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Sample records for neural plasticity learning

  1. Neural Plasticity in Speech Acquisition and Learning

    Science.gov (United States)

    Zhang, Yang; Wang, Yue

    2007-01-01

    Neural plasticity in speech acquisition and learning is concerned with the timeline trajectory and the mechanisms of experience-driven changes in the neural circuits that support or disrupt linguistic function. In this selective review, we discuss the role of phonetic learning in language acquisition, the "critical period" of learning, the agents…

  2. Neural plasticity of development and learning.

    Science.gov (United States)

    Galván, Adriana

    2010-06-01

    Development and learning are powerful agents of change across the lifespan that induce robust structural and functional plasticity in neural systems. An unresolved question in developmental cognitive neuroscience is whether development and learning share the same neural mechanisms associated with experience-related neural plasticity. In this article, I outline the conceptual and practical challenges of this question, review insights gleaned from adult studies, and describe recent strides toward examining this topic across development using neuroimaging methods. I suggest that development and learning are not two completely separate constructs and instead, that they exist on a continuum. While progressive and regressive changes are central to both, the behavioral consequences associated with these changes are closely tied to the existing neural architecture of maturity of the system. Eventually, a deeper, more mechanistic understanding of neural plasticity will shed light on behavioral changes across development and, more broadly, about the underlying neural basis of cognition. (c) 2010 Wiley-Liss, Inc.

  3. Shaping the learning curve: epigenetic dynamics in neural plasticity.

    Science.gov (United States)

    Bronfman, Zohar Z; Ginsburg, Simona; Jablonka, Eva

    2014-01-01

    A key characteristic of learning and neural plasticity is state-dependent acquisition dynamics reflected by the non-linear learning curve that links increase in learning with practice. Here we propose that the manner by which epigenetic states of individual cells change during learning contributes to the shape of the neural and behavioral learning curve. We base our suggestion on recent studies showing that epigenetic mechanisms such as DNA methylation, histone acetylation, and RNA-mediated gene regulation are intimately involved in the establishment and maintenance of long-term neural plasticity, reflecting specific learning-histories and influencing future learning. Our model, which is the first to suggest a dynamic molecular account of the shape of the learning curve, leads to several testable predictions regarding the link between epigenetic dynamics at the promoter, gene-network, and neural-network levels. This perspective opens up new avenues for therapeutic interventions in neurological pathologies.

  4. Developmental plasticity in neural circuits for a learned behavior.

    Science.gov (United States)

    Bottjer, S W; Arnold, A P

    1997-01-01

    The neural substrate underlying learned vocal behavior in songbirds provides a textbook illustration of anatomical localization of function for a complex learned behavior in vertebrates. The song-control system has become an important model for studying neural systems related to learning, behavior, and development. The song system of zebra finches is characterized by a heightened capacity for both neural and behavioral change during development and has taught us valuable information regarding sensitive periods, rearrangement of synaptic connections, topographic specificity, cell death and neurogenesis, experience-dependent neural plasticity, and sexual differentiation. The song system differs in some interesting ways from some well-studied mammalian model systems and thus offers fresh perspectives on specific theoretical issues. In this highly selective review, we concentrate on two major questions: What are the developmental changes in the song system responsible for song learning and the restriction of learning to a sensitive period, and what factors explain the highly sexually dimorphic development of this system? We discuss the important role of sex steroid hormones and of neurotrophins in creating a male-typical neural song circuit (which can learn to produce complex vocalizations) instead of a reduced, female-typical song circuit that does not produce learned song.

  5. Learning-induced neural plasticity of speech processing before birth.

    Science.gov (United States)

    Partanen, Eino; Kujala, Teija; Näätänen, Risto; Liitola, Auli; Sambeth, Anke; Huotilainen, Minna

    2013-09-10

    Learning, the foundation of adaptive and intelligent behavior, is based on plastic changes in neural assemblies, reflected by the modulation of electric brain responses. In infancy, auditory learning implicates the formation and strengthening of neural long-term memory traces, improving discrimination skills, in particular those forming the prerequisites for speech perception and understanding. Although previous behavioral observations show that newborns react differentially to unfamiliar sounds vs. familiar sound material that they were exposed to as fetuses, the neural basis of fetal learning has not thus far been investigated. Here we demonstrate direct neural correlates of human fetal learning of speech-like auditory stimuli. We presented variants of words to fetuses; unlike infants with no exposure to these stimuli, the exposed fetuses showed enhanced brain activity (mismatch responses) in response to pitch changes for the trained variants after birth. Furthermore, a significant correlation existed between the amount of prenatal exposure and brain activity, with greater activity being associated with a higher amount of prenatal speech exposure. Moreover, the learning effect was generalized to other types of similar speech sounds not included in the training material. Consequently, our results indicate neural commitment specifically tuned to the speech features heard before birth and their memory representations.

  6. Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity

    Science.gov (United States)

    Li, Xiumin; Wang, Wei; Xue, Fangzheng; Song, Yongduan

    2018-02-01

    Recently there has been continuously increasing interest in building up computational models of spiking neural networks (SNN), such as the Liquid State Machine (LSM). The biologically inspired self-organized neural networks with neural plasticity can enhance the capability of computational performance, with the characteristic features of dynamical memory and recurrent connection cycles which distinguish them from the more widely used feedforward neural networks. Despite a variety of computational models for brain-like learning and information processing have been proposed, the modeling of self-organized neural networks with multi-neural plasticity is still an important open challenge. The main difficulties lie in the interplay among different forms of neural plasticity rules and understanding how structures and dynamics of neural networks shape the computational performance. In this paper, we propose a novel approach to develop the models of LSM with a biologically inspired self-organizing network based on two neural plasticity learning rules. The connectivity among excitatory neurons is adapted by spike-timing-dependent plasticity (STDP) learning; meanwhile, the degrees of neuronal excitability are regulated to maintain a moderate average activity level by another learning rule: intrinsic plasticity (IP). Our study shows that LSM with STDP+IP performs better than LSM with a random SNN or SNN obtained by STDP alone. The noticeable improvement with the proposed method is due to the better reflected competition among different neurons in the developed SNN model, as well as the more effectively encoded and processed relevant dynamic information with its learning and self-organizing mechanism. This result gives insights to the optimization of computational models of spiking neural networks with neural plasticity.

  7. On the relationships between generative encodings, regularity, and learning abilities when evolving plastic artificial neural networks.

    Directory of Open Access Journals (Sweden)

    Paul Tonelli

    Full Text Available A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1 the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2 synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT. Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1 in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2 whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities.

  8. On the relationships between generative encodings, regularity, and learning abilities when evolving plastic artificial neural networks.

    Science.gov (United States)

    Tonelli, Paul; Mouret, Jean-Baptiste

    2013-01-01

    A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1) the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2) synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT). Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1) in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2) whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities.

  9. Spiking Neural Network With Distributed Plasticity Reproduces Cerebellar Learning in Eye Blink Conditioning Paradigms.

    Science.gov (United States)

    Antonietti, Alberto; Casellato, Claudia; Garrido, Jesús A; Luque, Niceto R; Naveros, Francisco; Ros, Eduardo; D' Angelo, Egidio; Pedrocchi, Alessandra

    2016-01-01

    In this study, we defined a realistic cerebellar model through the use of artificial spiking neural networks, testing it in computational simulations that reproduce associative motor tasks in multiple sessions of acquisition and extinction. By evolutionary algorithms, we tuned the cerebellar microcircuit to find out the near-optimal plasticity mechanism parameters that better reproduced human-like behavior in eye blink classical conditioning, one of the most extensively studied paradigms related to the cerebellum. We used two models: one with only the cortical plasticity and another including two additional plasticity sites at nuclear level. First, both spiking cerebellar models were able to well reproduce the real human behaviors, in terms of both "timing" and "amplitude", expressing rapid acquisition, stable late acquisition, rapid extinction, and faster reacquisition of an associative motor task. Even though the model with only the cortical plasticity site showed good learning capabilities, the model with distributed plasticity produced faster and more stable acquisition of conditioned responses in the reacquisition phase. This behavior is explained by the effect of the nuclear plasticities, which have slow dynamics and can express memory consolidation and saving. We showed how the spiking dynamics of multiple interactive neural mechanisms implicitly drive multiple essential components of complex learning processes.  This study presents a very advanced computational model, developed together by biomedical engineers, computer scientists, and neuroscientists. Since its realistic features, the proposed model can provide confirmations and suggestions about neurophysiological and pathological hypotheses and can be used in challenging clinical applications.

  10. Predispositions and plasticity in music and speech learning: neural correlates and implications.

    Science.gov (United States)

    Zatorre, Robert J

    2013-11-01

    Speech and music are remarkable aspects of human cognition and sensory-motor processing. Cognitive neuroscience has focused on them to understand how brain function and structure are modified by learning. Recent evidence indicates that individual differences in anatomical and functional properties of the neural architecture also affect learning and performance in these domains. Here, neuroimaging findings are reviewed that reiterate evidence of experience-dependent brain plasticity, but also point to the predictive validity of such data in relation to new learning in speech and music domains. Indices of neural sensitivity to certain stimulus features have been shown to predict individual rates of learning; individual network properties of brain activity are especially relevant in this regard, as they may reflect anatomical connectivity. Similarly, numerous studies have shown that anatomical features of auditory cortex and other structures, and their anatomical connectivity, are predictive of new sensory-motor learning ability. Implications of this growing body of literature are discussed.

  11. What Is Neural Plasticity?

    Science.gov (United States)

    von Bernhardi, Rommy; Bernhardi, Laura Eugenín-von; Eugenín, Jaime

    2017-01-01

    "Neural plasticity" refers to the capacity of the nervous system to modify itself, functionally and structurally, in response to experience and injury. As the various chapters in this volume show, plasticity is a key component of neural development and normal functioning of the nervous system, as well as a response to the changing environment, aging, or pathological insult. This chapter discusses how plasticity is necessary not only for neural networks to acquire new functional properties, but also for them to remain robust and stable. The article also reviews the seminal proposals developed over the years that have driven experiments and strongly influenced concepts of neural plasticity.

  12. Experience-dependent neural plasticity, learning, and memory in the era of epitranscriptomics.

    Science.gov (United States)

    Leighton, L J; Ke, K; Zajaczkowski, E L; Edmunds, J; Spitale, R C; Bredy, T W

    2017-09-19

    In this short review, we highlight recent findings in the emerging field of epitranscriptomic mechanisms and discuss their potential role in neural plasticity, learning and memory. These include the influence of RNA modifications on activity-induced RNA structure states, RNA editing and RNA localization, and how qualitative state changes in RNA increase the functional diversity and information-carrying capacity of RNA molecules. We predict that RNA modifications may be just as important for synaptic plasticity and memory as quantitative changes in transcript and protein abundance, but with the added advantage of not being required to signal back to the nucleus, and therefore better suited to be coordinated with the temporal dynamics of learning. © 2017 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

  13. Consciousness and neural plasticity

    DEFF Research Database (Denmark)

    In contemporary consciousness studies the phenomenon of neural plasticity has received little attention despite the fact that neural plasticity is of still increased interest in neuroscience. We will, however, argue that neural plasticity could be of great importance to consciousness studies....... If consciousness is related to neural processes it seems, at least prima facie, that the ability of the neural structures to change should be reflected in a theory of this relationship "Neural plasticity" refers to the fact that the brain can change due to its own activity. The brain is not static but rather...... a dynamic entity, which physical structure changes according to its use and environment. This change may take the form of growth of new neurons, the creation of new networks and structures, and change within network structures, that is, changes in synaptic strengths. Plasticity raises questions about...

  14. Histone Deacetylase (HDAC) Inhibitors - emerging roles in neuronal memory, learning, synaptic plasticity and neural regeneration.

    Science.gov (United States)

    Ganai, Shabir Ahmad; Ramadoss, Mahalakshmi; Mahadevan, Vijayalakshmi

    2016-01-01

    Epigenetic regulation of neuronal signalling through histone acetylation dictates transcription programs that govern neuronal memory, plasticity and learning paradigms. Histone Acetyl Transferases (HATs) and Histone Deacetylases (HDACs) are antagonistic enzymes that regulate gene expression through acetylation and deacetylation of histone proteins around which DNA is wrapped inside a eukaryotic cell nucleus. The epigenetic control of HDACs and the cellular imbalance between HATs and HDACs dictate disease states and have been implicated in muscular dystrophy, loss of memory, neurodegeneration and autistic disorders. Altering gene expression profiles through inhibition of HDACs is now emerging as a powerful technique in therapy. This review presents evolving applications of HDAC inhibitors as potential drugs in neurological research and therapy. Mechanisms that govern their expression profiles in neuronal signalling, plasticity and learning will be covered. Promising and exciting possibilities of HDAC inhibitors in memory formation, fear conditioning, ischemic stroke and neural regeneration have been detailed.

  15. Global and local missions of cAMP signaling in neural plasticity, learning, and memory.

    Science.gov (United States)

    Lee, Daewoo

    2015-01-01

    The fruit fly Drosophila melanogaster has been a popular model to study cAMP signaling and resultant behaviors due to its powerful genetic approaches. All molecular components (AC, PDE, PKA, CREB, etc) essential for cAMP signaling have been identified in the fly. Among them, adenylyl cyclase (AC) gene rutabaga and phosphodiesterase (PDE) gene dunce have been intensively studied to understand the role of cAMP signaling. Interestingly, these two mutant genes were originally identified on the basis of associative learning deficits. This commentary summarizes findings on the role of cAMP in Drosophila neuronal excitability, synaptic plasticity and memory. It mainly focuses on two distinct mechanisms (global versus local) regulating excitatory and inhibitory synaptic plasticity related to cAMP homeostasis. This dual regulatory role of cAMP is to increase the strength of excitatory neural circuits on one hand, but to act locally on postsynaptic GABA receptors to decrease inhibitory synaptic plasticity on the other. Thus the action of cAMP could result in a global increase in the neural circuit excitability and memory. Implications of this cAMP signaling related to drug discovery for neural diseases are also described.

  16. Learning and retrieval behavior in recurrent neural networks with pre-synaptic dependent homeostatic plasticity

    Science.gov (United States)

    Mizusaki, Beatriz E. P.; Agnes, Everton J.; Erichsen, Rubem; Brunnet, Leonardo G.

    2017-08-01

    The plastic character of brain synapses is considered to be one of the foundations for the formation of memories. There are numerous kinds of such phenomenon currently described in the literature, but their role in the development of information pathways in neural networks with recurrent architectures is still not completely clear. In this paper we study the role of an activity-based process, called pre-synaptic dependent homeostatic scaling, in the organization of networks that yield precise-timed spiking patterns. It encodes spatio-temporal information in the synaptic weights as it associates a learned input with a specific response. We introduce a correlation measure to evaluate the precision of the spiking patterns and explore the effects of different inhibitory interactions and learning parameters. We find that large learning periods are important in order to improve the network learning capacity and discuss this ability in the presence of distinct inhibitory currents.

  17. Distributed Neural Plasticity for Shape Learning in the Human Visual Cortex

    Science.gov (United States)

    Betts, Lisa R; Sarkheil, Pegah; Welchman, Andrew E

    2005-01-01

    Expertise in recognizing objects in cluttered scenes is a critical skill for our interactions in complex environments and is thought to develop with learning. However, the neural implementation of object learning across stages of visual analysis in the human brain remains largely unknown. Using combined psychophysics and functional magnetic resonance imaging (fMRI), we show a link between shape-specific learning in cluttered scenes and distributed neuronal plasticity in the human visual cortex. We report stronger fMRI responses for trained than untrained shapes across early and higher visual areas when observers learned to detect low-salience shapes in noisy backgrounds. However, training with high-salience pop-out targets resulted in lower fMRI responses for trained than untrained shapes in higher occipitotemporal areas. These findings suggest that learning of camouflaged shapes is mediated by increasing neural sensitivity across visual areas to bolster target segmentation and feature integration. In contrast, learning of prominent pop-out shapes is mediated by associations at higher occipitotemporal areas that support sparser coding of the critical features for target recognition. We propose that the human brain learns novel objects in complex scenes by reorganizing shape processing across visual areas, while taking advantage of natural image correlations that determine the distinctiveness of target shapes. PMID:15934786

  18. Learning to Produce Syllabic Speech Sounds via Reward-Modulated Neural Plasticity.

    Science.gov (United States)

    Warlaumont, Anne S; Finnegan, Megan K

    2016-01-01

    At around 7 months of age, human infants begin to reliably produce well-formed syllables containing both consonants and vowels, a behavior called canonical babbling. Over subsequent months, the frequency of canonical babbling continues to increase. How the infant's nervous system supports the acquisition of this ability is unknown. Here we present a computational model that combines a spiking neural network, reinforcement-modulated spike-timing-dependent plasticity, and a human-like vocal tract to simulate the acquisition of canonical babbling. Like human infants, the model's frequency of canonical babbling gradually increases. The model is rewarded when it produces a sound that is more auditorily salient than sounds it has previously produced. This is consistent with data from human infants indicating that contingent adult responses shape infant behavior and with data from deaf and tracheostomized infants indicating that hearing, including hearing one's own vocalizations, is critical for canonical babbling development. Reward receipt increases the level of dopamine in the neural network. The neural network contains a reservoir with recurrent connections and two motor neuron groups, one agonist and one antagonist, which control the masseter and orbicularis oris muscles, promoting or inhibiting mouth closure. The model learns to increase the number of salient, syllabic sounds it produces by adjusting the base level of muscle activation and increasing their range of activity. Our results support the possibility that through dopamine-modulated spike-timing-dependent plasticity, the motor cortex learns to harness its natural oscillations in activity in order to produce syllabic sounds. It thus suggests that learning to produce rhythmic mouth movements for speech production may be supported by general cortical learning mechanisms. The model makes several testable predictions and has implications for our understanding not only of how syllabic vocalizations develop in

  19. Learning to Produce Syllabic Speech Sounds via Reward-Modulated Neural Plasticity

    Science.gov (United States)

    Warlaumont, Anne S.; Finnegan, Megan K.

    2016-01-01

    At around 7 months of age, human infants begin to reliably produce well-formed syllables containing both consonants and vowels, a behavior called canonical babbling. Over subsequent months, the frequency of canonical babbling continues to increase. How the infant’s nervous system supports the acquisition of this ability is unknown. Here we present a computational model that combines a spiking neural network, reinforcement-modulated spike-timing-dependent plasticity, and a human-like vocal tract to simulate the acquisition of canonical babbling. Like human infants, the model’s frequency of canonical babbling gradually increases. The model is rewarded when it produces a sound that is more auditorily salient than sounds it has previously produced. This is consistent with data from human infants indicating that contingent adult responses shape infant behavior and with data from deaf and tracheostomized infants indicating that hearing, including hearing one’s own vocalizations, is critical for canonical babbling development. Reward receipt increases the level of dopamine in the neural network. The neural network contains a reservoir with recurrent connections and two motor neuron groups, one agonist and one antagonist, which control the masseter and orbicularis oris muscles, promoting or inhibiting mouth closure. The model learns to increase the number of salient, syllabic sounds it produces by adjusting the base level of muscle activation and increasing their range of activity. Our results support the possibility that through dopamine-modulated spike-timing-dependent plasticity, the motor cortex learns to harness its natural oscillations in activity in order to produce syllabic sounds. It thus suggests that learning to produce rhythmic mouth movements for speech production may be supported by general cortical learning mechanisms. The model makes several testable predictions and has implications for our understanding not only of how syllabic vocalizations develop

  20. Developmental Pathway Genes and Neural Plasticity Underlying Emotional Learning and Stress-Related Disorders

    Science.gov (United States)

    Maheau, Marissa E.; Ressler, Kerry J.

    2017-01-01

    The manipulation of neural plasticity as a means of intervening in the onset and progression of stress-related disorders retains its appeal for many researchers, despite our limited success in translating such interventions from the laboratory to the clinic. Given the challenges of identifying individual genetic variants that confer increased risk…

  1. Learning to Generate Sequences with Combination of Hebbian and Non-hebbian Plasticity in Recurrent Spiking Neural Networks.

    Science.gov (United States)

    Panda, Priyadarshini; Roy, Kaushik

    2017-01-01

    Synaptic Plasticity, the foundation for learning and memory formation in the human brain, manifests in various forms. Here, we combine the standard spike timing correlation based Hebbian plasticity with a non-Hebbian synaptic decay mechanism for training a recurrent spiking neural model to generate sequences. We show that inclusion of the adaptive decay of synaptic weights with standard STDP helps learn stable contextual dependencies between temporal sequences, while reducing the strong attractor states that emerge in recurrent models due to feedback loops. Furthermore, we show that the combined learning scheme suppresses the chaotic activity in the recurrent model substantially, thereby enhancing its' ability to generate sequences consistently even in the presence of perturbations.

  2. Neural plasticity: the biological substrate for neurorehabilitation.

    Science.gov (United States)

    Warraich, Zuha; Kleim, Jeffrey A

    2010-12-01

    Decades of basic science have clearly demonstrated the capacity of the central nervous system (CNS) to structurally and functionally adapt in response to experience. The field of neurorehabilitation has begun to use this body of work to develop neurobiologically informed therapies that harness the key behavioral and neural signals that drive neural plasticity. The present review describes how neural plasticity supports both learning in the intact CNS and functional improvement in the damaged or diseased CNS. A pragmatic, interdisciplinary definition of neural plasticity is presented that may be used by both clinical and basic scientists studying neurorehabilitation. Furthermore, a description of how neural plasticity may act to drive different neural strategies underlying functional improvement after CNS injury or disease is provided. The understanding of the relationship between these different neural strategies, mechanisms of neural plasticity, and changes in behavior may facilitate the development of novel, more effective rehabilitation interventions. Copyright © 2010 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

  3. A peptide mimetic targeting trans-homophilic NCAM binding sites promotes spatial learning and neural plasticity in the hippocampus

    DEFF Research Database (Denmark)

    Kraev, Igor; Henneberger, Christian; Rossetti, Clara

    2011-01-01

    cultures and improves spatial learning in rats, both under basal conditions and under conditions involving a deficit in a key plasticity-promoting posttranslational modification of NCAM, its polysialylation. We also found that plannexin enhances excitatory synaptic transmission in hippocampal area CA1......, where it also increases the number of mushroom spines and the synaptic expression of the AMPAR subunits GluA1 and GluA2. Altogether, these findings provide compelling evidence that plannexin is an important facilitator of synaptic functional, structural and molecular plasticity in the hippocampal CA1......The key roles played by the neural cell adhesion molecule (NCAM) in plasticity and cognition underscore this membrane protein as a relevant target to develop cognitive-enhancing drugs. However, NCAM is a structurally and functionally complex molecule with multiple domains engaged in a variety...

  4. Learning, neural plasticity and sensitive periods: implications for language acquisition, music training and transfer across the lifespan.

    Science.gov (United States)

    White, Erin J; Hutka, Stefanie A; Williams, Lynne J; Moreno, Sylvain

    2013-11-20

    Sensitive periods in human development have often been proposed to explain age-related differences in the attainment of a number of skills, such as a second language (L2) and musical expertise. It is difficult to reconcile the negative consequence this traditional view entails for learning after a sensitive period with our current understanding of the brain's ability for experience-dependent plasticity across the lifespan. What is needed is a better understanding of the mechanisms underlying auditory learning and plasticity at different points in development. Drawing on research in language development and music training, this review examines not only what we learn and when we learn it, but also how learning occurs at different ages. First, we discuss differences in the mechanism of learning and plasticity during and after a sensitive period by examining how language exposure versus training forms language-specific phonetic representations in infants and adult L2 learners, respectively. Second, we examine the impact of musical training that begins at different ages on behavioral and neural indices of auditory and motor processing as well as sensorimotor integration. Third, we examine the extent to which childhood training in one auditory domain can enhance processing in another domain via the transfer of learning between shared neuro-cognitive systems. Specifically, we review evidence for a potential bi-directional transfer of skills between music and language by examining how speaking a tonal language may enhance music processing and, conversely, how early music training can enhance language processing. We conclude with a discussion of the role of attention in auditory learning for learning during and after sensitive periods and outline avenues of future research.

  5. Learning, neural plasticity and sensitive periods: implications for language acquisition, music training and transfer across the lifespan

    Science.gov (United States)

    White, Erin J.; Hutka, Stefanie A.; Williams, Lynne J.; Moreno, Sylvain

    2013-01-01

    Sensitive periods in human development have often been proposed to explain age-related differences in the attainment of a number of skills, such as a second language (L2) and musical expertise. It is difficult to reconcile the negative consequence this traditional view entails for learning after a sensitive period with our current understanding of the brain’s ability for experience-dependent plasticity across the lifespan. What is needed is a better understanding of the mechanisms underlying auditory learning and plasticity at different points in development. Drawing on research in language development and music training, this review examines not only what we learn and when we learn it, but also how learning occurs at different ages. First, we discuss differences in the mechanism of learning and plasticity during and after a sensitive period by examining how language exposure versus training forms language-specific phonetic representations in infants and adult L2 learners, respectively. Second, we examine the impact of musical training that begins at different ages on behavioral and neural indices of auditory and motor processing as well as sensorimotor integration. Third, we examine the extent to which childhood training in one auditory domain can enhance processing in another domain via the transfer of learning between shared neuro-cognitive systems. Specifically, we review evidence for a potential bi-directional transfer of skills between music and language by examining how speaking a tonal language may enhance music processing and, conversely, how early music training can enhance language processing. We conclude with a discussion of the role of attention in auditory learning for learning during and after sensitive periods and outline avenues of future research. PMID:24312022

  6. Learning, neural plasticity and sensitive periods: implications for language acquisition, music training and transfer across the lifespan

    Directory of Open Access Journals (Sweden)

    Erin Jacquelyn White

    2013-11-01

    Full Text Available Sensitive periods in human development have often been proposed to explain age-related differences in the attainment of a number of skills, such as a second language and musical expertise. It is difficult to reconcile the negative consequence this traditional view entails for learning after a sensitive period with our current understanding of the brain’s ability for experience-dependent plasticity across the lifespan. What is needed is a better understanding of the mechanisms underlying auditory learning and plasticity at different points in development. Drawing on research in language development and music training, this review examines not only what we learn and when we learn it, but also how learning occurs at different ages. First, we discuss differences in the mechanism of learning and plasticity during and after a sensitive period by examining how language exposure versus training forms language-specific phonetic representations in infants and adult second language learners, respectively. Second, we examine the impact of musical training that begins at different ages on behavioural and neural indices of auditory and motor processing as well as sensorimotor integration. Third, we examine the extent to which childhood training in one auditory domain can enhance processing in another domain via the transfer of learning between shared neuro-cognitive systems. Specifically, we review evidence for a potential bi-directional transfer of skills between music and language by examining how speaking a tonal language may enhance music processing and, conversely, how early music training can enhance language processing. We conclude with a discussion of the role of attention in auditory learning for learning during and after sensitive periods and outline avenues of future research.

  7. Neural plasticity across the lifespan.

    Science.gov (United States)

    Power, Jonathan D; Schlaggar, Bradley L

    2017-01-01

    An essential feature of the brain is its capacity to change. Neuroscientists use the term 'plasticity' to describe the malleability of neuronal connectivity and circuitry. How does plasticity work? A review of current data suggests that plasticity encompasses many distinct phenomena, some of which operate across most or all of the lifespan, and others that operate exclusively in early development. This essay surveys some of the key concepts related to neural plasticity, beginning with how current patterns of neural activity (e.g., as you read this essay) come to impact future patterns of activity (e.g., your memory of this essay), and then extending this framework backward into more development-specific mechanisms of plasticity. WIREs Dev Biol 2017, 6:e216. doi: 10.1002/wdev.216 For further resources related to this article, please visit the WIREs website. © 2016 Wiley Periodicals, Inc.

  8. Myelin plasticity, neural activity, and traumatic neural injury.

    Science.gov (United States)

    Kondiles, Bethany R; Horner, Philip J

    2018-02-01

    The possibility that adult organisms exhibit myelin plasticity has recently become a topic of great interest. Many researchers are exploring the role of myelin growth and adaptation in daily functions such as memory and motor learning. Here we consider evidence for three different potential categories of myelin plasticity: the myelination of previously bare axons, remodeling of existing sheaths, and the removal of a sheath with replacement by a new internode. We also review evidence that points to the importance of neural activity as a mechanism by which oligodendrocyte precursor cells (OPCs) are cued to differentiate into myelinating oligodendrocytes, which may potentially be an important component of myelin plasticity. Finally, we discuss demyelination in the context of traumatic neural injury and present an argument for altering neural activity as a potential therapeutic target for remyelination following injury. © 2017 Wiley Periodicals, Inc. Develop Neurobiol 78: 108-122, 2018. © 2017 Wiley Periodicals, Inc.

  9. On the relationships between generative encodings, regularity, and learning abilities when evolving plastic artificial neural networks

    National Research Council Canada - National Science Library

    Tonelli, Paul; Mouret, Jean-Baptiste

    2013-01-01

    .... It is commonly believed that two keys for evolving nature-like artificial neural networks are (1) the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks...

  10. [Glutamate signaling and neural plasticity].

    Science.gov (United States)

    Watanabe, Masahiko

    2013-07-01

    Proper functioning of the nervous system relies on the precise formation of neural circuits during development. At birth, neurons have redundant synaptic connections not only to their proper targets but also to other neighboring cells. Then, functional neural circuits are formed during early postnatal development by the selective strengthening of necessary synapses and weakening of surplus connections. Synaptic connections are also modified so that projection fields of active afferents expand at the expense of lesser ones. We have studied the molecular mechanisms underlying these activity-dependent prunings and the plasticity of synaptic circuitry using gene-engineered mice defective in the glutamatergic signaling system. NMDA-type glutamate receptors are critically involved in the establishment of the somatosensory pathway ascending from the brainstem trigeminal nucleus to the somatosensory cortex. Without NMDA receptors, whisker-related patterning fails to develop, whereas lesion-induced plasticity occurs normally during the critical period. In contrast, mice lacking the glutamate transporters GLAST or GLT1 are selectively impaired in the lesion-induced critical plasticity of cortical barrels, although whisker-related patterning itself develops normally. In the developing cerebellum, multiple climbing fibers initially innervating given Purkinje cells are eliminated one by one until mono-innervation is achieved. In this pruning process, P/Q-type Ca2+ channels expressed on Purkinje cells are critically involved by the selective strengthening of single main climbing fibers against other lesser afferents. Therefore, the activation of glutamate receptors that leads to an activity-dependent increase in the intracellular Ca2+ concentration plays a key role in the pruning of immature synaptic circuits into functional circuits. On the other hand, glutamate transporters appear to control activity-dependent plasticity among afferent fields, presumably through adjusting

  11. Stable learning of functional maps in self-organizing spiking neural networks with continuous synaptic plasticity.

    Science.gov (United States)

    Srinivasa, Narayan; Jiang, Qin

    2013-01-01

    This study describes a spiking model that self-organizes for stable formation and maintenance of orientation and ocular dominance maps in the visual cortex (V1). This self-organization process simulates three development phases: an early experience-independent phase, a late experience-independent phase and a subsequent refinement phase during which experience acts to shape the map properties. The ocular dominance maps that emerge accommodate the two sets of monocular inputs that arise from the lateral geniculate nucleus (LGN) to layer 4 of V1. The orientation selectivity maps that emerge feature well-developed iso-orientation domains and fractures. During the last two phases of development the orientation preferences at some locations appear to rotate continuously through ±180° along circular paths and referred to as pinwheel-like patterns but without any corresponding point discontinuities in the orientation gradient maps. The formation of these functional maps is driven by balanced excitatory and inhibitory currents that are established via synaptic plasticity based on spike timing for both excitatory and inhibitory synapses. The stability and maintenance of the formed maps with continuous synaptic plasticity is enabled by homeostasis caused by inhibitory plasticity. However, a prolonged exposure to repeated stimuli does alter the formed maps over time due to plasticity. The results from this study suggest that continuous synaptic plasticity in both excitatory neurons and interneurons could play a critical role in the formation, stability, and maintenance of functional maps in the cortex.

  12. A review of evidence linking disrupted neural plasticity to schizophrenia.

    Science.gov (United States)

    Voineskos, Daphne; Rogasch, Nigel C; Rajji, Tarek K; Fitzgerald, Paul B; Daskalakis, Zafiris J

    2013-02-01

    The adaptations resulting from neural plasticity lead to changes in cognition and behaviour, which are strengthened through repeated exposure to the novel environment or stimulus. Learning and memory have been hypothesized to occur through modifications of the strength of neural circuits, particularly in the hippocampus and cortex. Cognitive deficits, specifically in executive functioning and negative symptoms, may be a corollary to deficits in neural plasticity. Moreover, the main excitatory and inhibitory neurotransmitters associated with neural plasticity have also been extensively investigated for their role in the cognitive deficits associated with schizophrenia. Transcranial magnetic stimulation (TMS) represents some of the most promising approaches to directly explore the physiological manifestations of neural plasticity in the human brain. Three TMS paradigms (use-dependent plasticity, paired associative stimulation, and repetitive TMS) have been used to evaluate neurophysiological measures of neural plasticity in the healthy brain and in patients with schizophrenia, and to examine the brain's responses to such stimulation. In schizophrenia, deficits in neural plasticity have been consistently shown which parallel the molecular evidence appearing to be entwined with this debilitating disorder. Such pathophysiology may underlie the learning and memory deficits that are key symptoms of this disorder and may even be a key mechanism involved in treatment with antipsychotics.

  13. Neural prostheses and brain plasticity

    Science.gov (United States)

    Fallon, James B.; Irvine, Dexter R. F.; Shepherd, Robert K.

    2009-12-01

    The success of modern neural prostheses is dependent on a complex interplay between the devices' hardware and software and the dynamic environment in which the devices operate: the patient's body or 'wetware'. Over 120 000 severe/profoundly deaf individuals presently receive information enabling auditory awareness and speech perception from cochlear implants. The cochlear implant therefore provides a useful case study for a review of the complex interactions between hardware, software and wetware, and of the important role of the dynamic nature of wetware. In the case of neural prostheses, the most critical component of that wetware is the central nervous system. This paper will examine the evidence of changes in the central auditory system that contribute to changes in performance with a cochlear implant, and discuss how these changes relate to electrophysiological and functional imaging studies in humans. The relationship between the human data and evidence from animals of the remarkable capacity for plastic change of the central auditory system, even into adulthood, will then be examined. Finally, we will discuss the role of brain plasticity in neural prostheses in general.

  14. Learning to Perceive Structure from Motion and Neural Plasticity in Patients with Alzheimer's Disease

    Science.gov (United States)

    Kim, Nam-Gyoon; Park, Jong-Hee

    2010-01-01

    Recent research has demonstrated that Alzheimer's disease (AD) affects the visual sensory pathways, producing a variety of visual deficits, including the capacity to perceive structure-from-motion (SFM). Because the sensory areas of the adult brain are known to retain a large degree of plasticity, the present study was conducted to explore whether…

  15. Phantom limbs and neural plasticity.

    Science.gov (United States)

    Ramachandran, V S; Rogers-Ramachandran, D

    2000-03-01

    The study of phantom limbs has received tremendous impetus from recent studies linking changes in cortical topography with perceptual experience. Systematic psychophysical testing and functional imaging studies on patients with phantom limbs provide 2 unique opportunities. First, they allow us to demonstrate neural plasticity in the adult human brain. Second, by tracking perceptual changes (such as referred sensations) and changes in cortical topography in individual patients, we can begin to explore how the activity of sensory maps gives rise to conscious experience. Finally, phantom limbs also allow us to explore intersensory effects and the manner in which the brain constructs and updates a "body image" throughout life.

  16. Neural oscillations and information flow associated with synaptic plasticity.

    Science.gov (United States)

    Zhang, Tao

    2011-10-25

    As a rhythmic neural activity, neural oscillation exists all over the nervous system, in structures as diverse as the cerebral cortex, hippocampus, subcortical nuclei and sense organs. This review firstly presents some evidence that synchronous neural oscillations in theta and gamma bands reveal much about the origin and nature of cognitive processes such as learning and memory. And then it introduces the novel analyzing algorithms of neural oscillations, which is a directionality index of neural information flow (NIF) as a measure of synaptic plasticity. An example of application used such an analyzing algorithms of neural oscillations has been provided.

  17. Neural plasticity during motor learning with motor imagery practice: Review and perspectives.

    Science.gov (United States)

    Ruffino, Célia; Papaxanthis, Charalambos; Lebon, Florent

    2017-01-26

    In the last decade, many studies confirmed the benefits of mental practice with motor imagery. In this review we first aimed to compile data issued from fundamental and clinical investigations and to provide the key-components for the optimization of motor imagery strategy. We focused on transcranial magnetic stimulation studies, supported by brain imaging research, that sustain the current hypothesis of a functional link between cortical reorganization and behavioral improvement. As perspectives, we suggest a model of neural adaptation following mental practice, in which synapse conductivity and inhibitory mechanisms at the spinal level may also play an important role. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

  18. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule.

    Science.gov (United States)

    Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin

    2015-11-01

    In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

  19. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin, E-mail: xmli@cqu.edu.cn [Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044 (China); College of Automation, Chongqing University, Chongqing 400044 (China)

    2015-11-15

    In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

  20. Sites of Plasticity in the Neural Circuit Mediating Tentacle Withdrawal in the Snail Helix aspersa: Implications for Behavioral Change and Learning Kinetics

    Science.gov (United States)

    Prescott, Steven A.; Chase, Ronald

    1999-01-01

    The tentacle withdrawal reflex of the snail Helix aspersa exhibits a complex combination of habituation and sensitization consistent with the dual-process theory of plasticity. Habituation, sensitization, or a combination of both were elicited by varying stimulation parameters and lesion condition. Analysis of response plasticity shows that the late phase of the response is selectively enhanced by sensitization, whereas all phases are decreased by habituation. Previous data have shown that tentacle withdrawal is mediated conjointly by parallel monosynaptic and polysynaptic pathways. The former mediates the early phase, whereas the latter mediates the late phase of the response. Plastic loci were identified by stimulating and recording at different points within the neural circuit, in combination with selective lesions. Results indicate that depression occurs at an upstream locus, before circuit divergence, and is therefore expressed in all pathways, whereas facilitation requires downstream facilitatory neurons and is selectively expressed in polysynaptic pathways. Differential expression of plasticity between pathways helps explain the behavioral manifestation of depression and facilitation. A simple mathematical model is used to show how serial positioning of depression and facilitation can explain the kinetics of dual-process learning. These results illustrate how the position of cellular plasticity in the network affects behavioral change and how forms of plasticity can interact to determine the kinetics of the net changes. PMID:10509707

  1. Computational modeling of neural plasticity for self-organization of neural networks.

    Science.gov (United States)

    Chrol-Cannon, Joseph; Jin, Yaochu

    2014-11-01

    Self-organization in biological nervous systems during the lifetime is known to largely occur through a process of plasticity that is dependent upon the spike-timing activity in connected neurons. In the field of computational neuroscience, much effort has been dedicated to building up computational models of neural plasticity to replicate experimental data. Most recently, increasing attention has been paid to understanding the role of neural plasticity in functional and structural neural self-organization, as well as its influence on the learning performance of neural networks for accomplishing machine learning tasks such as classification and regression. Although many ideas and hypothesis have been suggested, the relationship between the structure, dynamics and learning performance of neural networks remains elusive. The purpose of this article is to review the most important computational models for neural plasticity and discuss various ideas about neural plasticity's role. Finally, we suggest a few promising research directions, in particular those along the line that combines findings in computational neuroscience and systems biology, and their synergetic roles in understanding learning, memory and cognition, thereby bridging the gap between computational neuroscience, systems biology and computational intelligence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  2. Neural plasticity after spinal cord injury.

    Science.gov (United States)

    Liu, Jian; Yang, Xiaoyu; Jiang, Lianying; Wang, Chunxin; Yang, Maoguang

    2012-02-15

    Plasticity changes of uninjured nerves can result in a novel neural circuit after spinal cord injury, which can restore sensory and motor functions to different degrees. Although processes of neural plasticity have been studied, the mechanism and treatment to effectively improve neural plasticity changes remain controversial. The present study reviewed studies regarding plasticity of the central nervous system and methods for promoting plasticity to improve repair of injured central nerves. The results showed that synaptic reorganization, axonal sprouting, and neurogenesis are critical factors for neural circuit reconstruction. Directed functional exercise, neurotrophic factor and transplantation of nerve-derived and non-nerve-derived tissues and cells can effectively ameliorate functional disturbances caused by spinal cord injury and improve quality of life for patients.

  3. Modulation of Hippocampal Neural Plasticity by Glucose-Related Signaling

    OpenAIRE

    Marco Mainardi; Salvatore Fusco; Claudio Grassi

    2015-01-01

    Hormones and peptides involved in glucose homeostasis are emerging as important modulators of neural plasticity. In this regard, increasing evidence shows that molecules such as insulin, insulin-like growth factor-I, glucagon-like peptide-1, and ghrelin impact on the function of the hippocampus, which is a key area for learning and memory. Indeed, all these factors affect fundamental hippocampal properties including synaptic plasticity (i.e., synapse potentiation and depression), structural p...

  4. Neural Plasticity in the Gustatory System

    OpenAIRE

    Hill, David L.

    2004-01-01

    Sensory systems adapt to changing environmental influences by coordinated alterations in structure and function. These alterations are referred to as plastic changes. The gustatory system displays numerous plastic changes even in receptor cells. This review focuses on the plasticity of gustatory structures through the first synaptic relay in the brain. Unlike other sensory systems, there is a remarkable amount of environmentally induced changes in these peripheral-most neural structures. The ...

  5. Neural Circuitry and Plasticity Mechanisms Underlying Delay Eyeblink Conditioning

    Science.gov (United States)

    Freeman, John H.; Steinmetz, Adam B.

    2011-01-01

    Pavlovian eyeblink conditioning has been used extensively as a model system for examining the neural mechanisms underlying associative learning. Delay eyeblink conditioning depends on the intermediate cerebellum ipsilateral to the conditioned eye. Evidence favors a two-site plasticity model within the cerebellum with long-term depression of…

  6. A Neural Circuit for Acoustic Navigation combining Heterosynaptic and Non-synaptic Plasticity that learns Stable Trajectories

    DEFF Research Database (Denmark)

    Shaikh, Danish; Manoonpong, Poramate

    2017-01-01

    Reactive spatial robot navigation in goal-directed tasks such as phonotaxis requires generating consistent and stable trajectories towards an acoustic target while avoiding obstacles. High-level goal-directed steering behaviour can steer a robot towards the target by mapping sound direction...... controllers be resolved in a manner that generates consistent and stable robot trajectories? We propose a neural circuit that minimises this conflict by learning sensorimotor mappings as neuronal transfer functions between the perceived sound direction and wheel velocities of a simulated non-holonomic mobile...... robot. These mappings constitute the high-level goal-directed steering behaviour. Sound direction information is obtained from a model of the lizard peripheral auditory system. The parameters of the transfer functions are learned via an online unsupervised correlation learning algorithm through...

  7. Dynamic Neural Fields with Intrinsic Plasticity.

    Science.gov (United States)

    Strub, Claudius; Schöner, Gregor; Wörgötter, Florentin; Sandamirskaya, Yulia

    2017-01-01

    Dynamic neural fields (DNFs) are dynamical systems models that approximate the activity of large, homogeneous, and recurrently connected neural networks based on a mean field approach. Within dynamic field theory, the DNFs have been used as building blocks in architectures to model sensorimotor embedding of cognitive processes. Typically, the parameters of a DNF in an architecture are manually tuned in order to achieve a specific dynamic behavior (e.g., decision making, selection, or working memory) for a given input pattern. This manual parameters search requires expert knowledge and time to find and verify a suited set of parameters. The DNF parametrization may be particular challenging if the input distribution is not known in advance, e.g., when processing sensory information. In this paper, we propose the autonomous adaptation of the DNF resting level and gain by a learning mechanism of intrinsic plasticity (IP). To enable this adaptation, an input and output measure for the DNF are introduced, together with a hyper parameter to define the desired output distribution. The online adaptation by IP gives the possibility to pre-define the DNF output statistics without knowledge of the input distribution and thus, also to compensate for changes in it. The capabilities and limitations of this approach are evaluated in a number of experiments.

  8. A data science approach to candidate gene selection of pain regarded as a process of learning and neural plasticity.

    Science.gov (United States)

    Ultsch, Alfred; Kringel, Dario; Kalso, Eija; Mogil, Jeffrey S; Lötsch, Jörn

    2016-12-01

    The increasing availability of "big data" enables novel research approaches to chronic pain while also requiring novel techniques for data mining and knowledge discovery. We used machine learning to combine the knowledge about n = 535 genes identified empirically as relevant to pain with the knowledge about the functions of thousands of genes. Starting from an accepted description of chronic pain as displaying systemic features described by the terms "learning" and "neuronal plasticity," a functional genomics analysis proposed that among the functions of the 535 "pain genes," the biological processes "learning or memory" (P = 8.6 × 10) and "nervous system development" (P = 2.4 × 10) are statistically significantly overrepresented as compared with the annotations to these processes expected by chance. After establishing that the hypothesized biological processes were among important functional genomics features of pain, a subset of n = 34 pain genes were found to be annotated with both Gene Ontology terms. Published empirical evidence supporting their involvement in chronic pain was identified for almost all these genes, including 1 gene identified in March 2016 as being involved in pain. By contrast, such evidence was virtually absent in a randomly selected set of 34 other human genes. Hence, the present computational functional genomics-based method can be used for candidate gene selection, providing an alternative to established methods.

  9. Neural Plasticity: For Good and Bad

    Science.gov (United States)

    Møller, A. R.

    The brain's ability to change its organization and function is necessary for normal development of the nervous system and it makes it possible to adapt to changing demands but it can also cause disorders when going awry. This property, known as neural plasticity, is only evident when induced, very much like genes. Plastic changes may be programmed and providing a ``midcourse correction" during childhood development. If that is not executed in the normal way severe developmental disorders such as autism may results. Normal development of functions and anatomical organization of the brain and the spinal cord depend on appropriate sensory stimulation and motor activations. So-called enriched sensory environments have been shown to be beneficial for cognitive development and enriched acoustic environment may even slow the progression of age-related hearing loss. It is possible that the beneficial effect of physical exercise is achieved through activation of neural plasticity. The beneficial effect of training after trauma to the brain or spinal cord is mainly achieved through shifting functions from damaged brain area to other parts of the central nervous system and adapting these parts to take over the functions that are lost. This is accomplished through activation of neural plasticity. Plastic changes can also be harmful and cause symptoms and signs of disorders such as some forms of chronic pain (central neuropathic pain) and severe tinnitus. We will call such disorders ``plasticity disorders".

  10. Tinnitus and neural plasticity of the brain

    NARCIS (Netherlands)

    Bartels, Hilke; Staal, Michiel J.; Albers, Frans W. J.

    Objective: To describe the current ideas about the manifestations of neural plasticity in generating tinnitus. Data Sources: Recently published source articles were identified using MEDLINE, PubMed, and Cochrane Library according to the key words mentioned below. Study Selection: Review articles and

  11. On the Relationships between Generative Encodings, Regularity, and Learning Abilities when Evolving Plastic Artificial Neural Networks: e79138

    National Research Council Canada - National Science Library

    Paul Tonelli; Jean-Baptiste Mouret

    2013-01-01

    .... It is commonly believed that two keys for evolving nature-like artificial neural networks are (1) the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks...

  12. Neural plasticity in pancreatitis and pancreatic cancer.

    Science.gov (United States)

    Demir, Ihsan Ekin; Friess, Helmut; Ceyhan, Güralp O

    2015-11-01

    Pancreatic nerves undergo prominent alterations during the evolution and progression of human chronic pancreatitis and pancreatic cancer. Intrapancreatic nerves increase in size (neural hypertrophy) and number (increased neural density). The proportion of autonomic and sensory fibres (neural remodelling) is switched, and are infiltrated by perineural inflammatory cells (pancreatic neuritis) or invaded by pancreatic cancer cells (neural invasion). These neuropathic alterations also correlate with neuropathic pain. Instead of being mere histopathological manifestations of disease progression, pancreatic neural plasticity synergizes with the enhanced excitability of sensory neurons, with Schwann cell recruitment toward cancer and with central nervous system alterations. These alterations maintain a bidirectional interaction between nerves and non-neural pancreatic cells, as demonstrated by tissue and neural damage inducing neuropathic pain, and activated neurons releasing mediators that modulate inflammation and cancer growth. Owing to the prognostic effects of pain and neural invasion in pancreatic cancer, dissecting the mechanism of pancreatic neuroplasticity holds major translational relevance. However, current in vivo models of pancreatic cancer and chronic pancreatitis contain many discrepancies from human disease that overshadow their translational value. The present Review discusses novel possibilities for mechanistically uncovering the role of the nervous system in pancreatic disease progression.

  13. Spasticity, Motor Recovery, and Neural Plasticity after Stroke.

    Science.gov (United States)

    Li, Sheng

    2017-01-01

    Spasticity and weakness (spastic paresis) are the primary motor impairments after stroke and impose significant challenges for treatment and patient care. Spasticity emerges and disappears in the course of complete motor recovery. Spasticity and motor recovery are both related to neural plasticity after stroke. However, the relation between the two remains poorly understood among clinicians and researchers. Recovery of strength and motor function is mainly attributed to cortical plastic reorganization in the early recovery phase, while reticulospinal (RS) hyperexcitability as a result of maladaptive plasticity, is the most plausible mechanism for poststroke spasticity. It is important to differentiate and understand that motor recovery and spasticity have different underlying mechanisms. Facilitation and modulation of neural plasticity through rehabilitative strategies, such as early interventions with repetitive goal-oriented intensive therapy, appropriate non-invasive brain stimulation, and pharmacological agents, are the keys to promote motor recovery. Individualized rehabilitation protocols could be developed to utilize or avoid the maladaptive plasticity, such as RS hyperexcitability, in the course of motor recovery. Aggressive and appropriate spasticity management with botulinum toxin therapy is an example of how to create a transient plastic state of the neuromotor system that allows motor re-learning and recovery in chronic stages.

  14. Neural ECM molecules in axonal and synaptic homeostatic plasticity.

    Science.gov (United States)

    Frischknecht, Renato; Chang, Kae-Jiun; Rasband, Matthew N; Seidenbecher, Constanze I

    2014-01-01

    Neural circuits can express different forms of plasticity. So far, Hebbian synaptic plasticity was considered the most important plastic phenomenon, but over the last decade, homeostatic mechanisms gained more interest because they can explain how a neuronal network maintains stable baseline function despite multiple plastic challenges, like developmental plasticity, learning, or lesion. Such destabilizing influences can be counterbalanced by the mechanisms of homeostatic plasticity, which restore the stability of neuronal circuits. Synaptic scaling is a mechanism in which neurons can detect changes in their own firing rates through a set of molecular sensors that then regulate receptor trafficking to scale the accumulation of glutamate receptors at synaptic sites. Additional homeostatic mechanisms allow local changes in synaptic activation to generate local synaptic adaptations and network-wide changes in activity, which lead to adjustments in the balance between excitation and inhibition. The molecular pathways underlying these forms of homeostatic plasticity are currently under intense investigation, and it becomes clear that the extracellular matrix (ECM) of the brain, which surrounds individual neurons and integrates them into the tissue, is an important element in these processes. As a highly dynamic structure, which can be remodeled and degraded in an activity-dependent manner and in concerted action of neurons and glial cells, it can on one hand promote structural and functional plasticity and on the other hand stabilize neural microcircuits. This chapter highlights the composition of brain ECM with particular emphasis on perisynaptic and axonal matrix formations and its involvement in plastic and adaptive processes of the central nervous system.

  15. Environmental enrichment promotes neural plasticity and cognitive ability in fish.

    Science.gov (United States)

    Salvanes, Anne Gro Vea; Moberg, Olav; Ebbesson, Lars O E; Nilsen, Tom Ole; Jensen, Knut Helge; Braithwaite, Victoria A

    2013-09-22

    Different kinds of experience during early life can play a significant role in the development of an animal's behavioural phenotype. In natural contexts, this influences behaviours from anti-predator responses to navigation abilities. By contrast, for animals reared in captive environments, the homogeneous nature of their experience tends to reduce behavioural flexibility. Studies with cage-reared rodents indicate that captivity often compromises neural development and neural plasticity. Such neural and behavioural deficits can be problematic if captive-bred animals are being reared with the intention of releasing them as part of a conservation strategy. Over the last decade, there has been growing interest in the use of environmental enrichment to promote behavioural flexibility in animals that are bred for release. Here, we describe the positive effects of environmental enrichment on neural plasticity and cognition in juvenile Atlantic salmon (Salmo salar). Exposing fish to enriched conditions upregulated the forebrain expression of NeuroD1 mRNA and improved learning ability assessed in a spatial task. The addition of enrichment to the captive environment thus promotes neural and behavioural changes that are likely to promote behavioural flexibility and improve post-release survival.

  16. Modulation of hippocampal neural plasticity by glucose-related signaling.

    Science.gov (United States)

    Mainardi, Marco; Fusco, Salvatore; Grassi, Claudio

    2015-01-01

    Hormones and peptides involved in glucose homeostasis are emerging as important modulators of neural plasticity. In this regard, increasing evidence shows that molecules such as insulin, insulin-like growth factor-I, glucagon-like peptide-1, and ghrelin impact on the function of the hippocampus, which is a key area for learning and memory. Indeed, all these factors affect fundamental hippocampal properties including synaptic plasticity (i.e., synapse potentiation and depression), structural plasticity (i.e., dynamics of dendritic spines), and adult neurogenesis, thus leading to modifications in cognitive performance. Here, we review the main mechanisms underlying the effects of glucose metabolism on hippocampal physiology. In particular, we discuss the role of these signals in the modulation of cognitive functions and their potential implications in dysmetabolism-related cognitive decline.

  17. Modulation of Hippocampal Neural Plasticity by Glucose-Related Signaling

    Directory of Open Access Journals (Sweden)

    Marco Mainardi

    2015-01-01

    Full Text Available Hormones and peptides involved in glucose homeostasis are emerging as important modulators of neural plasticity. In this regard, increasing evidence shows that molecules such as insulin, insulin-like growth factor-I, glucagon-like peptide-1, and ghrelin impact on the function of the hippocampus, which is a key area for learning and memory. Indeed, all these factors affect fundamental hippocampal properties including synaptic plasticity (i.e., synapse potentiation and depression, structural plasticity (i.e., dynamics of dendritic spines, and adult neurogenesis, thus leading to modifications in cognitive performance. Here, we review the main mechanisms underlying the effects of glucose metabolism on hippocampal physiology. In particular, we discuss the role of these signals in the modulation of cognitive functions and their potential implications in dysmetabolism-related cognitive decline.

  18. Reading in the dark: neural correlates and cross-modal plasticity for learning to read entire words without visual experience.

    Science.gov (United States)

    Sigalov, Nadine; Maidenbaum, Shachar; Amedi, Amir

    2016-03-01

    Cognitive neuroscience has long attempted to determine the ways in which cortical selectivity develops, and the impact of nature vs. nurture on it. Congenital blindness (CB) offers a unique opportunity to test this question as the brains of blind individuals develop without visual experience. Here we approach this question through the reading network. Several areas in the visual cortex have been implicated as part of the reading network, and one of the main ones among them is the VWFA, which is selective to the form of letters and words. But what happens in the CB brain? On the one hand, it has been shown that cross-modal plasticity leads to the recruitment of occipital areas, including the VWFA, for linguistic tasks. On the other hand, we have recently demonstrated VWFA activity for letters in contrast to other visual categories when the information is provided via other senses such as touch or audition. Which of these tasks is more dominant? By which mechanism does the CB brain process reading? Using fMRI and visual-to-auditory sensory substitution which transfers the topographical features of the letters we compare reading with semantic and scrambled conditions in a group of CB. We found activation in early auditory and visual cortices during the early processing phase (letter), while the later phase (word) showed VWFA and bilateral dorsal-intraparietal activations for words. This further supports the notion that many visual regions in general, even early visual areas, also maintain a predilection for task processing even when the modality is variable and in spite of putative lifelong linguistic cross-modal plasticity. Furthermore, we find that the VWFA is recruited preferentially for letter and word form, while it was not recruited, and even exhibited deactivation, for an immediately subsequent semantic task suggesting that despite only short sensory substitution experience orthographic task processing can dominate semantic processing in the VWFA. On a wider

  19. Neural plasticity lessons from disorders of consciousness

    Directory of Open Access Journals (Sweden)

    Athena eDemertzi

    2011-02-01

    Full Text Available Communication and intentional behavior are supported by the brain’s integrity at a structural and a functional level. When widespread loss of cerebral connectivity is brought about as a result of a severe brain injury, in many cases patients are not capable of conscious interactive behavior and are said to suffer from disorders of consciousness (e.g., coma, vegetative state /unresponsive wakefulness syndrome, minimally conscious states. This lesion paradigm has offered not only clinical insights, as how to improve diagnosis, prognosis and treatment, but also put forward scientific opportunities to study the brain’s plastic abilities. We here review interventional and observational studies performed in severely brain-injured patients with regards to recovery of consciousness. The study of the recovered conscious brain (spontaneous and/or after surgical or pharmacologic interventions, suggests a link between some specific brain areas and the capacity of the brain to sustain conscious experience, challenging at the same time the notion of fixed temporal boundaries in rehabilitative processes. Altered functional connectivity, cerebral structural reorganization as well as behavioral amelioration after invasive treatments will be discussed as the main indices for plasticity in these challenging patients. The study of patients with chronic disorders of consciousness may, thus, provide further insights not only at a clinical level (i.e., medical management and rehabilitation but also from a scientific-theoretical perspective (i.e., the brain’s plastic abilities and the pursuit of the neural correlate of consciousness.

  20. The neurobiology of sound-specific auditory plasticity: a core neural circuit.

    Science.gov (United States)

    Xiong, Ying; Zhang, Yonghai; Yan, Jun

    2009-09-01

    Auditory learning or experience induces large-scale neural plasticity in not only the auditory cortex but also in the auditory thalamus and midbrain. Such plasticity is guided by acquired sound (sound-specific auditory plasticity). The mechanisms involved in this process have been studied from various approaches and support the presence of a core neural circuit consisting of a subcortico-cortico-subcortical tonotopic loop supplemented by neuromodulatory (e.g., cholinergic) inputs. This circuit has three key functions essential for establishing large-scale and sound-specific plasticity in the auditory cortex, auditory thalamus and auditory midbrain. They include the presence of sound information for guiding the plasticity, the communication between the cortex, thalamus and midbrain for coordinating the plastic changes and the adjustment of the circuit status for augmenting the plasticity. This review begins with an overview of sound-specific auditory plasticity in the central auditory system. It then introduces the core neural circuit which plays an essential role in inducing sound-specific auditory plasticity. Finally, the core neural circuit and its relationship to auditory learning and experience are discussed.

  1. Activity-dependent neural plasticity from bench to bedside.

    Science.gov (United States)

    Ganguly, Karunesh; Poo, Mu-Ming

    2013-10-30

    Much progress has been made in understanding how behavioral experience and neural activity can modify the structure and function of neural circuits during development and in the adult brain. Studies of physiological and molecular mechanisms underlying activity-dependent plasticity in animal models have suggested potential therapeutic approaches for a wide range of brain disorders in humans. Physiological and electrical stimulations as well as plasticity-modifying molecular agents may facilitate functional recovery by selectively enhancing existing neural circuits or promoting the formation of new functional circuits. Here, we review the advances in basic studies of neural plasticity mechanisms in developing and adult nervous systems and current clinical treatments that harness neural plasticity, and we offer perspectives on future development of plasticity-based therapy. Copyright © 2013 Elsevier Inc. All rights reserved.

  2. [Mechanism of neural plasticity of acupuncture on chronic migraine].

    Science.gov (United States)

    Xu, Xiaobai; Liu, Lu; Zhao, Luopeng; Qu, Zhengyang; Zhu, Yupu; Zhang, Yajie; Wang, Linpeng

    2017-10-12

    Chronic migraine is one of neurological disorders with high rate of disability, but sufficient attention has not been paid in this field. A large number of clinical studies have shown traditional Chinese acupuncture is a kind of effective treatment with less side effects. Through the analysis of literature regarding acupuncture and migraine published from 1981 to 2017 in CNKI and PubMed databases, the mechanism of neural plasticity of acupuncture on chronic migraine was explored. It was believed the progress of chronic migraine involved the changes of neural plasticity in neural structure and function, and the neural plasticity related with neural sensitization during the process of chronic migraine was discussed from three aspects of electrophysiology, molecular chemistry and radiography. Acupuncture could treat and prevent chronic migraine via the mechanism of neural plasticity, but there was no related literature, hindering the further spreading and development of acupuncture for chronic migraine.

  3. [Progress in activity-dependent structural plasticity of neural circuits in cortex].

    Science.gov (United States)

    Rao, Xiao-Ping; Xu, Zhi-Xiang; Xu, Fu-Qiang

    2012-10-01

    Neural circuits of mammalian cerebral cortex have exhibited amazing abilities of structural and functional plasticity in development, learning and memory, neurological and psychiatric diseases. With the new imaging techniques and the application of molecular biology methods, observation neural circuits' structural dynamics within the cortex in vivo at the cellular and synaptic level was possible, so there were many great progresses in the field of the activity-dependent structural plasticity over the past decade. This paper reviewed some of the aspects of the experimental results, focused on the characteristics of dendritic structural plasticity in individual growth and development, rich environment, sensory deprivation, and pathological conditions, as well as learning and memory, especially the dynamics of dendritic spines on morphology and quantity; after that, we introduced axonal structural plasticity, the molecular and cellular mechanisms of structural plasticity, and proposed some future problems to be solved at last.

  4. Neural plasticity and locomotor recovery after spinal cord injury.

    Science.gov (United States)

    Tansey, Keith E

    2010-12-01

    The discussion of neural plasticity and locomotor recovery after spinal cord injury (SCI) focuses on 2 main themes, the issues associated with detecting neural plasticity in human beings and the issue of how to translate information from animal models, in which neural plasticity can be more readily studied, to human clinical research and application. This article discusses the importance of studying neural plasticity to better understand the effects of current rehabilitation interventions and to devise the next generation of therapies. It reviews the current spectrum of clinical, functional, anatomical, and neurophysiological assessments of patients that can be made in neurorehabilitation and the relationship between those measures and the study of neural plasticity. Then the similarities and differences between animal models and human SCI are discussed in relation to the severity of injury, the effect of locomotor training on gait recovery, the localization of neural plasticity associated with that gait recovery, and the implications for interpreting the "translatability" of animal model data to human study and clinical practice. In summary, it is concluded that the study of neural plasticity and locomotor recovery after SCI is really in its infancy but that it is critical for the advancement of the science of neurorehabilitation and "restorative neurology." Copyright © 2010 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

  5. Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

    DEFF Research Database (Denmark)

    Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin

    2015-01-01

    dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural...... mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online...... correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking...

  6. Neural plasticity and its initiating conditions in tinnitus.

    Science.gov (United States)

    Roberts, L E

    2017-12-12

    Deafferentation caused by cochlear pathology (which can be hidden from the audiogram) activates forms of neural plasticity in auditory pathways, generating tinnitus and its associated conditions including hyperacusis. This article discusses tinnitus mechanisms and suggests how these mechanisms may relate to those involved in normal auditory information processing. Research findings from animal models of tinnitus and from electromagnetic imaging of tinnitus patients are reviewed which pertain to the role of deafferentation and neural plasticity in tinnitus and hyperacusis. Auditory neurons compensate for deafferentation by increasing their input/output functions (gain) at multiple levels of the auditory system. Forms of homeostatic plasticity are believed to be responsible for this neural change, which increases the spontaneous and driven activity of neurons in central auditory structures in animals expressing behavioral evidence of tinnitus. Another tinnitus correlate, increased neural synchrony among the affected neurons, is forged by spike-timing-dependent neural plasticity in auditory pathways. Slow oscillations generated by bursting thalamic neurons verified in tinnitus animals appear to modulate neural plasticity in the cortex, integrating tinnitus neural activity with information in brain regions supporting memory, emotion, and consciousness which exhibit increased metabolic activity in tinnitus patients. The latter process may be induced by transient auditory events in normal processing but it persists in tinnitus, driven by phantom signals from the auditory pathway. Several tinnitus therapies attempt to suppress tinnitus through plasticity, but repeated sessions will likely be needed to prevent tinnitus activity from returning owing to deafferentation as its initiating condition.

  7. A framework for plasticity implementation on the SpiNNaker neural architecture

    Directory of Open Access Journals (Sweden)

    Francesco eGalluppi

    2015-01-01

    Full Text Available Many of the precise biological mechanisms of synaptic plasticity remain elusive, but simulations of neural networks have greatly enhanced our understanding of how specific global functions arise from the massively parallel computation of neurons and local Hebbian or spike-timing dependent plasticity rules. For simulating large portions of neural tissue, this has created an increasingly strong need for large scale simulations of plastic neural networks on special purpose hardware platforms, because synaptic transmissions and updates are badly matched to computing style supported by current architectures. Because of the great diversity of biological plasticity phenomena and the corresponding diversity of models, there is a great need for testing various hypotheses about plasticity before committing to one hardware implementation. Here we present a novel framework for investigating different plasticity approaches on the SpiNNaker distributed digital neural simulation platform.The key innovation of the proposed architecture is to exploit the reconfigurability of the ARM processors inside SpiNNaker, dedicating a subset of them exclusively to process synaptic plasticity updates, while the rest perform the usual neural and synaptic simulations. We demonstrate the flexibility of the proposed approach by showing the implementation of a variety of spike- and rate-based learning rules, including standard Spike-Timing dependent plasticity (STDP, voltage-dependent STDP, and the rate-based BCM rule.We analyze their performance and validate them by running classical learning experiments in real time on a 4-chip SpiNNaker board. The result is an efficient, modular, flexible and scalable framework, which provides a valuable tool for the fast and easy exploration of learning models of very different kinds on the parallel and reconfigurable SpiNNaker system.

  8. A framework for plasticity implementation on the SpiNNaker neural architecture.

    Science.gov (United States)

    Galluppi, Francesco; Lagorce, Xavier; Stromatias, Evangelos; Pfeiffer, Michael; Plana, Luis A; Furber, Steve B; Benosman, Ryad B

    2014-01-01

    Many of the precise biological mechanisms of synaptic plasticity remain elusive, but simulations of neural networks have greatly enhanced our understanding of how specific global functions arise from the massively parallel computation of neurons and local Hebbian or spike-timing dependent plasticity rules. For simulating large portions of neural tissue, this has created an increasingly strong need for large scale simulations of plastic neural networks on special purpose hardware platforms, because synaptic transmissions and updates are badly matched to computing style supported by current architectures. Because of the great diversity of biological plasticity phenomena and the corresponding diversity of models, there is a great need for testing various hypotheses about plasticity before committing to one hardware implementation. Here we present a novel framework for investigating different plasticity approaches on the SpiNNaker distributed digital neural simulation platform. The key innovation of the proposed architecture is to exploit the reconfigurability of the ARM processors inside SpiNNaker, dedicating a subset of them exclusively to process synaptic plasticity updates, while the rest perform the usual neural and synaptic simulations. We demonstrate the flexibility of the proposed approach by showing the implementation of a variety of spike- and rate-based learning rules, including standard Spike-Timing dependent plasticity (STDP), voltage-dependent STDP, and the rate-based BCM rule. We analyze their performance and validate them by running classical learning experiments in real time on a 4-chip SpiNNaker board. The result is an efficient, modular, flexible and scalable framework, which provides a valuable tool for the fast and easy exploration of learning models of very different kinds on the parallel and reconfigurable SpiNNaker system.

  9. Neural Plasticity and Neurorehabilitation: Teaching the New Brain Old Tricks

    Science.gov (United States)

    Kleim, Jeffrey A.

    2011-01-01

    Following brain injury or disease there are widespread biochemical, anatomical and physiological changes that result in what might be considered a new, very different brain. This adapted brain is forced to reacquire behaviors lost as a result of the injury or disease and relies on neural plasticity within the residual neural circuits. The same…

  10. Principles of Experience-Dependent Neural Plasticity: Implications for Rehabilitation after Brain Damage

    Science.gov (United States)

    Kleim, Jeffrey A.; Jones, Theresa A.

    2008-01-01

    Purpose: This paper reviews 10 principles of experience-dependent neural plasticity and considerations in applying them to the damaged brain. Method: Neuroscience research using a variety of models of learning, neurological disease, and trauma are reviewed from the perspective of basic neuroscientists but in a manner intended to be useful for the…

  11. Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills.

    Science.gov (United States)

    Koralek, Aaron C; Jin, Xin; Long, John D; Costa, Rui M; Carmena, Jose M

    2012-03-04

    The ability to learn new skills and perfect them with practice applies not only to physical skills but also to abstract skills, like motor planning or neuroprosthetic actions. Although plasticity in corticostriatal circuits has been implicated in learning physical skills, it remains unclear if similar circuits or processes are required for abstract skill learning. Here we use a novel behavioural task in rodents to investigate the role of corticostriatal plasticity in abstract skill learning. Rodents learned to control the pitch of an auditory cursor to reach one of two targets by modulating activity in primary motor cortex irrespective of physical movement. Degradation of the relation between action and outcome, as well as sensory-specific devaluation and omission tests, demonstrate that these learned neuroprosthetic actions are intentional and goal-directed, rather than habitual. Striatal neurons change their activity with learning, with more neurons modulating their activity in relation to target-reaching as learning progresses. Concomitantly, strong relations between the activity of neurons in motor cortex and the striatum emerge. Specific deletion of striatal NMDA receptors impairs the development of this corticostriatal plasticity, and disrupts the ability to learn neuroprosthetic skills. These results suggest that corticostriatal plasticity is necessary for abstract skill learning, and that neuroprosthetic movements capitalize on the neural circuitry involved in natural motor learning. © 2012 Macmillan Publishers Limited. All rights reserved

  12. Neural plasticity and functional recovery of human central nervous system with special reference to spinal cord injury.

    Science.gov (United States)

    Wang, D; Sun, T

    2011-04-01

    Literature review. To study the progress that has been made in neural plasticity for the past few decades. United Kingdom/China. An electronic search of relevant publications through PubMed was conducted using two key words: 'axonal regeneration' and 'neural plasticity'. The search included publications of the past three decades of all languages and of both animal and human studies. After confirmation of immense increase of publications on neural plasticity, reviewing of neural plasticity alone was conducted. The review covered only the most important and clinically relevant publications. For convenience of reading by busy clinicians, discussions focused on cellular and functional levels, and only the most investigated molecules were mentioned. The size of references is also planned to be concise rather than comprehensive into three digits. Neural plasticity is about memory and learning. The entire process of neural plasticity is presented in the sequence of (1) lesion-induced plasticity, (2) clearance of debris, (3) collateral sprouting (4) potentiation. The recent discovery and understanding of the important role of Chondroitinase in clearance of debris is discussed in detail. Neural plasticity has enormous potentials in facilitating functional recovery. It is a realistic target than structural axonal regeneration at current level of neuroscience.

  13. Short-term synaptic plasticity and heterogeneity in neural systems

    Science.gov (United States)

    Mejias, J. F.; Kappen, H. J.; Longtin, A.; Torres, J. J.

    2013-01-01

    We review some recent results on neural dynamics and information processing which arise when considering several biophysical factors of interest, in particular, short-term synaptic plasticity and neural heterogeneity. The inclusion of short-term synaptic plasticity leads to enhanced long-term memory capacities, a higher robustness of memory to noise, and irregularity in the duration of the so-called up cortical states. On the other hand, considering some level of neural heterogeneity in neuron models allows neural systems to optimize information transmission in rate coding and temporal coding, two strategies commonly used by neurons to codify information in many brain areas. In all these studies, analytical approximations can be made to explain the underlying dynamics of these neural systems.

  14. Neural plasticity and behavior - sixty years of conceptual advances.

    Science.gov (United States)

    Sweatt, J David

    2016-10-01

    This brief review summarizes 60 years of conceptual advances that have demonstrated a role for active changes in neuronal connectivity as a controller of behavior and behavioral change. Seminal studies in the first phase of the six-decade span of this review firmly established the cellular basis of behavior - a concept that we take for granted now, but which was an open question at the time. Hebbian plasticity, including long-term potentiation and long-term depression, was then discovered as being important for local circuit refinement in the context of memory formation and behavioral change and stabilization in the mammalian central nervous system. Direct demonstration of plasticity of neuronal circuit function in vivo, for example, hippocampal neurons forming place cell firing patterns, extended this concept. However, additional neurophysiologic and computational studies demonstrated that circuit development and stabilization additionally relies on non-Hebbian, homoeostatic, forms of plasticity, such as synaptic scaling and control of membrane intrinsic properties. Activity-dependent neurodevelopment was found to be associated with cell-wide adjustments in post-synaptic receptor density, and found to occur in conjunction with synaptic pruning. Pioneering cellular neurophysiologic studies demonstrated the critical roles of transmembrane signal transduction, NMDA receptor regulation, regulation of neural membrane biophysical properties, and back-propagating action potential in critical time-dependent coincidence detection in behavior-modifying circuits. Concerning the molecular mechanisms underlying these processes, regulation of gene transcription was found to serve as a bridge between experience and behavioral change, closing the 'nature versus nurture' divide. Both active DNA (de)methylation and regulation of chromatin structure have been validated as crucial regulators of gene transcription during learning. The discovery of protein synthesis dependence on the

  15. The Role of Neural Plasticity in Depression: From Hippocampus to Prefrontal Cortex

    Directory of Open Access Journals (Sweden)

    Wei Liu

    2017-01-01

    Full Text Available Neural plasticity, a fundamental mechanism of neuronal adaptation, is disrupted in depression. The changes in neural plasticity induced by stress and other negative stimuli play a significant role in the onset and development of depression. Antidepressant treatments have also been found to exert their antidepressant effects through regulatory effects on neural plasticity. However, the detailed mechanisms of neural plasticity in depression still remain unclear. Therefore, in this review, we summarize the recent literature to elaborate the possible mechanistic role of neural plasticity in depression. Taken together, these findings may pave the way for future progress in neural plasticity studies.

  16. The Role of Neural Plasticity in Depression: From Hippocampus to Prefrontal Cortex.

    Science.gov (United States)

    Liu, Wei; Ge, Tongtong; Leng, Yashu; Pan, Zhenxiang; Fan, Jie; Yang, Wei; Cui, Ranji

    2017-01-01

    Neural plasticity, a fundamental mechanism of neuronal adaptation, is disrupted in depression. The changes in neural plasticity induced by stress and other negative stimuli play a significant role in the onset and development of depression. Antidepressant treatments have also been found to exert their antidepressant effects through regulatory effects on neural plasticity. However, the detailed mechanisms of neural plasticity in depression still remain unclear. Therefore, in this review, we summarize the recent literature to elaborate the possible mechanistic role of neural plasticity in depression. Taken together, these findings may pave the way for future progress in neural plasticity studies.

  17. Neural networks and statistical learning

    CERN Document Server

    Du, Ke-Lin

    2014-01-01

    Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...

  18. Perineuronal net, CSPG receptor and their regulation of neural plasticity.

    Science.gov (United States)

    Miao, Qing-Long; Ye, Qian; Zhang, Xiao-Hui

    2014-08-25

    Perineuronal nets (PNNs) are reticular structures resulting from the aggregation of extracellular matrix (ECM) molecules around the cell body and proximal neurite of specific population of neurons in the central nervous system (CNS). Since the first description of PNNs by Camillo Golgi in 1883, the molecular composition, developmental formation and potential functions of these specialized extracellular matrix structures have only been intensively studied over the last few decades. The main components of PNNs are hyaluronan (HA), chondroitin sulfate proteoglycans (CSPGs) of the lectican family, link proteins and tenascin-R. PNNs appear late in neural development, inversely correlating with the level of neural plasticity. PNNs have long been hypothesized to play a role in stabilizing the extracellular milieu, which secures the characteristic features of enveloped neurons and protects them from the influence of malicious agents. Aberrant PNN signaling can lead to CNS dysfunctions like epilepsy, stroke and Alzheimer's disease. On the other hand, PNNs create a barrier which constrains the neural plasticity and counteracts the regeneration after nerve injury. Digestion of PNNs with chondroitinase ABC accelerates functional recovery from the spinal cord injury and restores activity-dependent mechanisms for modifying neuronal connections in the adult animals, indicating that PNN is an important regulator of neural plasticity. Here, we review recent progress in the studies on the formation of PNNs during early development and the identification of CSPG receptor - an essential molecular component of PNN signaling, along with a discussion on their unique regulatory roles in neural plasticity.

  19. Studies of Neuronal Gene Regulation Controlling the Molecular Mechanisms Underlying Neural Plasticity.

    Science.gov (United States)

    Fukuchi, Mamoru

    2017-01-01

    The regulation of the development and function of the nervous system is not preprogramed but responds to environmental stimuli to change neural development and function flexibly. This neural plasticity is a characteristic property of the nervous system. For example, strong synaptic activation evoked by environmental stimuli leads to changes in synaptic functions (known as synaptic plasticity). Long-lasting synaptic plasticity is one of the molecular mechanisms underlying long-term learning and memory. Since discovering the role of the transcription factor cAMP-response element-binding protein in learning and memory, it has been widely accepted that gene regulation in neurons contributes to long-lasting changes in neural functions. However, it remains unclear how synaptic activation is converted into gene regulation that results in long-lasting neural functions like long-term memory. We continue to address this question. This review introduces our recent findings on the gene regulation of brain-derived neurotrophic factor and discusses how regulation of the gene participates in long-lasting changes in neural functions.

  20. Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware

    Science.gov (United States)

    Knight, James C.; Tully, Philip J.; Kaplan, Bernhard A.; Lansner, Anders; Furber, Steve B.

    2016-01-01

    SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 2.0 × 104 neurons and 5.1 × 107 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately 45× more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models. PMID:27092061

  1. Large-scale simulations of plastic neural networks on neuromorphic hardware

    Directory of Open Access Journals (Sweden)

    James Courtney Knight

    2016-04-01

    Full Text Available SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 20000 neurons and 51200000 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models.

  2. The Role of Neural Plasticity in Depression: From Hippocampus to Prefrontal Cortex

    OpenAIRE

    Wei Liu; Tongtong Ge; Yashu Leng; Zhenxiang Pan; Jie Fan; Wei Yang; Ranji Cui

    2017-01-01

    Neural plasticity, a fundamental mechanism of neuronal adaptation, is disrupted in depression. The changes in neural plasticity induced by stress and other negative stimuli play a significant role in the onset and development of depression. Antidepressant treatments have also been found to exert their antidepressant effects through regulatory effects on neural plasticity. However, the detailed mechanisms of neural plasticity in depression still remain unclear. Therefore, in this review, we su...

  3. Early life nutrition and neural plasticity

    OpenAIRE

    Georgieff, Michael K.; Brunette, Katya E; Tran, Phu V

    2015-01-01

    The human brain undergoes a remarkable transformation during fetal life and the first postnatal years from a relatively undifferentiated but pluripotent organ to a highly specified and organized one. The outcome of this developmental maturation is highly dependent on a sequence of environmental exposures that can have either positive or negative influences on the ultimate plasticity of the adult brain. Many environmental exposures are beyond the control of the individual, but nutrition is not...

  4. Brain plasticity through the life span: learning to learn and action video games.

    Science.gov (United States)

    Bavelier, Daphne; Green, C Shawn; Pouget, Alexandre; Schrater, Paul

    2012-01-01

    The ability of the human brain to learn is exceptional. Yet, learning is typically quite specific to the exact task used during training, a limiting factor for practical applications such as rehabilitation, workforce training, or education. The possibility of identifying training regimens that have a broad enough impact to transfer to a variety of tasks is thus highly appealing. This work reviews how complex training environments such as action video game play may actually foster brain plasticity and learning. This enhanced learning capacity, termed learning to learn, is considered in light of its computational requirements and putative neural mechanisms.

  5. Motor cortical plasticity induced by motor learning through mental practice.

    Directory of Open Access Journals (Sweden)

    Laura eAvanzino

    2015-04-01

    Full Text Available Several investigations suggest that actual and mental actions trigger similar neural substrates. Motor learning via physical practice results in long-term potentiation (LTP-like plasticity processes, namely potentiation of M1 and a temporary occlusion of additional LTP-like plasticity. However, whether this neuroplasticity process contributes to improve motor performance through mental practice remains to be determined. Here, we tested skill learning-dependent changes in primary motor cortex (M1 excitability and plasticity by means of transcranial magnetic stimulation in subjects trained to physically execute or mentally perform a sequence of finger opposition movements. Before and after physical practice and motor-imagery practice, M1 excitability was evaluated by measuring the input-output (IO curve of motor evoked potentials. M1 long-term potentiation (LTP and long-term depression (LTD-like plasticity was assessed with paired-associative stimulation (PAS of the median nerve and motor cortex using an interstimulus interval of 25 ms (PAS25 or 10 ms (PAS10, respectively. We found that even if after both practice sessions subjects significantly improved their movement speed, M1 excitability and plasticity were differentially influenced by the two practice sessions. First, we observed an increase in the slope of IO curve after physical but not after motor-imagery practice. Second, there was a reversal of the PAS25 effect from LTP-like plasticity to LTD-like plasticity following physical and motor-imagery practice. Third, LTD-like plasticity (PAS10 protocol increased after physical practice, whilst it was occluded after motor-imagery practice. In conclusion, we demonstrated that motor-imagery practice lead to the development of neuroplasticity, as it affected the PAS25- and PAS10- induced plasticity in M1. These results, expanding the current knowledge on how motor-imagery training shapes M1 plasticity, might have a potential impact in

  6. Enhancement of signal sensitivity in a heterogeneous neural network refined from synaptic plasticity

    Energy Technology Data Exchange (ETDEWEB)

    Li Xiumin; Small, Michael, E-mail: ensmall@polyu.edu.h, E-mail: 07901216r@eie.polyu.edu.h [Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon (Hong Kong)

    2010-08-15

    Long-term synaptic plasticity induced by neural activity is of great importance in informing the formation of neural connectivity and the development of the nervous system. It is reasonable to consider self-organized neural networks instead of prior imposition of a specific topology. In this paper, we propose a novel network evolved from two stages of the learning process, which are respectively guided by two experimentally observed synaptic plasticity rules, i.e. the spike-timing-dependent plasticity (STDP) mechanism and the burst-timing-dependent plasticity (BTDP) mechanism. Due to the existence of heterogeneity in neurons that exhibit different degrees of excitability, a two-level hierarchical structure is obtained after the synaptic refinement. This self-organized network shows higher sensitivity to afferent current injection compared with alternative archetypal networks with different neural connectivity. Statistical analysis also demonstrates that it has the small-world properties of small shortest path length and high clustering coefficients. Thus the selectively refined connectivity enhances the ability of neuronal communications and improves the efficiency of signal transmission in the network.

  7. Intrinsic Plasticity for Natural Competition in Koniocortex-Like Neural Networks.

    Science.gov (United States)

    Peláez, Francisco Javier Ropero; Aguiar-Furucho, Mariana Antonia; Andina, Diego

    2016-08-01

    In this paper, we use the neural property known as intrinsic plasticity to develop neural network models that resemble the koniocortex, the fourth layer of sensory cortices. These models evolved from a very basic two-layered neural network to a complex associative koniocortex network. In the initial network, intrinsic and synaptic plasticity govern the shifting of the activation function, and the modification of synaptic weights, respectively. In this first version, competition is forced, so that the most activated neuron is arbitrarily set to one and the others to zero, while in the second, competition occurs naturally due to inhibition between second layer neurons. In the third version of the network, whose architecture is similar to the koniocortex, competition also occurs naturally owing to the interplay between inhibitory interneurons and synaptic and intrinsic plasticity. A more complex associative neural network was developed based on this basic koniocortex-like neural network, capable of dealing with incomplete patterns and ideally suited to operating similarly to a learning vector quantization network. We also discuss the biological plausibility of the networks and their role in a more complex thalamocortical model.

  8. Environmental effects on fish neural plasticity and cognition.

    Science.gov (United States)

    Ebbesson, L O E; Braithwaite, V A

    2012-12-01

    Most fishes experiencing challenging environments are able to adjust and adapt their physiology and behaviour to help them cope more effectively. Much of this flexibility is supported and influenced by cognition and neural plasticity. The understanding of fish cognition and the role played by different regions of the brain has improved significantly in recent years. Techniques such as lesioning, tract tracing and quantifying changes in gene expression help in mapping specialized brain areas. It is now recognized that the fish brain remains plastic throughout a fish's life and that it continues to be sensitive to environmental challenges. The early development of fish brains is shaped by experiences with the environment and this can promote positive and negative effects on both neural plasticity and cognitive ability. This review focuses on what is known about the interactions between the environment, the telencephalon and cognition. Examples are used from a diverse array of fish species, but there could be a lot to be gained by focusing research on neural plasticity and cognition in fishes for which there is already a wealth of knowledge relating to their physiology, behaviour and natural history, e.g. the Salmonidae. © 2012 The Authors. Journal of Fish Biology © 2012 The Fisheries Society of the British Isles.

  9. Cannabinoid function in learning, memory and plasticity.

    Science.gov (United States)

    Riedel, G; Davies, S N

    2005-01-01

    Marijuana and its psychoactive constituents induce a multitude of effects on brain function. These include deficits in memory formation, but care needs to be exercised since many human studies are flawed by multiple drug abuse, small sample sizes, sample selection and sensitivity of psychological tests for subtle differences. The most robust finding with respect to memory is a deficit in working and short-term memory. This requires intact hippocampus and prefrontal cortex, two brain regions richly expressing CB1 receptors. Animal studies, which enable a more controlled drug regime and more constant behavioural testing, have confirmed human results and suggest, with respect to hippocampus, that exogenous cannabinoid treatment selectively affects encoding processes. This may be different in other brain areas, for instance the amygdala, where a predominant involvement in memory consolidation and forgetting has been firmly established. While cannabinoid receptor agonists impair memory formation, antagonists reverse these deficits or act as memory enhancers. These results are in good agreement with data obtained from electrophysiological recordings, which reveal reduction in neural plasticity following cannabinoid treatment, and increased plasticity following antagonist exposure. The mixed receptor properties of the pharmacological tool, however, make it difficult to define the exact role of any CB1 receptor population in memory processes with any certainty. This makes it all the more important that behavioural studies use selective administration of drugs to specific brain areas, rather than global administration to whole animals. The emerging role of the endogenous cannabinoid system in the hippocampus may be to facilitate the induction of long-term potentiation/the encoding of information. Administration of exogenous selective CB1 agonists may therefore disrupt hippocampus-dependent learning and memory by 'increasing the noise', rather than 'decreasing the signal' at

  10. A neuromorphic implementation of multiple spike-timing synaptic plasticity rules for large-scale neural networks

    Directory of Open Access Journals (Sweden)

    Runchun Mark Wang

    2015-05-01

    Full Text Available We present a neuromorphic implementation of multiple synaptic plasticity learning rules, which include both Spike Timing Dependent Plasticity (STDP and Spike Timing Dependent Delay Plasticity (STDDP. We present a fully digital implementation as well as a mixed-signal implementation, both of which use a novel dynamic-assignment time-multiplexing approach and support up to 2^26 (64M synaptic plasticity elements. Rather than implementing dedicated synapses for particular types of synaptic plasticity, we implemented a more generic synaptic plasticity adaptor array that is separate from the neurons in the neural network. Each adaptor performs synaptic plasticity according to the arrival times of the pre- and post-synaptic spikes assigned to it, and sends out a weighted and/or delayed pre-synaptic spike to the target synapse in the neural network. This strategy provides great flexibility for building complex large-scale neural networks, as a neural network can be configured for multiple synaptic plasticity rules without changing its structure. We validate the proposed neuromorphic implementations with measurement results and illustrate that the circuits are capable of performing both STDP and STDDP. We argue that it is practical to scale the work presented here up to 2^36 (64G synaptic adaptors on a current high-end FPGA platform.

  11. A neuromorphic implementation of multiple spike-timing synaptic plasticity rules for large-scale neural networks.

    Science.gov (United States)

    Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan C; van Schaik, André

    2015-01-01

    We present a neuromorphic implementation of multiple synaptic plasticity learning rules, which include both Spike Timing Dependent Plasticity (STDP) and Spike Timing Dependent Delay Plasticity (STDDP). We present a fully digital implementation as well as a mixed-signal implementation, both of which use a novel dynamic-assignment time-multiplexing approach and support up to 2(26) (64M) synaptic plasticity elements. Rather than implementing dedicated synapses for particular types of synaptic plasticity, we implemented a more generic synaptic plasticity adaptor array that is separate from the neurons in the neural network. Each adaptor performs synaptic plasticity according to the arrival times of the pre- and post-synaptic spikes assigned to it, and sends out a weighted or delayed pre-synaptic spike to the post-synaptic neuron in the neural network. This strategy provides great flexibility for building complex large-scale neural networks, as a neural network can be configured for multiple synaptic plasticity rules without changing its structure. We validate the proposed neuromorphic implementations with measurement results and illustrate that the circuits are capable of performing both STDP and STDDP. We argue that it is practical to scale the work presented here up to 2(36) (64G) synaptic adaptors on a current high-end FPGA platform.

  12. Learning to learn - intrinsic plasticity as a metaplasticity mechanism for memory formation.

    Science.gov (United States)

    Sehgal, Megha; Song, Chenghui; Ehlers, Vanessa L; Moyer, James R

    2013-10-01

    "Use it or lose it" is a popular adage often associated with use-dependent enhancement of cognitive abilities. Much research has focused on understanding exactly how the brain changes as a function of experience. Such experience-dependent plasticity involves both structural and functional alterations that contribute to adaptive behaviors, such as learning and memory, as well as maladaptive behaviors, including anxiety disorders, phobias, and posttraumatic stress disorder. With the advancing age of our population, understanding how use-dependent plasticity changes across the lifespan may also help to promote healthy brain aging. A common misconception is that such experience-dependent plasticity (e.g., associative learning) is synonymous with synaptic plasticity. Other forms of plasticity also play a critical role in shaping adaptive changes within the nervous system, including intrinsic plasticity - a change in the intrinsic excitability of a neuron. Intrinsic plasticity can result from a change in the number, distribution or activity of various ion channels located throughout the neuron. Here, we review evidence that intrinsic plasticity is an important and evolutionarily conserved neural correlate of learning. Intrinsic plasticity acts as a metaplasticity mechanism by lowering the threshold for synaptic changes. Thus, learning-related intrinsic changes can facilitate future synaptic plasticity and learning. Such intrinsic changes can impact the allocation of a memory trace within a brain structure, and when compromised, can contribute to cognitive decline during the aging process. This unique role of intrinsic excitability can provide insight into how memories are formed and, more interestingly, how neurons that participate in a memory trace are selected. Most importantly, modulation of intrinsic excitability can allow for regulation of learning ability - this can prevent or provide treatment for cognitive decline not only in patients with clinical disorders but

  13. Learning to learn – intrinsic plasticity as a metaplasticity mechanism for memory formation

    Science.gov (United States)

    Sehgal, Megha; Song, Chenghui; Ehlers, Vanessa L.; Moyer, James R.

    2013-01-01

    “Use it or lose it” is a popular adage often associated with use-dependent enhancement of cognitive abilities. Much research has focused on understanding exactly how the brain changes as a function of experience. Such experience-dependent plasticity involves both structural and functional alterations that contribute to adaptive behaviors, such as learning and memory, as well as maladaptive behaviors, including anxiety disorders, phobias, and posttraumatic stress disorder. With the advancing age of our population, understanding how use-dependent plasticity changes across the lifespan may also help to promote healthy brain aging. A common misconception is that such experience-dependent plasticity (e.g., associative learning) is synonymous with synaptic plasticity. Other forms of plasticity also play a critical role in shaping adaptive changes within the nervous system, including intrinsic plasticity – a change in the intrinsic excitability of a neuron. Intrinsic plasticity can result from a change in the number, distribution or activity of various ion channels located throughout the neuron. Here, we review evidence that intrinsic plasticity is an important and evolutionarily conserved neural correlate of learning. Intrinsic plasticity acts as a metaplasticity mechanism by lowering the threshold for synaptic changes. Thus, learning-related intrinsic changes can facilitate future synaptic plasticity and learning. Such intrinsic changes can impact the allocation of a memory trace within a brain structure, and when compromised, can contribute to cognitive decline during the aging process. This unique role of intrinsic excitability can provide insight into how memories are formed and, more interestingly, how neurons that participate in a memory trace are selected. Most importantly, modulation of intrinsic excitability can allow for regulation of learning ability – this can prevent or provide treatment for cognitive decline not only in patients with clinical

  14. Neural Plasticity in Common Forms of Chronic Headaches.

    Science.gov (United States)

    Lai, Tzu-Hsien; Protsenko, Ekaterina; Cheng, Yu-Chen; Loggia, Marco L; Coppola, Gianluca; Chen, Wei-Ta

    2015-01-01

    Headaches are universal experiences and among the most common disorders. While headache may be physiological in the acute setting, it can become a pathological and persistent condition. The mechanisms underlying the transition from episodic to chronic pain have been the subject of intense study. Using physiological and imaging methods, researchers have identified a number of different forms of neural plasticity associated with migraine and other headaches, including peripheral and central sensitization, and alterations in the endogenous mechanisms of pain modulation. While these changes have been proposed to contribute to headache and pain chronification, some findings are likely the results of repetitive noxious stimulation, such as atrophy of brain areas involved in pain perception and modulation. In this review, we provide a narrative overview of recent advances on the neuroimaging, electrophysiological and genetic aspects of neural plasticity associated with the most common forms of chronic headaches, including migraine, cluster headache, tension-type headache, and medication overuse headache.

  15. Simulating dynamic plastic continuous neural networks by finite elements.

    Science.gov (United States)

    Joghataie, Abdolreza; Torghabehi, Omid Oliyan

    2014-08-01

    We introduce dynamic plastic continuous neural network (DPCNN), which is comprised of neurons distributed in a nonlinear plastic medium where wire-like connections of neural networks are replaced with the continuous medium. We use finite element method to model the dynamic phenomenon of information processing within the DPCNNs. During the training, instead of weights, the properties of the continuous material at its different locations and some properties of neurons are modified. Input and output can be vectors and/or continuous functions over lines and/or areas. Delay and feedback from neurons to themselves and from outputs occur in the DPCNNs. We model a simple form of the DPCNN where the medium is a rectangular plate of bilinear material, and the neurons continuously fire a signal, which is a function of the horizontal displacement.

  16. Advances in neurocognitive rehabilitation research from 1992 to 2017: The ascension of neural plasticity.

    Science.gov (United States)

    Crosson, Bruce; Hampstead, Benjamin M; Krishnamurthy, Lisa C; Krishnamurthy, Venkatagiri; McGregor, Keith M; Nocera, Joe R; Roberts, Simone; Rodriguez, Amy D; Tran, Stella M

    2017-11-01

    The last 25 years have seen profound changes in neurocognitive rehabilitation that continue to motivate its evolution. Although the concept of nervous system plasticity was discussed by William James (1890), the foundation for experience-based plasticity had not reached the critical empirical mass to seriously impact rehabilitation research until after 1992. The objective of this review is to describe how the emergence of neural plasticity has changed neurocognitive rehabilitation research. The important developments included (a) introduction of a widely available tool that could measure brain plasticity (i.e., functional MRI); (b) development of new structural imaging techniques that could define limits of and opportunities for neural plasticity; (c) deployment of noninvasive brain stimulation to leverage neural plasticity for rehabilitation; (d) growth of a literature indicating that exercise has positively impacts neural plasticity, especially for older persons; and (e) enhancement of neural plasticity by creating interventions that generalize beyond the boundaries of treatment activities. Given the massive literature, each of these areas is developed by example. The expanding influence of neural plasticity has provided new models and tools for neurocognitive rehabilitation in neural injuries and disorders, as well as methods for measuring neural plasticity and predicting its limits and opportunities. Early clinical trials have provided very encouraging results. Now that neural plasticity has gained a firm foothold, it will continue to influence the evolution of neurocognitive rehabilitation research for the next 25 years and advance rehabilitation for neural injuries and disease. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  17. Neural plasticity and network remodeling: From concepts to pathology.

    Science.gov (United States)

    Cohen, Erez James; Quarta, Eros; Bravi, Riccardo; Granato, Alberto; Minciacchi, Diego

    2017-03-06

    Neuroplasticity has been subject to a great deal of research in the last century. Recently, significant emphasis has been placed on the global effect of localized plastic changes throughout the central nervous system, and on how these changes integrate in a pathological context. Specifically, alterations of network functionality have been described in various pathological contexts to which corresponding structural alterations have been proposed. However, considering the amount of literature and the different pathological contexts, an integration of this information is still lacking. In this paper we will review the concepts of neural plasticity as well as their repercussions on network remodeling and provide a possible explanation to how these two concepts relate to each other. We will further examine how alterations in different pathological contexts may relate to each other and will discuss the concept of plasticity diseases, its models and implications. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

  18. The role of histone acetylation in cocaine-induced neural plasticity and behavior.

    Science.gov (United States)

    Rogge, George A; Wood, Marcelo A

    2013-01-01

    How do drugs of abuse, such as cocaine, cause stable changes in neural plasticity that in turn drive long-term changes in behavior? What kind of mechanism can underlie such stable changes in neural plasticity? One prime candidate mechanism is epigenetic mechanisms of chromatin regulation. Chromatin regulation has been shown to generate short-term and long-term molecular memory within an individual cell. They have also been shown to underlie cell fate decisions (or cellular memory). Now, there is accumulating evidence that in the CNS, these same mechanisms may be pivotal for drug-induced changes in gene expression and ultimately long-term behavioral changes. As these mechanisms are also being found to be fundamental for learning and memory, an exciting new possibility is the extinction of drug-seeking behavior by manipulation of epigenetic mechanisms. In this review, we critically discuss the evidence demonstrating a key role for chromatin regulation via histone acetylation in cocaine action.

  19. Neural correlates of face gender discrimination learning.

    Science.gov (United States)

    Su, Junzhu; Tan, Qingleng; Fang, Fang

    2013-04-01

    Using combined psychophysics and event-related potentials (ERPs), we investigated the effect of perceptual learning on face gender discrimination and probe the neural correlates of the learning effect. Human subjects were trained to perform a gender discrimination task with male or female faces. Before and after training, they were tested with the trained faces and other faces with the same and opposite genders. ERPs responding to these faces were recorded. Psychophysical results showed that training significantly improved subjects' discrimination performance and the improvement was specific to the trained gender, as well as to the trained identities. The training effect indicates that learning occurs at two levels-the category level (gender) and the exemplar level (identity). ERP analyses showed that the gender and identity learning was associated with the N170 latency reduction at the left occipital-temporal area and the N170 amplitude reduction at the right occipital-temporal area, respectively. These findings provide evidence for the facilitation model and the sharpening model on neuronal plasticity from visual experience, suggesting a faster processing speed and a sparser representation of face induced by perceptual learning.

  20. Neural networks and perceptual learning

    Science.gov (United States)

    Tsodyks, Misha; Gilbert, Charles

    2005-01-01

    Sensory perception is a learned trait. The brain strategies we use to perceive the world are constantly modified by experience. With practice, we subconsciously become better at identifying familiar objects or distinguishing fine details in our environment. Current theoretical models simulate some properties of perceptual learning, but neglect the underlying cortical circuits. Future neural network models must incorporate the top-down alteration of cortical function by expectation or perceptual tasks. These newly found dynamic processes are challenging earlier views of static and feedforward processing of sensory information. PMID:15483598

  1. The malleable brain: plasticity of neural circuits and behavior - a review from students to students.

    Science.gov (United States)

    Schaefer, Natascha; Rotermund, Carola; Blumrich, Eva-Maria; Lourenco, Mychael V; Joshi, Pooja; Hegemann, Regina U; Jamwal, Sumit; Ali, Nilufar; García Romero, Ezra Michelet; Sharma, Sorabh; Ghosh, Shampa; Sinha, Jitendra K; Loke, Hannah; Jain, Vishal; Lepeta, Katarzyna; Salamian, Ahmad; Sharma, Mahima; Golpich, Mojtaba; Nawrotek, Katarzyna; Paidi, Ramesh K; Shahidzadeh, Sheila M; Piermartiri, Tetsade; Amini, Elham; Pastor, Veronica; Wilson, Yvette; Adeniyi, Philip A; Datusalia, Ashok K; Vafadari, Benham; Saini, Vedangana; Suárez-Pozos, Edna; Kushwah, Neetu; Fontanet, Paula; Turner, Anthony J

    2017-06-20

    One of the most intriguing features of the brain is its ability to be malleable, allowing it to adapt continually to changes in the environment. Specific neuronal activity patterns drive long-lasting increases or decreases in the strength of synaptic connections, referred to as long-term potentiation and long-term depression, respectively. Such phenomena have been described in a variety of model organisms, which are used to study molecular, structural, and functional aspects of synaptic plasticity. This review originated from the first International Society for Neurochemistry (ISN) and Journal of Neurochemistry (JNC) Flagship School held in Alpbach, Austria (Sep 2016), and will use its curriculum and discussions as a framework to review some of the current knowledge in the field of synaptic plasticity. First, we describe the role of plasticity during development and the persistent changes of neural circuitry occurring when sensory input is altered during critical developmental stages. We then outline the signaling cascades resulting in the synthesis of new plasticity-related proteins, which ultimately enable sustained changes in synaptic strength. Going beyond the traditional understanding of synaptic plasticity conceptualized by long-term potentiation and long-term depression, we discuss system-wide modifications and recently unveiled homeostatic mechanisms, such as synaptic scaling. Finally, we describe the neural circuits and synaptic plasticity mechanisms driving associative memory and motor learning. Evidence summarized in this review provides a current view of synaptic plasticity in its various forms, offers new insights into the underlying mechanisms and behavioral relevance, and provides directions for future research in the field of synaptic plasticity. Read the Editorial Highlight for this article on doi: 10.1111/jnc.14102. © 2017 International Society for Neurochemistry.

  2. NMDA Receptors Mediate Stimulus-Timing-Dependent Plasticity and Neural Synchrony in the Dorsal Cochlear Nucleus.

    Science.gov (United States)

    Stefanescu, Roxana A; Shore, Susan E

    2015-01-01

    Auditory information relayed by auditory nerve fibers and somatosensory information relayed by granule cell parallel fibers converge on the fusiform cells (FCs) of the dorsal cochlear nucleus, the first brain station of the auditory pathway. In vitro, parallel fiber synapses on FCs exhibit spike-timing-dependent plasticity with Hebbian learning rules, partially mediated by the NMDA receptor (NMDAr). Well-timed bimodal auditory-somatosensory stimulation, in vivo equivalent of spike-timing-dependent plasticity, can induce stimulus-timing-dependent plasticity (StTDP) of the FCs spontaneous and tone-evoked firing rates. In healthy guinea pigs, the resulting distribution of StTDP learning rules across a FC neural population is dominated by a Hebbian profile while anti-Hebbian, suppressive and enhancing LRs are less frequent. In this study, we investigate in vivo, the NMDAr contribution to FC baseline activity and long term plasticity. We find that blocking the NMDAr decreases the synchronization of FC- spontaneous activity and mediates differential modulation of FC rate-level functions such that low, and high threshold units are more likely to increase, and decrease, respectively, their maximum amplitudes. Three significant alterations in mean learning-rule profiles were identified: transitions from an initial Hebbian profile towards (1) an anti-Hebbian; (2) a suppressive profile; and (3) transitions from an anti-Hebbian to a Hebbian profile. FC units preserving their learning rules showed instead, NMDAr-dependent plasticity to unimodal acoustic stimulation, with persistent depression of tone-evoked responses changing to persistent enhancement following the NMDAr antagonist. These results reveal a crucial role of the NMDAr in mediating FC baseline activity and long-term plasticity which have important implications for signal processing and auditory pathologies related to maladaptive plasticity of dorsal cochlear nucleus circuitry.

  3. Neural plasticity in the gastrointestinal tract: chronic inflammation, neurotrophic signals, and hypersensitivity.

    Science.gov (United States)

    Demir, Ihsan Ekin; Schäfer, Karl-Herbert; Tieftrunk, Elke; Friess, Helmut; Ceyhan, Güralp O

    2013-04-01

    Neural plasticity is not only the adaptive response of the central nervous system to learning, structural damage or sensory deprivation, but also an increasingly recognized common feature of the gastrointestinal (GI) nervous system during pathological states. Indeed, nearly all chronic GI disorders exhibit a disease-stage-dependent, structural and functional neuroplasticity. At structural level, GI neuroplasticity usually comprises local tissue hyperinnervation (neural sprouting, neural, and ganglionic hypertrophy) next to hypoinnervated areas, a switch in the neurochemical (neurotransmitter/neuropeptide) code toward preferential expression of neuropeptides which are frequently present in nociceptive neurons (e.g., substance P/SP, calcitonin-gene-related-peptide/CGRP) and of ion channels (TRPV1, TRPA1, PAR2), and concomitant activation of peripheral neural glia. The functional counterpart of these structural alterations is altered neuronal electric activity, leading to organ dysfunction (e.g., impaired motility and secretion), together with reduced sensory thresholds, resulting in hypersensitivity and pain. The present review underlines that neural plasticity in all GI organs, starting from esophagus, stomach, small and large intestine to liver, gallbladder, and pancreas, actually exhibits common phenotypes and mechanisms. Careful appraisal of these GI neuroplastic alterations reveals that--no matter which etiology, i.e., inflammatory, infectious, neoplastic/malignant, or degenerative--neural plasticity in the GI tract primarily occurs in the presence of chronic tissue- and neuro-inflammation. It seems that studying the abundant trophic and activating signals which are generated during this neuro-immune-crosstalk represents the key to understand the remarkable neuroplasticity of the GI tract.

  4. Different propagation speeds of recalled sequences in plastic spiking neural networks

    Science.gov (United States)

    Huang, Xuhui; Zheng, Zhigang; Hu, Gang; Wu, Si; Rasch, Malte J.

    2015-03-01

    Neural networks can generate spatiotemporal patterns of spike activity. Sequential activity learning and retrieval have been observed in many brain areas, and e.g. is crucial for coding of episodic memory in the hippocampus or generating temporal patterns during song production in birds. In a recent study, a sequential activity pattern was directly entrained onto the neural activity of the primary visual cortex (V1) of rats and subsequently successfully recalled by a local and transient trigger. It was observed that the speed of activity propagation in coordinates of the retinotopically organized neural tissue was constant during retrieval regardless how the speed of light stimulation sweeping across the visual field during training was varied. It is well known that spike-timing dependent plasticity (STDP) is a potential mechanism for embedding temporal sequences into neural network activity. How training and retrieval speeds relate to each other and how network and learning parameters influence retrieval speeds, however, is not well described. We here theoretically analyze sequential activity learning and retrieval in a recurrent neural network with realistic synaptic short-term dynamics and STDP. Testing multiple STDP rules, we confirm that sequence learning can be achieved by STDP. However, we found that a multiplicative nearest-neighbor (NN) weight update rule generated weight distributions and recall activities that best matched the experiments in V1. Using network simulations and mean-field analysis, we further investigated the learning mechanisms and the influence of network parameters on recall speeds. Our analysis suggests that a multiplicative STDP rule with dominant NN spike interaction might be implemented in V1 since recall speed was almost constant in an NMDA-dominant regime. Interestingly, in an AMPA-dominant regime, neural circuits might exhibit recall speeds that instead follow the change in stimulus speeds. This prediction could be tested in

  5. Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity

    Directory of Open Access Journals (Sweden)

    Wiebke ePotjans

    2010-11-01

    Full Text Available A major puzzle in the field of computational neuroscience is how to relate system-level learning in higher organisms to synaptic plasticity. Recently, plasticity rules depending not only on pre- and post-synaptic activity but also on a third, non-local neuromodulatory signal have emerged as key candidates to bridge the gap between the macroscopic and the microscopic level of learning. Crucial insights into this topic are expected to be gained from simulations of neural systems, as these allow the simultaneous study of the multiple spatial and temporal scales that are involved in the problem. In particular, synaptic plasticity can be studied during the whole learning process, i.e. on a time scale of minutes to hours and across multiple brain areas. Implementing neuromodulated plasticity in large-scale network simulations where the neuromodulatory signal is dynamically generated by the network itself is challenging, because the network structure is commonly defined purely by the connectivity graph without explicit reference to the embedding of the nodes in physical space. Furthermore, the simulation of networks with realistic connectivity entails the use of distributed computing. A neuromodulated synapse must therefore be informed in an efficient way about the neuromodulatory signal, which is typically generated by a population of neurons located on different machines than either the pre- or post-synaptic neuron. Here, we develop a general framework to solve the problem of implementing neuromodulated plasticity in a time-driven distributed simulation, without reference to a particular implementation language, neuromodulator or neuromodulated plasticity mechanism. We implement our framework in the simulator NEST and demonstrate excellent scaling up to 1024 processors for simulations of a recurrent network incorporating neuromodulated spike-timing dependent plasticity.

  6. Neural Plastic Effects of Cognitive Training on Aging Brain.

    Science.gov (United States)

    Leung, Natalie T Y; Tam, Helena M K; Chu, Leung W; Kwok, Timothy C Y; Chan, Felix; Lam, Linda C W; Woo, Jean; Lee, Tatia M C

    2015-01-01

    Increasing research has evidenced that our brain retains a capacity to change in response to experience until late adulthood. This implies that cognitive training can possibly ameliorate age-associated cognitive decline by inducing training-specific neural plastic changes at both neural and behavioral levels. This longitudinal study examined the behavioral effects of a systematic thirteen-week cognitive training program on attention and working memory of older adults who were at risk of cognitive decline. These older adults were randomly assigned to the Cognitive Training Group (n = 109) and the Active Control Group (n = 100). Findings clearly indicated that training induced improvement in auditory and visual-spatial attention and working memory. The training effect was specific to the experience provided because no significant difference in verbal and visual-spatial memory between the two groups was observed. This pattern of findings is consistent with the prediction and the principle of experience-dependent neuroplasticity. Findings of our study provided further support to the notion that the neural plastic potential continues until older age. The baseline cognitive status did not correlate with pre- versus posttraining changes to any cognitive variables studied, suggesting that the initial cognitive status may not limit the neuroplastic potential of the brain at an old age.

  7. Auditory processing, plasticity, and learning in the barn owl.

    Science.gov (United States)

    Pena, Jose L; DeBello, William M

    2010-01-01

    The human brain has accumulated many useful building blocks over its evolutionary history, and the best knowledge of these has often derived from experiments performed in animal species that display finely honed abilities. In this article we review a model system at the forefront of investigation into the neural bases of information processing, plasticity, and learning: the barn owl auditory localization pathway. In addition to the broadly applicable principles gleaned from three decades of work in this system, there are good reasons to believe that continued exploration of the owl brain will be invaluable for further advances in understanding of how neuronal networks give rise to behavior.

  8. Plasticity in the neural coding of auditory space in the mammalian brain

    Science.gov (United States)

    King, Andrew J.; Parsons, Carl H.; Moore, David R.

    2000-10-01

    Sound localization relies on the neural processing of monaural and binaural spatial cues that arise from the way sounds interact with the head and external ears. Neurophysiological studies of animals raised with abnormal sensory inputs show that the map of auditory space in the superior colliculus is shaped during development by both auditory and visual experience. An example of this plasticity is provided by monaural occlusion during infancy, which leads to compensatory changes in auditory spatial tuning that tend to preserve the alignment between the neural representations of visual and auditory space. Adaptive changes also take place in sound localization behavior, as demonstrated by the fact that ferrets raised and tested with one ear plugged learn to localize as accurately as control animals. In both cases, these adjustments may involve greater use of monaural spectral cues provided by the other ear. Although plasticity in the auditory space map seems to be restricted to development, adult ferrets show some recovery of sound localization behavior after long-term monaural occlusion. The capacity for behavioral adaptation is, however, task dependent, because auditory spatial acuity and binaural unmasking (a measure of the spatial contribution to the "cocktail party effect") are permanently impaired by chronically plugging one ear, both in infancy but especially in adulthood. Experience-induced plasticity allows the neural circuitry underlying sound localization to be customized to individual characteristics, such as the size and shape of the head and ears, and to compensate for natural conductive hearing losses, including those associated with middle ear disease in infancy.

  9. Rehabilitation with Poststroke Motor Recovery: A Review with a Focus on Neural Plasticity

    OpenAIRE

    Naoyuki Takeuchi; Shin-Ichi Izumi

    2013-01-01

    Motor recovery after stroke is related to neural plasticity, which involves developing new neuronal interconnections, acquiring new functions, and compensating for impairment. However, neural plasticity is impaired in the stroke-affected hemisphere. Therefore, it is important that motor recovery therapies facilitate neural plasticity to compensate for functional loss. Stroke rehabilitation programs should include meaningful, repetitive, intensive, and task-specific movement training in an enr...

  10. Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

    Directory of Open Access Journals (Sweden)

    Eduard eGrinke

    2015-10-01

    Full Text Available Walking animals, like insects, with little neural computing can effectively perform complex behaviors. They can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a walking robot is a challenging task. In this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors in the network to generate different turning angles with short-term memory for a biomechanical walking robot. The turning information is transmitted as descending steering signals to the locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations as well as escaping from sharp corners or deadlocks. Using backbone joint control embedded in the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments.

  11. Plasticity in memristive devices for Spiking Neural Networks

    Directory of Open Access Journals (Sweden)

    Sylvain eSaïghi

    2015-03-01

    Full Text Available Memristive devices present a new device technology allowing for the realization of compact nonvolatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse physical mechanisms, and their plastic behaviors differ strongly from one technology to another. Here, we present measurements performed on different memristive devices and the opportunities that they provide. We show that they can be used to implement different learning rules whose properties emerge directly from device physics: real time or accelerated operation, deterministic or stochastic behavior, long term or short term plasticity. We then discuss how such devices might be integrated into a complete architecture. These results highlight that there is no unique way to exploit memristive devices in neuromorphic systems. Understanding and embracing device physics is the key for their optimal use.

  12. Neural correlates of motor learning, transfer of learning, and learning to learn.

    Science.gov (United States)

    Seidler, Rachael D

    2010-01-01

    Recent studies on the neural bases of sensorimotor adaptation demonstrate that the cerebellar and striatal thalamocortical pathways contribute to early learning. Transfer of learning involves a reduction in the contribution of early learning networks and increased reliance on the cerebellum. The neural correlates of learning to learn remain to be determined but likely involve enhanced functioning of the general aspects of early learning.

  13. Unsupervised discrimination of patterns in spiking neural networks with excitatory and inhibitory synaptic plasticity.

    Science.gov (United States)

    Srinivasa, Narayan; Cho, Youngkwan

    2014-01-01

    A spiking neural network model is described for learning to discriminate among spatial patterns in an unsupervised manner. The network anatomy consists of source neurons that are activated by external inputs, a reservoir that resembles a generic cortical layer with an excitatory-inhibitory (EI) network and a sink layer of neurons for readout. Synaptic plasticity in the form of STDP is imposed on all the excitatory and inhibitory synapses at all times. While long-term excitatory STDP enables sparse and efficient learning of the salient features in inputs, inhibitory STDP enables this learning to be stable by establishing a balance between excitatory and inhibitory currents at each neuron in the network. The synaptic weights between source and reservoir neurons form a basis set for the input patterns. The neural trajectories generated in the reservoir due to input stimulation and lateral connections between reservoir neurons can be readout by the sink layer neurons. This activity is used for adaptation of synapses between reservoir and sink layer neurons. A new measure called the discriminability index (DI) is introduced to compute if the network can discriminate between old patterns already presented in an initial training session. The DI is also used to compute if the network adapts to new patterns without losing its ability to discriminate among old patterns. The final outcome is that the network is able to correctly discriminate between all patterns-both old and new. This result holds as long as inhibitory synapses employ STDP to continuously enable current balance in the network. The results suggest a possible direction for future investigation into how spiking neural networks could address the stability-plasticity question despite having continuous synaptic plasticity.

  14. Unsupervised Discrimination of Patterns in Spiking Neural Networks with Excitatory and Inhibitory Synaptic Plasticity

    Directory of Open Access Journals (Sweden)

    Narayan eSrinivasa

    2014-12-01

    Full Text Available A spiking neural network model is described for learning to discriminate among spatial patterns in an unsupervised manner. The network anatomy consists of source neurons that are activated by external inputs, a reservoir that resembles a generic cortical layer with an excitatory-inhibitory (EI network and a sink layer of neurons for readout. Synaptic plasticity in the form of STDP is imposed on all the excitatory and inhibitory synapses at all times. While long-term excitatory STDP enables sparse and efficient learning of the salient features in inputs, inhibitory STDP enables this learning to be stable by establishing a balance between excitatory and inhibitory currents at each neuron in the network. The synaptic weights between source and reservoir neurons form a basis set for the input patterns. The neural trajectories generated in the reservoir due to input stimulation and lateral connections between reservoir neurons can be readout by the sink layer neurons. This activity is used for adaptation of synapses between reservoir and sink layer neurons. A new measure called the discriminability index (DI is introduced to compute if the network can discriminate between old patterns already presented in an initial training session. The DI is also used to compute if the network adapts to new patterns without losing its ability to discriminate among old patterns. The final outcome is that the network is able to correctly discriminate between all patterns – both old and new. This result holds as long as inhibitory synapses employ STDP to continuously enable current balance in the network. The results suggest a possible direction for future investigation into how spiking neural networks could address the stability-plasticity question despite having continuous synaptic plasticity.

  15. The Effects of Leptin Replacement on Neural Plasticity.

    Science.gov (United States)

    Paz-Filho, Gilberto J

    2016-01-01

    Leptin, an adipokine synthesized and secreted mainly by the adipose tissue, has multiple effects on the regulation of food intake, energy expenditure, and metabolism. Its recently-approved analogue, metreleptin, has been evaluated in clinical trials for the treatment of patients with leptin deficiency due to mutations in the leptin gene, lipodystrophy syndromes, and hypothalamic amenorrhea. In such patients, leptin replacement therapy has led to changes in brain structure and function in intra- and extrahypothalamic areas, including the hippocampus. Furthermore, in one of those patients, improvements in neurocognitive development have been observed. In addition to this evidence linking leptin to neural plasticity and function, observational studies evaluating leptin-sufficient humans have also demonstrated direct correlation between blood leptin levels and brain volume and inverse associations between circulating leptin and risk for the development of dementia. This review summarizes the evidence in the literature on the role of leptin in neural plasticity (in leptin-deficient and in leptin-sufficient individuals) and its effects on synaptic activity, glutamate receptor trafficking, neuronal morphology, neuronal development and survival, and microglial function.

  16. The Effects of Leptin Replacement on Neural Plasticity

    Directory of Open Access Journals (Sweden)

    Gilberto J. Paz-Filho

    2016-01-01

    Full Text Available Leptin, an adipokine synthesized and secreted mainly by the adipose tissue, has multiple effects on the regulation of food intake, energy expenditure, and metabolism. Its recently-approved analogue, metreleptin, has been evaluated in clinical trials for the treatment of patients with leptin deficiency due to mutations in the leptin gene, lipodystrophy syndromes, and hypothalamic amenorrhea. In such patients, leptin replacement therapy has led to changes in brain structure and function in intra- and extrahypothalamic areas, including the hippocampus. Furthermore, in one of those patients, improvements in neurocognitive development have been observed. In addition to this evidence linking leptin to neural plasticity and function, observational studies evaluating leptin-sufficient humans have also demonstrated direct correlation between blood leptin levels and brain volume and inverse associations between circulating leptin and risk for the development of dementia. This review summarizes the evidence in the literature on the role of leptin in neural plasticity (in leptin-deficient and in leptin-sufficient individuals and its effects on synaptic activity, glutamate receptor trafficking, neuronal morphology, neuronal development and survival, and microglial function.

  17. Rapid neural circuit switching mediated by synaptic plasticity during neural morphallactic regeneration.

    Science.gov (United States)

    Lybrand, Zane R; Zoran, Mark J

    2012-09-01

    The aquatic oligochaete, Lumbriculus variegatus (Lumbriculidae), undergoes a rapid regenerative transformation of its neural circuits following body fragmentation. This type of nervous system plasticity, called neural morphallaxis, involves the remodeling of the giant fiber pathways that mediate rapid head and tail withdrawal behaviors. Extra- and intracellular electrophysiological recordings demonstrated that changes in cellular properties and synaptic connections underlie neurobehavioral plasticity during morphallaxis. Sensory-to-giant interneuron connections, undetectable prior to body injury, emerged within hours of segment amputation. The appearance of functional synaptic transmission was followed by interneuron activation, coupling of giant fiber spiking to motor outputs and overt segmental shortening. The onset of morphallactic plasticity varied along the body axis and emerged more rapidly in segments closer to regions of sensory field overlap between the two giant fiber pathways. The medial and lateral giant fibers were simultaneously activated during a transient phase of network remodeling. Thus, synaptic plasticity at sensory-to-giant interneuron connections mediates escape circuit morphallaxis in this regenerating annelid worm. Copyright © 2011 Wiley Periodicals, Inc.

  18. Study of neural plasticity in braille reading visually challenged individuals

    Directory of Open Access Journals (Sweden)

    Nikhat Yasmeen, Mohammed Muslaiuddin Khalid, Abdul Raoof Omer siddique, Madhuri Taranikanti, Sanghamitra Panda, D.Usha Rani

    2014-03-01

    Full Text Available Background: Neural plasticity includes a wide range of adaptive changes due to loss or absence of a particular sense. Cortical mapping or reorganization is evolutionary conserved mechanism which involves either an unmasking of previously silent connections and/or sprouting of new neural elements. Aims & Objectives -To compare the Somatosensory evoked potentials (SSEPs wave form in normal and visually challenged individuals. Materials & Methods: 20 visually challenged males in the age group of 21 -31 yrs were included in the study along with 20 age & sex matched individuals. Subjects were screened for general physical health to rule out any medical disorder, tactile sensibility i.e., sensation of light touch, pressure, tactile localization & discrimination to rule out any delay in the peripheral conduction disorder. Somatosensory evoked potentials were recorded on Nicolet Viking select neuro diagnostic system version 10.0.The placement of electrodes & recording of potentials were done based on methodology in chiappa. Data was subjected to various statistical analyses using SPSS version 17.0 software. N20 & P25 latencies were shorter and amplitudes were larger in visually challenged individuals compared to age & sex matched individuals. Conclusions: In visually challenged individuals, decrease in latencies indicate greatly improved of information in the nervous system & increase in amplitudes indicate the extent and synchronization of neural network involved in processing of vision.

  19. Cognitive correlates of visual neural plasticity in schizophrenia.

    Science.gov (United States)

    Jahshan, Carol; Wynn, Jonathan K; Mathalon, Daniel H; Green, Michael F

    2017-12-01

    Neuroplasticity may be an important treatment target to improve the cognitive deficits in schizophrenia (SZ). Yet, it is poorly understood and difficult to assess. Recently, a visual high-frequency stimulation (HFS) paradigm that potentiates electroencephalography (EEG)-based visual evoked potentials (VEP) has been developed to assess neural plasticity in the visual cortex. Using this paradigm, we examined visual plasticity in SZ patients (N=64) and its correlations with clinical symptoms, neurocognition, functional capacity, and community functioning. VEPs were assessed prior to (baseline), and 2-, 4-, and 20-min after (Post-1, Post-2, and Post-3, respectively) 2min of visual HFS. Cluster-based permutation tests were conducted to identify time points and electrodes at which VEP amplitudes were significantly different after HFS. Compared to baseline, there was increased negativity between 140 and 227ms for the early post-HFS block (average of Post-1 and Post-2), and increased positivity between 180 and 281ms for the late post-HFS block (Post-3), at parieto-occipital and occipital electrodes. The increased negativity in the early post-HFS block did not correlate with any of the measures, whereas increased positivity in the late post-HFS block correlated with better neurocognitive performance. Results suggest that SZ patients exhibit both short- and long-term plasticity. The long-term plasticity effect, which was present 22min after HFS, was evident relatively late in the VEP, suggesting that neuroplastic changes in higher-order visual association areas, rather than earlier short-term changes in primary and secondary visual cortex, may be particularly important for the maintenance of neurocognitive function in SZ. Published by Elsevier B.V.

  20. Learning Processes of Layered Neural Networks

    OpenAIRE

    Fujiki, Sumiyoshi; FUJIKI, Nahomi, M.

    1995-01-01

    A positive reinforcement type learning algorithm is formulated for a stochastic feed-forward neural network, and a learning equation similar to that of the Boltzmann machine algorithm is obtained. By applying a mean field approximation to the same stochastic feed-forward neural network, a deterministic analog feed-forward network is obtained and the back-propagation learning rule is re-derived.

  1. Rapid-onset antidepressant efficacy of glutamatergic system modulators: the neural plasticity hypothesis of depression.

    Science.gov (United States)

    Wang, Jing; Jing, Liang; Toledo-Salas, Juan-Carlos; Xu, Lin

    2015-02-01

    Depression is a devastating psychiatric disorder widely attributed to deficient monoaminergic signaling in the central nervous system. However, most clinical antidepressants enhance monoaminergic neurotransmission with little delay but require 4-8 weeks to reach therapeutic efficacy, a paradox suggesting that the monoaminergic hypothesis of depression is an oversimplification. In contrast to the antidepressants targeting the monoaminergic system, a single dose of the N-methyl-D-aspartate receptor (NMDAR) antagonist ketamine produces rapid (within 2 h) and sustained (over 7 days) antidepressant efficacy in treatment-resistant patients. Glutamatergic transmission mediated by NMDARs is critical for experience-dependent synaptic plasticity and learning, processes that can be modified indirectly by the monoaminergic system. To better understand the mechanisms of action of the new antidepressants like ketamine, we review and compare the monoaminergic and glutamatergic antidepressants, with emphasis on neural plasticity. The pathogenesis of depression may involve maladaptive neural plasticity in glutamatergic circuits that may serve as a new class of targets to produce rapid antidepressant effects.

  2. Complement emerges as a masterful regulator of CNS homeostasis, neural synaptic plasticity and cognitive function.

    Science.gov (United States)

    Mastellos, Dimitrios C

    2014-11-01

    Growing evidence points to a previously elusive role of complement-modulated pathways in CNS development, neurogenesis and synaptic plasticity. Distinct complement effectors appear to play a multifaceted role in brain homeostasis by regulating synaptic pruning in the retinogeniculate system and sculpting functional neural circuits both in the developing and adult mammalian brain. A recent study by Perez-Alcazar et al. (2014) provides novel insights into this intricate interplay between complement and the dynamically regulated brain synaptic circuitry, by reporting that mice deficient in C3 exhibit enhanced hippocampus-dependent spatial learning and cognitive performance. This behavioral pattern is associated with an impact of C3 on the functional capacity of glutamatergic synapses, supporting a crucial role for complement in excitatory synapse elimination in the hippocampus. These findings add a fresh twist to this rapidly evolving research field, suggesting that discrete complement components may differentially modulate synaptic connectivity by wiring up with diverse neural effectors in different regions of the brain. The emerging role of complement in synaptogenesis and neural network plasticity opens new conceptual avenues for considering complement interception as a potential therapeutic modality for ameliorating progressive cognitive impairment in age-related, debilitating brain diseases with a prominent inflammatory signature. Copyright © 2014 Elsevier Inc. All rights reserved.

  3. Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot.

    Science.gov (United States)

    Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin; Manoonpong, Poramate

    2015-01-01

    Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles

  4. Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

    Science.gov (United States)

    Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin; Manoonpong, Poramate

    2015-01-01

    Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles

  5. Evolving Neural Turing Machines for Reward-based Learning

    DEFF Research Database (Denmark)

    Greve, Rasmus Boll; Jacobsen, Emil Juul; Risi, Sebastian

    2016-01-01

    and integrating new information without losing previously acquired skills. Here we build on recent work by Graves et al. [5] who extended the capabilities of an ANN by combining it with an external memory bank trained through gradient descent. In this paper, we introduce an evolvable version of their Neural...... version of the double T-Maze, a complex reinforcement-like learning problem. In the T-Maze learning task the agent uses the memory bank to display adaptive behavior that normally requires a plastic ANN, thereby suggesting a complementary and effective mechanism for adaptive behavior in NE....

  6. Remodeling myelination: implications for mechanisms of neural plasticity.

    Science.gov (United States)

    Chang, Kae-Jiun; Redmond, Stephanie A; Chan, Jonah R

    2016-02-01

    One of the most significant paradigm shifts in membrane remodeling is the emerging view that membrane transformation is not exclusively controlled by cytoskeletal rearrangement, but also by biophysical constraints, adhesive forces, membrane curvature and compaction. One of the most exquisite examples of membrane remodeling is myelination. The advent of myelin was instrumental in advancing the nervous system during vertebrate evolution. With more rapid and efficient communication between neurons, faster and more complex computations could be performed in a given time and space. Our knowledge of how myelin-forming oligodendrocytes select and wrap axons has been limited by insufficient spatial and temporal resolution. By virtue of recent technological advances, progress has clarified longstanding controversies in the field. Here we review insights into myelination, from target selection to axon wrapping and membrane compaction, and discuss how understanding these processes has unexpectedly opened new avenues of insight into myelination-centered mechanisms of neural plasticity.

  7. Learning language with the wrong neural scaffolding: The cost of neural commitment to sounds.

    Directory of Open Access Journals (Sweden)

    Amy Sue Finn

    2013-11-01

    Full Text Available Does tuning to one’s native language explain the sensitive period for language learning? We explore the idea that tuning to (or becoming more selective for the properties of one’s native-language could result in being less open (or plastic for tuning to the properties of a new language. To explore how this might lead to the sensitive period for grammar learning, we ask if tuning to an earlier-learned aspect of language (sound structure has an impact on the neural representation of a later-learned aspect (grammar. English-speaking adults learned one of two miniature artificial languages over 4 days in the lab. Compared to English, both languages had novel grammar, but only one was comprised of novel sounds. After learning a language, participants were scanned while judging the grammaticality of sentences. Judgments were performed for the newly learned language and English. Learners of the similar-sounds language recruited regions that overlapped more with English. Learners of the distinct-sounds language, however, recruited the Superior Temporal Gyrus (STG to a greater extent, which was coactive with the Inferior Frontal Gyrus (IFG. Across learners, recruitment of IFG (but not STG predicted both learning success in tests conducted prior to the scan and grammatical judgment ability during the scan. Data suggest that adults’ difficulty learning language, especially grammar, could be due, at least in part, to the neural commitments they have made to the lower level linguistic components of their native language.

  8. Learning language with the wrong neural scaffolding: the cost of neural commitment to sounds

    Science.gov (United States)

    Finn, Amy S.; Hudson Kam, Carla L.; Ettlinger, Marc; Vytlacil, Jason; D'Esposito, Mark

    2013-01-01

    Does tuning to one's native language explain the “sensitive period” for language learning? We explore the idea that tuning to (or becoming more selective for) the properties of one's native-language could result in being less open (or plastic) for tuning to the properties of a new language. To explore how this might lead to the sensitive period for grammar learning, we ask if tuning to an earlier-learned aspect of language (sound structure) has an impact on the neural representation of a later-learned aspect (grammar). English-speaking adults learned one of two miniature artificial languages (MALs) over 4 days in the lab. Compared to English, both languages had novel grammar, but only one was comprised of novel sounds. After learning a language, participants were scanned while judging the grammaticality of sentences. Judgments were performed for the newly learned language and English. Learners of the similar-sounds language recruited regions that overlapped more with English. Learners of the distinct-sounds language, however, recruited the Superior Temporal Gyrus (STG) to a greater extent, which was coactive with the Inferior Frontal Gyrus (IFG). Across learners, recruitment of IFG (but not STG) predicted both learning success in tests conducted prior to the scan and grammatical judgment ability during the scan. Data suggest that adults' difficulty learning language, especially grammar, could be due, at least in part, to the neural commitments they have made to the lower level linguistic components of their native language. PMID:24273497

  9. Continual Learning through Evolvable Neural Turing Machines

    DEFF Research Database (Denmark)

    Lüders, Benno; Schläger, Mikkel; Risi, Sebastian

    2016-01-01

    Continual learning, i.e. the ability to sequentially learn tasks without catastrophic forgetting of previously learned ones, is an important open challenge in machine learning. In this paper we take a step in this direction by showing that the recently proposed Evolving Neural Turing Machine (ENT......) approach is able to perform one-shot learning in a reinforcement learning task without catastrophic forgetting of previously stored associations.......Continual learning, i.e. the ability to sequentially learn tasks without catastrophic forgetting of previously learned ones, is an important open challenge in machine learning. In this paper we take a step in this direction by showing that the recently proposed Evolving Neural Turing Machine (ENTM...

  10. Temporal plasticity in auditory cortex improves neural discrimination of speech sounds.

    Science.gov (United States)

    Engineer, Crystal T; Shetake, Jai A; Engineer, Navzer D; Vrana, Will A; Wolf, Jordan T; Kilgard, Michael P

    Many individuals with language learning impairments exhibit temporal processing deficits and degraded neural responses to speech sounds. Auditory training can improve both the neural and behavioral deficits, though significant deficits remain. Recent evidence suggests that vagus nerve stimulation (VNS) paired with rehabilitative therapies enhances both cortical plasticity and recovery of normal function. We predicted that pairing VNS with rapid tone trains would enhance the primary auditory cortex (A1) response to unpaired novel speech sounds. VNS was paired with tone trains 300 times per day for 20 days in adult rats. Responses to isolated speech sounds, compressed speech sounds, word sequences, and compressed word sequences were recorded in A1 following the completion of VNS-tone train pairing. Pairing VNS with rapid tone trains resulted in stronger, faster, and more discriminable A1 responses to speech sounds presented at conversational rates. This study extends previous findings by documenting that VNS paired with rapid tone trains altered the neural response to novel unpaired speech sounds. Future studies are necessary to determine whether pairing VNS with appropriate auditory stimuli could potentially be used to improve both neural responses to speech sounds and speech perception in individuals with receptive language disorders. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Neural signatures of phonetic learning in adulthood: a magnetoencephalography study.

    Science.gov (United States)

    Zhang, Yang; Kuhl, Patricia K; Imada, Toshiaki; Iverson, Paul; Pruitt, John; Stevens, Erica B; Kawakatsu, Masaki; Tohkura, Yoh'ichi; Nemoto, Iku

    2009-05-15

    The present study used magnetoencephalography (MEG) to examine perceptual learning of American English /r/ and /l/ categories by Japanese adults who had limited English exposure. A training software program was developed based on the principles of infant phonetic learning, featuring systematic acoustic exaggeration, multi-talker variability, visible articulation, and adaptive listening. The program was designed to help Japanese listeners utilize an acoustic dimension relevant for phonemic categorization of /r-l/ in English. Although training did not produce native-like phonetic boundary along the /r-l/ synthetic continuum in the second language learners, success was seen in highly significant identification improvement over twelve training sessions and transfer of learning to novel stimuli. Consistent with behavioral results, pre-post MEG measures showed not only enhanced neural sensitivity to the /r-l/ distinction in the left-hemisphere mismatch field (MMF) response but also bilateral decreases in equivalent current dipole (ECD) cluster and duration measures for stimulus coding in the inferior parietal region. The learning-induced increases in neural sensitivity and efficiency were also found in distributed source analysis using Minimum Current Estimates (MCE). Furthermore, the pre-post changes exhibited significant brain-behavior correlations between speech discrimination scores and MMF amplitudes as well as between the behavioral scores and ECD measures of neural efficiency. Together, the data provide corroborating evidence that substantial neural plasticity for second-language learning in adulthood can be induced with adaptive and enriched linguistic exposure. Like the MMF, the ECD cluster and duration measures are sensitive neural markers of phonetic learning.

  12. Plasticity-related genes in brain development and amygdala-dependent learning.

    Science.gov (United States)

    Ehrlich, D E; Josselyn, S A

    2016-01-01

    Learning about motivationally important stimuli involves plasticity in the amygdala, a temporal lobe structure. Amygdala-dependent learning involves a growing number of plasticity-related signaling pathways also implicated in brain development, suggesting that learning-related signaling in juveniles may simultaneously influence development. Here, we review the pleiotropic functions in nervous system development and amygdala-dependent learning of a signaling pathway that includes brain-derived neurotrophic factor (BDNF), extracellular signaling-related kinases (ERKs) and cyclic AMP-response element binding protein (CREB). Using these canonical, plasticity-related genes as an example, we discuss the intersection of learning-related and developmental plasticity in the immature amygdala, when aversive and appetitive learning may influence the developmental trajectory of amygdala function. We propose that learning-dependent activation of BDNF, ERK and CREB signaling in the immature amygdala exaggerates and accelerates neural development, promoting amygdala excitability and environmental sensitivity later in life. © 2015 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

  13. Is Sleep Essential for Neural Plasticity in Humans, and How Does It Affect Motor and Cognitive Recovery?

    Directory of Open Access Journals (Sweden)

    Maurizio Gorgoni

    2013-01-01

    Full Text Available There is a general consensus that sleep is strictly linked to memory, learning, and, in general, to the mechanisms of neural plasticity, and that this link may directly affect recovery processes. In fact, a coherent pattern of empirical findings points to beneficial effect of sleep on learning and plastic processes, and changes in synaptic plasticity during wakefulness induce coherent modifications in EEG slow wave cortical topography during subsequent sleep. However, the specific nature of the relation between sleep and synaptic plasticity is not clear yet. We reported findings in line with two models conflicting with respect to the underlying mechanisms, that is, the “synaptic homeostasis hypothesis” and the “consolidation” hypothesis, and some recent results that may reconcile them. Independently from the specific mechanisms involved, sleep loss is associated with detrimental effects on plastic processes at a molecular and electrophysiological level. Finally, we reviewed growing evidence supporting the notion that plasticity-dependent recovery could be improved managing sleep quality, while monitoring EEG during sleep may help to explain how specific rehabilitative paradigms work. We conclude that a better understanding of the sleep-plasticity link could be crucial from a rehabilitative point of view.

  14. Is sleep essential for neural plasticity in humans, and how does it affect motor and cognitive recovery?

    Science.gov (United States)

    Gorgoni, Maurizio; D'Atri, Aurora; Lauri, Giulia; Rossini, Paolo Maria; Ferlazzo, Fabio; De Gennaro, Luigi

    2013-01-01

    There is a general consensus that sleep is strictly linked to memory, learning, and, in general, to the mechanisms of neural plasticity, and that this link may directly affect recovery processes. In fact, a coherent pattern of empirical findings points to beneficial effect of sleep on learning and plastic processes, and changes in synaptic plasticity during wakefulness induce coherent modifications in EEG slow wave cortical topography during subsequent sleep. However, the specific nature of the relation between sleep and synaptic plasticity is not clear yet. We reported findings in line with two models conflicting with respect to the underlying mechanisms, that is, the "synaptic homeostasis hypothesis" and the "consolidation" hypothesis, and some recent results that may reconcile them. Independently from the specific mechanisms involved, sleep loss is associated with detrimental effects on plastic processes at a molecular and electrophysiological level. Finally, we reviewed growing evidence supporting the notion that plasticity-dependent recovery could be improved managing sleep quality, while monitoring EEG during sleep may help to explain how specific rehabilitative paradigms work. We conclude that a better understanding of the sleep-plasticity link could be crucial from a rehabilitative point of view.

  15. How learning to abstract shapes neural sound representations

    Directory of Open Access Journals (Sweden)

    Anke eLey

    2014-06-01

    Full Text Available The transformation of acoustic signals into abstract perceptual representations is the essence of the efficient and goal-directed neural processing of sounds in complex natural environments. While the human and animal auditory system is perfectly equipped to process the spectrotemporal sound features, adequate sound identification and categorization require neural sound representations that are invariant to irrelevant stimulus parameters. Crucially, what is relevant and irrelevant is not necessarily intrinsic to the physical stimulus structure but needs to be learned over time, often through integration of information from other senses. This review discusses the main principles underlying categorical sound perception with a special focus on the role of learning and neural plasticity. We examine the role of different neural structures along the auditory processing pathway in the formation of abstract sound representations with respect to hierarchical as well as dynamic and distributed processing models. Whereas most fMRI studies on categorical sound processing employed speech sounds, the emphasis of the current review lies on the contribution of empirical studies using natural or artificial sounds that enable separating acoustic and perceptual processing levels and avoid interference with existing category representations. Finally, we discuss the opportunities of modern analyses techniques (such as multivariate pattern analysis in studying categorical sound representations. With their increased sensitivity to distributed activation changes - even in absence of changes in overall signal level - these analyses techniques provide a promising tool to reveal the neural underpinnings of perceptually invariant sound representations.

  16. Rehabilitation with poststroke motor recovery: a review with a focus on neural plasticity.

    Science.gov (United States)

    Takeuchi, Naoyuki; Izumi, Shin-Ichi

    2013-01-01

    Motor recovery after stroke is related to neural plasticity, which involves developing new neuronal interconnections, acquiring new functions, and compensating for impairment. However, neural plasticity is impaired in the stroke-affected hemisphere. Therefore, it is important that motor recovery therapies facilitate neural plasticity to compensate for functional loss. Stroke rehabilitation programs should include meaningful, repetitive, intensive, and task-specific movement training in an enriched environment to promote neural plasticity and motor recovery. Various novel stroke rehabilitation techniques for motor recovery have been developed based on basic science and clinical studies of neural plasticity. However, the effectiveness of rehabilitative interventions among patients with stroke varies widely because the mechanisms underlying motor recovery are heterogeneous. Neurophysiological and neuroimaging studies have been developed to evaluate the heterogeneity of mechanisms underlying motor recovery for effective rehabilitation interventions after stroke. Here, we review novel stroke rehabilitation techniques associated with neural plasticity and discuss individualized strategies to identify appropriate therapeutic goals, prevent maladaptive plasticity, and maximize functional gain in patients with stroke.

  17. Computational neurorehabilitation: modeling plasticity and learning to predict recovery

    National Research Council Canada - National Science Library

    Reinkensmeyer, David J; Burdet, Etienne; Casadio, Maura; Krakauer, John W; Kwakkel, Gert; Lang, Catherine E; Swinnen, Stephan P; Ward, Nick S; Schweighofer, Nicolas

    2016-01-01

    .... Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment...

  18. An Inquiry into the Neural Plasticity Underlying Everyday Actions

    Directory of Open Access Journals (Sweden)

    Garrett Tisdale

    2017-11-01

    Full Text Available How does the brain change with respect to how we live our daily lives? Modern studies on how specific actions affect the anatomy of the brain have shown that different actions shape the way the brain is oriented. While individual studies might point towards these effects occurring in daily actions, the concept that morphological changes occur throughout the numerous fields of neuroplasticity based on daily actions has yet to become a well established and discussed phenomena. It is the goal of this article to view a few fields of neuroplasticity to answer this overarching question and review brain imaging studies indicating such morphological changes associated with the fields of neuroplasticity and everyday actions. To achieve this goal, a systematic approach revolving around scholarly search engines was used to briefly explore each studied field of interest. In this article, the activities of music production, video game play, and sleep are analyzed indicating such morphological change. These activities show changes to the respective areas of the brain in which the tasks are processed with a trend arising from the amount of time spent performing each action. It is shown from these fields of study that this classification of relating everyday actions to morphological change through neural plasticity does hold validity with respect to experimental studies.

  19. Self-control of chaos in neural circuits with plastic electrical synapses

    Science.gov (United States)

    Zhigulin, V. P.; Rabinovich, M. I.

    2004-10-01

    Two kinds of connections are known to exist in neural circuits: electrical (also called gap junctions) and chemical. Whereas chemical synapses are known to be plastic (i. e., modifiable), but slow, electrical transmission through gap junctions is not modifiable, but is very fast. We suggest the new artificial synapse that combines the best properties of both: the fast reaction of a gap junction and the plasticity of a chemical synapse. Such a plastic electrical synapse can be used in hybrid neural circuits and for the development of neural prosthetics, i.e., implanted devices that can interact with the real nervous system. Based on the computer modelling we show that such a plastic electrical synapse regularizes chaos in the minimal neural circuit consisting of two chaotic bursting neurons.

  20. Neural plasticity is affected by stress and heritable variation in stress coping style

    DEFF Research Database (Denmark)

    Johansen, I.B.; Sørensen, C.; Sandvik, G.K.

    2012-01-01

    Here we use a comparative model to investigate how behavioral and physiological traits correlate with neural plasticity. Selection for divergent post-stress cortisol levels in rainbow trout (Oncorhynchus mykiss) has yielded low- (LR) and high responsive (HR) lines. Recent reports show low...... behavioral flexibility in LR compared to HR fish and we hypothesize that this divergence is caused by differences in neural plasticity. Genes involved in neural plasticity and neurogenesis were investigated by quantitative PCR in brains of LR and HR fish at baseline conditions and in response to two...... also being affected by STC – and LTS stress in a biphasic manner. A higher degree of neural plasticity in HR fish may provide the substrate for enhanced behavioral flexibility...

  1. Music training enhances rapid neural plasticity of n1 and p2 source activation for unattended sounds.

    Science.gov (United States)

    Seppänen, Miia; Hämäläinen, Jarmo; Pesonen, Anu-Katriina; Tervaniemi, Mari

    2012-01-01

    Neurocognitive studies have demonstrated that long-term music training enhances the processing of unattended sounds. It is not clear, however, whether music training also modulates rapid (within tens of minutes) neural plasticity for sound encoding. To study this phenomenon, we examined whether adult musicians display enhanced rapid neural plasticity compared to non-musicians. More specifically, we compared the modulation of P1, N1, and P2 responses to standard sounds between four unattended passive blocks. Among the standard sounds, infrequently presented deviant sounds were presented (the so-called oddball paradigm). In the middle of the experiment (after two blocks), an active task was presented. Source analysis for event-related potentials (ERPs) showed that N1 and P2 source activation was selectively decreased in musicians after 15 min of passive exposure to sounds and that P2 source activation was found to be re-enhanced after the active task in musicians. Additionally, ERP analysis revealed that in both musicians and non-musicians, P2 ERP amplitude was enhanced after 15 min of passive exposure but only at the frontal electrodes. Furthermore, in musicians, the N1 ERP was enhanced after the active discrimination task but only at the parietal electrodes. Musical training modulates the rapid neural plasticity reflected in N1 and P2 source activation for unattended regular standard sounds. Enhanced rapid plasticity of N1 and P2 is likely to reflect faster auditory perceptual learning in musicians.

  2. Habit learning and memory in mammals: behavioral and neural characteristics.

    Science.gov (United States)

    Gasbarri, Antonella; Pompili, Assunta; Packard, Mark G; Tomaz, Carlos

    2014-10-01

    Goal-direct behavior and habit learning represent two forms of instrumental learning; whereas the former is rapidly acquired and regulated by its outcome, the latter is reflexive, elicited by antecedent stimuli rather than their consequences. Habit learning can be generally defined as the acquisition of associations between stimuli and responses. Habits are acquired via experience-dependent plasticity, occurring repeatedly over the course of days or years and becoming remarkably fixed. The distinction between habit learning, as a product of a procedural learning brain system, and a declarative learning system for encoding facts and episodes is based on the hypothesis that memory is composed of multiple systems that have distinct neuroanatomy and operating principles. Here we review recent research analyzing the main behavioral and neural characteristics of habit learning. In particular, we focus on the distinction between goal-directed and habitual behavior, and describe the brain areas and neurotransmitters systems involved in habit learning. The emotional modulation of habit learning in rodents and primates is reviewed, and the implications of habit learning in psychopathology are briefly described. Copyright © 2014 Elsevier Inc. All rights reserved.

  3. Logarithmic learning for generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2014-12-01

    Generalized classifier neural network is introduced as an efficient classifier among the others. Unless the initial smoothing parameter value is close to the optimal one, generalized classifier neural network suffers from convergence problem and requires quite a long time to converge. In this work, to overcome this problem, a logarithmic learning approach is proposed. The proposed method uses logarithmic cost function instead of squared error. Minimization of this cost function reduces the number of iterations used for reaching the minima. The proposed method is tested on 15 different data sets and performance of logarithmic learning generalized classifier neural network is compared with that of standard one. Thanks to operation range of radial basis function included by generalized classifier neural network, proposed logarithmic approach and its derivative has continuous values. This makes it possible to adopt the advantage of logarithmic fast convergence by the proposed learning method. Due to fast convergence ability of logarithmic cost function, training time is maximally decreased to 99.2%. In addition to decrease in training time, classification performance may also be improved till 60%. According to the test results, while the proposed method provides a solution for time requirement problem of generalized classifier neural network, it may also improve the classification accuracy. The proposed method can be considered as an efficient way for reducing the time requirement problem of generalized classifier neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.

  4. Translating Principles of Neural Plasticity into Research on Speech Motor Control Recovery and Rehabilitation

    Science.gov (United States)

    Ludlow, Christy L.; Hoit, Jeannette; Kent, Raymond; Ramig, Lorraine O.; Shrivastav, Rahul; Strand, Edythe; Yorkston, Kathryn; Sapienza, Christine M.

    2008-01-01

    Purpose: To review the principles of neural plasticity and make recommendations for research on the neural bases for rehabilitation of neurogenic speech disorders. Method: A working group in speech motor control and disorders developed this report, which examines the potential relevance of basic research on the brain mechanisms involved in neural…

  5. Synaptic Plasticity onto Dopamine Neurons Shapes Fear Learning.

    Science.gov (United States)

    Pignatelli, Marco; Umanah, George Kwabena Essien; Ribeiro, Sissi Palma; Chen, Rong; Karuppagounder, Senthilkumar Senthil; Yau, Hau-Jie; Eacker, Stephen; Dawson, Valina Lynn; Dawson, Ted Murray; Bonci, Antonello

    2017-01-18

    Fear learning is a fundamental behavioral process that requires dopamine (DA) release. Experience-dependent synaptic plasticity occurs on DA neurons while an organism is engaged in aversive experiences. However, whether synaptic plasticity onto DA neurons is causally involved in aversion learning is unknown. Here, we show that a stress priming procedure enhances fear learning by engaging VTA synaptic plasticity. Moreover, we took advantage of the ability of the ATPase Thorase to regulate the internalization of AMPA receptors (AMPARs) in order to selectively manipulate glutamatergic synaptic plasticity on DA neurons. Genetic ablation of Thorase in DAT + neurons produced increased AMPAR surface expression and function that lead to impaired induction of both long-term depression (LTD) and long-term potentiation (LTP). Strikingly, animals lacking Thorase in DAT + neurons expressed greater associative learning in a fear conditioning paradigm. In conclusion, our data provide a novel, causal link between synaptic plasticity onto DA neurons and fear learning. Published by Elsevier Inc.

  6. Online neural monitoring of statistical learning.

    Science.gov (United States)

    Batterink, Laura J; Paller, Ken A

    2017-05-01

    The extraction of patterns in the environment plays a critical role in many types of human learning, from motor skills to language acquisition. This process is known as statistical learning. Here we propose that statistical learning has two dissociable components: (1) perceptual binding of individual stimulus units into integrated composites and (2) storing those integrated representations for later use. Statistical learning is typically assessed using post-learning tasks, such that the two components are conflated. Our goal was to characterize the online perceptual component of statistical learning. Participants were exposed to a structured stream of repeating trisyllabic nonsense words and a random syllable stream. Online learning was indexed by an EEG-based measure that quantified neural entrainment at the frequency of the repeating words relative to that of individual syllables. Statistical learning was subsequently assessed using conventional measures in an explicit rating task and a reaction-time task. In the structured stream, neural entrainment to trisyllabic words was higher than in the random stream, increased as a function of exposure to track the progression of learning, and predicted performance on the reaction time (RT) task. These results demonstrate that monitoring this critical component of learning via rhythmic EEG entrainment reveals a gradual acquisition of knowledge whereby novel stimulus sequences are transformed into familiar composites. This online perceptual transformation is a critical component of learning. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Neuron-Glia Interactions in Neural Plasticity: Contributions of Neural Extracellular Matrix and Perineuronal Nets

    Directory of Open Access Journals (Sweden)

    Egor Dzyubenko

    2016-01-01

    Full Text Available Synapses are specialized structures that mediate rapid and efficient signal transmission between neurons and are surrounded by glial cells. Astrocytes develop an intimate association with synapses in the central nervous system (CNS and contribute to the regulation of ion and neurotransmitter concentrations. Together with neurons, they shape intercellular space to provide a stable milieu for neuronal activity. Extracellular matrix (ECM components are synthesized by both neurons and astrocytes and play an important role in the formation, maintenance, and function of synapses in the CNS. The components of the ECM have been detected near glial processes, which abut onto the CNS synaptic unit, where they are part of the specialized macromolecular assemblies, termed perineuronal nets (PNNs. PNNs have originally been discovered by Golgi and represent a molecular scaffold deposited in the interface between the astrocyte and subsets of neurons in the vicinity of the synapse. Recent reports strongly suggest that PNNs are tightly involved in the regulation of synaptic plasticity. Moreover, several studies have implicated PNNs and the neural ECM in neuropsychiatric diseases. Here, we highlight current concepts relating to neural ECM and PNNs and describe an in vitro approach that allows for the investigation of ECM functions for synaptogenesis.

  8. Plasticity in the rat prefrontal cortex: linking gene expression and an operant learning with a computational theory.

    Directory of Open Access Journals (Sweden)

    Maximiliano Rapanelli

    Full Text Available The plasticity in the medial Prefrontal Cortex (mPFC of rodents or lateral prefrontal cortex in non human primates (lPFC, plays a key role neural circuits involved in learning and memory. Several genes, like brain-derived neurotrophic factor (BDNF, cAMP response element binding (CREB, Synapsin I, Calcium/calmodulin-dependent protein kinase II (CamKII, activity-regulated cytoskeleton-associated protein (Arc, c-jun and c-fos have been related to plasticity processes. We analysed differential expression of related plasticity genes and immediate early genes in the mPFC of rats during learning an operant conditioning task. Incompletely and completely trained animals were studied because of the distinct events predicted by our computational model at different learning stages. During learning an operant conditioning task, we measured changes in the mRNA levels by Real-Time RT-PCR during learning; expression of these markers associated to plasticity was incremented while learning and such increments began to decline when the task was learned. The plasticity changes in the lPFC during learning predicted by the model matched up with those of the representative gene BDNF. Herein, we showed for the first time that plasticity in the mPFC in rats during learning of an operant conditioning is higher while learning than when the task is learned, using an integrative approach of a computational model and gene expression.

  9. Neural Plasticity Is Involved in Physiological Sleep, Depressive Sleep Disturbances, and Antidepressant Treatments

    Directory of Open Access Journals (Sweden)

    Meng-Qi Zhang

    2017-01-01

    Full Text Available Depression, which is characterized by a pervasive and persistent low mood and anhedonia, greatly impacts patients, their families, and society. The associated and recurring sleep disturbances further reduce patient’s quality of life. However, therapeutic sleep deprivation has been regarded as a rapid and robust antidepressant treatment for several decades, which suggests a complicated role of sleep in development of depression. Changes in neural plasticity are observed during physiological sleep, therapeutic sleep deprivation, and depression. This correlation might help us to understand better the mechanism underlying development of depression and the role of sleep. In this review, we first introduce the structure of sleep and the facilitated neural plasticity caused by physiological sleep. Then, we introduce sleep disturbances and changes in plasticity in patients with depression. Finally, the effects and mechanisms of antidepressants and therapeutic sleep deprivation on neural plasticity are discussed.

  10. Neural Plasticity Is Involved in Physiological Sleep, Depressive Sleep Disturbances, and Antidepressant Treatments.

    Science.gov (United States)

    Zhang, Meng-Qi; Li, Rui; Wang, Yi-Qun; Huang, Zhi-Li

    2017-01-01

    Depression, which is characterized by a pervasive and persistent low mood and anhedonia, greatly impacts patients, their families, and society. The associated and recurring sleep disturbances further reduce patient's quality of life. However, therapeutic sleep deprivation has been regarded as a rapid and robust antidepressant treatment for several decades, which suggests a complicated role of sleep in development of depression. Changes in neural plasticity are observed during physiological sleep, therapeutic sleep deprivation, and depression. This correlation might help us to understand better the mechanism underlying development of depression and the role of sleep. In this review, we first introduce the structure of sleep and the facilitated neural plasticity caused by physiological sleep. Then, we introduce sleep disturbances and changes in plasticity in patients with depression. Finally, the effects and mechanisms of antidepressants and therapeutic sleep deprivation on neural plasticity are discussed.

  11. Robustness of learning that is based on covariance-driven synaptic plasticity.

    Directory of Open Access Journals (Sweden)

    Yonatan Loewenstein

    2008-03-01

    Full Text Available It is widely believed that learning is due, at least in part, to long-lasting modifications of the strengths of synapses in the brain. Theoretical studies have shown that a family of synaptic plasticity rules, in which synaptic changes are driven by covariance, is particularly useful for many forms of learning, including associative memory, gradient estimation, and operant conditioning. Covariance-based plasticity is inherently sensitive. Even a slight mistuning of the parameters of a covariance-based plasticity rule is likely to result in substantial changes in synaptic efficacies. Therefore, the biological relevance of covariance-based plasticity models is questionable. Here, we study the effects of mistuning parameters of the plasticity rule in a decision making model in which synaptic plasticity is driven by the covariance of reward and neural activity. An exact covariance plasticity rule yields Herrnstein's matching law. We show that although the effect of slight mistuning of the plasticity rule on the synaptic efficacies is large, the behavioral effect is small. Thus, matching behavior is robust to mistuning of the parameters of the covariance-based plasticity rule. Furthermore, the mistuned covariance rule results in undermatching, which is consistent with experimentally observed behavior. These results substantiate the hypothesis that approximate covariance-based synaptic plasticity underlies operant conditioning. However, we show that the mistuning of the mean subtraction makes behavior sensitive to the mistuning of the properties of the decision making network. Thus, there is a tradeoff between the robustness of matching behavior to changes in the plasticity rule and its robustness to changes in the properties of the decision making network.

  12. Mimicking Synaptic Plasticity and Neural Network Using Memtranstors.

    Science.gov (United States)

    Shen, Jian-Xin; Shang, Da-Shan; Chai, Yi-Sheng; Wang, Shou-Guo; Shen, Bao-Gen; Sun, Young

    2018-02-05

    Artificial synaptic devices that mimic the functions of biological synapses have drawn enormous interest because of their potential in developing brain-inspired computing. Current studies are focusing on memristive devices in which the change of the conductance state is used to emulate synaptic behaviors. Here, a new type of artificial synaptic devices based on the memtranstor is demonstrated, which is a fundamental circuit memelement in addition to the memristor, memcapacitor, and meminductor. The state of transtance (presented by the magnetoelectric voltage) in memtranstors acting as the synaptic weight can be tuned continuously with a large number of nonvolatile levels by engineering the applied voltage pulses. Synaptic behaviors including the long-term potentiation, long-term depression, and spiking-time-dependent plasticity are implemented in memtranstors made of Ni/0.7Pb(Mg 1/3 Nb 2/3 )O 3 -0.3PbTiO 3 /Ni multiferroic heterostructures. Simulations reveal the capability of pattern learning in a memtranstor network. The work elucidates the promise of memtranstors as artificial synaptic devices with low energy consumption. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  13. [Involvement of aquaporin-4 in synaptic plasticity, learning and memory].

    Science.gov (United States)

    Wu, Xin; Gao, Jian-Feng

    2017-06-25

    Aquaporin-4 (AQP-4) is the predominant water channel in the central nervous system (CNS) and primarily expressed in astrocytes. Astrocytes have been generally believed to play important roles in regulating synaptic plasticity and information processing. However, the role of AQP-4 in regulating synaptic plasticity, learning and memory, cognitive function is only beginning to be investigated. It is well known that synaptic plasticity is the prime candidate for mediating of learning and memory. Long term potentiation (LTP) and long term depression (LTD) are two forms of synaptic plasticity, and they share some but not all the properties and mechanisms. Hippocampus is a part of limbic system that is particularly important in regulation of learning and memory. This article is to review some research progresses of the function of AQP-4 in synaptic plasticity, learning and memory, and propose the possible role of AQP-4 as a new target in the treatment of cognitive dysfunction.

  14. Deep Learning in Neural Networks: An Overview

    OpenAIRE

    Schmidhuber, Juergen

    2014-01-01

    In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpr...

  15. Reorganization and plastic changes of the human brain associated with skill learning and expertise

    Directory of Open Access Journals (Sweden)

    Yongmin eChang

    2014-02-01

    Full Text Available Novel experience and learning new skills are known as modulators of brain function. Advances in non-invasive brain imaging have provided new insight into structural and functional reorganization associated with skill learning and expertise. Especially, significant imaging evidences come from the domains of sports and music. Data from in vivo imaging studies in sports and music have provided vital information on plausible neural substrates contributing to brain reorganization underlying skill acquisition in humans. This mini review will attempt to take a narrow snapshot of imaging findings demonstrating functional and structural plasticity that mediate skill learning and expertise while identifying converging areas of interest and possible avenues for future research.

  16. Homeostatic synaptic plasticity: from single synapses to neural circuits.

    OpenAIRE

    Vitureira, Nathalia; Letellier, Mathieu; Goda, Yukiko

    2012-01-01

    Homeostatic synaptic plasticity remains an enigmatic form of synaptic plasticity. Increasing interest on the topic has fuelled a surge of recent studies that have identified key molecular players and the signaling pathways involved. However, the new findings also highlight our lack of knowledge concerning some of the basic properties of homeostatic synaptic plasticity. In this review we address how homeostatic mechanisms balance synaptic strengths between the presynaptic and the postsynaptic ...

  17. Menstrual cycle-dependent neural plasticity in the adult human brain is hormone, task, and region specific.

    NARCIS (Netherlands)

    Fernandez, G.S.E.; Weis, S.; Stoffel-Wagner, B.; Tendolkar, I.; Reuber, M.; Beyenburg, S.; Klaver, P.; Fell, J.; Greiff, A. de; Ruhlmann, J.; Reul, J.; Elger, C.E.

    2003-01-01

    In rodents, cyclically fluctuating levels of gonadal steroid hormones modulate neural plasticity by altering synaptic transmission and synaptogenesis. Alterations of mood and cognition observed during the menstrual cycle suggest that steroid-related plasticity also occurs in humans. Cycle

  18. Epigenetic learning in non-neural organisms

    Indian Academy of Sciences (India)

    2008-09-19

    Sep 19, 2008 ... ... some physical traces of the relation persist and can later be the basis of a more effective response. Using toy models we show that this characterization applies not only to the paradigmatic case of neural learning, but also to cellular responses that are based on epigenetic mechanisms of cell memory.

  19. Learning chaotic attractors by neural networks

    NARCIS (Netherlands)

    Bakker, R; Schouten, JC; Giles, CL; Takens, F; van den Bleek, CM

    2000-01-01

    An algorithm is introduced that trains a neural network to identify chaotic dynamics from a single measured time series. During training, the algorithm learns to short-term predict the time series. At the same time a criterion, developed by Diks, van Zwet, Takens, and de Goede (1996) is monitored

  20. The super-Turing computational power of plastic recurrent neural networks.

    Science.gov (United States)

    Cabessa, Jérémie; Siegelmann, Hava T

    2014-12-01

    We study the computational capabilities of a biologically inspired neural model where the synaptic weights, the connectivity pattern, and the number of neurons can evolve over time rather than stay static. Our study focuses on the mere concept of plasticity of the model so that the nature of the updates is assumed to be not constrained. In this context, we show that the so-called plastic recurrent neural networks (RNNs) are capable of the precise super-Turing computational power--as the static analog neural networks--irrespective of whether their synaptic weights are modeled by rational or real numbers, and moreover, irrespective of whether their patterns of plasticity are restricted to bi-valued updates or expressed by any other more general form of updating. Consequently, the incorporation of only bi-valued plastic capabilities in a basic model of RNNs suffices to break the Turing barrier and achieve the super-Turing level of computation. The consideration of more general mechanisms of architectural plasticity or of real synaptic weights does not further increase the capabilities of the networks. These results support the claim that the general mechanism of plasticity is crucially involved in the computational and dynamical capabilities of biological neural networks. They further show that the super-Turing level of computation reflects in a suitable way the capabilities of brain-like models of computation.

  1. Developmental plasticity in the neural control of breathing.

    Science.gov (United States)

    Bavis, Ryan W; MacFarlane, Peter M

    2017-01-01

    The respiratory control system undergoes a diversity of morphological and physiological transformational stages during intrauterine development as it prepares to transition into an air-breathing lifestyle. Following birth, the respiratory system continues to develop and may pass through critical periods of heightened vulnerability to acute environmental stressors. Over a similar time course, however, the developing respiratory control system exhibits substantial capacity to undergo plasticity in response to chronic or repeated environmental stimuli. A hallmark of developmental plasticity is that it requires an interaction between a stimulus (e.g., hypoxia, hyperoxia, or psychosocial stress) and a unique window of development; the same stimulus experienced beyond the boundaries of this critical window of plasticity (e.g., at maturity), therefore, will have little if any appreciable effect on the phenotype. However, there are major gaps in our understanding of the mechanistic basis of developmental plasticity. Filling these gaps in our knowledge may be crucial to advancing our understanding of the developmental origin of adult health and disease. In this review, we: i) begin by clarifying some ambiguities in the definitions of plasticity and related terms that have arisen in recent years; ii) describe various levels of the respiratory control system where plasticity can (or has been identified to) occur; iii) emphasize the importance of understanding the mechanistic basis of developmental plasticity; iv) consider factors that influence whether developmental plasticity is permanent or whether function can be restored; v) discuss genetic and sex-based variation in the expression of developmental plasticity; and vi) provide a translational perspective to developmental plasticity. Copyright © 2016 Elsevier Inc. All rights reserved.

  2. From altered synaptic plasticity to atypical learning: A computational model of Down syndrome.

    Science.gov (United States)

    Tovar, Ángel Eugenio; Westermann, Gert; Torres, Alvaro

    2017-11-02

    Learning and memory rely on the adaptation of synaptic connections. Research on the neurophysiology of Down syndrome has characterized an atypical pattern of synaptic plasticity with limited long-term potentiation (LTP) and increased long-term depression (LTD). Here we present a neurocomputational model that instantiates this LTP/LTD imbalance to explore its impact on tasks of associative learning. In Study 1, we ran a series of computational simulations to analyze the learning of simple and overlapping stimulus associations in a model of Down syndrome compared with a model of typical development. Learning in the Down syndrome model was slower and more susceptible to interference effects. We found that interference effects could be overcome with dedicated stimulation schedules. In Study 2, we ran a second set of simulations and an empirical study with participants with Down syndrome and typically developing children to test the predictions of our model. The model adequately predicted the performance of the human participants in a serial reaction time task, an implicit learning task that relies on associative learning mechanisms. Critically, typical and atypical behavior was explained by the interactions between neural plasticity constraints and the stimulation schedule. Our model provides a mechanistic account of learning impairments based on these interactions, and a causal link between atypical synaptic plasticity and associative learning. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. Research on quasi-dynamic calibration model of plastic sensitive element based on neural networks

    Science.gov (United States)

    Wang, Fang; Kong, Deren; Yang, Lixia; Zhang, Zouzou

    2017-08-01

    Quasi-dynamic calibration accuracy of the plastic sensitive element depends on the accuracy of the fitting model between pressure and deformation. By using the excellent nonlinear mapping ability of RBF (Radial Basis Function) neural network, a calibration model is established which use the peak pressure as the input and use the deformation of the plastic sensitive element as the output in this paper. The calibration experiments of a batch of copper cylinders are carried out on the quasi-dynamic pressure calibration device, which pressure range is within the range of 200MPa to 700MPa. The experiment data are acquired according to the standard pressure monitoring system. The network train and study are done to quasi dynamic calibration model based on neural network by using MATLAB neural network toolbox. Taking the testing samples as the research object, the prediction accuracy of neural network model is compared with the exponential fitting model and the second-order polynomial fitting model. The results show that prediction of the neural network model is most close to the testing samples, and the accuracy of prediction model based on neural network is better than 0.5%, respectively one order higher than the second-order polynomial fitting model and two orders higher than the exponential fitting model. The quasi-dynamic calibration model between pressure peak and deformation of plastic sensitive element, which is based on neural network, provides important basis for creating higher accuracy quasi-dynamic calibration table.

  4. Brain plasticity, cognitive functions and neural stem cells: a pivotal role for the brain-specific neural master gene |-SRGAP2-FAM72-|.

    Science.gov (United States)

    Ho, Nguyen Thi Thanh; Kutzner, Arne; Heese, Klaus

    2017-12-20

    Due to an aging society with an increased dementia-induced threat to higher cognitive functions, it has become imperative to understand the molecular and cellular events controlling the memory and learning processes in the brain. Here, we suggest that the novel master gene pair |-SRGAP2-FAM72-| (SLIT-ROBO Rho GTPase activating the protein 2, family with sequence similarity to 72) reveals a new dogma for the regulation of neural stem cell (NSC) gene expression and is a distinctive player in the control of human brain plasticity. Insight into the specific regulation of the brain-specific neural master gene |-SRGAP2-FAM72-| may essentially contribute to novel therapeutic approaches to restore or improve higher cognitive functions.

  5. Cortical plasticity induced by rapid Hebbian learning of novel tonal word-forms : Evidence from mismatch negativity

    NARCIS (Netherlands)

    Yue, Jinxing; Bastiaanse, Roelien; Alter, Kai

    2014-01-01

    Although several experiments reported rapid cortical plasticity induced by passive exposure to novel segmental patterns, few studies have devoted attention to the neural dynamics during the rapid learning of novel tonal word-forms in tonal languages, such as Chinese. In the current study, native

  6. Physiological Plasticity of Neural-Crest-Derived Stem Cells in the Adult Mammalian Carotid Body

    Directory of Open Access Journals (Sweden)

    Valentina Annese

    2017-04-01

    Full Text Available Adult stem cell plasticity, or the ability of somatic stem cells to cross boundaries and differentiate into unrelated cell types, has been a matter of debate in the last decade. Neural-crest-derived stem cells (NCSCs display a remarkable plasticity during development. Whether adult populations of NCSCs retain this plasticity is largely unknown. Herein, we describe that neural-crest-derived adult carotid body stem cells (CBSCs are able to undergo endothelial differentiation in addition to their reported role in neurogenesis, contributing to both neurogenic and angiogenic processes taking place in the organ during acclimatization to hypoxia. Moreover, CBSC conversion into vascular cell types is hypoxia inducible factor (HIF dependent and sensitive to hypoxia-released vascular cytokines such as erythropoietin. Our data highlight a remarkable physiological plasticity in an adult population of tissue-specific stem cells and could have impact on the use of these cells for cell therapy.

  7. Learning-Related Changes in Adolescents' Neural Networks during Hypothesis-Generating and Hypothesis-Understanding Training

    Science.gov (United States)

    Lee, Jun-Ki; Kwon, Yongju

    2012-01-01

    Fourteen science high school students participated in this study, which investigated neural-network plasticity associated with hypothesis-generating and hypothesis-understanding in learning. The students were divided into two groups and participated in either hypothesis-generating or hypothesis-understanding type learning programs, which were…

  8. Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks.

    Science.gov (United States)

    Walter, Florian; Röhrbein, Florian; Knoll, Alois

    2015-12-01

    The application of biologically inspired methods in design and control has a long tradition in robotics. Unlike previous approaches in this direction, the emerging field of neurorobotics not only mimics biological mechanisms at a relatively high level of abstraction but employs highly realistic simulations of actual biological nervous systems. Even today, carrying out these simulations efficiently at appropriate timescales is challenging. Neuromorphic chip designs specially tailored to this task therefore offer an interesting perspective for neurorobotics. Unlike Von Neumann CPUs, these chips cannot be simply programmed with a standard programming language. Like real brains, their functionality is determined by the structure of neural connectivity and synaptic efficacies. Enabling higher cognitive functions for neurorobotics consequently requires the application of neurobiological learning algorithms to adjust synaptic weights in a biologically plausible way. In this paper, we therefore investigate how to program neuromorphic chips by means of learning. First, we provide an overview over selected neuromorphic chip designs and analyze them in terms of neural computation, communication systems and software infrastructure. On the theoretical side, we review neurobiological learning techniques. Based on this overview, we then examine on-die implementations of these learning algorithms on the considered neuromorphic chips. A final discussion puts the findings of this work into context and highlights how neuromorphic hardware can potentially advance the field of autonomous robot systems. The paper thus gives an in-depth overview of neuromorphic implementations of basic mechanisms of synaptic plasticity which are required to realize advanced cognitive capabilities with spiking neural networks. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. Mechanisms of Long Non-Coding RNAs in the Assembly and Plasticity of Neural Circuitry.

    Science.gov (United States)

    Wang, Andi; Wang, Junbao; Liu, Ying; Zhou, Yan

    2017-01-01

    The mechanisms underlying development processes and functional dynamics of neural circuits are far from understood. Long non-coding RNAs (lncRNAs) have emerged as essential players in defining identities of neural cells, and in modulating neural activities. In this review, we summarized latest advances concerning roles and mechanisms of lncRNAs in assembly, maintenance and plasticity of neural circuitry, as well as lncRNAs' implications in neurological disorders. We also discussed technical advances and challenges in studying functions and mechanisms of lncRNAs in neural circuitry. Finally, we proposed that lncRNA studies would advance our understanding on how neural circuits develop and function in physiology and disease conditions.

  10. Learning of N-layers neural network

    Directory of Open Access Journals (Sweden)

    Vladimír Konečný

    2005-01-01

    Full Text Available In the last decade we can observe increasing number of applications based on the Artificial Intelligence that are designed to solve problems from different areas of human activity. The reason why there is so much interest in these technologies is that the classical way of solutions does not exist or these technologies are not suitable because of their robustness. They are often used in applications like Business Intelligence that enable to obtain useful information for high-quality decision-making and to increase competitive advantage.One of the most widespread tools for the Artificial Intelligence are the artificial neural networks. Their high advantage is relative simplicity and the possibility of self-learning based on set of pattern situations.For the learning phase is the most commonly used algorithm back-propagation error (BPE. The base of BPE is the method minima of error function representing the sum of squared errors on outputs of neural net, for all patterns of the learning set. However, while performing BPE and in the first usage, we can find out that it is necessary to complete the handling of the learning factor by suitable method. The stability of the learning process and the rate of convergence depend on the selected method. In the article there are derived two functions: one function for the learning process management by the relative great error function value and the second function when the value of error function approximates to global minimum.The aim of the article is to introduce the BPE algorithm in compact matrix form for multilayer neural networks, the derivation of the learning factor handling method and the presentation of the results.

  11. [Robustness analysis of adaptive neural network model based on spike timing-dependent plasticity].

    Science.gov (United States)

    Chen, Yunzhi; Xu, Guizhi; Zhou, Qian; Guo, Miaomiao; Guo, Lei; Wan, Xiaowei

    2015-02-01

    To explore the self-organization robustness of the biological neural network, and thus to provide new ideas and methods for the electromagnetic bionic protection, we studied both the information transmission mechanism of neural network and spike timing-dependent plasticity (STDP) mechanism, and then investigated the relationship between synaptic plastic and adaptive characteristic of biology. Then a feedforward neural network with the Izhikevich model and the STDP mechanism was constructed, and the adaptive robust capacity of the network was analyzed. Simulation results showed that the neural network based on STDP mechanism had good rubustness capacity, and this characteristics is closely related to the STDP mechanisms. Based on this simulation work, the cell circuit with neurons and synaptic circuit which can simulate the information processing mechanisms of biological nervous system will be further built, then the electronic circuits with adaptive robustness will be designed based on the cell circuit.

  12. Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding.

    Science.gov (United States)

    Gardner, Brian; Grüning, André

    2016-01-01

    Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule's error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism.

  13. Learning Topologies with the Growing Neural Forest.

    Science.gov (United States)

    Palomo, Esteban José; López-Rubio, Ezequiel

    2016-06-01

    In this work, a novel self-organizing model called growing neural forest (GNF) is presented. It is based on the growing neural gas (GNG), which learns a general graph with no special provisions for datasets with separated clusters. On the contrary, the proposed GNF learns a set of trees so that each tree represents a connected cluster of data. High dimensional datasets often contain large empty regions among clusters, so this proposal is better suited to them than other self-organizing models because it represents these separated clusters as connected components made of neurons. Experimental results are reported which show the self-organization capabilities of the model. Moreover, its suitability for unsupervised clustering and foreground detection applications is demonstrated. In particular, the GNF is shown to correctly discover the connected component structure of some datasets. Moreover, it outperforms some well-known foreground detectors both in quantitative and qualitative terms.

  14. Temporal-pattern learning in neural models

    CERN Document Server

    Genís, Carme Torras

    1985-01-01

    While the ability of animals to learn rhythms is an unquestionable fact, the underlying neurophysiological mechanisms are still no more than conjectures. This monograph explores the requirements of such mechanisms, reviews those previously proposed and postulates a new one based on a direct electric coding of stimulation frequencies. Experi­ mental support for the option taken is provided both at the single neuron and neural network levels. More specifically, the material presented divides naturally into four parts: a description of the experimental and theoretical framework where this work becomes meaningful (Chapter 2), a detailed specifica­ tion of the pacemaker neuron model proposed together with its valida­ tion through simulation (Chapter 3), an analytic study of the behavior of this model when submitted to rhythmic stimulation (Chapter 4) and a description of the neural network model proposed for learning, together with an analysis of the simulation results obtained when varying seve­ ral factors r...

  15. Learning in Neural Networks: VLSI Implementation Strategies

    Science.gov (United States)

    Duong, Tuan Anh

    1995-01-01

    Fully-parallel hardware neural network implementations may be applied to high-speed recognition, classification, and mapping tasks in areas such as vision, or can be used as low-cost self-contained units for tasks such as error detection in mechanical systems (e.g. autos). Learning is required not only to satisfy application requirements, but also to overcome hardware-imposed limitations such as reduced dynamic range of connections.

  16. A neural model of hierarchical reinforcement learning.

    Science.gov (United States)

    Rasmussen, Daniel; Voelker, Aaron; Eliasmith, Chris

    2017-01-01

    We develop a novel, biologically detailed neural model of reinforcement learning (RL) processes in the brain. This model incorporates a broad range of biological features that pose challenges to neural RL, such as temporally extended action sequences, continuous environments involving unknown time delays, and noisy/imprecise computations. Most significantly, we expand the model into the realm of hierarchical reinforcement learning (HRL), which divides the RL process into a hierarchy of actions at different levels of abstraction. Here we implement all the major components of HRL in a neural model that captures a variety of known anatomical and physiological properties of the brain. We demonstrate the performance of the model in a range of different environments, in order to emphasize the aim of understanding the brain's general reinforcement learning ability. These results show that the model compares well to previous modelling work and demonstrates improved performance as a result of its hierarchical ability. We also show that the model's behaviour is consistent with available data on human hierarchical RL, and generate several novel predictions.

  17. Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task

    Science.gov (United States)

    2017-01-01

    Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP) are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both reward modulated and non-reward modulated STDP and implemented multiple mechanisms for homeostatic regulation of synaptic efficacy, including heterosynaptic plasticity, gain control, output balancing, activity normalization of rewarded STDP and hard limits on synaptic strength. We found that addition of a hidden layer of neurons employing non-rewarded STDP created neurons that responded to the specific combinations of inputs and thus performed basic classification of the input patterns. When combined with a following layer of neurons implementing rewarded STDP, the network was able to learn, despite the absence of labeled training data, discrimination between rewarding patterns and the patterns designated as punishing. Synaptic noise allowed for trial-and-error learning that helped to identify the goal-oriented strategies which were effective in task solving. The study predicts a critical set of properties of the spiking neuronal network with STDP that was sufficient to solve a complex foraging task involving pattern classification and decision making. PMID:28961245

  18. Learning structure of sensory inputs with synaptic plasticity leads to interference.

    Science.gov (United States)

    Chrol-Cannon, Joseph; Jin, Yaochu

    2015-01-01

    Synaptic plasticity is often explored as a form of unsupervised adaptation in cortical microcircuits to learn the structure of complex sensory inputs and thereby improve performance of classification and prediction. The question of whether the specific structure of the input patterns is encoded in the structure of neural networks has been largely neglected. Existing studies that have analyzed input-specific structural adaptation have used simplified, synthetic inputs in contrast to complex and noisy patterns found in real-world sensory data. In this work, input-specific structural changes are analyzed for three empirically derived models of plasticity applied to three temporal sensory classification tasks that include complex, real-world visual and auditory data. Two forms of spike-timing dependent plasticity (STDP) and the Bienenstock-Cooper-Munro (BCM) plasticity rule are used to adapt the recurrent network structure during the training process before performance is tested on the pattern recognition tasks. It is shown that synaptic adaptation is highly sensitive to specific classes of input pattern. However, plasticity does not improve the performance on sensory pattern recognition tasks, partly due to synaptic interference between consecutively presented input samples. The changes in synaptic strength produced by one stimulus are reversed by the presentation of another, thus largely preventing input-specific synaptic changes from being retained in the structure of the network. To solve the problem of interference, we suggest that models of plasticity be extended to restrict neural activity and synaptic modification to a subset of the neural circuit, which is increasingly found to be the case in experimental neuroscience.

  19. Neural Plasticity in Multiple Sclerosis: The Functional and Molecular Background

    Science.gov (United States)

    Glabinski, Andrzej

    2015-01-01

    Multiple sclerosis is an autoimmune neurodegenerative disorder resulting in motor dysfunction and cognitive decline. The inflammatory and neurodegenerative changes seen in the brains of MS patients lead to progressive disability and increasing brain atrophy. The most common type of MS is characterized by episodes of clinical exacerbations and remissions. This suggests the presence of compensating mechanisms for accumulating damage. Apart from the widely known repair mechanisms like remyelination, another important phenomenon is neuronal plasticity. Initially, neuroplasticity was connected with the developmental stages of life; however, there is now growing evidence confirming that structural and functional reorganization occurs throughout our lifetime. Several functional studies, utilizing such techniques as fMRI, TBS, or MRS, have provided valuable data about the presence of neuronal plasticity in MS patients. CNS ability to compensate for neuronal damage is most evident in RR-MS; however it has been shown that brain plasticity is also preserved in patients with substantial brain damage. Regardless of the numerous studies, the molecular background of neuronal plasticity in MS is still not well understood. Several factors, like IL-1β, BDNF, PDGF, or CB1Rs, have been implicated in functional recovery from the acute phase of MS and are thus considered as potential therapeutic targets. PMID:26229689

  20. Neural plasticity is affected by stress and heritable variation in stress coping style.

    Science.gov (United States)

    Johansen, Ida B; Sørensen, Christina; Sandvik, Guro K; Nilsson, Göran E; Höglund, Erik; Bakken, Morten; Overli, Oyvind

    2012-06-01

    Here we use a comparative model to investigate how behavioral and physiological traits correlate with neural plasticity. Selection for divergent post-stress cortisol levels in rainbow trout (Oncorhynchus mykiss) has yielded low- (LR) and high responsive (HR) lines. Recent reports show low behavioral flexibility in LR compared to HR fish and we hypothesize that this divergence is caused by differences in neural plasticity. Genes involved in neural plasticity and neurogenesis were investigated by quantitative PCR in brains of LR and HR fish at baseline conditions and in response to two different stress paradigms: short-term confinement (STC) and long-term social (LTS) stress. Expression of proliferating cell nuclear antigen (PCNA), neurogenic differentiation factor (NeuroD) and doublecortin (DCX) was generally higher in HR compared to LR fish. STC stress led to increased expression of PCNA and brain-derived neurotrophic factor (BDNF) in both lines, whereas LTS stress generally suppressed PCNA and NeuroD expression while leaving BDNF expression unaltered. These results indicate that the transcription of neuroplasticity-related genes is associated with variation in coping style, while also being affected by STC - and LTS stress in a biphasic manner. A higher degree of neural plasticity in HR fish may provide the substrate for enhanced behavioral flexibility. Copyright © 2012 Elsevier Inc. All rights reserved.

  1. Neural Plasticity and the Issue of Mimicry Tasks in L2 Pronunciation Studies.

    Science.gov (United States)

    Stapp, Yvonne F.

    1999-01-01

    In an investigation of the relationship between mimicry skill and neural plasticity, 28 monolingual Japanese subjects aged 4-17 repeated a list of simple English words containing /r/ and /l/. Analyses were made of individual and age-group scores and of consistency of individuals' pronunciation across word tokens. Results indicated mimicry ability…

  2. Swallowing and Dysphagia Rehabilitation: Translating Principles of Neural Plasticity into Clinically Oriented Evidence

    Science.gov (United States)

    Robbins, JoAnne; Butler, Susan G.; Daniels, Stephanie K.; Gross, Roxann Diez; Langmore, Susan; Lazarus, Cathy L.; Martin-Harris, Bonnie; McCabe, Daniel; Musson, Nan; Rosenbek, John

    2008-01-01

    Purpose: This review presents the state of swallowing rehabilitation science as it relates to evidence for neural plastic changes in the brain. The case is made for essential collaboration between clinical and basic scientists to expand the positive influences of dysphagia rehabilitation in synergy with growth in technology and knowledge. The…

  3. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung

    2018-02-01

    Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  4. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung

    2018-02-01

    Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  5. Enhancement of Spike-Timing-Dependent Plasticity in Spiking Neural Systems with Noise.

    Science.gov (United States)

    Nobukawa, Sou; Nishimura, Haruhiko

    2016-08-01

    Synaptic plasticity is widely recognized to support adaptable information processing in the brain. Spike-timing-dependent plasticity, one subtype of plasticity, can lead to synchronous spike propagation with temporal spiking coding information. Recently, it was reported that in a noisy environment, like the actual brain, the spike-timing-dependent plasticity may be made efficient by the effect of stochastic resonance. In the stochastic resonance, the presence of noise helps a nonlinear system in amplifying a weak (under barrier) signal. However, previous studies have ignored the full variety of spiking patterns and many relevant factors in neural dynamics. Thus, in order to prove the physiological possibility for the enhancement of spike-timing-dependent plasticity by stochastic resonance, it is necessary to demonstrate that this stochastic resonance arises in realistic cortical neural systems. In this study, we evaluate this stochastic resonance phenomenon in the realistic cortical neural system described by the Izhikevich neuron model and compare the characteristics of typical spiking patterns of regular spiking, intrinsically bursting and chattering experimentally observed in the cortex.

  6. Neural circuit remodeling and structural plasticity in the cortex during chronic pain.

    Science.gov (United States)

    Kim, Woojin; Kim, Sun Kwang

    2016-01-01

    Damage in the periphery or spinal cord induces maladaptive plastic changes along the somatosensory nervous system from the periphery to the cortex, often leading to chronic pain. Although the role of neural circuit remodeling and structural synaptic plasticity in the 'pain matrix' cortices in chronic pain has been thought as a secondary epiphenomenon to altered nociceptive signaling in the spinal cord, progress in whole brain imaging studies on human patients and animal models has suggested a possibility that plastic changes in cortical neural circuits may actively contribute to chronic pain symptoms. Furthermore, recent development in two-photon microscopy and fluorescence labeling techniques have enabled us to longitudinally trace the structural and functional changes in local circuits, single neurons and even individual synapses in the brain of living animals. These technical advances has started to reveal that cortical structural remodeling following tissue or nerve damage could rapidly occur within days, which are temporally correlated with functional plasticity of cortical circuits as well as the development and maintenance of chronic pain behavior, thereby modifying the previous concept that it takes much longer periods (e.g. months or years). In this review, we discuss the relation of neural circuit plasticity in the 'pain matrix' cortices, such as the anterior cingulate cortex, prefrontal cortex and primary somatosensory cortex, with chronic pain. We also introduce how to apply long-term in vivo two-photon imaging approaches for the study of pathophysiological mechanisms of chronic pain.

  7. Neural associative memory with optimal Bayesian learning.

    Science.gov (United States)

    Knoblauch, Andreas

    2011-06-01

    Neural associative memories are perceptron-like single-layer networks with fast synaptic learning typically storing discrete associations between pairs of neural activity patterns. Previous work optimized the memory capacity for various models of synaptic learning: linear Hopfield-type rules, the Willshaw model employing binary synapses, or the BCPNN rule of Lansner and Ekeberg, for example. Here I show that all of these previous models are limit cases of a general optimal model where synaptic learning is determined by probabilistic Bayesian considerations. Asymptotically, for large networks and very sparse neuron activity, the Bayesian model becomes identical to an inhibitory implementation of the Willshaw and BCPNN-type models. For less sparse patterns, the Bayesian model becomes identical to Hopfield-type networks employing the covariance rule. For intermediate sparseness or finite networks, the optimal Bayesian learning rule differs from the previous models and can significantly improve memory performance. I also provide a unified analytical framework to determine memory capacity at a given output noise level that links approaches based on mutual information, Hamming distance, and signal-to-noise ratio.

  8. Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

    Science.gov (United States)

    Chernoded, Andrey; Dudko, Lev; Myagkov, Igor; Volkov, Petr

    2017-10-01

    Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

  9. Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

    Directory of Open Access Journals (Sweden)

    Chernoded Andrey

    2017-01-01

    Full Text Available Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

  10. Simulated apoptosis/neurogenesis regulates learning and memory capabilities of adaptive neural networks.

    Science.gov (United States)

    Chambers, R Andrew; Potenza, Marc N; Hoffman, Ralph E; Miranker, Willard

    2004-04-01

    Characterization of neuronal death and neurogenesis in the adult brain of birds, humans, and other mammals raises the possibility that neuronal turnover represents a special form of neuroplasticity associated with stress responses, cognition, and the pathophysiology and treatment of psychiatric disorders. Multilayer neural network models capable of learning alphabetic character representations via incremental synaptic connection strength changes were used to assess additional learning and memory effects incurred by simulation of coordinated apoptotic and neurogenic events in the middle layer. Using a consistent incremental learning capability across all neurons and experimental conditions, increasing the number of middle layer neurons undergoing turnover increased network learning capacity for new information, and increased forgetting of old information. Simulations also showed that specific patterns of neural turnover based on individual neuronal connection characteristics, or the temporal-spatial pattern of neurons chosen for turnover during new learning impacts new learning performance. These simulations predict that apoptotic and neurogenic events could act together to produce specific learning and memory effects beyond those provided by ongoing mechanisms of connection plasticity in neuronal populations. Regulation of rates as well as patterns of neuronal turnover may serve an important function in tuning the informatic properties of plastic networks according to novel informational demands. Analogous regulation in the hippocampus may provide for adaptive cognitive and emotional responses to novel and stressful contexts, or operate suboptimally as a basis for psychiatric disorders. The implications of these elementary simulations for future biological and neural modeling research on apoptosis and neurogenesis are discussed.

  11. Brain-controlled neuromuscular stimulation to drive neural plasticity and functional recovery.

    Science.gov (United States)

    Ethier, C; Gallego, J A; Miller, L E

    2015-08-01

    There is mounting evidence that appropriately timed neuromuscular stimulation can induce neural plasticity and generate functional recovery from motor disorders. This review addresses the idea that coordinating stimulation with a patient's voluntary effort might further enhance neurorehabilitation. Studies in cell cultures and behaving animals have delineated the rules underlying neural plasticity when single neurons are used as triggers. However, the rules governing more complex stimuli and larger networks are less well understood. We argue that functional recovery might be optimized if stimulation were modulated by a brain machine interface, to match the details of the patient's voluntary intent. The potential of this novel approach highlights the need for a better understanding of the complex rules underlying this form of plasticity. Copyright © 2015 Elsevier Ltd. All rights reserved.

  12. The Radical Plasticity Thesis: How the Brain Learns to be Conscious

    Science.gov (United States)

    Cleeremans, Axel

    2011-01-01

    In this paper, I explore the idea that consciousness is something that the brain learns to do rather than an intrinsic property of certain neural states and not others. Starting from the idea that neural activity is inherently unconscious, the question thus becomes: How does the brain learn to be conscious? I suggest that consciousness arises as a result of the brain's continuous attempts at predicting not only the consequences of its actions on the world and on other agents, but also the consequences of activity in one cerebral region on activity in other regions. By this account, the brain continuously and unconsciously learns to redescribe its own activity to itself, so developing systems of meta-representations that characterize and qualify the target first-order representations. Such learned redescriptions, enriched by the emotional value associated with them, form the basis of conscious experience. Learning and plasticity are thus central to consciousness, to the extent that experiences only occur in experiencers that have learned to know they possess certain first-order states and that have learned to care more about certain states than about others. This is what I call the “Radical Plasticity Thesis.” In a sense thus, this is the enactive perspective, but turned both inwards and (further) outwards. Consciousness involves “signal detection on the mind”; the conscious mind is the brain's (non-conceptual, implicit) theory about itself. I illustrate these ideas through neural network models that simulate the relationships between performance and awareness in different tasks. PMID:21687455

  13. Enhancing neural activity to drive respiratory plasticity following cervical spinal cord injury.

    Science.gov (United States)

    Hormigo, Kristiina M; Zholudeva, Lyandysha V; Spruance, Victoria M; Marchenko, Vitaliy; Cote, Marie-Pascale; Vinit, Stephane; Giszter, Simon; Bezdudnaya, Tatiana; Lane, Michael A

    2017-01-01

    Cervical spinal cord injury (SCI) results in permanent life-altering sensorimotor deficits, among which impaired breathing is one of the most devastating and life-threatening. While clinical and experimental research has revealed that some spontaneous respiratory improvement (functional plasticity) can occur post-SCI, the extent of the recovery is limited and significant deficits persist. Thus, increasing effort is being made to develop therapies that harness and enhance this neuroplastic potential to optimize long-term recovery of breathing in injured individuals. One strategy with demonstrated therapeutic potential is the use of treatments that increase neural and muscular activity (e.g. locomotor training, neural and muscular stimulation) and promote plasticity. With a focus on respiratory function post-SCI, this review will discuss advances in the use of neural interfacing strategies and activity-based treatments, and highlights some recent results from our own research. Copyright © 2016 Elsevier Inc. All rights reserved.

  14. Topological dynamics in spike-timing dependent plastic model neural networks

    Directory of Open Access Journals (Sweden)

    David B. Stone

    2013-04-01

    Full Text Available Spike-timing dependent plasticity (STDP is a biologically constrained unsupervised form of learning that potentiates or depresses synaptic connections based on the precise timing of pre-synaptic and post-synaptic firings. The effects of on-going STDP on the topology of evolving model neural networks were assessed in 50 unique simulations which modeled two hours of activity. After a period of stabilization, a number of global and local topological features were monitored periodically to quantify on-going changes in network structure. Global topological features included the total number of remaining synapses, average synaptic strengths, and average number of synapses per neuron (degree. Under a range of different input regimes and initial network configurations, each network maintained a robust and highly stable global structure across time. Local topology was monitored by assessing state changes of all three-neuron subgraphs (triads present in the networks. Overall counts and the range of triad configurations varied little across the simulations; however, a substantial set of individual triads continued to undergo rapid state changes and revealed a dynamic local topology. In addition, specific small-world properties also fluctuated across time. These findings suggest that on-going STDP provides an efficient means of selecting and maintaining a stable yet flexible network organization.

  15. Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks.

    Science.gov (United States)

    Zenke, Friedemann; Agnes, Everton J; Gerstner, Wulfram

    2015-04-21

    Synaptic plasticity, the putative basis of learning and memory formation, manifests in various forms and across different timescales. Here we show that the interaction of Hebbian homosynaptic plasticity with rapid non-Hebbian heterosynaptic plasticity is, when complemented with slower homeostatic changes and consolidation, sufficient for assembly formation and memory recall in a spiking recurrent network model of excitatory and inhibitory neurons. In the model, assemblies were formed during repeated sensory stimulation and characterized by strong recurrent excitatory connections. Even days after formation, and despite ongoing network activity and synaptic plasticity, memories could be recalled through selective delay activity following the brief stimulation of a subset of assembly neurons. Blocking any component of plasticity prevented stable functioning as a memory network. Our modelling results suggest that the diversity of plasticity phenomena in the brain is orchestrated towards achieving common functional goals.

  16. Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks

    Science.gov (United States)

    Zenke, Friedemann; Agnes, Everton J.; Gerstner, Wulfram

    2015-01-01

    Synaptic plasticity, the putative basis of learning and memory formation, manifests in various forms and across different timescales. Here we show that the interaction of Hebbian homosynaptic plasticity with rapid non-Hebbian heterosynaptic plasticity is, when complemented with slower homeostatic changes and consolidation, sufficient for assembly formation and memory recall in a spiking recurrent network model of excitatory and inhibitory neurons. In the model, assemblies were formed during repeated sensory stimulation and characterized by strong recurrent excitatory connections. Even days after formation, and despite ongoing network activity and synaptic plasticity, memories could be recalled through selective delay activity following the brief stimulation of a subset of assembly neurons. Blocking any component of plasticity prevented stable functioning as a memory network. Our modelling results suggest that the diversity of plasticity phenomena in the brain is orchestrated towards achieving common functional goals. PMID:25897632

  17. Lithium's role in neural plasticity and its implications for mood disorders.

    Science.gov (United States)

    Gray, J D; McEwen, B S

    2013-11-01

    Lithium (Li) is often an effective treatment for mood disorders, especially bipolar disorder (BPD), and can mitigate the effects of stress on the brain by modulating several pathways to facilitate neural plasticity. This review seeks to summarize what is known about the molecular mechanisms underlying Li's actions in the brain in response to stress, particularly how Li is able to facilitate plasticity through regulation of the glutamate system and cytoskeletal components. The authors conducted an extensive search of the published literature using several search terms, including Li, plasticity, and stress. Relevant articles were retrieved, and their bibliographies consulted to expand the number of articles reviewed. The most relevant articles from both the clinical and preclinical literature were examined in detail. Chronic stress results in morphological and functional remodeling in specific brain regions where structural differences have been associated with mood disorders, such as BPD. Li has been shown to block stress-induced changes and facilitate neural plasticity. The onset of mood disorders may reflect an inability of the brain to properly respond after stress, where changes in certain regions may become 'locked in' when plasticity is lost. Li can enhance plasticity through several molecular mechanisms, which have been characterized in animal models. Further, the expanding number of clinical imaging studies has provided evidence that these mechanisms may be at work in the human brain. This work supports the hypothesis that Li is able to improve clinical symptoms by facilitating neural plasticity and thereby helps to 'unlock' the brain from its maladaptive state in patients with mood disorders. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  18. General anesthesia: a gateway to modulate synapse formation and neural plasticity?

    Science.gov (United States)

    Vutskits, Laszlo

    2012-11-01

    Appropriate balance between excitatory and inhibitory neural activity patterns is of utmost importance in the maintenance of neuronal homeostasis. General anesthetic-induced pharmacological interference with this equilibrium results not only in a temporary loss of consciousness but can also initiate long-term changes in brain function. Although these alterations were initially considered deleterious, recent observations suggest that at least under some specific conditions, they may eventually improve neural function. The goal of this review is to provide insights into the mechanisms underlying these dual effects. Basic science issues on the important role of critical periods during neural circuitry assembly will be discussed to better understand how even brief exposures to general anesthetics could initiate context-dependent lasting changes in neuronal structure and function. Recent series of observations suggesting a developmental stage-dependent impact of these drugs on synaptogenesis will then be summarized together with currently known molecular mechanisms underlying these effects. Particular emphasis will be placed on how anesthetic drugs modulate neural plasticity in the adult brain and how this may improve neural function under some pathological states. The ensemble of these new observations strongly suggests that general anesthetics should not merely be considered toxic drugs but rather acknowledged as robust, context-dependent modulators of neural plasticity.

  19. Neural and Cognitive Plasticity: From Maps to Minds

    Science.gov (United States)

    Mercado, Eduardo, III

    2008-01-01

    Some species and individuals are able to learn cognitive skills more flexibly than others. Learning experiences and cortical function are known to contribute to such differences, but the specific factors that determine an organism's intellectual capacities remain unclear. Here, an integrative framework is presented suggesting that variability in…

  20. Learning Structure of Sensory Inputs with Synaptic Plasticity Leads to Interference

    Directory of Open Access Journals (Sweden)

    Joseph eChrol-Cannon

    2015-08-01

    Full Text Available Synaptic plasticity is often explored as a form of unsupervised adaptationin cortical microcircuits to learn the structure of complex sensoryinputs and thereby improve performance of classification and prediction. The question of whether the specific structure of the input patterns is encoded in the structure of neural networks has been largely neglected. Existing studies that have analyzed input-specific structural adaptation have used simplified, synthetic inputs in contrast to complex and noisy patterns found in real-world sensory data.In this work, input-specific structural changes are analyzed forthree empirically derived models of plasticity applied to three temporal sensory classification tasks that include complex, real-world visual and auditory data. Two forms of spike-timing dependent plasticity (STDP and the Bienenstock-Cooper-Munro (BCM plasticity rule are used to adapt the recurrent network structure during the training process before performance is tested on the pattern recognition tasks.It is shown that synaptic adaptation is highly sensitive to specific classes of input pattern. However, plasticity does not improve the performance on sensory pattern recognition tasks, partly due to synaptic interference between consecutively presented input samples. The changes in synaptic strength produced by one stimulus are reversed by thepresentation of another, thus largely preventing input-specific synaptic changes from being retained in the structure of the network.To solve the problem of interference, we suggest that models of plasticitybe extended to restrict neural activity and synaptic modification to a subset of the neural circuit, which is increasingly found to be the casein experimental neuroscience.

  1. Sensorimotor learning biases choice behavior: a learning neural field model for decision making.

    Directory of Open Access Journals (Sweden)

    Christian Klaes

    Full Text Available According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action

  2. Learning about the Types of Plastic Wastes: Effectiveness of Inquiry Learning Strategies

    Science.gov (United States)

    So, Wing-Mui Winnie; Cheng, Nga-Yee Irene; Chow, Cheuk-Fai; Zhan, Ying

    2016-01-01

    This study aims to examine the impacts of the inquiry learning strategies employed in a "Plastic Education Project" on primary students' knowledge, beliefs and intended behaviour in Hong Kong. Student questionnaires and a test on plastic types were adopted for data collection. Results reveal that the inquiry learning strategies…

  3. Temporal entrainment of cognitive functions: musical mnemonics induce brain plasticity and oscillatory synchrony in neural networks underlying memory.

    Science.gov (United States)

    Thaut, Michael H; Peterson, David A; McIntosh, Gerald C

    2005-12-01

    In a series of experiments, we have begun to investigate the effect of music as a mnemonic device on learning and memory and the underlying plasticity of oscillatory neural networks. We used verbal learning and memory tests (standardized word lists, AVLT) in conjunction with electroencephalographic analysis to determine differences between verbal learning in either a spoken or musical (verbal materials as song lyrics) modality. In healthy adults, learning in both the spoken and music condition was associated with significant increases in oscillatory synchrony across all frequency bands. A significant difference between the spoken and music condition emerged in the cortical topography of the learning-related synchronization. When using EEG measures as predictors during learning for subsequent successful memory recall, significantly increased coherence (phase-locked synchronization) within and between oscillatory brain networks emerged for music in alpha and gamma bands. In a similar study with multiple sclerosis patients, superior learning and memory was shown in the music condition when controlled for word order recall, and subjects were instructed to sing back the word lists. Also, the music condition was associated with a significant power increase in the low-alpha band in bilateral frontal networks, indicating increased neuronal synchronization. Musical learning may access compensatory pathways for memory functions during compromised PFC functions associated with learning and recall. Music learning may also confer a neurophysiological advantage through the stronger synchronization of the neuronal cell assemblies underlying verbal learning and memory. Collectively our data provide evidence that melodic-rhythmic templates as temporal structures in music may drive internal rhythm formation in recurrent cortical networks involved in learning and memory.

  4. Premotor synaptic plasticity limited to the critical period for song learning.

    Science.gov (United States)

    Sizemore, Max; Perkel, David J

    2011-10-18

    Synaptic plasticity has been hypothesized to underlie learning and memory. Understanding of how such plasticity might produce motor learning is limited, in part because of the paucity of model systems with a tractable learned behavior under control of a discrete neural circuit. Songbirds possess both of these traits, thereby providing an excellent model for studying vertebrate motor learning. We report unique evidence of long-term depression (LTD) in the juvenile songbird premotor robust nucleus of the arcopallium (RA). LTD induction at RA recurrent collateral synapses requires NMDA receptors, postsynaptic depolarization, and postsynaptic calcium, and can be reversed by high-frequency stimulation. In adult birds, which have exited the critical period for sensorimotor learning and cannot modify their song, we were no longer able to induce LTD at RA collateral synapses. Furthermore, testosterone-induced premature maturation of song in juveniles abolishes LTD. LTD in nucleus RA therefore makes an excellent candidate mechanism to mediate song learning during development and is well-suited to provide insight into other forms of vertebrate motor learning.

  5. Neural plasticity and the development of attention: Intrinsic and extrinsic influences.

    Science.gov (United States)

    Swingler, Margaret M; Perry, Nicole B; Calkins, Susan D

    2015-05-01

    The development of attention has been strongly linked to the regulation of emotion and behavior and has therefore been of particular interest to researchers aiming to better understand precursors to behavioral maladjustment. In the current paper, we utilize a developmental psychopathology and neural plasticity framework to highlight the importance of both intrinsic (i.e., infant neural functioning) and extrinsic (i.e., caregiver behavior) factors for the development of attentional control across the first year. We begin by highlighting the importance of attention for children's emotion regulation abilities and mental health. We then review the development of attention behavior and underscore the importance of neural development and caregiver behavior for shaping attentional control. Finally, we posit that neural activation associated with the development of the executive attention network may be one mechanism through which maternal caregiving behavior influences the development of infants' attentional control and subsequent emotion regulation abilities known to be influential to childhood psychopathology.

  6. Complement peptide C3a stimulates neural plasticity after experimental brain ischaemia.

    Science.gov (United States)

    Stokowska, Anna; Atkins, Alison L; Morán, Javier; Pekny, Tulen; Bulmer, Linda; Pascoe, Michaela C; Barnum, Scott R; Wetsel, Rick A; Nilsson, Jonas A; Dragunow, Mike; Pekna, Marcela

    2017-02-01

    Ischaemic stroke induces endogenous repair processes that include proliferation and differentiation of neural stem cells and extensive rewiring of the remaining neural connections, yet about 50% of stroke survivors live with severe long-term disability. There is an unmet need for drug therapies to improve recovery by promoting brain plasticity in the subacute to chronic phase after ischaemic stroke. We previously showed that complement-derived peptide C3a regulates neural progenitor cell migration and differentiation in vitro and that C3a receptor signalling stimulates neurogenesis in unchallenged adult mice. To determine the role of C3a-C3a receptor signalling in ischaemia-induced neural plasticity, we subjected C3a receptor-deficient mice, GFAP-C3a transgenic mice expressing biologically active C3a in the central nervous system, and their respective wild-type controls to photothrombotic stroke. We found that C3a overexpression increased, whereas C3a receptor deficiency decreased post-stroke expression of GAP43 (P plasticity, in the peri-infarct cortex. To verify the translational potential of these findings, we used a pharmacological approach. Daily intranasal treatment of wild-type mice with C3a beginning 7 days after stroke induction robustly increased synaptic density (P neural plasticity and intranasal treatment with C3a receptor agonists is an attractive approach to improve functional recovery after ischaemic brain injury. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  7. Towards a Cross-Level Theory of Neural Learning

    Science.gov (United States)

    Bell, Anthony J.

    2007-11-01

    This paper reviews ideas and results from unsupervised learning theory that have given the best explanation yet of how neural firing rates self-organise to code natural images in area V1 of visual cortex. It then discusses the generalisation of these ideas to self-organising spike-coding networks. A mismatch between the resulting spike-learning algorithm and the known physiological processes of synaptic plasticity is then used as a motivation to introduce the rather obvious idea that neurons are not sending their information to other neurons, but to synapses—more microscopic structures. This prompts a survey of other inter-level communications in the brain and inside cells. It is proposed on the basis of this that information flows all the way up and down the reductionist hierarchy—an idea that transforms many of our ideas about machine learning and neuroscience. What it transforms them into is not yet clear, but the remainder of the paper discusses this.

  8. Are the neural correlates of conscious contents stable or plastic?

    DEFF Research Database (Denmark)

    Sandberg, Kristian; Overgaard, Morten; Rees, Geraint

    2012-01-01

    examine the second assumption. We recorded the magnetoencephalographic (MEG) signal from healthy human participants while they viewed an intermittently presented binocular rivalry stimulus consisting of a face and a grating. During binocular rivalry, the stimulus is kept constant, but the conscious...... of the same recording session. Very similar accuracies were obtained when the data used to train and test the classifier were gathered on different days within a week. However, when training/testing data were separated by 2.5 years, prediction accuracy was reduced drastically, to a level comparable to when...... the classifier was trained on a different participant. We discuss whether this drop in accuracy can best be explained by changes in the predictive signal in terms of timing, topography or underlying sources. Our results thus show that the neural correlates of conscious perception of a particular stimulus...

  9. Cognitive-affective neural plasticity following active-controlled mindfulness intervention

    DEFF Research Database (Denmark)

    Allen, Micah Galen

    Mindfulness meditation is a set of attention-based, regulatory and self-inquiry training regimes. Although the impact of mindfulness meditation training (MT) on self-regulation is well established, the neural mechanisms supporting such plasticity are poorly understood. MT is thought to act through...... prefrontal cortex (mPFC), and right anterior insula during negative valence processing. Our findings highlight the importance of active control in MT research, indicate unique neural mechanisms for progressive stages of mindfulness training, and suggest that optimal application of MT may differ depending...

  10. On the Role of Neurogenesis and Neural Plasticity in the Evolution of Animal Personalities and Stress Coping Styles.

    Science.gov (United States)

    Øverli, Øyvind; Sørensen, Christina

    2016-08-24

    Individual variation in how animals react to stress and environmental change has become a central topic in a wide range of biological disciplines, from evolutionary ecology to biomedicine. Such variation manifests phenotypically as correlated trait-clusters (referred to as coping styles, behavioral syndromes, shyness-boldness, or personality traits). Thresholds for switching from active coping (fight-flight) to inhibition and passive behavior when exposed to stress depend on experience and genetic factors. Comparative research has revealed a range of neuroendocrine-behavioral associations which are conserved throughout the vertebrate subphylum, including factors affecting perception, learning, and memory of stimuli and events. Here we review conserved aspects of the contribution of neurogenesis and other aspects of neural plasticity to stress coping. In teleost fish, brain cell proliferation and neurogenesis have received recent attention. This work reveals that brain cell proliferation and neurogenesis are associated with heritable variation in stress coping style, and they are also differentially affected by short- and long-term stress in a biphasic manner. Routine-dependent and inflexible behavior in proactive individuals is associated with limited neural plasticity. These evolutionarily conserved relationships hold the potential to illuminate the biological background for stress-related neurobiological disorders. © 2016 S. Karger AG, Basel.

  11. Synaptic Plasticity in the Nucleus Accumbens: Lessons Learned from Experience.

    Science.gov (United States)

    Turner, Brandon D; Kashima, Daniel T; Manz, Kevin M; Grueter, Carrie A; Grueter, Brad A

    2018-01-24

    Synaptic plasticity contributes to behavioral adaptations. As a key node in the reward pathway, the nucleus accumbens (NAc) is important for determining motivation-to-action outcomes. Across animal models of motivation including addiction, depression, anxiety, and hedonic feeding, selective recruitment of neuromodulatory signals and plasticity mechanisms have been a focus of physiologists and behaviorists alike. Experience-dependent plasticity mechanisms within the NAc vary depending on the distinct afferents and cell-types over time. A greater understanding of molecular mechanisms determining how these changes in synaptic strength track with behavioral adaptations will provide insight into the process of learning and memory along with identifying maladaptations underlying pathological behavior. Here, we summarize recent findings detailing how changes in NAc synaptic strength and mechanisms of plasticity manifest in various models of motivational disorders.

  12. Neural Plasticity Is Involved in Physiological Sleep, Depressive Sleep Disturbances, and Antidepressant Treatments

    OpenAIRE

    Meng-Qi Zhang; Rui Li; Yi-Qun Wang; Zhi-Li Huang

    2017-01-01

    Depression, which is characterized by a pervasive and persistent low mood and anhedonia, greatly impacts patients, their families, and society. The associated and recurring sleep disturbances further reduce patient’s quality of life. However, therapeutic sleep deprivation has been regarded as a rapid and robust antidepressant treatment for several decades, which suggests a complicated role of sleep in development of depression. Changes in neural plasticity are observed during physiological sl...

  13. Music Training Enhances Rapid Neural Plasticity of N1 and P2 Source Activation for Unattended Sounds

    OpenAIRE

    Seppänen, Miia; Hämäläinen, Jarmo; Pesonen, Anu-Katriina; Tervaniemi, Mari

    2012-01-01

    Neurocognitive studies have demonstrated that long-term music training enhances the processing of unattended sounds. It is not clear, however, whether music training also modulates rapid (within tens of minutes) neural plasticity for sound encoding. To study this phenomenon, we examined whether adult musicians display enhanced rapid neural plasticity compared to non-musicians. More specifically, we compared the modulation of P1, N1, and P2 responses to standard sounds between four unattended ...

  14. Calcium dependent plasticity applied to repetitive transcranial magnetic stimulation with a neural field model.

    Science.gov (United States)

    Wilson, M T; Fung, P K; Robinson, P A; Shemmell, J; Reynolds, J N J

    2016-08-01

    The calcium dependent plasticity (CaDP) approach to the modeling of synaptic weight change is applied using a neural field approach to realistic repetitive transcranial magnetic stimulation (rTMS) protocols. A spatially-symmetric nonlinear neural field model consisting of populations of excitatory and inhibitory neurons is used. The plasticity between excitatory cell populations is then evaluated using a CaDP approach that incorporates metaplasticity. The direction and size of the plasticity (potentiation or depression) depends on both the amplitude of stimulation and duration of the protocol. The breaks in the inhibitory theta-burst stimulation protocol are crucial to ensuring that the stimulation bursts are potentiating in nature. Tuning the parameters of a spike-timing dependent plasticity (STDP) window with a Monte Carlo approach to maximize agreement between STDP predictions and the CaDP results reproduces a realistically-shaped window with two regions of depression in agreement with the existing literature. Developing understanding of how TMS interacts with cells at a network level may be important for future investigation.

  15. Physiological Plasticity of Neural-Crest-Derived Stem Cells in the Adult Mammalian Carotid Body.

    Science.gov (United States)

    Annese, Valentina; Navarro-Guerrero, Elena; Rodríguez-Prieto, Ismael; Pardal, Ricardo

    2017-04-18

    Adult stem cell plasticity, or the ability of somatic stem cells to cross boundaries and differentiate into unrelated cell types, has been a matter of debate in the last decade. Neural-crest-derived stem cells (NCSCs) display a remarkable plasticity during development. Whether adult populations of NCSCs retain this plasticity is largely unknown. Herein, we describe that neural-crest-derived adult carotid body stem cells (CBSCs) are able to undergo endothelial differentiation in addition to their reported role in neurogenesis, contributing to both neurogenic and angiogenic processes taking place in the organ during acclimatization to hypoxia. Moreover, CBSC conversion into vascular cell types is hypoxia inducible factor (HIF) dependent and sensitive to hypoxia-released vascular cytokines such as erythropoietin. Our data highlight a remarkable physiological plasticity in an adult population of tissue-specific stem cells and could have impact on the use of these cells for cell therapy. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

  16. Cerebellar motor learning: when is cortical plasticity not enough?

    Directory of Open Access Journals (Sweden)

    John Porrill

    2007-10-01

    Full Text Available Classical Marr-Albus theories of cerebellar learning employ only cortical sites of plasticity. However, tests of these theories using adaptive calibration of the vestibulo-ocular reflex (VOR have indicated plasticity in both cerebellar cortex and the brainstem. To resolve this long-standing conflict, we attempted to identify the computational role of the brainstem site, by using an adaptive filter version of the cerebellar microcircuit to model VOR calibration for changes in the oculomotor plant. With only cortical plasticity, introducing a realistic delay in the retinal-slip error signal of 100 ms prevented learning at frequencies higher than 2.5 Hz, although the VOR itself is accurate up to at least 25 Hz. However, the introduction of an additional brainstem site of plasticity, driven by the correlation between cerebellar and vestibular inputs, overcame the 2.5 Hz limitation and allowed learning of accurate high-frequency gains. This "cortex-first" learning mechanism is consistent with a wide variety of evidence concerning the role of the flocculus in VOR calibration, and complements rather than replaces the previously proposed "brainstem-first" mechanism that operates when ocular tracking mechanisms are effective. These results (i describe a process whereby information originally learnt in one area of the brain (cerebellar cortex can be transferred and expressed in another (brainstem, and (ii indicate for the first time why a brainstem site of plasticity is actually required by Marr-Albus type models when high-frequency gains must be learned in the presence of error delay.

  17. Supervised Learning with Complex-valued Neural Networks

    CERN Document Server

    Suresh, Sundaram; Savitha, Ramasamy

    2013-01-01

    Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks.  Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computati...

  18. Rules of engagement: factors that regulate activity-dependent synaptic plasticity during neural network development.

    Science.gov (United States)

    Stoneham, Emily T; Sanders, Erin M; Sanyal, Mohima; Dumas, Theodore C

    2010-10-01

    Overproduction and pruning during development is a phenomenon that can be observed in the number of organisms in a population, the number of cells in many tissue types, and even the number of synapses on individual neurons. The sculpting of synaptic connections in the brain of a developing organism is guided by its personal experience, which on a neural level translates to specific patterns of activity. Activity-dependent plasticity at glutamatergic synapses is an integral part of neuronal network formation and maturation in developing vertebrate and invertebrate brains. As development of the rodent forebrain transitions away from an over-proliferative state, synaptic plasticity undergoes modification. Late developmental changes in synaptic plasticity signal the establishment of a more stable network and relate to pronounced perceptual and cognitive abilities. In large part, activation of glutamate-sensitive N-methyl-d-aspartate (NMDA) receptors regulates synaptic stabilization during development and is a necessary step in memory formation processes that occur in the forebrain. A developmental change in the subunits that compose NMDA receptors coincides with developmental modifications in synaptic plasticity and cognition, and thus much research in this area focuses on NMDA receptor composition. We propose that there are additional, equally important developmental processes that influence synaptic plasticity, including mechanisms that are upstream (factors that influence NMDA receptors) and downstream (intracellular processes regulated by NMDA receptors) from NMDA receptor activation. The goal of this review is to summarize what is known and what is not well understood about developmental changes in functional plasticity at glutamatergic synapses, and in the end, attempt to relate these changes to maturation of neural networks.

  19. Perspectives of TRPV1 Function on the Neurogenesis and Neural Plasticity.

    Science.gov (United States)

    Ramírez-Barrantes, R; Cordova, C; Poblete, H; Muñoz, P; Marchant, I; Wianny, F; Olivero, P

    2016-01-01

    The development of new strategies to renew and repair neuronal networks using neural plasticity induced by stem cell graft could enable new therapies to cure diseases that were considered lethal until now. In adequate microenvironment a neuronal progenitor must receive molecular signal of a specific cellular context to determine fate, differentiation, and location. TRPV1, a nonselective calcium channel, is expressed in neurogenic regions of the brain like the subgranular zone of the hippocampal dentate gyrus and the telencephalic subventricular zone, being valuable for neural differentiation and neural plasticity. Current data show that TRPV1 is involved in several neuronal functions as cytoskeleton dynamics, cell migration, survival, and regeneration of injured neurons, incorporating several stimuli in neurogenesis and network integration. The function of TRPV1 in the brain is under intensive investigation, due to multiple places where it has been detected and its sensitivity for different chemical and physical agonists, and a new role of TRPV1 in brain function is now emerging as a molecular tool for survival and control of neural stem cells.

  20. Neural interactions mediating conflict control and its training-induced plasticity.

    Science.gov (United States)

    Hu, Min; Wang, Xiangpeng; Zhang, Wenwen; Hu, Xueping; Chen, Antao

    2017-12-01

    Cognitive control is of great plasticity. Training programs targeted on improving it have been suggested to yield neural changes in the brain. However, until recently, the relationship between training-induced brain changes and improvements in cognitive control is still an open issue. Besides, although the literature has attributed the operation of cognitive control to interactions between large-scale networks, the neural pathways directly associated with it remain unclear. The current study aimed to examine these issues by focusing on conflict processing. In particular, we employed a training program with a randomized controlled design. The main findings were as follows: 1) In behavior, the training group showed reduced conflict effect after training, relative to the control group; 2) In the pretest stage, the behavioral conflict effect was negatively correlated with a number of neural pathways, including the connectivity from the cingulo-opercular network (CON) to the cerebellum and to sub-regions of the dorsal visual network; 3) increase in the connectivity strength of several network interactions, such as the connectivity from the CON to the cerebellum and to the primary visual network, was associated with behavioral gains; 4) there were also nonlinear correlations between behavioral and neural changes. These findings highlighted a critical role of the modulation of CON on other networks in mediating conflict processing and its plasticity, and raised the significance of investigating nonlinear relationship in the field of cognitive training. Copyright © 2017 Elsevier Inc. All rights reserved.

  1. Dynamic neural networking as a basis for plasticity in the control of heart rate.

    Science.gov (United States)

    Kember, G; Armour, J A; Zamir, M

    2013-01-21

    A model is proposed in which the relationship between individual neurons within a neural network is dynamically changing to the effect of providing a measure of "plasticity" in the control of heart rate. The neural network on which the model is based consists of three populations of neurons residing in the central nervous system, the intrathoracic extracardiac nervous system, and the intrinsic cardiac nervous system. This hierarchy of neural centers is used to challenge the classical view that the control of heart rate, a key clinical index, resides entirely in central neuronal command (spinal cord, medulla oblongata, and higher centers). Our results indicate that dynamic networking allows for the possibility of an interplay among the three populations of neurons to the effect of altering the order of control of heart rate among them. This interplay among the three levels of control allows for different neural pathways for the control of heart rate to emerge under different blood flow demands or disease conditions and, as such, it has significant clinical implications because current understanding and treatment of heart rate anomalies are based largely on a single level of control and on neurons acting in unison as a single entity rather than individually within a (plastically) interconnected network. Copyright © 2012 Elsevier Ltd. All rights reserved.

  2. Inactivity-induced respiratory plasticity: protecting the drive to breathe in disorders that reduce respiratory neural activity.

    Science.gov (United States)

    Strey, K A; Baertsch, N A; Baker-Herman, T L

    2013-11-01

    Multiple forms of plasticity are activated following reduced respiratory neural activity. For example, in ventilated rats, a central neural apnea elicits a rebound increase in phrenic and hypoglossal burst amplitude upon resumption of respiratory neural activity, forms of plasticity called inactivity-induced phrenic and hypoglossal motor facilitation (iPMF and iHMF), respectively. Here, we provide a conceptual framework for plasticity following reduced respiratory neural activity to guide future investigations. We review mechanisms giving rise to iPMF and iHMF, present new data suggesting that inactivity-induced plasticity is observed in inspiratory intercostals (iIMF) and point out gaps in our knowledge. We then survey conditions relevant to human health characterized by reduced respiratory neural activity and discuss evidence that inactivity-induced plasticity is elicited during these conditions. Understanding the physiological impact and circumstances in which inactivity-induced respiratory plasticity is elicited may yield novel insights into the treatment of disorders characterized by reductions in respiratory neural activity. Copyright © 2013 Elsevier B.V. All rights reserved.

  3. Inactivity-induced respiratory plasticity: Protecting the drive to breathe in disorders that reduce respiratory neural activity☆

    Science.gov (United States)

    Strey, K.A.; Baertsch, N.A.; Baker-Herman, T.L.

    2013-01-01

    Multiple forms of plasticity are activated following reduced respiratory neural activity. For example, in ventilated rats, a central neural apnea elicits a rebound increase in phrenic and hypoglossal burst amplitude upon resumption of respiratory neural activity, forms of plasticity called inactivity-induced phrenic and hypoglossal motor facilitation (iPMF and iHMF), respectively. Here, we provide a conceptual framework for plasticity following reduced respiratory neural activity to guide future investigations. We review mechanisms giving rise to iPMF and iHMF, present new data suggesting that inactivity-induced plasticity is observed in inspiratory intercostals (iIMF) and point out gaps in our knowledge. We then survey conditions relevant to human health characterized by reduced respiratory neural activity and discuss evidence that inactivity-induced plasticity is elicited during these conditions. Understanding the physiological impact and circumstances in which inactivity-induced respiratory plasticity is elicited may yield novel insights into the treatment of disorders characterized by reductions in respiratory neural activity. PMID:23816599

  4. Improving the Neural GPU Architecture for Algorithm Learning

    OpenAIRE

    Freivalds, Karlis; Liepins, Renars

    2017-01-01

    Algorithm learning is a core problem in artificial intelligence with significant implications on automation level that can be achieved by machines. Recently deep learning methods are emerging for synthesizing an algorithm from its input-output examples, the most successful being the Neural GPU, capable of learning multiplication. We present several improvements to the Neural GPU that substantially reduces training time and improves generalization. We introduce a technique of general applicabi...

  5. Learning, plasticity, and atypical generalization in children with autism.

    Science.gov (United States)

    Church, Barbara A; Rice, Courtney L; Dovgopoly, Alexander; Lopata, Christopher J; Thomeer, Marcus L; Nelson, Andrew; Mercado, Eduardo

    2015-10-01

    Individuals with autism spectrum disorder (ASD) show accelerated learning in some tasks, degraded learning in others, and distinct deficits when generalizing to novel situations. Recent simulations with connectionist models suggest that deficits in cortical plasticity mechanisms can account for atypical patterns of generalization shown by some children with ASD. We tested the surprising theoretical prediction, from past simulations, that the children with ASD who show atypical generalization in perceptual categorization tasks will benefit more from training with a single prototypical member of the category than from training with multiple examples, but children with ASD who generalize normally will be comparatively harmed. The experimental results confirmed this prediction, suggesting that plasticity deficits may well underlie the difficulties that some children with ASD have generalizing skills, and these deficits are not specific to the acquisition of social skills, but rather reflect a more general perceptual learning deficit that may impact many abilities.

  6. Filopodia: A Rapid Structural Plasticity Substrate for Fast Learning

    Directory of Open Access Journals (Sweden)

    Ahmet S. Ozcan

    2017-06-01

    Full Text Available Formation of new synapses between neurons is an essential mechanism for learning and encoding memories. The vast majority of excitatory synapses occur on dendritic spines, therefore, the growth dynamics of spines is strongly related to the plasticity timescales. Especially in the early stages of the developing brain, there is an abundant number of long, thin and motile protrusions (i.e., filopodia, which develop in timescales of seconds and minutes. Because of their unique morphology and motility, it has been suggested that filopodia can have a dual role in both spinogenesis and environmental sampling of potential axonal partners. I propose that filopodia can lower the threshold and reduce the time to form new dendritic spines and synapses, providing a substrate for fast learning. Based on this proposition, the functional role of filopodia during brain development is discussed in relation to learning and memory. Specifically, it is hypothesized that the postnatal brain starts with a single-stage memory system with filopodia playing a significant role in rapid structural plasticity along with the stability provided by the mushroom-shaped spines. Following the maturation of the hippocampus, this highly-plastic unitary system transitions to a two-stage memory system, which consists of a plastic temporary store and a long-term stable store. In alignment with these architectural changes, it is posited that after brain maturation, filopodia-based structural plasticity will be preserved in specific areas, which are involved in fast learning (e.g., hippocampus in relation to episodic memory. These propositions aim to introduce a unifying framework for a diversity of phenomena in the brain such as synaptogenesis, pruning and memory consolidation.

  7. Neurometaplasticity: Glucoallostasis control of plasticity of the neural networks of error commission, detection, and correction modulates neuroplasticity to influence task precision

    Science.gov (United States)

    Welcome, Menizibeya O.; Dane, Şenol; Mastorakis, Nikos E.; Pereverzev, Vladimir A.

    2017-12-01

    The term "metaplasticity" is a recent one, which means plasticity of synaptic plasticity. Correspondingly, neurometaplasticity simply means plasticity of neuroplasticity, indicating that a previous plastic event determines the current plasticity of neurons. Emerging studies suggest that neurometaplasticity underlie many neural activities and neurobehavioral disorders. In our previous work, we indicated that glucoallostasis is essential for the control of plasticity of the neural network that control error commission, detection and correction. Here we review recent works, which suggest that task precision depends on the modulatory effects of neuroplasticity on the neural networks of error commission, detection, and correction. Furthermore, we discuss neurometaplasticity and its role in error commission, detection, and correction.

  8. One pass learning for generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2016-01-01

    Generalized classifier neural network introduced as a kind of radial basis function neural network, uses gradient descent based optimized smoothing parameter value to provide efficient classification. However, optimization consumes quite a long time and may cause a drawback. In this work, one pass learning for generalized classifier neural network is proposed to overcome this disadvantage. Proposed method utilizes standard deviation of each class to calculate corresponding smoothing parameter. Since different datasets may have different standard deviations and data distributions, proposed method tries to handle these differences by defining two functions for smoothing parameter calculation. Thresholding is applied to determine which function will be used. One of these functions is defined for datasets having different range of values. It provides balanced smoothing parameters for these datasets through logarithmic function and changing the operation range to lower boundary. On the other hand, the other function calculates smoothing parameter value for classes having standard deviation smaller than the threshold value. Proposed method is tested on 14 datasets and performance of one pass learning generalized classifier neural network is compared with that of probabilistic neural network, radial basis function neural network, extreme learning machines, and standard and logarithmic learning generalized classifier neural network in MATLAB environment. One pass learning generalized classifier neural network provides more than a thousand times faster classification than standard and logarithmic generalized classifier neural network. Due to its classification accuracy and speed, one pass generalized classifier neural network can be considered as an efficient alternative to probabilistic neural network. Test results show that proposed method overcomes computational drawback of generalized classifier neural network and may increase the classification performance. Copyright

  9. Pharmacological approach for targeting dysfunctional brain plasticity: Focus on neural cell adhesion molecule (NCAM).

    Science.gov (United States)

    Aonurm-Helm, Anu; Jaako, Külli; Jürgenson, Monika; Zharkovsky, Alexander

    2016-11-01

    Brain plasticity refers to the ability of the brain to undergo functionally relevant adaptations in response to external and internal stimuli. Alterations in brain plasticity have been associated with several neuropsychiatric disorders, and current theories suggest that dysfunctions in neuronal circuits and synaptogenesis have a major impact in the development of these diseases. Among the molecules that regulate brain plasticity, neural cell adhesion molecule (NCAM) and its polysialylated form PSA-NCAM have been of particular interest for years because alterations in NCAM and PSA-NCAM levels have been associated with memory impairment, depression, autistic spectrum disorders and schizophrenia. In this review, we discuss the roles of NCAM and PSA-NCAM in the regulation of brain plasticity and, in particular, their roles in the mechanisms of depression. We also demonstrate that the NCAM-mimetic peptides FGL and Enreptin are able to restore disrupted neuronal plasticity. FGL peptide has also been demonstrated to ameliorate the symptoms of depressive-like behavior in NCAM-deficient mice and therefore, may be considered a new drug candidate for the treatment of depression as well as other neuropsychiatric disorders with disrupted neuroplasticity. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Structural brain plasticity in adult learning and development.

    Science.gov (United States)

    Lövdén, Martin; Wenger, Elisabeth; Mårtensson, Johan; Lindenberger, Ulman; Bäckman, Lars

    2013-11-01

    Recent research using magnetic resonance imaging has documented changes in the adult human brain's grey matter structure induced by alterations in experiential demands. We review this research and relate it to models of brain plasticity from related strands of research, such as work on animal models. This allows us to generate recommendations and predictions for future research that may advance the understanding of the function, sequential progression, and microstructural nature of experience-dependent changes in regional brain volumes. Informed by recent evidence on adult age differences in structural brain plasticity, we show how understanding learning-related changes in human brain structure can expand our knowledge about adult development and aging. We hope that this review will promote research on the mechanisms regulating experience-dependent structural plasticity of the adult human brain. Copyright © 2013 Elsevier Ltd. All rights reserved.

  11. Synaptic plasticity-related neural oscillations on hippocampus-prefrontal cortex pathway in depression.

    Science.gov (United States)

    Zheng, C; Zhang, T

    2015-04-30

    It is believed that phase synchronization facilitates neural communication and neural plasticity throughout the hippocampal-cortical network, and further supports cognition and memory. The pathway from the ventral hippocampus to the medial prefrontal cortex (mPFC) is thought to play a significant role in emotional memory processing. Therefore, the information transmission on the pathway was hypothesized to be disrupted in the depressive state, which could be related to its impaired synaptic plasticity. In this study, local field potentials (LFPs) from both ventral CA1 (vCA1) and mPFC were recorded in both normal and chronic unpredictable stress (CUS) model rats under urethane anesthesia. LFPs of all rats were recorded before and after the long-term potentiation (LTP) induced on the vCA1-mPFC pathway in order to figure out the correlation of oscillatory synchronization of LFPs and synaptic plasticity. Our results showed the vCA1-to-mPFC unidirectional phase coupling of the theta rhythm, rather than the power of either region, was significantly enhanced by LTP induction, with less enhancement in the CUS model rats compared to that in the normal rats. In addition, theta phase coupling was positively correlated with synaptic plasticity on vCA1-mPFC pathway. Moreover, the theta-slow gamma phase-amplitude coupling in vCA1 was long-term enhanced after high frequency stimulation. These results suggest that the impaired synaptic plasticity in vCA1-mPFC pathway could be reflected by the attenuated theta phase coupling and theta-gamma cross frequency coupling of LFPs in the depression state. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

  12. An ART neural network model of discrimination shift learning

    NARCIS (Netherlands)

    Raijmakers, M.E.J.; Coffey, E.; Stevenson, C.; Winkel, J.; Berkeljon, A.; Taatgen, N.; van Rijn, H.

    2009-01-01

    We present an ART-based neural network model (adapted from [2]) of the development of discrimination-shift learning that models the trial-by-trial learning process in great detail. In agreement with the results of human participants (4-20 years of age) in [1] the model revealed two distinct learning

  13. Synaptic organizations and dynamical properties of weakly connected neural oscillators. II. Learning phase information.

    Science.gov (United States)

    Hoppensteadt, F C; Izhikevich, E M

    1996-08-01

    This is the second of two articles devoted to analyzing the relationship between synaptic organizations (anatomy) and dynamical properties (function) of networks of neural oscillators near multiple supercritical Andronov-Hopf bifurcation points. Here we analyze learning processes in such networks. Regarding learning dynamics, we assume (1) learning is local (i.e. synaptic modification depends on pre- and postsynaptic neurons but not on others), (2) synapses modify slowly relative to characteristic neuron response times, (3) in the absence of either pre- or postsynaptic activity, the synapse weakens (forgets). Our major goal is to analyze all synaptic organizations of oscillatory neural networks that can memorize and retrieve phase information or time delays. We show that such network have the following attributes: (1) the rate of synaptic plasticity connected with learning is determined locally by the presynaptic neurons, (2) the excitatory neurons must be long-axon relay neurons capable of forming distant connections with other excitatory and inhibitory neurons, (3) if inhibitory neurons have long axons, then the network can learn, passively forget and actively unlearn information by adjusting synaptic plasticity rates.

  14. Music mnemonics aid Verbal Memory and Induce Learning - Related Brain Plasticity in Multiple Sclerosis.

    Science.gov (United States)

    Thaut, Michael H; Peterson, David A; McIntosh, Gerald C; Hoemberg, Volker

    2014-01-01

    Recent research on music and brain function has suggested that the temporal pattern structure in music and rhythm can enhance cognitive functions. To further elucidate this question specifically for memory, we investigated if a musical template can enhance verbal learning in patients with multiple sclerosis (MS) and if music-assisted learning will also influence short-term, system-level brain plasticity. We measured systems-level brain activity with oscillatory network synchronization during music-assisted learning. Specifically, we measured the spectral power of 128-channel electroencephalogram (EEG) in alpha and beta frequency bands in 54 patients with MS. The study sample was randomly divided into two groups, either hearing a spoken or a musical (sung) presentation of Rey's auditory verbal learning test. We defined the "learning-related synchronization" (LRS) as the percent change in EEG spectral power from the first time the word was presented to the average of the subsequent word encoding trials. LRS differed significantly between the music and the spoken conditions in low alpha and upper beta bands. Patients in the music condition showed overall better word memory and better word order memory and stronger bilateral frontal alpha LRS than patients in the spoken condition. The evidence suggests that a musical mnemonic recruits stronger oscillatory network synchronization in prefrontal areas in MS patients during word learning. It is suggested that the temporal structure implicit in musical stimuli enhances "deep encoding" during verbal learning and sharpens the timing of neural dynamics in brain networks degraded by demyelination in MS.

  15. Adaptation, perceptual learning, and plasticity of brain functions.

    Science.gov (United States)

    Horton, Jonathan C; Fahle, Manfred; Mulder, Theo; Trauzettel-Klosinski, Susanne

    2017-03-01

    The capacity for functional restitution after brain damage is quite different in the sensory and motor systems. This series of presentations highlights the potential for adaptation, plasticity, and perceptual learning from an interdisciplinary perspective. The chances for restitution in the primary visual cortex are limited. Some patterns of visual field loss and recovery after stroke are common, whereas others are impossible, which can be explained by the arrangement and plasticity of the cortical map. On the other hand, compensatory mechanisms are effective, can occur spontaneously, and can be enhanced by training. In contrast to the human visual system, the motor system is highly flexible. This is based on special relationships between perception and action and between cognition and action. In addition, the healthy adult brain can learn new functions, e.g. increasing resolution above the retinal one. The significance of these studies for rehabilitation after brain damage will be discussed.

  16. Synaptic plasticity, neural circuits, and the emerging role of altered short-term information processing in schizophrenia.

    Science.gov (United States)

    Crabtree, Gregg W; Gogos, Joseph A

    2014-01-01

    Synaptic plasticity alters the strength of information flow between presynaptic and postsynaptic neurons and thus modifies the likelihood that action potentials in a presynaptic neuron will lead to an action potential in a postsynaptic neuron. As such, synaptic plasticity and pathological changes in synaptic plasticity impact the synaptic computation which controls the information flow through the neural microcircuits responsible for the complex information processing necessary to drive adaptive behaviors. As current theories of neuropsychiatric disease suggest that distinct dysfunctions in neural circuit performance may critically underlie the unique symptoms of these diseases, pathological alterations in synaptic plasticity mechanisms may be fundamental to the disease process. Here we consider mechanisms of both short-term and long-term plasticity of synaptic transmission and their possible roles in information processing by neural microcircuits in both health and disease. As paradigms of neuropsychiatric diseases with strongly implicated risk genes, we discuss the findings in schizophrenia and autism and consider the alterations in synaptic plasticity and network function observed in both human studies and genetic mouse models of these diseases. Together these studies have begun to point toward a likely dominant role of short-term synaptic plasticity alterations in schizophrenia while dysfunction in autism spectrum disorders (ASDs) may be due to a combination of both short-term and long-term synaptic plasticity alterations.

  17. Using machine learning, neural networks and statistics to predict bankruptcy

    NARCIS (Netherlands)

    Pompe, P.P.M.; Feelders, A.J.; Feelders, A.J.

    1997-01-01

    Recent literature strongly suggests that machine learning approaches to classification outperform "classical" statistical methods. We make a comparison between the performance of linear discriminant analysis, classification trees, and neural networks in predicting corporate bankruptcy. Linear

  18. Digital associative memory neural network with optical learning capability

    Science.gov (United States)

    Watanabe, Minoru; Ohtsubo, Junji

    1994-12-01

    A digital associative memory neural network system with optical learning and recalling capabilities is proposed by using liquid crystal television spatial light modulators and an Optic RAM detector. In spite of the drawback of the limited memory capacity compared with optical analogue associative memory neural network, the proposed optical digital neural network has the advantage of all optical learning and recalling capabilities, thus an all optics network system is easily realized. Some experimental results of the learning and the recalling for character recognitions are presented. This new optical architecture offers compactness of the system and the fast learning and recalling properties. Based on the results, the practical system for the implementation of a faster optical digital associative memory neural network system with ferro-electric liquid crystal SLMs is also proposed.

  19. A constructive algorithm for unsupervised learning with incremental neural network

    OpenAIRE

    Wang, Jenq-Haur; Wang, Hsin-Yang; Chen, Yen-Lin; Liu, Chuan-Ming

    2015-01-01

    Artificial neural network (ANN) has wide applications such as data processing and classification. However, comparing with other classification methods, ANN needs enormous memory space and training time to build the model. This makes ANN infeasible in practical applications. In this paper, we try to integrate the ideas of human learning mechanism with the existing models of ANN. We propose an incremental neural network construction framework for unsupervised learning. In this framework, a neur...

  20. Modulating neural plasticity with non-invasive brain stimulation in schizophrenia.

    Science.gov (United States)

    Hasan, Alkomiet; Wobrock, Thomas; Rajji, Tarek; Malchow, Berend; Daskalakis, Zafiris J

    2013-12-01

    Schizophrenia is a severe mental disorder characterised by a complex phenotype including positive, negative, affective and cognitive symptoms. Various theories have been developed to integrate the clinical phenotype into a strong neurobiological framework. One theory describes schizophrenia as a disorder of impaired neural plasticity. Recently, non-invasive brain stimulation techniques have garnered much attention to their ability to modulate plasticity and treat schizophrenia. The aim of this review is to introduce the basic physiological principles of conventional non-invasive brain stimulation techniques and to review the available evidence for schizophrenia. Despite promising evidence for efficacy in a large number of clinical trials, we continue to have a rudimentary understanding of the underlying neurobiology. Additional investigation is required to improve the response rates to non-invasive brain stimulation, to reduce the interindividual variability and to improve the understanding of non-invasive brain stimulation in schizophrenia.

  1. Nogo Receptor Signaling Restricts Adult Neural Plasticity by Limiting Synaptic AMPA Receptor Delivery.

    Science.gov (United States)

    Jitsuki, Susumu; Nakajima, Waki; Takemoto, Kiwamu; Sano, Akane; Tada, Hirobumi; Takahashi-Jitsuki, Aoi; Takahashi, Takuya

    2016-01-01

    Experience-dependent plasticity is limited in the adult brain, and its molecular and cellular mechanisms are poorly understood. Removal of the myelin-inhibiting signaling protein, Nogo receptor (NgR1), restores adult neural plasticity. Here we found that, in NgR1-deficient mice, whisker experience-driven synaptic α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) insertion in the barrel cortex, which is normally complete by 2 weeks after birth, lasts into adulthood. In vivo live imaging by two-photon microscopy revealed more AMPAR on the surface of spines in the adult barrel cortex of NgR1-deficient than on those of wild-type (WT) mice. Furthermore, we observed that whisker stimulation produced new spines in the adult barrel cortex of mutant but not WT mice, and that the newly synthesized spines contained surface AMPAR. These results suggest that Nogo signaling limits plasticity by restricting synaptic AMPAR delivery in coordination with anatomical plasticity. © The Author 2015. Published by Oxford University Press.

  2. Sparing of descending axons rescues interneuron plasticity in the lumbar cord to allow adaptive learning after thoracic spinal cord injury

    Directory of Open Access Journals (Sweden)

    Christopher Nelson Hansen

    2016-03-01

    Full Text Available This study evaluated the role of spared axons on structural and behavioral neuroplasticity in the lumbar enlargement after a thoracic spinal cord injury (SCI. Previous work has demonstrated that recovery in the presence of spared axons after an incomplete lesion increases behavioral output after a subsequent complete spinal cord transection (TX. This suggests that spared axons direct adaptive changes in below-level neuronal networks of the lumbar cord. In response to spared fibers, we postulate that lumbar neuron networks support behavioral gains by preventing aberrant plasticity. As such, the present study measured histological and functional changes in the isolated lumbar cord after complete TX or incomplete contusion (SCI. To measure functional plasticity in the lumbar cord, we used an established instrumental learning paradigm. In this paradigm, neural circuits within isolated lumbar segments demonstrate learning by an increase in flexion duration that reduces exposure to a noxious leg shock. We employed this model using a proof-of-principle design to evaluate the role of sparing on lumbar learning and plasticity early (7 days or late (42 days after midthoracic SCI in a rodent model. Early after SCI or TX at 7d, spinal learning was unattainable regardless of whether the animal recovered with or without axonal substrate. Failed learning occurred alongside measures of cell soma atrophy and aberrant dendritic spine expression within interneuron populations responsible for sensorimotor integration and learning. Alternatively, exposure of the lumbar cord to a small amount of spared axons for 6 weeks produced near-normal learning late after SCI. This coincided with greater cell soma volume and fewer aberrant dendritic spines on interneurons. Thus, an opportunity to influence activity-based learning in locomotor networks depends on spared axons limiting maladaptive plasticity. Together, this work identifies a time dependent interaction between

  3. Sparing of Descending Axons Rescues Interneuron Plasticity in the Lumbar Cord to Allow Adaptive Learning After Thoracic Spinal Cord Injury.

    Science.gov (United States)

    Hansen, Christopher N; Faw, Timothy D; White, Susan; Buford, John A; Grau, James W; Basso, D Michele

    2016-01-01

    This study evaluated the role of spared axons on structural and behavioral neuroplasticity in the lumbar enlargement after a thoracic spinal cord injury (SCI). Previous work has demonstrated that recovery in the presence of spared axons after an incomplete lesion increases behavioral output after a subsequent complete spinal cord transection (TX). This suggests that spared axons direct adaptive changes in below-level neuronal networks of the lumbar cord. In response to spared fibers, we postulate that lumbar neuron networks support behavioral gains by preventing aberrant plasticity. As such, the present study measured histological and functional changes in the isolated lumbar cord after complete TX or incomplete contusion (SCI). To measure functional plasticity in the lumbar cord, we used an established instrumental learning paradigm (ILP). In this paradigm, neural circuits within isolated lumbar segments demonstrate learning by an increase in flexion duration that reduces exposure to a noxious leg shock. We employed this model using a proof-of-principle design to evaluate the role of sparing on lumbar learning and plasticity early (7 days) or late (42 days) after midthoracic SCI in a rodent model. Early after SCI or TX at 7 days, spinal learning was unattainable regardless of whether the animal recovered with or without axonal substrate. Failed learning occurred alongside measures of cell soma atrophy and aberrant dendritic spine expression within interneuron populations responsible for sensorimotor integration and learning. Alternatively, exposure of the lumbar cord to a small amount of spared axons for 6 weeks produced near-normal learning late after SCI. This coincided with greater cell soma volume and fewer aberrant dendritic spines on interneurons. Thus, an opportunity to influence activity-based learning in locomotor networks depends on spared axons limiting maladaptive plasticity. Together, this work identifies a time dependent interaction between spared

  4. Self-organized noise resistance of oscillatory neural networks with spike timing-dependent plasticity.

    Science.gov (United States)

    Popovych, Oleksandr V; Yanchuk, Serhiy; Tass, Peter A

    2013-10-11

    Intuitively one might expect independent noise to be a powerful tool for desynchronizing a population of synchronized neurons. We here show that, intriguingly, for oscillatory neural populations with adaptive synaptic weights governed by spike timing-dependent plasticity (STDP) the opposite is true. We found that the mean synaptic coupling in such systems increases dynamically in response to the increase of the noise intensity, and there is an optimal noise level, where the amount of synaptic coupling gets maximal in a resonance-like manner as found for the stochastic or coherence resonances, although the mechanism in our case is different. This constitutes a noise-induced self-organization of the synaptic connectivity, which effectively counteracts the desynchronizing impact of independent noise over a wide range of the noise intensity. Given the attempts to counteract neural synchrony underlying tinnitus with noisers and maskers, our results may be of clinical relevance.

  5. A decision-making model based on a spiking neural circuit and synaptic plasticity.

    Science.gov (United States)

    Wei, Hui; Bu, Yijie; Dai, Dawei

    2017-10-01

    To adapt to the environment and survive, most animals can control their behaviors by making decisions. The process of decision-making and responding according to cues in the environment is stable, sustainable, and learnable. Understanding how behaviors are regulated by neural circuits and the encoding and decoding mechanisms from stimuli to responses are important goals in neuroscience. From results observed in Drosophila experiments, the underlying decision-making process is discussed, and a neural circuit that implements a two-choice decision-making model is proposed to explain and reproduce the observations. Compared with previous two-choice decision making models, our model uses synaptic plasticity to explain changes in decision output given the same environment. Moreover, biological meanings of parameters of our decision-making model are discussed. In this paper, we explain at the micro-level (i.e., neurons and synapses) how observable decision-making behavior at the macro-level is acquired and achieved.

  6. Synchronization and long-time memory in neural networks with inhibitory hubs and synaptic plasticity

    Science.gov (United States)

    Bertolotti, Elena; Burioni, Raffaella; di Volo, Matteo; Vezzani, Alessandro

    2017-01-01

    We investigate the dynamical role of inhibitory and highly connected nodes (hub) in synchronization and input processing of leaky-integrate-and-fire neural networks with short term synaptic plasticity. We take advantage of a heterogeneous mean-field approximation to encode the role of network structure and we tune the fraction of inhibitory neurons fI and their connectivity level to investigate the cooperation between hub features and inhibition. We show that, depending on fI, highly connected inhibitory nodes strongly drive the synchronization properties of the overall network through dynamical transitions from synchronous to asynchronous regimes. Furthermore, a metastable regime with long memory of external inputs emerges for a specific fraction of hub inhibitory neurons, underlining the role of inhibition and connectivity also for input processing in neural networks.

  7. Zinc in the Monoaminergic Theory of Depression: Its Relationship to Neural Plasticity.

    Science.gov (United States)

    Doboszewska, Urszula; Wlaź, Piotr; Nowak, Gabriel; Radziwoń-Zaleska, Maria; Cui, Ranji; Młyniec, Katarzyna

    2017-01-01

    Preclinical and clinical studies have demonstrated that zinc possesses antidepressant properties and that it may augment the therapy with conventional, that is, monoamine-based, antidepressants. In this review we aim to discuss the role of zinc in the pathophysiology and treatment of depression with regard to the monoamine hypothesis of the disease. Particular attention will be paid to the recently described zinc-sensing GPR39 receptor as well as aspects of zinc deficiency. Furthermore, an attempt will be made to give a possible explanation of the mechanisms by which zinc interacts with the monoamine system in the context of depression and neural plasticity.

  8. Emerging role of non-coding RNA in neural plasticity, cognitive function, and neuropsychiatric disorders

    Directory of Open Access Journals (Sweden)

    Paola eSpadaro

    2012-07-01

    Full Text Available Non-coding RNAs have emerged as critical regulators of transcription, epigenetic processes, and gene silencing, which make them ideal candidates for insight into molecular evolution and a better understanding of the molecular pathways of neuropsychiatric disease. Here we provide an overview of the current state of knowledge regarding various classes of ncRNAs and their role in neural plasticity and cognitive function, and highlight the potential contribution they may make to the development of a variety of neuropsychiatric disorders, including schizophrenia, addiction and fear-related anxiety disorders.

  9. Embedding responses in spontaneous neural activity shaped through sequential learning.

    Science.gov (United States)

    Kurikawa, Tomoki; Kaneko, Kunihiko

    2013-01-01

    Recent experimental measurements have demonstrated that spontaneous neural activity in the absence of explicit external stimuli has remarkable spatiotemporal structure. This spontaneous activity has also been shown to play a key role in the response to external stimuli. To better understand this role, we proposed a viewpoint, "memories-as-bifurcations," that differs from the traditional "memories-as-attractors" viewpoint. Memory recall from the memories-as-bifurcations viewpoint occurs when the spontaneous neural activity is changed to an appropriate output activity upon application of an input, known as a bifurcation in dynamical systems theory, wherein the input modifies the flow structure of the neural dynamics. Learning, then, is a process that helps create neural dynamical systems such that a target output pattern is generated as an attractor upon a given input. Based on this novel viewpoint, we introduce in this paper an associative memory model with a sequential learning process. Using a simple hebbian-type learning, the model is able to memorize a large number of input/output mappings. The neural dynamics shaped through the learning exhibit different bifurcations to make the requested targets stable upon an increase in the input, and the neural activity in the absence of input shows chaotic dynamics with occasional approaches to the memorized target patterns. These results suggest that these dynamics facilitate the bifurcations to each target attractor upon application of the corresponding input, which thus increases the capacity for learning. This theoretical finding about the behavior of the spontaneous neural activity is consistent with recent experimental observations in which the neural activity without stimuli wanders among patterns evoked by previously applied signals. In addition, the neural networks shaped by learning properly reflect the correlations of input and target-output patterns in a similar manner to those designed in our previous study.

  10. Embedding responses in spontaneous neural activity shaped through sequential learning.

    Directory of Open Access Journals (Sweden)

    Tomoki Kurikawa

    Full Text Available Recent experimental measurements have demonstrated that spontaneous neural activity in the absence of explicit external stimuli has remarkable spatiotemporal structure. This spontaneous activity has also been shown to play a key role in the response to external stimuli. To better understand this role, we proposed a viewpoint, "memories-as-bifurcations," that differs from the traditional "memories-as-attractors" viewpoint. Memory recall from the memories-as-bifurcations viewpoint occurs when the spontaneous neural activity is changed to an appropriate output activity upon application of an input, known as a bifurcation in dynamical systems theory, wherein the input modifies the flow structure of the neural dynamics. Learning, then, is a process that helps create neural dynamical systems such that a target output pattern is generated as an attractor upon a given input. Based on this novel viewpoint, we introduce in this paper an associative memory model with a sequential learning process. Using a simple hebbian-type learning, the model is able to memorize a large number of input/output mappings. The neural dynamics shaped through the learning exhibit different bifurcations to make the requested targets stable upon an increase in the input, and the neural activity in the absence of input shows chaotic dynamics with occasional approaches to the memorized target patterns. These results suggest that these dynamics facilitate the bifurcations to each target attractor upon application of the corresponding input, which thus increases the capacity for learning. This theoretical finding about the behavior of the spontaneous neural activity is consistent with recent experimental observations in which the neural activity without stimuli wanders among patterns evoked by previously applied signals. In addition, the neural networks shaped by learning properly reflect the correlations of input and target-output patterns in a similar manner to those designed in

  11. Changed Synaptic Plasticity in Neural Circuits of Depressive-Like and Escitalopram-Treated Rats.

    Science.gov (United States)

    Li, Xiao-Li; Yuan, Yong-Gui; Xu, Hua; Wu, Di; Gong, Wei-Gang; Geng, Lei-Yu; Wu, Fang-Fang; Tang, Hao; Xu, Lin; Zhang, Zhi-Jun

    2015-04-21

    Although progress has been made in the detection and characterization of neural plasticity in depression, it has not been fully understood in individual synaptic changes in the neural circuits under chronic stress and antidepressant treatment. Using electron microscopy and Western-blot analyses, the present study quantitatively examined the changes in the Gray's Type I synaptic ultrastructures and the expression of synapse-associated proteins in the key brain regions of rats' depressive-related neural circuit after chronic unpredicted mild stress and/or escitalopram administration. Meanwhile, their depressive behaviors were also determined by several tests. The Type I synapses underwent considerable remodeling after chronic unpredicted mild stress, which resulted in the changed width of the synaptic cleft, length of the active zone, postsynaptic density thickness, and/or synaptic curvature in the subregions of medial prefrontal cortex and hippocampus, as well as the basolateral amygdaloid nucleus of the amygdala, accompanied by changed expression of several synapse-associated proteins. Chronic escitalopram administration significantly changed the above alternations in the chronic unpredicted mild stress rats but had little effect on normal controls. Also, there was a positive correlation between the locomotor activity and the maximal synaptic postsynaptic density thickness in the stratum radiatum of the Cornu Ammonis 1 region and a negative correlation between the sucrose preference and the length of the active zone in the basolateral amygdaloid nucleus region in chronic unpredicted mild stress rats. These findings strongly indicate that chronic stress and escitalopram can alter synaptic plasticity in the neural circuits, and the remodeled synaptic ultrastructure was correlated with the rats' depressive behaviors, suggesting a therapeutic target for further exploration. © The Author 2015. Published by Oxford University Press on behalf of CINP.

  12. Changed Synaptic Plasticity in Neural Circuits of Depressive-Like and Escitalopram-Treated Rats

    Science.gov (United States)

    Li, Xiao-Li; Yuan, Yong-Gui; Xu, Hua; Wu, Di; Gong, Wei-Gang; Geng, Lei-Yu; Wu, Fang-Fang; Tang, Hao; Xu, Lin

    2015-01-01

    Background: Although progress has been made in the detection and characterization of neural plasticity in depression, it has not been fully understood in individual synaptic changes in the neural circuits under chronic stress and antidepressant treatment. Methods: Using electron microscopy and Western-blot analyses, the present study quantitatively examined the changes in the Gray’s Type I synaptic ultrastructures and the expression of synapse-associated proteins in the key brain regions of rats’ depressive-related neural circuit after chronic unpredicted mild stress and/or escitalopram administration. Meanwhile, their depressive behaviors were also determined by several tests. Results: The Type I synapses underwent considerable remodeling after chronic unpredicted mild stress, which resulted in the changed width of the synaptic cleft, length of the active zone, postsynaptic density thickness, and/or synaptic curvature in the subregions of medial prefrontal cortex and hippocampus, as well as the basolateral amygdaloid nucleus of the amygdala, accompanied by changed expression of several synapse-associated proteins. Chronic escitalopram administration significantly changed the above alternations in the chronic unpredicted mild stress rats but had little effect on normal controls. Also, there was a positive correlation between the locomotor activity and the maximal synaptic postsynaptic density thickness in the stratum radiatum of the Cornu Ammonis 1 region and a negative correlation between the sucrose preference and the length of the active zone in the basolateral amygdaloid nucleus region in chronic unpredicted mild stress rats. Conclusion: These findings strongly indicate that chronic stress and escitalopram can alter synaptic plasticity in the neural circuits, and the remodeled synaptic ultrastructure was correlated with the rats’ depressive behaviors, suggesting a therapeutic target for further exploration. PMID:25899067

  13. Formation and remodeling of the brain extracellular matrix in neural plasticity: Roles of chondroitin sulfate and hyaluronan.

    Science.gov (United States)

    Miyata, Shinji; Kitagawa, Hiroshi

    2017-10-01

    The extracellular matrix (ECM) of the brain is rich in glycosaminoglycans such as chondroitin sulfate (CS) and hyaluronan. These glycosaminoglycans are organized into either diffuse or condensed ECM. Diffuse ECM is distributed throughout the brain and fills perisynaptic spaces, whereas condensed ECM selectively surrounds parvalbumin-expressing inhibitory neurons (PV cells) in mesh-like structures called perineuronal nets (PNNs). The brain ECM acts as a non-specific physical barrier that modulates neural plasticity and axon regeneration. Here, we review recent progress in understanding of the molecular basis of organization and remodeling of the brain ECM, and the involvement of several types of experience-dependent neural plasticity, with a particular focus on the mechanism that regulates PV cell function through specific interactions between CS chains and their binding partners. We also discuss how the barrier function of the brain ECM restricts dendritic spine dynamics and limits axon regeneration after injury. The brain ECM not only forms physical barriers that modulate neural plasticity and axon regeneration, but also forms molecular brakes that actively controls maturation of PV cells and synapse plasticity in which sulfation patterns of CS chains play a key role. Structural remodeling of the brain ECM modulates neural function during development and pathogenesis. Genetic or enzymatic manipulation of the brain ECM may restore neural plasticity and enhance recovery from nerve injury. This article is part of a Special Issue entitled Neuro-glycoscience, edited by Kenji Kadomatsu and Hiroshi Kitagawa. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. STAT3 signal that mediates the neural plasticity is involved in willed-movement training in focal ischemic rats.

    Science.gov (United States)

    Tang, Qing-Ping; Shen, Qin; Wu, Li-Xiang; Feng, Xiang-Ling; Liu, Hui; Wu, Bei; Huang, Xiao-Song; Wang, Gai-Qing; Li, Zhong-Hao; Liu, Zun-Jing

    2016-07-01

    Willed-movement training has been demonstrated to be a promising approach to increase motor performance and neural plasticity in ischemic rats. However, little is known regarding the molecular signals that are involved in neural plasticity following willed-movement training. To investigate the potential signals related to neural plasticity following willed-movement training, littermate rats were randomly assigned into three groups: middle cerebral artery occlusion, environmental modification, and willed-movement training. The infarct volume was measured 18 d after occlusion of the right middle cerebral artery. Reverse transcription-polymerase chain reaction (PCR) and immunofluorescence staining were used to detect the changes in the signal transducer and activator of transcription 3 (STAT3) mRNA and protein, respectively. A chromatin immunoprecipitation was used to investigate whether STAT3 bound to plasticity-related genes, such as brain-derived neurotrophic factor (BDNF), synaptophysin, and protein interacting with C kinase 1 (PICK1). In this study, we demonstrated that STAT3 mRNA and protein were markedly increased following 15-d willed-movement training in the ischemic hemispheres of the treated rats. STAT3 bound to BDNF, PICK1, and synaptophysin promoters in the neocortical cells of rats. These data suggest that the increased STAT3 levels after willed-movement training might play critical roles in the neural plasticity by directly regulating plasticity-related genes.

  15. A Calcium-Dependent Plasticity Rule for HCN Channels Maintains Activity Homeostasis and Stable Synaptic Learning

    Science.gov (United States)

    Honnuraiah, Suraj; Narayanan, Rishikesh

    2013-01-01

    Theoretical and computational frameworks for synaptic plasticity and learning have a long and cherished history, with few parallels within the well-established literature for plasticity of voltage-gated ion channels. In this study, we derive rules for plasticity in the hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, and assess the synergy between synaptic and HCN channel plasticity in establishing stability during synaptic learning. To do this, we employ a conductance-based model for the hippocampal pyramidal neuron, and incorporate synaptic plasticity through the well-established Bienenstock-Cooper-Munro (BCM)-like rule for synaptic plasticity, wherein the direction and strength of the plasticity is dependent on the concentration of calcium influx. Under this framework, we derive a rule for HCN channel plasticity to establish homeostasis in synaptically-driven firing rate, and incorporate such plasticity into our model. In demonstrating that this rule for HCN channel plasticity helps maintain firing rate homeostasis after bidirectional synaptic plasticity, we observe a linear relationship between synaptic plasticity and HCN channel plasticity for maintaining firing rate homeostasis. Motivated by this linear relationship, we derive a calcium-dependent rule for HCN-channel plasticity, and demonstrate that firing rate homeostasis is maintained in the face of synaptic plasticity when moderate and high levels of cytosolic calcium influx induced depression and potentiation of the HCN-channel conductance, respectively. Additionally, we show that such synergy between synaptic and HCN-channel plasticity enhances the stability of synaptic learning through metaplasticity in the BCM-like synaptic plasticity profile. Finally, we demonstrate that the synergistic interaction between synaptic and HCN-channel plasticity preserves robustness of information transfer across the neuron under a rate-coding schema. Our results establish specific physiological roles

  16. Boltzmann learning of parameters in cellular neural networks

    DEFF Research Database (Denmark)

    Hansen, Lars Kai

    1992-01-01

    The use of Bayesian methods to design cellular neural networks for signal processing tasks and the Boltzmann machine learning rule for parameter estimation is discussed. The learning rule can be used for models with hidden units, or for completely unsupervised learning. The latter is exemplified...... by unsupervised adaptation of an image segmentation cellular network. The learning rule is applied to adaptive segmentation of satellite imagery...

  17. The neural basis of implicit learning and memory: a review of neuropsychological and neuroimaging research.

    Science.gov (United States)

    Reber, Paul J

    2013-08-01

    Memory systems research has typically described the different types of long-term memory in the brain as either declarative versus non-declarative or implicit versus explicit. These descriptions reflect the difference between declarative, conscious, and explicit memory that is dependent on the medial temporal lobe (MTL) memory system, and all other expressions of learning and memory. The other type of memory is generally defined by an absence: either the lack of dependence on the MTL memory system (nondeclarative) or the lack of conscious awareness of the information acquired (implicit). However, definition by absence is inherently underspecified and leaves open questions of how this type of memory operates, its neural basis, and how it differs from explicit, declarative memory. Drawing on a variety of studies of implicit learning that have attempted to identify the neural correlates of implicit learning using functional neuroimaging and neuropsychology, a theory of implicit memory is presented that describes it as a form of general plasticity within processing networks that adaptively improve function via experience. Under this model, implicit memory will not appear as a single, coherent, alternative memory system but will instead be manifested as a principle of improvement from experience based on widespread mechanisms of cortical plasticity. The implications of this characterization for understanding the role of implicit learning in complex cognitive processes and the effects of interactions between types of memory will be discussed for examples within and outside the psychology laboratory. Copyright © 2013 Elsevier Ltd. All rights reserved.

  18. The neural circuit basis of learning

    Science.gov (United States)

    Patrick, Kaifosh William John

    The astounding capacity for learning ranks among the nervous system's most impressive features. This thesis comprises studies employing varied approaches to improve understanding, at the level of neural circuits, of the brain's capacity for learning. The first part of the thesis contains investigations of hippocampal circuitry -- both theoretical work and experimental work in the mouse Mus musculus -- as a model system for declarative memory. To begin, Chapter 2 presents a theory of hippocampal memory storage and retrieval that reflects nonlinear dendritic processing within hippocampal pyramidal neurons. As a prelude to the experimental work that comprises the remainder of this part, Chapter 3 describes an open source software platform that we have developed for analysis of data acquired with in vivo Ca2+ imaging, the main experimental technique used throughout the remainder of this part of the thesis. As a first application of this technique, Chapter 4 characterizes the content of signaling at synapses between GABAergic neurons of the medial septum and interneurons in stratum oriens of hippocampal area CA1. Chapter 5 then combines these techniques with optogenetic, pharmacogenetic, and pharmacological manipulations to uncover inhibitory circuit mechanisms underlying fear learning. The second part of this thesis focuses on the cerebellum-like electrosensory lobe in the weakly electric mormyrid fish Gnathonemus petersii, as a model system for non-declarative memory. In Chapter 6, we study how short-duration EOD motor commands are recoded into a complex temporal basis in the granule cell layer, which can be used to cancel Purkinje-like cell firing to the longer duration and temporally varying EOD-driven sensory responses. In Chapter 7, we consider not only the temporal aspects of the granule cell code, but also the encoding of body position provided from proprioceptive and efference copy sources. Together these studies clarify how the cerebellum-like circuitry of the

  19. Stability and plasticity in neural encoding of linguistically relevant pitch patterns.

    Science.gov (United States)

    Xie, Zilong; Reetzke, Rachel; Chandrasekaran, Bharath

    2017-03-01

    While lifelong language experience modulates subcortical encoding of pitch patterns, there is emerging evidence that short-term training introduced in adulthood also shapes subcortical pitch encoding. Here we use a cross-language design to examine the stability of language experience-dependent subcortical plasticity over multiple days. We then examine the extent to which behavioral relevance induced by sound-to-category training leads to plastic changes in subcortical pitch encoding in adulthood relative to adolescence, a period of ongoing maturation of subcortical and cortical auditory processing. Frequency-following responses (FFRs), which reflect phase-locked activity from subcortical neural ensembles, were elicited while participants passively listened to pitch patterns reflective of Mandarin tones. In experiment 1 , FFRs were recorded across three consecutive days from native Chinese-speaking ( n = 10) and English-speaking ( n = 10) adults. In experiment 2 , FFRs were recorded from native English-speaking adolescents ( n = 20) and adults ( n = 15) before, during, and immediately after a session of sound-to-category training, as well as a day after training ceased. Experiment 1 demonstrated the stability of language experience-dependent subcortical plasticity in pitch encoding across multiple days of passive exposure to linguistic pitch patterns. In contrast, experiment 2 revealed an enhancement in subcortical pitch encoding that emerged a day after the sound-to-category training, with some developmental differences observed. Taken together, these findings suggest that behavioral relevance is a critical component for the observation of plasticity in the subcortical encoding of pitch. NEW & NOTEWORTHY We examine the timescale of experience-dependent auditory plasticity to linguistically relevant pitch patterns. We find extreme stability in lifelong experience-dependent plasticity. We further demonstrate that subcortical function in adolescents and adults is

  20. Behavioral and neural bases of extinction learning in Hermissenda

    Directory of Open Access Journals (Sweden)

    Joel S. Cavallo

    2014-08-01

    Full Text Available Extinction of classical conditioning is thought to produce new learning that masks or interferes with the original memory. However, research in the nudibranch Hermissenda crassicornis (H.c. has challenged this view, and instead suggested that extinction erased the original associative memory. We have re-examined extinction in H.c. to test whether extinguished associative memories can be detected on the behavioral and cellular levels, and to characterize the temporal variables involved. Associative conditioning using pairings of light (CS and rotation (US produced characteristic suppression of H.c. phototactic behavior. A single session of extinction training (repeated light-alone presentations reversed suppressed behavior back to pre-training levels when administered 15 min after associative conditioning. This effect was abolished if extinction was delayed by 23 hr, and yet was recovered using extended extinction training (three consecutive daily extinction sessions. Extinguished phototactic suppression did not spontaneously recover at any retention interval tested (2-, 24-, 48-, 72-hr, or after additional US presentations (no observed reinstatement. Extinction training (single session, 15 min interval also reversed the pairing-produced increases in light-evoked spike frequencies of Type B photoreceptors, an identified site of associative memory storage that is causally related to phototactic suppression. These results suggest that the behavioral effects of extinction training are not due to temporary suppression of associative memories, but instead represent a reversal of the underlying cellular changes necessary for the expression of learning. In the companion article, we further elucidate mechanisms responsible for extinction-produced reversal of memory-related neural plasticity in Type B photoreceptors.

  1. Behavioral and neural bases of extinction learning in Hermissenda

    Science.gov (United States)

    Cavallo, Joel S.; Hamilton, Brittany N.; Farley, Joseph

    2014-01-01

    Extinction of classical conditioning is thought to produce new learning that masks or interferes with the original memory. However, research in the nudibranch Hermissenda crassicornis (H.c.) has challenged this view, and instead suggested that extinction erased the original associative memory. We have re-examined extinction in H.c. to test whether extinguished associative memories can be detected on the behavioral and cellular levels, and to characterize the temporal variables involved. Associative conditioning using pairings of light (CS) and rotation (US) produced characteristic suppression of H.c. phototactic behavior. A single session of extinction training (repeated light-alone presentations) reversed suppressed behavior back to pre-training levels when administered 15 min after associative conditioning. This effect was abolished if extinction was delayed by 23 h, and yet was recovered using extended extinction training (three consecutive daily extinction sessions). Extinguished phototactic suppression did not spontaneously recover at any retention interval (RI) tested (2-, 24-, 48-, 72-h), or after additional US presentations (no observed reinstatement). Extinction training (single session, 15 min interval) also reversed the pairing-produced increases in light-evoked spike frequencies of Type B photoreceptors, an identified site of associative memory storage that is causally related to phototactic suppression. These results suggest that the behavioral effects of extinction training are not due to temporary suppression of associative memories, but instead represent a reversal of the underlying cellular changes necessary for the expression of learning. In the companion article, we further elucidate mechanisms responsible for extinction-produced reversal of memory-related neural plasticity in Type B photoreceptors. PMID:25191236

  2. Biologically based neural circuit modelling for the study of fear learning and extinction

    Science.gov (United States)

    Nair, Satish S.; Paré, Denis; Vicentic, Aleksandra

    2016-11-01

    The neuronal systems that promote protective defensive behaviours have been studied extensively using Pavlovian conditioning. In this paradigm, an initially neutral-conditioned stimulus is paired with an aversive unconditioned stimulus leading the subjects to display behavioural signs of fear. Decades of research into the neural bases of this simple behavioural paradigm uncovered that the amygdala, a complex structure comprised of several interconnected nuclei, is an essential part of the neural circuits required for the acquisition, consolidation and expression of fear memory. However, emerging evidence from the confluence of electrophysiological, tract tracing, imaging, molecular, optogenetic and chemogenetic methodologies, reveals that fear learning is mediated by multiple connections between several amygdala nuclei and their distributed targets, dynamical changes in plasticity in local circuit elements as well as neuromodulatory mechanisms that promote synaptic plasticity. To uncover these complex relations and analyse multi-modal data sets acquired from these studies, we argue that biologically realistic computational modelling, in conjunction with experiments, offers an opportunity to advance our understanding of the neural circuit mechanisms of fear learning and to address how their dysfunction may lead to maladaptive fear responses in mental disorders.

  3. A Unifying Framework of Synaptic and Intrinsic Plasticity in Neural Populations.

    Science.gov (United States)

    Leugering, Johannes; Pipa, Gordon

    2018-01-17

    A neuronal population is a computational unit that receives a multivariate, time-varying input signal and creates a related multivariate output. These neural signals are modeled as stochastic processes that transmit information in real time, subject to stochastic noise. In a stationary environment, where the input signals can be characterized by constant statistical properties, the systematic relationship between its input and output processes determines the computation carried out by a population. When these statistical characteristics unexpectedly change, the population needs to adapt to its new environment if it is to maintain stable operation. Based on the general concept of homeostatic plasticity, we propose a simple compositional model of adaptive networks that achieve invariance with regard to undesired changes in the statistical properties of their input signals and maintain outputs with well-defined joint statistics. To achieve such invariance, the network model combines two functionally distinct types of plasticity. An abstract stochastic process neuron model implements a generalized form of intrinsic plasticity that adapts marginal statistics, relying only on mechanisms locally confined within each neuron and operating continuously in time, while a simple form of Hebbian synaptic plasticity operates on synaptic connections, thus shaping the interrelation between neurons as captured by a copula function. The combined effect of both mechanisms allows a neuron population to discover invariant representations of its inputs that remain stable under a wide range of transformations (e.g., shifting, scaling and (affine linear) mixing). The probabilistic model of homeostatic adaptation on a population level as presented here allows us to isolate and study the individual and the interaction dynamics of both mechanisms of plasticity and could guide the future search for computationally beneficial types of adaptation.

  4. Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

    DEFF Research Database (Denmark)

    Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin

    2015-01-01

    Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements...... correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking...... robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables...

  5. Upper Limb Immobilisation: A Neural Plasticity Model with Relevance to Poststroke Motor Rehabilitation.

    Science.gov (United States)

    Furlan, Leonardo; Conforto, Adriana Bastos; Cohen, Leonardo G; Sterr, Annette

    2016-01-01

    Advances in our understanding of the neural plasticity that occurs after hemiparetic stroke have contributed to the formulation of theories of poststroke motor recovery. These theories, in turn, have underpinned contemporary motor rehabilitation strategies for treating motor deficits after stroke, such as upper limb hemiparesis. However, a relative drawback has been that, in general, these strategies are most compatible with the recovery profiles of relatively high-functioning stroke survivors and therefore do not easily translate into benefit to those individuals sustaining low-functioning upper limb hemiparesis, who otherwise have poorer residual function. For these individuals, alternative motor rehabilitation strategies are currently needed. In this paper, we will review upper limb immobilisation studies that have been conducted with healthy adult humans and animals. Then, we will discuss how the findings from these studies could inspire the creation of a neural plasticity model that is likely to be of particular relevance to the context of motor rehabilitation after stroke. For instance, as will be elaborated, such model could contribute to the development of alternative motor rehabilitation strategies for treating poststroke upper limb hemiparesis. The implications of the findings from those immobilisation studies for contemporary motor rehabilitation strategies will also be discussed and perspectives for future research in this arena will be provided as well.

  6. Pushing the Limits: Cognitive, Affective, & Neural Plasticity Revealed by an Intensive Multifaceted Intervention

    Directory of Open Access Journals (Sweden)

    Michael David Mrazek

    2016-03-01

    Full Text Available Scientific understanding of how much the adult brain can be shaped by experience requires examination of how multiple influences combine to elicit cognitive, affective, and neural plasticity. Using an intensive multifaceted intervention, we discovered that substantial and enduring improvements can occur in parallel across multiple cognitive and neuroimaging measures in healthy young adults. The intervention elicited substantial improvements in physical health, working memory, standardized test performance, mood, self-esteem, self-efficacy, mindfulness, and life satisfaction. Improvements in mindfulness were associated with increased degree centrality of the insula, greater functional connectivity between insula and somatosensory cortex, and reduced functional connectivity between posterior cingulate cortex and somatosensory cortex. Improvements in working memory and reading comprehension were associated with increased degree centrality of a region within the middle temporal gyrus that was extensively and predominately integrated with the executive control network. The scope and magnitude of the observed improvements represent the most extensive demonstration to date of the considerable human capacity for change. These findings point to higher limits for rapid and concurrent cognitive, affective, and neural plasticity than is widely assumed.

  7. Neural mechanisms of brain plasticity with complex cognitive training in healthy seniors.

    Science.gov (United States)

    Chapman, Sandra B; Aslan, Sina; Spence, Jeffrey S; Hart, John J; Bartz, Elizabeth K; Didehbani, Nyaz; Keebler, Molly W; Gardner, Claire M; Strain, Jeremy F; DeFina, Laura F; Lu, Hanzhang

    2015-02-01

    Complex mental activity induces improvements in cognition, brain function, and structure in animals and young adults. It is not clear to what extent the aging brain is capable of such plasticity. This study expands previous evidence of generalized cognitive gains after mental training in healthy seniors. Using 3 MRI-based measurements, that is, arterial spin labeling MRI, functional connectivity, and diffusion tensor imaging, we examined brain changes across 3 time points pre, mid, and post training (12 weeks) in a randomized sample (n = 37) who received cognitive training versus a control group. We found significant training-related brain state changes at rest; specifically, 1) increases in global and regional cerebral blood flow (CBF), particularly in the default mode network and the central executive network, 2) greater connectivity in these same networks, and 3) increased white matter integrity in the left uncinate demonstrated by an increase in fractional anisotropy. Improvements in cognition were identified along with significant CBF correlates of the cognitive gains. We propose that cognitive training enhances resting-state neural activity and connectivity, increasing the blood supply to these regions via neurovascular coupling. These convergent results provide preliminary evidence that neural plasticity can be harnessed to mitigate brain losses with cognitive training in seniors. © The Author 2013. Published by Oxford University Press.

  8. Upper Limb Immobilisation: A Neural Plasticity Model with Relevance to Poststroke Motor Rehabilitation

    Science.gov (United States)

    Furlan, Leonardo; Conforto, Adriana Bastos; Cohen, Leonardo G.; Sterr, Annette

    2016-01-01

    Advances in our understanding of the neural plasticity that occurs after hemiparetic stroke have contributed to the formulation of theories of poststroke motor recovery. These theories, in turn, have underpinned contemporary motor rehabilitation strategies for treating motor deficits after stroke, such as upper limb hemiparesis. However, a relative drawback has been that, in general, these strategies are most compatible with the recovery profiles of relatively high-functioning stroke survivors and therefore do not easily translate into benefit to those individuals sustaining low-functioning upper limb hemiparesis, who otherwise have poorer residual function. For these individuals, alternative motor rehabilitation strategies are currently needed. In this paper, we will review upper limb immobilisation studies that have been conducted with healthy adult humans and animals. Then, we will discuss how the findings from these studies could inspire the creation of a neural plasticity model that is likely to be of particular relevance to the context of motor rehabilitation after stroke. For instance, as will be elaborated, such model could contribute to the development of alternative motor rehabilitation strategies for treating poststroke upper limb hemiparesis. The implications of the findings from those immobilisation studies for contemporary motor rehabilitation strategies will also be discussed and perspectives for future research in this arena will be provided as well. PMID:26843992

  9. Upper Limb Immobilisation: A Neural Plasticity Model with Relevance to Poststroke Motor Rehabilitation

    Directory of Open Access Journals (Sweden)

    Leonardo Furlan

    2016-01-01

    Full Text Available Advances in our understanding of the neural plasticity that occurs after hemiparetic stroke have contributed to the formulation of theories of poststroke motor recovery. These theories, in turn, have underpinned contemporary motor rehabilitation strategies for treating motor deficits after stroke, such as upper limb hemiparesis. However, a relative drawback has been that, in general, these strategies are most compatible with the recovery profiles of relatively high-functioning stroke survivors and therefore do not easily translate into benefit to those individuals sustaining low-functioning upper limb hemiparesis, who otherwise have poorer residual function. For these individuals, alternative motor rehabilitation strategies are currently needed. In this paper, we will review upper limb immobilisation studies that have been conducted with healthy adult humans and animals. Then, we will discuss how the findings from these studies could inspire the creation of a neural plasticity model that is likely to be of particular relevance to the context of motor rehabilitation after stroke. For instance, as will be elaborated, such model could contribute to the development of alternative motor rehabilitation strategies for treating poststroke upper limb hemiparesis. The implications of the findings from those immobilisation studies for contemporary motor rehabilitation strategies will also be discussed and perspectives for future research in this arena will be provided as well.

  10. Learning from large scale neural simulations

    DEFF Research Database (Denmark)

    Serban, Maria

    2017-01-01

    -possibly explanations of neural and cognitive phenomena, or they can be used to test explanatory hypotheses and identify sufficient causal factors in specific neurobiological mechanisms, or yet again they can be deployed exploratorily to evaluate the hierarchy of multiple neural and cognitive models that are put...

  11. A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks.

    Directory of Open Access Journals (Sweden)

    Alireza Alemi

    2015-08-01

    Full Text Available Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, whose prototype is the Hopfield model. The model simplicity and the locality of the synaptic update rules come at the cost of a poor storage capacity, compared with the capacity achieved with perceptron learning algorithms. Here, by transforming the perceptron learning rule, we present an online learning rule for a recurrent neural network that achieves near-maximal storage capacity without an explicit supervisory error signal, relying only upon locally accessible information. The fully-connected network consists of excitatory binary neurons with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the memory patterns to be memorized are presented online as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs. Synapses corresponding to active inputs are modified as a function of the value of the local fields with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. We simulated and analyzed a network of binary neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction. The storage capacity obtained through numerical simulations is shown to be close to the value predicted by analytical calculations. We also measured the dependence of capacity on the strength of external inputs. Finally, we quantified the statistics of the resulting synaptic connectivity matrix, and found that both the fraction of zero weight synapses and the degree of symmetry of the weight matrix increase with the

  12. Neural Correlates of High Performance in Foreign Language Vocabulary Learning

    Science.gov (United States)

    Macedonia, Manuela; Muller, Karsten; Friederici, Angela D.

    2010-01-01

    Learning vocabulary in a foreign language is a laborious task which people perform with varying levels of success. Here, we investigated the neural underpinning of high performance on this task. In a within-subjects paradigm, participants learned 92 vocabulary items under two multimodal conditions: one condition paired novel words with iconic…

  13. Neural plasticity and proliferation in the generation of antidepressant effects: hippocampal implication.

    Science.gov (United States)

    Pilar-Cuéllar, Fuencisla; Vidal, Rebeca; Díaz, Alvaro; Castro, Elena; dos Anjos, Severiano; Pascual-Brazo, Jesús; Linge, Raquel; Vargas, Veronica; Blanco, Helena; Martínez-Villayandre, Beatriz; Pazos, Ángel; Valdizán, Elsa M

    2013-01-01

    It is widely accepted that changes underlying depression and antidepressant-like effects involve not only alterations in the levels of neurotransmitters as monoamines and their receptors in the brain, but also structural and functional changes far beyond. During the last two decades, emerging theories are providing new explanations about the neurobiology of depression and the mechanism of action of antidepressant strategies based on cellular changes at the CNS level. The neurotrophic/plasticity hypothesis of depression, proposed more than a decade ago, is now supported by multiple basic and clinical studies focused on the role of intracellular-signalling cascades that govern neural proliferation and plasticity. Herein, we review the state-of-the-art of the changes in these signalling pathways which appear to underlie both depressive disorders and antidepressant actions. We will especially focus on the hippocampal cellularity and plasticity modulation by serotonin, trophic factors as brain-derived neurotrophic factor (BDNF), and vascular endothelial growth factor (VEGF) through intracellular signalling pathways-cAMP, Wnt/ β -catenin, and mTOR. Connecting the classic monoaminergic hypothesis with proliferation/neuroplasticity-related evidence is an appealing and comprehensive attempt for improving our knowledge about the neurobiological events leading to depression and associated to antidepressant therapies.

  14. Erythropoietin Promotes Neural Plasticity and Spatial Memory Recovery in Fimbria-Fornix-Lesioned Rats.

    Science.gov (United States)

    Almaguer-Melian, William; Mercerón-Martínez, Daymara; Pavón-Fuentes, Nancy; Alberti-Amador, Esteban; Leon-Martinez, Rilda; Ledón, Nuris; Delgado Ocaña, Susana; Bergado Rosado, Jorge A

    2015-01-01

    Erythropoietin (EPO) upregulates the mitogen activated protein kinase (MAPK) cascade, a central signaling pathway in cellular plastic mechanisms, and is critical for normal brain development. We hypothesized that EPO could modulate the plasticity mechanisms supporting spatial memory recovery in fimbria-fornix-transected animals. Fimbria-fornix was transected in 3 groups of rats. Seven days later, EPO was injected daily for 4 consecutive days within 10 minutes after training on a water maze task. Our results show that EPO injections 10 minutes after training produced a substantial spatial memory recovery in fimbria-fornix-lesioned animals. In contrast, an EPO injection shortly after fimbria-fornix lesion surgery does not promote spatial-memory recovery. Neither does daily EPO injection 5 hours after the water maze performance. EPO, on the other hand, induced the expression of plasticity-related genes like arc and bdnf, but this effect was independent of training or lesion. This finding supports our working hypothesis that EPO can modulate transient neuroplastic mechanisms triggered by training in lesioned animals. Consequently, we propose that EPO administration can be a useful trophic factor to promote neural restoration when given in combination with training. © The Author(s) 2015.

  15. Prediction of Damage Factor in end Milling of Glass Fibre Reinforced Plastic Composites Using Artificial Neural Network

    Science.gov (United States)

    Erkan, Ömer; Işık, Birhan; Çiçek, Adem; Kara, Fuat

    2013-08-01

    Glass fibre reinforced plastic (GFRP) composites are an economic alternative to engineering materials because of their superior properties. Some damages on the surface occur due to their complex cutting mechanics in cutting process. Minimisation of the damages is fairly important in terms of product quality. In this study, a GFRP composite material was milled to experimentally minimise the damages on the machined surfaces, using two, three and four flute end mills at different combinations of cutting parameters. Experimental results showed that the damage factor increased with increasing cutting speed and feed rate, on the other hand, it was found that the damage factor decreased with increasing depth of cut and number of the flutes. In addition, analysis of variance (ANOVA) results clearly revealed that the feed rate was the most influential parameter affecting the damage factor in end milling of GFRP composites. Also, in present study, Artificial Neural Network (ANN) models with five learning algorithms were used in predicting the damage factor to reduce number of expensive and time-consuming experiments. The highest performance was obtained by 4-10-1 network structure with LM learning algorithm. ANN was notably successful in predicting the damage factor due to higher R2 and lower RMSE and MEP.

  16. Neural Stem Cell Plasticity: Advantages in Therapy for the Injured Central Nervous System.

    Science.gov (United States)

    Ottoboni, Linda; Merlini, Arianna; Martino, Gianvito

    2017-01-01

    The physiological and pathological properties of the neural germinal stem cell niche have been well-studied in the past 30 years, mainly in animals and within given limits in humans, and knowledge is available for the cyto-architectonic structure, the cellular components, the timing of development and the energetic maintenance of the niche, as well as for the therapeutic potential and the cross talk between neural and immune cells. In recent years we have gained detailed understanding of the potentiality of neural stem cells (NSCs), although we are only beginning to understand their molecular, metabolic, and epigenetic profile in physiopathology and, further, more can be invested to measure quantitatively the activity of those cells, to model in vitro their therapeutic responses or to predict interactions in silico . Information in this direction has been put forward for other organs but is still limited in the complex and very less accessible context of the brain. A comprehensive understanding of the behavior of endogenous NSCs will help to tune or model them toward a desired response in order to treat complex neurodegenerative diseases. NSCs have the ability to modulate multiple cellular functions and exploiting their plasticity might make them into potent and versatile cellular drugs.

  17. [Phenotypic plasticity of neural crest-derived melanocytes and Schwann cells].

    Science.gov (United States)

    Dupin, Elisabeth

    2011-01-01

    expression. This review considers the issue of whether neural crest-derived lineages are endowed with some phenotypic plasticity. Emphasis is put on the ability of pigment cells and Schwann cells to dedifferentiate and reprogram their fate in vitro. To address this question, we have studied the clonal progeny of differentiated Schwann cells and melanocytes after their isolation from the sciatic nerve and the back skin of quail embryos, respectively. When stimulated to proliferate in vitro in the presence of endothelin-3, both cell types were able to dedifferentiate and produce alternative neural crest-derived cell lineages. Individual Schwann cells isolated by FACS, using a glial-specific surface marker, gave rise in culture to pigment cells and myofibroblasts/smooth muscle cells. Treatment of the cultures with endothelin-3 was required for Schwann cell conversion into melanocytes, which involved acquisition of multipotency. Moreover, Schwann cell plasticity could also be induced in vivo: following transplantation into the branchial arch of a young chick host embryo, dedifferentiating Schwann cells were able to integrate the forming head structures of the host and, specifically, to contribute smooth muscle cells to the wall of cranial blood vessels. We also analyzed the in vitro behavior of individual pigment cells obtained by microdissection and enzymatic treatment of quail epidermis at embryonic and hatching stages. In single cell cultures treated with endothelin-3, pigment cells strongly proliferated while rapidly dedifferentiating into unpigmented cells, leading to the formation of large colonies that comprised glial cells and myofibroblasts in addition to melanocytes. By serially subcloning these primary colonies, we could efficiently propagate a bipotent glial-melanocytic precursor that is generated in the progeny of the melanocytic founder. These data therefore suggest that pigment cells have the ability to revert back to the state of self-renewing neural crest

  18. Neural Monkey: An Open-source Tool for Sequence Learning

    Directory of Open Access Journals (Sweden)

    Helcl Jindřich

    2017-04-01

    Full Text Available In this paper, we announce the development of Neural Monkey – an open-source neural machine translation (NMT and general sequence-to-sequence learning system built over the TensorFlow machine learning library. The system provides a high-level API tailored for fast prototyping of complex architectures with multiple sequence encoders and decoders. Models’ overall architecture is specified in easy-to-read configuration files. The long-term goal of the Neural Monkey project is to create and maintain a growing collection of implementations of recently proposed components or methods, and therefore it is designed to be easily extensible. Trained models can be deployed either for batch data processing or as a web service. In the presented paper, we describe the design of the system and introduce the reader to running experiments using Neural Monkey.

  19. Neural networks for relational learning: An experimental comparison

    OpenAIRE

    Uwents, Werner; Monfardini, Gabriele; Blockeel, Hendrik; Gori, Marco De; Scarselli, Franco

    2011-01-01

    In the last decade, connectionist models have been proposed that can process structured information directly. These methods, which are based on the use of graphs for the representation of the data and the relationships within the data, are particularly suitable for handling relational learning tasks. In this paper, two recently proposed architectures of this kind, i.e. Graph Neural Networks (GNNs) and Relational Neural Networks (RelNNs), are compared and discussed, along with their correspond...

  20. Self-Organisation of Neural Topologies by Evolutionary Reinforcement Learning

    OpenAIRE

    Siebel, Nils T; Krause, Jochen; Sommer, Gerald

    2007-01-01

    In this article we present EANT, "Evolutionary Acquisition of Neural Topologies", a method that creates neural networks (NNs) by evolutionary reinforcement learning. The structure of NNs is developed using mutation operators, starting from a minimal structure. Their parameters are optimised using CMA-ES. EANT can create NNs that are very specialised; they achieve a very good performance while being relatively small. This can be seen in experiments where our method competes with a different on...

  1. Neural Multi-task Learning in Automated Assessment

    OpenAIRE

    Cummins, Ronan; Rei, Marek

    2018-01-01

    Grammatical error detection and automated essay scoring are two tasks in the area of automated assessment. Traditionally these tasks have been treated independently with different machine learning models and features used for each task. In this paper, we develop a multi-task neural network model that jointly optimises for both tasks, and in particular we show that neural automated essay scoring can be significantly improved. We show that while the essay score provides little evidence to infor...

  2. Ischemic long-term-potentiation (iLTP): perspectives to set the threshold of neural plasticity toward therapy.

    Science.gov (United States)

    Lenz, Maximilian; Vlachos, Andreas; Maggio, Nicola

    2015-10-01

    The precise role of neural plasticity under pathological conditions remains not well understood. It appears to be well accepted, however, that changes in the ability of neurons to express plasticity accompany neurological diseases. Here, we discuss recent experimental evidence, which suggests that synaptic plasticity induced by a pathological stimulus, i.e., ischemic long-term-potentiation (iLTP) of excitatory synapses, could play an important role for post-stroke recovery by influencing the post-lesional reorganization of surviving neuronal networks.

  3. Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks.

    Science.gov (United States)

    Miconi, Thomas

    2017-02-23

    Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.

  4. Learning about time: plastic changes and interindividual brain differences.

    Science.gov (United States)

    Bueti, Domenica; Lasaponara, Stefano; Cercignani, Mara; Macaluso, Emiliano

    2012-08-23

    Learning the timing of rapidly changing sensory events is crucial to construct a reliable representation of the environment and to efficiently control behavior. The neurophysiological mechanisms underlying the learning of time are unknown. We used functional and structural magnetic resonance imaging to investigate neurophysiological changes and individual brain differences underlying the learning of time in the millisecond range. We found that the representation of a trained visual temporal interval was associated with functional and structural changes in a sensory-motor network including occipital, parietal, and insular cortices, plus the cerebellum. We show that both types of neurophysiological changes correlated with changes of performance accuracy and that activity and gray-matter volume of sensorimotor cortices predicted individual learning abilities. These findings represent neurophysiological evidence of functional and structural plasticity associated with the learning of time in humans and highlight the role of sensory-motor circuits in the perceptual representation of time in the millisecond range. Copyright © 2012 Elsevier Inc. All rights reserved.

  5. A neural circuit model of emotional learning using two pathways with different granularity and speed of information processing.

    Science.gov (United States)

    Murakoshi, Kazushi; Saito, Mayuko

    2009-02-01

    We propose a neural circuit model of emotional learning using two pathways with different granularity and speed of information processing. In order to derive a precise time process, we utilized a spiking model neuron proposed by Izhikevich and spike-timing-dependent synaptic plasticity (STDP) of both excitatory and inhibitory synapses. We conducted computer simulations to evaluate the proposed model. We demonstrate some aspects of emotional learning from the perspective of the time process. The agreement of the results with the previous behavioral experiments suggests that the structure and learning process of the proposed model are appropriate.

  6. Neural network models of learning and categorization in multigame experiments

    Directory of Open Access Journals (Sweden)

    Davide eMarchiori

    2011-12-01

    Full Text Available Previous research has shown that regret-driven neural networks predict behavior in repeated completely mixed games remarkably well, substantially equating the performance of the most accurate established models of learning. This result prompts the question of what is the added value of modeling learning through neural networks. We submit that this modeling approach allows for models that are able to distinguish among and respond differently to different payoff structures. Moreover, the process of categorization of a game is implicitly carried out by these models, thus without the need of any external explicit theory of similarity between games. To validate our claims, we designed and ran two multigame experiments in which subjects faced, in random sequence, different instances of two completely mixed 2x2 games. Then, we tested on our experimental data two regret-driven neural network models, and compared their performance with that of other established models of learning and Nash equilibrium.

  7. Reaction-diffusion-like formalism for plastic neural networks reveals dissipative solitons at criticality

    Science.gov (United States)

    Grytskyy, Dmytro; Diesmann, Markus; Helias, Moritz

    2016-06-01

    Self-organized structures in networks with spike-timing dependent synaptic plasticity (STDP) are likely to play a central role for information processing in the brain. In the present study we derive a reaction-diffusion-like formalism for plastic feed-forward networks of nonlinear rate-based model neurons with a correlation sensitive learning rule inspired by and being qualitatively similar to STDP. After obtaining equations that describe the change of the spatial shape of the signal from layer to layer, we derive a criterion for the nonlinearity necessary to obtain stable dynamics for arbitrary input. We classify the possible scenarios of signal evolution and find that close to the transition to the unstable regime metastable solutions appear. The form of these dissipative solitons is determined analytically and the evolution and interaction of several such coexistent objects is investigated.

  8. Exploring the spatio-temporal neural basis of face learning.

    Science.gov (United States)

    Yang, Ying; Xu, Yang; Jew, Carol A; Pyles, John A; Kass, Robert E; Tarr, Michael J

    2017-06-01

    Humans are experts at face individuation. Although previous work has identified a network of face-sensitive regions and some of the temporal signatures of face processing, as yet, we do not have a clear understanding of how such face-sensitive regions support learning at different time points. To study the joint spatio-temporal neural basis of face learning, we trained subjects to categorize two groups of novel faces and recorded their neural responses using magnetoencephalography (MEG) throughout learning. A regression analysis of neural responses in face-sensitive regions against behavioral learning curves revealed significant correlations with learning in the majority of the face-sensitive regions in the face network, mostly between 150-250 ms, but also after 300 ms. However, the effect was smaller in nonventral regions (within the superior temporal areas and prefrontal cortex) than that in the ventral regions (within the inferior occipital gyri (IOG), midfusiform gyri (mFUS) and anterior temporal lobes). A multivariate discriminant analysis also revealed that IOG and mFUS, which showed strong correlation effects with learning, exhibited significant discriminability between the two face categories at different time points both between 150-250 ms and after 300 ms. In contrast, the nonventral face-sensitive regions, where correlation effects with learning were smaller, did exhibit some significant discriminability, but mainly after 300 ms. In sum, our findings indicate that early and recurring temporal components arising from ventral face-sensitive regions are critically involved in learning new faces.

  9. The Neural Cell Adhesion Molecule-Derived Peptide FGL Facilitates Long-Term Plasticity in the Dentate Gyrus in Vivo

    Science.gov (United States)

    Dallerac, Glenn; Zerwas, Meike; Novikova, Tatiana; Callu, Delphine; Leblanc-Veyrac, Pascale; Bock, Elisabeth; Berezin, Vladimir; Rampon, Claire; Doyere, Valerie

    2011-01-01

    The neural cell adhesion molecule (NCAM) is known to play a role in developmental and structural processes but also in synaptic plasticity and memory of the adult animal. Recently, FGL, a NCAM mimetic peptide that binds to the Fibroblast Growth Factor Receptor 1 (FGFR-1), has been shown to have a beneficial impact on normal memory functioning, as…

  10. The neural plasticity theory of depression: assessing the roles of adult neurogenesis and PSA-NCAM within the hippocampus.

    Science.gov (United States)

    Wainwright, Steven R; Galea, Liisa A M

    2013-01-01

    Depression is a devastating and prevalent disease, with profound effects on neural structure and function; however the etiology and neuropathology of depression remain poorly understood. Though antidepressant drugs exist, they are not ideal, as only a segment of patients are effectively treated, therapeutic onset is delayed, and the exact mechanism of these drugs remains to be elucidated. Several theories of depression do exist, including modulation of monoaminergic neurotransmission, alterations in neurotrophic factors, and the upregulation of adult hippocampal neurogenesis, and are briefly mentioned in the review. However none of these theories sufficiently explains the pathology and treatment of depression unto itself. Recently, neural plasticity theories of depression have postulated that multiple aspects of brain plasticity, beyond neurogenesis, may bridge the prevailing theories. The term "neural plasticity" encompasses an array of mechanisms, from the birth, survival, migration, and integration of new neurons to neurite outgrowth, synaptogenesis, and the modulation of mature synapses. This review critically assesses the role of adult hippocampal neurogenesis and the cell adhesion molecule, PSA-NCAM (which is known to be involved in many facets of neural plasticity), in depression and antidepressant treatment.

  11. Computational neural learning formalisms for manipulator inverse kinematics

    Science.gov (United States)

    Gulati, Sandeep; Barhen, Jacob; Iyengar, S. Sitharama

    1989-01-01

    An efficient, adaptive neural learning paradigm for addressing the inverse kinematics of redundant manipulators is presented. The proposed methodology exploits the infinite local stability of terminal attractors - a new class of mathematical constructs which provide unique information processing capabilities to artificial neural systems. For robotic applications, synaptic elements of such networks can rapidly acquire the kinematic invariances embedded within the presented samples. Subsequently, joint-space configurations, required to follow arbitrary end-effector trajectories, can readily be computed. In a significant departure from prior neuromorphic learning algorithms, this methodology provides mechanisms for incorporating an in-training skew to handle kinematics and environmental constraints.

  12. DNA methyltransferase activity is required for memory-related neural plasticity in the lateral amygdala.

    Science.gov (United States)

    Maddox, Stephanie A; Watts, Casey S; Schafe, Glenn E

    2014-01-01

    We have previously shown that auditory Pavlovian fear conditioning is associated with an increase in DNA methyltransferase (DNMT) expression in the lateral amygdala (LA) and that intra-LA infusion or bath application of an inhibitor of DNMT activity impairs the consolidation of an auditory fear memory and long-term potentiation (LTP) at thalamic and cortical inputs to the LA, in vitro. In the present study, we use awake behaving neurophysiological techniques to examine the role of DNMT activity in memory-related neurophysiological changes accompanying fear memory consolidation and reconsolidation in the LA, in vivo. We show that auditory fear conditioning results in a training-related enhancement in the amplitude of short-latency auditory-evoked field potentials (AEFPs) in the LA. Intra-LA infusion of a DNMT inhibitor impairs both fear memory consolidation and, in parallel, the consolidation of training-related neural plasticity in the LA; that is, short-term memory (STM) and short-term training-related increases in AEFP amplitude in the LA are intact, while long-term memory (LTM) and long-term retention of training-related increases in AEFP amplitudes are impaired. In separate experiments, we show that intra-LA infusion of a DNMT inhibitor following retrieval of an auditory fear memory has no effect on post-retrieval STM or short-term retention of training-related changes in AEFP amplitude in the LA, but significantly impairs both post-retrieval LTM and long-term retention of AEFP amplitude changes in the LA. These findings are the first to demonstrate the necessity of DNMT activity in the consolidation and reconsolidation of memory-associated neural plasticity, in vivo. Copyright © 2013 Elsevier Inc. All rights reserved.

  13. Oscillations, Timing, Plasticity, and Learning in the Cerebellum.

    Science.gov (United States)

    Cheron, G; Márquez-Ruiz, J; Dan, B

    2016-04-01

    The highly stereotyped, crystal-like architecture of the cerebellum has long served as a basis for hypotheses with regard to the function(s) that it subserves. Historically, most clinical observations and experimental work have focused on the involvement of the cerebellum in motor control, with particular emphasis on coordination and learning. Two main models have been suggested to account for cerebellar functioning. According to Llinás's theory, the cerebellum acts as a control machine that uses the rhythmic activity of the inferior olive to synchronize Purkinje cell populations for fine-tuning of coordination. In contrast, the Ito-Marr-Albus theory views the cerebellum as a motor learning machine that heuristically refines synaptic weights of the Purkinje cell based on error signals coming from the inferior olive. Here, we review the role of timing of neuronal events, oscillatory behavior, and synaptic and non-synaptic influences in functional plasticity that can be recorded in awake animals in various physiological and pathological models in a perspective that also includes non-motor aspects of cerebellar function. We discuss organizational levels from genes through intracellular signaling, synaptic network to system and behavior, as well as processes from signal production and processing to memory, delegation, and actual learning. We suggest an integrative concept for control and learning based on articulated oscillation templates.

  14. Dynamic Hebbian Cross-Correlation Learning Resolves the Spike Timing Dependent Plasticity Conundrum

    Directory of Open Access Journals (Sweden)

    Tjeerd V. olde Scheper

    2018-01-01

    Full Text Available Spike Timing-Dependent Plasticity has been found to assume many different forms. The classic STDP curve, with one potentiating and one depressing window, is only one of many possible curves that describe synaptic learning using the STDP mechanism. It has been shown experimentally that STDP curves may contain multiple LTP and LTD windows of variable width, and even inverted windows. The underlying STDP mechanism that is capable of producing such an extensive, and apparently incompatible, range of learning curves is still under investigation. In this paper, it is shown that STDP originates from a combination of two dynamic Hebbian cross-correlations of local activity at the synapse. The correlation of the presynaptic activity with the local postsynaptic activity is a robust and reliable indicator of the discrepancy between the presynaptic neuron and the postsynaptic neuron's activity. The second correlation is between the local postsynaptic activity with dendritic activity which is a good indicator of matching local synaptic and dendritic activity. We show that this simple time-independent learning rule can give rise to many forms of the STDP learning curve. The rule regulates synaptic strength without the need for spike matching or other supervisory learning mechanisms. Local differences in dendritic activity at the synapse greatly affect the cross-correlation difference which determines the relative contributions of different neural activity sources. Dendritic activity due to nearby synapses, action potentials, both forward and back-propagating, as well as inhibitory synapses will dynamically modify the local activity at the synapse, and the resulting STDP learning rule. The dynamic Hebbian learning rule ensures furthermore, that the resulting synaptic strength is dynamically stable, and that interactions between synapses do not result in local instabilities. The rule clearly demonstrates that synapses function as independent localized

  15. Music mnemonics aid Verbal Memory and Induce Learning – Related Brain Plasticity in Multiple Sclerosis

    Science.gov (United States)

    Thaut, Michael H.; Peterson, David A.; McIntosh, Gerald C.; Hoemberg, Volker

    2014-01-01

    Recent research on music and brain function has suggested that the temporal pattern structure in music and rhythm can enhance cognitive functions. To further elucidate this question specifically for memory, we investigated if a musical template can enhance verbal learning in patients with multiple sclerosis (MS) and if music-assisted learning will also influence short-term, system-level brain plasticity. We measured systems-level brain activity with oscillatory network synchronization during music-assisted learning. Specifically, we measured the spectral power of 128-channel electroencephalogram (EEG) in alpha and beta frequency bands in 54 patients with MS. The study sample was randomly divided into two groups, either hearing a spoken or a musical (sung) presentation of Rey’s auditory verbal learning test. We defined the “learning-related synchronization” (LRS) as the percent change in EEG spectral power from the first time the word was presented to the average of the subsequent word encoding trials. LRS differed significantly between the music and the spoken conditions in low alpha and upper beta bands. Patients in the music condition showed overall better word memory and better word order memory and stronger bilateral frontal alpha LRS than patients in the spoken condition. The evidence suggests that a musical mnemonic recruits stronger oscillatory network synchronization in prefrontal areas in MS patients during word learning. It is suggested that the temporal structure implicit in musical stimuli enhances “deep encoding” during verbal learning and sharpens the timing of neural dynamics in brain networks degraded by demyelination in MS. PMID:24982626

  16. Coordinated neural, behavioral, and phenomenological changes in perceptual plasticity through overtraining of synesthetic associations.

    Science.gov (United States)

    Rothen, Nicolas; Schwartzman, David J; Bor, Daniel; Seth, Anil K

    2018-01-31

    Synesthesia is associated with additional perceptual experiences, which are automatically and consistently triggered by specific inducing stimuli. Synesthesia is also accompanied by more general sensory and cortical changes, including enhanced modality-specific cortical excitability. Extensive cognitive training has been shown to generate synesthesia-like phenomenology but whether these experiences are accompanied by neurophysiological changes characteristic of synesthesia remains unknown. Addressing this question provides a unique opportunity to elucidate the neural basis of perceptual plasticity relevant to conscious experiences. Here we investigate whether extensive training of letter-color associations leads not only to synesthetic experiences, but also to changes in cortical excitability. We confirm that overtraining synesthetic associations results in synesthetic phenomenology. Stroop tasks further reveal synesthesia-like performance following training. Electroencephalography and transcranial magnetic stimulation show, respectively, enhanced visual evoked potentials (in response to untrained patterns) and lower phosphene thresholds, demonstrating specific cortical changes. An active (using letter-symbol training) and a passive control confirmed these results were due to letter-color training and not simply to repeated testing. Summarizing, we demonstrate specific cortical changes, following training-induced acquisition of synesthetic phenomenology that are characteristic of genuine synesthesia. Collectively, our data reveal dramatic plasticity in human visual perception, expressed through a coordinated set of behavioral, neurophysiological, and phenomenological changes. Copyright © 2018 Elsevier Ltd. All rights reserved.

  17. Central Sensitization: A Generator of Pain Hypersensitivity by Central Neural Plasticity

    Science.gov (United States)

    Latremoliere, Alban; Woolf, Clifford J.

    2009-01-01

    Central sensitization represents an enhancement in the function of neurons and circuits in nociceptive pathways caused by increases in membrane excitability and synaptic efficacy as well as to reduced inhibition and is a manifestation of the remarkable plasticity of the somatosensory nervous system in response to activity, inflammation, and neural injury. The net effect of central sensitization is to recruit previously subthreshold synaptic inputs to nociceptive neurons, generating an increased or augmented action potential output: a state of facilitation, potentiation, augmentation, or amplification. Central sensitization is responsible for many of the temporal, spatial, and threshold changes in pain sensibility in acute and chronic clinical pain settings and exemplifies the fundamental contribution of the central nervous system to the generation of pain hypersensitivity. Because central sensitization results from changes in the properties of neurons in the central nervous system, the pain is no longer coupled, as acute nociceptive pain is, to the presence, intensity, or duration of noxious peripheral stimuli. Instead, central sensitization produces pain hypersensitivity by changing the sensory response elicited by normal inputs, including those that usually evoke innocuous sensations. Perspective In this article, we review the major triggers that initiate and maintain central sensitization in healthy individuals in response to nociceptor input and in patients with inflammatory and neuropathic pain, emphasizing the fundamental contribution and multiple mechanisms of synaptic plasticity caused by changes in the density, nature, and properties of ionotropic and metabotropic glutamate receptors. PMID:19712899

  18. Neural bases of goal-directed implicit learning.

    Science.gov (United States)

    Rostami, Maryam; Hosseini, S M Hadi; Takahashi, Makoto; Sugiura, Motoaki; Kawashima, Ryuta

    2009-10-15

    Several neuropsychological and neuroimaging studies have been performed to clarify the neural bases of implicit learning, but the question of which brain regions are involved in different forms of implicit learning, including goal-directed learning and habit learning, has not yet been resolved. The present study sought to clarify the mechanisms of goal-directed implicit learning by examining the sugar production factory (SPF) task in conjunction with functional magnetic resonance imaging (fMRI). Several brain regions were identified that contribute to learning in the SPF task. Significant learning-related decreases in brain activity were found in the right inferior parietal lobule (IPL), left superior frontal gyrus, right medial frontal gyrus, cerebellar vermis, and left inferior frontal gyrus, while significant learning-related increases in activity were observed in the right inferior frontal gyrus, left precenteral gyrus and, left precuneus. Among these regions, we speculate that the IPL and medial frontal gyrus may specifically be involved in the early stage of goal-directed implicit learning. We also attempted to investigate the role of the striatum, which has a significant role in habit learning, during learning of the SPF task. The results of ROI analysis showed no learning-related change in the activity of the striatum. Although some of the observed learning-related activations in this study have also been previously reported in neuroimaging studies of habit learning, the possibility that specific brain regions involved in goal-direct implicit learning cannot be excluded.

  19. Functional plasticity before the cradle: a review of neural functional imaging in the human fetus.

    Science.gov (United States)

    Anderson, Amy L; Thomason, Moriah E

    2013-11-01

    The organization of the brain is highly plastic in fetal life. Establishment of healthy neural functional systems during the fetal period is essential to normal growth and development. Across the last several decades, remarkable progress has been made in understanding the development of human fetal functional brain systems. This is largely due to advances in imaging methodologies. Fetal neuroimaging began in the 1950-1970's with fetal electroencephalography (EEG) applied during labor. Later, in the 1980's, magnetoencephalography (MEG) emerged as an effective approach for investigating fetal brain function. Most recently, functional magnetic resonance imaging (fMRI) has arisen as an additional powerful approach for examining fetal brain function. This review will discuss major developmental findings from fetal imaging studies such as the maturation of prenatal sensory system functions, functional hemispheric asymmetry, and sensory-driven neurodevelopment. We describe how with improved imaging and analysis techniques, functional imaging of the fetus has the potential to assess the earliest point of neural maturation and provide insight into the patterning and sequence of normal and abnormal brain development. Copyright © 2013 Elsevier Ltd. All rights reserved.

  20. Chronic pain resolution after a lucid dream: a case for neural plasticity?

    Science.gov (United States)

    Zappaterra, Mauro; Jim, Lysander; Pangarkar, Sanjog

    2014-03-01

    Chronic pain is often managed using a multidisciplinary, biopsychosocial approach. Interventions targeting the biological, psychological, and social aspects of both the patient and the pain have been demonstrated to provide objective and subjective improvement in chronic pain symptoms. The mechanism by which pain attenuation occurs after these interventions remains to be elucidated. While there is a relatively large body of empirical literature suggesting that functional and structural changes in the peripheral and central nervous systems are key in the development and maintenance of chronic pain states, less is known about changes that take place in the nervous system as a whole after biopsychosocial interventions. Using as a model the unique case of Mr. S, a patient suffering with chronic pain for 22 years who experienced a complete resolution of pain after a lucid dream following 2 years of biopsychosocial treatments, we postulate that central nervous system (CNS) reorganization (i.e., neural plasticity) serves as a possible mechanism for the therapeutic benefit of multidisciplinary treatments, and may set a neural framework for healing, in this case via a lucid dream. Copyright © 2013 Elsevier Ltd. All rights reserved.

  1. Neural plasticity expressed in central auditory structures with and without tinnitus

    Directory of Open Access Journals (Sweden)

    Larry E Roberts

    2012-05-01

    Full Text Available Sensory training therapies for tinnitus are based on the assumption that, notwithstanding neural changes related to tinnitus, auditory training can alter the response properties of neurons in auditory pathways. To address this question, we investigated whether brain changes induced by sensory training in tinnitus sufferers and measured by EEG are similar to those induced in age and hearing loss matched individuals without tinnitus trained on the same auditory task. Auditory training was given using a 5 kHz 40-Hz amplitude-modulated sound that was in the tinnitus frequency region of the tinnitus subjects and enabled extraction of the 40-Hz auditory steady-state response (ASSR and P2 transient response known to localize to primary and nonprimary auditory cortex, respectively. P2 amplitude increased with training equally in participants with tinnitus and in control subjects, suggesting normal remodeling of nonprimary auditory regions in tinnitus. However, training-induced changes in the ASSR differed between the tinnitus and control groups. In controls ASSR phase advanced toward the stimulus waveform by about ten degrees over training, in agreement with previous results obtained in young normal hearing individuals. However, ASSR phase did not change significantly with training in the tinnitus group, although some participants showed phase shifts resembling controls. On the other hand, ASSR amplitude increased with training in the tinnitus group, whereas in controls this response (which is difficult to remodel in young normal hearing subjects did not change with training. These results suggest that neural changes related to tinnitus altered how neural plasticity was expressed in the region of primary but not nonprimary auditory cortex. Auditory training did not reduce tinnitus loudness although a small effect on the tinnitus spectrum was detected.

  2. Neural-Fitted TD-Leaf Learning for Playing Othello With Structured Neural Networks

    NARCIS (Netherlands)

    van den Dries, Sjoerd; Wiering, Marco A.

    2012-01-01

    This paper describes a methodology for quickly learning to play games at a strong level. The methodology consists of a novel combination of three techniques, and a variety of experiments on the game of Othello demonstrates their usefulness. First, structures or topologies in neural network

  3. Vocal learning in elephants: neural bases and adaptive context.

    Science.gov (United States)

    Stoeger, Angela S; Manger, Paul

    2014-10-01

    In the last decade clear evidence has accumulated that elephants are capable of vocal production learning. Examples of vocal imitation are documented in African (Loxodonta africana) and Asian (Elephas maximus) elephants, but little is known about the function of vocal learning within the natural communication systems of either species. We are also just starting to identify the neural basis of elephant vocalizations. The African elephant diencephalon and brainstem possess specializations related to aspects of neural information processing in the motor system (affecting the timing and learning of trunk movements) and the auditory and vocalization system. Comparative interdisciplinary (from behavioral to neuroanatomical) studies are strongly warranted to increase our understanding of both vocal learning and vocal behavior in elephants. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

  4. Genetic learning in rule-based and neural systems

    Science.gov (United States)

    Smith, Robert E.

    1993-01-01

    The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.

  5. Development of neural mechanisms for machine learning.

    Science.gov (United States)

    Arsenio, Artur M

    2005-01-01

    The goal of this work is to develop a humanoid robot's perceptual mechanisms through the use of learning aids. We describe methods to enable learning on a humanoid robot using learning aids such as books, drawing materials, boards, educational videos or other children toys. Visual properties of objects are learned and inserted into a recognition scheme, which is then applied to acquire new object representations - we propose learning through developmental stages. Inspired in infant development, we will also boost the robot's perceptual capabilities by having a human caregiver performing educational and play activities with the robot (such as drawing, painting or playing with a toy train on a railway). We describe original algorithms to extract meaningful percepts from such learning experiments. Experimental evaluation of the algorithms corroborates the theoretical framework.

  6. Functional consequences of experience-dependent plasticity on tactile perception following perceptual learning

    Science.gov (United States)

    Trzcinski, Natalie K; Gomez-Ramirez, Manuel; Hsiao, Steven S.

    2016-01-01

    Continuous training enhances perceptual discrimination and promotes neural changes in areas encoding the experienced stimuli. This type of experience-dependent plasticity has been demonstrated in several sensory and motor systems. Particularly, non-human primates trained to detect consecutive tactile bar indentations across multiple digits showed expanded excitatory receptive fields (RFs) in somatosensory cortex. However, the perceptual implications of these anatomical changes remain undetermined. Here, we trained human participants for nine days on a tactile task that promoted expansion of multi-digit RFs. Participants were required to detect consecutive indentations of bar stimuli spanning multiple digits. Throughout the training regime we tracked participants’ discrimination thresholds on spatial (grating orientation) and temporal tasks on the trained and untrained hands in separate sessions. We hypothesized that training on the multi-digit task would decrease perceptual thresholds on tasks that require stimulus processing across multiple digits, while also increasing thresholds on tasks requiring discrimination on single digits. We observed an increase in orientation thresholds on a single-digit. Importantly, this effect was selective for the stimulus orientation and hand used during multi-digit training. We also found that temporal acuity between digits improved across trained digits, suggesting that discriminating the temporal order of multi-digit stimuli can transfer to temporal discrimination of other tactile stimuli. These results suggest that experience-dependent plasticity following perceptual learning improves and interferes with tactile abilities in manners predictive of the task and stimulus features used during training. PMID:27422224

  7. Thermodynamic efficiency of learning a rule in neural networks

    Science.gov (United States)

    Goldt, Sebastian; Seifert, Udo

    2017-11-01

    Biological systems have to build models from their sensory input data that allow them to efficiently process previously unseen inputs. Here, we study a neural network learning a binary classification rule for these inputs from examples provided by a teacher. We analyse the ability of the network to apply the rule to new inputs, that is to generalise from past experience. Using stochastic thermodynamics, we show that the thermodynamic costs of the learning process provide an upper bound on the amount of information that the network is able to learn from its teacher for both batch and online learning. This allows us to introduce a thermodynamic efficiency of learning. We analytically compute the dynamics and the efficiency of a noisy neural network performing online learning in the thermodynamic limit. In particular, we analyse three popular learning algorithms, namely Hebbian, Perceptron and AdaTron learning. Our work extends the methods of stochastic thermodynamics to a new type of learning problem and might form a suitable basis for investigating the thermodynamics of decision-making.

  8. Competitive Learning Neural Network Ensemble Weighted by Predicted Performance

    Science.gov (United States)

    Ye, Qiang

    2010-01-01

    Ensemble approaches have been shown to enhance classification by combining the outputs from a set of voting classifiers. Diversity in error patterns among base classifiers promotes ensemble performance. Multi-task learning is an important characteristic for Neural Network classifiers. Introducing a secondary output unit that receives different…

  9. A Neural Mechanism for Background Information-Gated Learning Based on Axonal-Dendritic Overlaps

    Science.gov (United States)

    Mainetti, Matteo; Ascoli, Giorgio A.

    2015-01-01

    Experiencing certain events triggers the acquisition of new memories. Although necessary, however, actual experience is not sufficient for memory formation. One-trial learning is also gated by knowledge of appropriate background information to make sense of the experienced occurrence. Strong neurobiological evidence suggests that long-term memory storage involves formation of new synapses. On the short time scale, this form of structural plasticity requires that the axon of the pre-synaptic neuron be physically proximal to the dendrite of the post-synaptic neuron. We surmise that such “axonal-dendritic overlap” (ADO) constitutes the neural correlate of background information-gated (BIG) learning. The hypothesis is based on a fundamental neuroanatomical constraint: an axon must pass close to the dendrites that are near other neurons it contacts. The topographic organization of the mammalian cortex ensures that nearby neurons encode related information. Using neural network simulations, we demonstrate that ADO is a suitable mechanism for BIG learning. We model knowledge as associations between terms, concepts or indivisible units of thought via directed graphs. The simplest instantiation encodes each concept by single neurons. Results are then generalized to cell assemblies. The proposed mechanism results in learning real associations better than spurious co-occurrences, providing definitive cognitive advantages. PMID:25767887

  10. Unsupervised Learning of Digit Recognition Using Spike-Timing-Dependent Plasticity

    Directory of Open Access Journals (Sweden)

    Peter U. Diehl

    2015-08-01

    Full Text Available In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in recent years there is an increasing interest in how spiking neural networks (SNN can be used to perform complex computations or solve pattern recognition tasks. However, it remains a challenging task to design SNNs which use biologically plausible mechanisms (especially for learning new patterns, since most of such SNN architectures rely on training in a rate-based network and subsequent conversion to a SNN. We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e. conductance-based instead of current-based synapses, spike-timing-dependent plasticity with time-dependent weight change, lateral inhibition, and an adaptive spiking threshold. Unlike most other systems, we do not use a teaching signal and do not present any class labels to the network. Using this unsupervised learning scheme, our architecture achieves 95% accuracy on the MNIST benchmark, which is better than previous SNN implementations without supervision. The fact that we used no domain-specific knowledge points toward the general applicability of our network design. Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks.

  11. Continuous Online Sequence Learning with an Unsupervised Neural Network Model.

    Science.gov (United States)

    Cui, Yuwei; Ahmad, Subutar; Hawkins, Jeff

    2016-09-14

    The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory recently has been proposed as a theoretical framework for sequence learning in the cortex. In this letter, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variableorder temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods: autoregressive integrated moving average; feedforward neural networks-time delay neural network and online sequential extreme learning machine; and recurrent neural networks-long short-term memory and echo-state networks on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high-order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyperparameter tuning. Therefore, the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real-world sequence learning problems from continuous data streams.

  12. Tracking neural correlates of successful learning over repeated sequence observations

    Science.gov (United States)

    Steinemann, Natalie A.; Moisello, Clara; Ghilardi, M. Felice; Kelly, Simon P.

    2016-01-01

    The neural correlates of memory formation in humans have long been investigated by exposing subjects to diverse material and comparing responses to items later remembered to those forgotten. Tasks requiring memorization of sensory sequences afford unique possibilities for linking neural memorization processes to behavior, because, rather than comparing across different items of varying content, each individual item can be examined across the successive learning states of being initially unknown, newly learned, and eventually, fully known. Sequence learning paradigms have not yet been exploited in this way, however. Here, we analyze the event-related potentials of subjects attempting to memorize sequences of visual locations over several blocks of repeated observation, with respect to pre- and post-block recall tests. Over centro-parietal regions, we observed a rapid P300 component superimposed on a broader positivity, which exhibited distinct modulations across learning states that were replicated in two separate experiments. Consistent with its well-known encoding of surprise, the P300 deflection monotonically decreased over blocks as locations became better learned and hence more expected. In contrast, the broader positivity was especially elevated at the point when a given item was newly learned, i.e., started being successfully recalled. These results implicate the Broad Positivity in endogenously-driven, intentional memory formation, whereas the P300, in processing the current stimulus to the degree that it was previously uncertain, indexes the cumulative knowledge thereby gained. The decreasing surprise/P300 effect significantly predicted learning success both across blocks and across subjects. This presents a new, neural-based means to evaluate learning capabilities independent of verbal reports, which could have considerable value in distinguishing genuine learning disabilities from difficulties to communicate the outcomes of learning, or perceptual

  13. Performance Enhancement at the Cost of Potential Brain Plasticity: Neural Ramifications of Nootropic Drugs in the Healthy Developing Brain

    Directory of Open Access Journals (Sweden)

    Kimberly R. Urban

    2014-05-01

    Full Text Available Cognitive enhancement is perhaps one of the most intriguing and controversial topics in neuroscience today. Currently, the main classes of drugs used as potential cognitive enhancers include psychostimulants (methylphenidate, amphetamine, but wakefulness-promoting agents (modafinil and glutamate activators (ampakine are also frequently used. Pharmacologically, substances that enhance the components of the memory/learning circuits - dopamine, glutamate (neuronal excitation, and/or norepinephrine - stand to improve brain function in healthy individuals beyond their baseline functioning. In particular, non-medical use of prescription stimulants such as methylphenidate and illicit use of psychostimulants for cognitive enhancement have seen a recent rise among teens and young adults in schools and college campuses. However, this enhancement likely comes with a neuronal, as well as ethical, cost. Altering glutamate function via the use of psychostimulants may impair behavioral flexibility, leading to the development and/or potentiation of addictive behaviors. Furthermore, dopamine and norepinephrine do not display linear effects; instead, their modulation of cognitive and neuronal function maps on an inverted-U curve. Healthy individuals run the risk of pushing themselves beyond optimal levels into hyperdopaminergic and hypernoradrenergic states, thus vitiating the very behaviors they are striving to improve. Finally, recent studies have begun to highlight potential damaging effects of stimulant exposure in healthy juveniles. This review explains how the main classes of cognitive enhancing drugs affect the learning and memory circuits, and highlights the potential risks and concerns in healthy individuals, particularly juveniles and adolescents. We emphasize the performance enhancement at the potential cost of brain plasticity that is associated with the neural ramifications of nootropic drugs in the healthy developing brain.

  14. Performance enhancement at the cost of potential brain plasticity: neural ramifications of nootropic drugs in the healthy developing brain.

    Science.gov (United States)

    Urban, Kimberly R; Gao, Wen-Jun

    2014-01-01

    Cognitive enhancement is perhaps one of the most intriguing and controversial topics in neuroscience today. Currently, the main classes of drugs used as potential cognitive enhancers include psychostimulants (methylphenidate (MPH), amphetamine), but wakefulness-promoting agents (modafinil) and glutamate activators (ampakine) are also frequently used. Pharmacologically, substances that enhance the components of the memory/learning circuits-dopamine, glutamate (neuronal excitation), and/or norepinephrine-stand to improve brain function in healthy individuals beyond their baseline functioning. In particular, non-medical use of prescription stimulants such as MPH and illicit use of psychostimulants for cognitive enhancement have seen a recent rise among teens and young adults in schools and college campuses. However, this enhancement likely comes with a neuronal, as well as ethical, cost. Altering glutamate function via the use of psychostimulants may impair behavioral flexibility, leading to the development and/or potentiation of addictive behaviors. Furthermore, dopamine and norepinephrine do not display linear effects; instead, their modulation of cognitive and neuronal function maps on an inverted-U curve. Healthy individuals run the risk of pushing themselves beyond optimal levels into hyperdopaminergic and hypernoradrenergic states, thus vitiating the very behaviors they are striving to improve. Finally, recent studies have begun to highlight potential damaging effects of stimulant exposure in healthy juveniles. This review explains how the main classes of cognitive enhancing drugs affect the learning and memory circuits, and highlights the potential risks and concerns in healthy individuals, particularly juveniles and adolescents. We emphasize the performance enhancement at the potential cost of brain plasticity that is associated with the neural ramifications of nootropic drugs in the healthy developing brain.

  15. Robust adaptive learning of feedforward neural networks via LMI optimizations.

    Science.gov (United States)

    Jing, Xingjian

    2012-07-01

    Feedforward neural networks (FNNs) have been extensively applied to various areas such as control, system identification, function approximation, pattern recognition etc. A novel robust control approach to the learning problems of FNNs is further investigated in this study in order to develop efficient learning algorithms which can be implemented with optimal parameter settings and considering noise effect in the data. To this aim, the learning problem of a FNN is cast into a robust output feedback control problem of a discrete time-varying linear dynamic system. New robust learning algorithms with adaptive learning rate are therefore developed, using linear matrix inequality (LMI) techniques to find the appropriate learning rates and to guarantee the fast and robust convergence. Theoretical analysis and examples are given to illustrate the theoretical results. Copyright © 2012 Elsevier Ltd. All rights reserved.

  16. Neural Network Machine Learning and Dimension Reduction for Data Visualization

    Science.gov (United States)

    Liles, Charles A.

    2014-01-01

    Neural network machine learning in computer science is a continuously developing field of study. Although neural network models have been developed which can accurately predict a numeric value or nominal classification, a general purpose method for constructing neural network architecture has yet to be developed. Computer scientists are often forced to rely on a trial-and-error process of developing and improving accurate neural network models. In many cases, models are constructed from a large number of input parameters. Understanding which input parameters have the greatest impact on the prediction of the model is often difficult to surmise, especially when the number of input variables is very high. This challenge is often labeled the "curse of dimensionality" in scientific fields. However, techniques exist for reducing the dimensionality of problems to just two dimensions. Once a problem's dimensions have been mapped to two dimensions, it can be easily plotted and understood by humans. The ability to visualize a multi-dimensional dataset can provide a means of identifying which input variables have the highest effect on determining a nominal or numeric output. Identifying these variables can provide a better means of training neural network models; models can be more easily and quickly trained using only input variables which appear to affect the outcome variable. The purpose of this project is to explore varying means of training neural networks and to utilize dimensional reduction for visualizing and understanding complex datasets.

  17. Effective learning in recurrent max-min neural networks.

    Science.gov (United States)

    Loe, Kia Fock; Teow, Loo Nin

    1998-04-01

    Max and min operations have interesting properties that facilitate the exchange of information between the symbolic and real-valued domains. As such, neural networks that employ max-min activation functions have been a subject of interest in recent years. Since max-min functions are not strictly differentiable, we propose a mathematically sound learning method based on using Fourier convergence analysis of side-derivatives to derive a gradient descent technique for max-min error functions. We then propose a novel recurrent max-min neural network model that is trained to perform grammatical inference as an application example. Comparisons made between this model and recurrent sigmoidal neural networks show that our model not only performs better in terms of learning speed and generalization, but that its final weight configuration allows a deterministic finite automation (DFA) to be extracted in a straightforward manner. In essence, we are able to demonstrate that our proposed gradient descent technique does allow max-min neural networks to learn effectively.

  18. Learning strategy trumps motivational level in determining learning-induced auditory cortical plasticity.

    Science.gov (United States)

    Bieszczad, Kasia M; Weinberger, Norman M

    2010-02-01

    Associative memory for auditory-cued events involves specific plasticity in the primary auditory cortex (A1) that facilitates responses to tones which gain behavioral significance, by modifying representational parameters of sensory coding. Learning strategy, rather than the amount or content of learning, can determine this learning-induced cortical (high order) associative representational plasticity (HARP). Thus, tone-contingent learning with signaled errors can be accomplished either by (1) responding only during tone duration ("tone-duration" strategy, T-Dur), or (2) responding from tone onset until receiving an error signal for responses made immediately after tone offset ("tone-onset-to-error", TOTE). While rats using both strategies achieve the same high level of performance, only those using the TOTE strategy develop HARP, viz., frequency-specific decreased threshold (increased sensitivity) and decreased bandwidth (increased selectivity) (Berlau & Weinberger, 2008). The present study challenged the generality of learning strategy by determining if high motivation dominates in the formation of HARP. Two groups of adult male rats were trained to bar-press during a 5.0kHz (10s, 70dB) tone for a water reward under either high (HiMot) or moderate (ModMot) levels of motivation. The HiMot group achieved a higher level of correct performance. However, terminal mapping of A1 showed that only the ModMot group developed HARP, i.e., increased sensitivity and selectivity in the signal-frequency band. Behavioral analysis revealed that the ModMot group used the TOTE strategy while HiMot subjects used the T-Dur strategy. Thus, type of learning strategy, not level of learning or motivation, is dominant for the formation of cortical plasticity. Copyright 2009 Elsevier Inc. All rights reserved.

  19. The neural correlates of speech motor sequence learning.

    Science.gov (United States)

    Segawa, Jennifer A; Tourville, Jason A; Beal, Deryk S; Guenther, Frank H

    2015-04-01

    Speech is perhaps the most sophisticated example of a species-wide movement capability in the animal kingdom, requiring split-second sequencing of approximately 100 muscles in the respiratory, laryngeal, and oral movement systems. Despite the unique role speech plays in human interaction and the debilitating impact of its disruption, little is known about the neural mechanisms underlying speech motor learning. Here, we studied the behavioral and neural correlates of learning new speech motor sequences. Participants repeatedly produced novel, meaningless syllables comprising illegal consonant clusters (e.g., GVAZF) over 2 days of practice. Following practice, participants produced the sequences with fewer errors and shorter durations, indicative of motor learning. Using fMRI, we compared brain activity during production of the learned illegal sequences and novel illegal sequences. Greater activity was noted during production of novel sequences in brain regions linked to non-speech motor sequence learning, including the BG and pre-SMA. Activity during novel sequence production was also greater in brain regions associated with learning and maintaining speech motor programs, including lateral premotor cortex, frontal operculum, and posterior superior temporal cortex. Measures of learning success correlated positively with activity in left frontal operculum and white matter integrity under left posterior superior temporal sulcus. These findings indicate speech motor sequence learning relies not only on brain areas involved generally in motor sequencing learning but also those associated with feedback-based speech motor learning. Furthermore, learning success is modulated by the integrity of structural connectivity between these motor and sensory brain regions.

  20. Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.

    Science.gov (United States)

    Shim, Yoonsik; Philippides, Andrew; Staras, Kevin; Husbands, Phil

    2016-10-01

    We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.

  1. Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.

    Directory of Open Access Journals (Sweden)

    Yoonsik Shim

    2016-10-01

    Full Text Available We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP. The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.

  2. The E3 ubiquitin ligase IDOL regulates synaptic ApoER2 levels and is important for plasticity and learning.

    Science.gov (United States)

    Gao, Jie; Marosi, Mate; Choi, Jinkuk; Achiro, Jennifer M; Kim, Sangmok; Li, Sandy; Otis, Klara; Martin, Kelsey C; Portera-Cailliau, Carlos; Tontonoz, Peter

    2017-09-11

    Neuronal ApoE receptors are linked to learning and memory, but the pathways governing their abundance, and the mechanisms by which they affect the function of neural circuits are incompletely understood. Here we demonstrate that the E3 ubiquitin ligase IDOL determines synaptic ApoER2 protein levels in response to neuronal activation and regulates dendritic spine morphogenesis and plasticity. IDOL-dependent changes in ApoER2 abundance modulate dendritic filopodia initiation and synapse maturation. Loss of IDOL in neurons results in constitutive overexpression of ApoER2 and is associated with impaired activity-dependent structural remodeling of spines and defective LTP in primary neuron cultures and hippocampal slices. IDOL-deficient mice show profound impairment in experience-dependent reorganization of synaptic circuits in the barrel cortex, as well as diminished spatial and associative learning. These results identify control of lipoprotein receptor abundance by IDOL as a post-transcriptional mechanism underlying the structural and functional plasticity of synapses and neural circuits.

  3. Regularized negative correlation learning for neural network ensembles.

    Science.gov (United States)

    Chen, Huanhuan; Yao, Xin

    2009-12-01

    Negative correlation learning (NCL) is a neural network ensemble learning algorithm that introduces a correlation penalty term to the cost function of each individual network so that each neural network minimizes its mean square error (MSE) together with the correlation of the ensemble. This paper analyzes NCL and reveals that the training of NCL (when lambda = 1) corresponds to training the entire ensemble as a single learning machine that only minimizes the MSE without regularization. This analysis explains the reason why NCL is prone to overfitting the noise in the training set. This paper also demonstrates that tuning the correlation parameter lambda in NCL by cross validation cannot overcome the overfitting problem. The paper analyzes this problem and proposes the regularized negative correlation learning (RNCL) algorithm which incorporates an additional regularization term for the whole ensemble. RNCL decomposes the ensemble's training objectives, including MSE and regularization, into a set of sub-objectives, and each sub-objective is implemented by an individual neural network. In this paper, we also provide a Bayesian interpretation for RNCL and provide an automatic algorithm to optimize regularization parameters based on Bayesian inference. The RNCL formulation is applicable to any nonlinear estimator minimizing the MSE. The experiments on synthetic as well as real-world data sets demonstrate that RNCL achieves better performance than NCL, especially when the noise level is nontrivial in the data set.

  4. The molecular evidence of neural plasticity induced by cerebellar repetitive transcranial magnetic stimulation in the rat brain: a preliminary report.

    Science.gov (United States)

    Lee, Seung Ah; Oh, Byung-Mo; Kim, Sang Jeong; Paik, Nam-Jong

    2014-07-11

    Cerebellar repetitive transcranial magnetic stimulation (rTMS) has been applied to treat several pathological conditions with insufficient evidence of molecular mechanism. Neural plasticity is proposed as one of mechanism. This study aimed to (1) confirm the feasibility of focal stimulation over cerebellar cortex and (2) investigate cerebellar rTMS effects on molecular changes associated with neural plasticity in the rat. For feasibility, six male Sprague-Dawley rats underwent (18)F-FDG positron emission tomography (PET) to confirm focal stimulation on the cerebellar cortex after rTMS. For molecular evidence, thirty rats underwent a single (N=15) or 10 sessions (N=15) of rTMS with low-, high-frequency, or sham stimulation. In cerebellar cortex, reverse-transcriptase polymerase chain reaction and western blotting were performed on mRNA and proteins associated with neural plasticity: metabotrophic glutamate receptor 1 (GluR1), 2-amino-5-methyl-4-isoxazole-propionatic acid (AMPA) receptor (GluR2) and protein kinase C (PKC). As a result, (18)F-FDG-PET showed an increase of glucose metabolism in the cerebellar cortex. The transcription of mGluR1 decreased following a single session of high-frequency rTMS. Synthesis of mGluR, PKC and GluR2 was reduced after rTMS, especially high frequency stimulation. It is suggested that rTMS could focus on the cerebellar cortex in the rat and induce neural plasticity associated with long-term depression. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  5. Statistical learning of parts and wholes: A neural network approach.

    Science.gov (United States)

    Plaut, David C; Vande Velde, Anna K

    2017-03-01

    Statistical learning is often considered to be a means of discovering the units of perception, such as words and objects, and representing them as explicit "chunks." However, entities are not undifferentiated wholes but often contain parts that contribute systematically to their meanings. Studies of incidental auditory or visual statistical learning suggest that, as participants learn about wholes they become insensitive to parts embedded within them, but this seems difficult to reconcile with a broad range of findings in which parts and wholes work together to contribute to behavior. Bayesian approaches provide a principled description of how parts and wholes can contribute simultaneously to performance, but are generally not intended to model the computations that actually give rise to this performance. In the current work, we develop an account based on learning in artificial neural networks in which the representation of parts and wholes is a matter of degree, and the extent to which they cooperate or compete arises naturally through incidental learning. We show that the approach accounts for a wide range of findings concerning the relationship between parts and wholes in auditory and visual statistical learning, including some findings previously thought to be problematic for neural network approaches. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  6. Projection learning algorithm for threshold - controlled neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Reznik, A.M.

    1995-03-01

    The projection learning algorithm proposed in [1, 2] and further developed in [3] substantially improves the efficiency of memorizing information and accelerates the learning process in neural networks. This algorithm is compatible with the completely connected neural network architecture (the Hopfield network [4]), but its application to other networks involves a number of difficulties. The main difficulties include constraints on interconnection structure and the need to eliminate the state uncertainty of latent neurons if such are present in the network. Despite the encouraging preliminary results of [3], further extension of the applications of the projection algorithm therefore remains problematic. In this paper, which is a continuation of the work begun in [3], we consider threshold-controlled neural networks. Networks of this type are quite common. They represent the receptor neuron layers in some neurocomputer designs. A similar structure is observed in the lower divisions of biological sensory systems [5]. In multilayer projection neural networks with lateral interconnections, the neuron layers or parts of these layers may also have the structure of a threshold-controlled completely connected network. Here the thresholds are the potentials delivered through the projection connections from other parts of the network. The extension of the projection algorithm to the class of threshold-controlled networks may accordingly prove to be useful both for extending its technical applications and for better understanding of the operation of the nervous system in living organisms.

  7. Media Multitasking and Cognitive, Psychological, Neural, and Learning Differences.

    Science.gov (United States)

    Uncapher, Melina R; Lin, Lin; Rosen, Larry D; Kirkorian, Heather L; Baron, Naomi S; Bailey, Kira; Cantor, Joanne; Strayer, David L; Parsons, Thomas D; Wagner, Anthony D

    2017-11-01

    American youth spend more time with media than any other waking activity: an average of 7.5 hours per day, every day. On average, 29% of that time is spent juggling multiple media streams simultaneously (ie, media multitasking). This phenomenon is not limited to American youth but is paralleled across the globe. Given that a large number of media multitaskers (MMTs) are children and young adults whose brains are still developing, there is great urgency to understand the neurocognitive profiles of MMTs. It is critical to understand the relation between the relevant cognitive domains and underlying neural structure and function. Of equal importance is understanding the types of information processing that are necessary in 21st century learning environments. The present review surveys the growing body of evidence demonstrating that heavy MMTs show differences in cognition (eg, poorer memory), psychosocial behavior (eg, increased impulsivity), and neural structure (eg, reduced volume in anterior cingulate cortex). Furthermore, research indicates that multitasking with media during learning (in class or at home) can negatively affect academic outcomes. Until the direction of causality is understood (whether media multitasking causes such behavioral and neural differences or whether individuals with such differences tend to multitask with media more often), the data suggest that engagement with concurrent media streams should be thoughtfully considered. Findings from such research promise to inform policy and practice on an increasingly urgent societal issue while significantly advancing our understanding of the intersections between cognitive, psychosocial, neural, and academic factors. Copyright © 2017 by the American Academy of Pediatrics.

  8. Behavioral and neural plasticity caused by early social experiences: the case of the honeybee.

    Science.gov (United States)

    Arenas, Andrés; Ramírez, Gabriela P; Balbuena, María Sol; Farina, Walter M

    2013-01-01

    Cognitive experiences during the early stages of life play an important role in shaping future behavior. Behavioral and neural long-term changes after early sensory and associative experiences have been recently reported in the honeybee. This invertebrate is an excellent model for assessing the role of precocious experiences on later behavior due to its extraordinarily tuned division of labor based on age polyethism. These studies are mainly focused on the role and importance of experiences occurred during the first days of the adult lifespan, their impact on foraging decisions, and their contribution to coordinate food gathering. Odor-rewarded experiences during the first days of honeybee adulthood alter the responsiveness to sucrose, making young hive bees more sensitive to assess gustatory features about the nectar brought back to the hive and affecting the dynamic of the food transfers and the propagation of food-related information within the colony. Early olfactory experiences lead to stable and long-term associative memories that can be successfully recalled after many days, even at foraging ages. Also they improve memorizing of new associative learning events later in life. The establishment of early memories promotes stable reorganization of the olfactory circuits inducing structural and functional changes in the antennal lobe (AL). Early rewarded experiences have relevant consequences at the social level too, biasing dance and trophallaxis partner choice and affecting recruitment. Here, we revised recent results in bees' physiology, behavior, and sociobiology to depict how the early experiences affect their cognition abilities and neural-related circuits.

  9. A Novel Learning Scheme for Chebyshev Functional Link Neural Networks

    Directory of Open Access Journals (Sweden)

    Satchidananda Dehuri

    2011-01-01

    dimensional-space where linear separability is possible. Moreover, the proposed HCFLNN combines the best attribute of particle swarm optimization (PSO, back propagation learning (BP learning, and functional link neural networks (FLNNs. The proposed method eliminates the need of hidden layer by expanding the input patterns using Chebyshev orthogonal polynomials. We have shown its effectiveness of classifying the unknown pattern using the publicly available datasets obtained from UCI repository. The computational results are then compared with functional link neural network (FLNN with a generic basis functions, PSO-based FLNN, and EFLN. From the comparative study, we observed that the performance of the HCFLNN outperforms FLNN, PSO-based FLNN, and EFLN in terms of classification accuracy.

  10. Bio-Inspired Neural Model for Learning Dynamic Models

    Science.gov (United States)

    Duong, Tuan; Duong, Vu; Suri, Ronald

    2009-01-01

    A neural-network mathematical model that, relative to prior such models, places greater emphasis on some of the temporal aspects of real neural physical processes, has been proposed as a basis for massively parallel, distributed algorithms that learn dynamic models of possibly complex external processes by means of learning rules that are local in space and time. The algorithms could be made to perform such functions as recognition and prediction of words in speech and of objects depicted in video images. The approach embodied in this model is said to be "hardware-friendly" in the following sense: The algorithms would be amenable to execution by special-purpose computers implemented as very-large-scale integrated (VLSI) circuits that would operate at relatively high speeds and low power demands.

  11. The neural cell adhesion molecule-derived peptide FGL facilitates long-term plasticity in the dentate gyrus in vivo

    DEFF Research Database (Denmark)

    Dallérac, Glenn; Zerwas, Meike; Novikova, Tatiana

    2011-01-01

    The neural cell adhesion molecule (NCAM) is known to play a role in developmental and structural processes but also in synaptic plasticity and memory of the adult animal. Recently, FGL, a NCAM mimetic peptide that binds to the Fibroblast Growth Factor Receptor 1 (FGFR-1), has been shown to have...... and maintenance of synaptic plasticity in the dentate gyrus (DG) in vivo. For this, we first assessed the effect of the FGL peptide on synaptic functions at perforant path-dentate gyrus synapses in the anesthetized rat. FGL, or its control inactive peptide, was injected locally 60 min before applying high......-frequency stimulation (HFS) to the medial perforant path. The results suggest that although FGL did not alter basal synaptic transmission, it facilitated both the induction and maintenance of LTP. Interestingly, FGL also modified the heterosynaptic plasticity observed at the neighboring lateral perforant path synapses...

  12. Dynamic transcriptional signature and cell fate analysis reveals plasticity of individual neural plate border cells.

    Science.gov (United States)

    Roellig, Daniela; Tan-Cabugao, Johanna; Esaian, Sevan; Bronner, Marianne E

    2017-03-29

    The 'neural plate border' of vertebrate embryos contains precursors of neural crest and placode cells, both defining vertebrate characteristics. How these lineages segregate from neural and epidermal fates has been a matter of debate. We address this by performing a fine-scale quantitative temporal analysis of transcription factor expression in the neural plate border of chick embryos. The results reveal significant overlap of transcription factors characteristic of multiple lineages in individual border cells from gastrula through neurula stages. Cell fate analysis using a Sox2 (neural) enhancer reveals that cells that are initially Sox2+ cells can contribute not only to neural tube but also to neural crest and epidermis. Moreover, modulating levels of Sox2 or Pax7 alters the apportionment of neural tube versus neural crest fates. Our results resolve a long-standing question and suggest that many individual border cells maintain ability to contribute to multiple ectodermal lineages until or beyond neural tube closure.

  13. Neural correlates of learning to attend.

    Science.gov (United States)

    Kelley, Todd A; Yantis, Steven

    2010-01-01

    Recent work has shown that training can improve attentional focus. Little is known, however, about how training in attention and multitasking affects the brain. We used functional magnetic resonance imaging (fMRI) to measure changes in cortical responses to distracting stimuli during training on a visual categorization task. Training led to a reduction in behavioral distraction effects, and these improvements in performance generalized to untrained conditions. Although large regions of early visual and posterior parietal cortices responded to the presence of distractors, these regions did not exhibit significant changes in their response following training. In contrast, middle frontal gyrus did exhibit decreased distractor-related responses with practice, showing the same trend as behavior for previously observed distractor locations. However, the neural response in this region diverged from behavior for novel distractor locations, showing greater activity. We conclude that training did not change the robustness of the initial sensory response, but led to increased efficiency in late-stage filtering in the trained conditions.

  14. Neural correlates of learning to attend

    Directory of Open Access Journals (Sweden)

    Todd A Kelley

    2010-11-01

    Full Text Available Recent work has shown that training can improve attentional focus. Little is known, however, about how training in attention and multitasking affects the brain. We used functional magnetic resonance imaging (fMRI to measure changes in cortical responses to distracting stimuli during training on a visual categorization task. Training led to a reduction in behavioural distraction effects, and these improvements in performance generalized to untrained conditions. Although large regions of early visual and posterior parietal cortices responded to the presence of distractors, these regions did not exhibit significant changes in their response following training. In contrast, middle frontal gyrus did exhibit decreased distractor-related responses with practice, showing the same trend as behaviour for previously observed distractor locations. However, the neural response in this region diverged from behaviour for novel distractor locations, showing greater activity. We conclude that training did not change the robustness of the initial sensory response, but led to increased efficiency in late-stage filtering in the trained conditions.

  15. Neural systems for choice and valuation with counterfactual learning signals.

    Science.gov (United States)

    Tobia, M J; Guo, R; Schwarze, U; Boehmer, W; Gläscher, J; Finckh, B; Marschner, A; Büchel, C; Obermayer, K; Sommer, T

    2014-04-01

    The purpose of this experiment was to test a computational model of reinforcement learning with and without fictive prediction error (FPE) signals to investigate how counterfactual consequences contribute to acquired representations of action-specific expected value, and to determine the functional neuroanatomy and neuromodulator systems that are involved. 80 male participants underwent dietary depletion of either tryptophan or tyrosine/phenylalanine to manipulate serotonin (5HT) and dopamine (DA), respectively. They completed 80 rounds (240 trials) of a strategic sequential investment task that required accepting interim losses in order to access a lucrative state and maximize long-term gains, while being scanned. We extended the standard Q-learning model by incorporating both counterfactual gains and losses into separate error signals. The FPE model explained the participants' data significantly better than a model that did not include counterfactual learning signals. Expected value from the FPE model was significantly correlated with BOLD signal change in the ventromedial prefrontal cortex (vmPFC) and posterior orbitofrontal cortex (OFC), whereas expected value from the standard model did not predict changes in neural activity. The depletion procedure revealed significantly different neural responses to expected value in the vmPFC, caudate, and dopaminergic midbrain in the vicinity of the substantia nigra (SN). Differences in neural activity were not evident in the standard Q-learning computational model. These findings demonstrate that FPE signals are an important component of valuation for decision making, and that the neural representation of expected value incorporates cortical and subcortical structures via interactions among serotonergic and dopaminergic modulator systems. Copyright © 2013 Elsevier Inc. All rights reserved.

  16. The effects of cultural learning in populations of neural networks.

    Science.gov (United States)

    Curran, Dara; O'Riordan, Colm

    2007-01-01

    Population learning can be described as the iterative Darwinian process of fitness-based selection and genetic transfer of information leading to populations of higher fitness and is often simulated using genetic algorithms. Cultural learning describes the process of information transfer between individuals in a population through non-genetic means. Cultural learning has been simulated by combining genetic algorithms and neural networks using a teacher-pupil scenario where highly fit individuals are selected as teachers and instruct the next generation. By examining the innate fitness of a population (i.e., the fitness of the population measured before any cultural learning takes place), it is possible to examine the effects of cultural learning on the population's genetic makeup. Our model explores the effect of cultural learning on a population and employs three benchmark sequential decision tasks as the evolutionary task for the population: connect-four, tic-tac-toe, and blackjack. Experiments are conducted with populations employing population learning alone and populations combining population and cultural learning. The article presents results showing the gradual transfer of knowledge from genes to the cultural process, illustrated by the simultaneous decrease in the population's innate fitness and the increase of its acquired fitness measured after learning takes place.

  17. Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks.

    Science.gov (United States)

    Emary, E; Zawbaa, Hossam M; Grosan, Crina

    2017-01-10

    In this paper, a variant of gray wolf optimization (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by reinforced learning, the common challenge of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate, which influences the performance of the algorithm. Rather than using a global way to change this parameter for all the agents, we use reinforcement learning to set it on an individual basis. The adaptation of the exploration rate for each agent depends on the agent's own experience and the current terrain of the search space. In order to achieve this, experience repository is built based on the neural network to map a set of agents' states to a set of corresponding actions that specifically influence the exploration rate. The experience repository is updated by all the search agents to reflect experience and to enhance the future actions continuously. The resulted algorithm is called experienced GWO (EGWO) and its performance is assessed on solving feature selection problems and on finding optimal weights for neural networks algorithm. We use a set of performance indicators to evaluate the efficiency of the method. Results over various data sets demonstrate an advance of the EGWO over the original GWO and over other metaheuristics, such as genetic algorithms and particle swarm optimization.

  18. NMDA receptors control cue-outcome selectivity and plasticity of orbitofrontal firing patterns during associative stimulus-reward learning.

    Science.gov (United States)

    van Wingerden, Marijn; Vinck, Martin; Tijms, Vincent; Ferreira, Irene R S; Jonker, Allert J; Pennartz, Cyriel M A

    2012-11-21

    Neural activity in orbitofrontal cortex has been linked to flexible representations of stimulus-outcome associations. Such value representations are known to emerge with learning, but the neural mechanisms supporting this phenomenon are not well understood. Here, we provide evidence for a causal role for NMDA receptors (NMDARs) in mediating spike pattern discriminability, neural plasticity, and rhythmic synchronization in relation to evaluative stimulus processing and decision making. Using tetrodes, single-unit spike trains and local field potentials were recorded during local, unilateral perfusion of an NMDAR blocker in rat OFC. In the absence of behavioral effects, NMDAR blockade severely hampered outcome-selective spike pattern formation to olfactory cues, relative to control perfusions. Moreover, NMDAR blockade shifted local rhythmic synchronization to higher frequencies and degraded its linkage to stimulus-outcome selective coding. These results demonstrate the importance of NMDARs for cue-outcome associative coding in OFC during learning and illustrate how NMDAR blockade disrupts network dynamics. Copyright © 2012 Elsevier Inc. All rights reserved.

  19. Short-term plasticity in the auditory system: differential neural responses to perception and imagery of speech and music.

    Science.gov (United States)

    Meyer, Martin; Elmer, Stefan; Baumann, Simon; Jancke, Lutz

    2007-01-01

    In this EEG study we sought to examine the neuronal underpinnings of short-term plasticity as a top-down guided auditory learning process. We hypothesized, that (i) auditory imagery should elicit proper auditory evoked effects (N1/P2 complex) and a late positive component (LPC). Generally, based on recent human brain mapping studies we expected (ii) to observe the involvement of different temporal and parietal lobe areas in imagery and in perception of acoustic stimuli. Furthermore we predicted (iii) that temporal regions show an asymmetric trend due to the different specialization of the temporal lobes in processing speech and non-speech sounds. Finally we sought evidence supporting the notion that short-term training is sufficient to drive top-down activity in brain regions that are not normally recruited by sensory induced bottom up processing. 18 non-musicians partook in a 30 channels based EEG session that investigated spatio-temporal dynamics of auditory imagery of "consonant-vowel" (CV) syllables and piano triads. To control for conditioning effects, we split the volunteers in two matched groups comprising the same conditions (visual, auditory or bimodal stimulation) presented in a slightly different serial order. Furthermore the study presents electromagnetic source localization (LORETA) of perception and imagery of CV- and piano stimuli. Our results imply that auditory imagery elicited similar electrophysiological effects at an early stage (N1/P2) as auditory stimulation. However, we found an additional LPC following the N1/P2 for auditory imagery only. Source estimation evinced bilateral engagement of anterior temporal cortex, which was generally stronger for imagery of music relative to imagery of speech. While we did not observe lateralized activity for the imagery of syllables we noted significantly increased rightward activation over the anterior supratemporal plane for musical imagery. Thus, we conclude that short-term top-down training based

  20. Learning speaker-specific characteristics with a deep neural architecture.

    Science.gov (United States)

    Chen, Ke; Salman, Ahmad

    2011-11-01

    Speech signals convey various yet mixed information ranging from linguistic to speaker-specific information. However, most of acoustic representations characterize all different kinds of information as whole, which could hinder either a speech or a speaker recognition (SR) system from producing a better performance. In this paper, we propose a novel deep neural architecture (DNA) especially for learning speaker-specific characteristics from mel-frequency cepstral coefficients, an acoustic representation commonly used in both speech recognition and SR, which results in a speaker-specific overcomplete representation. In order to learn intrinsic speaker-specific characteristics, we come up with an objective function consisting of contrastive losses in terms of speaker similarity/dissimilarity and data reconstruction losses used as regularization to normalize the interference of non-speaker-related information. Moreover, we employ a hybrid learning strategy for learning parameters of the deep neural networks: i.e., local yet greedy layerwise unsupervised pretraining for initialization and global supervised learning for the ultimate discriminative goal. With four Linguistic Data Consortium (LDC) benchmarks and two non-English corpora, we demonstrate that our overcomplete representation is robust in characterizing various speakers, no matter whether their utterances have been used in training our DNA, and highly insensitive to text and languages spoken. Extensive comparative studies suggest that our approach yields favorite results in speaker verification and segmentation. Finally, we discuss several issues concerning our proposed approach.

  1. Outsmarting neural networks: an alternative paradigm for machine learning

    Energy Technology Data Exchange (ETDEWEB)

    Protopopescu, V.; Rao, N.S.V.

    1996-10-01

    We address three problems in machine learning, namely: (i) function learning, (ii) regression estimation, and (iii) sensor fusion, in the Probably and Approximately Correct (PAC) framework. We show that, under certain conditions, one can reduce the three problems above to the regression estimation. The latter is usually tackled with artificial neural networks (ANNs) that satisfy the PAC criteria, but have high computational complexity. We propose several computationally efficient PAC alternatives to ANNs to solve the regression estimation. Thereby we also provide efficient PAC solutions to the function learning and sensor fusion problems. The approach is based on cross-fertilizing concepts and methods from statistical estimation, nonlinear algorithms, and the theory of computational complexity, and is designed as part of a new, coherent paradigm for machine learning.

  2. Neural basis of reinforcement learning and decision making.

    Science.gov (United States)

    Lee, Daeyeol; Seo, Hyojung; Jung, Min Whan

    2012-01-01

    Reinforcement learning is an adaptive process in which an animal utilizes its previous experience to improve the outcomes of future choices. Computational theories of reinforcement learning play a central role in the newly emerging areas of neuroeconomics and decision neuroscience. In this framework, actions are chosen according to their value functions, which describe how much future reward is expected from each action. Value functions can be adjusted not only through reward and penalty, but also by the animal's knowledge of its current environment. Studies have revealed that a large proportion of the brain is involved in representing and updating value functions and using them to choose an action. However, how the nature of a behavioral task affects the neural mechanisms of reinforcement learning remains incompletely understood. Future studies should uncover the principles by which different computational elements of reinforcement learning are dynamically coordinated across the entire brain.

  3. Neural Basis of Reinforcement Learning and Decision Making

    Science.gov (United States)

    Lee, Daeyeol; Seo, Hyojung; Jung, Min Whan

    2012-01-01

    Reinforcement learning is an adaptive process in which an animal utilizes its previous experience to improve the outcomes of future choices. Computational theories of reinforcement learning play a central role in the newly emerging areas of neuroeconomics and decision neuroscience. In this framework, actions are chosen according to their value functions, which describe how much future reward is expected from each action. Value functions can be adjusted not only through reward and penalty, but also by the animal’s knowledge of its current environment. Studies have revealed that a large proportion of the brain is involved in representing and updating value functions and using them to choose an action. However, how the nature of a behavioral task affects the neural mechanisms of reinforcement learning remains incompletely understood. Future studies should uncover the principles by which different computational elements of reinforcement learning are dynamically coordinated across the entire brain. PMID:22462543

  4. Are deep neural networks really learning relevant features?

    DEFF Research Database (Denmark)

    Kereliuk, Corey; Sturm, Bob L.; Larsen, Jan

    In recent years deep neural networks (DNNs) have become a popular choice for audio content analysis. This may be attributed to various factors including advancements in training algorithms, computational power, and the potential for DNNs to implicitly learn a set of feature detectors. We have...... recently re-examined two works \\cite{sigtiaimproved}\\cite{hamel2010learning} that consider DNNs for the task of music genre recognition (MGR). These papers conclude that frame-level features learned by DNNs offer an improvement over traditional, hand-crafted features such as Mel-frequency cepstrum...... leads one to question the degree to which the learned frame-level features are actually useful for MGR. We make available a reproducible software package allowing other researchers to completely duplicate our figures and results....

  5. Association of contextual cues with morphine reward increases neural and synaptic plasticity in the ventral hippocampus of rats.

    Science.gov (United States)

    Alvandi, Mina Sadighi; Bourmpoula, Maria; Homberg, Judith R; Fathollahi, Yaghoub

    2017-11-01

    Drug addiction is associated with aberrant memory and permanent functional changes in neural circuits. It is known that exposure to drugs like morphine is associated with positive emotional states and reward-related memory. However, the underlying mechanisms in terms of neural plasticity in the ventral hippocampus, a region involved in associative memory and emotional behaviors, are not fully understood. Therefore, we measured adult neurogenesis, dendritic spine density and brain-derived neurotrophic factor (BDNF) and TrkB mRNA expression as parameters for synaptic plasticity in the ventral hippocampus. Male Sprague Dawley rats were subjected to the CPP (conditioned place preference) paradigm and received 10 mg/kg morphine. Half of the rats were used to evaluate neurogenesis by immunohistochemical markers Ki67 and doublecortin (DCX). The other half was used for Golgi staining to measure spine density and real-time quantitative reverse transcription-polymerase chain reaction to assess BDNF/TrkB expression levels. We found that morphine-treated rats exhibited more place conditioning as compared with saline-treated rats and animals that were exposed to the CPP without any injections. Locomotor activity did not change significantly. Morphine-induced CPP significantly increased the number of Ki67 and DCX-labeled cells in the ventral dentate gyrus. Additionally, we found increased dendritic spine density in both CA1 and dentate gyrus and an enhancement of BDNF/TrkB mRNA levels in the whole ventral hippocampus. Ki67, DCX and spine density were significantly correlated with CPP scores. In conclusion, we show that morphine-induced reward-related memory is associated with neural and synaptic plasticity changes in the ventral hippocampus. Such neural changes could underlie context-induced drug relapse. © 2017 Society for the Study of Addiction.

  6. Transfer functions for protein signal transduction: application to a model of striatal neural plasticity.

    Directory of Open Access Journals (Sweden)

    Gabriele Scheler

    Full Text Available We present a novel formulation for biochemical reaction networks in the context of protein signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of "source" species, which are interpreted as input signals. Signals are transmitted to all other species in the system (the "target" species with a specific delay and with a specific transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and even recalled to build dynamical models on the basis of state changes. By separating the temporal and the magnitudinal domain we can greatly simplify the computational model, circumventing typical problems of complex dynamical systems. The transfer function transformation of biochemical reaction systems can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant novel insights while remaining a fully testable and executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modularizations that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. Remarkably, we found that overall interconnectedness depends on the magnitude of inputs, with higher connectivity at low input concentrations and significant modularization at moderate to high input concentrations. This general result, which directly follows from the properties of

  7. Examining neural plasticity and cognitive benefit through the unique lens of musical training.

    Science.gov (United States)

    Moreno, Sylvain; Bidelman, Gavin M

    2014-02-01

    Training programs aimed to alleviate or improve auditory-cognitive abilities have either experienced mixed success or remain to be fully validated. The limited benefits of such regimens are largely attributable to our weak understanding of (i) how (and which) interventions provide the most robust and long lasting improvements to cognitive and perceptual abilities and (ii) how the neural mechanisms which underlie such abilities are positively modified by certain activities and experience. Recent studies indicate that music training provides robust, long-lasting biological benefits to auditory function. Importantly, the behavioral advantages conferred by musical experience extend beyond simple enhancements to perceptual abilities and even impact non-auditory functions necessary for higher-order aspects of cognition (e.g., working memory, intelligence). Collectively, preliminary findings indicate that alternative forms of arts engagement (e.g., visual arts training) may not yield such widespread enhancements, suggesting that music expertise uniquely taps and refines a hierarchy of brain networks subserving a variety of auditory as well as domain-general cognitive mechanisms. We infer that transfer from specific music experience to broad cognitive benefit might be mediated by the degree to which a listener's musical training tunes lower- (e.g., perceptual) and higher-order executive functions, and the coordination between these processes. Ultimately, understanding the broad impact of music on the brain will not only provide a more holistic picture of auditory processing and plasticity, but may help inform and tailor remediation and training programs designed to improve perceptual and cognitive benefits in human listeners. Copyright © 2013 Elsevier B.V. All rights reserved.

  8. A saturation hypothesis to explain both enhanced and impaired learning with enhanced plasticity

    Science.gov (United States)

    Nguyen-Vu, TD Barbara; Zhao, Grace Q; Lahiri, Subhaneil; Kimpo, Rhea R; Lee, Hanmi; Ganguli, Surya; Shatz, Carla J; Raymond, Jennifer L

    2017-01-01

    Across many studies, animals with enhanced synaptic plasticity exhibit either enhanced or impaired learning, raising a conceptual puzzle: how enhanced plasticity can yield opposite learning outcomes? Here, we show that the recent history of experience can determine whether mice with enhanced plasticity exhibit enhanced or impaired learning in response to the same training. Mice with enhanced cerebellar LTD, due to double knockout (DKO) of MHCI H2-Kb/H2-Db (KbDb−/−), exhibited oculomotor learning deficits. However, the same mice exhibited enhanced learning after appropriate pre-training. Theoretical analysis revealed that synapses with history-dependent learning rules could recapitulate the data, and suggested that saturation may be a key factor limiting the ability of enhanced plasticity to enhance learning. Optogenetic stimulation designed to saturate LTD produced the same impairment in WT as observed in DKO mice. Overall, our results suggest that the recent history of activity and the threshold for synaptic plasticity conspire to effect divergent learning outcomes. DOI: http://dx.doi.org/10.7554/eLife.20147.001 PMID:28234229

  9. Behavioral and neural plasticity caused by early social experiences: the case of the honeybee

    Directory of Open Access Journals (Sweden)

    Andrés eArenas

    2013-08-01

    Full Text Available Cognitive experiences during the early stages of life play an important role in shaping future behavior. Behavioral and neural long-term changes after early sensory and associative experiences have been recently reported in the honeybee. This invertebrate is an excellent model for assessing the role of precocious experiences on later behavior due to its extraordinarily tuned division of labor based on age polyethism. These studies are mainly focused on the role and importance of experiences occurred during the first days of the adult lifespan, their impact on foraging decisions and their contribution to coordinate food gathering. Odor-rewarded experiences during the first days of honeybee adulthood alter the responsiveness to sucrose, making young hive bees more sensitive to assess gustatory features about the nectar brought back to the hive and affecting the dynamic of the food transfers and the propagation of food-related information within the colony as well. Early olfactory experiences lead to stable and long-term associative memories that can be successfully recalled after many days, even at foraging ages. Also they improve memorizing of new associative learning events later in life. The establishment of early memories promotes stable reorganization of the olfactory circuits inducing structural and functional changes in the antennal lobe. Early rewarded experiences have relevant consequences at the social level too, biasing dance and trophallaxis partner choice and affecting recruitment. Here, we revised recent results in bees´ physiology, behavior and sociobiology to depict how the early experiences affect their cognition abilities and neural-related circuits.

  10. Prespeech motor learning in a neural network using reinforcement☆

    Science.gov (United States)

    Warlaumont, Anne S.; Westermann, Gert; Buder, Eugene H.; Oller, D. Kimbrough

    2012-01-01

    Vocal motor development in infancy provides a crucial foundation for language development. Some significant early accomplishments include learning to control the process of phonation (the production of sound at the larynx) and learning to produce the sounds of one’s language. Previous work has shown that social reinforcement shapes the kinds of vocalizations infants produce. We present a neural network model that provides an account of how vocal learning may be guided by reinforcement. The model consists of a self-organizing map that outputs to muscles of a realistic vocalization synthesizer. Vocalizations are spontaneously produced by the network. If a vocalization meets certain acoustic criteria, it is reinforced, and the weights are updated to make similar muscle activations increasingly likely to recur. We ran simulations of the model under various reinforcement criteria and tested the types of vocalizations it produced after learning in the differ-ent conditions. When reinforcement was contingent on the production of phonated (i.e. voiced) sounds, the network’s post learning productions were almost always phonated, whereas when reinforcement was not contingent on phonation, the network’s post-learning productions were almost always not phonated. When reinforcement was contingent on both phonation and proximity to English vowels as opposed to Korean vowels, the model’s post-learning productions were more likely to resemble the English vowels and vice versa. PMID:23275137

  11. Prespeech motor learning in a neural network using reinforcement.

    Science.gov (United States)

    Warlaumont, Anne S; Westermann, Gert; Buder, Eugene H; Oller, D Kimbrough

    2013-02-01

    Vocal motor development in infancy provides a crucial foundation for language development. Some significant early accomplishments include learning to control the process of phonation (the production of sound at the larynx) and learning to produce the sounds of one's language. Previous work has shown that social reinforcement shapes the kinds of vocalizations infants produce. We present a neural network model that provides an account of how vocal learning may be guided by reinforcement. The model consists of a self-organizing map that outputs to muscles of a realistic vocalization synthesizer. Vocalizations are spontaneously produced by the network. If a vocalization meets certain acoustic criteria, it is reinforced, and the weights are updated to make similar muscle activations increasingly likely to recur. We ran simulations of the model under various reinforcement criteria and tested the types of vocalizations it produced after learning in the different conditions. When reinforcement was contingent on the production of phonated (i.e. voiced) sounds, the network's post-learning productions were almost always phonated, whereas when reinforcement was not contingent on phonation, the network's post-learning productions were almost always not phonated. When reinforcement was contingent on both phonation and proximity to English vowels as opposed to Korean vowels, the model's post-learning productions were more likely to resemble the English vowels and vice versa. Copyright © 2012 Elsevier Ltd. All rights reserved.

  12. Adaptive Neural Network Nonparametric Identifier With Normalized Learning Laws.

    Science.gov (United States)

    Chairez, Isaac

    2017-05-01

    This paper addresses the design of a normalized convergent learning law for neural networks (NNs) with continuous dynamics. The NN is used here to obtain a nonparametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties is the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on normalized algorithms was used to adjust the weights of the NN. The adaptive algorithm was derived by means of a nonstandard logarithmic Lyapunov function (LLF). Two identifiers were designed using two variations of LLFs leading to a normalized learning law for the first identifier and a variable gain normalized learning law. In the case of the second identifier, the inclusion of normalized learning laws yields to reduce the size of the convergence region obtained as solution of the practical stability analysis. On the other hand, the velocity of convergence for the learning laws depends on the norm of errors in inverse form. This fact avoids the peaking transient behavior in the time evolution of weights that accelerates the convergence of identification error. A numerical example demonstrates the improvements achieved by the algorithm introduced in this paper compared with classical schemes with no-normalized continuous learning methods. A comparison of the identification performance achieved by the no-normalized identifier and the ones developed in this paper shows the benefits of the learning law proposed in this paper.

  13. Neural correlates of threat perception: neural equivalence of conspecific and heterospecific mobbing calls is learned.

    Science.gov (United States)

    Avey, Marc T; Hoeschele, Marisa; Moscicki, Michele K; Bloomfield, Laurie L; Sturdy, Christopher B

    2011-01-01

    Songbird auditory areas (i.e., CMM and NCM) are preferentially activated to playback of conspecific vocalizations relative to heterospecific and arbitrary noise. Here, we asked if the neural response to auditory stimulation is not simply preferential for conspecific vocalizations but also for the information conveyed by the vocalization. Black-capped chickadees use their chick-a-dee mobbing call to recruit conspecifics and other avian species to mob perched predators. Mobbing calls produced in response to smaller, higher-threat predators contain more "D" notes compared to those produced in response to larger, lower-threat predators and thus convey the degree of threat of predators. We specifically asked whether the neural response varies with the degree of threat conveyed by the mobbing calls of chickadees and whether the neural response is the same for actual predator calls that correspond to the degree of threat of the chickadee mobbing calls. Our results demonstrate that, as degree of threat increases in conspecific chickadee mobbing calls, there is a corresponding increase in immediate early gene (IEG) expression in telencephalic auditory areas. We also demonstrate that as the degree of threat increases for the heterospecific predator, there is a corresponding increase in IEG expression in the auditory areas. Furthermore, there was no significant difference in the amount IEG expression between conspecific mobbing calls or heterospecific predator calls that were the same degree of threat. In a second experiment, using hand-reared chickadees without predator experience, we found more IEG expression in response to mobbing calls than corresponding predator calls, indicating that degree of threat is learned. Our results demonstrate that degree of threat corresponds to neural activity in the auditory areas and that threat can be conveyed by different species signals and that these signals must be learned.

  14. Neural correlates of threat perception: neural equivalence of conspecific and heterospecific mobbing calls is learned.

    Directory of Open Access Journals (Sweden)

    Marc T Avey

    Full Text Available Songbird auditory areas (i.e., CMM and NCM are preferentially activated to playback of conspecific vocalizations relative to heterospecific and arbitrary noise. Here, we asked if the neural response to auditory stimulation is not simply preferential for conspecific vocalizations but also for the information conveyed by the vocalization. Black-capped chickadees use their chick-a-dee mobbing call to recruit conspecifics and other avian species to mob perched predators. Mobbing calls produced in response to smaller, higher-threat predators contain more "D" notes compared to those produced in response to larger, lower-threat predators and thus convey the degree of threat of predators. We specifically asked whether the neural response varies with the degree of threat conveyed by the mobbing calls of chickadees and whether the neural response is the same for actual predator calls that correspond to the degree of threat of the chickadee mobbing calls. Our results demonstrate that, as degree of threat increases in conspecific chickadee mobbing calls, there is a corresponding increase in immediate early gene (IEG expression in telencephalic auditory areas. We also demonstrate that as the degree of threat increases for the heterospecific predator, there is a corresponding increase in IEG expression in the auditory areas. Furthermore, there was no significant difference in the amount IEG expression between conspecific mobbing calls or heterospecific predator calls that were the same degree of threat. In a second experiment, using hand-reared chickadees without predator experience, we found more IEG expression in response to mobbing calls than corresponding predator calls, indicating that degree of threat is learned. Our results demonstrate that degree of threat corresponds to neural activity in the auditory areas and that threat can be conveyed by different species signals and that these signals must be learned.

  15. Neural model for learning-to-learn of novel task sets in the motor domain.

    Science.gov (United States)

    Pitti, Alexandre; Braud, Raphaël; Mahé, Sylvain; Quoy, Mathias; Gaussier, Philippe

    2013-01-01

    During development, infants learn to differentiate their motor behaviors relative to various contexts by exploring and identifying the correct structures of causes and effects that they can perform; these structures of actions are called task sets or internal models. The ability to detect the structure of new actions, to learn them and to select on the fly the proper one given the current task set is one great leap in infants cognition. This behavior is an important component of the child's ability of learning-to-learn, a mechanism akin to the one of intrinsic motivation that is argued to drive cognitive development. Accordingly, we propose to model a dual system based on (1) the learning of new task sets and on (2) their evaluation relative to their uncertainty and prediction error. The architecture is designed as a two-level-based neural system for context-dependent behavior (the first system) and task exploration and exploitation (the second system). In our model, the task sets are learned separately by reinforcement learning in the first network after their evaluation and selection in the second one. We perform two different experimental setups to show the sensorimotor mapping and switching between tasks, a first one in a neural simulation for modeling cognitive tasks and a second one with an arm-robot for motor task learning and switching. We show that the interplay of several intrinsic mechanisms drive the rapid formation of the neural populations with respect to novel task sets.

  16. Are deep neural networks really learning relevant features?

    DEFF Research Database (Denmark)

    Kereliuk, Corey Mose; Larsen, Jan; Sturm, Bob L.

    In recent years deep neural networks (DNNs) have become a popular choice for audio content analysis. This may be attributed to various factors including advancements in training algorithms, computational power, and the potential for DNNs to implicitly learn a set of feature detectors. We have...... recently re-examined two works that consider DNNs for the task of music genre recognition (MGR). These papers conclude that frame-level features learned by DNNs offer an improvement over traditional, hand-crafted features such as Mel-frequency cepstrum coefficients (MFCCs). However, these conclusions were...... drawn based on training/testing using the GTZAN dataset, which is now known to contain several flaws including replicated observations and artists. We illustrate how considering these flaws dramatically changes the results, which leads one to question the degree to which the learned frame-level features...

  17. Maladaptive spinal plasticity opposes spinal learning and recovery in spinal cord injury

    Science.gov (United States)

    Ferguson, Adam R.; Huie, J. Russell; Crown, Eric D.; Baumbauer, Kyle M.; Hook, Michelle A.; Garraway, Sandra M.; Lee, Kuan H.; Hoy, Kevin C.; Grau, James W.

    2012-01-01

    Synaptic plasticity within the spinal cord has great potential to facilitate recovery of function after spinal cord injury (SCI). Spinal plasticity can be induced in an activity-dependent manner even without input from the brain after complete SCI. A mechanistic basis for these effects is provided by research demonstrating that spinal synapses have many of the same plasticity mechanisms that are known to underlie learning and memory in the brain. In addition, the lumbar spinal cord can sustain several forms of learning and memory, including limb-position training. However, not all spinal plasticity promotes recovery of function. Central sensitization of nociceptive (pain) pathways in the spinal cord may emerge in response to various noxious inputs, demonstrating that plasticity within the spinal cord may contribute to maladaptive pain states. In this review we discuss interactions between adaptive and maladaptive forms of activity-dependent plasticity in the spinal cord below the level of SCI. The literature demonstrates that activity-dependent plasticity within the spinal cord must be carefully tuned to promote adaptive spinal training. Prior work from our group has shown that stimulation that is delivered in a limb position-dependent manner or on a fixed interval can induce adaptive plasticity that promotes future spinal cord learning and reduces nociceptive hyper-reactivity. On the other hand, stimulation that is delivered in an unsynchronized fashion, such as randomized electrical stimulation or peripheral skin injuries, can generate maladaptive spinal plasticity that undermines future spinal cord learning, reduces recovery of locomotor function, and promotes nociceptive hyper-reactivity after SCI. We review these basic phenomena, how these findings relate to the broader spinal plasticity literature, discuss the cellular and molecular mechanisms, and finally discuss implications of these and other findings for improved rehabilitative therapies after SCI. PMID

  18. Learning to play Go using recursive neural networks.

    Science.gov (United States)

    Wu, Lin; Baldi, Pierre

    2008-11-01

    Go is an ancient board game that poses unique opportunities and challenges for artificial intelligence. Currently, there are no computer Go programs that can play at the level of a good human player. However, the emergence of large repositories of games is opening the door for new machine learning approaches to address this challenge. Here we develop a machine learning approach to Go, and related board games, focusing primarily on the problem of learning a good evaluation function in a scalable way. Scalability is essential at multiple levels, from the library of local tactical patterns, to the integration of patterns across the board, to the size of the board itself. The system we propose is capable of automatically learning the propensity of local patterns from a library of games. Propensity and other local tactical information are fed into recursive neural networks, derived from a probabilistic Bayesian network architecture. The recursive neural networks in turn integrate local information across the board in all four cardinal directions and produce local outputs that represent local territory ownership probabilities. The aggregation of these probabilities provides an effective strategic evaluation function that is an estimate of the expected area at the end, or at various other stages, of the game. Local area targets for training can be derived from datasets of games played by human players. In this approach, while requiring a learning time proportional to N(4), skills learned on a board of size N(2) can easily be transferred to boards of other sizes. A system trained using only 9 x 9 amateur game data performs surprisingly well on a test set derived from 19 x 19 professional game data. Possible directions for further improvements are briefly discussed.

  19. Influence of aerobic exercise training on the neural correlates of motor learning in Parkinson's disease individuals.

    Science.gov (United States)

    Duchesne, C; Gheysen, F; Bore, A; Albouy, G; Nadeau, A; Robillard, M E; Bobeuf, F; Lafontaine, A L; Lungu, O; Bherer, L; Doyon, J

    Aerobic exercise training (AET) has been shown to provide general health benefits, and to improve motor behaviours in particular, in individuals with Parkinson's disease (PD). However, the influence of AET on their motor learning capacities, as well as the change in neural substrates mediating this effect remains to be explored. In the current study, we employed functional Magnetic Resonance Imaging (fMRI) to assess the effect of a 3-month AET program on the neural correlates of implicit motor sequence learning (MSL). 20 healthy controls (HC) and 19 early PD individuals participated in a supervised, high-intensity, stationary recumbent bike training program (3 times/week for 12 weeks). Exercise prescription started at 20 min (+ 5 min/week up to 40 min) based on participant's maximal aerobic power. Before and after the AET program, participants' brain was scanned while performing an implicit version of the serial reaction time task. Brain data revealed pre-post MSL-related increases in functional activity in the hippocampus, striatum and cerebellum in PD patients, as well as in the striatum in HC individuals. Importantly, the functional brain changes in PD individuals correlated with changes in aerobic fitness: a positive relationship was found with increased activity in the hippocampus and striatum, while a negative relationship was observed with the cerebellar activity. Our results reveal, for the first time, that exercise training produces functional changes in known motor learning related brain structures that are consistent with improved behavioural performance observed in PD patients. As such, AET can be a valuable non-pharmacological intervention to promote, not only physical fitness in early PD, but also better motor learning capacity useful in day-to-day activities through increased plasticity in motor related structures.

  20. Odor experiences during preimaginal stages cause behavioral and neural plasticity in adult honeybees

    Directory of Open Access Journals (Sweden)

    Gabriela eRamirez

    2016-06-01

    Full Text Available In eusocial insects, experiences acquired during the development have long-term consequences on mature behavior. In the honeybee that suffers profound changes associated with metamorphosis, the effect of odor experiences at larval instars on the subsequent physiological and behavioral response is still unclear. To address the impact of preimaginal experiences on the adult honeybee, colonies containing larvae were fed scented food. The effect of the preimaginal experiences with the food odor was assessed in learning performance, memory retention and generalization in 3-5- and 17-19-day-old bees, in the regulation of their expression of synaptic-related genes and in theperception and morphology of their antennae. Three-5 day old bees that experienced 1-hexanol (1-HEX as food scent responded more to the presentation of the odor during the 1-HEX conditioning than control bees (i.e. bees reared in colonies fed unscented food. Higher levels of PER to 1-HEX in this group also extent to HEXA, the most perceptually similar odor to the experienced one that we tested. These results were not observed for the group tested at older ages. In the brain of young adults, larval experiences triggered similar levels of neurexins and neuroligins expression, two proteins that have been involved in synaptic formation after associative learning. At the sensory periphery, the experience did not alter the number of the olfactory sensilla placoidea, but did reduce the electrical response of the antennae to the experienced and novel odor. Our study provides a new insight into the effects of preimaginal experiences in the honeybee and the mechanisms underlying olfactory plasticity at larval stage of holometabolous insects.

  1. Comparison between extreme learning machine and wavelet neural networks in data classification

    Science.gov (United States)

    Yahia, Siwar; Said, Salwa; Jemai, Olfa; Zaied, Mourad; Ben Amar, Chokri

    2017-03-01

    Extreme learning Machine is a well known learning algorithm in the field of machine learning. It's about a feed forward neural network with a single-hidden layer. It is an extremely fast learning algorithm with good generalization performance. In this paper, we aim to compare the Extreme learning Machine with wavelet neural networks, which is a very used algorithm. We have used six benchmark data sets to evaluate each technique. These datasets Including Wisconsin Breast Cancer, Glass Identification, Ionosphere, Pima Indians Diabetes, Wine Recognition and Iris Plant. Experimental results have shown that both extreme learning machine and wavelet neural networks have reached good results.

  2. Behavioral and neural properties of social reinforcement learning.

    Science.gov (United States)

    Jones, Rebecca M; Somerville, Leah H; Li, Jian; Ruberry, Erika J; Libby, Victoria; Glover, Gary; Voss, Henning U; Ballon, Douglas J; Casey, B J

    2011-09-14

    Social learning is critical for engaging in complex interactions with other individuals. Learning from positive social exchanges, such as acceptance from peers, may be similar to basic reinforcement learning. We formally test this hypothesis by developing a novel paradigm that is based on work in nonhuman primates and human imaging studies of reinforcement learning. The probability of receiving positive social reinforcement from three distinct peers was parametrically manipulated while brain activity was recorded in healthy adults using event-related functional magnetic resonance imaging. Over the course of the experiment, participants responded more quickly to faces of peers who provided more frequent positive social reinforcement, and rated them as more likeable. Modeling trial-by-trial learning showed ventral striatum and orbital frontal cortex activity correlated positively with forming expectations about receiving social reinforcement. Rostral anterior cingulate cortex activity tracked positively with modulations of expected value of the cues (peers). Together, the findings across three levels of analysis--social preferences, response latencies, and modeling neural responses--are consistent with reinforcement learning theory and nonhuman primate electrophysiological studies of reward. This work highlights the fundamental influence of acceptance by one's peers in altering subsequent behavior.

  3. How instructed knowledge modulates the neural systems of reward learning.

    Science.gov (United States)

    Li, Jian; Delgado, Mauricio R; Phelps, Elizabeth A

    2011-01-04

    Recent research in neuroeconomics has demonstrated that the reinforcement learning model of reward learning captures the patterns of both behavioral performance and neural responses during a range of economic decision-making tasks. However, this powerful theoretical model has its limits. Trial-and-error is only one of the means by which individuals can learn the value associated with different decision options. Humans have also developed efficient, symbolic means of communication for learning without the necessity for committing multiple errors across trials. In the present study, we observed that instructed knowledge of cue-reward probabilities improves behavioral performance and diminishes reinforcement learning-related blood-oxygen level-dependent (BOLD) responses to feedback in the nucleus accumbens, ventromedial prefrontal cortex, and hippocampal complex. The decrease in BOLD responses in these brain regions to reward-feedback signals was functionally correlated with activation of the dorsolateral prefrontal cortex (DLPFC). These results suggest that when learning action values, participants use the DLPFC to dynamically adjust outcome responses in valuation regions depending on the usefulness of action-outcome information.

  4. Behavioral and neural properties of social reinforcement learning

    Science.gov (United States)

    Jones, Rebecca M.; Somerville, Leah H.; Li, Jian; Ruberry, Erika J.; Libby, Victoria; Glover, Gary; Voss, Henning U.; Ballon, Douglas J.; Casey, BJ

    2011-01-01

    Social learning is critical for engaging in complex interactions with other individuals. Learning from positive social exchanges, such as acceptance from peers, may be similar to basic reinforcement learning. We formally test this hypothesis by developing a novel paradigm that is based upon work in non-human primates and human imaging studies of reinforcement learning. The probability of receiving positive social reinforcement from three distinct peers was parametrically manipulated while brain activity was recorded in healthy adults using event-related functional magnetic resonance imaging (fMRI). Over the course of the experiment, participants responded more quickly to faces of peers who provided more frequent positive social reinforcement, and rated them as more likeable. Modeling trial-by-trial learning showed ventral striatum and orbital frontal cortex activity correlated positively with forming expectations about receiving social reinforcement. Rostral anterior cingulate cortex activity tracked positively with modulations of expected value of the cues (peers). Together, the findings across three levels of analysis - social preferences, response latencies and modeling neural responses – are consistent with reinforcement learning theory and non-human primate electrophysiological studies of reward. This work highlights the fundamental influence of acceptance by one’s peers in altering subsequent behavior. PMID:21917787

  5. Forecasting financial asset processes: stochastic dynamics via learning neural networks.

    Science.gov (United States)

    Giebel, S; Rainer, M

    2010-01-01

    Models for financial asset dynamics usually take into account their inherent unpredictable nature by including a suitable stochastic component into their process. Unknown (forward) values of financial assets (at a given time in the future) are usually estimated as expectations of the stochastic asset under a suitable risk-neutral measure. This estimation requires the stochastic model to be calibrated to some history of sufficient length in the past. Apart from inherent limitations, due to the stochastic nature of the process, the predictive power is also limited by the simplifying assumptions of the common calibration methods, such as maximum likelihood estimation and regression methods, performed often without weights on the historic time series, or with static weights only. Here we propose a novel method of "intelligent" calibration, using learning neural networks in order to dynamically adapt the parameters of the stochastic model. Hence we have a stochastic process with time dependent parameters, the dynamics of the parameters being themselves learned continuously by a neural network. The back propagation in training the previous weights is limited to a certain memory length (in the examples we consider 10 previous business days), which is similar to the maximal time lag of autoregressive processes. We demonstrate the learning efficiency of the new algorithm by tracking the next-day forecasts for the EURTRY and EUR-HUF exchange rates each.

  6. Protein kinase C substrate phosphorylation in relation to neural growth and synaptic plasticity: a common molecular mechanism underlying multiple neural functions

    Energy Technology Data Exchange (ETDEWEB)

    Nelson, R.B.

    1987-01-01

    In these studies, we addressed the issues of: (1) whether neural protein kinase C (PKC) substrates might be altered in phosphorylation following induction of long-term potentiation (LTP); (2) whether PKC substrate phosphorylation might be specifically related to a model of neural plasticity other than LTP; and (3) whether the PKC substrates implicated in adult synaptic plasticity might be present in axonal growth cones given reports that high concentrations of PKC are found in these structures. Using quantitative analysis of multiple two-dimensional gels, we found that the two major substrates of exogenous purified PKC in adult hippocampal homogenate are both directly correlated to persistence of LTP. In rhesus monkey cerebral cortex, the proteins corresponding to protein F1 and 80k displayed topographical gradients in /sup 32/P-incorporation along the occipitotemporal visual processing pathway. The phosphorylation of both proteins was 11- and 14-fold higher, respectively, in temporal regions of this pathway implicated in the storage of visual representations, than in occipital regions, which do not appear to directly participate in visual memory functions.

  7. Dynamic transcriptional signature and cell fate analysis reveals plasticity of individual neural plate border cells

    Science.gov (United States)

    Roellig, Daniela; Tan-Cabugao, Johanna; Esaian, Sevan; Bronner, Marianne E

    2017-01-01

    The ‘neural plate border’ of vertebrate embryos contains precursors of neural crest and placode cells, both defining vertebrate characteristics. How these lineages segregate from neural and epidermal fates has been a matter of debate. We address this by performing a fine-scale quantitative temporal analysis of transcription factor expression in the neural plate border of chick embryos. The results reveal significant overlap of transcription factors characteristic of multiple lineages in individual border cells from gastrula through neurula stages. Cell fate analysis using a Sox2 (neural) enhancer reveals that cells that are initially Sox2+ cells can contribute not only to neural tube but also to neural crest and epidermis. Moreover, modulating levels of Sox2 or Pax7 alters the apportionment of neural tube versus neural crest fates. Our results resolve a long-standing question and suggest that many individual border cells maintain ability to contribute to multiple ectodermal lineages until or beyond neural tube closure. DOI: http://dx.doi.org/10.7554/eLife.21620.001 PMID:28355135

  8. Monocular denervation of visual nuclei modulates APP processing and sAPPα production: A possible role on neural plasticity.

    Science.gov (United States)

    Vasques, Juliana Ferreira; Heringer, Pedro Vinícius Bastos; Gonçalves, Renata Guedes de Jesus; Campello-Costa, Paula; Serfaty, Claudio Alberto; Faria-Melibeu, Adriana da Cunha

    2017-08-01

    Amyloid precursor protein (APP) is essential to physiological processes such as synapse formation and neural plasticity. Sequential proteolysis of APP by beta- and gamma-secretases generates amyloid-beta peptide (Aβ), the main component of senile plaques in Alzheimer Disease. Alternative APP cleavage by alpha-secretase occurs within Aβ domain, releasing soluble α-APP (sAPPα), a neurotrophic fragment. Among other functions, sAPPα is important to synaptogenesis, neural survival and axonal growth. APP and sAPPα levels are increased in models of neuroplasticity, which suggests an important role for APP and its metabolites, especially sAPPα, in the rearranging brain. In this work we analyzed the effects of monocular enucleation (ME), a classical model of lesion-induced plasticity, upon APP content, processing and also in secretases levels. Besides, we addressed whether α-secretase activity is crucial for retinotectal remodeling after ME. Our results showed that ME induced a transient reduction in total APP content. We also detected an increase in α-secretase expression and in sAPP production concomitant with a reduction in Aβ and β-secretase contents. These data suggest that ME facilitates APP processing by the non-amyloidogenic pathway, increasing sAPPα levels. Indeed, the pharmacological inhibition of α-secretase activity reduced the axonal sprouting of ipsilateral retinocollicular projections from the intact eye after ME, suggesting that sAPPα is necessary for synaptic structural rearrangement. Understanding how APP processing is regulated under lesion conditions may provide new insights into APP physiological role on neural plasticity. Copyright © 2017 ISDN. Published by Elsevier Ltd. All rights reserved.

  9. Weighted spiking neural P systems with structural plasticity working in sequential mode based on maximum spike number

    Science.gov (United States)

    Sun, Mingming; Qu, Jianhua

    2017-10-01

    Spiking neural P systems (SNP systems, in short) are a group of parallel and distributed computing devices inspired by the function and structure of spiking neurons. Recently, a new variant of SNP systems, called SNP systems with structural plasticity (SNPSP systems, in short) was proposed. In SNPSP systems, neuron can use plasticity ru les to create and delete synapses. In this work, we consider many restrictions sequentiality on SNPSP systems: (1) neuron with the maximum number of spikes is chosen to fire; (2) we use the weighted synapses. Specifically, we investigate the computational power of weighted SNPSP systems working in the sequential mode based on maximum spike number (WSNPSPM systems, in short) and we proved that SNPSP systems with these new restrictions are universal as generating devices.

  10. Supervised learning of probability distributions by neural networks

    Science.gov (United States)

    Baum, Eric B.; Wilczek, Frank

    1988-01-01

    Supervised learning algorithms for feedforward neural networks are investigated analytically. The back-propagation algorithm described by Werbos (1974), Parker (1985), and Rumelhart et al. (1986) is generalized by redefining the values of the input and output neurons as probabilities. The synaptic weights are then varied to follow gradients in the logarithm of likelihood rather than in the error. This modification is shown to provide a more rigorous theoretical basis for the algorithm and to permit more accurate predictions. A typical application involving a medical-diagnosis expert system is discussed.

  11. Multiple levels of impaired neural plasticity and cellular resilience in bipolar disorder: developing treatments using an integrated translational approach.

    Science.gov (United States)

    Machado-Vieira, Rodrigo; Soeiro-De-Souza, Marcio G; Richards, Erica M; Teixeira, Antonio L; Zarate, Carlos A

    2014-02-01

    This paper reviews the neurobiology of bipolar disorder (BD), particularly findings associated with impaired cellular resilience and plasticity. PubMed/Medline articles and book chapters published over the last 20 years were identified using the following keyword combinations: BD, calcium, cytokines, endoplasmic reticulum (ER), genetics, glucocorticoids, glutamate, imaging, ketamine, lithium, mania, mitochondria, neuroplasticity, neuroprotection, neurotrophic, oxidative stress, plasticity, resilience, and valproate. BD is associated with impaired cellular resilience and synaptic dysfunction at multiple levels, associated with impaired cellular resilience and plasticity. These findings were partially prevented or even reversed with the use of mood stabilizers, but longitudinal studies associated with clinical outcome remain scarce. Evidence consistently suggests that BD involves impaired neural plasticity and cellular resilience at multiple levels. This includes the genetic and intra- and intercellular signalling levels, their impact on brain structure and function, as well as the final translation into behaviour/cognitive changes. Future studies are expected to adopt integrated translational approaches using a variety of methods (e.g., microarray approaches, neuroimaging, genetics, electrophysiology, and the new generation of -omics techniques). These studies will likely focus on more precise diagnoses and a personalized medicine paradigm in order to develop better treatments for those who need them most.

  12. Repeated anodal transcranial direct current stimulation induces neural plasticity-associated gene expression in the rat cortex and hippocampus.

    Science.gov (United States)

    Kim, Min Sun; Koo, Ho; Han, Sang Who; Paulus, Walter; Nitsche, Michael A; Kim, Yun-Hee; Yoon, Jin A; Shin, Yong-Il

    2017-01-01

    Anodal transcranial direct current stimulation (A-tDCS) induces a long-lasting increase in cortical excitability that can increase gene transcription in the brain. The purpose of this study was to evaluate the expression of genes related to activity-dependent neuronal plasticity in the sensorimotor cortex and hippocampus of young Sprague-Dawley rats following A-tDCS. We applied A-tDCS over the right sensorimotor cortex epicranially with a circular electrode (3 mm diameter) at 250 μA for 20 min per day for 7 consecutive days. Levels of mRNA for brain-derived neurotrophic factor (BDNF), cAMP response element-binding protein (CREB), synapsin I, Ca2+/calmodulin-dependent protein kinase II (CaMKII), activity-regulated cytoskeleton-associated protein (Arc), and c-Fos were analyzed using SYBR Green quantitative real-time polymerase chain reaction (PCR). We found that 7 days of unilateral A-tDCS resulted in significant increases in transcription of all plasticity-related genes tested in the ipsilateral cortex. Daily A-tDCS also resulted in a significant increase in c-Fos mRNA in the ipsilateral hippocampus. These results indicate that altered expression of plasticity-associated genes in the cortex and hippocampus is a molecular substrate of A-tDCS-induced neural plasticity.

  13. Neural Network Based Simulation of Micro Creeping Fibrous Composites SiC/Al6061 for Plastic Behaviour

    Science.gov (United States)

    Monfared, Vahid

    2017-03-01

    The present work presents a new approach based on neural network prediction for simple and fast estimation of the creep plastic behaviour of the short fiber composites. Also, this approach is proposed to reduce the solution procedure. Moreover, as a significant application of the method, shuttles and spaceships, turbine blades and discs are generally subjected to the creep effects. Consequently, analysis of the creep phenomenon is required and vital in different industries. Analysis of the creep behaviour is required for failure, fracture, fatigue, and creep resistance of the optoelectronic/photonic composites, and sensors. One of the main applications of the present work is in designing the composites with optical fibers and devices. At last, a good agreement is seen among the present prediction by neural network approach, finite element method (FEM), and the experimental results.

  14. A novel Bayesian learning method for information aggregation in modular neural networks

    DEFF Research Database (Denmark)

    Wang, Pan; Xu, Lida; Zhou, Shang-Ming

    2010-01-01

    Modular neural network is a popular neural network model which has many successful applications. In this paper, a sequential Bayesian learning (SBL) is proposed for modular neural networks aiming at efficiently aggregating the outputs of members of the ensemble. The experimental results on eight...... benchmark problems have demonstrated that the proposed method can perform information aggregation efficiently in data modeling....

  15. Learning Orthographic Structure With Sequential Generative Neural Networks.

    Science.gov (United States)

    Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

    2016-04-01

    Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. Copyright © 2015 Cognitive Science Society, Inc.

  16. Composite learning from adaptive backstepping neural network control.

    Science.gov (United States)

    Pan, Yongping; Sun, Tairen; Liu, Yiqi; Yu, Haoyong

    2017-11-01

    In existing neural network (NN) learning control methods, the trajectory of NN inputs must be recurrent to satisfy a stringent condition termed persistent excitation (PE) so that NN parameter convergence is obtainable. This paper focuses on command-filtered backstepping adaptive control for a class of strict-feedback nonlinear systems with functional uncertainties, where an NN composite learning technique is proposed to guarantee convergence of NN weights to their ideal values without the PE condition. In the NN composite learning, spatially localized NN approximation is employed to handle functional uncertainties, online historical data together with instantaneous data are exploited to generate prediction errors, and both tracking errors and prediction errors are employed to update NN weights. The influence of NN approximation errors on the control performance is also clearly shown. The distinctive feature of the proposed NN composite learning is that NN parameter convergence is guaranteed without the requirement of the trajectory of NN inputs being recurrent. Illustrative results have verified effectiveness and superiority of the proposed method compared with existing NN learning control methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Common neural mechanisms underlying reversal learning by reward and punishment.

    Science.gov (United States)

    Xue, Gui; Xue, Feng; Droutman, Vita; Lu, Zhong-Lin; Bechara, Antoine; Read, Stephen

    2013-01-01

    Impairments in flexible goal-directed decisions, often examined by reversal learning, are associated with behavioral abnormalities characterized by impulsiveness and disinhibition. Although the lateral orbital frontal cortex (OFC) has been consistently implicated in reversal learning, it is still unclear whether this region is involved in negative feedback processing, behavioral control, or both, and whether reward and punishment might have different effects on lateral OFC involvement. Using a relatively large sample (N = 47), and a categorical learning task with either monetary reward or moderate electric shock as feedback, we found overlapping activations in the right lateral OFC (and adjacent insula) for reward and punishment reversal learning when comparing correct reversal trials with correct acquisition trials, whereas we found overlapping activations in the right dorsolateral prefrontal cortex (DLPFC) when negative feedback signaled contingency change. The right lateral OFC and DLPFC also showed greater sensitivity to punishment than did their left homologues, indicating an asymmetry in how punishment is processed. We propose that the right lateral OFC and anterior insula are important for transforming affective feedback to behavioral adjustment, whereas the right DLPFC is involved in higher level attention control. These results provide insight into the neural mechanisms of reversal learning and behavioral flexibility, which can be leveraged to understand risky behaviors among vulnerable populations.

  18. Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory

    DEFF Research Database (Denmark)

    Lüders, Benno; Schläger, Mikkel; Korach, Aleksandra

    2017-01-01

    Training neural networks to quickly learn new skills without forgetting previously learned skills is an important open challenge in machine learning. A common problem for adaptive networks that can learn during their lifetime is that the weights encoding a particular task are often overridden when...... a new task is learned. This paper takes a step in overcoming this limitation by building on the recently proposed Evolving Neural Turing Machine (ENTM) approach. In the ENTM, neural networks are augmented with an external memory component that they can write to and read from, which allows them to store...

  19. Enhanced neural function in highly aberrated eyes following perceptual learning with adaptive optics.

    Science.gov (United States)

    Sabesan, Ramkumar; Barbot, Antoine; Yoon, Geunyoung

    2017-03-01

    Highly aberrated keratoconic (KC) eyes do not elicit the expected visual advantage from customized optical corrections. This is attributed to the neural insensitivity arising from chronic visual experience with poor retinal image quality, dominated by low spatial frequencies. The goal of this study was to investigate if targeted perceptual learning with adaptive optics (AO) can stimulate neural plasticity in these highly aberrated eyes. The worse eye of 2 KC subjects was trained in a contrast threshold test under AO correction. Prior to training, tumbling 'E' visual acuity and contrast sensitivity at 4, 8, 12, 16, 20, 24 and 28 c/deg were measured in both the trained and untrained eyes of each subject with their routine prescription and with AO correction for a 6mm pupil. The high spatial frequency requiring 50% contrast for detection with AO correction was picked as the training frequency. Subjects were required to train on a contrast detection test with AO correction for 1h for 5 consecutive days. During each training session, threshold contrast measurement at the training frequency with AO was conducted. Pre-training measures were repeated after the 5 training sessions in both eyes (i.e., post-training). After training, contrast sensitivity under AO correction improved on average across spatial frequency by a factor of 1.91 (range: 1.77-2.04) and 1.75 (1.22-2.34) for the two subjects. This improvement in contrast sensitivity transferred to visual acuity with the two subjects improving by 1.5 and 1.3 lines respectively with AO following training. One of the two subjects denoted an interocular transfer of training and an improvement in performance with their routine prescription post-training. This training-induced visual benefit demonstrates the potential of AO as a tool for neural rehabilitation in patients with abnormal corneas. Moreover, it reveals a sufficient degree of neural plasticity in normally developed adults who have a long history of abnormal visual

  20. Structure Learning for Deep Neural Networks Based on Multiobjective Optimization.

    Science.gov (United States)

    Liu, Jia; Gong, Maoguo; Miao, Qiguang; Wang, Xiaogang; Li, Hao

    2017-05-05

    This paper focuses on the connecting structure of deep neural networks and proposes a layerwise structure learning method based on multiobjective optimization. A model with better generalization can be obtained by reducing the connecting parameters in deep networks. The aim is to find the optimal structure with high representation ability and better generalization for each layer. Then, the visible data are modeled with respect to structure based on the products of experts. In order to mitigate the difficulty of estimating the denominator in PoE, the denominator is simplified and taken as another objective, i.e., the connecting sparsity. Moreover, for the consideration of the contradictory nature between the representation ability and the network connecting sparsity, the multiobjective model is established. An improved multiobjective evolutionary algorithm is used to solve this model. Two tricks are designed to decrease the computational cost according to the properties of input data. The experiments on single-layer level, hierarchical level, and application level demonstrate the effectiveness of the proposed algorithm, and the learned structures can improve the performance of deep neural networks.

  1. Interaction of plasticity and circuit organization during the acquisition of cerebellum-dependent motor learning.

    Science.gov (United States)

    Yang, Yan; Lisberger, Stephen G

    2013-12-31

    Motor learning occurs through interactions between the cerebellar circuit and cellular plasticity at different sites. Previous work has established plasticity in brain slices and suggested plausible sites of behavioral learning. We now reveal what actually happens in the cerebellum during short-term learning. We monitor the expression of plasticity in the simple-spike firing of cerebellar Purkinje cells during trial-over-trial learning in smooth pursuit eye movements of monkeys. Our findings imply that: 1) a single complex-spike response driven by one instruction for learning causes short-term plasticity in a Purkinje cell's mossy fiber/parallel-fiber input pathways; 2) complex-spike responses and simple-spike firing rate are correlated across the Purkinje cell population; and 3) simple-spike firing rate at the time of an instruction for learning modulates the probability of a complex-spike response, possibly through a disynaptic feedback pathway to the inferior olive. These mechanisms may participate in long-term motor learning. DOI: http://dx.doi.org/10.7554/eLife.01574.001.

  2. A role for calcium-permeable AMPA receptors in synaptic plasticity and learning.

    Directory of Open Access Journals (Sweden)

    Brian J Wiltgen

    2010-09-01

    Full Text Available A central concept in the field of learning and memory is that NMDARs are essential for synaptic plasticity and memory formation. Surprisingly then, multiple studies have found that behavioral experience can reduce or eliminate the contribution of these receptors to learning. The cellular mechanisms that mediate learning in the absence of NMDAR activation are currently unknown. To address this issue, we examined the contribution of Ca(2+-permeable AMPARs to learning and plasticity in the hippocampus. Mutant mice were engineered with a conditional genetic deletion of GluR2 in the CA1 region of the hippocampus (GluR2-cKO mice. Electrophysiology experiments in these animals revealed a novel form of long-term potentiation (LTP that was independent of NMDARs and mediated by GluR2-lacking Ca(2+-permeable AMPARs. Behavioral analyses found that GluR2-cKO mice were impaired on multiple hippocampus-dependent learning tasks that required NMDAR activation. This suggests that AMPAR-mediated LTP interferes with NMDAR-dependent plasticity. In contrast, NMDAR-independent learning was normal in knockout mice and required the activation of Ca(2+-permeable AMPARs. These results suggest that GluR2-lacking AMPARs play a functional and previously unidentified role in learning; they appear to mediate changes in synaptic strength that occur after plasticity has been established by NMDARs.

  3. Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

    Science.gov (United States)

    Albers, Christian; Westkott, Maren; Pawelzik, Klaus

    2016-01-01

    Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

  4. Learning sequential control in a Neural Blackboard Architecture for in situ concept reasoning

    NARCIS (Netherlands)

    van der Velde, Frank; van der Velde, Frank; Besold, Tarek R.; Lamb, Luis; Serafini, Luciano; Tabor, Whitney

    2016-01-01

    Simulations are presented and discussed of learning sequential control in a Neural Blackboard Architecture (NBA) for in situ concept-based reasoning. Sequential control is learned in a reservoir network, consisting of columns with neural circuits. This allows the reservoir to control the dynamics of

  5. Spaced Learning Enhances Subsequent Recognition Memory by Reducing Neural Repetition Suppression

    Science.gov (United States)

    Xue, Gui; Mei, Leilei; Chen, Chuansheng; Lu, Zhong-Lin; Poldrack, Russell; Dong, Qi

    2011-01-01

    Spaced learning usually leads to better recognition memory as compared with massed learning, yet the underlying neural mechanisms remain elusive. One open question is whether the spacing effect is achieved by reducing neural repetition suppression. In this fMRI study, participants were scanned while intentionally memorizing 120 novel faces, half…

  6. Ontology Mapping Neural Network: An Approach to Learning and Inferring Correspondences among Ontologies

    Science.gov (United States)

    Peng, Yefei

    2010-01-01

    An ontology mapping neural network (OMNN) is proposed in order to learn and infer correspondences among ontologies. It extends the Identical Elements Neural Network (IENN)'s ability to represent and map complex relationships. The learning dynamics of simultaneous (interlaced) training of similar tasks interact at the shared connections of the…

  7. Neural Networks that Learn Temporal Sequences by Selection

    Science.gov (United States)

    Dehaene, Stanislas; Changeux, Jean-Pierre; Nadal, Jean-Pierre

    1987-05-01

    A model for formal neural networks that learn temporal sequences by selection is proposed on the basis of observations on the acquisition of song by birds, on sequence-detecting neurons, and on allosteric receptors. The model relies on hypothetical elementary devices made up of three neurons, the synaptic triads, which yield short-term modification of synaptic efficacy through heterosynaptic interactions, and on a local Hebbian learning rule. The functional units postulated are mutually inhibiting clusters of synergic neurons and bundles of synapses. Networks formalized on this basis display capacities for passive recognition and for production of temporal sequences that may include repetitions. Introduction of the learning rule leads to the differentiation of sequence-detecting neurons and to the stabilization of ongoing temporal sequences. A network architecture composed of three layers of neuronal clusters is shown to exhibit active recognition and learning of time sequences by selection: the network spontaneously produces prerepresentations that are selected according to their resonance with the input percepts. Predictions of the model are discussed.

  8. Neural plasticity in hypocretin neurons: the basis of hypocretinergic regulation of physiological and behavioral functions in animals

    Directory of Open Access Journals (Sweden)

    Xiao-Bing eGao

    2015-10-01

    Full Text Available The neuronal system that resides in the perifornical and lateral hypothalamus (Pf/LH and synthesizes the neuropeptide hypocretin/orexin participates in critical brain functions across species from fish to human. The hypocretin system regulates neural activity responsible for daily functions (such as sleep/wake homeostasis, energy balance, appetite, etc and long-term behavioral changes (such as reward seeking and addiction, stress response, etc in animals. The most recent evidence suggests that the hypocretin system undergoes substantial plastic changes in response to both daily fluctuations (such as food intake and sleep-wake regulation and long-term changes (such as cocaine seeking in neuronal activity in the brain. The understanding of these changes in the hypocretin system is essential in addressing the role of the hypocretin system in normal physiological functions and pathological conditions in animals and humans. In this review, the evidence demonstrating that neural plasticity occurs in hypocretin-containing neurons in the Pf/LH will be presented and possible physiological behavioral, and mental health implications of these findings will be discussed.

  9. Neural plasticity in hypocretin neurons: the basis of hypocretinergic regulation of physiological and behavioral functions in animals

    Science.gov (United States)

    Gao, Xiao-Bing; Hermes, Gretchen

    2015-01-01

    The neuronal system that resides in the perifornical and lateral hypothalamus (Pf/LH) and synthesizes the neuropeptide hypocretin/orexin participates in critical brain functions across species from fish to human. The hypocretin system regulates neural activity responsible for daily functions (such as sleep/wake homeostasis, energy balance, appetite, etc.) and long-term behavioral changes (such as reward seeking and addiction, stress response, etc.) in animals. The most recent evidence suggests that the hypocretin system undergoes substantial plastic changes in response to both daily fluctuations (such as food intake and sleep-wake regulation) and long-term changes (such as cocaine seeking) in neuronal activity in the brain. The understanding of these changes in the hypocretin system is essential in addressing the role of the hypocretin system in normal physiological functions and pathological conditions in animals and humans. In this review, the evidence demonstrating that neural plasticity occurs in hypocretin-containing neurons in the Pf/LH will be presented and possible physiological, behavioral, and mental health implications of these findings will be discussed. PMID:26539086

  10. NPY gene transfer in the hippocampus attenuates synaptic plasticity and learning

    DEFF Research Database (Denmark)

    Sørensen, Andreas T; Kanter-Schlifke, Irene; Carli, Mirjana

    2008-01-01

    . Future clinical progress, however, requires more detailed evaluation of possible side effects of this treatment. Until now it has been unknown whether rAAV vector-based NPY overexpression in the hippocampus alters normal synaptic transmission and plasticity, which could disturb learning and memory...... processing. Here we show, by electrophysiological recordings in CA1 of the hippocampal formation of rats, that hippocampal NPY gene transfer into the intact brain does not affect basal synaptic transmission, but slightly alters short-term synaptic plasticity, most likely via NPY Y2 receptor...... is partially impaired and animals have a slower rate of hippocampal-based spatial discrimination learning. These data provide the first evidence that rAAV-based gene therapy using NPY exerts relative limited effect on synaptic plasticity and learning in the hippocampus, and therefore this approach could...

  11. The frog vestibular system as a model for lesion-induced plasticity: basic neural principles and implications for posture control

    Directory of Open Access Journals (Sweden)

    Francois M Lambert

    2012-04-01

    Full Text Available Studies of behavioral consequences after unilateral labyrinthectomy have a long tradition in the quest of determining rules and limitations of the CNS to exert plastic changes that assist the recuperation from the loss of sensory inputs. Frogs were among the first animal models to illustrate general principles of regenerative capacity and reorganizational neural flexibility after a vestibular lesion. The continuous successful use of the latter animals is in part based on the easy access and identifiability of nerve branches to inner ear organs for surgical intervention, the possibility to employ whole brain preparations for in vitro studies and the limited degree of freedom of postural reflexes for quantification of behavioral impairments and subsequent improvements. Major discoveries that increased the knowledge of post-lesional reactive mechanisms in the central nervous system include alterations in vestibular commissural signal processing and activation of cooperative changes in excitatory and inhibitory inputs to disfacilitated neurons. Moreover, the observed increase of synaptic efficacy in propriospinal circuits illustrates the importance of limb proprioceptive inputs for postural recovery. Accumulated evidence suggests that the lesion-induced neural plasticity is not a goal-directed process that aims towards a meaningful restoration of vestibular reflexes but rather attempts a survival of those neurons that have lost their excitatory inputs. Accordingly, the reaction mechanism causes an improvement of some components but also a deterioration of other aspects as seen by spatio-temporally inappropriate vestibulo-motor responses, similar to the consequences of plasticity processes in various sensory systems and species. The generality of the findings indicate that frogs continue to form a highly amenable vertebrate model system for exploring molecular and physiological events during cellular and network reorganization after a loss of

  12. Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

    Science.gov (United States)

    Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

    2015-04-01

    A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to

  13. Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills

    Science.gov (United States)

    Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

    2015-01-01

    A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to

  14. Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

    Directory of Open Access Journals (Sweden)

    Kai Olav Ellefsen

    2015-04-01

    Full Text Available A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand. To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1 that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2 that one benefit of the modularity ubiquitous in the brains of natural animals

  15. Plasticity in central neural drive with short-term disuse and recovery - effects on muscle strength and influence of aging.

    Science.gov (United States)

    Hvid, L G; Aagaard, P; Ørtenblad, N; Kjaer, M; Suetta, C

    2018-02-21

    While short-term disuse negatively affects mechanical muscle function (e.g. isometric muscle strength) little is known of the relative contribution of adaptions in central neural drive and peripheral muscle contractility. The present study investigated the relative contribution of adaptations in central neural drive and peripheral muscle contractility on changes in isometric muscle strength following short-term unilateral disuse (4 days, knee brace) and subsequent active recovery (7 days, one session of resistance training) in young (n = 11, 24 yrs) and old healthy men (n = 11, 67 yrs). Maximal isometric knee extensor strength (MVC) (isokinetic dynamometer), voluntary muscle activation (superimposed twitch technique), and electrically evoked muscle twitch force (single and doublet twitch stimulation) were assessed prior to and after disuse, and after recovery. Following disuse, relative decreases in MVC did not differ statistically between old (16.4 ± 3.7%, p plasticity in voluntary muscle activation (~central neural drive) is a dominant mechanism affecting short-term disuse- and recovery-induced changes in muscle strength in older adults. Copyright © 2017. Published by Elsevier Inc.

  16. Altered synaptic plasticity in Tourette's syndrome and its relationship to motor skill learning.

    Directory of Open Access Journals (Sweden)

    Valerie Cathérine Brandt

    Full Text Available Gilles de la Tourette syndrome is a neuropsychiatric disorder characterized by motor and phonic tics that can be considered motor responses to preceding inner urges. It has been shown that Tourette patients have inferior performance in some motor learning tasks and reduced synaptic plasticity induced by transcranial magnetic stimulation. However, it has not been investigated whether altered synaptic plasticity is directly linked to impaired motor skill acquisition in Tourette patients. In this study, cortical plasticity was assessed by measuring motor-evoked potentials before and after paired associative stimulation in 14 Tourette patients (13 male; age 18-39 and 15 healthy controls (12 male; age 18-33. Tic and urge severity were assessed using the Yale Global Tic Severity Scale and the Premonitory Urges for Tics Scale. Motor learning was assessed 45 minutes after inducing synaptic plasticity and 9 months later, using the rotary pursuit task. On average, long-term potentiation-like effects in response to the paired associative stimulation were present in healthy controls but not in patients. In Tourette patients, long-term potentiation-like effects were associated with more and long-term depression-like effects with less severe urges and tics. While motor learning did not differ between patients and healthy controls 45 minutes after inducing synaptic plasticity, the learning curve of the healthy controls started at a significantly higher level than the Tourette patients' 9 months later. Induced synaptic plasticity correlated positively with motor skills in healthy controls 9 months later. The present study confirms previously found long-term improvement in motor performance after paired associative stimulation in healthy controls but not in Tourette patients. Tourette patients did not show long-term potentiation in response to PAS and also showed reduced levels of motor skill consolidation after 9 months compared to healthy controls. Moreover

  17. A Computational Model of the Temporal Dynamics of Plasticity in Procedural Learning: Sensitivity to Feedback Timing

    Directory of Open Access Journals (Sweden)

    Vivian V. Valentin

    2014-07-01

    Full Text Available The evidence is now good that different memory systems mediate the learning of different types of category structures. In particular, declarative memory dominates rule-based (RB category learning and procedural memory dominates information-integration (II category learning. For example, several studies have reported that feedback timing is critical for II category learning, but not for RB category learning – results that have broad support within the memory systems literature. Specifically, II category learning has been shown to be best with feedback delays of 500ms compared to delays of 0 and 1000ms, and highly impaired with delays of 2.5 seconds or longer. In contrast, RB learning is unaffected by any feedback delay up to 10 seconds. We propose a neurobiologically detailed theory of procedural learning that is sensitive to different feedback delays. The theory assumes that procedural learning is mediated by plasticity at cortical-striatal synapses that are modified by dopamine-mediated reinforcement learning. The model captures the time-course of the biochemical events in the striatum that cause synaptic plasticity, and thereby accounts for the empirical effects of various feedback delays on II category learning.

  18. Astrocytes: Orchestrating synaptic plasticity?

    Science.gov (United States)

    De Pittà, M; Brunel, N; Volterra, A

    2016-05-26

    Synaptic plasticity is the capacity of a preexisting connection between two neurons to change in strength as a function of neural activity. Because synaptic plasticity is the major candidate mechanism for learning and memory, the elucidation of its constituting mechanisms is of crucial importance in many aspects of normal and pathological brain function. In particular, a prominent aspect that remains debated is how the plasticity mechanisms, that encompass a broad spectrum of temporal and spatial scales, come to play together in a concerted fashion. Here we review and discuss evidence that pinpoints to a possible non-neuronal, glial candidate for such orchestration: the regulation of synaptic plasticity by astrocytes. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

  19. Markov Chain Monte Carlo Bayesian Learning for Neural Networks

    Science.gov (United States)

    Goodrich, Michael S.

    2011-01-01

    Conventional training methods for neural networks involve starting al a random location in the solution space of the network weights, navigating an error hyper surface to reach a minimum, and sometime stochastic based techniques (e.g., genetic algorithms) to avoid entrapment in a local minimum. It is further typically necessary to preprocess the data (e.g., normalization) to keep the training algorithm on course. Conversely, Bayesian based learning is an epistemological approach concerned with formally updating the plausibility of competing candidate hypotheses thereby obtaining a posterior distribution for the network weights conditioned on the available data and a prior distribution. In this paper, we developed a powerful methodology for estimating the full residual uncertainty in network weights and therefore network predictions by using a modified Jeffery's prior combined with a Metropolis Markov Chain Monte Carlo method.

  20. Neural architecture design based on extreme learning machine.

    Science.gov (United States)

    Bueno-Crespo, Andrés; García-Laencina, Pedro J; Sancho-Gómez, José-Luis

    2013-12-01

    Selection of the optimal neural architecture to solve a pattern classification problem entails to choose the relevant input units, the number of hidden neurons and its corresponding interconnection weights. This problem has been widely studied in many research works but their solutions usually involve excessive computational cost in most of the problems and they do not provide a unique solution. This paper proposes a new technique to efficiently design the MultiLayer Perceptron (MLP) architecture for classification using the Extreme Learning Machine (ELM) algorithm. The proposed method provides a high generalization capability and a unique solution for the architecture design. Moreover, the selected final network only retains those input connections that are relevant for the classification task. Experimental results show these advantages. Copyright © 2013 Elsevier Ltd. All rights reserved.

  1. Modification of tenascin-R expression following unilateral labyrinthectomy in rats indicates its possible role in neural plasticity of the vestibular neural circuit.

    Science.gov (United States)

    Gaal, Botond; Jóhannesson, Einar Örn; Dattani, Amit; Magyar, Agnes; Wéber, Ildikó; Matesz, Clara

    2015-09-01

    We have previously found that unilateral labyrinthectomy is accompanied by modification of hyaluronan and chondroitin sulfate proteoglycan staining in the lateral vestibular nucleus of rats and the time course of subsequent reorganization of extracellular matrix assembly correlates to the restoration of impaired vestibular function. The tenascin-R has repelling effect on pathfinding during axonal growth/regrowth, and thus inhibits neural circuit repair. By using immunohistochemical method, we studied the modification of tenascin-R expression in the superior, medial, lateral, and descending vestibular nuclei of the rat following unilateral labyrinthectomy. On postoperative day 1, tenascin-R reaction in the perineuronal nets disappeared on the side of labyrinthectomy in the superior, lateral, medial, and rostral part of the descending vestibular nuclei. On survival day 3, the staining intensity of tenascin-R reaction in perineuronal nets recovered on the operated side of the medial vestibular nucleus, whereas it was restored by the time of postoperative day 7 in the superior, lateral and rostral part of the descending vestibular nuclei. The staining intensity of tenascin-R reaction remained unchanged in the caudal part of the descending vestibular nucleus bilaterally. Regional differences in the modification of tenascin-R expression presented here may be associated with different roles of individual vestibular nuclei in the compensatory processes. The decreased expression of the tenascin-R may suggest the extracellular facilitation of plastic modifications in the vestibular neural circuit after lesion of the labyrinthine receptors.

  2. Searching for learning-dependent changes in the antennal lobe: simultaneous recording of neural activity and aversive olfactory learning in honeybees

    Directory of Open Access Journals (Sweden)

    Edith Roussel

    2010-09-01

    Full Text Available Plasticity in the honeybee brain has been studied using the appetitive olfactory conditioning of the proboscis extension reflex, in which a bee learns the association between an odor and a sucrose reward. In this framework, coupling behavioral measurements of proboscis extension and invasive recordings of neural activity has been difficult because proboscis movements usually introduce brain movements that affect physiological preparations. Here we took advantage of a new conditioning protocol, the aversive olfactory conditioning of the sting extension reflex, which does not generate this problem. We achieved the first simultaneous recordings of conditioned sting extension responses and calcium imaging of antennal lobe activity, thus revealing on-line processing of olfactory information during conditioning trials. Based on behavioral output we distinguished learners and non-learners and analyzed possible learning-dependent changes in antennal lobe activity. We did not find differences between glomerular responses to the CS+ and the CS- in learners. Unexpectedly, we found that during conditioning trials non-learners exhibited a progressive decrease in physiological responses to odors, irrespective of their valence. This effect could neither be attributed to a fitness problem nor to abnormal dye bleaching. We discuss the absence of learning-induced changes in the antennal lobe of learners and the decrease in calcium responses found in non-learners. Further studies will have to extend the search for functional plasticity related to aversive learning to other brain areas and to look on a broader range of temporal scales

  3. Spatiotemporal computations of an excitable and plastic brain: neuronal plasticity leads to noise-robust and noise-constructive computations.

    Science.gov (United States)

    Toutounji, Hazem; Pipa, Gordon

    2014-03-01

    It is a long-established fact that neuronal plasticity occupies the central role in generating neural function and computation. Nevertheless, no unifying account exists of how neurons in a recurrent cortical network learn to compute on temporally and spatially extended stimuli. However, these stimuli constitute the norm, rather than the exception, of the brain's input. Here, we introduce a geometric theory of learning spatiotemporal computations through neuronal plasticity. To that end, we rigorously formulate the problem of neural representations as a relation in space between stimulus-induced neural activity and the asymptotic dynamics of excitable cortical networks. Backed up by computer simulations and numerical analysis, we show that two canonical and widely spread forms of neuronal plasticity, that is, spike-timing-dependent synaptic plasticity and intrinsic plasticity, are both necessary for creating neural representations, such that these computations become realizable. Interestingly, the effects of these forms of plasticity on the emerging neural code relate to properties necessary for both combating and utilizing noise. The neural dynamics also exhibits features of the most likely stimulus in the network's spontaneous activity. These properties of the spatiotemporal neural code resulting from plasticity, having their grounding in nature, further consolidate the biological relevance of our findings.

  4. Recurrent Neural Network for Text Classification with Multi-Task Learning

    OpenAIRE

    Liu, Pengfei; Qiu, Xipeng; Huang, Xuanjing

    2016-01-01

    Neural network based methods have obtained great progress on a variety of natural language processing tasks. However, in most previous works, the models are learned based on single-task supervised objectives, which often suffer from insufficient training data. In this paper, we use the multi-task learning framework to jointly learn across multiple related tasks. Based on recurrent neural network, we propose three different mechanisms of sharing information to model text with task-specific and...

  5. Self-organization of neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Clark, J.W.; Winston, J.V.; Rafelski, J.

    1984-05-14

    The plastic development of a neural-network model operating autonomously in discrete time is described by the temporal modification of interneuronal coupling strengths according to momentary neural activity. A simple algorithm (brainwashing) is found which, applied to nets with initially quasirandom connectivity, leads to model networks with properties conducive to the simulation of memory and learning phenomena. 18 references, 2 figures.

  6. Brain Plasticity and the Art of Teaching to Learn

    Science.gov (United States)

    Martinez, Margaret

    2005-01-01

    "Everyone thinks of changing the world, but no one thinks of changing himself, "wrote Leo Tolstoy. Have you ever thought about how learning changes your brain? If yes, this paper may help you explore the research that will change our learning landscape in the next few years! Recent developers in the neurosciences and education research…

  7. Three-terminal ferroelectric synapse device with concurrent learning function for artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Nishitani, Y.; Kaneko, Y.; Ueda, M.; Fujii, E. [Advanced Technology Research Laboratories, Panasonic Corporation, Seika, Kyoto 619-0237 (Japan); Morie, T. [Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Wakamatsu-ku, Kitakyushu 808-0196 (Japan)

    2012-06-15

    Spike-timing-dependent synaptic plasticity (STDP) is demonstrated in a synapse device based on a ferroelectric-gate field-effect transistor (FeFET). STDP is a key of the learning functions observed in human brains, where the synaptic weight changes only depending on the spike timing of the pre- and post-neurons. The FeFET is composed of the stacked oxide materials with ZnO/Pr(Zr,Ti)O{sub 3} (PZT)/SrRuO{sub 3}. In the FeFET, the channel conductance can be altered depending on the density of electrons induced by the polarization of PZT film, which can be controlled by applying the gate voltage in a non-volatile manner. Applying a pulse gate voltage enables the multi-valued modulation of the conductance, which is expected to be caused by a change in PZT polarization. This variation depends on the height and the duration of the pulse gate voltage. Utilizing these characteristics, symmetric and asymmetric STDP learning functions are successfully implemented in the FeFET-based synapse device by applying the non-linear pulse gate voltage generated from a set of two pulses in a sampling circuit, in which the two pulses correspond to the spikes from the pre- and post-neurons. The three-terminal structure of the synapse device enables the concurrent learning, in which the weight update can be performed without canceling signal transmission among neurons, while the neural networks using the previously reported two-terminal synapse devices need to stop signal transmission for learning.

  8. A comparison of learning abilities of spiking networks with different spike timing-dependent plasticity forms

    Science.gov (United States)

    Sboev, Alexander; Vlasov, Danila; Serenko, Alexey; Rybka, Roman; Moloshnikov, Ivan

    2016-02-01

    A study of possibility to model the learning process on base of different forms of timing-dependent plasticity (STDP) was performed. It is shown that the learning ability depends on the choice of spike pairing scheme and the type of input signal used for learning. The comparison of performance of several STDP rules along with several neuron models (leaky integrate-and-fire, static, Izhikevich and Hodgkin-Huxley) was carried out using the NEST simulator. The combinations of input signal and STDP spike pairing scheme, which demonstrate the best learning abilities, were extracted.

  9. Random neural Q-learning for obstacle avoidance of a mobile robot in unknown environments

    Directory of Open Access Journals (Sweden)

    Jing Yang

    2016-07-01

    Full Text Available The article presents a random neural Q-learning strategy for the obstacle avoidance problem of an autonomous mobile robot in unknown environments. In the proposed strategy, two independent modules, namely, avoidance without considering the target and goal-seeking without considering obstacles, are first trained using the proposed random neural Q-learning algorithm to obtain their best control policies. Then, the two trained modules are combined based on a switching function to realize the obstacle avoidance in unknown environments. For the proposed random neural Q-learning algorithm, a single-hidden layer feedforward network is used to approximate the Q-function to estimate the Q-value. The parameters of the single-hidden layer feedforward network are modified using the recently proposed neural algorithm named the online sequential version of extreme learning machine, where the parameters of the hidden nodes are assigned randomly and the sample data can come one by one. However, different from the original online sequential version of extreme learning machine algorithm, the initial output weights are estimated subjected to quadratic inequality constraint to improve the convergence speed. Finally, the simulation results demonstrate that the proposed random neural Q-learning strategy can successfully solve the obstacle avoidance problem. Also, the higher learning efficiency and better generalization ability are achieved by the proposed random neural Q-learning algorithm compared with the Q-learning based on the back-propagation method.

  10. musical mnemonics aid verbal memory and induce learning related brain plasticity in multiple sclerosis

    OpenAIRE

    Michael eThaut; David ePeterson; gerald c mcintosh; Volker eHoemberg

    2014-01-01

    Recent research in music and brain function has suggested that the temporal pattern structure in music andrhythm can enhance cognitive functions. To further elucidate this question specifically for memory weinvestigated if a musical template can enhance verbal learning in patients with multiple sclerosis and ifmusic assisted learning will also influence short-term, system-level brain plasticity. We measuredsystems-level brain activity with oscillatory network synchronization during music assi...

  11. Music mnemonics aid Verbal Memory and Induce Learning – Related Brain Plasticity in Multiple Sclerosis

    OpenAIRE

    Michael H. Thaut; Peterson, David A.; gerald c mcintosh; Hoemberg, Volker

    2014-01-01

    Recent research on music and brain function has suggested that the temporal pattern structure in music and rhythm can enhance cognitive functions. To further elucidate this question specifically for memory, we investigated if a musical template can enhance verbal learning in patients with multiple sclerosis (MS) and if music-assisted learning will also influence short-term, system-level brain plasticity. We measured systems-level brain activity with oscillatory network synchronization during ...

  12. Unsupervised learning in neural networks with short range synapses

    Science.gov (United States)

    Brunnet, L. G.; Agnes, E. J.; Mizusaki, B. E. P.; Erichsen, R., Jr.

    2013-01-01

    Different areas of the brain are involved in specific aspects of the information being processed both in learning and in memory formation. For example, the hippocampus is important in the consolidation of information from short-term memory to long-term memory, while emotional memory seems to be dealt by the amygdala. On the microscopic scale the underlying structures in these areas differ in the kind of neurons involved, in their connectivity, or in their clustering degree but, at this level, learning and memory are attributed to neuronal synapses mediated by longterm potentiation and long-term depression. In this work we explore the properties of a short range synaptic connection network, a nearest neighbor lattice composed mostly by excitatory neurons and a fraction of inhibitory ones. The mechanism of synaptic modification responsible for the emergence of memory is Spike-Timing-Dependent Plasticity (STDP), a Hebbian-like rule, where potentiation/depression is acquired when causal/non-causal spikes happen in a synapse involving two neurons. The system is intended to store and recognize memories associated to spatial external inputs presented as simple geometrical forms. The synaptic modifications are continuously applied to excitatory connections, including a homeostasis rule and STDP. In this work we explore the different scenarios under which a network with short range connections can accomplish the task of storing and recognizing simple connected patterns.

  13. Future health care applications resulting from progress in the neurosciences: The significance of neural plasticity research

    NARCIS (Netherlands)

    Gelijns, A.C.; Graaff, P.J.; Lopes da Silva, F.A.; Gispen, W.H.

    1987-01-01

    Neurological, communicative and behavioral disorders afflict a significant part of the population in industrialized countries, and these disorders can be expected to gain in importance in the coming decades. In a considerable number of these dis-orders impairments in plasticity, i.e. deficiencies in

  14. Emergence of slow collective oscillations in neural networks with spike-timing dependent plasticity

    DEFF Research Database (Denmark)

    Mikkelsen, Kaare; Imparato, Alberto; Torcini, Alessandro

    2013-01-01

    The collective dynamics of excitatory pulse coupled neurons with spike timing dependent plasticity (STDP) is studied. The introduction of STDP induces persistent irregular oscillations between strongly and weakly synchronized states, reminiscent of brain activity during slow-wave sleep. We explain...

  15. Learning and Memory, Part II: Molecular Mechanisms of Synaptic Plasticity

    Science.gov (United States)

    Lombroso, Paul; Ogren, Marilee

    2009-01-01

    The molecular events that are responsible for strengthening synaptic connections and how these are linked to memory and learning are discussed. The laboratory preparations that allow the investigation of these events are also described.

  16. Intelligence moderates reinforcement learning: a mini-review of the neural evidence.

    Science.gov (United States)

    Chen, Chong

    2015-06-01

    Our understanding of the neural basis of reinforcement learning and intelligence, two key factors contributing to human strivings, has progressed significantly recently. However, the overlap of these two lines of research, namely, how intelligence affects neural responses during reinforcement learning, remains uninvestigated. A mini-review of three existing studies suggests that higher IQ (especially fluid IQ) may enhance the neural signal of positive prediction error in dorsolateral prefrontal cortex, dorsal anterior cingulate cortex, and striatum, several brain substrates of reinforcement learning or intelligence. Copyright © 2015 the American Physiological Society.

  17. A hybrid ART-GRNN online learning neural network with a epsilon -insensitive loss function.

    Science.gov (United States)

    Yap, Keem Siah; Lim, Chee Peng; Abidin, Izham Zainal

    2008-09-01

    In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical studies to assess the effectiveness of GART in classification, regression, and time series prediction tasks is conducted. The results demonstrate that GART is able to produce good performances as compared with those of other methods, including the online sequential extreme learning machine (OSELM) and sequential learning radial basis function (RBF) neural network models.

  18. A Model for Improving the Learning Curves of Artificial Neural Networks.

    Directory of Open Access Journals (Sweden)

    Roberto L S Monteiro

    Full Text Available In this article, the performance of a hybrid artificial neural network (i.e. scale-free and small-world was analyzed and its learning curve compared to three other topologies: random, scale-free and small-world, as well as to the chemotaxis neural network of the nematode Caenorhabditis Elegans. One hundred equivalent networks (same number of vertices and average degree for each topology were generated and each was trained for one thousand epochs. After comparing the mean learning curves of each network topology with the C. elegans neural network, we found that the networks that exhibited preferential attachment exhibited the best learning curves.

  19. Larval neurogenesis in Sabellaria alveolata reveals plasticity in polychaete neural patterning

    DEFF Research Database (Denmark)

    Brinkmann, Nora; Wanninger, Andreas

    2008-01-01

    reconstruction software. The overall pattern of neurogenesis in S. alveolata resembles the condition found in other planktonic polychaete trochophores where the larval neural body plan including a serotonergic prototroch nerve ring is directly followed by adult features of the nervous system...

  20. Melanoma Spheroids Grown Under Neural Crest Cell Conditions Are Highly Plastic Migratory/Invasive Tumor Cells Endowed with Immunomodulator Function

    Science.gov (United States)

    Lalou, Claude; Lauden, Laura; Michel, Laurence; de la Grange, Pierre; Khatib, Abdel-Majid; Aoudjit, Fawzi; Charron, Dominique; Alcaide-Loridan, Catherine; Al-Daccak, Reem

    2011-01-01

    Background The aggressiveness of melanoma tumors is likely to rely on their well-recognized heterogeneity and plasticity. Melanoma comprises multi-subpopulations of cancer cells some of which may possess stem cell-like properties. Although useful, the sphere-formation assay to identify stem cell-like or tumor initiating cell subpopulations in melanoma has been challenged, and it is unclear if this model can predict a functional phenotype associated with aggressive tumor cells. Methodology/Principal Findings We analyzed the molecular and functional phenotypes of melanoma spheroids formed in neural crest cell medium. Whether from metastatic or advanced primary tumors, spheroid cells expressed melanoma-associated markers. They displayed higher capacity to differentiate along mesenchymal lineages and enhanced expression of SOX2, NANOG, KLF4, and/or OCT4 transcription factors, but not enhanced self-renewal or tumorigenicity when compared to their adherent counterparts. Gene expression profiling attributed a neural crest cell signature to these spheroids and indicated that a migratory/invasive and immune-function modulating program could be associated with these cells. In vitro assays confirmed that spheroids display enhanced migratory/invasive capacities. In immune activation assays, spheroid cells elicited a poorer allogenic response from immune cells and inhibited mitogen-dependent T cells activation and proliferation more efficiently than their adherent counterparts. Our findings reveal a novel immune-modulator function of melanoma spheroids and suggest specific roles for spheroids in invasion and in evasion of antitumor immunity. Conclusion/Significance The association of a more plastic, invasive and evasive, thus a more aggressive tumor phenotype with melanoma spheroids reveals a previously unrecognized aspect of tumor cells expanded as spheroid cultures. While of limited efficiency for melanoma initiating cell identification, our melanoma spheroid model predicted

  1. The neural plasticity of early-passage human bone marrow-derived mesenchymal stem cells and their modulation with chromatin-modifying agents.

    Science.gov (United States)

    Zhang, Zhiying; Alexanian, Arshak R

    2014-05-01

    Mesenchymal stem cells (MSCs) in their immature state express a variety of genes of the three germ layers at relatively low or moderate levels that might explain their phenomenal plasticity. Numerous recent studies have demonstrated that under the appropriate conditions in vitro and in vivo the expression of different sets of these genes can be upregulated, turning MSCs into variety of cell lineages of mesodermal, ectodermal and endodermal origin. While transdifferentiation of MSCs is still controversial, these unique properties make MSCs an ideal autologous source of easily reprogrammable cells. Recently, using the approach of cell reprogramming by biological active compounds that interfere with chromatin structure and function, as well as with specific signalling pathways that promote neural fate commitment, we have been able to generate neural-like cells from human bone marrow (BM)-derived MSCs (hMSCs). However, the efficiency of neural transformation of hMSCs induced by this approach gradually declined with passaging. To elucidate the mechanisms that underlie the higher plasticity of early-passage hMSCs, comparative analysis of the expression levels of several pluripotent and neural genes was conducted for early- and late-passage hMSCs. The results demonstrated that early-passage hMSCs expressed the majority of these genes at low and moderate levels that gradually declined at late passages. Neural induction further increased the expression of some of these genes in hMSCs, accompanied by morphological changes into neural-like cells. We concluded that low and moderate expression of several pluripotent and neural genes in early-passage hMSCs could explain their higher plasticity and pliability for neural induction. Copyright © 2012 John Wiley & Sons, Ltd.

  2. Utilising reinforcement learning to develop strategies for driving auditory neural implants

    Science.gov (United States)

    Lee, Geoffrey W.; Zambetta, Fabio; Li, Xiaodong; Paolini, Antonio G.

    2016-08-01

    Objective. In this paper we propose a novel application of reinforcement learning to the area of auditory neural stimulation. We aim to develop a simulation environment which is based off real neurological responses to auditory and electrical stimulation in the cochlear nucleus (CN) and inferior colliculus (IC) of an animal model. Using this simulator we implement closed loop reinforcement learning algorithms to determine which methods are most effective at learning effective acoustic neural stimulation strategies. Approach. By recording a comprehensive set of acoustic frequency presentations and neural responses from a set of animals we created a large database of neural responses to acoustic stimulation. Extensive electrical stimulation in the CN and the recording of neural responses in the IC provides a mapping of how the auditory system responds to electrical stimuli. The combined dataset is used as the foundation for the simulator, which is used to implement and test learning algorithms. Main results. Reinforcement learning, utilising a modified n-Armed Bandit solution, is implemented to demonstrate the model’s function. We show the ability to effectively learn stimulation patterns which mimic the cochlea’s ability to covert acoustic frequencies to neural activity. Time taken to learn effective replication using neural stimulation takes less than 20 min under continuous testing. Significance. These results show the utility of reinforcement learning in the field of neural stimulation. These results can be coupled with existing sound processing technologies to develop new auditory prosthetics that are adaptable to the recipients current auditory pathway. The same process can theoretically be abstracted to other sensory and motor systems to develop similar electrical replication of neural signals.

  3. A Multiple-Plasticity Spiking Neural Network Embedded in a Closed-Loop Control System to Model Cerebellar Pathologies.

    Science.gov (United States)

    Geminiani, Alice; Casellato, Claudia; Antonietti, Alberto; D'Angelo, Egidio; Pedrocchi, Alessandra

    2017-01-10

    The cerebellum plays a crucial role in sensorimotor control and cerebellar disorders compromise adaptation and learning of motor responses. However, the link between alterations at network level and cerebellar dysfunction is still unclear. In principle, this understanding would benefit of the development of an artificial system embedding the salient neuronal and plastic properties of the cerebellum and operating in closed-loop. To this aim, we have exploited a realistic spiking computational model of the cerebellum to analyze the network correlates of cerebellar impairment. The model was modified to reproduce three different damages of the cerebellar cortex: (i) a loss of the main output neurons (Purkinje Cells), (ii) a lesion to the main cerebellar afferents (Mossy Fibers), and (iii) a damage to a major mechanism of synaptic plasticity (Long Term Depression). The modified network models were challenged with an Eye-Blink Classical Conditioning test, a standard learning paradigm used to evaluate cerebellar impairment, in which the outcome was compared to reference results obtained in human or animal experiments. In all cases, the model reproduced the partial and delayed conditioning typical of the pathologies, indicating that an intact cerebellar cortex functionality is required to accelerate learning by transferring acquired information to the cerebellar nuclei. Interestingly, depending on the type of lesion, the redistribution of synaptic plasticity and response timing varied greatly generating specific adaptation patterns. Thus, not only the present work extends the generalization capabilities of the cerebellar spiking model to pathological cases, but also predicts how changes at the neuronal level are distributed across the network, making it usable to infer cerebellar circuit alterations occurring in cerebellar pathologies.

  4. Learning-Dependent Plasticity of the Barrel Cortex Is Impaired by Restricting GABA-Ergic Transmission.

    Directory of Open Access Journals (Sweden)

    Anna Posluszny

    Full Text Available Experience-induced plastic changes in the cerebral cortex are accompanied by alterations in excitatory and inhibitory transmission. Increased excitatory drive, necessary for plasticity, precedes the occurrence of plastic change, while decreased inhibitory signaling often facilitates plasticity. However, an increase of inhibitory interactions was noted in some instances of experience-dependent changes. We previously reported an increase in the number of inhibitory markers in the barrel cortex of mice after fear conditioning engaging vibrissae, observed concurrently with enlargement of the cortical representational area of the row of vibrissae receiving conditioned stimulus (CS. We also observed that an increase of GABA level accompanied the conditioning. Here, to find whether unaltered GABAergic signaling is necessary for learning-dependent rewiring in the murine barrel cortex, we locally decreased GABA production in the barrel cortex or reduced transmission through GABAA receptors (GABAARs at the time of the conditioning. Injections of 3-mercaptopropionic acid (3-MPA, an inhibitor of glutamic acid decarboxylase (GAD, into the barrel cortex prevented learning-induced enlargement of the conditioned vibrissae representation. A similar effect was observed after injection of gabazine, an antagonist of GABAARs. At the behavioral level, consistent conditioned response (cessation of head movements in response to CS was impaired. These results show that appropriate functioning of the GABAergic system is required for both manifestation of functional cortical representation plasticity and for the development of a conditioned response.

  5. Cognitive and Neural Plasticity in Older Adults’ Prospective Memory Following Training with the Virtual Week Computer Game

    Directory of Open Access Journals (Sweden)

    Nathan S Rose

    2015-10-01

    Full Text Available Prospective memory (PM – the ability to remember and successfully execute our intentions and planned activities – is critical for functional independence and declines with age, yet few studies have attempted to train PM in older adults. We developed a PM training program using the Virtual Week computer game. Trained participants played the game in twelve, 1-hour sessions over one month. Measures of neuropsychological functions, lab-based PM, event-related potentials (ERPs during performance on a lab-based PM task, instrumental activities of daily living, and real-world PM were assessed before and after training. Performance was compared to both no-contact and active (music training control groups. PM on the Virtual Week game dramatically improved following training relative to controls, suggesting PM plasticity is preserved in older adults. Relative to control participants, training did not produce reliable transfer to laboratory-based tasks, but was associated with a reduction of an ERP component (sustained negativity over occipito-parietal cortex associated with processing PM cues, indicative of more automatic PM retrieval. Most importantly, training produced far transfer to real-world outcomes including improvements in performance on real-world PM and activities of daily living. Real-world gains were not observed in either control group. Our findings demonstrate that short-term training with the Virtual Week game produces cognitive and neural plasticity that may result in real-world benefits to supporting functional independence in older adulthood.

  6. Cognitive and neural plasticity in older adults' prospective memory following training with the Virtual Week computer game.

    Science.gov (United States)

    Rose, Nathan S; Rendell, Peter G; Hering, Alexandra; Kliegel, Matthias; Bidelman, Gavin M; Craik, Fergus I M

    2015-01-01

    Prospective memory (PM) - the ability to remember and successfully execute our intentions and planned activities - is critical for functional independence and declines with age, yet few studies have attempted to train PM in older adults. We developed a PM training program using the Virtual Week computer game. Trained participants played the game in 12, 1-h sessions over 1 month. Measures of neuropsychological functions, lab-based PM, event-related potentials (ERPs) during performance on a lab-based PM task, instrumental activities of daily living, and real-world PM were assessed before and after training. Performance was compared to both no-contact and active (music training) control groups. PM on the Virtual Week game dramatically improved following training relative to controls, suggesting PM plasticity is preserved in older adults. Relative to control participants, training did not produce reliable transfer to laboratory-based tasks, but was associated with a reduction of an ERP component (sustained negativity over occipito-parietal cortex) associated with processing PM cues, indicative of more automatic PM retrieval. Most importantly, training produced far transfer to real-world outcomes including improvements in performance on real-world PM and activities of daily living. Real-world gains were not observed in either control group. Our findings demonstrate that short-term training with the Virtual Week game produces cognitive and neural plasticity that may result in real-world benefits to supporting functional independence in older adulthood.

  7. Cognitive and neural plasticity in older adults’ prospective memory following training with the Virtual Week computer game

    Science.gov (United States)

    Rose, Nathan S.; Rendell, Peter G.; Hering, Alexandra; Kliegel, Matthias; Bidelman, Gavin M.; Craik, Fergus I. M.

    2015-01-01

    Prospective memory (PM) – the ability to remember and successfully execute our intentions and planned activities – is critical for functional independence and declines with age, yet few studies have attempted to train PM in older adults. We developed a PM training program using the Virtual Week computer game. Trained participants played the game in 12, 1-h sessions over 1 month. Measures of neuropsychological functions, lab-based PM, event-related potentials (ERPs) during performance on a lab-based PM task, instrumental activities of daily living, and real-world PM were assessed before and after training. Performance was compared to both no-contact and active (music training) control groups. PM on the Virtual Week game dramatically improved following training relative to controls, suggesting PM plasticity is preserved in older adults. Relative to control participants, training did not produce reliable transfer to laboratory-based tasks, but was associated with a reduction of an ERP component (sustained negativity over occipito-parietal cortex) associated with processing PM cues, indicative of more automatic PM retrieval. Most importantly, training produced far transfer to real-world outcomes including improvements in performance on real-world PM and activities of daily living. Real-world gains were not observed in either control group. Our findings demonstrate that short-term training with the Virtual Week game produces cognitive and neural plasticity that may result in real-world benefits to supporting functional independence in older adulthood. PMID:26578936

  8. Training the brain: practical applications of neural plasticity from the intersection of cognitive neuroscience, developmental psychology, and prevention science.

    Science.gov (United States)

    Bryck, Richard L; Fisher, Philip A

    2012-01-01

    Prior researchers have shown that the brain has a remarkable ability for adapting to environmental changes. The positive effects of such neural plasticity include enhanced functioning in specific cognitive domains and shifts in cortical representation following naturally occurring cases of sensory deprivation; however, maladaptive changes in brain function and development owing to early developmental adversity and stress have also been well documented. Researchers examining enriched rearing environments in animals have revealed the potential for inducing positive brain plasticity effects and have helped to popularize methods for training the brain to reverse early brain deficits or to boost normal cognitive functioning. In this article, two classes of empirically based methods of brain training in children are reviewed and critiqued: laboratory-based, mental process training paradigms and ecological interventions based upon neurocognitive conceptual models. Given the susceptibility of executive function disruption, special attention is paid to training programs that emphasize executive function enhancement. In addition, a third approach to brain training, aimed at tapping into compensatory processes, is postulated. Study results showing the effectiveness of this strategy in the field of neurorehabilitation and in terms of naturally occurring compensatory processing in human aging lend credence to the potential of this approach. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

  9. Coordinated Plasticity among Glutamatergic and GABAergic Neurons and Synapses in the Barrel Cortex Is Correlated to Learning Efficiency

    Science.gov (United States)

    Zhao, Xin; Huang, Li; Guo, Rui; Liu, Yulong; Zhao, Shidi; Guan, Sudong; Ge, Rongjing; Cui, Shan; Wang, Shirlene; Wang, Jin-Hui

    2017-01-01

    Functional plasticity at cortical synapses and neurons is presumably associated with learning and memory. Additionally, coordinated refinement between glutamatergic and GABAergic neurons occurs in associative memory. If these assumptions are present, neuronal plasticity strength and learning efficiency should be correlated. We have examined whether neuronal plasticity strength and learning efficiency are quantitatively correlated in a mouse model of associative learning. Paired whisker and odor stimulations in mice induce odorant-induced whisker motions. The fully establishment of this associative memory appears fast and slow, which are termed as high learning efficiency and low learning efficiency, respectively. In the study of cellular mechanisms underlying this differential learning efficiency, we have compared the strength of neuronal plasticity in the barrel cortices that store associative signals from the mice with high vs. low learning efficiencies. Our results indicate that the levels of learning efficiency are linearly correlated with the upregulated strengths of excitatory synaptic transmission on glutamatergic neurons and their excitability, as well as the downregulated strengths of GABAergic neurons' excitability, their excitatory synaptic inputs and inhibitory synaptic outputs in layers II~III of barrel cortices. The correlations between learning efficiency in associative memory formation and coordinated plasticity at cortical glutamatergic and GABAergic neurons support the notion that the plasticity of associative memory cells is a basis for memory strength. PMID:28798668

  10. Neural correlates of individual differences in fear learning.

    Science.gov (United States)

    MacNamara, Annmarie; Rabinak, Christine A; Fitzgerald, Daniel A; Zhou, Xiaohong Joe; Shankman, Stewart A; Milad, Mohammed R; Phan, K Luan

    2015-01-01

    Variability in fear conditionability is common, and clarity regarding the neural regions responsible for individual differences in fear conditionability could uncover brain-based biomarkers of resilience or vulnerability to trauma-based psychopathologies (e.g., post-traumatic stress disorder). In recent years, neuroimaging work has yielded a detailed understanding of the neural mechanisms underlying fear conditioning common across participants, however only a minority of studies have investigated the brain basis of inter-individual variation in fear learning. Moreover, the majority of these studies have employed small sample sizes (mean n=17; range n=5-27) and all have failed to meet the minimum recommended sample size for functional magnetic resonance imaging (fMRI) studies of individual differences. Here, using fMRI, we analyzed blood-oxygenation level dependent (BOLD) response recorded simultaneously with skin conductance response (SCR) and ratings of unconditioned stimulus (US) expectancy in 49 participants undergoing Pavlovian fear conditioning. On average, participants became conditioned to the conditioned stimulus (CS+; higher US expectancy ratings and SCR for the CS+ compared to the unpaired conditioned stimulus, CS-); the CS+ also robustly increased activation in the bilateral insula. Amygdala activation was revealed from a regression analysis that incorporated individual differences in fear conditionability (i.e., a between-subjects regressor of mean CS+>CS- SCR). By replicating results observed using much smaller sample sizes, the results confirm that variation in amygdala reactivity covaries with individual differences in fear conditionability. The link between behavior (SCR) and brain (amygdala reactivity) may be a putative endophenotype for the acquisition of fear memories. Copyright © 2015 Elsevier B.V. All rights reserved.

  11. Neural plasticity in functional and anatomical MRI studies of children with Tourette syndrome

    DEFF Research Database (Denmark)

    Eichele, Heike; Plessen, Kerstin J

    2012-01-01

    Background: Tourette syndrome (TS) is a neuropsychiatric disorder with childhood onset characterized by chronic motor and vocal tics. The typical clinical course of an attenuation of symptoms during adolescence in parallel with the emerging self-regulatory control during development suggests...... that plastic processes may play an important role in the development of tic symptoms. Methods: We conducted a systematic search to identify existing imaging studies (both anatomical and functional magnetic resonance imaging [fMRI]) in young persons under the age of 19 years with TS. Results: The final search...... resulted in 13 original studies, which were reviewed with a focus on findings suggesting adaptive processes (using fMRI) and plasticity (using anatomical MRI). Differences in brain activation compared to healthy controls during tasks that require overriding of prepotent responses help to understand...

  12. Neural plasticity following lesions of the primate occipital lobe: The marmoset as an animal model for studies of blindsight.

    Science.gov (United States)

    Hagan, Maureen A; Rosa, Marcello G P; Lui, Leo L

    2017-03-01

    For nearly a century it has been observed that some residual visually guided behavior can persist after damage to the primary visual cortex (V1) in primates. The age at which damage to V1 occurs leads to different outcomes, with V1 lesions in infancy allowing better preservation of visual faculties in comparison with those incurred in adulthood. While adult V1 lesions may still allow retention of some limited visual abilities, these are subconscious-a characteristic that has led to this form of residual vision being referred to as blindsight. The neural basis of blindsight has been of great interest to the neuroscience community, with particular focus on understanding the contributions of the different subcortical pathways and cortical areas that may underlie this phenomenon. More recently, research has started to address which forms of neural plasticity occur following V1 lesions at different ages, including work using marmoset monkeys. The relatively rapid postnatal development of this species, allied to the lissencephalic brains and well-characterized visual cortex provide significant technical advantages, which allow controlled experiments exploring visual function in the absence of V1. © 2016 Wiley Periodicals, Inc. Develop Neurobiol 77: 314-327, 2017. © 2016 Wiley Periodicals, Inc.

  13. Refinement and Pattern Formation in Neural Circuits by the Interaction of Traveling Waves with Spike-Timing Dependent Plasticity

    Science.gov (United States)

    Bennett, James E. M.; Bair, Wyeth

    2015-01-01

    Traveling waves in the developing brain are a prominent source of highly correlated spiking activity that may instruct the refinement of neural circuits. A candidate mechanism for mediating such refinement is spike-timing dependent plasticity (STDP), which translates correlated activity patterns into changes in synaptic strength. To assess the potential of these phenomena to build useful structure in developing neural circuits, we examined the interaction of wave activity with STDP rules in simple, biologically plausible models of spiking neurons. We derive an expression for the synaptic strength dynamics showing that, by mapping the time dependence of STDP into spatial interactions, traveling waves can build periodic synaptic connectivity patterns into feedforward circuits with a broad class of experimentally observed STDP rules. The spatial scale of the connectivity patterns increases with wave speed and STDP time constants. We verify these results with simulations and demonstrate their robustness to likely sources of noise. We show how this pattern formation ability, which is analogous to solutions of reaction-diffusion systems that have been widely applied to biological pattern formation, can be harnessed to instruct the refinement of postsynaptic receptive fields. Our results hold for rich, complex wave patterns in two dimensions and over several orders of magnitude in wave speeds and STDP time constants, and they provide predictions that can be tested under existing experimental paradigms. Our model generalizes across brain areas and STDP rules, allowing broad application to the ubiquitous occurrence of traveling waves and to wave-like activity patterns induced by moving stimuli. PMID:26308406

  14. In search for the neural mechanisms of individual development: behavior-driven differential Hebbian learning

    Directory of Open Access Journals (Sweden)

    Ralf eDer

    2016-01-01

    Full Text Available When Donald Hebb published his 1949 book ``The Organization of Behavior'' he opened a new way of thinking in theoretical neuroscience which, in retrospective, is very close to contemporary ideas in self-organization. His metaphor of ``wiring'' together what ``fires together'' matches very closely the commonparadigm that global organization can derive from simple local rules. While ingenious at his time and inspiring the research over decades, the results still fall short of the expectations. For instance,unsupervised as they are, such neural mechanisms should be able to explain and realize the self-organizedacquisition of sensorimotor competencies. This paper proposes a new synaptic law which replaces Hebb's original metaphor by that of ``chaining together'' what ``changes together''. Starting from differential Hebbian learning,the new rule grounds the behavior of the agent directly in the internal synaptic dynamics.Therefore, one may call this a behavior-driven synaptic plasticity.Neurorobotics is an ideal testing ground for this new, unsupervised learning rule. This paper focuses on the close coupling between body, control, and environmentin challenging physical settings. The examples demonstrate how the new synaptic mechanism induces a self-determined ``search and converge'' strategy in behavior space, generating spontaneously a variety of sensorimotor competencies. The emerging behavior patterns are qualified by involving body and environment inan irreducible conjunction with the internal mechanism.The results may not only be of immediate interest for the further development of embodied intelligence.They also offer a new view on the role of self-learning processes in natural evolutionand in the brain.Videos and further details may be found under url{http://robot.informatik.uni-leipzig.de/research/supplementary/NeuroAutonomy/}.

  15. α-Tocopherol and Hippocampal Neural Plasticity in Physiological and Pathological Conditions.

    Science.gov (United States)

    Ambrogini, Patrizia; Betti, Michele; Galati, Claudia; Di Palma, Michael; Lattanzi, Davide; Savelli, David; Galli, Francesco; Cuppini, Riccardo; Minelli, Andrea

    2016-12-15

    Neuroplasticity is an "umbrella term" referring to the complex, multifaceted physiological processes that mediate the ongoing structural and functional modifications occurring, at various time- and size-scales, in the ever-changing immature and adult brain, and that represent the basis for fundamental neurocognitive behavioral functions; in addition, maladaptive neuroplasticity plays a role in the pathophysiology of neuropsychiatric dysfunctions. Experiential cues and several endogenous and exogenous factors can regulate neuroplasticity; among these, vitamin E, and in particular α-tocopherol (α-T), the isoform with highest bioactivity, exerts potent effects on many plasticity-related events in both the physiological and pathological brain. In this review, the role of vitamin E/α-T in regulating diverse aspects of neuroplasticity is analyzed and discussed, focusing on the hippocampus, a brain structure that remains highly plastic throughout the lifespan and is involved in cognitive functions. Vitamin E-mediated influences on hippocampal synaptic plasticity and related cognitive behavior, on post-natal development and adult hippocampal neurogenesis, as well as on cellular and molecular disruptions in kainate-induced temporal seizures are described. Besides underscoring the relevance of its antioxidant properties, non-antioxidant functions of vitamin E/α-T, mainly involving regulation of cell signaling molecules and their target proteins, have been highlighted to help interpret the possible mechanisms underlying the effects on neuroplasticity.

  16. Anodal transcranial direct current stimulation of the motor cortex increases cortical voluntary activation and neural plasticity.

    Science.gov (United States)

    Frazer, Ashlyn; Williams, Jacqueline; Spittles, Michael; Rantalainen, Timo; Kidgell, Dawson

    2016-11-01

    We examined the cumulative effect of 4 consecutive bouts of noninvasive brain stimulation on corticospinal plasticity and motor performance, and whether these responses were influenced by the brain-derived neurotrophic factor (BDNF) polymorphism. In a randomized double-blinded cross-over design, changes in strength and indices of corticospinal plasticity were analyzed in 14 adults who were exposed to 4 consecutive sessions of anodal and sham transcranial direct current stimulation (tDCS). Participants also undertook a blood sample for BDNF genotyping (N = 13). We observed a significant increase in isometric wrist flexor strength with transcranial magnetic stimulation revealing increased corticospinal excitability, decreased silent period duration, and increased cortical voluntary activation compared with sham tDCS. The results show that 4 consecutive sessions of anodal tDCS increased cortical voluntary activation manifested as an improvement in strength. Induction of corticospinal plasticity appears to be influenced by the BDNF polymorphism. Muscle Nerve 54: 903-913, 2016. © 2016 Wiley Periodicals, Inc.

  17. Spred1 is required for synaptic plasticity and hippocampus-dependent learning.

    Science.gov (United States)

    Denayer, Ellen; Ahmed, Tariq; Brems, Hilde; Van Woerden, Geeske; Borgesius, Nils Zuiderveen; Callaerts-Vegh, Zsuzsanna; Yoshimura, Akihiko; Hartmann, Dieter; Elgersma, Ype; D'Hooge, Rudi; Legius, Eric; Balschun, Detlef

    2008-12-31

    Germline mutations in SPRED1, a negative regulator of Ras, have been described in a neurofibromatosis type 1 (NF1)-like syndrome (NFLS) that included learning difficulties in some affected individuals. NFLS belongs to the group of phenotypically overlapping neuro-cardio-facial-cutaneous syndromes that are all caused by germ line mutations in genes of the Ras/mitogen-activated protein kinase extracellular signal-regulated kinase (ERK) pathway and that present with some degree of learning difficulties or mental retardation. We investigated hippocampus-dependent learning and memory as well as synaptic plasticity in Spred1(-/-) mice, an animal model of this newly discovered human syndrome. Spred1(-/-) mice show decreased learning and memory performance in the Morris water maze and visual-discrimination T-maze, but normal basic neuromotor and sensory abilities. Electrophysiological recordings on brain slices from these animals identified defects in short- and long-term synaptic hippocampal plasticity, including a disequilibrium between long-term potentiation (LTP) and long-term depression in CA1 region. Biochemical analysis, 4 h after LTP induction, demonstrated increased ERK-phosphorylation in Spred1(-/-) slices compared with those of wild-type littermates. This indicates that deficits in hippocampus-dependent learning and synaptic plasticity induced by SPRED1 deficiency are related to hyperactivation of the Ras/ERK pathway.

  18. Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules

    Science.gov (United States)

    Frémaux, Nicolas; Gerstner, Wulfram

    2016-01-01

    Classical Hebbian learning puts the emphasis on joint pre- and postsynaptic activity, but neglects the potential role of neuromodulators. Since neuromodulators convey information about novelty or reward, the influence of neuromodulators on synaptic plasticity is useful not just for action learning in classical conditioning, but also to decide “when” to create new memories in response to a flow of sensory stimuli. In this review, we focus on timing requirements for pre- and postsynaptic activity in conjunction with one or several phasic neuromodulatory signals. While the emphasis of the text is on conceptual models and mathematical theories, we also discuss some experimental evidence for neuromodulation of Spike-Timing-Dependent Plasticity. We highlight the importance of synaptic mechanisms in bridging the temporal gap between sensory stimulation and neuromodulatory signals, and develop a framework for a class of neo-Hebbian three-factor learning rules that depend on presynaptic activity, postsynaptic variables as well as the influence of neuromodulators. PMID:26834568

  19. Supervised learning with complex spikes and spike-timing-dependent plasticity.

    Directory of Open Access Journals (Sweden)

    Conor Houghton

    Full Text Available One distinctive feature of Purkinje cells is that they have two types of discharge: in addition to simple spikes they fire complex spikes in response to input from the climbing fibers. These complex spikes have an initial rapid burst of spikes and spikelets followed by a sustained depolarization; in some models of cerebellar function this climbing fiber input supervises learning in Purkinje cells. On the other hand, synaptic plasticity is often thought to rely on the timing of pre-synaptic and post-synaptic spikes. It is suggested here that the period of depolarization following a complex spike, combined with a simple spike-timing-dependent plasticity rule, gives a mechanism for the climbing fiber to supervise learning in the Purkinje cell. This proposal is illustrated using a simple simulation in which it is seen that the climbing fiber succeeds in supervising the learning.

  20. Cerebellar plasticity and motor learning deficits in a copy-number variation mouse model of autism.

    Science.gov (United States)

    Piochon, Claire; Kloth, Alexander D; Grasselli, Giorgio; Titley, Heather K; Nakayama, Hisako; Hashimoto, Kouichi; Wan, Vivian; Simmons, Dana H; Eissa, Tahra; Nakatani, Jin; Cherskov, Adriana; Miyazaki, Taisuke; Watanabe, Masahiko; Takumi, Toru; Kano, Masanobu; Wang, Samuel S-H; Hansel, Christian

    2014-11-24

    A common feature of autism spectrum disorder (ASD) is the impairment of motor control and learning, occurring in a majority of children with autism, consistent with perturbation in cerebellar function. Here we report alterations in motor behaviour and cerebellar synaptic plasticity in a mouse model (patDp/+) for the human 15q11-13 duplication, one of the most frequently observed genetic aberrations in autism. These mice show ASD-resembling social behaviour deficits. We find that in patDp/+ mice delay eyeblink conditioning--a form of cerebellum-dependent motor learning--is impaired, and observe deregulation of a putative cellular mechanism for motor learning, long-term depression (LTD) at parallel fibre-Purkinje cell synapses. Moreover, developmental elimination of surplus climbing fibres--a model for activity-dependent synaptic pruning--is impaired. These findings point to deficits in synaptic plasticity and pruning as potential causes for motor problems and abnormal circuit development in autism.

  1. Active random noise control using adaptive learning rate neural networks with an immune feedback law

    Science.gov (United States)

    Sasaki, Minoru; Kuribayashi, Takumi; Ito, Satoshi

    2005-12-01

    In this paper an active random noise control using adaptive learning rate neural networks with an immune feedback law is presented. The adaptive learning rate strategy increases the learning rate by a small constant if the current partial derivative of the objective function with respect to the weight and the exponential average of the previous derivatives have the same sign, otherwise the learning rate is decreased by a proportion of its value. The use of an adaptive learning rate attempts to keep the learning step size as large as possible without leading to oscillation. In the proposed method, because of the immune feedback law change a learning rate of the neural networks individually and adaptively, it is expected that a cost function minimize rapidly and training time is decreased. Numerical simulations and experiments of active random noise control with the transfer function of the error path will be performed, to validate the convergence properties of the adaptive learning rate Neural Networks with the immune feedback law. Control results show that adaptive learning rate Neural Networks control structure can outperform linear controllers and conventional neural network controller for the active random noise control.

  2. Neural Plasticity following Abacus Training in Humans: A Review and Future Directions.

    Science.gov (United States)

    Li, Yongxin; Chen, Feiyan; Huang, Wenhua

    2016-01-01

    The human brain has an enormous capacity to adapt to a broad variety of environmental demands. Previous studies in the field of abacus training have shown that this training can induce specific changes in the brain. However, the neural mechanism underlying these changes remains elusive. Here, we reviewed the behavioral and imaging findings of comparisons between abacus experts and average control subjects and focused on changes in activation patterns and changes in brain structure. Finally, we noted the limitations and the future directions of this field. We concluded that although current studies have provided us with information about the mechanisms of abacus training, more research on abacus training is needed to understand its neural impact.

  3. Neural Plasticity following Abacus Training in Humans: A Review and Future Directions

    Directory of Open Access Journals (Sweden)

    Yongxin Li

    2016-01-01

    Full Text Available The human brain has an enormous capacity to adapt to a broad variety of environmental demands. Previous studies in the field of abacus training have shown that this training can induce specific changes in the brain. However, the neural mechanism underlying these changes remains elusive. Here, we reviewed the behavioral and imaging findings of comparisons between abacus experts and average control subjects and focused on changes in activation patterns and changes in brain structure. Finally, we noted the limitations and the future directions of this field. We concluded that although current studies have provided us with information about the mechanisms of abacus training, more research on abacus training is needed to understand its neural impact.

  4. A neural network model for familiarity and context learning during honeybee foraging flights.

    Science.gov (United States)

    Müller, Jurek; Nawrot, Martin; Menzel, Randolf; Landgraf, Tim

    2017-09-15

    How complex is the memory structure that honeybees use to navigate? Recently, an insect-inspired parsimonious spiking neural network model was proposed that enabled simulated ground-moving agents to follow learned routes. We adapted this model to flying insects and evaluate the route following performance in three different worlds with gradually decreasing object density. In addition, we propose an extension to the model to enable the model to associate sensory input with a behavioral context, such as foraging or homing. The spiking neural network model makes use of a sparse stimulus representation in the mushroom body and reward-based synaptic plasticity at its output synapses. In our experiments, simulated bees were able to navigate correctly even when panoramic cues were missing. The context extension we propose enabled agents to successfully discriminate partly overlapping routes. The structure of the visual environment, however, crucially determines the success rate. We find that the model fails more often in visually rich environments due to the overlap of features represented by the Kenyon cell layer. Reducing the landmark density improves the agents route following performance. In very sparse environments, we find that extended landmarks, such as roads or field edges, may help the agent stay on its route, but often act as strong distractors yielding poor route following performance. We conclude that the presented model is valid for simple route following tasks and may represent one component of insect navigation. Additional components might still be necessary for guidance and action selection while navigating along different memorized routes in complex natural environments.

  5. Learning to see again: biological constraints on cortical plasticity and the implications for sight restoration technologies

    Science.gov (United States)

    Beyeler, Michael; Rokem, Ariel; Boynton, Geoffrey M.; Fine, Ione

    2017-10-01

    The ‘bionic eye’—so long a dream of the future—is finally becoming a reality with retinal prostheses available to patients in both the US and Europe. However, clinical experience with these implants has made it apparent that the visual information provided by these devices differs substantially from normal sight. Consequently, the ability of patients to learn to make use of this abnormal retinal input plays a critical role in whether or not some functional vision is successfully regained. The goal of the present review is to summarize the vast basic science literature on developmental and adult cortical plasticity with an emphasis on how this literature might relate to the field of prosthetic vision. We begin with describing the distortion and information loss likely to be experienced by visual prosthesis users. We then define cortical plasticity and perceptual learning, and describe what is known, and what is unknown, about visual plasticity across the hierarchy of brain regions involved in visual processing, and across different stages of life. We close by discussing what is known about brain plasticity in sight restoration patients and discuss biological mechanisms that might eventually be harnessed to improve visual learning in these patients.

  6. Neural Plasticity following Abacus Training in Humans: A Review and Future Directions

    OpenAIRE

    Yongxin Li; Feiyan Chen; Wenhua Huang

    2016-01-01

    The human brain has an enormous capacity to adapt to a broad variety of environmental demands. Previous studies in the field of abacus training have shown that this training can induce specific changes in the brain. However, the neural mechanism underlying these changes remains elusive. Here, we reviewed the behavioral and imaging findings of comparisons between abacus experts and average control subjects and focused on changes in activation patterns and changes in brain structure. Finally, w...

  7. The Learning Hippocampus: Education and Experience-Dependent Plasticity

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    Wenger, Elisabeth; Lövdén, Martin

    2016-01-01

    The hippocampal formation of the brain plays a crucial role in declarative learning and memory while at the same time being particularly susceptible to environmental influences. Education requires a well-functioning hippocampus, but may also influence the development of this brain structure. Understanding these bidirectional influences may have…

  8. Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

    Directory of Open Access Journals (Sweden)

    Min Wang

    2017-01-01

    Full Text Available This paper focuses on neural learning from adaptive neural control (ANC for a class of flexible joint manipulator under the output tracking constraint. To facilitate the design, a new transformed function is introduced to convert the constrained tracking error into unconstrained error variable. Then, a novel adaptive neural dynamic surface control scheme is proposed by combining the neural universal approximation. The proposed control scheme not only decreases the dimension of neural inputs but also reduces the number of neural approximators. Moreover, it can be verified that all the closed-loop signals are uniformly ultimately bounded and the constrained tracking error converges to a small neighborhood around zero in a finite time. Particularly, the reduction of the number of neural input variables simplifies the verification of persistent excitation (PE condition for neural networks (NNs. Subsequently, the proposed ANC scheme is verified recursively to be capable of acquiring and storing knowledge of unknown system dynamics in constant neural weights. By reusing the stored knowledge, a neural learning controller is developed for better control performance. Simulation results on a single-link flexible joint manipulator and experiment results on Baxter robot are given to illustrate the effectiveness of the proposed scheme.

  9. Neural markers of neuropathic pain associated with maladaptive plasticity in spinal cord injury.

    Science.gov (United States)

    Pascoal-Faria, Paula; Yalcin, Nilufer; Fregni, Felipe

    2015-04-01

    Given the potential use of neural markers for the development of novel treatments in spinal cord pain, we aimed to characterize the most effective neural markers of neuropathic pain following spinal cord injury (SCI). A systematic PubMed review was conducted, compiling studies that were published prior to April, 2014 that examined neural markers associated with neuropathic pain after SCI using electrophysiological and neuroimaging techniques. We identified 6 studies: Four using electroencephalogram (EEG); 1 using magnetic resonance imaging (MRI) and FDG-PET (positron emission tomography); and 1 using MR spectroscopy. The EEG recordings suggested a reduction in alpha EEG peak frequency activity in the frontal regions of SCI patients with neuropathic pain. The MRI scans showed volume loss, primarily in the gray matter of the left dorsolateral prefrontal cortex, and by FDG-PET, hypometabolism in the medial prefrontal cortex was observed in SCI patients with neuropathic pain compared with healthy subjects. In the MR spectroscopy findings, the presence of pain was associated with changes in the prefrontal cortex and anterior cingulate cortex. When analyzed together, the results of these studies seem to point out to a common marker of pain in SCI characterized by decreased cortical activity in frontal areas and possibly increased subcortical activity. These results may contribute to planning further mechanistic studies as to better understand the mechanisms by which neuropathic pain is modulated in patients with SCI as well as clinical studies investigating best responders of treatment. © 2014 World Institute of Pain.

  10. The influence of personality on neural mechanisms of observational fear and reward learning.

    Science.gov (United States)

    Hooker, Christine I; Verosky, Sara C; Miyakawa, Asako; Knight, Robert T; D'Esposito, Mark

    2008-09-01

    Fear and reward learning can occur through direct experience or observation. Both channels can enhance survival or create maladaptive behavior. We used fMRI to isolate neural mechanisms of observational fear and reward learning and investigate whether neural response varied according to individual differences in neuroticism and extraversion. Participants learned object-emotion associations by observing a woman respond with fearful (or neutral) and happy (or neutral) facial expressions to novel objects. The amygdala-hippocampal complex was active when learning the object-fear association, and the hippocampus was active when learning the object-happy association. After learning, objects were presented alone; amygdala activity was greater for the fear (vs. neutral) and happy (vs. neutral) associated object. Importantly, greater amygdala-hippocampal activity during fear (vs. neutral) learning predicted better recognition of learned objects on a subsequent memory test. Furthermore, personality modulated neural mechanisms of learning. Neuroticism positively correlated with neural activity in the amygdala and hippocampus during fear (vs. neutral) learning. Low extraversion/high introversion was related to faster behavioral predictions of the fearful and neutral expressions during fear learning. In addition, low extraversion/high introversion was related to greater amygdala activity during happy (vs. neutral) learning, happy (vs. neutral) object recognition, and faster reaction times for predicting happy and neutral expressions during reward learning. These findings suggest that neuroticism is associated with an increased sensitivity in the neural mechanism for fear learning which leads to enhanced encoding of fear associations, and that low extraversion/high introversion is related to enhanced conditionability for both fear and reward learning.

  11. Learning expectation in insects: a recurrent spiking neural model for spatio-temporal representation.

    Science.gov (United States)

    Arena, Paolo; Patané, Luca; Termini, Pietro Savio

    2012-08-01

    Insects are becoming a reference point in Neuroscience for the study of biological aspects at the basis of cognitive processes. These animals have much simpler brains with respect to higher animals, showing, at the same time, impressive capability to adaptively react and take decisions in front of complex environmental situations. In this paper we propose a neural model inspired by the insect olfactory system, with particular attention to the fruit fly Drosophila melanogaster. This architecture is a multilayer spiking network, where each layer is inspired by the structures of the insect brain mainly involved in olfactory information processing, namely the Mushroom Bodies, the Lateral Horns and the Antennal Lobes. In the Antennal Lobes layer olfactory signals lead to a competition among sets of neurons, resulting in a pattern which is projected to the Mushroom Bodies layer. Here a competitive reaction-diffusion process leads to a spontaneous emerging of clusters. The Lateral Horns have been modeled as a delayed input-triggered resetting system. Using plastic recurrent connections, with the addition of simple learning mechanisms, the structure is able to realize a top-down modulation at the input level. This leads to the emergence of an attentional loop as well as to the arousal of basic expectation behaviors in case of subsequently presented stimuli. Simulation results and analysis on the biological plausibility of the architecture are provided and the role of noise in the network is reported. Copyright © 2012 Elsevier Ltd. All rights reserved.

  12. Hybrid Spintronic-CMOS Spiking Neural Network with On-Chip Learning: Devices, Circuits, and Systems

    Science.gov (United States)

    Sengupta, Abhronil; Banerjee, Aparajita; Roy, Kaushik

    2016-12-01

    Over the past decade, spiking neural networks (SNNs) have emerged as one of the popular architectures to emulate the brain. In SNNs, information is temporally encoded and communication between neurons is accomplished by means of spikes. In such networks, spike-timing-dependent plasticity mechanisms require the online programing of synapses based on the temporal information of spikes transmitted by spiking neurons. In this work, we propose a spintronic synapse with decoupled spike-transmission and programing-current paths. The spintronic synapse consists of a ferromagnet-heavy-metal heterostructure where the programing current through the heavy metal generates spin-orbit torque to modulate the device conductance. Low programing energy and fast programing times demonstrate the efficacy of the proposed device as a nanoelectronic synapse. We perform a simulation study based on an experimentally benchmarked device-simulation framework to demonstrate the interfacing of such spintronic synapses with CMOS neurons and learning circuits operating in the transistor subthreshold region to form a network of spiking neurons that can be utilized for pattern-recognition problems.

  13. Neural mechanisms of reinforcement learning in unmedicated patients with major depressive disorder.

    Science.gov (United States)

    Rothkirch, Marcus; Tonn, Jonas; Köhler, Stephan; Sterzer, Philipp

    2017-04-01

    According to current concepts, major depressive disorder is strongly related to dysfunctional neural processing of motivational information, entailing impairments in reinforcement learning. While computational modelling can reveal the precise nature of neural learning signals, it has not been used to study learning-related neural dysfunctions in unmedicated patients with major depressive disorder so far. We thus aimed at comparing the neural coding of reward and punishment prediction errors, representing indicators of neural learning-related processes, between unmedicated patients with major depressive disorder and healthy participants. To this end, a group of unmedicated patients with major depressive disorder (n = 28) and a group of age- and sex-matched healthy control participants (n = 30) completed an instrumental learning task involving monetary gains and losses during functional magnetic resonance imaging. The two groups did not differ in their learning performance. Patients and control participants showed the same level of prediction error-related activity in the ventral striatum and the anterior insula. In contrast, neural coding of reward prediction errors in the medial orbitofrontal cortex was reduced in patients. Moreover, neural reward prediction error signals in the medial orbitofrontal cortex and ventral striatum showed negative correlations with anhedonia severity. Using a standard instrumental learning paradigm we found no evidence for an overall impairment of reinforcement learning in medication-free patients with major depressive disorder. Importantly, however, the attenuated neural coding of reward in the medial orbitofrontal cortex and the relation between anhedonia and reduced reward prediction error-signalling in the medial orbitofrontal cortex and ventral striatum likely reflect an impairment in experiencing pleasure from rewarding events as a key mechanism of anhedonia in major depressive disorder. © The Author (2017). Published by Oxford

  14. Plasticity of Cortical Excitatory-Inhibitory Balance

    Science.gov (United States)

    Froemke, Robert C.

    2015-01-01

    Synapses are highly plastic and are modified by changes in patterns of neural activity or sensory experience. Plasticity of cortical excitatory synapses is thought to be important for learning and memory, leading to alterations in sensory representations and cognitive maps. However, these changes must be coordinated across other synapses within local circuits to preserve neural coding schemes and the organization of excitatory and inhibitory inputs, i.e., excitatory-inhibitory balance. Recent studies indicate that inhibitory synapses are also plastic and are controlled directly by a large number of neuromodulators, particularly during episodes of learning. Many modulators transiently alter excitatory-inhibitory balance by decreasing inhibition, and thus disinhibition has emerged as a major mechanism by which neuromodulation might enable long-term synaptic modifications naturally. This review examines the relationships between neuromodulation and synaptic plasticity, focusing on the induction of long-term changes that collectively enhance cortical excitatory-inhibitory balance for improving perception and behavior. PMID:25897875

  15. Synaptic Plasticity and Learning Behaviors Mimicked in Single Inorganic Synapses of Pt/HfOx/ZnOx/TiN Memristive System

    Science.gov (United States)

    Wang, Lai-Guo; Zhang, Wei; Chen, Yan; Cao, Yan-Qiang; Li, Ai-Dong; Wu, Di

    2017-01-01

    In this work, a kind of new memristor with the simple structure of Pt/HfOx/ZnOx/TiN was fabricated completely via combination of thermal-atomic layer deposition (TALD) and plasma-enhanced ALD (PEALD). The synaptic plasticity and learning behaviors of Pt/HfOx/ZnOx/TiN memristive system have been investigated deeply. Multilevel resistance states are obtained by varying the programming voltage amplitudes during the pulse cycling. The device conductance can be continuously increased or decreased from cycle to cycle with better endurance characteristics up to about 3 × 103 cycles. Several essential synaptic functions are simultaneously achieved in such a single double-layer of HfOx/ZnOx device, including nonlinear transmission properties, such as long-term plasticity (LTP), short-term plasticity (STP), and spike-timing-dependent plasticity. The transformation from STP to LTP induced by repetitive pulse stimulation is confirmed in Pt/HfOx/ZnOx/TiN memristive device. Above all, simple structure of Pt/HfOx/ZnOx/TiN by ALD technique is a kind of promising memristor device for applications in artificial neural network.

  16. Image aesthetic quality evaluation using convolution neural network embedded learning

    Science.gov (United States)

    Li, Yu-xin; Pu, Yuan-yuan; Xu, Dan; Qian, Wen-hua; Wang, Li-peng

    2017-11-01

    A way of embedded learning convolution neural network (ELCNN) based on the image content is proposed to evaluate the image aesthetic quality in this paper. Our approach can not only solve the problem of small-scale data but also score the image aesthetic quality. First, we chose Alexnet and VGG_S to compare for confirming which is more suitable for this image aesthetic quality evaluation task. Second, to further boost the image aesthetic quality classification performance, we employ the image content to train aesthetic quality classification models. But the training samples become smaller and only using once fine-tuning cannot make full use of the small-scale data set. Third, to solve the problem in second step, a way of using twice fine-tuning continually based on the aesthetic quality label and content label respective is proposed, the classification probability of the trained CNN models is used to evaluate the image aesthetic quality. The experiments are carried on the small-scale data set of Photo Quality. The experiment results show that the classification accuracy rates of our approach are higher than the existing image aesthetic quality evaluation approaches.

  17. The plasticity of extinction: contribution of the prefrontal cortex in treating addiction though inhibitory learning

    Directory of Open Access Journals (Sweden)

    Lawrence Judson Chandler

    2013-05-01

    Full Text Available Theories of drug addiction that incorporate various concepts from the fields of learning and memory have led to the idea that classical and operant conditioning principles underlie the compulsiveness of addictive behaviors. Relapse often results from exposure to drug-associated cues, and the ability to extinguish these conditioned behaviors through inhibitory learning could serve as a potential therapeutic mechanism for those who suffer from addiction. This review will examine the evidence that extinction learning alters neuronal plasticity in specific brain regions and pathways. In particular, subregions of the prefrontal cortex and their projections to other brain regions have been shown to differentially modulate drug-seeking and extinction behavior. Additionally, there is a growing body of research demonstrating that manipulation of neuronal plasticity can alter extinction learning. Therefore, the ability to alter plasticity within areas of the prefrontal cortex through pharmacological manipulation could facilitate the acquisition of extinction and provide a novel intervention to aid in the extinction of drug-related memories.

  18. Stochastic spike synchronization in a small-world neural network with spike-timing-dependent plasticity.

    Science.gov (United States)

    Kim, Sang-Yoon; Lim, Woochang

    2018-01-01

    We consider the Watts-Strogatz small-world network (SWN) consisting of subthreshold neurons which exhibit noise-induced spikings. This neuronal network has adaptive dynamic synaptic strengths governed by the spike-timing-dependent plasticity (STDP). In previous works without STDP, stochastic spike synchronization (SSS) between noise-induced spikings of subthreshold neurons was found to occur in a range of intermediate noise intensities. Here, we investigate the effect of additive STDP on the SSS by varying the noise intensity. Occurrence of a "Matthew" effect in synaptic plasticity is found due to a positive feedback process. As a result, good synchronization gets better via long-term potentiation of synaptic strengths, while bad synchronization gets worse via long-term depression. Emergences of long-term potentiation and long-term depression of synaptic strengths are intensively investigated via microscopic studies based on the pair-correlations between the pre- and the post-synaptic IISRs (instantaneous individual spike rates) as well as the distributions of time delays between the pre- and the post-synaptic spike times. Furthermore, the effects of multiplicative STDP (which depends on states) on the SSS are studied and discussed in comparison with the case of additive STDP (independent of states). These effects of STDP on the SSS in the SWN are also compared with those in the regular lattice and the random graph. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Can adult neural stem cells create new brains? Plasticity in the adult mammalian neurogenic niches: realities and expectations in the era of regenerative biology.

    Science.gov (United States)

    Kazanis, Ilias

    2012-02-01

    Since the first experimental reports showing the persistence of neurogenic activity in the adult mammalian brain, this field of neurosciences has expanded significantly. It is now widely accepted that neural stem and precursor cells survive during adulthood and are able to respond to various endogenous and exogenous cues by altering their proliferation and differentiation activity. Nevertheless, the pathway to therapeutic applications still seems to be long. This review attempts to summarize and revisit the available data regarding the plasticity potential of adult neural stem cells and of their normal microenvironment, the neurogenic niche. Recent data have demonstrated that adult neural stem cells retain a high level of pluripotency and that adult neurogenic systems can switch the balance between neurogenesis and gliogenesis and can generate a range of cell types with an efficiency that was not initially expected. Moreover, adult neural stem and precursor cells seem to be able to self-regulate their interaction with the microenvironment and even to contribute to its synthesis, altogether revealing a high level of plasticity potential. The next important step will be to elucidate the factors that limit this plasticity in vivo, and such a restrictive role for the microenvironment is discussed in more details.

  20. High-density microelectrode array recordings and real-time spike sorting for closed-loop experiments: an emerging technology to study neural plasticity.

    Science.gov (United States)

    Franke, Felix; Jäckel, David; Dragas, Jelena; Müller, Jan; Radivojevic, Milos; Bakkum, Douglas; Hierlemann, Andreas

    2012-01-01

    Understanding plasticity of neural networks is a key to comprehending their development and function. A powerful technique to study neural plasticity includes recording and control of pre- and post-synaptic neural activity, e.g., by using simultaneous intracellular recording and stimulation of several neurons. Intracellular recording is, however, a demanding technique and has its limitations in that only a small number of neurons can be stimulated and recorded from at the same time. Extracellular techniques offer the possibility to simultaneously record from larger numbers of neurons with relative ease, at the expenses of increased efforts to sort out single neuronal activities from the recorded mixture, which is a time consuming and error prone step, referred to as spike sorting. In this mini-review, we describe recent technological developments in two separate fields, namely CMOS-based high-density microelectrode arrays, which also allow for extracellular stimulation of neurons, and real-time spike sorting. We argue that these techniques, when combined, will provide a powerful tool to study plasticity in neural networks consisting of several thousand neurons in vitro.

  1. Dopamine Promotes Motor Cortex Plasticity and Motor Skill Learning via PLC Activation.

    Science.gov (United States)

    Rioult-Pedotti, Mengia-Seraina; Pekanovic, Ana; Atiemo, Clement Osei; Marshall, John; Luft, Andreas Rüdiger

    2015-01-01

    Dopaminergic neurons in the ventral tegmental area, the major midbrain nucleus projecting to the motor cortex, play a key role in motor skill learning and motor cortex synaptic plasticity. Dopamine D1 and D2 receptor antagonists exert parallel effects in the motor system: they impair motor skill learning and reduce long-term potentiation. Traditionally, D1 and D2 receptor modulate adenylyl cyclase activity and cyclic adenosine monophosphate accumulation in opposite directions via different G-proteins and bidirectionally modulate protein kinase A (PKA), leading to distinct physiological and behavioral effects. Here we show that D1 and D2 receptor activity influences motor skill acquisition and long term synaptic potentiation via phospholipase C (PLC) activation in rat primary motor cortex. Learning a new forelimb reaching task is severely impaired in the presence of PLC, but not PKA-inhibitor. Similarly, long term potentiation in motor cortex, a mechanism involved in motor skill learning, is reduced when PLC is inhibited but remains unaffected by the PKA inhibitor. Skill learning deficits and reduced synaptic plasticity caused by dopamine antagonists are prevented by co-administration of a PLC agonist. These results provide evidence for a role of intracellular PLC signaling in motor skill learning and associated cortical synaptic plasticity, challenging the traditional view of bidirectional modulation of PKA by D1 and D2 receptors. These findings reveal a novel and important action of dopamine in motor cortex that might be a future target for selective therapeutic interventions to support learning and recovery of movement resulting from injury and disease.

  2. How the Blind “See” Braille and the Deaf “Hear” Sign: Lessons from fMRI on the Cross-Modal Plasticity, Integration, and Learning

    Directory of Open Access Journals (Sweden)

    Norihiro Sadato

    2011-10-01

    Full Text Available What does the visual cortex of the blind do during Braille reading? This process involves converting simple tactile information into meaningful patterns that have lexical and semantic properties. The perceptual processing of Braille might be mediated by the somatosensory system, whereas visual letter identity is accomplished within the visual system in sighted people. Recent advances in functional neuroimaging techniques have enabled exploration of the neural substrates of Braille reading (Sadato et al. 1996, 1998, 2002, Cohen et al. 1997, 1999. The primary visual cortex of early-onset blind subjects is functionally relevant to Braille reading, suggesting that the brain shows remarkable plasticity that potentially permits the additional processing of tactile information in the visual cortical areas. Similar cross-modal plasticity is observed by the auditory deprivation: Sign language activates the auditory cortex of deaf subjects (Neville et al. 1999, Nishimura et al. 1999, Sadato et al. 2004. Cross-modal activation can be seen in the sighted and hearing subjects. For example, the tactile shape discrimination of two dimensional (2D shapes (Mah-Jong tiles activated the visual cortex by expert players (Saito et al. 2006, and the lip-reading (visual phonetics (Sadato et al. 2004 or key touch reading by pianists (Hasegawa et al. 2004 activates the auditory cortex of hearing subjects. Thus the cross-modal plasticity by sensory deprivation and cross-modal integration through the learning may share their neural substrates. To clarify the distribution of the neural substrates and their dynamics during cross-modal association learning within several hours, we conducted audio-visual paired association learning of delayed-matching-to-sample type tasks (Tanabe et al. 2005. Each trial consisted of the successive presentation of a pair of stimuli. Subjects had to find pre-defined audio-visual or visuo-visual pairs in a trial and error manner with feedback in

  3. CCR5 is a suppressor for cortical plasticity and hippocampal learning and memory.

    Science.gov (United States)

    Zhou, Miou; Greenhill, Stuart; Huang, Shan; Silva, Tawnie K; Sano, Yoshitake; Wu, Shumin; Cai, Ying; Nagaoka, Yoshiko; Sehgal, Megha; Cai, Denise J; Lee, Yong-Seok; Fox, Kevin; Silva, Alcino J

    2016-12-20

    Although the role of CCR5 in immunity and in HIV infection has been studied widely, its role in neuronal plasticity, learning and memory is not understood. Here, we report that decreasing the function of CCR5 increases MAPK/CREB signaling, long-term potentiation (LTP), and hippocampus-dependent memory in mice, while neuronal CCR5 overexpression caused memory deficits. Decreasing CCR5 function in mouse barrel cortex also resulted in enhanced spike timing dependent plasticity and consequently, dramatically accelerated experience-dependent plasticity. These results suggest that CCR5 is a powerful suppressor for plasticity and memory, and CCR5 over-activation by viral proteins may contribute to HIV-associated cognitive deficits. Consistent with this hypothesis, the HIV V3 peptide caused LTP, signaling and memory deficits that were prevented by Ccr5 knockout or knockdown. Overall, our results demonstrate that CCR5 plays an important role in neuroplasticity, learning and memory, and indicate that CCR5 has a role in the cognitive deficits caused by HIV.

  4. CCR5 is a suppressor for cortical plasticity and hippocampal learning and memory

    Science.gov (United States)

    Zhou, Miou; Greenhill, Stuart; Huang, Shan; Silva, Tawnie K; Sano, Yoshitake; Wu, Shumin; Cai, Ying; Nagaoka, Yoshiko; Sehgal, Megha; Cai, Denise J; Lee, Yong-Seok; Fox, Kevin; Silva, Alcino J

    2016-01-01

    Although the role of CCR5 in immunity and in HIV infection has been studied widely, its role in neuronal plasticity, learning and memory is not understood. Here, we report that decreasing the function of CCR5 increases MAPK/CREB signaling, long-term potentiation (LTP), and hippocampus-dependent memory in mice, while neuronal CCR5 overexpression caused memory deficits. Decreasing CCR5 function in mouse barrel cortex also resulted in enhanced spike timing dependent plasticity and consequently, dramatically accelerated experience-dependent plasticity. These results suggest that CCR5 is a powerful suppressor for plasticity and memory, and CCR5 over-activation by viral proteins may contribute to HIV-associated cognitive deficits. Consistent with this hypothesis, the HIV V3 peptide caused LTP, signaling and memory deficits that were prevented by Ccr5 knockout or knockdown. Overall, our results demonstrate that CCR5 plays an important role in neuroplasticity, learning and memory, and indicate that CCR5 has a role in the cognitive deficits caused by HIV. DOI: http://dx.doi.org/10.7554/eLife.20985.001 PMID:27996938

  5. Increased spinal reflex excitability is not associated with neural plasticity underlying the cross-education effect.

    Science.gov (United States)

    Lagerquist, Olle; Zehr, E Paul; Docherty, David

    2006-01-01

    The purpose of this study was to examine the effects of a 5-wk unilateral, isometric strength-training program on plasticity in the spinal Hoffmann (H-) reflex in both the trained and untrained legs. Sixteen participants, 22-42 yr old, were assigned to either a control (n = 6) or an exercise group (n = 10). Both groups were tested for plantar flexion maximal voluntary isometric contractions (MVIC) and soleus H-reflex amplitude in both limbs, at the beginning and at the end of a 5-wk interval. Participants in the exercise group showed significantly increased MVIC in both legs after training (P cross-education effect of strength training may be due to supraspinal to a greater extent than spinal mechanisms.

  6. Chaos and Correlated Avalanches in Excitatory Neural Networks with Synaptic Plasticity

    Science.gov (United States)

    Pittorino, Fabrizio; Ibáñez-Berganza, Miguel; di Volo, Matteo; Vezzani, Alessandro; Burioni, Raffaella

    2017-03-01

    A collective chaotic phase with power law scaling of activity events is observed in a disordered mean field network of purely excitatory leaky integrate-and-fire neurons with short-term synaptic plasticity. The dynamical phase diagram exhibits two transitions from quasisynchronous and asynchronous regimes to the nontrivial, collective, bursty regime with avalanches. In the homogeneous case without disorder, the system synchronizes and the bursty behavior is reflected into a period doubling transition to chaos for a two dimensional discrete map. Numerical simulations show that the bursty chaotic phase with avalanches exhibits a spontaneous emergence of persistent time correlations and enhanced Kolmogorov complexity. Our analysis reveals a mechanism for the generation of irregular avalanches that emerges from the combination of disorder and deterministic underlying chaotic dynamics.

  7. Cellular Plasticity within the Pancreas— Lessons Learned from Development

    Science.gov (United States)

    Puri, Sapna; Hebrok, Matthias

    2014-01-01

    The pancreas has been the subject of intense research due to the debilitating diseases that result from its dysfunction. In this review, we summarize current understanding of the critical tissue interactions and intracellular regulatory events that take place during formation of the pancreas from a small cluster of cells in the foregut domain of the mouse embryo. Importantly, an understanding of principles that govern the development of this organ has equipped us with the means to manipulate both embryonic and differentiated adult cells in the context of regenerative medicine. The emerging area of lineage modulation within the adult pancreas is of particular interest, and this review summarizes recent findings that exemplify how lessons learned from development are being applied to reveal the potential of fully differentiated cells to change fate. PMID:20230744

  8. Differential roles of nonsynaptic and synaptic plasticity in operant reward learning-induced compulsive behavior.

    Science.gov (United States)

    Sieling, Fred; Bédécarrats, Alexis; Simmers, John; Prinz, Astrid A; Nargeot, Romuald

    2014-05-05

    Rewarding stimuli in associative learning can transform the irregularly and infrequently generated motor patterns underlying motivated behaviors into output for accelerated and stereotyped repetitive action. This transition to compulsive behavioral expression is associated with modified synaptic and membrane properties of central neurons, but establishing the causal relationships between cellular plasticity and motor adaptation has remained a challenge. We found previously that changes in the intrinsic excitability and electrical synapses of identified neurons in Aplysia's central pattern-generating network for feeding are correlated with a switch to compulsive-like motor output expression induced by in vivo operant conditioning. Here, we used specific computer-simulated ionic currents in vitro to selectively replicate or suppress the membrane and synaptic plasticity resulting from this learning. In naive in vitro preparations, such experimental manipulation of neuronal membrane properties alone increased the frequency but not the regularity of feeding motor output found in preparations from operantly trained animals. On the other hand, changes in synaptic strength alone switched the regularity but not the frequency of feeding output from naive to trained states. However, simultaneously imposed changes in both membrane and synaptic properties reproduced both major aspects of the motor plasticity. Conversely, in preparations from trained animals, experimental suppression of the membrane and synaptic plasticity abolished the increase in frequency and regularity of the learned motor output expression. These data establish direct causality for the contributions of distinct synaptic and nonsynaptic adaptive processes to complementary facets of a compulsive behavior resulting from operant reward learning. Copyright © 2014 Elsevier Ltd. All rights reserved.

  9. Phantom lower limb as a perceptual marker of neural plasticity in the mature human brain.

    Science.gov (United States)

    Aglioti, S; Bonazzi, A; Cortese, F

    1994-03-22

    Three lower limb amputees, who reported phantom sensations, referred somatic stimuli delivered to skin regions proximal to the stump to select points on the phantom limb. Stimuli on the rectum and anus (e.g. during defecation) and on genital areas (e.g. during sexual intercourse) induced analogous, although less precise, mislocation to the phantom limb. Although the representation of the stump in the somatosensory pathway is lateral to that of the amputated lower limb, both anus and genitals are mapped medially to the areas formerly subserving the amputated lower limb. Therefore the mislocalization phenomenon can be considered as a perceptual landmark of new functional connections between the deprived areas and the adjacent ones, thus suggesting a dynamic neural remodelling in the mature nervous system, which was previously considered as a static entity.