Neural basis of self and other representation in autism: an FMRI study of self-face recognition.

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Lucina Q Uddin

Full Text Available Autism is a developmental disorder characterized by decreased interest and engagement in social interactions and by enhanced self-focus. While previous theoretical approaches to understanding autism have emphasized social impairments and altered interpersonal interactions, there is a recent shift towards understanding the nature of the representation of the self in individuals with autism spectrum disorders (ASD. Still, the neural mechanisms subserving self-representations in ASD are relatively unexplored.We used event-related fMRI to investigate brain responsiveness to images of the subjects' own face and to faces of others. Children with ASD and typically developing (TD children viewed randomly presented digital morphs between their own face and a gender-matched other face, and made "self/other" judgments. Both groups of children activated a right premotor/prefrontal system when identifying images containing a greater percentage of the self face. However, while TD children showed activation of this system during both self- and other-processing, children with ASD only recruited this system while viewing images containing mostly their own face.This functional dissociation between the representation of self versus others points to a potential neural substrate for the characteristic self-focus and decreased social understanding exhibited by these individuals, and suggests that individuals with ASD lack the shared neural representations for self and others that TD children and adults possess and may use to understand others.

Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning.

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Michaels, Jonathan A; Dann, Benjamin; Scherberger, Hansjörg

2016-11-01

Recent models of movement generation in motor cortex have sought to explain neural activity not as a function of movement parameters, known as representational models, but as a dynamical system acting at the level of the population. Despite evidence supporting this framework, the evaluation of representational models and their integration with dynamical systems is incomplete in the literature. Using a representational velocity-tuning based simulation of center-out reaching, we show that incorporating variable latency offsets between neural activity and kinematics is sufficient to generate rotational dynamics at the level of neural populations, a phenomenon observed in motor cortex. However, we developed a covariance-matched permutation test (CMPT) that reassigns neural data between task conditions independently for each neuron while maintaining overall neuron-to-neuron relationships, revealing that rotations based on the representational model did not uniquely depend on the underlying condition structure. In contrast, rotations based on either a dynamical model or motor cortex data depend on this relationship, providing evidence that the dynamical model more readily explains motor cortex activity. Importantly, implementing a recurrent neural network we demonstrate that both representational tuning properties and rotational dynamics emerge, providing evidence that a dynamical system can reproduce previous findings of representational tuning. Finally, using motor cortex data in combination with the CMPT, we show that results based on small numbers of neurons or conditions should be interpreted cautiously, potentially informing future experimental design. Together, our findings reinforce the view that representational models lack the explanatory power to describe complex aspects of single neuron and population level activity.

Identifying autism from neural representations of social interactions: neurocognitive markers of autism.

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Just, Marcel Adam; Cherkassky, Vladimir L; Buchweitz, Augusto; Keller, Timothy A; Mitchell, Tom M

2014-01-01

Autism is a psychiatric/neurological condition in which alterations in social interaction (among other symptoms) are diagnosed by behavioral psychiatric methods. The main goal of this study was to determine how the neural representations and meanings of social concepts (such as to insult) are altered in autism. A second goal was to determine whether these alterations can serve as neurocognitive markers of autism. The approach is based on previous advances in fMRI analysis methods that permit (a) the identification of a concept, such as the thought of a physical object, from its fMRI pattern, and (b) the ability to assess the semantic content of a concept from its fMRI pattern. These factor analysis and machine learning methods were applied to the fMRI activation patterns of 17 adults with high-functioning autism and matched controls, scanned while thinking about 16 social interactions. One prominent neural representation factor that emerged (manifested mainly in posterior midline regions) was related to self-representation, but this factor was present only for the control participants, and was near-absent in the autism group. Moreover, machine learning algorithms classified individuals as autistic or control with 97% accuracy from their fMRI neurocognitive markers. The findings suggest that psychiatric alterations of thought can begin to be biologically understood by assessing the form and content of the altered thought's underlying brain activation patterns.

Transformation-invariant visual representations in self-organizing spiking neural networks.

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Evans, Benjamin D; Stringer, Simon M

2012-01-01

The ventral visual pathway achieves object and face recognition by building transformation-invariant representations from elementary visual features. In previous computer simulation studies with rate-coded neural networks, the development of transformation-invariant representations has been demonstrated using either of two biologically plausible learning mechanisms, Trace learning and Continuous Transformation (CT) learning. However, it has not previously been investigated how transformation-invariant representations may be learned in a more biologically accurate spiking neural network. A key issue is how the synaptic connection strengths in such a spiking network might self-organize through Spike-Time Dependent Plasticity (STDP) where the change in synaptic strength is dependent on the relative times of the spikes emitted by the presynaptic and postsynaptic neurons rather than simply correlated activity driving changes in synaptic efficacy. Here we present simulations with conductance-based integrate-and-fire (IF) neurons using a STDP learning rule to address these gaps in our understanding. It is demonstrated that with the appropriate selection of model parameters and training regime, the spiking network model can utilize either Trace-like or CT-like learning mechanisms to achieve transform-invariant representations.

Transform-invariant visual representations in self-organizing spiking neural networks

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Benjamin eEvans

2012-07-01

Full Text Available The ventral visual pathway achieves object and face recognition by building transform-invariant representations from elementary visual features. In previous computer simulation studies with rate-coded neural networks, the development of transform invariant representations has been demonstrated using either of two biologically plausible learning mechanisms, Trace learning and Continuous Transformation (CT learning. However, it has not previously been investigated how transform invariant representations may be learned in a more biologically accurate spiking neural network. A key issue is how the synaptic connection strengths in such a spiking network might self-organize through Spike-Time Dependent Plasticity (STDP where the change in synaptic strength is dependent on the relative times of the spikes emitted by the pre- and postsynaptic neurons rather than simply correlated activity driving changes in synaptic efficacy. Here we present simulations with conductance-based integrate-and-fire (IF neurons using a STDP learning rule to address these gaps in our understanding. It is demonstrated that with the appropriate selection of model pa- rameters and training regime, the spiking network model can utilize either Trace-like or CT-like learning mechanisms to achieve transform-invariant representations.

Representation of neural networks as Lotka-Volterra systems

International Nuclear Information System (INIS)

Moreau, Yves; Vandewalle, Joos; Louies, Stephane; Brenig, Leon

1999-01-01

We study changes of coordinates that allow the representation of the ordinary differential equations describing continuous-time recurrent neural networks into differential equations describing predator-prey models--also called Lotka-Volterra systems. We transform the equations for the neural network first into quasi-monomial form, where we express the vector field of the dynamical system as a linear combination of products of powers of the variables. In practice, this transformation is possible only if the activation function is the hyperbolic tangent or the logistic sigmoied. From this quasi-monomial form, we can directly transform the system further into Lotka-Volterra equations. The resulting Lotka-Volterra system is of higher dimension than the original system, but the behavior of its first variables is equivalent to the behavior of the original neural network

Neural representation of face familiarity in an awake chimpanzee

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Hirokata Fukushima

2013-12-01

Full Text Available Evaluating the familiarity of faces is critical for social animals as it is the basis of individual recognition. In the present study, we examined how face familiarity is reflected in neural activities in our closest living relative, the chimpanzee. Skin-surface event-related brain potentials (ERPs were measured while a fully awake chimpanzee observed photographs of familiar and unfamiliar chimpanzee faces (Experiment 1 and human faces (Experiment 2. The ERPs evoked by chimpanzee faces differentiated unfamiliar individuals from familiar ones around midline areas centered on vertex sites at approximately 200 ms after the stimulus onset. In addition, the ERP response to the image of the subject’s own face did not significantly diverge from those evoked by familiar chimpanzees, suggesting that the subject’s brain at a minimum remembered the image of her own face. The ERPs evoked by human faces were not influenced by the familiarity of target individuals. These results indicate that chimpanzee neural representations are more sensitive to the familiarity of conspecific than allospecific faces.

Multi-Connection Pattern Analysis: Decoding the representational content of neural communication.

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Li, Yuanning; Richardson, Robert Mark; Ghuman, Avniel Singh

2017-11-15

The lack of multivariate methods for decoding the representational content of interregional neural communication has left it difficult to know what information is represented in distributed brain circuit interactions. Here we present Multi-Connection Pattern Analysis (MCPA), which works by learning mappings between the activity patterns of the populations as a factor of the information being processed. These maps are used to predict the activity from one neural population based on the activity from the other population. Successful MCPA-based decoding indicates the involvement of distributed computational processing and provides a framework for probing the representational structure of the interaction. Simulations demonstrate the efficacy of MCPA in realistic circumstances. In addition, we demonstrate that MCPA can be applied to different signal modalities to evaluate a variety of hypothesis associated with information coding in neural communications. We apply MCPA to fMRI and human intracranial electrophysiological data to provide a proof-of-concept of the utility of this method for decoding individual natural images and faces in functional connectivity data. We further use a MCPA-based representational similarity analysis to illustrate how MCPA may be used to test computational models of information transfer among regions of the visual processing stream. Thus, MCPA can be used to assess the information represented in the coupled activity of interacting neural circuits and probe the underlying principles of information transformation between regions. Copyright © 2017 Elsevier Inc. All rights reserved.

Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream.

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Güçlü, Umut; van Gerven, Marcel A J

2015-07-08

Converging evidence suggests that the primate ventral visual pathway encodes increasingly complex stimulus features in downstream areas. We quantitatively show that there indeed exists an explicit gradient for feature complexity in the ventral pathway of the human brain. This was achieved by mapping thousands of stimulus features of increasing complexity across the cortical sheet using a deep neural network. Our approach also revealed a fine-grained functional specialization of downstream areas of the ventral stream. Furthermore, it allowed decoding of representations from human brain activity at an unsurpassed degree of accuracy, confirming the quality of the developed approach. Stimulus features that successfully explained neural responses indicate that population receptive fields were explicitly tuned for object categorization. This provides strong support for the hypothesis that object categorization is a guiding principle in the functional organization of the primate ventral stream. Copyright © 2015 the authors 0270-6474/15/3510005-10$15.00/0.

Aging affects hemispheric asymmetry in the neural representation of speech sounds.

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Bellis, T J; Nicol, T; Kraus, N

2000-01-15

Hemispheric asymmetries in the processing of elemental speech sounds appear to be critical for normal speech perception. This study investigated the effects of age on hemispheric asymmetry observed in the neurophysiological responses to speech stimuli in three groups of normal hearing, right-handed subjects: children (ages, 8-11 years), young adults (ages, 20-25 years), and older adults (ages > 55 years). Peak-to-peak response amplitudes of the auditory cortical P1-N1 complex obtained over right and left temporal lobes were examined to determine the degree of left/right asymmetry in the neurophysiological responses elicited by synthetic speech syllables in each of the three subject groups. In addition, mismatch negativity (MMN) responses, which are elicited by acoustic change, were obtained. Whereas children and young adults demonstrated larger P1-N1-evoked response amplitudes over the left temporal lobe than over the right, responses from elderly subjects were symmetrical. In contrast, MMN responses, which reflect an echoic memory process, were symmetrical in all subject groups. The differences observed in the neurophysiological responses were accompanied by a finding of significantly poorer ability to discriminate speech syllables involving rapid spectrotemporal changes in the older adult group. This study demonstrates a biological, age-related change in the neural representation of basic speech sounds and suggests one possible underlying mechanism for the speech perception difficulties exhibited by aging adults. Furthermore, results of this study support previous findings suggesting a dissociation between neural mechanisms underlying those processes that reflect the basic representation of sound structure and those that represent auditory echoic memory and stimulus change.

The neural representation of typical and atypical experiences of negative images: comparing fear, disgust and morbid fascination

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Lindquist, Kristen A.; Adebayo, Morenikeji; Barrett, Lisa Feldman

2016-01-01

Negative stimuli do not only evoke fear or disgust, but can also evoke a state of ‘morbid fascination’ which is an urge to approach and explore a negative stimulus. In the present neuroimaging study, we applied an innovative method to investigate the neural systems involved in typical and atypical conceptualizations of negative images. Participants received false feedback labeling their mental experience as fear, disgust or morbid fascination. This manipulation was successful; participants judged the false feedback correct for 70% of the trials on average. The neuroimaging results demonstrated differential activity within regions in the ‘neural reference space for discrete emotion’ depending on the type of feedback. We found robust differences in the ventrolateral prefrontal cortex, the dorsomedial prefrontal cortex and the lateral orbitofrontal cortex comparing morbid fascination to control feedback. More subtle differences in the dorsomedial prefrontal cortex and the lateral orbitofrontal cortex were also found between morbid fascination feedback and the other emotion feedback conditions. This study is the first to forward evidence about the neural representation of the experimentally unexplored state of morbid fascination. In line with a constructionist framework, our findings suggest that neural resources associated with the process of conceptualization contribute to the neural representation of this state. PMID:26180088

Doctor, Teacher, and Stethoscope: Neural Representation of Different Types of Semantic Relations.

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Xu, Yangwen; Wang, Xiaosha; Wang, Xiaoying; Men, Weiwei; Gao, Jia-Hong; Bi, Yanchao

2018-03-28

Concepts can be related in many ways. They can belong to the same taxonomic category (e.g., "doctor" and "teacher," both in the category of people) or be associated with the same event context (e.g., "doctor" and "stethoscope," both associated with medical scenarios). How are these two major types of semantic relations coded in the brain? We constructed stimuli from three taxonomic categories (people, manmade objects, and locations) and three thematic categories (school, medicine, and sports) and investigated the neural representations of these two dimensions using representational similarity analyses in human participants (10 men and nine women). In specific regions of interest, the left anterior temporal lobe (ATL) and the left temporoparietal junction (TPJ), we found that, whereas both areas had significant effects of taxonomic information, the taxonomic relations had stronger effects in the ATL than in the TPJ ("doctor" and "teacher" closer in ATL neural activity), with the reverse being true for thematic relations ("doctor" and "stethoscope" closer in TPJ neural activity). A whole-brain searchlight analysis revealed that widely distributed regions, mainly in the left hemisphere, represented the taxonomic dimension. Interestingly, the significant effects of the thematic relations were only observed after the taxonomic differences were controlled for in the left TPJ, the right superior lateral occipital cortex, and other frontal, temporal, and parietal regions. In summary, taxonomic grouping is a primary organizational dimension across distributed brain regions, with thematic grouping further embedded within such taxonomic structures. SIGNIFICANCE STATEMENT How are concepts organized in the brain? It is well established that concepts belonging to the same taxonomic categories (e.g., "doctor" and "teacher") share neural representations in specific brain regions. How concepts are associated in other manners (e.g., "doctor" and "stethoscope," which are thematically

Dynamic cultural influences on neural representations of the self.

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Chiao, Joan Y; Harada, Tokiko; Komeda, Hidetsugu; Li, Zhang; Mano, Yoko; Saito, Daisuke; Parrish, Todd B; Sadato, Norihiro; Iidaka, Tetsuya

2010-01-01

People living in multicultural environments often encounter situations which require them to acquire different cultural schemas and to switch between these cultural schemas depending on their immediate sociocultural context. Prior behavioral studies show that priming cultural schemas reliably impacts mental processes and behavior underlying self-concept. However, less well understood is whether or not cultural priming affects neurobiological mechanisms underlying the self. Here we examined whether priming cultural values of individualism and collectivism in bicultural individuals affects neural activity in cortical midline structures underlying self-relevant processes using functional magnetic resonance imaging. Biculturals primed with individualistic values showed increased activation within medial prefrontal cortex (MPFC) and posterior cingulate cortex (PCC) during general relative to contextual self-judgments, whereas biculturals primed with collectivistic values showed increased response within MPFC and PCC during contextual relative to general self-judgments. Moreover, degree of cultural priming was positively correlated with degree of MPFC and PCC activity during culturally congruent self-judgments. These findings illustrate the dynamic influence of culture on neural representations underlying the self and, more broadly, suggest a neurobiological basis by which people acculturate to novel environments.

Incremental learning of perceptual and conceptual representations and the puzzle of neural repetition suppression.

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Gotts, Stephen J

2016-08-01

Incremental learning models of long-term perceptual and conceptual knowledge hold that neural representations are gradually acquired over many individual experiences via Hebbian-like activity-dependent synaptic plasticity across cortical connections of the brain. In such models, variation in task relevance of information, anatomic constraints, and the statistics of sensory inputs and motor outputs lead to qualitative alterations in the nature of representations that are acquired. Here, the proposal that behavioral repetition priming and neural repetition suppression effects are empirical markers of incremental learning in the cortex is discussed, and research results that both support and challenge this position are reviewed. Discussion is focused on a recent fMRI-adaptation study from our laboratory that shows decoupling of experience-dependent changes in neural tuning, priming, and repetition suppression, with representational changes that appear to work counter to the explicit task demands. Finally, critical experiments that may help to clarify and resolve current challenges are outlined.

A neural network model of semantic memory linking feature-based object representation and words.

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Cuppini, C; Magosso, E; Ursino, M

2009-06-01

Recent theories in cognitive neuroscience suggest that semantic memory is a distributed process, which involves many cortical areas and is based on a multimodal representation of objects. The aim of this work is to extend a previous model of object representation to realize a semantic memory, in which sensory-motor representations of objects are linked with words. The model assumes that each object is described as a collection of features, coded in different cortical areas via a topological organization. Features in different objects are segmented via gamma-band synchronization of neural oscillators. The feature areas are further connected with a lexical area, devoted to the representation of words. Synapses among the feature areas, and among the lexical area and the feature areas are trained via a time-dependent Hebbian rule, during a period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from acoustic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits).

Briefly Cuing Memories Leads to Suppression of Their Neural Representations

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Norman, Kenneth A.

2014-01-01

Previous studies have linked partial memory activation with impaired subsequent memory retrieval (e.g., Detre et al., 2013) but have not provided an account of this phenomenon at the level of memory representations: How does partial activation change the neural pattern subsequently elicited when the memory is cued? To address this question, we conducted a functional magnetic resonance imaging (fMRI) experiment in which participants studied word-scene paired associates. Later, we weakly reactivated some memories by briefly presenting the cue word during a rapid serial visual presentation (RSVP) task; other memories were more strongly reactivated or not reactivated at all. We tested participants' memory for the paired associates before and after RSVP. Cues that were briefly presented during RSVP triggered reduced levels of scene activity on the post-RSVP memory test, relative to the other conditions. We used pattern similarity analysis to assess how representations changed as a function of the RSVP manipulation. For briefly cued pairs, we found that neural patterns elicited by the same cue on the pre- and post-RSVP tests (preA–postA; preB–postB) were less similar than neural patterns elicited by different cues (preA–postB; preB–postA). These similarity reductions were predicted by neural measures of memory activation during RSVP. Through simulation, we show that our pattern similarity results are consistent with a model in which partial memory activation triggers selective weakening of the strongest parts of the memory. PMID:24899722

Predicting behavior change from persuasive messages using neural representational similarity and social network analyses.

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Pegors, Teresa K; Tompson, Steven; O'Donnell, Matthew Brook; Falk, Emily B

2017-08-15

Neural activity in medial prefrontal cortex (MPFC), identified as engaging in self-related processing, predicts later health behavior change. However, it is unknown to what extent individual differences in neural representation of content and lived experience influence this brain-behavior relationship. We examined whether the strength of content-specific representations during persuasive messaging relates to later behavior change, and whether these relationships change as a function of individuals' social network composition. In our study, smokers viewed anti-smoking messages while undergoing fMRI and we measured changes in their smoking behavior one month later. Using representational similarity analyses, we found that the degree to which message content (i.e. health, social, or valence information) was represented in a self-related processing MPFC region was associated with later smoking behavior, with increased representations of negatively valenced (risk) information corresponding to greater message-consistent behavior change. Furthermore, the relationship between representations and behavior change depended on social network composition: smokers who had proportionally fewer smokers in their network showed increases in smoking behavior when social or health content was strongly represented in MPFC, whereas message-consistent behavior (i.e., less smoking) was more likely for those with proportionally more smokers in their social network who represented social or health consequences more strongly. These results highlight the dynamic relationship between representations in MPFC and key outcomes such as health behavior change; a complete understanding of the role of MPFC in motivation and action should take into account individual differences in neural representation of stimulus attributes and social context variables such as social network composition. Copyright © 2017 Elsevier Inc. All rights reserved.

Neural overlap of L1 and L2 semantic representations in speech: A decoding approach.

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Van de Putte, Eowyn; De Baene, Wouter; Brass, Marcel; Duyck, Wouter

2017-11-15

Although research has now converged towards a consensus that both languages of a bilingual are represented in at least partly shared systems for language comprehension, it remains unclear whether both languages are represented in the same neural populations for production. We investigated the neural overlap between L1 and L2 semantic representations of translation equivalents using a production task in which the participants had to name pictures in L1 and L2. Using a decoding approach, we tested whether brain activity during the production of individual nouns in one language allowed predicting the production of the same concepts in the other language. Because both languages only share the underlying semantic representation (sensory and lexical overlap was maximally avoided), this would offer very strong evidence for neural overlap in semantic representations of bilinguals. Based on the brain activation for the individual concepts in one language in the bilateral occipito-temporal cortex and the inferior and the middle temporal gyrus, we could accurately predict the equivalent individual concepts in the other language. This indicates that these regions share semantic representations across L1 and L2 word production. Copyright © 2017 Elsevier Inc. All rights reserved.

Crossmodal integration enhances neural representation of task-relevant features in audiovisual face perception.

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Li, Yuanqing; Long, Jinyi; Huang, Biao; Yu, Tianyou; Wu, Wei; Liu, Yongjian; Liang, Changhong; Sun, Pei

2015-02-01

Previous studies have shown that audiovisual integration improves identification performance and enhances neural activity in heteromodal brain areas, for example, the posterior superior temporal sulcus/middle temporal gyrus (pSTS/MTG). Furthermore, it has also been demonstrated that attention plays an important role in crossmodal integration. In this study, we considered crossmodal integration in audiovisual facial perception and explored its effect on the neural representation of features. The audiovisual stimuli in the experiment consisted of facial movie clips that could be classified into 2 gender categories (male vs. female) or 2 emotion categories (crying vs. laughing). The visual/auditory-only stimuli were created from these movie clips by removing the auditory/visual contents. The subjects needed to make a judgment about the gender/emotion category for each movie clip in the audiovisual, visual-only, or auditory-only stimulus condition as functional magnetic resonance imaging (fMRI) signals were recorded. The neural representation of the gender/emotion feature was assessed using the decoding accuracy and the brain pattern-related reproducibility indices, obtained by a multivariate pattern analysis method from the fMRI data. In comparison to the visual-only and auditory-only stimulus conditions, we found that audiovisual integration enhanced the neural representation of task-relevant features and that feature-selective attention might play a role of modulation in the audiovisual integration. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Behavioral and Neural Representations of Spatial Directions across Words, Schemas, and Images.

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Weisberg, Steven M; Marchette, Steven A; Chatterjee, Anjan

2018-05-23

Modern spatial navigation requires fluency with multiple representational formats, including visual scenes, signs, and words. These formats convey different information. Visual scenes are rich and specific but contain extraneous details. Arrows, as an example of signs, are schematic representations in which the extraneous details are eliminated, but analog spatial properties are preserved. Words eliminate all spatial information and convey spatial directions in a purely abstract form. How does the human brain compute spatial directions within and across these formats? To investigate this question, we conducted two experiments on men and women: a behavioral study that was preregistered and a neuroimaging study using multivoxel pattern analysis of fMRI data to uncover similarities and differences among representational formats. Participants in the behavioral study viewed spatial directions presented as images, schemas, or words (e.g., "left"), and responded to each trial, indicating whether the spatial direction was the same or different as the one viewed previously. They responded more quickly to schemas and words than images, despite the visual complexity of stimuli being matched. Participants in the fMRI study performed the same task but responded only to occasional catch trials. Spatial directions in images were decodable in the intraparietal sulcus bilaterally but were not in schemas and words. Spatial directions were also decodable between all three formats. These results suggest that intraparietal sulcus plays a role in calculating spatial directions in visual scenes, but this neural circuitry may be bypassed when the spatial directions are presented as schemas or words. SIGNIFICANCE STATEMENT Human navigators encounter spatial directions in various formats: words ("turn left"), schematic signs (an arrow showing a left turn), and visual scenes (a road turning left). The brain must transform these spatial directions into a plan for action. Here, we investigate

Computational neuroanatomy: ontology-based representation of neural components and connectivity.

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Rubin, Daniel L; Talos, Ion-Florin; Halle, Michael; Musen, Mark A; Kikinis, Ron

2009-02-05

A critical challenge in neuroscience is organizing, managing, and accessing the explosion in neuroscientific knowledge, particularly anatomic knowledge. We believe that explicit knowledge-based approaches to make neuroscientific knowledge computationally accessible will be helpful in tackling this challenge and will enable a variety of applications exploiting this knowledge, such as surgical planning. We developed ontology-based models of neuroanatomy to enable symbolic lookup, logical inference and mathematical modeling of neural systems. We built a prototype model of the motor system that integrates descriptive anatomic and qualitative functional neuroanatomical knowledge. In addition to modeling normal neuroanatomy, our approach provides an explicit representation of abnormal neural connectivity in disease states, such as common movement disorders. The ontology-based representation encodes both structural and functional aspects of neuroanatomy. The ontology-based models can be evaluated computationally, enabling development of automated computer reasoning applications. Neuroanatomical knowledge can be represented in machine-accessible format using ontologies. Computational neuroanatomical approaches such as described in this work could become a key tool in translational informatics, leading to decision support applications that inform and guide surgical planning and personalized care for neurological disease in the future.

Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations

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van Noord, Rik; Bos, Johannes

2017-01-01

We evaluate the character-level translation method for neural semantic parsing on a large corpus of sentences annotated with Abstract Meaning Representations (AMRs). Using a sequence-to-sequence model, and some trivial preprocessing and postprocessing of AMRs, we obtain a baseline accuracy of 53.1

Neural speech recognition: continuous phoneme decoding using spatiotemporal representations of human cortical activity

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Moses, David A.; Mesgarani, Nima; Leonard, Matthew K.; Chang, Edward F.

2016-10-01

Objective. The superior temporal gyrus (STG) and neighboring brain regions play a key role in human language processing. Previous studies have attempted to reconstruct speech information from brain activity in the STG, but few of them incorporate the probabilistic framework and engineering methodology used in modern speech recognition systems. In this work, we describe the initial efforts toward the design of a neural speech recognition (NSR) system that performs continuous phoneme recognition on English stimuli with arbitrary vocabulary sizes using the high gamma band power of local field potentials in the STG and neighboring cortical areas obtained via electrocorticography. Approach. The system implements a Viterbi decoder that incorporates phoneme likelihood estimates from a linear discriminant analysis model and transition probabilities from an n-gram phonemic language model. Grid searches were used in an attempt to determine optimal parameterizations of the feature vectors and Viterbi decoder. Main results. The performance of the system was significantly improved by using spatiotemporal representations of the neural activity (as opposed to purely spatial representations) and by including language modeling and Viterbi decoding in the NSR system. Significance. These results emphasize the importance of modeling the temporal dynamics of neural responses when analyzing their variations with respect to varying stimuli and demonstrate that speech recognition techniques can be successfully leveraged when decoding speech from neural signals. Guided by the results detailed in this work, further development of the NSR system could have applications in the fields of automatic speech recognition and neural prosthetics.

Vocal Tract Images Reveal Neural Representations of Sensorimotor Transformation During Speech Imitation

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Carey, Daniel; Miquel, Marc E.; Evans, Bronwen G.; Adank, Patti; McGettigan, Carolyn

2017-01-01

Abstract Imitating speech necessitates the transformation from sensory targets to vocal tract motor output, yet little is known about the representational basis of this process in the human brain. Here, we address this question by using real-time MR imaging (rtMRI) of the vocal tract and functional MRI (fMRI) of the brain in a speech imitation paradigm. Participants trained on imitating a native vowel and a similar nonnative vowel that required lip rounding. Later, participants imitated these vowels and an untrained vowel pair during separate fMRI and rtMRI runs. Univariate fMRI analyses revealed that regions including left inferior frontal gyrus were more active during sensorimotor transformation (ST) and production of nonnative vowels, compared with native vowels; further, ST for nonnative vowels activated somatomotor cortex bilaterally, compared with ST of native vowels. Using test representational similarity analysis (RSA) models constructed from participants’ vocal tract images and from stimulus formant distances, we found that RSA searchlight analyses of fMRI data showed either type of model could be represented in somatomotor, temporal, cerebellar, and hippocampal neural activation patterns during ST. We thus provide the first evidence of widespread and robust cortical and subcortical neural representation of vocal tract and/or formant parameters, during prearticulatory ST. PMID:28334401

Neural representations of kinematic laws of motion: evidence for action-perception coupling.

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Dayan, Eran; Casile, Antonino; Levit-Binnun, Nava; Giese, Martin A; Hendler, Talma; Flash, Tamar

2007-12-18

Behavioral and modeling studies have established that curved and drawing human hand movements obey the 2/3 power law, which dictates a strong coupling between movement curvature and velocity. Human motion perception seems to reflect this constraint. The functional MRI study reported here demonstrates that the brain's response to this law of motion is much stronger and more widespread than to other types of motion. Compliance with this law is reflected in the activation of a large network of brain areas subserving motor production, visual motion processing, and action observation functions. Hence, these results strongly support the notion of similar neural coding for motion perception and production. These findings suggest that cortical motion representations are optimally tuned to the kinematic and geometrical invariants characterizing biological actions.

Examining overlap in behavioral and neural representations of morals, facts, and preferences.

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Theriault, Jordan; Waytz, Adam; Heiphetz, Larisa; Young, Liane

2017-11-01

Metaethical judgments refer to judgments about the information expressed by moral claims. Moral objectivists generally believe that moral claims are akin to facts, whereas moral subjectivists generally believe that moral claims are more akin to preferences. Evidence from developmental and social psychology has generally favored an objectivist view; however, this work has typically relied on few examples, and analyses have disallowed statistical generalizations beyond these few stimuli. The present work addresses whether morals are represented as fact-like or preference-like, using behavioral and neuroimaging methods, in combination with statistical techniques that can (a) generalize beyond our sample stimuli, and (b) test whether particular item features are associated with neural activity. Behaviorally, and contrary to prior work, morals were perceived as more preference-like than fact-like. Neurally, morals and preferences elicited common magnitudes and spatial patterns of activity, particularly within the dorsal-medial prefrontal cortex (DMPFC), a critical region for social cognition. This common DMPFC activity for morals and preferences was present across whole-brain conjunctions, and in individually localized functional regions of interest (targeting the theory of mind network). By contrast, morals and facts did not elicit any neural activity in common. Follow-up item analyses suggested that the activity elicited in common by morals and preferences was explained by their shared tendency to evoke representations of mental states. We conclude that morals are represented as far more subjective than prior work has suggested. This conclusion is consistent with recent theoretical research, which has argued that morality is fundamentally about regulating social relationships. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Hierarchical Neural Representation of Dreamed Objects Revealed by Brain Decoding with Deep Neural Network Features.

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Horikawa, Tomoyasu; Kamitani, Yukiyasu

2017-01-01

Dreaming is generally thought to be generated by spontaneous brain activity during sleep with patterns common to waking experience. This view is supported by a recent study demonstrating that dreamed objects can be predicted from brain activity during sleep using statistical decoders trained with stimulus-induced brain activity. However, it remains unclear whether and how visual image features associated with dreamed objects are represented in the brain. In this study, we used a deep neural network (DNN) model for object recognition as a proxy for hierarchical visual feature representation, and DNN features for dreamed objects were analyzed with brain decoding of fMRI data collected during dreaming. The decoders were first trained with stimulus-induced brain activity labeled with the feature values of the stimulus image from multiple DNN layers. The decoders were then used to decode DNN features from the dream fMRI data, and the decoded features were compared with the averaged features of each object category calculated from a large-scale image database. We found that the feature values decoded from the dream fMRI data positively correlated with those associated with dreamed object categories at mid- to high-level DNN layers. Using the decoded features, the dreamed object category could be identified at above-chance levels by matching them to the averaged features for candidate categories. The results suggest that dreaming recruits hierarchical visual feature representations associated with objects, which may support phenomenal aspects of dream experience.

Neural dynamics underlying attentional orienting to auditory representations in short-term memory.

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Backer, Kristina C; Binns, Malcolm A; Alain, Claude

2015-01-21

Sounds are ephemeral. Thus, coherent auditory perception depends on "hearing" back in time: retrospectively attending that which was lost externally but preserved in short-term memory (STM). Current theories of auditory attention assume that sound features are integrated into a perceptual object, that multiple objects can coexist in STM, and that attention can be deployed to an object in STM. Recording electroencephalography from humans, we tested these assumptions, elucidating feature-general and feature-specific neural correlates of auditory attention to STM. Alpha/beta oscillations and frontal and posterior event-related potentials indexed feature-general top-down attentional control to one of several coexisting auditory representations in STM. Particularly, task performance during attentional orienting was correlated with alpha/low-beta desynchronization (i.e., power suppression). However, attention to one feature could occur without simultaneous processing of the second feature of the representation. Therefore, auditory attention to memory relies on both feature-specific and feature-general neural dynamics. Copyright © 2015 the authors 0270-6474/15/351307-12$15.00/0.

Young Children's Understanding of Conflicting Mental Representation Predicts Suggestibility.

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Welch-Ross, Melissa K.; And Others

1997-01-01

Examined the relation between developmental suggestibility effects and preschoolers' emerging ability to reason about conflicting mental representations. Subjects were 42 three- to five-year-olds. Found in the children significant initial encoding and ability to retrieve event details. Also found an integration between children's theory of mind…

Deep neural networks rival the representation of primate IT cortex for core visual object recognition.

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Charles F Cadieu

2014-12-01

Full Text Available The primate visual system achieves remarkable visual object recognition performance even in brief presentations, and under changes to object exemplar, geometric transformations, and background variation (a.k.a. core visual object recognition. This remarkable performance is mediated by the representation formed in inferior temporal (IT cortex. In parallel, recent advances in machine learning have led to ever higher performing models of object recognition using artificial deep neural networks (DNNs. It remains unclear, however, whether the representational performance of DNNs rivals that of the brain. To accurately produce such a comparison, a major difficulty has been a unifying metric that accounts for experimental limitations, such as the amount of noise, the number of neural recording sites, and the number of trials, and computational limitations, such as the complexity of the decoding classifier and the number of classifier training examples. In this work, we perform a direct comparison that corrects for these experimental limitations and computational considerations. As part of our methodology, we propose an extension of "kernel analysis" that measures the generalization accuracy as a function of representational complexity. Our evaluations show that, unlike previous bio-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual object recognition task. Furthermore, we show that models that perform well on measures of representational performance also perform well on measures of representational similarity to IT, and on measures of predicting individual IT multi-unit responses. Whether these DNNs rely on computational mechanisms similar to the primate visual system is yet to be determined, but, unlike all previous bio-inspired models, that possibility cannot be ruled out merely on representational performance grounds.

Implicit Race Bias Decreases the Similarity of Neural Representations of Black and White Faces

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Brosch, Tobias; Bar-David, Eyal; Phelps, Elizabeth A.

2013-01-01

Implicit race bias has been shown to affect decisions and behaviors. It may also change perceptual experience by increasing perceived differences between social groups. We investigated how this phenomenon may be expressed at the neural level by testing whether the distributed blood-oxygenation-level-dependent (BOLD) patterns representing Black and White faces are more dissimilar in participants with higher implicit race bias. We used multivoxel pattern analysis to predict the race of faces participants were viewing. We successfully predicted the race of the faces on the basis of BOLD activation patterns in early occipital visual cortex, occipital face area, and fusiform face area (FFA). Whereas BOLD activation patterns in early visual regions, likely reflecting different perceptual features, allowed successful prediction for all participants, successful prediction on the basis of BOLD activation patterns in FFA, a high-level face-processing region, was restricted to participants with high pro-White bias. These findings suggest that stronger implicit pro-White bias decreases the similarity of neural representations of Black and White faces. PMID:23300228

Neural representations of the self and the mother for Chinese individuals.

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Gaowa Wuyun

Full Text Available An important question in social neuroscience is the similarities and differences in the neural representations between the self and close others. Most studies examining this topic have identified the medial prefrontal cortex (MPFC region as the primary area involved in this process. However, several studies have reported conflicting data, making further investigation of this topic very important. In this functional magnetic resonance imaging (fMRI study, we investigated the brain activity in the anterior cingulate cortex (ACC when Chinese participants passively listened to their self-name (SN, their mother's name (MN, and unknown names (UN. The results showed that compared with UN recognition, SN perception was associated with a robust activation in a widely distributed bilateral network, including the cortical midline structure (the MPFC and ACC, the inferior frontal gyrus, and the middle temporal gyrus. The SN invoked the bilateral superior temporal gyrus in contrast to the MN; the MN recognition provoked a stronger activation in the central and posterior brain regions in contrast to the SN recognition. The SN and MN caused an activation of overlapping areas, namely, the ACC, MPFC, and superior frontal gyrus. These results suggest that Chinese individuals utilize certain common brain region in processing both the SN and the MN. The present findings provide evidence for the neural basis of the self and close others for Chinese individuals.

The representation of object viewpoint in human visual cortex.

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Andresen, David R; Vinberg, Joakim; Grill-Spector, Kalanit

2009-04-01

Understanding the nature of object representations in the human brain is critical for understanding the neural basis of invariant object recognition. However, the degree to which object representations are sensitive to object viewpoint is unknown. Using fMRI we employed a parametric approach to examine the sensitivity to object view as a function of rotation (0 degrees-180 degrees ), category (animal/vehicle) and fMRI-adaptation paradigm (short or long-lagged). For both categories and fMRI-adaptation paradigms, object-selective regions recovered from adaptation when a rotated view of an object was shown after adaptation to a specific view of that object, suggesting that representations are sensitive to object rotation. However, we found evidence for differential representations across categories and ventral stream regions. Rotation cross-adaptation was larger for animals than vehicles, suggesting higher sensitivity to vehicle than animal rotation, and was largest in the left fusiform/occipito-temporal sulcus (pFUS/OTS), suggesting that this region has low sensitivity to rotation. Moreover, right pFUS/OTS and FFA responded more strongly to front than back views of animals (without adaptation) and rotation cross-adaptation depended both on the level of rotation and the adapting view. This result suggests a prevalence of neurons that prefer frontal views of animals in fusiform regions. Using a computational model of view-tuned neurons, we demonstrate that differential neural view tuning widths and relative distributions of neural-tuned populations in fMRI voxels can explain the fMRI results. Overall, our findings underscore the utility of parametric approaches for studying the neural basis of object invariance and suggest that there is no complete invariance to object view in the human ventral stream.

Neural Dynamics and Information Representation in Microcircuits of Motor Cortex

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Yasuhiro eTsubo

2013-05-01

Full Text Available The brain has to analyze and respond to external events that can change rapidly from time to time, suggesting that information processing by the brain may be essentially dynamic rather than static. The dynamical features of neural computation are of significant importance in motor cortex that governs the process of movement generation and learning. In this paper, we discuss these features based primarily on our recent findings on neural dynamics and information coding in the microcircuit of rat motor cortex. In fact, cortical neurons show a variety of dynamical behavior from rhythmic activity in various frequency bands to highly irregular spike firing. Of particular interest are the similarity and dissimilarity of the neuronal response properties in different layers of motor cortex. By conducting electrophysiological recordings in slice preparation, we report the phase response curves of neurons in different cortical layers to demonstrate their layer-dependent synchronization properties. We then study how motor cortex recruits task-related neurons in different layers for voluntary arm movements by simultaneous juxtacellular and multiunit recordings from behaving rats. The results suggest an interesting difference in the spectrum of functional activity between the superficial and deep layers. Furthermore, the task-related activities recorded from various layers exhibited power law distributions of inter-spike intervals (ISIs, in contrast to a general belief that ISIs obey Poisson or Gamma distributions in cortical neurons. We present a theoretical argument that this power law of in vivo neurons may represent the maximization of the entropy of firing rate with limited energy consumption of spike generation. Though further studies are required to fully clarify the functional implications of this coding principle, it may shed new light on information representations by neurons and circuits in motor cortex.

Social behaviour shapes hypothalamic neural ensemble representations of conspecific sex

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Remedios, Ryan; Kennedy, Ann; Zelikowsky, Moriel; Grewe, Benjamin F.; Schnitzer, Mark J.; Anderson, David J.

2017-10-01

All animals possess a repertoire of innate (or instinctive) behaviours, which can be performed without training. Whether such behaviours are mediated by anatomically distinct and/or genetically specified neural pathways remains unknown. Here we report that neural representations within the mouse hypothalamus, that underlie innate social behaviours, are shaped by social experience. Oestrogen receptor 1-expressing (Esr1+) neurons in the ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) control mating and fighting in rodents. We used microendoscopy to image Esr1+ neuronal activity in the VMHvl of male mice engaged in these social behaviours. In sexually and socially experienced adult males, divergent and characteristic neural ensembles represented male versus female conspecifics. However, in inexperienced adult males, male and female intruders activated overlapping neuronal populations. Sex-specific neuronal ensembles gradually separated as the mice acquired social and sexual experience. In mice permitted to investigate but not to mount or attack conspecifics, ensemble divergence did not occur. However, 30 minutes of sexual experience with a female was sufficient to promote the separation of male and female ensembles and to induce an attack response 24 h later. These observations uncover an unexpected social experience-dependent component to the formation of hypothalamic neural assemblies controlling innate social behaviours. More generally, they reveal plasticity and dynamic coding in an evolutionarily ancient deep subcortical structure that is traditionally viewed as a ‘hard-wired’ system.

Standard representation and unified stability analysis for dynamic artificial neural network models.

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Kim, Kwang-Ki K; Patrón, Ernesto Ríos; Braatz, Richard D

2018-02-01

An overview is provided of dynamic artificial neural network models (DANNs) for nonlinear dynamical system identification and control problems, and convex stability conditions are proposed that are less conservative than past results. The three most popular classes of dynamic artificial neural network models are described, with their mathematical representations and architectures followed by transformations based on their block diagrams that are convenient for stability and performance analyses. Classes of nonlinear dynamical systems that are universally approximated by such models are characterized, which include rigorous upper bounds on the approximation errors. A unified framework and linear matrix inequality-based stability conditions are described for different classes of dynamic artificial neural network models that take additional information into account such as local slope restrictions and whether the nonlinearities within the DANNs are odd. A theoretical example shows reduced conservatism obtained by the conditions. Copyright © 2017. Published by Elsevier Ltd.

Emotions in "Black and White" or Shades of Gray? How We Think About Emotion Shapes Our Perception and Neural Representation of Emotion.

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Satpute, Ajay B; Nook, Erik C; Narayanan, Sandhya; Shu, Jocelyn; Weber, Jochen; Ochsner, Kevin N

2016-11-01

The demands of social life often require categorically judging whether someone's continuously varying facial movements express "calm" or "fear," or whether one's fluctuating internal states mean one feels "good" or "bad." In two studies, we asked whether this kind of categorical, "black and white," thinking can shape the perception and neural representation of emotion. Using psychometric and neuroimaging methods, we found that (a) across participants, judging emotions using a categorical, "black and white" scale relative to judging emotions using a continuous, "shades of gray," scale shifted subjective emotion perception thresholds; (b) these shifts corresponded with activity in brain regions previously associated with affective responding (i.e., the amygdala and ventral anterior insula); and (c) connectivity of these regions with the medial prefrontal cortex correlated with the magnitude of categorization-related shifts. These findings suggest that categorical thinking about emotions may actively shape the perception and neural representation of the emotions in question. © The Author(s) 2016.

Two-stage neural-network-based technique for Urdu character two-dimensional shape representation, classification, and recognition

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Megherbi, Dalila B.; Lodhi, S. M.; Boulenouar, A. J.

2001-03-01

This work is in the field of automated document processing. This work addresses the problem of representation and recognition of Urdu characters using Fourier representation and a Neural Network architecture. In particular, we show that a two-stage Neural Network scheme is used here to make classification of 36 Urdu characters into seven sub-classes namely subclasses characterized by seven proposed and defined fuzzy features specifically related to Urdu characters. We show that here Fourier Descriptors and Neural Network provide a remarkably simple way to draw definite conclusions from vague, ambiguous, noisy or imprecise information. In particular, we illustrate the concept of interest regions and describe a framing method that provides a way to make the proposed technique for Urdu characters recognition robust and invariant to scaling and translation. We also show that a given character rotation is dealt with by using the Hotelling transform. This transform is based upon the eigenvalue decomposition of the covariance matrix of an image, providing a method of determining the orientation of the major axis of an object within an image. Finally experimental results are presented to show the power and robustness of the proposed two-stage Neural Network based technique for Urdu character recognition, its fault tolerance, and high recognition accuracy.

Effects of task-switching on neural representations of ambiguous sound input.

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Sussman, Elyse S; Bregman, Albert S; Lee, Wei-Wei

2014-11-01

The ability to perceive discrete sound streams in the presence of competing sound sources relies on multiple mechanisms that organize the mixture of the auditory input entering the ears. Many studies have focused on mechanisms that contribute to integrating sounds that belong together into one perceptual stream (integration) and segregating those that come from different sound sources (segregation). However, little is known about mechanisms that allow us to perceive individual sound sources within a dynamically changing auditory scene, when the input may be ambiguous, and heard as either integrated or segregated. This study tested the question of whether focusing on one of two possible sound organizations suppressed representation of the alternative organization. We presented listeners with ambiguous input and cued them to switch between tasks that used either the integrated or the segregated percept. Electrophysiological measures indicated which organization was currently maintained in memory. If mutual exclusivity at the neural level was the rule, attention to one of two possible organizations would preclude neural representation of the other. However, significant MMNs were elicited to both the target organization and the unattended, alternative organization, along with the target-related P3b component elicited only to the designated target organization. Results thus indicate that both organizations (integrated and segregated) were simultaneously maintained in memory regardless of which task was performed. Focusing attention to one aspect of the sounds did not abolish the alternative, unattended organization when the stimulus input was ambiguous. In noisy environments, such as walking on a city street, rapid and flexible adaptive processes are needed to help facilitate rapid switching to different sound sources in the environment. Having multiple representations available to the attentive system would allow for such flexibility, needed in everyday situations to

The neural representation of abstract words: the role of emotion.

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Vigliocco, Gabriella; Kousta, Stavroula-Thaleia; Della Rosa, Pasquale Anthony; Vinson, David P; Tettamanti, Marco; Devlin, Joseph T; Cappa, Stefano F

2014-07-01

It is generally assumed that abstract concepts are linguistically coded, in line with imaging evidence of greater engagement of the left perisylvian language network for abstract than concrete words (Binder JR, Desai RH, Graves WW, Conant LL. 2009. Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cerebral Cortex. 19:2767-2796; Wang J, Conder JA, Blitzer DN, Shinkareva SV. 2010. Neural representation of abstract and concrete concepts: A meta-analysis of neuroimaging studies. Hum Brain Map. 31:1459-1468). Recent behavioral work, which used tighter matching of items than previous studies, however, suggests that abstract concepts also entail affective processing to a greater extent than concrete concepts (Kousta S-T, Vigliocco G, Vinson DP, Andrews M, Del Campo E. The representation of abstract words: Why emotion matters. J Exp Psychol Gen. 140:14-34). Here we report a functional magnetic resonance imaging experiment that shows greater engagement of the rostral anterior cingulate cortex, an area associated with emotion processing (e.g., Etkin A, Egner T, Peraza DM, Kandel ER, Hirsch J. 2006. Resolving emotional conflict: A role for the rostral anterior cingulate cortex in modulating activity in the amygdala. Neuron. 52:871), in abstract processing. For abstract words, activation in this area was modulated by the hedonic valence (degree of positive or negative affective association) of our items. A correlation analysis of more than 1,400 English words further showed that abstract words, in general, receive higher ratings for affective associations (both valence and arousal) than concrete words, supporting the view that engagement of emotional processing is generally required for processing abstract words. We argue that these results support embodiment views of semantic representation, according to which, whereas concrete concepts are grounded in our sensory-motor experience, affective experience is crucial in the

Neurofeedback and the Neural Representation of Self: Lessons From Awake State and Sleep.

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Ioannides, Andreas A

2018-01-01

Neurofeedback has been around for half a century, but despite some promising results it is not yet widely appreciated. Recently, some of the concerns about neurofeedback have been addressed with functional magnetic resonance imaging and magnetoencephalography adding their contributions to the long history of neurofeedback with electroencephalography. Attempts to address other concerns related to methodological issues with new experiments and meta-analysis of earlier studies, have opened up new questions about its efficacy. A key concern about neurofeedback is the missing framework to explain how improvements in very different and apparently unrelated conditions are achieved. Recent advances in neuroscience begin to address this concern. A particularly promising approach is the analysis of resting state of fMRI data, which has revealed robust covariations in brain networks that maintain their integrity in sleep and even anesthesia. Aberrant activity in three brain wide networks (i.e., the default mode, central executive and salience networks) has been associated with a number of psychiatric disorders. Recent publications have also suggested that neurofeedback guides the restoration of "normal" activity in these three networks. Using very recent results from our analysis of whole night MEG sleep data together with key concepts from developmental psychology, cloaked in modern neuroscience terms, a theoretical framework is proposed for a neural representation of the self, located at the core of a double onion-like structure of the default mode network. This framework fits a number of old and recent neuroscientific findings, and unites the way attention and memory operate in awake state and during sleep. In the process, safeguards are uncovered, put in place by evolution, before any interference with the core representation of self can proceed. Within this framework, neurofeedback is seen as set of methods for restoration of aberrant activity in large scale networks

Neurofeedback and the Neural Representation of Self: Lessons From Awake State and Sleep

Directory of Open Access Journals (Sweden)

Andreas A. Ioannides

2018-04-01

Full Text Available Neurofeedback has been around for half a century, but despite some promising results it is not yet widely appreciated. Recently, some of the concerns about neurofeedback have been addressed with functional magnetic resonance imaging and magnetoencephalography adding their contributions to the long history of neurofeedback with electroencephalography. Attempts to address other concerns related to methodological issues with new experiments and meta-analysis of earlier studies, have opened up new questions about its efficacy. A key concern about neurofeedback is the missing framework to explain how improvements in very different and apparently unrelated conditions are achieved. Recent advances in neuroscience begin to address this concern. A particularly promising approach is the analysis of resting state of fMRI data, which has revealed robust covariations in brain networks that maintain their integrity in sleep and even anesthesia. Aberrant activity in three brain wide networks (i.e., the default mode, central executive and salience networks has been associated with a number of psychiatric disorders. Recent publications have also suggested that neurofeedback guides the restoration of “normal” activity in these three networks. Using very recent results from our analysis of whole night MEG sleep data together with key concepts from developmental psychology, cloaked in modern neuroscience terms, a theoretical framework is proposed for a neural representation of the self, located at the core of a double onion-like structure of the default mode network. This framework fits a number of old and recent neuroscientific findings, and unites the way attention and memory operate in awake state and during sleep. In the process, safeguards are uncovered, put in place by evolution, before any interference with the core representation of self can proceed. Within this framework, neurofeedback is seen as set of methods for restoration of aberrant activity in

Multivariate Cross-Classification: Applying machine learning techniques to characterize abstraction in neural representations

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Jonas eKaplan

2015-03-01

Full Text Available Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC, and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application.

A study on neural network representation of reactor power control procedures 2

International Nuclear Information System (INIS)

Moon, Byung Soo; Park, Jea Chang; Kim, Young Taek; Lee, Hee Cho; Yang, Sung Uoon; Hwang, Hee Sun; Hwang, In Ah

1998-12-01

The major results of this study are as follows; the first is the algorithm developed through this study for computing the spline interpolation coefficients without solving the matrix equation involved. This is expected to be used in various numerical analysis problems. If this algorithm can be extended to functions of two independent variables in the future, then it could be a big help for the finite element method used in solving various boundary value problems. The second is the method developed to reduce systematically the number of output fuzzy sets for fuzzy systems representing functions of two variables. this may be considered as an indication that the neural network representation of functions has advantages over other conventional methods. The third result is an artificial neural network system developed for automating the manual procedures being used to change the reactor power level by adding boric acid or water to the reactor coolant. This along with the neural networks developed earlier can be used in nuclear power plants as an operator aid after a verification process. (author). 8 refs., 13 tabs., 5 figs

A study on neural network representation of reactor power control procedures 2

Energy Technology Data Exchange (ETDEWEB)

Moon, Byung Soo; Park, Jea Chang; Kim, Young Taek; Lee, Hee Cho; Yang, Sung Uoon; Hwang, Hee Sun; Hwang, In Ah

1998-12-01

The major results of this study are as follows; the first is the algorithm developed through this study for computing the spline interpolation coefficients without solving the matrix equation involved. This is expected to be used in various numerical analysis problems. If this algorithm can be extended to functions of two independent variables in the future, then it could be a big help for the finite element method used in solving various boundary value problems. The second is the method developed to reduce systematically the number of output fuzzy sets for fuzzy systems representing functions of two variables. this may be considered as an indication that the neural network representation of functions has advantages over other conventional methods. The third result is an artificial neural network system developed for automating the manual procedures being used to change the reactor power level by adding boric acid or water to the reactor coolant. This along with the neural networks developed earlier can be used in nuclear power plants as an operator aid after a verification process. (author). 8 refs., 13 tabs., 5 figs.

Neural representations and the cortical body matrix: implications for sports medicine and future directions.

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Wallwork, Sarah B; Bellan, Valeria; Catley, Mark J; Moseley, G Lorimer

2016-08-01

Neural representations, or neurotags, refer to the idea that networks of brain cells, distributed across multiple brain areas, work in synergy to produce outputs. The brain can be considered then, a complex array of neurotags, each influencing and being influenced by each other. The output of some neurotags act on other systems, for example, movement, or on consciousness, for example, pain. This concept of neurotags has sparked a new body of research into pain and rehabilitation. We draw on this research and the concept of a cortical body matrix-a network of representations that subserves the regulation and protection of the body and the space around it-to suggest important implications for rehabilitation of sports injury and for sports performance. Protective behaviours associated with pain have been reinterpreted in light of these conceptual models. With a particular focus on rehabilitation of the injured athlete, this review presents the theoretical underpinnings of the cortical body matrix and its application within the sporting context. Therapeutic approaches based on these ideas are discussed and the efficacy of the most tested approaches is addressed. By integrating current thought in pain and cognitive neuroscience related to sports rehabilitation, recommendations for clinical practice and future research are suggested. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Predicting Neural Activity Patterns Associated with Sentences Using a Neurobiologically Motivated Model of Semantic Representation.

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Anderson, Andrew James; Binder, Jeffrey R; Fernandino, Leonardo; Humphries, Colin J; Conant, Lisa L; Aguilar, Mario; Wang, Xixi; Doko, Donias; Raizada, Rajeev D S

2017-09-01

We introduce an approach that predicts neural representations of word meanings contained in sentences then superposes these to predict neural representations of new sentences. A neurobiological semantic model based on sensory, motor, social, emotional, and cognitive attributes was used as a foundation to define semantic content. Previous studies have predominantly predicted neural patterns for isolated words, using models that lack neurobiological interpretation. Fourteen participants read 240 sentences describing everyday situations while undergoing fMRI. To connect sentence-level fMRI activation patterns to the word-level semantic model, we devised methods to decompose the fMRI data into individual words. Activation patterns associated with each attribute in the model were then estimated using multiple-regression. This enabled synthesis of activation patterns for trained and new words, which were subsequently averaged to predict new sentences. Region-of-interest analyses revealed that prediction accuracy was highest using voxels in the left temporal and inferior parietal cortex, although a broad range of regions returned statistically significant results, showing that semantic information is widely distributed across the brain. The results show how a neurobiologically motivated semantic model can decompose sentence-level fMRI data into activation features for component words, which can be recombined to predict activation patterns for new sentences. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior.

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Groen, Iris Ia; Greene, Michelle R; Baldassano, Christopher; Fei-Fei, Li; Beck, Diane M; Baker, Chris I

2018-03-07

Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimized a priori through stimulus preselection. Behavioral assessments of scene similarity reflected unique contributions from a functional feature model indicating potential actions in scenes as well as high-level visual features from a deep neural network (DNN). In contrast, similarity of cortical responses in scene-selective areas was uniquely explained by mid- and high-level DNN features only, while an object label model did not contribute uniquely to either domain. The striking dissociation between functional and DNN features in their contribution to behavioral and brain representations of scenes indicates that scene-selective cortex represents only a subset of behaviorally relevant scene information.

Action Potential Modulation of Neural Spin Networks Suggests Possible Role of Spin

CERN Document Server

Hu, H P

2004-01-01

In this paper we show that nuclear spin networks in neural membranes are modulated by action potentials through J-coupling, dipolar coupling and chemical shielding tensors and perturbed by microscopically strong and fluctuating internal magnetic fields produced largely by paramagnetic oxygen. We suggest that these spin networks could be involved in brain functions since said modulation inputs information carried by the neural spike trains into them, said perturbation activates various dynamics within them and the combination of the two likely produce stochastic resonance thus synchronizing said dynamics to the neural firings. Although quantum coherence is desirable and may indeed exist, it is not required for these spin networks to serve as the subatomic components for the conventional neural networks.

Neural network of body representation differs between transsexuals and cissexuals.

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Chia-Shu Lin

Full Text Available Body image is the internal representation of an individual's own physical appearance. Individuals with gender identity disorder (GID, commonly referred to as transsexuals (TXs, are unable to form a satisfactory body image due to the dissonance between their biological sex and gender identity. We reasoned that changes in the resting-state functional connectivity (rsFC network would neurologically reflect such experiential incongruence in TXs. Using graph theory-based network analysis, we investigated the regional changes of the degree centrality of the rsFC network. The degree centrality is an index of the functional importance of a node in a neural network. We hypothesized that three key regions of the body representation network, i.e., the primary somatosensory cortex, the superior parietal lobule and the insula, would show a higher degree centrality in TXs. Twenty-three pre-treatment TXs (11 male-to-female and 12 female-to-male TXs as one psychosocial group and 23 age-matched healthy cissexual control subjects (CISs, 11 males and 12 females were recruited. Resting-state functional magnetic resonance imaging was performed, and binarized rsFC networks were constructed. The TXs demonstrated a significantly higher degree centrality in the bilateral superior parietal lobule and the primary somatosensory cortex. In addition, the connectivity between the right insula and the bilateral primary somatosensory cortices was negatively correlated with the selfness rating of their desired genders. These data indicate that the key components of body representation manifest in TXs as critical function hubs in the rsFC network. The negative association may imply a coping mechanism that dissociates bodily emotion from body image. The changes in the functional connectome may serve as representational markers for the dysphoric bodily self of TXs.

Entire Sound Representations Are Time-Compressed in Sensory Memory: Evidence from MMN.

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Tamakoshi, Seiji; Minoura, Nanako; Katayama, Jun'ichi; Yagi, Akihiro

2016-01-01

In order to examine the encoding of partial silence included in a sound stimulus in neural representation, time flow of the sound representations was investigated using mismatch negativity (MMN), an ERP component that reflects neural representation in auditory sensory memory. Previous work suggested that time flow of auditory stimuli is compressed in neural representations. The stimuli used were a full-stimulus of 170 ms duration, an early-gap stimulus with silence for a 20-50 ms segment (i.e., an omitted segment), and a late-gap stimulus with an omitted segment of 110-140 ms. Peak MMNm latencies from oddball sequences of these stimuli, with a 500 ms SOA, did not reflect time point of the physical gap, suggesting that temporal information can be compressed in sensory memory. However, it was not clear whether the whole stimulus duration or only the omitted segment duration is compressed. Thus, stimuli were used in which the gap was replaced by a tone segment with a 1/4 sound pressure level (filled), as well as the gap stimuli. Combinations of full-stimuli and one of four gapped or filled stimuli (i.e., early gap, late gap, early filled, and late filled) were presented in an oddball sequence (85 vs. 15%). If compression occurs only for the gap duration, MMN latency for filled stimuli should show a different pattern from those for gap stimuli. MMN latencies for the filled conditions showed the same pattern as those for the gap conditions, indicating that the whole stimulus duration rather than only gap duration is compressed in sensory memory neural representation. These results suggest that temporal aspects of silence are encoded in the same manner as physical sound.

Identifying thematic roles from neural representations measured by functional magnetic resonance imaging.

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Wang, Jing; Cherkassky, Vladimir L; Yang, Ying; Chang, Kai-Min Kevin; Vargas, Robert; Diana, Nicholas; Just, Marcel Adam

2016-01-01

The generativity and complexity of human thought stem in large part from the ability to represent relations among concepts and form propositions. The current study reveals how a given object such as rabbit is neurally encoded differently and identifiably depending on whether it is an agent ("the rabbit punches the monkey") or a patient ("the monkey punches the rabbit"). Machine-learning classifiers were trained on functional magnetic resonance imaging (fMRI) data evoked by a set of short videos that conveyed agent-verb-patient propositions. When tested on a held-out video, the classifiers were able to reliably identify the thematic role of an object from its associated fMRI activation pattern. Moreover, when trained on one subset of the study participants, classifiers reliably identified the thematic roles in the data of a left-out participant (mean accuracy = .66), indicating that the neural representations of thematic roles were common across individuals.

Optimizing Semantic Pointer Representations for Symbol-Like Processing in Spiking Neural Networks.

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Gosmann, Jan; Eliasmith, Chris

2016-01-01

The Semantic Pointer Architecture (SPA) is a proposal of specifying the computations and architectural elements needed to account for cognitive functions. By means of the Neural Engineering Framework (NEF) this proposal can be realized in a spiking neural network. However, in any such network each SPA transformation will accumulate noise. By increasing the accuracy of common SPA operations, the overall network performance can be increased considerably. As well, the representations in such networks present a trade-off between being able to represent all possible values and being only able to represent the most likely values, but with high accuracy. We derive a heuristic to find the near-optimal point in this trade-off. This allows us to improve the accuracy of common SPA operations by up to 25 times. Ultimately, it allows for a reduction of neuron number and a more efficient use of both traditional and neuromorphic hardware, which we demonstrate here.

Transformed Neural Pattern Reinstatement during Episodic Memory Retrieval.

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Xiao, Xiaoqian; Dong, Qi; Gao, Jiahong; Men, Weiwei; Poldrack, Russell A; Xue, Gui

2017-03-15

Contemporary models of episodic memory posit that remembering involves the reenactment of encoding processes. Although encoding-retrieval similarity has been consistently reported and linked to memory success, the nature of neural pattern reinstatement is poorly understood. Using high-resolution fMRI on human subjects, our results obtained clear evidence for item-specific pattern reinstatement in the frontoparietal cortex, even when the encoding-retrieval pairs shared no perceptual similarity. No item-specific pattern reinstatement was found in the ventral visual cortex. Importantly, the brain regions and voxels carrying item-specific representation differed significantly between encoding and retrieval, and the item specificity for encoding-retrieval similarity was smaller than that for encoding or retrieval, suggesting different nature of representations between encoding and retrieval. Moreover, cross-region representational similarity analysis suggests that the encoded representation in the ventral visual cortex was reinstated in the frontoparietal cortex during retrieval. Together, these results suggest that, in addition to reinstatement of the originally encoded pattern in the brain regions that perform encoding processes, retrieval may also involve the reinstatement of a transformed representation of the encoded information. These results emphasize the constructive nature of memory retrieval that helps to serve important adaptive functions. SIGNIFICANCE STATEMENT Episodic memory enables humans to vividly reexperience past events, yet how this is achieved at the neural level is barely understood. A long-standing hypothesis posits that memory retrieval involves the faithful reinstatement of encoding-related activity. We tested this hypothesis by comparing the neural representations during encoding and retrieval. We found strong pattern reinstatement in the frontoparietal cortex, but not in the ventral visual cortex, that represents visual details. Critically

Neural Representations of Natural and Scrambled Movies Progressively Change from Rat Striate to Temporal Cortex

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Vinken, Kasper; Van den Bergh, Gert; Vermaercke, Ben; Op de Beeck, Hans P.

2016-01-01

In recent years, the rodent has come forward as a candidate model for investigating higher level visual abilities such as object vision. This view has been backed up substantially by evidence from behavioral studies that show rats can be trained to express visual object recognition and categorization capabilities. However, almost no studies have investigated the functional properties of rodent extrastriate visual cortex using stimuli that target object vision, leaving a gap compared with the primate literature. Therefore, we recorded single-neuron responses along a proposed ventral pathway in rat visual cortex to investigate hallmarks of primate neural object representations such as preference for intact versus scrambled stimuli and category-selectivity. We presented natural movies containing a rat or no rat as well as their phase-scrambled versions. Population analyses showed increased dissociation in representations of natural versus scrambled stimuli along the targeted stream, but without a clear preference for natural stimuli. Along the measured cortical hierarchy the neural response seemed to be driven increasingly by features that are not V1-like and destroyed by phase-scrambling. However, there was no evidence for category selectivity for the rat versus nonrat distinction. Together, these findings provide insights about differences and commonalities between rodent and primate visual cortex. PMID:27146315

Impact of the virtual reality on the neural representation of an environment.

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Mellet, Emmanuel; Laou, Laetitia; Petit, Laurent; Zago, Laure; Mazoyer, Bernard; Tzourio-Mazoyer, Nathalie

2010-07-01

Despite the increasing use of virtual reality, the impact on cerebral representation of topographical knowledge of learning by virtual reality rather than by actual locomotion has never been investigated. To tackle this challenging issue, we conducted an experiment wherein participants learned an immersive virtual environment using a joystick. The following day, participants' brain activity was monitored by functional magnetic resonance imaging while they mentally estimated distances in this environment. Results were compared with that of participants performing the same task but having learned the real version of the environment by actual walking. We detected a large set of areas shared by both groups including the parieto-frontal areas and the parahippocampal gyrus. More importantly, although participants of both groups performed the same mental task and exhibited similar behavioral performances, they differed at the brain activity level. Unlike real learners, virtual learners activated a left-lateralized network associated with tool manipulation and action semantics. This demonstrated that a neural fingerprint distinguishing virtual from real learning persists when subjects use a mental representation of the learnt environment with equivalent performances. (c) 2009 Wiley-Liss, Inc.

Emergence of task-dependent representations in working memory circuits

Directory of Open Access Journals (Sweden)

Cristina eSavin

2014-05-01

Full Text Available A wealth of experimental evidence suggests that working memory circuits preferentially represent information that is behaviorally relevant. Still, we are missing a mechanistic account of how these representations come about. Here we provide a simple explanation for a range of experimental findings, in light of prefrontal circuits adapting to task constraints by reward-dependent learning. In particular, we model a neural network shaped by reward-modulated spike-timing dependent plasticity (r-STDP and homeostatic plasticity (intrinsic excitability and synaptic scaling. We show that the experimentally-observed neural representations naturally emerge in an initially unstructured circuit as it learns to solve several working memory tasks. These results point to a critical, and previously unappreciated, role for reward-dependent learning in shaping prefrontal cortex activity.

Extracting the Behaviorally Relevant Stimulus: Unique Neural Representation of Farnesol, a Component of the Recruitment Pheromone of Bombus terrestris.

Directory of Open Access Journals (Sweden)

Martin F Strube-Bloss

Full Text Available To trigger innate behavior, sensory neural networks are pre-tuned to extract biologically relevant stimuli. Many male-female or insect-plant interactions depend on this phenomenon. Especially communication among individuals within social groups depends on innate behaviors. One example is the efficient recruitment of nest mates by successful bumblebee foragers. Returning foragers release a recruitment pheromone in the nest while they perform a 'dance' behavior to activate unemployed nest mates. A major component of this pheromone is the sesquiterpenoid farnesol. How farnesol is processed and perceived by the olfactory system, has not yet been identified. It is much likely that processing farnesol involves an innate mechanism for the extraction of relevant information to trigger a fast and reliable behavioral response. To test this hypothesis, we used population response analyses of 100 antennal lobe (AL neurons recorded in alive bumblebee workers under repeated stimulation with four behaviorally different, but chemically related odorants (geraniol, citronellol, citronellal and farnesol. The analysis identified a unique neural representation of the recruitment pheromone component compared to the other odorants that are predominantly emitted by flowers. The farnesol induced population activity in the AL allowed a reliable separation of farnesol from all other chemically related odor stimuli we tested. We conclude that the farnesol induced population activity may reflect a predetermined representation within the AL-neural network allowing efficient and fast extraction of a behaviorally relevant stimulus. Furthermore, the results show that population response analyses of multiple single AL-units may provide a powerful tool to identify distinct representations of behaviorally relevant odors.

The price of your soul: neural evidence for the non-utilitarian representation of sacred values.

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Berns, Gregory S; Bell, Emily; Capra, C Monica; Prietula, Michael J; Moore, Sara; Anderson, Brittany; Ginges, Jeremy; Atran, Scott

2012-03-05

Sacred values, such as those associated with religious or ethnic identity, underlie many important individual and group decisions in life, and individuals typically resist attempts to trade off their sacred values in exchange for material benefits. Deontological theory suggests that sacred values are processed based on rights and wrongs irrespective of outcomes, while utilitarian theory suggests that they are processed based on costs and benefits of potential outcomes, but which mode of processing an individual naturally uses is unknown. The study of decisions over sacred values is difficult because outcomes cannot typically be realized in a laboratory, and hence little is known about the neural representation and processing of sacred values. We used an experimental paradigm that used integrity as a proxy for sacredness and which paid real money to induce individuals to sell their personal values. Using functional magnetic resonance imaging (fMRI), we found that values that people refused to sell (sacred values) were associated with increased activity in the left temporoparietal junction and ventrolateral prefrontal cortex, regions previously associated with semantic rule retrieval. This suggests that sacred values affect behaviour through the retrieval and processing of deontic rules and not through a utilitarian evaluation of costs and benefits.

Neural representations of novel objects associated with olfactory experience.

Science.gov (United States)

Ghio, Marta; Schulze, Patrick; Suchan, Boris; Bellebaum, Christian

2016-07-15

Object conceptual knowledge comprises information related to several motor and sensory modalities (e.g. for tools, how they look like, how to manipulate them). Whether and to which extent conceptual object knowledge is represented in the same sensory and motor systems recruited during object-specific learning experience is still a controversial question. A direct approach to assess the experience-dependence of conceptual object representations is based on training with novel objects. The present study extended previous research, which focused mainly on the role of manipulation experience for tool-like stimuli, by considering sensory experience only. Specifically, we examined the impact of experience in the non-dominant olfactory modality on the neural representation of novel objects. Sixteen healthy participants visually explored a set of novel objects during the training phase while for each object an odor (e.g., peppermint) was presented (olfactory-visual training). As control conditions, a second set of objects was only visually explored (visual-only training), and a third set was not part of the training. In a post-training fMRI session, participants performed an old/new task with pictures of objects associated with olfactory-visual and visual-only training (old) and no training objects (new). Although we did not find any evidence of activations in primary olfactory areas, the processing of olfactory-visual versus visual-only training objects elicited greater activation in the right anterior hippocampus, a region included in the extended olfactory network. This finding is discussed in terms of different functional roles of the hippocampus in olfactory processes. Copyright © 2016 Elsevier B.V. All rights reserved.

Dissociable neural representations of grammatical gender in Broca's area investigated by the combination of satiation and TMS.

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Cattaneo, Zaira; Devlin, Joseph T; Vecchi, Tomaso; Silvanto, Juha

2009-08-15

Along with meaning and form, words can be described on the basis of their grammatical properties. Grammatical gender is often used to investigate the latter as it is a grammatical property that is independent of meaning. The left inferior frontal gyrus (IFG) has been implicated in the encoding of grammatical gender, but its causal role in this process in neurologically normal observers has not been demonstrated. Here we combined verbal satiation with transcranial magnetic stimulation (TMS) to demonstrate that subpopulations of neurons within Broca's area respond preferentially to different classes of grammatical gender. Subjects were asked to classify Italian nouns into living and nonliving categories; half of these words were of masculine and the other half of feminine grammatical gender. Prior to each test block, a satiation paradigm (a phenomenon in which verbal repetition of a category name leads to a reduced access to that category) was used to modulate the initial state of the representations of either masculine or feminine noun categories. In the No TMS condition, subjects were slower in responding to exemplars to the satiated category relative to exemplars of the nonsatiated category, implying that the neural representations for different classes of grammatical gender are partly dissociable. The application of TMS over Broca's area removed the behavioral impact of verbal (grammatical) satiation, demonstrating the causal role of this region in the encoding of grammatical gender. These results show that the neural representations for different cases of a grammatical property within Broca's area are dissociable.

Attention Effects on Neural Population Representations for Shape and Location Are Stronger in the Ventral than Dorsal Stream

Science.gov (United States)

2018-01-01

Abstract We examined how attention causes neural population representations of shape and location to change in ventral stream (AIT) and dorsal stream (LIP). Monkeys performed two identical delayed-match-to-sample (DMTS) tasks, attending either to shape or location. In AIT, shapes were more discriminable when directing attention to shape rather than location, measured by an increase in mean distance between population response vectors. In LIP, attending to location rather than shape did not increase the discriminability of different stimulus locations. Even when factoring out the change in mean vector response distance, multidimensional scaling (MDS) still showed a significant task difference in AIT, but not LIP, indicating that beyond increasing discriminability, attention also causes a nonlinear warping of representation space in AIT. Despite single-cell attentional modulations in both areas, our data show that attentional modulations of population representations are weaker in LIP, likely due to a need to maintain veridical representations for visuomotor control. PMID:29876521

A common neural code for perceived and inferred emotion.

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Skerry, Amy E; Saxe, Rebecca

2014-11-26

Although the emotions of other people can often be perceived from overt reactions (e.g., facial or vocal expressions), they can also be inferred from situational information in the absence of observable expressions. How does the human brain make use of these diverse forms of evidence to generate a common representation of a target's emotional state? In the present research, we identify neural patterns that correspond to emotions inferred from contextual information and find that these patterns generalize across different cues from which an emotion can be attributed. Specifically, we use functional neuroimaging to measure neural responses to dynamic facial expressions with positive and negative valence and to short animations in which the valence of a character's emotion could be identified only from the situation. Using multivoxel pattern analysis, we test for regions that contain information about the target's emotional state, identifying representations specific to a single stimulus type and representations that generalize across stimulus types. In regions of medial prefrontal cortex (MPFC), a classifier trained to discriminate emotional valence for one stimulus (e.g., animated situations) could successfully discriminate valence for the remaining stimulus (e.g., facial expressions), indicating a representation of valence that abstracts away from perceptual features and generalizes across different forms of evidence. Moreover, in a subregion of MPFC, this neural representation generalized to trials involving subjectively experienced emotional events, suggesting partial overlap in neural responses to attributed and experienced emotions. These data provide a step toward understanding how the brain transforms stimulus-bound inputs into abstract representations of emotion. Copyright © 2014 the authors 0270-6474/14/3315997-12$15.00/0.

Separate representation of stimulus frequency, intensity, and duration in auditory sensory memory: an event-related potential and dipole-model analysis.

Science.gov (United States)

Giard, M H; Lavikahen, J; Reinikainen, K; Perrin, F; Bertrand, O; Pernier, J; Näätänen, R

1995-01-01

Abstract The present study analyzed the neural correlates of acoustic stimulus representation in echoic sensory memory. The neural traces of auditory sensory memory were indirectly studied by using the mismatch negativity (MMN), an event-related potential component elicited by a change in a repetitive sound. The MMN is assumed to reflect change detection in a comparison process between the sensory input from a deviant stimulus and the neural representation of repetitive stimuli in echoic memory. The scalp topographies of the MMNs elicited by pure tones deviating from standard tones by either frequency, intensity, or duration varied according to the type of stimulus deviance, indicating that the MMNs for different attributes originate, at least in part, from distinct neural populations in the auditory cortex. This result was supported by dipole-model analysis. If the MMN generator process occurs where the stimulus information is stored, these findings strongly suggest that the frequency, intensity, and duration of acoustic stimuli have a separate neural representation in sensory memory.

Wittgenstein running: neural mechanisms of collective intentionality and we-mode.

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Becchio, Cristina; Bertone, Cesare

2004-03-01

In this paper we discuss the problem of the neural conditions of shared attitudes and intentions: which neural mechanisms underlie "we-mode" processes or serve as precursors to such processes? Neurophysiological and neuropsychological evidence suggests that in different areas of the brain neural representations are shared by several individuals. This situation, on the one hand, creates a potential problem for correct attribution. On the other hand, it may provide the conditions for shared attitudes and intentions.

Improving Feature Representation Based on a Neural Network for Author Profiling in Social Media Texts.

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Gómez-Adorno, Helena; Markov, Ilia; Sidorov, Grigori; Posadas-Durán, Juan-Pablo; Sanchez-Perez, Miguel A; Chanona-Hernandez, Liliana

2016-01-01

We introduce a lexical resource for preprocessing social media data. We show that a neural network-based feature representation is enhanced by using this resource. We conducted experiments on the PAN 2015 and PAN 2016 author profiling corpora and obtained better results when performing the data preprocessing using the developed lexical resource. The resource includes dictionaries of slang words, contractions, abbreviations, and emoticons commonly used in social media. Each of the dictionaries was built for the English, Spanish, Dutch, and Italian languages. The resource is freely available.

Global Neural Pattern Similarity as a Common Basis for Categorization and Recognition Memory

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Xue, Gui; Love, Bradley C.; Preston, Alison R.; Poldrack, Russell A.

2014-01-01

Familiarity, or memory strength, is a central construct in models of cognition. In previous categorization and long-term memory research, correlations have been found between psychological measures of memory strength and activation in the medial temporal lobes (MTLs), which suggests a common neural locus for memory strength. However, activation alone is insufficient for determining whether the same mechanisms underlie neural function across domains. Guided by mathematical models of categorization and long-term memory, we develop a theory and a method to test whether memory strength arises from the global similarity among neural representations. In human subjects, we find significant correlations between global similarity among activation patterns in the MTLs and both subsequent memory confidence in a recognition memory task and model-based measures of memory strength in a category learning task. Our work bridges formal cognitive theories and neuroscientific models by illustrating that the same global similarity computations underlie processing in multiple cognitive domains. Moreover, by establishing a link between neural similarity and psychological memory strength, our findings suggest that there may be an isomorphism between psychological and neural representational spaces that can be exploited to test cognitive theories at both the neural and behavioral levels. PMID:24872552

Decoding the dynamic representation of musical pitch from human brain activity.

Science.gov (United States)

Sankaran, N; Thompson, W F; Carlile, S; Carlson, T A

2018-01-16

In music, the perception of pitch is governed largely by its tonal function given the preceding harmonic structure of the music. While behavioral research has advanced our understanding of the perceptual representation of musical pitch, relatively little is known about its representational structure in the brain. Using Magnetoencephalography (MEG), we recorded evoked neural responses to different tones presented within a tonal context. Multivariate Pattern Analysis (MVPA) was applied to "decode" the stimulus that listeners heard based on the underlying neural activity. We then characterized the structure of the brain's representation using decoding accuracy as a proxy for representational distance, and compared this structure to several well established perceptual and acoustic models. The observed neural representation was best accounted for by a model based on the Standard Tonal Hierarchy, whereby differences in the neural encoding of musical pitches correspond to their differences in perceived stability. By confirming that perceptual differences honor those in the underlying neuronal population coding, our results provide a crucial link in understanding the cognitive foundations of musical pitch across psychological and neural domains.

Language, Perception, and the Schematic Representation of Spatial Relations

Science.gov (United States)

Amorapanth, Prin; Kranjec, Alexander; Bromberger, Bianca; Lehet, Matthew; Widick, Page; Woods, Adam J.; Kimberg, Daniel Y.; Chatterjee, Anjan

2012-01-01

Schemas are abstract nonverbal representations that parsimoniously depict spatial relations. Despite their ubiquitous use in maps and diagrams, little is known about their neural instantiation. We sought to determine the extent to which schematic representations are neurally distinguished from language on the one hand, and from rich perceptual…

Contributions of feature shapes and surface cues to the recognition and neural representation of facial identity.

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Andrews, Timothy J; Baseler, Heidi; Jenkins, Rob; Burton, A Mike; Young, Andrew W

2016-10-01

A full understanding of face recognition will involve identifying the visual information that is used to discriminate different identities and how this is represented in the brain. The aim of this study was to explore the importance of shape and surface properties in the recognition and neural representation of familiar faces. We used image morphing techniques to generate hybrid faces that mixed shape properties (more specifically, second order spatial configural information as defined by feature positions in the 2D-image) from one identity and surface properties from a different identity. Behavioural responses showed that recognition and matching of these hybrid faces was primarily based on their surface properties. These behavioural findings contrasted with neural responses recorded using a block design fMRI adaptation paradigm to test the sensitivity of Haxby et al.'s (2000) core face-selective regions in the human brain to the shape or surface properties of the face. The fusiform face area (FFA) and occipital face area (OFA) showed a lower response (adaptation) to repeated images of the same face (same shape, same surface) compared to different faces (different shapes, different surfaces). From the behavioural data indicating the critical contribution of surface properties to the recognition of identity, we predicted that brain regions responsible for familiar face recognition should continue to adapt to faces that vary in shape but not surface properties, but show a release from adaptation to faces that vary in surface properties but not shape. However, we found that the FFA and OFA showed an equivalent release from adaptation to changes in both shape and surface properties. The dissociation between the neural and perceptual responses suggests that, although they may play a role in the process, these core face regions are not solely responsible for the recognition of facial identity. Copyright © 2016 Elsevier Ltd. All rights reserved.

Musical Audio Synthesis Using Autoencoding Neural Nets

OpenAIRE

Sarroff, Andy; Casey, Michael A.

2014-01-01

With an optimal network topology and tuning of hyperpa-\\ud rameters, artificial neural networks (ANNs) may be trained\\ud to learn a mapping from low level audio features to one\\ud or more higher-level representations. Such artificial neu-\\ud ral networks are commonly used in classification and re-\\ud gression settings to perform arbitrary tasks. In this work\\ud we suggest repurposing autoencoding neural networks as\\ud musical audio synthesizers. We offer an interactive musi-\\ud cal audio synt...

Cortical pitch representations of complex tones in musicians and non-musicians

DEFF Research Database (Denmark)

Bianchi, Federica; Hjortkjær, Jens; Santurette, Sébastien

Musicians typically show enhanced pitch-discrimination ability compared to non-musicians, consistent with the fact that musicians are more sensitive to some acoustic features critical for both speech and music processing. However, it is still unclear which mechanisms underlie this perceptual...... enhancement. In a previous behavioral study, musicians showed an increased pitch-discrimination performance for both resolved and unresolved complex tones suggesting an enhanced neural representation of pitch at central stages of the auditory system. The aim of this study was to clarify whether musicians show...... (i) differential neural activation in response to complex tones as compared to non-musicians and/or (ii) finer fundamental frequency (F0) representation in the auditory cortex. Assuming that the right auditory cortex is specialized in processing fine spectral changes, we hypothesized that an enhanced...

Neural mechanisms by which attention modulates the comparison of remembered and perceptual representations.

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Bo-Cheng Kuo

Full Text Available Attention is important for effectively comparing incoming perceptual information with the contents of visual short-term memory (VSTM, such that any differences can be detected. However, how attentional mechanisms operate upon these comparison processes remains largely unknown. Here we investigate the underlying neural mechanisms by which attention modulates the comparisons between VSTM and perceptual representations using functional magnetic resonance imaging (fMRI. Participants performed a cued change detection task. Spatial cues were presented to orient their attention either to the location of an item in VSTM prior to its comparison (retro-cues, or simultaneously (simultaneous-cues with the probe array. A no-cue condition was also included. When attention cannot be effectively deployed in advance (i.e. following the simultaneous-cues, we observed a distributed and extensive activation pattern in the prefrontal and parietal cortices in support of successful change detection. This was not the case when participants can deploy their attention in advance (i.e. following the retro-cues. The region-of-interest analyses confirmed that neural responses for successful change detection versus correct rejection in the visual and parietal regions were significantly different for simultaneous-cues compared to retro-cues. Importantly, we found enhanced functional connectivity between prefrontal and parietal cortices when detecting changes on the simultaneous-cue trials. Moreover, we demonstrated a close relationship between this functional connectivity and d' scores. Together, our findings elucidate the attentional and neural mechanisms by which items held in VSTM are compared with incoming perceptual information.

The neural representation of social status in the extended face-processing network.

Science.gov (United States)

Koski, Jessica E; Collins, Jessica A; Olson, Ingrid R

2017-12-01

Social status is a salient cue that shapes our perceptions of other people and ultimately guides our social interactions. Despite the pervasive influence of status on social behavior, how information about the status of others is represented in the brain remains unclear. Here, we tested the hypothesis that social status information is embedded in our neural representations of other individuals. Participants learned to associate faces with names, job titles that varied in associated status, and explicit markers of reputational status (star ratings). Trained stimuli were presented in an functional magnetic resonance imaging experiment where participants performed a target detection task orthogonal to the variable of interest. A network of face-selective brain regions extending from the occipital lobe to the orbitofrontal cortex was localized and served as regions of interest. Using multivoxel pattern analysis, we found that face-selective voxels in the lateral orbitofrontal cortex - a region involved in social and nonsocial valuation, could decode faces based on their status. Similar effects were observed with two different status manipulations - one based on stored semantic knowledge (e.g., different careers) and one based on learned reputation (e.g., star ranking). These data suggest that a face-selective region of the lateral orbitofrontal cortex may contribute to the perception of social status, potentially underlying the preferential attention and favorable biases humans display toward high-status individuals. © 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Representation of neutron noise data using neural networks

International Nuclear Information System (INIS)

Korsah, K.; Damiano, B.; Wood, R.T.

1992-01-01

This paper describes a neural network-based method of representing neutron noise spectra using a model developed at the Oak Ridge National Laboratory (ORNL). The backpropagation neural network learned to represent neutron noise data in terms of four descriptors, and the network response matched calculated values to within 3.5 percent. These preliminary results are encouraging, and further research is directed towards the application of neural networks in a diagnostics system for the identification of the causes of changes in structural spectral resonances. This work is part of our current investigation of advanced technologies such as expert systems and neural networks for neutron noise data reduction, analysis, and interpretation. The objective is to improve the state-of-the-art of noise analysis as a diagnostic tool for nuclear power plants and other mechanical systems

What recent research on diagrams suggests about learning with rather than learning from visual representations in science

Science.gov (United States)

Tippett, Christine D.

2016-03-01

The move from learning science from representations to learning science with representations has many potential and undocumented complexities. This thematic analysis partially explores the trends of representational uses in science instruction, examining 80 research studies on diagram use in science. These studies, published during 2000-2014, were located through searches of journal databases and books. Open coding of the studies identified 13 themes, 6 of which were identified in at least 10% of the studies: eliciting mental models, classroom-based research, multimedia principles, teaching and learning strategies, representational competence, and student agency. A shift in emphasis on learning with rather than learning from representations was evident across the three 5-year intervals considered, mirroring a pedagogical shift from science instruction as transmission of information to constructivist approaches in which learners actively negotiate understanding and construct knowledge. The themes and topics in recent research highlight areas of active interest and reveal gaps that may prove fruitful for further research, including classroom-based studies, the role of prior knowledge, and the use of eye-tracking. The results of the research included in this thematic review of the 2000-2014 literature suggest that both interpreting and constructing representations can lead to better understanding of science concepts.

Identifying Emotions on the Basis of Neural Activation.

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Kassam, Karim S; Markey, Amanda R; Cherkassky, Vladimir L; Loewenstein, George; Just, Marcel Adam

2013-01-01

We attempt to determine the discriminability and organization of neural activation corresponding to the experience of specific emotions. Method actors were asked to self-induce nine emotional states (anger, disgust, envy, fear, happiness, lust, pride, sadness, and shame) while in an fMRI scanner. Using a Gaussian Naïve Bayes pooled variance classifier, we demonstrate the ability to identify specific emotions experienced by an individual at well over chance accuracy on the basis of: 1) neural activation of the same individual in other trials, 2) neural activation of other individuals who experienced similar trials, and 3) neural activation of the same individual to a qualitatively different type of emotion induction. Factor analysis identified valence, arousal, sociality, and lust as dimensions underlying the activation patterns. These results suggest a structure for neural representations of emotion and inform theories of emotional processing.

Distinguishing Representations as Origin and Representations as Input: Roles for Individual Cells

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Jonathan C.W. Edwards

2016-09-01

Full Text Available It is widely perceived that there is a problem in giving a naturalistic account of mental representation that deals adequately with meaning, interpretation or significance (semantic content. It is suggested here that this problem may arise partly from the conflation of two vernacular senses of representation: representation-as-origin and representation-as-input. The flash of a neon sign may in one sense represent a popular drink, but to function as representation it must provide an input to a ‘consumer’ in the street. The arguments presented draw on two principles – the neuron doctrine and the need for a venue for ‘presentation’ or ‘reception’ of a representation at a specified site, consistent with the locality principle. It is also argued that domains of representation cannot be defined by signal traffic, since they can be expected to include ‘null’ elements based on non-firing cells. In this analysis, mental representations-as-origin are distributed patterns of cell firing. Each firing cell is given semantic value in its own right - some form of atomic propositional significance – since different axonal branches may contribute to integration with different populations of signals at different downstream sites. Representations-as-input are patterns of local co-arrival of signals in the form of synaptic potentials in dendrites. Meaning then draws on the relationships between active and null inputs, forming ‘scenarios’ comprising a molecular combination of ‘premises’ from which a new output with atomic propositional significance is generated. In both types of representation, meaning, interpretation or significance pivots on events in an individual cell. (This analysis only applies to ‘occurrent’ representations based on current neural activity. The concept of representations-as-input emphasises the need for a ‘consumer’ of a representation and the dependence of meaning on the co-relationships involved in an

Identifying Emotions on the Basis of Neural Activation.

Directory of Open Access Journals (Sweden)

Karim S Kassam

Full Text Available We attempt to determine the discriminability and organization of neural activation corresponding to the experience of specific emotions. Method actors were asked to self-induce nine emotional states (anger, disgust, envy, fear, happiness, lust, pride, sadness, and shame while in an fMRI scanner. Using a Gaussian Naïve Bayes pooled variance classifier, we demonstrate the ability to identify specific emotions experienced by an individual at well over chance accuracy on the basis of: 1 neural activation of the same individual in other trials, 2 neural activation of other individuals who experienced similar trials, and 3 neural activation of the same individual to a qualitatively different type of emotion induction. Factor analysis identified valence, arousal, sociality, and lust as dimensions underlying the activation patterns. These results suggest a structure for neural representations of emotion and inform theories of emotional processing.

Response variance in functional maps: neural darwinism revisited.

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Hirokazu Takahashi

Full Text Available The mechanisms by which functional maps and map plasticity contribute to cortical computation remain controversial. Recent studies have revisited the theory of neural Darwinism to interpret the learning-induced map plasticity and neuronal heterogeneity observed in the cortex. Here, we hypothesize that the Darwinian principle provides a substrate to explain the relationship between neuron heterogeneity and cortical functional maps. We demonstrate in the rat auditory cortex that the degree of response variance is closely correlated with the size of its representational area. Further, we show that the response variance within a given population is altered through training. These results suggest that larger representational areas may help to accommodate heterogeneous populations of neurons. Thus, functional maps and map plasticity are likely to play essential roles in Darwinian computation, serving as effective, but not absolutely necessary, structures to generate diverse response properties within a neural population.

Response variance in functional maps: neural darwinism revisited.

Science.gov (United States)

Takahashi, Hirokazu; Yokota, Ryo; Kanzaki, Ryohei

2013-01-01

The mechanisms by which functional maps and map plasticity contribute to cortical computation remain controversial. Recent studies have revisited the theory of neural Darwinism to interpret the learning-induced map plasticity and neuronal heterogeneity observed in the cortex. Here, we hypothesize that the Darwinian principle provides a substrate to explain the relationship between neuron heterogeneity and cortical functional maps. We demonstrate in the rat auditory cortex that the degree of response variance is closely correlated with the size of its representational area. Further, we show that the response variance within a given population is altered through training. These results suggest that larger representational areas may help to accommodate heterogeneous populations of neurons. Thus, functional maps and map plasticity are likely to play essential roles in Darwinian computation, serving as effective, but not absolutely necessary, structures to generate diverse response properties within a neural population.

Discriminating lysosomal membrane protein types using dynamic neural network.

Science.gov (United States)

Tripathi, Vijay; Gupta, Dwijendra Kumar

2014-01-01

This work presents a dynamic artificial neural network methodology, which classifies the proteins into their classes from their sequences alone: the lysosomal membrane protein classes and the various other membranes protein classes. In this paper, neural networks-based lysosomal-associated membrane protein type prediction system is proposed. Different protein sequence representations are fused to extract the features of a protein sequence, which includes seven feature sets; amino acid (AA) composition, sequence length, hydrophobic group, electronic group, sum of hydrophobicity, R-group, and dipeptide composition. To reduce the dimensionality of the large feature vector, we applied the principal component analysis. The probabilistic neural network, generalized regression neural network, and Elman regression neural network (RNN) are used as classifiers and compared with layer recurrent network (LRN), a dynamic network. The dynamic networks have memory, i.e. its output depends not only on the input but the previous outputs also. Thus, the accuracy of LRN classifier among all other artificial neural networks comes out to be the highest. The overall accuracy of jackknife cross-validation is 93.2% for the data-set. These predicted results suggest that the method can be effectively applied to discriminate lysosomal associated membrane proteins from other membrane proteins (Type-I, Outer membrane proteins, GPI-Anchored) and Globular proteins, and it also indicates that the protein sequence representation can better reflect the core feature of membrane proteins than the classical AA composition.

The Challenges of Neural Mind-reading Paradigms

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Oscar eVilarroya

2013-06-01

Full Text Available Neural mind-reading studies, based on multivariate pattern analysis (MVPA methods, are providing exciting new studies. Some of the results obtained with these paradigms have raised high expectations, such as the possibility of creating brain reading devices. However, such hopes are based on the assumptions that: a the BOLD signal is a marker of neural activity; b the BOLD pattern identified by a MVPA is a neurally sound pattern; c the MVPA’s feature space is a good mapping of the neural representation of a stimulus, and d the pattern identified by a MVPA corresponds to a representation. I examine here the challenges that still have to be met before fully accepting such assumptions.

The challenges of neural mind-reading paradigms.

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Vilarroya, Oscar

2013-01-01

Neural mind-reading studies, based on multivariate pattern analysis (MVPA) methods, are providing exciting new studies. Some of the results obtained with these paradigms have raised high expectations, such as the possibility of creating brain reading devices. However, such hopes are based on the assumptions that: (a) the BOLD signal is a marker of neural activity; (b) the BOLD pattern identified by a MVPA is a neurally sound pattern; (c) the MVPA's feature space is a good mapping of the neural representation of a stimulus, and (d) the pattern identified by a MVPA corresponds to a representation. I examine here the challenges that still have to be met before fully accepting such assumptions.

Nonlinear programming with feedforward neural networks.

Energy Technology Data Exchange (ETDEWEB)

Reifman, J.

1999-06-02

We provide a practical and effective method for solving constrained optimization problems by successively training a multilayer feedforward neural network in a coupled neural-network/objective-function representation. Nonlinear programming problems are easily mapped into this representation which has a simpler and more transparent method of solution than optimization performed with Hopfield-like networks and poses very mild requirements on the functions appearing in the problem. Simulation results are illustrated and compared with an off-the-shelf optimization tool.

Evidence for similar patterns of neural activity elicited by picture- and word-based representations of natural scenes.

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Kumar, Manoj; Federmeier, Kara D; Fei-Fei, Li; Beck, Diane M

2017-07-15

A long-standing core question in cognitive science is whether different modalities and representation types (pictures, words, sounds, etc.) access a common store of semantic information. Although different input types have been shown to activate a shared network of brain regions, this does not necessitate that there is a common representation, as the neurons in these regions could still differentially process the different modalities. However, multi-voxel pattern analysis can be used to assess whether, e.g., pictures and words evoke a similar pattern of activity, such that the patterns that separate categories in one modality transfer to the other. Prior work using this method has found support for a common code, but has two limitations: they have either only examined disparate categories (e.g. animals vs. tools) that are known to activate different brain regions, raising the possibility that the pattern separation and inferred similarity reflects only large scale differences between the categories or they have been limited to individual object representations. By using natural scene categories, we not only extend the current literature on cross-modal representations beyond objects, but also, because natural scene categories activate a common set of brain regions, we identify a more fine-grained (i.e. higher spatial resolution) common representation. Specifically, we studied picture- and word-based representations of natural scene stimuli from four different categories: beaches, cities, highways, and mountains. Participants passively viewed blocks of either phrases (e.g. "sandy beach") describing scenes or photographs from those same scene categories. To determine whether the phrases and pictures evoke a common code, we asked whether a classifier trained on one stimulus type (e.g. phrase stimuli) would transfer (i.e. cross-decode) to the other stimulus type (e.g. picture stimuli). The analysis revealed cross-decoding in the occipitotemporal, posterior parietal and

Oscillatory Activity in the Infant Brain and the Representation of Small Numbers.

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Leung, Sumie; Mareschal, Denis; Rowsell, Renee; Simpson, David; Iaria, Leon; Grbic, Amanda; Kaufman, Jordy

2016-01-01

Gamma-band oscillatory activity (GBA) is an established neural signature of sustained occluded object representation in infants and adults. However, it is not yet known whether the magnitude of GBA in the infant brain reflects the quantity of occluded items held in memory. To examine this, we compared GBA of 6-8 month-old infants during occlusion periods after the representation of two objects vs. that of one object. We found that maintaining a representation of two objects during occlusion resulted in significantly greater GBA relative to maintaining a single object. Further, this enhancement was located in the right occipital region, which is consistent with previous object representation research in adults and infants. We conclude that enhanced GBA reflects neural processes underlying infants' representation of small numbers.

Distributed representations of action sequences in anterior cingulate cortex: A recurrent neural network approach.

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Shahnazian, Danesh; Holroyd, Clay B

2018-02-01

Anterior cingulate cortex (ACC) has been the subject of intense debate over the past 2 decades, but its specific computational function remains controversial. Here we present a simple computational model of ACC that incorporates distributed representations across a network of interconnected processing units. Based on the proposal that ACC is concerned with the execution of extended, goal-directed action sequences, we trained a recurrent neural network to predict each successive step of several sequences associated with multiple tasks. In keeping with neurophysiological observations from nonhuman animals, the network yields distributed patterns of activity across ACC neurons that track the progression of each sequence, and in keeping with human neuroimaging data, the network produces discrepancy signals when any step of the sequence deviates from the predicted step. These simulations illustrate a novel approach for investigating ACC function.

Normalization as a canonical neural computation

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Carandini, Matteo; Heeger, David J.

2012-01-01

There is increasing evidence that the brain relies on a set of canonical neural computations, repeating them across brain regions and modalities to apply similar operations to different problems. A promising candidate for such a computation is normalization, in which the responses of neurons are divided by a common factor that typically includes the summed activity of a pool of neurons. Normalization was developed to explain responses in the primary visual cortex and is now thought to operate throughout the visual system, and in many other sensory modalities and brain regions. Normalization may underlie operations such as the representation of odours, the modulatory effects of visual attention, the encoding of value and the integration of multisensory information. Its presence in such a diversity of neural systems in multiple species, from invertebrates to mammals, suggests that it serves as a canonical neural computation. PMID:22108672

Sequential neural models with stochastic layers

DEFF Research Database (Denmark)

Fraccaro, Marco; Sønderby, Søren Kaae; Paquet, Ulrich

2016-01-01

How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks? This paper introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural...... generative model. The clear separation of deterministic and stochastic layers allows a structured variational inference network to track the factorization of the model's posterior distribution. By retaining both the nonlinear recursive structure of a recurrent neural network and averaging over...

Neural basis of individualistic and collectivistic views of self.

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Chiao, Joan Y; Harada, Tokiko; Komeda, Hidetsugu; Li, Zhang; Mano, Yoko; Saito, Daisuke; Parrish, Todd B; Sadato, Norihiro; Iidaka, Tetsuya

2009-09-01

Individualism and collectivism refer to cultural values that influence how people construe themselves and their relation to the world. Individualists perceive themselves as stable entities, autonomous from other people and their environment, while collectivists view themselves as dynamic entities, continually defined by their social context and relationships. Despite rich understanding of how individualism and collectivism influence social cognition at a behavioral level, little is known about how these cultural values modulate neural representations underlying social cognition. Using cross-cultural functional magnetic resonance imaging (fMRI), we examined whether the cultural values of individualism and collectivism modulate neural activity within medial prefrontal cortex (MPFC) during processing of general and contextual self judgments. Here, we show that neural activity within the anterior rostral portion of the MPFC during processing of general and contextual self judgments positively predicts how individualistic or collectivistic a person is across cultures. These results reveal two kinds of neural representations of self (eg, a general self and a contextual self) within MPFC and demonstrate how cultural values of individualism and collectivism shape these neural representations. 2008 Wiley-Liss, Inc.

Action simulation: time course and representational mechanisms

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Springer, Anne; Parkinson, Jim; Prinz, Wolfgang

2013-01-01

The notion of action simulation refers to the ability to re-enact foreign actions (i.e., actions observed in other individuals). Simulating others' actions implies a mirroring of their activities, based on one's own sensorimotor competencies. Here, we discuss theoretical and experimental approaches to action simulation and the study of its representational underpinnings. One focus of our discussion is on the timing of internal simulation and its relation to the timing of external action, and a paradigm that requires participants to predict the future course of actions that are temporarily occluded from view. We address transitions between perceptual mechanisms (referring to action representation before and after occlusion) and simulation mechanisms (referring to action representation during occlusion). Findings suggest that action simulation runs in real-time; acting on newly created action representations rather than relying on continuous visual extrapolations. A further focus of our discussion pertains to the functional characteristics of the mechanisms involved in predicting other people's actions. We propose that two processes are engaged, dynamic updating and static matching, which may draw on both semantic and motor information. In a concluding section, we discuss these findings in the context of broader theoretical issues related to action and event representation, arguing that a detailed functional analysis of action simulation in cognitive, neural, and computational terms may help to further advance our understanding of action cognition and motor control. PMID:23847563

Oscillatory activity in the infant brain and the representation of small numbers

Directory of Open Access Journals (Sweden)

Sumie eLeung

2016-02-01

Full Text Available Gamma-band oscillatory activity (GBA is an established neural signature of sustained occluded object representation in infants and adults. However, it is not yet known whether the magnitude of GBA in the infant brain reflects the quantity of occluded items held in memory. To examine this, we compared GBA of 6- to 8-month-old infants during occlusion periods after the representation of two objects versus that of one object. We found that maintaining a representation of two objects during occlusion resulted in significantly greater GBA relative to maintaining a single object. Further, this enhancement was located in the right occipital region, which is consistent with previous object representation research in adults and infants. We conclude that enhanced GBA reflects neural processes underlying infants’ representation of small numbers.

The neural code for face orientation in the human fusiform face area.

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Ramírez, Fernando M; Cichy, Radoslaw M; Allefeld, Carsten; Haynes, John-Dylan

2014-09-03

Humans recognize faces and objects with high speed and accuracy regardless of their orientation. Recent studies have proposed that orientation invariance in face recognition involves an intermediate representation where neural responses are similar for mirror-symmetric views. Here, we used fMRI, multivariate pattern analysis, and computational modeling to investigate the neural encoding of faces and vehicles at different rotational angles. Corroborating previous studies, we demonstrate a representation of face orientation in the fusiform face-selective area (FFA). We go beyond these studies by showing that this representation is category-selective and tolerant to retinal translation. Critically, by controlling for low-level confounds, we found the representation of orientation in FFA to be compatible with a linear angle code. Aspects of mirror-symmetric coding cannot be ruled out when FFA mean activity levels are considered as a dimension of coding. Finally, we used a parametric family of computational models, involving a biased sampling of view-tuned neuronal clusters, to compare different face angle encoding models. The best fitting model exhibited a predominance of neuronal clusters tuned to frontal views of faces. In sum, our findings suggest a category-selective and monotonic code of face orientation in the human FFA, in line with primate electrophysiology studies that observed mirror-symmetric tuning of neural responses at higher stages of the visual system, beyond the putative homolog of human FFA. Copyright © 2014 the authors 0270-6474/14/3412155-13$15.00/0.

Learning speaker-specific characteristics with a deep neural architecture.

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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.

Semantic representations in the temporal pole predict false memories.

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Chadwick, Martin J; Anjum, Raeesa S; Kumaran, Dharshan; Schacter, Daniel L; Spiers, Hugo J; Hassabis, Demis

2016-09-06

Recent advances in neuroscience have given us unprecedented insight into the neural mechanisms of false memory, showing that artificial memories can be inserted into the memory cells of the hippocampus in a way that is indistinguishable from true memories. However, this alone is not enough to explain how false memories can arise naturally in the course of our daily lives. Cognitive psychology has demonstrated that many instances of false memory, both in the laboratory and the real world, can be attributed to semantic interference. Whereas previous studies have found that a diverse set of regions show some involvement in semantic false memory, none have revealed the nature of the semantic representations underpinning the phenomenon. Here we use fMRI with representational similarity analysis to search for a neural code consistent with semantic false memory. We find clear evidence that false memories emerge from a similarity-based neural code in the temporal pole, a region that has been called the "semantic hub" of the brain. We further show that each individual has a partially unique semantic code within the temporal pole, and this unique code can predict idiosyncratic patterns of memory errors. Finally, we show that the same neural code can also predict variation in true-memory performance, consistent with an adaptive perspective on false memory. Taken together, our findings reveal the underlying structure of neural representations of semantic knowledge, and how this semantic structure can both enhance and distort our memories.

Semantic representations in the temporal pole predict false memories

Science.gov (United States)

Chadwick, Martin J.; Anjum, Raeesa S.; Kumaran, Dharshan; Schacter, Daniel L.; Spiers, Hugo J.; Hassabis, Demis

2016-01-01

Recent advances in neuroscience have given us unprecedented insight into the neural mechanisms of false memory, showing that artificial memories can be inserted into the memory cells of the hippocampus in a way that is indistinguishable from true memories. However, this alone is not enough to explain how false memories can arise naturally in the course of our daily lives. Cognitive psychology has demonstrated that many instances of false memory, both in the laboratory and the real world, can be attributed to semantic interference. Whereas previous studies have found that a diverse set of regions show some involvement in semantic false memory, none have revealed the nature of the semantic representations underpinning the phenomenon. Here we use fMRI with representational similarity analysis to search for a neural code consistent with semantic false memory. We find clear evidence that false memories emerge from a similarity-based neural code in the temporal pole, a region that has been called the “semantic hub” of the brain. We further show that each individual has a partially unique semantic code within the temporal pole, and this unique code can predict idiosyncratic patterns of memory errors. Finally, we show that the same neural code can also predict variation in true-memory performance, consistent with an adaptive perspective on false memory. Taken together, our findings reveal the underlying structure of neural representations of semantic knowledge, and how this semantic structure can both enhance and distort our memories. PMID:27551087

神经振荡在人类视觉工作记忆表征维持中的作用%Role of neural oscillations in maintenance of human visual working memory representations

Institute of Scientific and Technical Information of China (English)

潘亚丽; 王亮

2016-01-01

Human visual system receives many cluttered stimuli simultaneously,but only a few of visual representations can be held in the mind to guide subsequent goal-oriented behaviors,which indicates the limited capacity of visual working memory.Meanwhile,the neural representations of visual stimuli can be noisy and hence the memory precision is varying.This review systematically illustrates the roles of neural oscillations in maintaining visual working memory and neural mechanisms underlying the capacity and precision.In the end,several potential directions of this field for future studies are discussed.A large amount of researches demonstrates that during the maintenance of visual working memory representations,the theta band oscillations can facilitate the neural information communications between brain regions throughout a relative long distance,in order to achieve the top-down cognitive control;the alpha band oscillations,which was initially considered as an electrophysiological correlate of cortical idling,are now generally treated to reflect functional inhibition or non-engagement of a given brain regions,in order to gate neural information to the task-relevant regions;as for gamma band,the high frequency oscillation property make it suitable for the integration of multiple object features in the local neural network and hold the relevant representations.Therefore,theta and gamma band engage in the maintenance of working memory representations directly,while alpha oscillations serve in an indirect way of functional inhibition.In addition,the cross frequency coupling and neural synchronization enable the collaboration of different brain regions in the working memory network.The contralateral delay activity (CDA) is widely acknowledged to be an online electrophysiological marker of working memory capacity.In addition,the alpha oscillations in irrelevant regions also have a strong correlation with individual capacity.Early studies that aimed to explore memory capacity

Illusions in the spatial sense of the eye: geometrical-optical illusions and the neural representation of space.

Science.gov (United States)

Westheimer, Gerald

2008-09-01

Differences between the geometrical properties of simple configurations and their visual percept are called geometrical-optical illusions. They can be differentiated from illusions in the brightness or color domains, from ambiguous figures and impossible objects, from trompe l'oeil and perspective drawing with perfectly valid views, and from illusory contours. They were discovered independently by several scientists in a short time span in the 1850's. The clear distinction between object and visual space that they imply allows the question to be raised whether the transformation between the two spaces can be productively investigated in terms of differential geometry and metrical properties. Perceptual insight and psychophysical research prepares the ground for investigation of the neural representation of space but, because visual attributes are processed separately in parallel, one looks in vain for a neural map that is isomorphic with object space or even with individual forms it contains. Geometrical-optical illusions help reveal parsing rules for sensory signals by showing how conflicts are resolved when there is mismatch in the output of the processing modules for various primitives as a perceptual pattern's unitary structure is assembled. They point to a hierarchical ordering of spatial primitives: cardinal directions and explicit contours predominate over oblique orientation and implicit contours (Poggendorff illusion); rectilinearity yields to continuity (Hering illusion), point position and line length to contour orientation (Ponzo). Hence the geometrical-optical illusions show promise as analytical tools in unraveling neural processing in vision.

What works in auditory working memory? A neural oscillations perspective.

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Wilsch, Anna; Obleser, Jonas

2016-06-01

Working memory is a limited resource: brains can only maintain small amounts of sensory input (memory load) over a brief period of time (memory decay). The dynamics of slow neural oscillations as recorded using magneto- and electroencephalography (M/EEG) provide a window into the neural mechanics of these limitations. Especially oscillations in the alpha range (8-13Hz) are a sensitive marker for memory load. Moreover, according to current models, the resultant working memory load is determined by the relative noise in the neural representation of maintained information. The auditory domain allows memory researchers to apply and test the concept of noise quite literally: Employing degraded stimulus acoustics increases memory load and, at the same time, allows assessing the cognitive resources required to process speech in noise in an ecologically valid and clinically relevant way. The present review first summarizes recent findings on neural oscillations, especially alpha power, and how they reflect memory load and memory decay in auditory working memory. The focus is specifically on memory load resulting from acoustic degradation. These findings are then contrasted with contextual factors that benefit neural as well as behavioral markers of memory performance, by reducing representational noise. We end on discussing the functional role of alpha power in auditory working memory and suggest extensions of the current methodological toolkit. This article is part of a Special Issue entitled SI: Auditory working memory. Published by Elsevier B.V.

Shared memories reveal shared structure in neural activity across individuals

Science.gov (United States)

Chen, J.; Leong, Y.C.; Honey, C.J.; Yong, C.H.; Norman, K.A.; Hasson, U.

2016-01-01

Our lives revolve around sharing experiences and memories with others. When different people recount the same events, how similar are their underlying neural representations? Participants viewed a fifty-minute movie, then verbally described the events during functional MRI, producing unguided detailed descriptions lasting up to forty minutes. As each person spoke, event-specific spatial patterns were reinstated in default-network, medial-temporal, and high-level visual areas. Individual event patterns were both highly discriminable from one another and similar between people, suggesting consistent spatial organization. In many high-order areas, patterns were more similar between people recalling the same event than between recall and perception, indicating systematic reshaping of percept into memory. These results reveal the existence of a common spatial organization for memories in high-level cortical areas, where encoded information is largely abstracted beyond sensory constraints; and that neural patterns during perception are altered systematically across people into shared memory representations for real-life events. PMID:27918531

Learning text representation using recurrent convolutional neural network with highway layers

OpenAIRE

Wen, Ying; Zhang, Weinan; Luo, Rui; Wang, Jun

2016-01-01

Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks. In this paper, we describe a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN) with highway layers. The highway network module is incorporated in the middle takes the output of the bi-directional Recurrent Neural Network (Bi-RNN) module in the first stage and provides the Convolutional Neural Network (CNN) module in the last stage with the i...

EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation.

Science.gov (United States)

Amidi, Afshine; Amidi, Shervine; Vlachakis, Dimitrios; Megalooikonomou, Vasileios; Paragios, Nikos; Zacharaki, Evangelia I

2018-01-01

During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to their excellent performance on computer vision problems. The size of the Protein Data Bank (PDB) has increased more than 15-fold since 1999, which enabled the expansion of models that aim at predicting enzymatic function via their amino acid composition. Amino acid sequence, however, is less conserved in nature than protein structure and therefore considered a less reliable predictor of protein function. This paper presents EnzyNet, a novel 3D convolutional neural networks classifier that predicts the Enzyme Commission number of enzymes based only on their voxel-based spatial structure. The spatial distribution of biochemical properties was also examined as complementary information. The two-layer architecture was investigated on a large dataset of 63,558 enzymes from the PDB and achieved an accuracy of 78.4% by exploiting only the binary representation of the protein shape. Code and datasets are available at https://github.com/shervinea/enzynet.

Neural codes of seeing architectural styles.

Science.gov (United States)

Choo, Heeyoung; Nasar, Jack L; Nikrahei, Bardia; Walther, Dirk B

2017-01-10

Images of iconic buildings, such as the CN Tower, instantly transport us to specific places, such as Toronto. Despite the substantial impact of architectural design on people's visual experience of built environments, we know little about its neural representation in the human brain. In the present study, we have found patterns of neural activity associated with specific architectural styles in several high-level visual brain regions, but not in primary visual cortex (V1). This finding suggests that the neural correlates of the visual perception of architectural styles stem from style-specific complex visual structure beyond the simple features computed in V1. Surprisingly, the network of brain regions representing architectural styles included the fusiform face area (FFA) in addition to several scene-selective regions. Hierarchical clustering of error patterns further revealed that the FFA participated to a much larger extent in the neural encoding of architectural styles than entry-level scene categories. We conclude that the FFA is involved in fine-grained neural encoding of scenes at a subordinate-level, in our case, architectural styles of buildings. This study for the first time shows how the human visual system encodes visual aspects of architecture, one of the predominant and longest-lasting artefacts of human culture.

Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback.

Science.gov (United States)

Orhan, A Emin; Ma, Wei Ji

2017-07-26

Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties associated with task variables and subsequent use of this representation. Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations. We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks. In a probabilistic categorization task, error-based learning in a generic network simultaneously explains a monkey's learning curve and the evolution of qualitative aspects of its choice behavior. In all tasks, the number of neurons required for a given level of performance grows sublinearly with the input population size, a substantial improvement on previous implementations of probabilistic inference. The trained networks develop a novel sparsity-based probabilistic population code. Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules.Behavioural tasks often require probability distributions to be inferred about task specific variables. Here, the authors demonstrate that generic neural networks can be trained using a simple error-based learning rule to perform such probabilistic computations efficiently without any need for task specific operations.

Windowed active sampling for reliable neural learning

NARCIS (Netherlands)

Barakova, E.I; Spaanenburg, L

The composition of the example set has a major impact on the quality of neural learning. The popular approach is focused on extensive pre-processing to bridge the representation gap between process measurement and neural presentation. In contrast, windowed active sampling attempts to solve these

Tracking Typological Traits of Uralic Languages in Distributed Language Representations

DEFF Research Database (Denmark)

Bjerva, Johannes; Augenstein, Isabelle

2018-01-01

Although linguistic typology has a long history, computational approaches have only recently gained popularity. The use of distributed representations in computational linguistics has also become increasingly popular. A recent development is to learn distributed representations of language...... for model transfer between Uralic languages in deep neural networks. We then investigate which typological features are encoded in these representations by attempting to predict features in the World Atlas of Language Structures, at various stages of fine-tuning of the representations. We focus on Uralic...

Hierarchical representation of shapes in visual cortex - from localized features to figural shape segregation

Directory of Open Access Journals (Sweden)

Stephan eTschechne

2014-08-01

Full Text Available Visual structures in the environment are effortlessly segmented into image regions and those combined to a representation of surfaces and prototypical objects. Such a perceptual organization is performed by complex neural mechanisms in the visual cortex of primates. Multiple mutually connected areas in the ventral cortical pathway receive visual input and extract local form features that are subsequently grouped into increasingly complex, more meaningful image elements. At this stage, highly articulated changes in shape boundary as well as very subtle curvature changes contribute to the perception of an object.We propose a recurrent computational network architecture that utilizes a hierarchical distributed representation of shape features to encode boundary features over different scales of resolution. Our model makes use of neural mechanisms that model the processing capabilities of early and intermediate stages in visual cortex, namely areas V1-V4 and IT. We suggest that multiple specialized component representations interact by feedforward hierarchical processing that is combined with feedback from representations generated at higher stages. In so doing, global configurational as well as local information is available to distinguish changes in the object's contour. Once the outline of a shape has been established, contextual contour configurations are used to assign border ownership directions and thus achieve segregation of figure and ground. This combines separate findings about the generation of cortical shape representation using hierarchical representations with figure-ground segregation mechanisms.Our model is probed with a selection of artificial and real world images to illustrate processing results at different processing stages. We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy.

Neural mechanisms of interference control in working memory capacity.

Science.gov (United States)

Bomyea, Jessica; Taylor, Charles T; Spadoni, Andrea D; Simmons, Alan N

2018-02-01

The extent to which one can use cognitive resources to keep information in working memory is known to rely on (1) active maintenance of target representations and (2) downregulation of interference from irrelevant representations. Neurobiologically, the global capacity of working memory is thought to depend on the prefrontal and parietal cortices; however, the neural mechanisms involved in controlling interference specifically in working memory capacity tasks remain understudied. In this study, 22 healthy participants completed a modified complex working memory capacity task (Reading Span) with trials of varying levels of interference control demands while undergoing functional MRI. Neural activity associated with interference control demands was examined separately during encoding and recall phases of the task. Results suggested a widespread network of regions in the prefrontal, parietal, and occipital cortices, and the cingulate and cerebellum associated with encoding, and parietal and occipital regions associated with recall. Results align with prior findings emphasizing the importance of frontoparietal circuits for working memory performance, including the role of the inferior frontal gyrus, cingulate, occipital cortex, and cerebellum in regulation of interference demands. © 2017 Wiley Periodicals, Inc.

Neural networks in continuous optical media

International Nuclear Information System (INIS)

Anderson, D.Z.

1987-01-01

The authors' interest is to see to what extent neural models can be implemented using continuous optical elements. Thus these optical networks represent a continuous distribution of neuronlike processors rather than a discrete collection. Most neural models have three characteristic features: interconnections; adaptivity; and nonlinearity. In their optical representation the interconnections are implemented with linear one- and two-port optical elements such as lenses and holograms. Real-time holographic media allow these interconnections to become adaptive. The nonlinearity is achieved with gain, for example, from two-beam coupling in photorefractive media or a pumped dye medium. Using these basic optical elements one can in principle construct continuous representations of a number of neural network models. The authors demonstrated two devices based on continuous optical elements: an associative memory which recalls an entire object when addressed with a partial object and a tracking novelty filter which identifies time-dependent features in an optical scene. These devices demonstrate the potential of distributed optical elements to implement more formal models of neural networks

Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation.

Science.gov (United States)

Xie, Jiaheng; Liu, Xiao; Dajun Zeng, Daniel

2018-01-01

Recent years have seen increased worldwide popularity of e-cigarette use. However, the risks of e-cigarettes are underexamined. Most e-cigarette adverse event studies have achieved low detection rates due to limited subject sample sizes in the experiments and surveys. Social media provides a large data repository of consumers' e-cigarette feedback and experiences, which are useful for e-cigarette safety surveillance. However, it is difficult to automatically interpret the informal and nontechnical consumer vocabulary about e-cigarettes in social media. This issue hinders the use of social media content for e-cigarette safety surveillance. Recent developments in deep neural network methods have shown promise for named entity extraction from noisy text. Motivated by these observations, we aimed to design a deep neural network approach to extract e-cigarette safety information in social media. Our deep neural language model utilizes word embedding as the representation of text input and recognizes named entity types with the state-of-the-art Bidirectional Long Short-Term Memory (Bi-LSTM) Recurrent Neural Network. Our Bi-LSTM model achieved the best performance compared to 3 baseline models, with a precision of 94.10%, a recall of 91.80%, and an F-measure of 92.94%. We identified 1591 unique adverse events and 9930 unique e-cigarette components (ie, chemicals, flavors, and devices) from our research testbed. Although the conditional random field baseline model had slightly better precision than our approach, our Bi-LSTM model achieved much higher recall, resulting in the best F-measure. Our method can be generalized to extract medical concepts from social media for other medical applications. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Discrete Neural Signatures of Basic Emotions.

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Saarimäki, Heini; Gotsopoulos, Athanasios; Jääskeläinen, Iiro P; Lampinen, Jouko; Vuilleumier, Patrik; Hari, Riitta; Sams, Mikko; Nummenmaa, Lauri

2016-06-01

Categorical models of emotions posit neurally and physiologically distinct human basic emotions. We tested this assumption by using multivariate pattern analysis (MVPA) to classify brain activity patterns of 6 basic emotions (disgust, fear, happiness, sadness, anger, and surprise) in 3 experiments. Emotions were induced with short movies or mental imagery during functional magnetic resonance imaging. MVPA accurately classified emotions induced by both methods, and the classification generalized from one induction condition to another and across individuals. Brain regions contributing most to the classification accuracy included medial and inferior lateral prefrontal cortices, frontal pole, precentral and postcentral gyri, precuneus, and posterior cingulate cortex. Thus, specific neural signatures across these regions hold representations of different emotional states in multimodal fashion, independently of how the emotions are induced. Similarity of subjective experiences between emotions was associated with similarity of neural patterns for the same emotions, suggesting a direct link between activity in these brain regions and the subjective emotional experience. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Ontogeny of neural circuits underlying spatial memory in the rat

Directory of Open Access Journals (Sweden)

James Alexander Ainge

2012-03-01

Full Text Available Spatial memory is a well characterised psychological function in both humans and rodents. The combined computations of a network of systems including place cells in the hippocampus, grid cells in the medial entorhinal cortex and head direction cells found in numerous structures in the brain have been suggested to form the neural instantiation of the cognitive map as first described by Tolman in 1948. However, while our understanding of the neural mechanisms underlying spatial representations in adults is relatively sophisticated, we know substantially less about how this network develops in young animals. In this article we review studies examining the developmental timescale that these systems follow. Electrophysiological recordings from very young rats show that directional information is at adult levels at the outset of navigational experience. The systems supporting allocentric memory, however, take longer to mature. This is consistent with behavioural studies of young rats which show that spatial memory based on head direction develops very early but that allocentric spatial memory takes longer to mature. We go on to report new data demonstrating that memory for associations between objects and their spatial locations is slower to develop than memory for objects alone. This is again consistent with previous reports suggesting that adult like spatial representations have a protracted development in rats and also suggests that the systems involved in processing non-spatial stimuli come online earlier.

Decomposing Gratitude: Representation and Integration of Cognitive Antecedents of Gratitude in the Brain.

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Yu, Hongbo; Gao, Xiaoxue; Zhou, Yuanyuan; Zhou, Xiaolin

2018-05-23

Gratitude is a typical social-moral emotion that plays a crucial role in maintaining human cooperative interpersonal relationship. Although neural correlates of gratitude have been investigated, the neurocognitive processes that lead to gratitude, namely, the representation and integration of its cognitive antecedents, remain largely unknown. Here, we combined fMRI and a human social interactive task to investigate how benefactor's cost and beneficiary's benefit, two critical antecedents of gratitude, are encoded and integrated in beneficiary's brain, and how the neural processing of gratitude is converted to reciprocity. A coplayer decided whether to help a human participant (either male or female) avoid pain at his/her own monetary cost; the participants could transfer monetary points to the benefactor with the knowledge that the benefactor was unaware of this transfer. By independently manipulating monetary cost and the degree of pain reduction, we could identify the neural signatures of benefactor's cost and recipient's benefit and examine how they were integrated. Recipient's self-benefit was encoded in reward-sensitive regions (e.g., ventral striatum), whereas benefactor-cost was encoded in regions associated with mentalizing (e.g., temporoparietal junction). Gratitude was represented in perigenual anterior cingulate cortex (pgACC), the strength of which correlated with trait gratitude. Dynamic causal modeling showed that the neural signals representing benefactor-cost and self-benefit passed to pgACC via effective connectivities, suggesting an integrative role of pgACC in generating gratitude. Moreover, gyral ACC plays an intermediary role in converting gratitude representation into reciprocal behaviors. Our findings provide a neural mechanistic account of gratitude and its role in social-moral life. SIGNIFICANCE STATEMENT Gratitude plays an integral role in subjective well-being and harmonious interpersonal relationships. However, the neurocognitive

The Molecular Basis of Neural Memory. Part 7: Neural Intelligence (NI versus Artificial Intelligence (AI

Directory of Open Access Journals (Sweden)

Gerard Marx

2017-07-01

Full Text Available The link of memory to intelligence is incontestable, though the development of electronic artifacts with memory has confounded cognitive and computer scientists’ conception of memory and its relevance to “intelligence”. We propose two categories of “Intelligence”: (1 Logical (objective — mathematics, numbers, pattern recognition, games, programmable in binary format. (2 Emotive (subjective — sensations, feelings, perceptions, goals desires, sociability, sex, food, love. The 1st has been reduced to computational algorithms of which we are well versed, witness global technology and the internet. The 2nd relates to the mysterious process whereby (psychic emotive states are achieved by neural beings sensing, comprehending, remembering and dealing with their surroundings. Many theories and philosophies have been forwarded to rationalize this process, but as neuroscientists, we remain dissatisfied. Our own musings on universal neural memory, suggest a tripartite mechanism involving neurons interacting with their surroundings, notably the neural extracellular matrix (nECM with dopants [trace metals and neurotransmitters (NTs]. In particular, the NTs are the molecular encoders of emotive states. We have developed a chemographic representation of such a molecular code.To quote Longuet-Higgins, “Perhaps it is time for the term ‘artificial intelligence’ to be replaced by something more modest and less provisional”. We suggest “artifact intelligence” (ARTI or “machine intelligence” (MI, neither of which imply emulation of emotive neural processes, but simply refer to the ‘demotive’ (lacking emotive quality capability of electronic artifacts that employ a recall function, to calculate algorithms.

Alpha-Band Activity Reveals Spontaneous Representations of Spatial Position in Visual Working Memory.

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Foster, Joshua J; Bsales, Emma M; Jaffe, Russell J; Awh, Edward

2017-10-23

An emerging view suggests that spatial position is an integral component of working memory (WM), such that non-spatial features are bound to locations regardless of whether space is relevant [1, 2]. For instance, past work has shown that stimulus position is spontaneously remembered when non-spatial features are stored. Item recognition is enhanced when memoranda appear at the same location where they were encoded [3-5], and accessing non-spatial information elicits shifts of spatial attention to the original position of the stimulus [6, 7]. However, these findings do not establish that a persistent, active representation of stimulus position is maintained in WM because similar effects have also been documented following storage in long-term memory [8, 9]. Here we show that the spatial position of the memorandum is actively coded by persistent neural activity during a non-spatial WM task. We used a spatial encoding model in conjunction with electroencephalogram (EEG) measurements of oscillatory alpha-band (8-12 Hz) activity to track active representations of spatial position. The position of the stimulus varied trial to trial but was wholly irrelevant to the tasks. We nevertheless observed active neural representations of the original stimulus position that persisted throughout the retention interval. Further experiments established that these spatial representations are dependent on the volitional storage of non-spatial features rather than being a lingering effect of sensory energy or initial encoding demands. These findings provide strong evidence that online spatial representations are spontaneously maintained in WM-regardless of task relevance-during the storage of non-spatial features. Copyright © 2017 Elsevier Ltd. All rights reserved.

What the success of brain imaging implies about the neural code.

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Guest, Olivia; Love, Bradley C

2017-01-19

The success of fMRI places constraints on the nature of the neural code. The fact that researchers can infer similarities between neural representations, despite fMRI's limitations, implies that certain neural coding schemes are more likely than others. For fMRI to succeed given its low temporal and spatial resolution, the neural code must be smooth at the voxel and functional level such that similar stimuli engender similar internal representations. Through proof and simulation, we determine which coding schemes are plausible given both fMRI's successes and its limitations in measuring neural activity. Deep neural network approaches, which have been forwarded as computational accounts of the ventral stream, are consistent with the success of fMRI, though functional smoothness breaks down in the later network layers. These results have implications for the nature of the neural code and ventral stream, as well as what can be successfully investigated with fMRI.

Hidden neural networks

DEFF Research Database (Denmark)

Krogh, Anders Stærmose; Riis, Søren Kamaric

1999-01-01

A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability...... parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum...... likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear...

Neural network recognition of mammographic lesions

International Nuclear Information System (INIS)

Oldham, W.J.B.; Downes, P.T.; Hunter, V.

1987-01-01

A method for recognition of mammographic lesions through the use of neural networks is presented. Neural networks have exhibited the ability to learn the shape andinternal structure of patterns. Digitized mammograms containing circumscribed and stelate lesions were used to train a feedfoward synchronous neural network that self-organizes to stable attractor states. Encoding of data for submission to the network was accomplished by performing a fractal analysis of the digitized image. This results in scale invariant representation of the lesions. Results are discussed

Mirror representations innate versus determined by experience: a viewpoint from learning theory.

Science.gov (United States)

Giese, Martin A

2014-04-01

From the viewpoint of pattern recognition and computational learning, mirror neurons form an interesting multimodal representation that links action perception and planning. While it seems unlikely that all details of such representations are specified by the genetic code, robust learning of such complex representations likely requires an appropriate interplay between plasticity, generalization, and anatomical constraints of the underlying neural architecture.

Application of neural networks to group technology

Science.gov (United States)

Caudell, Thomas P.; Smith, Scott D. G.; Johnson, G. C.; Wunsch, Donald C., II

1991-08-01

Adaptive resonance theory (ART) neural networks are being developed for application to the industrial engineering problem of group technology--the reuse of engineering designs. Two- and three-dimensional representations of engineering designs are input to ART-1 neural networks to produce groups or families of similar parts. These representations, in their basic form, amount to bit maps of the part, and can become very large when the part is represented in high resolution. This paper describes an enhancement to an algorithmic form of ART-1 that allows it to operate directly on compressed input representations and to generate compressed memory templates. The performance of this compressed algorithm is compared to that of the regular algorithm on real engineering designs and a significant savings in memory storage as well as a speed up in execution is observed. In additions, a `neural database'' system under development is described. This system demonstrates the feasibility of training an ART-1 network to first cluster designs into families, and then to recall the family when presented a similar design. This application is of large practical value to industry, making it possible to avoid duplication of design efforts.

A Model of Representational Spaces in Human Cortex.

Science.gov (United States)

Guntupalli, J Swaroop; Hanke, Michael; Halchenko, Yaroslav O; Connolly, Andrew C; Ramadge, Peter J; Haxby, James V

2016-06-01

Current models of the functional architecture of human cortex emphasize areas that capture coarse-scale features of cortical topography but provide no account for population responses that encode information in fine-scale patterns of activity. Here, we present a linear model of shared representational spaces in human cortex that captures fine-scale distinctions among population responses with response-tuning basis functions that are common across brains and models cortical patterns of neural responses with individual-specific topographic basis functions. We derive a common model space for the whole cortex using a new algorithm, searchlight hyperalignment, and complex, dynamic stimuli that provide a broad sampling of visual, auditory, and social percepts. The model aligns representations across brains in occipital, temporal, parietal, and prefrontal cortices, as shown by between-subject multivariate pattern classification and intersubject correlation of representational geometry, indicating that structural principles for shared neural representations apply across widely divergent domains of information. The model provides a rigorous account for individual variability of well-known coarse-scale topographies, such as retinotopy and category selectivity, and goes further to account for fine-scale patterns that are multiplexed with coarse-scale topographies and carry finer distinctions. © The Author 2016. Published by Oxford University Press.

Visual perception from the perspective of a representational, non-reductionistic, level-dependent account of perception and conscious awareness

Science.gov (United States)

Overgaard, Morten; Mogensen, Jesper

2014-01-01

This article proposes a new model to interpret seemingly conflicting evidence concerning the correlation of consciousness and neural processes. Based on an analysis of research of blindsight and subliminal perception, the reorganization of elementary functions and consciousness framework suggests that mental representations consist of functions at several different levels of analysis, including truly localized perceptual elementary functions and perceptual algorithmic modules, which are interconnections of the elementary functions. We suggest that conscious content relates to the ‘top level’ of analysis in a ‘situational algorithmic strategy’ that reflects the general state of an individual. We argue that conscious experience is intrinsically related to representations that are available to guide behaviour. From this perspective, we find that blindsight and subliminal perception can be explained partly by too coarse-grained methodology, and partly by top-down enhancing of representations that normally would not be relevant to action. PMID:24639581

Multi-stream CNN: Learning representations based on human-related regions for action recognition

NARCIS (Netherlands)

Tu, Zhigang; Xie, Wei; Qin, Qianqing; Poppe, R.W.; Veltkamp, R.C.; Li, Baoxin; Yuan, Junsong

2018-01-01

The most successful video-based human action recognition methods rely on feature representations extracted using Convolutional Neural Networks (CNNs). Inspired by the two-stream network (TS-Net), we propose a multi-stream Convolutional Neural Network (CNN) architecture to recognize human actions. We

SYNAPTIC DEPRESSION IN DEEP NEURAL NETWORKS FOR SPEECH PROCESSING.

Science.gov (United States)

Zhang, Wenhao; Li, Hanyu; Yang, Minda; Mesgarani, Nima

2016-03-01

A characteristic property of biological neurons is their ability to dynamically change the synaptic efficacy in response to variable input conditions. This mechanism, known as synaptic depression, significantly contributes to the formation of normalized representation of speech features. Synaptic depression also contributes to the robust performance of biological systems. In this paper, we describe how synaptic depression can be modeled and incorporated into deep neural network architectures to improve their generalization ability. We observed that when synaptic depression is added to the hidden layers of a neural network, it reduces the effect of changing background activity in the node activations. In addition, we show that when synaptic depression is included in a deep neural network trained for phoneme classification, the performance of the network improves under noisy conditions not included in the training phase. Our results suggest that more complete neuron models may further reduce the gap between the biological performance and artificial computing, resulting in networks that better generalize to novel signal conditions.

Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system

Science.gov (United States)

Born, Jannis; Stringer, Simon M.

2017-01-01

A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plausible, Hebbian learning mechanism may account for the development of localized hand-centred representations in a hierarchical neural network model of the primate visual system, VisNet. The hand-centered neurons developed in the model use an invariance learning mechanism known as continuous transformation (CT) learning. In contrast to previous theoretical proposals for the development of hand-centered visual representations, CT learning does not need a memory trace of recent neuronal activity to be incorporated in the synaptic learning rule. Instead, CT learning relies solely on a Hebbian learning rule, which is able to exploit the spatial overlap that naturally occurs between successive images of a hand-object configuration as it is shifted across different retinal locations due to saccades. Our simulations show how individual neurons in the network model can learn to respond selectively to target objects in particular locations with respect to the hand, irrespective of where the hand-object configuration occurs on the retina. The response properties of these hand-centred neurons further generalise to localised receptive fields in the hand-centred space when tested on novel hand-object configurations that have not been explored during training. Indeed, even when the network is trained with target objects presented across a near continuum of locations around the hand during training, the model continues to develop hand-centred neurons with localised receptive fields in hand-centred space. With the help of principal component analysis, we provide the first theoretical framework that explains the behavior of Hebbian learning

Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system.

Science.gov (United States)

Born, Jannis; Galeazzi, Juan M; Stringer, Simon M

2017-01-01

A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plausible, Hebbian learning mechanism may account for the development of localized hand-centred representations in a hierarchical neural network model of the primate visual system, VisNet. The hand-centered neurons developed in the model use an invariance learning mechanism known as continuous transformation (CT) learning. In contrast to previous theoretical proposals for the development of hand-centered visual representations, CT learning does not need a memory trace of recent neuronal activity to be incorporated in the synaptic learning rule. Instead, CT learning relies solely on a Hebbian learning rule, which is able to exploit the spatial overlap that naturally occurs between successive images of a hand-object configuration as it is shifted across different retinal locations due to saccades. Our simulations show how individual neurons in the network model can learn to respond selectively to target objects in particular locations with respect to the hand, irrespective of where the hand-object configuration occurs on the retina. The response properties of these hand-centred neurons further generalise to localised receptive fields in the hand-centred space when tested on novel hand-object configurations that have not been explored during training. Indeed, even when the network is trained with target objects presented across a near continuum of locations around the hand during training, the model continues to develop hand-centred neurons with localised receptive fields in hand-centred space. With the help of principal component analysis, we provide the first theoretical framework that explains the behavior of Hebbian learning

Different brain circuits underlie motor and perceptual representations of temporal intervals

DEFF Research Database (Denmark)

Bueti, Doemnica; Walsh, Vincent; Frith, Christopher

2008-01-01

V5/MT. Our findings point to a role for the parietal cortex as an interface between sensory and motor processes and suggest that it may be a key node in translation of temporal information into action. Furthermore, we discuss the potential importance of the extrastriate cortex in processing visual......In everyday life, temporal information is used for both perception and action, but whether these two functions reflect the operation of similar or different neural circuits is unclear. We used functional magnetic resonance imaging to investigate the neural correlates of processing temporal...... information when either a motor or a perceptual representation is used. Participants viewed two identical sequences of visual stimuli and used the information differently to perform either a temporal reproduction or a temporal estimation task. By comparing brain activity evoked by these tasks and control...

A network of spiking neurons for computing sparse representations in an energy-efficient way.

Science.gov (United States)

Hu, Tao; Genkin, Alexander; Chklovskii, Dmitri B

2012-11-01

Computing sparse redundant representations is an important problem in both applied mathematics and neuroscience. In many applications, this problem must be solved in an energy-efficient way. Here, we propose a hybrid distributed algorithm (HDA), which solves this problem on a network of simple nodes communicating by low-bandwidth channels. HDA nodes perform both gradient-descent-like steps on analog internal variables and coordinate-descent-like steps via quantized external variables communicated to each other. Interestingly, the operation is equivalent to a network of integrate-and-fire neurons, suggesting that HDA may serve as a model of neural computation. We show that the numerical performance of HDA is on par with existing algorithms. In the asymptotic regime, the representation error of HDA decays with time, t, as 1/t. HDA is stable against time-varying noise; specifically, the representation error decays as 1/√t for gaussian white noise.

Effects of selective attention on the electrophysiological representation of concurrent sounds in the human auditory cortex.

Science.gov (United States)

Bidet-Caulet, Aurélie; Fischer, Catherine; Besle, Julien; Aguera, Pierre-Emmanuel; Giard, Marie-Helene; Bertrand, Olivier

2007-08-29

In noisy environments, we use auditory selective attention to actively ignore distracting sounds and select relevant information, as during a cocktail party to follow one particular conversation. The present electrophysiological study aims at deciphering the spatiotemporal organization of the effect of selective attention on the representation of concurrent sounds in the human auditory cortex. Sound onset asynchrony was manipulated to induce the segregation of two concurrent auditory streams. Each stream consisted of amplitude modulated tones at different carrier and modulation frequencies. Electrophysiological recordings were performed in epileptic patients with pharmacologically resistant partial epilepsy, implanted with depth electrodes in the temporal cortex. Patients were presented with the stimuli while they either performed an auditory distracting task or actively selected one of the two concurrent streams. Selective attention was found to affect steady-state responses in the primary auditory cortex, and transient and sustained evoked responses in secondary auditory areas. The results provide new insights on the neural mechanisms of auditory selective attention: stream selection during sound rivalry would be facilitated not only by enhancing the neural representation of relevant sounds, but also by reducing the representation of irrelevant information in the auditory cortex. Finally, they suggest a specialization of the left hemisphere in the attentional selection of fine-grained acoustic information.

Attention enhances multi-voxel representation of novel objects in frontal, parietal and visual cortices.

Science.gov (United States)

Woolgar, Alexandra; Williams, Mark A; Rich, Anina N

2015-04-01

Selective attention is fundamental for human activity, but the details of its neural implementation remain elusive. One influential theory, the adaptive coding hypothesis (Duncan, 2001, An adaptive coding model of neural function in prefrontal cortex, Nature Reviews Neuroscience 2:820-829), proposes that single neurons in certain frontal and parietal regions dynamically adjust their responses to selectively encode relevant information. This selective representation may in turn support selective processing in more specialized brain regions such as the visual cortices. Here, we use multi-voxel decoding of functional magnetic resonance images to demonstrate selective representation of attended--and not distractor--objects in frontal, parietal, and visual cortices. In addition, we highlight a critical role for task demands in determining which brain regions exhibit selective coding. Strikingly, representation of attended objects in frontoparietal cortex was highest under conditions of high perceptual demand, when stimuli were hard to perceive and coding in early visual cortex was weak. Coding in early visual cortex varied as a function of attention and perceptual demand, while coding in higher visual areas was sensitive to the allocation of attention but robust to changes in perceptual difficulty. Consistent with high-profile reports, peripherally presented objects could also be decoded from activity at the occipital pole, a region which corresponds to the fovea. Our results emphasize the flexibility of frontoparietal and visual systems. They support the hypothesis that attention enhances the multi-voxel representation of information in the brain, and suggest that the engagement of this attentional mechanism depends critically on current task demands. Copyright © 2015 Elsevier Inc. All rights reserved.

Contralateral delay activity provides a neural measure of the number of representations in visual working memory.

Science.gov (United States)

Ikkai, Akiko; McCollough, Andrew W; Vogel, Edward K

2010-04-01

Visual working memory (VWM) helps to temporarily represent information from the visual environment and is severely limited in capacity. Recent work has linked various forms of neural activity to the ongoing representations in VWM. One piece of evidence comes from human event-related potential studies, which find a sustained contralateral negativity during the retention period of VWM tasks. This contralateral delay activity (CDA) has previously been shown to increase in amplitude as the number of memory items increases, up to the individual's working memory capacity limit. However, significant alternative hypotheses remain regarding the true nature of this activity. Here we test whether the CDA is modulated by the perceptual requirements of the memory items as well as whether it is determined by the number of locations that are being attended within the display. Our results provide evidence against these two alternative accounts and instead strongly support the interpretation that this activity reflects the current number of objects that are being represented in VWM.

Neural basis for generalized quantifier comprehension.

Science.gov (United States)

McMillan, Corey T; Clark, Robin; Moore, Peachie; Devita, Christian; Grossman, Murray

2005-01-01

Generalized quantifiers like "all cars" are semantically well understood, yet we know little about their neural representation. Our model of quantifier processing includes a numerosity device, operations that combine number elements and working memory. Semantic theory posits two types of quantifiers: first-order quantifiers identify a number state (e.g. "at least 3") and higher-order quantifiers additionally require maintaining a number state actively in working memory for comparison with another state (e.g. "less than half"). We used BOLD fMRI to test the hypothesis that all quantifiers recruit inferior parietal cortex associated with numerosity, while only higher-order quantifiers recruit prefrontal cortex associated with executive resources like working memory. Our findings showed that first-order and higher-order quantifiers both recruit right inferior parietal cortex, suggesting that a numerosity component contributes to quantifier comprehension. Moreover, only probes of higher-order quantifiers recruited right dorsolateral prefrontal cortex, suggesting involvement of executive resources like working memory. We also observed activation of thalamus and anterior cingulate that may be associated with selective attention. Our findings are consistent with a large-scale neural network centered in frontal and parietal cortex that supports comprehension of generalized quantifiers.

Hierarchical representation of shapes in visual cortex-from localized features to figural shape segregation.

Science.gov (United States)

Tschechne, Stephan; Neumann, Heiko

2014-01-01

Visual structures in the environment are segmented into image regions and those combined to a representation of surfaces and prototypical objects. Such a perceptual organization is performed by complex neural mechanisms in the visual cortex of primates. Multiple mutually connected areas in the ventral cortical pathway receive visual input and extract local form features that are subsequently grouped into increasingly complex, more meaningful image elements. Such a distributed network of processing must be capable to make accessible highly articulated changes in shape boundary as well as very subtle curvature changes that contribute to the perception of an object. We propose a recurrent computational network architecture that utilizes hierarchical distributed representations of shape features to encode surface and object boundary over different scales of resolution. Our model makes use of neural mechanisms that model the processing capabilities of early and intermediate stages in visual cortex, namely areas V1-V4 and IT. We suggest that multiple specialized component representations interact by feedforward hierarchical processing that is combined with feedback signals driven by representations generated at higher stages. Based on this, global configurational as well as local information is made available to distinguish changes in the object's contour. Once the outline of a shape has been established, contextual contour configurations are used to assign border ownership directions and thus achieve segregation of figure and ground. The model, thus, proposes how separate mechanisms contribute to distributed hierarchical cortical shape representation and combine with processes of figure-ground segregation. Our model is probed with a selection of stimuli to illustrate processing results at different processing stages. We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy.

Feature to prototype transition in neural networks

Science.gov (United States)

Krotov, Dmitry; Hopfield, John

Models of associative memory with higher order (higher than quadratic) interactions, and their relationship to neural networks used in deep learning are discussed. Associative memory is conventionally described by recurrent neural networks with dynamical convergence to stable points. Deep learning typically uses feedforward neural nets without dynamics. However, a simple duality relates these two different views when applied to problems of pattern classification. From the perspective of associative memory such models deserve attention because they make it possible to store a much larger number of memories, compared to the quadratic case. In the dual description, these models correspond to feedforward neural networks with one hidden layer and unusual activation functions transmitting the activities of the visible neurons to the hidden layer. These activation functions are rectified polynomials of a higher degree rather than the rectified linear functions used in deep learning. The network learns representations of the data in terms of features for rectified linear functions, but as the power in the activation function is increased there is a gradual shift to a prototype-based representation, the two extreme regimes of pattern recognition known in cognitive psychology. Simons Center for Systems Biology.

Deep supervised, but not unsupervised, models may explain IT cortical representation.

Directory of Open Access Journals (Sweden)

Seyed-Mahdi Khaligh-Razavi

2014-11-01

Full Text Available Inferior temporal (IT cortex in human and nonhuman primates serves visual object recognition. Computational object-vision models, although continually improving, do not yet reach human performance. It is unclear to what extent the internal representations of computational models can explain the IT representation. Here we investigate a wide range of computational model representations (37 in total, testing their categorization performance and their ability to account for the IT representational geometry. The models include well-known neuroscientific object-recognition models (e.g. HMAX, VisNet along with several models from computer vision (e.g. SIFT, GIST, self-similarity features, and a deep convolutional neural network. We compared the representational dissimilarity matrices (RDMs of the model representations with the RDMs obtained from human IT (measured with fMRI and monkey IT (measured with cell recording for the same set of stimuli (not used in training the models. Better performing models were more similar to IT in that they showed greater clustering of representational patterns by category. In addition, better performing models also more strongly resembled IT in terms of their within-category representational dissimilarities. Representational geometries were significantly correlated between IT and many of the models. However, the categorical clustering observed in IT was largely unexplained by the unsupervised models. The deep convolutional network, which was trained by supervision with over a million category-labeled images, reached the highest categorization performance and also best explained IT, although it did not fully explain the IT data. Combining the features of this model with appropriate weights and adding linear combinations that maximize the margin between animate and inanimate objects and between faces and other objects yielded a representation that fully explained our IT data. Overall, our results suggest that explaining

Decision Making under Uncertainty: A Neural Model based on Partially Observable Markov Decision Processes

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Rajesh P N Rao

2010-11-01

Full Text Available A fundamental problem faced by animals is learning to select actions based on noisy sensory information and incomplete knowledge of the world. It has been suggested that the brain engages in Bayesian inference during perception but how such probabilistic representations are used to select actions has remained unclear. Here we propose a neural model of action selection and decision making based on the theory of partially observable Markov decision processes (POMDPs. Actions are selected based not on a single optimal estimate of state but on the posterior distribution over states (the belief state. We show how such a model provides a unified framework for explaining experimental results in decision making that involve both information gathering and overt actions. The model utilizes temporal difference (TD learning for maximizing expected reward. The resulting neural architecture posits an active role for the neocortex in belief computation while ascribing a role to the basal ganglia in belief representation, value computation, and action selection. When applied to the random dots motion discrimination task, model neurons representing belief exhibit responses similar to those of LIP neurons in primate neocortex. The appropriate threshold for switching from information gathering to overt actions emerges naturally during reward maximization. Additionally, the time course of reward prediction error in the model shares similarities with dopaminergic responses in the basal ganglia during the random dots task. For tasks with a deadline, the model learns a decision making strategy that changes with elapsed time, predicting a collapsing decision threshold consistent with some experimental studies. The model provides a new framework for understanding neural decision making and suggests an important role for interactions between the neocortex and the basal ganglia in learning the mapping between probabilistic sensory representations and actions that maximize

Theories of Person Perception Predict Patterns of Neural Activity During Mentalizing.

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Thornton, Mark A; Mitchell, Jason P

2017-08-22

Social life requires making inferences about other people. What information do perceivers spontaneously draw upon to make such inferences? Here, we test 4 major theories of person perception, and 1 synthetic theory that combines their features, to determine whether the dimensions of such theories can serve as bases for describing patterns of neural activity during mentalizing. While undergoing functional magnetic resonance imaging, participants made social judgments about well-known public figures. Patterns of brain activity were then predicted using feature encoding models that represented target people's positions on theoretical dimensions such as warmth and competence. All 5 theories of person perception proved highly accurate at reconstructing activity patterns, indicating that each could describe the informational basis of mentalizing. Cross-validation indicated that the theories robustly generalized across both targets and participants. The synthetic theory consistently attained the best performance-approximately two-thirds of noise ceiling accuracy--indicating that, in combination, the theories considered here can account for much of the neural representation of other people. Moreover, encoding models trained on the present data could reconstruct patterns of activity associated with mental state representations in independent data, suggesting the use of a common neural code to represent others' traits and states. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

3D Polygon Mesh Compression with Multi Layer Feed Forward Neural Networks

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Emmanouil Piperakis

2003-06-01

Full Text Available In this paper, an experiment is conducted which proves that multi layer feed forward neural networks are capable of compressing 3D polygon meshes. Our compression method not only preserves the initial accuracy of the represented object but also enhances it. The neural network employed includes the vertex coordinates, the connectivity and normal information in one compact form, converting the discrete and surface polygon representation into an analytic, solid colloquial. Furthermore, the 3D object in its compressed neural form can be directly - without decompression - used for rendering. The neural compression - representation is viable to 3D transformations without the need of any anti-aliasing techniques - transformations do not disrupt the accuracy of the geometry. Our method does not su.er any scaling problem and was tested with objects of 300 to 107 polygons - such as the David of Michelangelo - achieving in all cases an order of O(b3 less bits for the representation than any other commonly known compression method. The simplicity of our algorithm and the established mathematical background of neural networks combined with their aptness for hardware implementation can establish this method as a good solution for polygon compression and if further investigated, a novel approach for 3D collision, animation and morphing.

A sparse neural code for some speech sounds but not for others.

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Mathias Scharinger

Full Text Available The precise neural mechanisms underlying speech sound representations are still a matter of debate. Proponents of 'sparse representations' assume that on the level of speech sounds, only contrastive or otherwise not predictable information is stored in long-term memory. Here, in a passive oddball paradigm, we challenge the neural foundations of such a 'sparse' representation; we use words that differ only in their penultimate consonant ("coronal" [t] vs. "dorsal" [k] place of articulation and for example distinguish between the German nouns Latz ([lats]; bib and Lachs ([laks]; salmon. Changes from standard [t] to deviant [k] and vice versa elicited a discernible Mismatch Negativity (MMN response. Crucially, however, the MMN for the deviant [lats] was stronger than the MMN for the deviant [laks]. Source localization showed this difference to be due to enhanced brain activity in right superior temporal cortex. These findings reflect a difference in phonological 'sparsity': Coronal [t] segments, but not dorsal [k] segments, are based on more sparse representations and elicit less specific neural predictions; sensory deviations from this prediction are more readily 'tolerated' and accordingly trigger weaker MMNs. The results foster the neurocomputational reality of 'representationally sparse' models of speech perception that are compatible with more general predictive mechanisms in auditory perception.

Event-driven processing for hardware-efficient neural spike sorting

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Liu, Yan; Pereira, João L.; Constandinou, Timothy G.

2018-02-01

Objective. The prospect of real-time and on-node spike sorting provides a genuine opportunity to push the envelope of large-scale integrated neural recording systems. In such systems the hardware resources, power requirements and data bandwidth increase linearly with channel count. Event-based (or data-driven) processing can provide here a new efficient means for hardware implementation that is completely activity dependant. In this work, we investigate using continuous-time level-crossing sampling for efficient data representation and subsequent spike processing. Approach. (1) We first compare signals (synthetic neural datasets) encoded with this technique against conventional sampling. (2) We then show how such a representation can be directly exploited by extracting simple time domain features from the bitstream to perform neural spike sorting. (3) The proposed method is implemented in a low power FPGA platform to demonstrate its hardware viability. Main results. It is observed that considerably lower data rates are achievable when using 7 bits or less to represent the signals, whilst maintaining the signal fidelity. Results obtained using both MATLAB and reconfigurable logic hardware (FPGA) indicate that feature extraction and spike sorting accuracies can be achieved with comparable or better accuracy than reference methods whilst also requiring relatively low hardware resources. Significance. By effectively exploiting continuous-time data representation, neural signal processing can be achieved in a completely event-driven manner, reducing both the required resources (memory, complexity) and computations (operations). This will see future large-scale neural systems integrating on-node processing in real-time hardware.

Bilingual Lexical Interactions in an Unsupervised Neural Network Model

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Zhao, Xiaowei; Li, Ping

2010-01-01

In this paper we present an unsupervised neural network model of bilingual lexical development and interaction. We focus on how the representational structures of the bilingual lexicons can emerge, develop, and interact with each other as a function of the learning history. The results show that: (1) distinct representations for the two lexicons…

Translating working memory into action: behavioral and neural evidence for using motor representations in encoding visuo-spatial sequences.

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Langner, Robert; Sternkopf, Melanie A; Kellermann, Tanja S; Grefkes, Christian; Kurth, Florian; Schneider, Frank; Zilles, Karl; Eickhoff, Simon B

2014-07-01

The neurobiological organization of action-oriented working memory is not well understood. To elucidate the neural correlates of translating visuo-spatial stimulus sequences into delayed (memory-guided) sequential actions, we measured brain activity using functional magnetic resonance imaging while participants encoded sequences of four to seven dots appearing on fingers of a left or right schematic hand. After variable delays, sequences were to be reproduced with the corresponding fingers. Recall became less accurate with longer sequences and was initiated faster after long delays. Across both hands, encoding and recall activated bilateral prefrontal, premotor, superior and inferior parietal regions as well as the basal ganglia, whereas hand-specific activity was found (albeit to a lesser degree during encoding) in contralateral premotor, sensorimotor, and superior parietal cortex. Activation differences after long versus short delays were restricted to motor-related regions, indicating that rehearsal during long delays might have facilitated the conversion of the memorandum into concrete motor programs at recall. Furthermore, basal ganglia activity during encoding selectively predicted correct recall. Taken together, the results suggest that to-be-reproduced visuo-spatial sequences are encoded as prospective action representations (motor intentions), possibly in addition to retrospective sensory codes. Overall, our study supports and extends multi-component models of working memory, highlighting the notion that sensory input can be coded in multiple ways depending on what the memorandum is to be used for. Copyright © 2013 Wiley Periodicals, Inc.

Attention and Representational Momentum

OpenAIRE

Hayes, Amy; Freyd, Jennifer J

1995-01-01

Representational momentum, the tendency for memory to be distorted in the direction of an implied transformation, suggests that dynamics are an intrinsic part of perceptual representations. We examined the effect of attention on dynamic representation by testing for representational momentum under conditions of distraction. Forward memory shifts increase when attention is divided. Attention may be involved in halting but not in maintaining dynamic representations.

Neural codes of seeing architectural styles

OpenAIRE

Choo, Heeyoung; Nasar, Jack L.; Nikrahei, Bardia; Walther, Dirk B.

2017-01-01

Images of iconic buildings, such as the CN Tower, instantly transport us to specific places, such as Toronto. Despite the substantial impact of architectural design on people′s visual experience of built environments, we know little about its neural representation in the human brain. In the present study, we have found patterns of neural activity associated with specific architectural styles in several high-level visual brain regions, but not in primary visual cortex (V1). This finding sugges...

The Representation of Object Viewpoint in Human Visual Cortex

OpenAIRE

Andresen, David R.; Vinberg, Joakim; Grill-Spector, Kalanit

2008-01-01

Understanding the nature of object representations in the human brain is critical for understanding the neural basis of invariant object recognition. However, the degree to which object representations are sensitive to object viewpoint is unknown. Using fMRI we employed a parametric approach to examine the sensitivity to object view as a function of rotation (0°–180°), category (animal/vehicle) and fMRI-adaptation paradigm (short or long-lagged). For both categories and fMRI-adaptation paradi...

Cortical representations of communication sounds.

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Heiser, Marc A; Cheung, Steven W

2008-10-01

This review summarizes recent research into cortical processing of vocalizations in animals and humans. There has been a resurgent interest in this topic accompanied by an increased number of studies using animal models with complex vocalizations and new methods in human brain imaging. Recent results from such studies are discussed. Experiments have begun to reveal the bilateral cortical fields involved in communication sound processing and the transformations of neural representations that occur among those fields. Advances have also been made in understanding the neuronal basis of interaction between developmental exposures and behavioral experiences with vocalization perception. Exposure to sounds during the developmental period produces large effects on brain responses, as do a variety of specific trained tasks in adults. Studies have also uncovered a neural link between the motor production of vocalizations and the representation of vocalizations in cortex. Parallel experiments in humans and animals are answering important questions about vocalization processing in the central nervous system. This dual approach promises to reveal microscopic, mesoscopic, and macroscopic principles of large-scale dynamic interactions between brain regions that underlie the complex phenomenon of vocalization perception. Such advances will yield a greater understanding of the causes, consequences, and treatment of disorders related to speech processing.

Decoding the Brain’s Algorithm for Categorization from its Neural Implementation

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Mack, Michael L.; Preston, Alison R.; Love, Bradley C.

2013-01-01

Summary Acts of cognition can be described at different levels of analysis: what behavior should characterize the act, what algorithms and representations underlie the behavior, and how the algorithms are physically realized in neural activity [1]. Theories that bridge levels of analysis offer more complete explanations by leveraging the constraints present at each level [2–4]. Despite the great potential for theoretical advances, few studies of cognition bridge levels of analysis. For example, formal cognitive models of category decisions accurately predict human decision making [5, 6], but whether model algorithms and representations supporting category decisions are consistent with underlying neural implementation remains unknown. This uncertainty is largely due to the hurdle of forging links between theory and brain [7–9]. Here, we tackle this critical problem by using brain response to characterize the nature of mental computations that support category decisions to evaluate two dominant, and opposing, models of categorization. We found that brain states during category decisions were significantly more consistent with latent model representations from exemplar [5] rather than prototype theory [10, 11]. Representations of individual experiences, not the abstraction of experiences, are critical for category decision making. Holding models accountable for behavior and neural implementation provides a means for advancing more complete descriptions of the algorithms of cognition. PMID:24094852

Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system.

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Jannis Born

Full Text Available A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plausible, Hebbian learning mechanism may account for the development of localized hand-centred representations in a hierarchical neural network model of the primate visual system, VisNet. The hand-centered neurons developed in the model use an invariance learning mechanism known as continuous transformation (CT learning. In contrast to previous theoretical proposals for the development of hand-centered visual representations, CT learning does not need a memory trace of recent neuronal activity to be incorporated in the synaptic learning rule. Instead, CT learning relies solely on a Hebbian learning rule, which is able to exploit the spatial overlap that naturally occurs between successive images of a hand-object configuration as it is shifted across different retinal locations due to saccades. Our simulations show how individual neurons in the network model can learn to respond selectively to target objects in particular locations with respect to the hand, irrespective of where the hand-object configuration occurs on the retina. The response properties of these hand-centred neurons further generalise to localised receptive fields in the hand-centred space when tested on novel hand-object configurations that have not been explored during training. Indeed, even when the network is trained with target objects presented across a near continuum of locations around the hand during training, the model continues to develop hand-centred neurons with localised receptive fields in hand-centred space. With the help of principal component analysis, we provide the first theoretical framework that explains the behavior

Internal representations for face detection: an application of noise-based image classification to BOLD responses.

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Nestor, Adrian; Vettel, Jean M; Tarr, Michael J

2013-11-01

What basic visual structures underlie human face detection and how can we extract such structures directly from the amplitude of neural responses elicited by face processing? Here, we address these issues by investigating an extension of noise-based image classification to BOLD responses recorded in high-level visual areas. First, we assess the applicability of this classification method to such data and, second, we explore its results in connection with the neural processing of faces. To this end, we construct luminance templates from white noise fields based on the response of face-selective areas in the human ventral cortex. Using behaviorally and neurally-derived classification images, our results reveal a family of simple but robust image structures subserving face representation and detection. Thus, we confirm the role played by classical face selective regions in face detection and we help clarify the representational basis of this perceptual function. From a theory standpoint, our findings support the idea of simple but highly diagnostic neurally-coded features for face detection. At the same time, from a methodological perspective, our work demonstrates the ability of noise-based image classification in conjunction with fMRI to help uncover the structure of high-level perceptual representations. Copyright © 2012 Wiley Periodicals, Inc.

Hierarchical Organization of Auditory and Motor Representations in Speech Perception: Evidence from Searchlight Similarity Analysis.

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Evans, Samuel; Davis, Matthew H

2015-12-01

How humans extract the identity of speech sounds from highly variable acoustic signals remains unclear. Here, we use searchlight representational similarity analysis (RSA) to localize and characterize neural representations of syllables at different levels of the hierarchically organized temporo-frontal pathways for speech perception. We asked participants to listen to spoken syllables that differed considerably in their surface acoustic form by changing speaker and degrading surface acoustics using noise-vocoding and sine wave synthesis while we recorded neural responses with functional magnetic resonance imaging. We found evidence for a graded hierarchy of abstraction across the brain. At the peak of the hierarchy, neural representations in somatomotor cortex encoded syllable identity but not surface acoustic form, at the base of the hierarchy, primary auditory cortex showed the reverse. In contrast, bilateral temporal cortex exhibited an intermediate response, encoding both syllable identity and the surface acoustic form of speech. Regions of somatomotor cortex associated with encoding syllable identity in perception were also engaged when producing the same syllables in a separate session. These findings are consistent with a hierarchical account of how variable acoustic signals are transformed into abstract representations of the identity of speech sounds. © The Author 2015. Published by Oxford University Press.

A Meta-Analysis Suggests Different Neural Correlates for Implicit and Explicit Learning.

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Loonis, Roman F; Brincat, Scott L; Antzoulatos, Evan G; Miller, Earl K

2017-10-11

A meta-analysis of non-human primates performing three different tasks (Object-Match, Category-Match, and Category-Saccade associations) revealed signatures of explicit and implicit learning. Performance improved equally following correct and error trials in the Match (explicit) tasks, but it improved more after correct trials in the Saccade (implicit) task, a signature of explicit versus implicit learning. Likewise, error-related negativity, a marker for error processing, was greater in the Match (explicit) tasks. All tasks showed an increase in alpha/beta (10-30 Hz) synchrony after correct choices. However, only the implicit task showed an increase in theta (3-7 Hz) synchrony after correct choices that decreased with learning. In contrast, in the explicit tasks, alpha/beta synchrony increased with learning and decreased thereafter. Our results suggest that explicit versus implicit learning engages different neural mechanisms that rely on different patterns of oscillatory synchrony. Copyright © 2017 Elsevier Inc. All rights reserved.

3D Maps Representation Using GNG

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Vicente Morell

2014-01-01

Full Text Available Current RGB-D sensors provide a big amount of valuable information for mobile robotics tasks like 3D map reconstruction, but the storage and processing of the incremental data provided by the different sensors through time quickly become unmanageable. In this work, we focus on 3D maps representation and propose the use of the Growing Neural Gas (GNG network as a model to represent 3D input data. GNG method is able to represent the input data with a desired amount of neurons or resolution while preserving the topology of the input space. Experiments show how GNG method yields a better input space adaptation than other state-of-the-art 3D map representation methods.

A study on neural network representation of reactor power control procedures

International Nuclear Information System (INIS)

Moon, Byung Soo; Park, J. C.; Kim, Y. T.; Yang, S. U.; Lee, H. C.; Hwang, I. A.; Hwang, H. S.

1997-12-01

A neural algorithm to carry out the curve readings and arithmetic computations necessary for reactor power control is described in this report. The curve readings are for functions of the form z=f(x,y) and require fairly good interpolations. One of the functions is the total power defect as a function of reactor power and boron concentration. The second is the new position of control rod as a function of the current rod position and the increment of total power defect needed for the required power change. The curves involving xenon effect are also considered separately. We represented these curves by cubic spline interpolations first and then converted them to fuzzy systems so that they perform the identical interpolations as the splines. The resulting fuzzy systems are then converted to artificial neural networks similar to the RBF type neural network. These networks still carry the O(h'4) accuracy as the cubic spline interpolating functions. Also included is a description of an important result on how to find the spline interpolation coefficients without solving the matrix equation, when the function is a polynomial of the form f(t)=t'm. This result provides a systematic way of presenting continuous functions by fuzzy systems and hence by artificial neural networks without any training. (author). 10 refs., 2 tabs., 10 figs

Synaptic E-I Balance Underlies Efficient Neural Coding.

Science.gov (United States)

Zhou, Shanglin; Yu, Yuguo

2018-01-01

Both theoretical and experimental evidence indicate that synaptic excitation and inhibition in the cerebral cortex are well-balanced during the resting state and sensory processing. Here, we briefly summarize the evidence for how neural circuits are adjusted to achieve this balance. Then, we discuss how such excitatory and inhibitory balance shapes stimulus representation and information propagation, two basic functions of neural coding. We also point out the benefit of adopting such a balance during neural coding. We conclude that excitatory and inhibitory balance may be a fundamental mechanism underlying efficient coding.

Dynamically Partitionable Autoassociative Networks as a Solution to the Neural Binding Problem

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Kenneth Jeffrey Hayworth

2012-09-01

Full Text Available An outstanding question in theoretical neuroscience is how the brain solves the neural binding problem. In vision, binding can be summarized as the ability to represent that certain properties belong to one object while other properties belong to a different object. I review the binding problem in visual and other domains, and review its simplest proposed solution – the anatomical binding hypothesis. This hypothesis has traditionally been rejected as a true solution because it seems to require a type of one-to-one wiring of neurons that would be impossible in a biological system (as opposed to an engineered system like a computer. I show that this requirement for one-to-one wiring can be loosened by carefully considering how the neural representation is actually put to use by the rest of the brain. This leads to a solution where a symbol is represented not as a particular pattern of neural activation but instead as a piece of a global stable attractor state. I introduce the Dynamically Partitionable AutoAssociative Network (DPAAN as an implementation of this solution and show how DPANNs can be used in systems which perform perceptual binding and in systems that implement syntax-sensitive rules. Finally I show how the core parts of the cognitive architecture ACT-R can be neurally implemented using a DPAAN as ACT-R’s global workspace. Because the DPAAN solution to the binding problem requires only ‘flat’ neural representations (as opposed to the phase encoded representation hypothesized in neural synchrony solutions it is directly compatible with the most well developed neural models of learning, memory, and pattern recognition.

"Neural overlap of L1 and L2 semantic representations across visual and auditory modalities: a decoding approach".

Science.gov (United States)

Van de Putte, Eowyn; De Baene, Wouter; Price, Cathy J; Duyck, Wouter

2018-05-01

This study investigated whether brain activity in Dutch-French bilinguals during semantic access to concepts from one language could be used to predict neural activation during access to the same concepts from another language, in different language modalities/tasks. This was tested using multi-voxel pattern analysis (MVPA), within and across language comprehension (word listening and word reading) and production (picture naming). It was possible to identify the picture or word named, read or heard in one language (e.g. maan, meaning moon) based on the brain activity in a distributed bilateral brain network while, respectively, naming, reading or listening to the picture or word in the other language (e.g. lune). The brain regions identified differed across tasks. During picture naming, brain activation in the occipital and temporal regions allowed concepts to be predicted across languages. During word listening and word reading, across-language predictions were observed in the rolandic operculum and several motor-related areas (pre- and postcentral, the cerebellum). In addition, across-language predictions during reading were identified in regions typically associated with semantic processing (left inferior frontal, middle temporal cortex, right cerebellum and precuneus) and visual processing (inferior and middle occipital regions and calcarine sulcus). Furthermore, across modalities and languages, the left lingual gyrus showed semantic overlap across production and word reading. These findings support the idea of at least partially language- and modality-independent semantic neural representations. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Visual attention mitigates information loss in small- and large-scale neural codes

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Sprague, Thomas C; Saproo, Sameer; Serences, John T

2015-01-01

Summary The visual system transforms complex inputs into robust and parsimonious neural codes that efficiently guide behavior. Because neural communication is stochastic, the amount of encoded visual information necessarily decreases with each synapse. This constraint requires processing sensory signals in a manner that protects information about relevant stimuli from degradation. Such selective processing – or selective attention – is implemented via several mechanisms, including neural gain and changes in tuning properties. However, examining each of these effects in isolation obscures their joint impact on the fidelity of stimulus feature representations by large-scale population codes. Instead, large-scale activity patterns can be used to reconstruct representations of relevant and irrelevant stimuli, providing a holistic understanding about how neuron-level modulations collectively impact stimulus encoding. PMID:25769502

Encoding of phonology in a recurrent neural model of grounded speech

NARCIS (Netherlands)

Alishahi, Afra; Barking, Marie; Chrupala, Grzegorz; Levy, Roger; Specia, Lucia

2017-01-01

We study the representation and encoding of phonemes in a recurrent neural network model of grounded speech. We use a model which processes images and their spoken descriptions, and projects the visual and auditory representations into the same semantic space. We perform a number of analyses on how

Visual motion transforms visual space representations similarly throughout the human visual hierarchy.

Science.gov (United States)

Harvey, Ben M; Dumoulin, Serge O

2016-02-15

Several studies demonstrate that visual stimulus motion affects neural receptive fields and fMRI response amplitudes. Here we unite results of these two approaches and extend them by examining the effects of visual motion on neural position preferences throughout the hierarchy of human visual field maps. We measured population receptive field (pRF) properties using high-field fMRI (7T), characterizing position preferences simultaneously over large regions of the visual cortex. We measured pRFs properties using sine wave gratings in stationary apertures, moving at various speeds in either the direction of pRF measurement or the orthogonal direction. We find direction- and speed-dependent changes in pRF preferred position and size in all visual field maps examined, including V1, V3A, and the MT+ map TO1. These effects on pRF properties increase up the hierarchy of visual field maps. However, both within and between visual field maps the extent of pRF changes was approximately proportional to pRF size. This suggests that visual motion transforms the representation of visual space similarly throughout the visual hierarchy. Visual motion can also produce an illusory displacement of perceived stimulus position. We demonstrate perceptual displacements using the same stimulus configuration. In contrast to effects on pRF properties, perceptual displacements show only weak effects of motion speed, with far larger speed-independent effects. We describe a model where low-level mechanisms could underlie the observed effects on neural position preferences. We conclude that visual motion induces similar transformations of visuo-spatial representations throughout the visual hierarchy, which may arise through low-level mechanisms. Copyright © 2015 Elsevier Inc. All rights reserved.

Perisaccadic Updating of Visual Representations and Attentional States: Linking Behavior and Neurophysiology

Science.gov (United States)

Marino, Alexandria C.; Mazer, James A.

2016-01-01

During natural vision, saccadic eye movements lead to frequent retinal image changes that result in different neuronal subpopulations representing the same visual feature across fixations. Despite these potentially disruptive changes to the neural representation, our visual percept is remarkably stable. Visual receptive field remapping, characterized as an anticipatory shift in the position of a neuron’s spatial receptive field immediately before saccades, has been proposed as one possible neural substrate for visual stability. Many of the specific properties of remapping, e.g., the exact direction of remapping relative to the saccade vector and the precise mechanisms by which remapping could instantiate stability, remain a matter of debate. Recent studies have also shown that visual attention, like perception itself, can be sustained across saccades, suggesting that the attentional control system can also compensate for eye movements. Classical remapping could have an attentional component, or there could be a distinct attentional analog of visual remapping. At this time we do not yet fully understand how the stability of attentional representations relates to perisaccadic receptive field shifts. In this review, we develop a vocabulary for discussing perisaccadic shifts in receptive field location and perisaccadic shifts of attentional focus, review and synthesize behavioral and neurophysiological studies of perisaccadic perception and perisaccadic attention, and identify open questions that remain to be experimentally addressed. PMID:26903820

Part-based deep representation for product tagging and search

Science.gov (United States)

Chen, Keqing

2017-06-01

Despite previous studies, tagging and indexing the product images remain challenging due to the large inner-class variation of the products. In the traditional methods, the quantized hand-crafted features such as SIFTs are extracted as the representation of the product images, which are not discriminative enough to handle the inner-class variation. For discriminative image representation, this paper firstly presents a novel deep convolutional neural networks (DCNNs) architect true pre-trained on a large-scale general image dataset. Compared to the traditional features, our DCNNs representation is of more discriminative power with fewer dimensions. Moreover, we incorporate the part-based model into the framework to overcome the negative effect of bad alignment and cluttered background and hence the descriptive ability of the deep representation is further enhanced. Finally, we collect and contribute a well-labeled shoe image database, i.e., the TBShoes, on which we apply the part-based deep representation for product image tagging and search, respectively. The experimental results highlight the advantages of the proposed part-based deep representation.

Neural and Behavioral Evidence for an Online Resetting Process in Visual Working Memory.

Science.gov (United States)

Balaban, Halely; Luria, Roy

2017-02-01

Visual working memory (VWM) guides behavior by holding a set of active representations and modifying them according to changes in the environment. This updating process relies on a unique mapping between each VWM representation and an actual object in the environment. Here, we destroyed this mapping by either presenting a coherent object but then breaking it into independent parts or presenting an object but then abruptly replacing it with a different object. This allowed us to introduce the neural marker and behavioral consequence of an online resetting process in humans' VWM. Across seven experiments, we demonstrate that this resetting process involves abandoning the old VWM contents because they no longer correspond to the objects in the environment. Then, VWM encodes the novel information and reestablishes the correspondence between the new representations and the objects. The resetting process was marked by a unique neural signature: a sharp drop in the amplitude of the electrophysiological index of VWM contents (the contralateral delay activity), presumably indicating the loss of the existent object-to-representation mappings. This marker was missing when an updating process occurred. Moreover, when tracking moving items, VWM failed to detect salient changes in the object's shape when these changes occurred during the resetting process. This happened despite the object being fully visible, presumably because the mapping between the object and a VWM representation was lost. Importantly, we show that resetting, its neural marker, and the behavioral cost it entails, are specific to situations that involve a destruction of the objects-to-representations correspondence. Visual working memory (VWM) maintains task-relevant information in an online state. Previous studies showed that VWM representations are accessed and modified after changes in the environment. Here, we show that this updating process critically depends on an ongoing mapping between the

Sleep modulates the neural substrates of both spatial and contextual memory consolidation.

Directory of Open Access Journals (Sweden)

Géraldine Rauchs

Full Text Available It is known that sleep reshapes the neural representations that subtend the memories acquired while navigating in a virtual environment. However, navigation is not process-pure, as manifold learning components contribute to performance, notably the spatial and contextual memory constituents. In this context, it remains unclear whether post-training sleep globally promotes consolidation of all of the memory components embedded in virtual navigation, or rather favors the development of specific representations. Here, we investigated the effect of post-training sleep on the neural substrates of the consolidation of spatial and contextual memories acquired while navigating in a complex 3D, naturalistic virtual town. Using fMRI, we mapped regional cerebral activity during various tasks designed to tap either the spatial or the contextual memory component, or both, 72 h after encoding with or without sleep deprivation during the first post-training night. Behavioral performance was not dependent upon post-training sleep deprivation, neither in a natural setting that engages both spatial and contextual memory processes nor when looking more specifically at each of these memory representations. At the neuronal level however, analyses that focused on contextual memory revealed distinct correlations between performance and neuronal activity in frontal areas associated with recollection processes after post-training sleep, and in the parahippocampal gyrus associated with familiarity processes in sleep-deprived participants. Likewise, efficient spatial memory was associated with posterior cortical activity after sleep whereas it correlated with parahippocampal/medial temporal activity after sleep deprivation. Finally, variations in place-finding efficiency in a natural setting encompassing spatial and contextual elements were associated with caudate activity after post-training sleep, suggesting the automation of navigation. These data indicate that post

A modular architecture for transparent computation in recurrent neural networks.

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Carmantini, Giovanni S; Beim Graben, Peter; Desroches, Mathieu; Rodrigues, Serafim

2017-01-01

Computation is classically studied in terms of automata, formal languages and algorithms; yet, the relation between neural dynamics and symbolic representations and operations is still unclear in traditional eliminative connectionism. Therefore, we suggest a unique perspective on this central issue, to which we would like to refer as transparent connectionism, by proposing accounts of how symbolic computation can be implemented in neural substrates. In this study we first introduce a new model of dynamics on a symbolic space, the versatile shift, showing that it supports the real-time simulation of a range of automata. We then show that the Gödelization of versatile shifts defines nonlinear dynamical automata, dynamical systems evolving on a vectorial space. Finally, we present a mapping between nonlinear dynamical automata and recurrent artificial neural networks. The mapping defines an architecture characterized by its granular modularity, where data, symbolic operations and their control are not only distinguishable in activation space, but also spatially localizable in the network itself, while maintaining a distributed encoding of symbolic representations. The resulting networks simulate automata in real-time and are programmed directly, in the absence of network training. To discuss the unique characteristics of the architecture and their consequences, we present two examples: (i) the design of a Central Pattern Generator from a finite-state locomotive controller, and (ii) the creation of a network simulating a system of interactive automata that supports the parsing of garden-path sentences as investigated in psycholinguistics experiments. Copyright © 2016 Elsevier Ltd. All rights reserved.

Particle identification with neural networks using a rotational invariant moment representation

International Nuclear Information System (INIS)

Sinkus, R.

1997-01-01

A feed-forward neural network is used to identify electromagnetic particles based upon their showering properties within a segmented calorimeter. The novel feature is the expansion of the energy distribution in terms of moments of the so-called Zernike functions which are invariant under rotation. The multidimensional input distribution for the neural network is transformed via a principle component analysis and rescaled by its respective variances to ensure input values of the order of one. This results is a better performance in identifying and separating electromagnetic from hadronic particles, especially at low energies. (orig.)

Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning.

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Zhu, Lusha; Mathewson, Kyle E; Hsu, Ming

2012-01-31

Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents' beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs.

A model of interval timing by neural integration.

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Simen, Patrick; Balci, Fuat; de Souza, Laura; Cohen, Jonathan D; Holmes, Philip

2011-06-22

We show that simple assumptions about neural processing lead to a model of interval timing as a temporal integration process, in which a noisy firing-rate representation of time rises linearly on average toward a response threshold over the course of an interval. Our assumptions include: that neural spike trains are approximately independent Poisson processes, that correlations among them can be largely cancelled by balancing excitation and inhibition, that neural populations can act as integrators, and that the objective of timed behavior is maximal accuracy and minimal variance. The model accounts for a variety of physiological and behavioral findings in rodents, monkeys, and humans, including ramping firing rates between the onset of reward-predicting cues and the receipt of delayed rewards, and universally scale-invariant response time distributions in interval timing tasks. It furthermore makes specific, well-supported predictions about the skewness of these distributions, a feature of timing data that is usually ignored. The model also incorporates a rapid (potentially one-shot) duration-learning procedure. Human behavioral data support the learning rule's predictions regarding learning speed in sequences of timed responses. These results suggest that simple, integration-based models should play as prominent a role in interval timing theory as they do in theories of perceptual decision making, and that a common neural mechanism may underlie both types of behavior.

Gravity influences the visual representation of object tilt in parietal cortex.

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Rosenberg, Ari; Angelaki, Dora E

2014-10-22

Sensory systems encode the environment in egocentric (e.g., eye, head, or body) reference frames, creating inherently unstable representations that shift and rotate as we move. However, it is widely speculated that the brain transforms these signals into an allocentric, gravity-centered representation of the world that is stable and independent of the observer's spatial pose. Where and how this representation may be achieved is currently unknown. Here we demonstrate that a subpopulation of neurons in the macaque caudal intraparietal area (CIP) visually encodes object tilt in nonegocentric coordinates defined relative to the gravitational vector. Neuronal responses to the tilt of a visually presented planar surface were measured with the monkey in different spatial orientations (upright and rolled left/right ear down) and then compared. This revealed a continuum of representations in which planar tilt was encoded in a gravity-centered reference frame in approximately one-tenth of the comparisons, intermediate reference frames ranging between gravity-centered and egocentric in approximately two-tenths of the comparisons, and in an egocentric reference frame in less than half of the comparisons. Altogether, almost half of the comparisons revealed a shift in the preferred tilt and/or a gain change consistent with encoding object orientation in nonegocentric coordinates. Through neural network modeling, we further show that a purely gravity-centered representation of object tilt can be achieved directly from the population activity of CIP-like units. These results suggest that area CIP may play a key role in creating a stable, allocentric representation of the environment defined relative to an "earth-vertical" direction. Copyright © 2014 the authors 0270-6474/14/3414170-11$15.00/0.

Convolutional Neural Networks - Generalizability and Interpretations

DEFF Research Database (Denmark)

Malmgren-Hansen, David

from data despite it being limited in amount or context representation. Within Machine Learning this thesis focuses on Convolutional Neural Networks for Computer Vision. The research aims to answer how to explore a model's generalizability to the whole population of data samples and how to interpret...

Visual attention mitigates information loss in small- and large-scale neural codes.

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Sprague, Thomas C; Saproo, Sameer; Serences, John T

2015-04-01

The visual system transforms complex inputs into robust and parsimonious neural codes that efficiently guide behavior. Because neural communication is stochastic, the amount of encoded visual information necessarily decreases with each synapse. This constraint requires that sensory signals are processed in a manner that protects information about relevant stimuli from degradation. Such selective processing--or selective attention--is implemented via several mechanisms, including neural gain and changes in tuning properties. However, examining each of these effects in isolation obscures their joint impact on the fidelity of stimulus feature representations by large-scale population codes. Instead, large-scale activity patterns can be used to reconstruct representations of relevant and irrelevant stimuli, thereby providing a holistic understanding about how neuron-level modulations collectively impact stimulus encoding. Copyright © 2015 Elsevier Ltd. All rights reserved.

Intrusive images in psychological disorders: characteristics, neural mechanisms, and treatment implications.

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Brewin, Chris R; Gregory, James D; Lipton, Michelle; Burgess, Neil

2010-01-01

Involuntary images and visual memories are prominent in many types of psychopathology. Patients with posttraumatic stress disorder, other anxiety disorders, depression, eating disorders, and psychosis frequently report repeated visual intrusions corresponding to a small number of real or imaginary events, usually extremely vivid, detailed, and with highly distressing content. Both memory and imagery appear to rely on common networks involving medial prefrontal regions, posterior regions in the medial and lateral parietal cortices, the lateral temporal cortex, and the medial temporal lobe. Evidence from cognitive psychology and neuroscience implies distinct neural bases to abstract, flexible, contextualized representations (C-reps) and to inflexible, sensory-bound representations (S-reps). We revise our previous dual representation theory of posttraumatic stress disorder to place it within a neural systems model of healthy memory and imagery. The revised model is used to explain how the different types of distressing visual intrusions associated with clinical disorders arise, in terms of the need for correct interaction between the neural systems supporting S-reps and C-reps via visuospatial working memory. Finally, we discuss the treatment implications of the new model and relate it to existing forms of psychological therapy.

A k-mer-based barcode DNA classification methodology based on spectral representation and a neural gas network.

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Fiannaca, Antonino; La Rosa, Massimo; Rizzo, Riccardo; Urso, Alfonso

2015-07-01

In this paper, an alignment-free method for DNA barcode classification that is based on both a spectral representation and a neural gas network for unsupervised clustering is proposed. In the proposed methodology, distinctive words are identified from a spectral representation of DNA sequences. A taxonomic classification of the DNA sequence is then performed using the sequence signature, i.e., the smallest set of k-mers that can assign a DNA sequence to its proper taxonomic category. Experiments were then performed to compare our method with other supervised machine learning classification algorithms, such as support vector machine, random forest, ripper, naïve Bayes, ridor, and classification tree, which also consider short DNA sequence fragments of 200 and 300 base pairs (bp). The experimental tests were conducted over 10 real barcode datasets belonging to different animal species, which were provided by the on-line resource "Barcode of Life Database". The experimental results showed that our k-mer-based approach is directly comparable, in terms of accuracy, recall and precision metrics, with the other classifiers when considering full-length sequences. In addition, we demonstrate the robustness of our method when a classification is performed task with a set of short DNA sequences that were randomly extracted from the original data. For example, the proposed method can reach the accuracy of 64.8% at the species level with 200-bp fragments. Under the same conditions, the best other classifier (random forest) reaches the accuracy of 20.9%. Our results indicate that we obtained a clear improvement over the other classifiers for the study of short DNA barcode sequence fragments. Copyright © 2015 Elsevier B.V. All rights reserved.

Why Overlearned Sequences are Special: Distinct Neural Networks for Ordinal Sequences

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Vani ePariyadath

2012-12-01

Full Text Available Several observations suggest that overlearned ordinal categories (e.g., letters, numbers, weekdays, months are processed differently than non-ordinal categories in the brain. In synesthesia, for example, anomalous perceptual experiences are most often triggered by members of ordinal categories (Rich et al., 2005; Eagleman, 2009. In semantic dementia, the processing of ordinal stimuli appears to be preserved relative to non-ordinal ones (Cappelletti et al., 2001. Moreover, ordinal stimuli often map onto unconscious spatial representations, as observed in the SNARC effect (Dehaene et al, 1993; Fias, 1996. At present, little is known about the neural representation of ordinal categories. Using functional neuroimaging, we show that words in ordinal categories are processed in a fronto-temporo-parietal network biased toward the right hemisphere. This differs from words in non-ordinal categories (such as names of furniture, animals, cars and fruit, which show an expected bias toward the left hemisphere. Further, we find that increased predictability of stimulus order correlates with smaller regions of BOLD activation, a phenomenon we term prediction suppression. Our results provide new insights into the processing of ordinal stimuli, and suggest a new anatomical framework for understanding the patterns seen in synesthesia, unconscious spatial representation, and semantic dementia.

Representation of dynamic interaural phase difference in auditory cortex of awake rhesus macaques.

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Scott, Brian H; Malone, Brian J; Semple, Malcolm N

2009-04-01

Neurons in auditory cortex of awake primates are selective for the spatial location of a sound source, yet the neural representation of the binaural cues that underlie this tuning remains undefined. We examined this representation in 283 single neurons across the low-frequency auditory core in alert macaques, trained to discriminate binaural cues for sound azimuth. In response to binaural beat stimuli, which mimic acoustic motion by modulating the relative phase of a tone at the two ears, these neurons robustly modulate their discharge rate in response to this directional cue. In accordance with prior studies, the preferred interaural phase difference (IPD) of these neurons typically corresponds to azimuthal locations contralateral to the recorded hemisphere. Whereas binaural beats evoke only transient discharges in anesthetized cortex, neurons in awake cortex respond throughout the IPD cycle. In this regard, responses are consistent with observations at earlier stations of the auditory pathway. Discharge rate is a band-pass function of the frequency of IPD modulation in most neurons (73%), but both discharge rate and temporal synchrony are independent of the direction of phase modulation. When subjected to a receiver operator characteristic analysis, the responses of individual neurons are insufficient to account for the perceptual acuity of these macaques in an IPD discrimination task, suggesting the need for neural pooling at the cortical level.

Disordered semantic representation in schizophrenic temporal cortex revealed by neuromagnetic response patterns

Directory of Open Access Journals (Sweden)

Silberman Yaron

2006-05-01

Full Text Available Abstract Background Loosening of associations and thought disruption are key features of schizophrenic psychopathology. Alterations in neural networks underlying this basic abnormality have not yet been sufficiently identified. Previously, we demonstrated that spatio-temporal clustering of magnetic brain responses to pictorial stimuli map categorical representations in temporal cortex. This result has opened the possibility to quantify associative strength within and across semantic categories in schizophrenic patients. We hypothesized that in contrast to controls, schizophrenic patients exhibit disordered representations of semantic categories. Methods The spatio-temporal clusters of brain magnetic activities elicited by object pictures related to super-ordinate (flowers, animals, furniture, clothes and base-level (e.g. tulip, rose, orchid, sunflower categories were analysed in the source space for the time epochs 170–210 and 210–450 ms following stimulus onset and were compared between 10 schizophrenic patients and 10 control subjects. Results Spatio-temporal correlations of responses elicited by base-level concepts and the difference of within vs. across super-ordinate categories were distinctly lower in patients than in controls. Additionally, in contrast to the well-defined categorical representation in control subjects, unsupervised clustering indicated poorly defined representation of semantic categories in patients. Within the patient group, distinctiveness of categorical representation in the temporal cortex was positively related to negative symptoms and tended to be inversely related to positive symptoms. Conclusion Schizophrenic patients show a less organized representation of semantic categories in clusters of magnetic brain responses than healthy adults. This atypical neural network architecture may be a correlate of loosening of associations, promoting positive symptoms.

A neural model of figure-ground organization.

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Craft, Edward; Schütze, Hartmut; Niebur, Ernst; von der Heydt, Rüdiger

2007-06-01

Psychophysical studies suggest that figure-ground organization is a largely autonomous process that guides--and thus precedes--allocation of attention and object recognition. The discovery of border-ownership representation in single neurons of early visual cortex has confirmed this view. Recent theoretical studies have demonstrated that border-ownership assignment can be modeled as a process of self-organization by lateral interactions within V2 cortex. However, the mechanism proposed relies on propagation of signals through horizontal fibers, which would result in increasing delays of the border-ownership signal with increasing size of the visual stimulus, in contradiction with experimental findings. It also remains unclear how the resulting border-ownership representation would interact with attention mechanisms to guide further processing. Here we present a model of border-ownership coding based on dedicated neural circuits for contour grouping that produce border-ownership assignment and also provide handles for mechanisms of selective attention. The results are consistent with neurophysiological and psychophysical findings. The model makes predictions about the hypothetical grouping circuits and the role of feedback between cortical areas.

Fast and automatic activation of an abstract representation of money in the human ventral visual pathway.

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Catherine Tallon-Baudry

Full Text Available Money, when used as an incentive, activates the same neural circuits as rewards associated with physiological needs. However, unlike physiological rewards, monetary stimuli are cultural artifacts: how are monetary stimuli identified in the first place? How and when does the brain identify a valid coin, i.e. a disc of metal that is, by social agreement, endowed with monetary properties? We took advantage of the changes in the Euro area in 2002 to compare neural responses to valid coins (Euros, Australian Dollars with neural responses to invalid coins that have lost all monetary properties (French Francs, Finnish Marks. We show in magneto-encephalographic recordings, that the ventral visual pathway automatically distinguishes between valid and invalid coins, within only ∼150 ms. This automatic categorization operates as well on coins subjects were familiar with as on unfamiliar coins. No difference between neural responses to scrambled controls could be detected. These results could suggest the existence of a generic, all-purpose neural representation of money that is independent of experience. This finding is reminiscent of a central assumption in economics, money fungibility, or the fact that a unit of money is substitutable to another. From a neural point of view, our findings may indicate that the ventral visual pathway, a system previously thought to analyze visual features such as shape or color and to be influenced by daily experience, could also able to use conceptual attributes such as monetary validity to categorize familiar as well as unfamiliar visual objects. The symbolic abilities of the posterior fusiform region suggested here could constitute an efficient neural substrate to deal with culturally defined symbols, independently of experience, which probably fostered money's cultural emergence and success.

Associative learning changes cross-modal representations in the gustatory cortex.

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Vincis, Roberto; Fontanini, Alfredo

2016-08-30

A growing body of literature has demonstrated that primary sensory cortices are not exclusively unimodal, but can respond to stimuli of different sensory modalities. However, several questions concerning the neural representation of cross-modal stimuli remain open. Indeed, it is poorly understood if cross-modal stimuli evoke unique or overlapping representations in a primary sensory cortex and whether learning can modulate these representations. Here we recorded single unit responses to auditory, visual, somatosensory, and olfactory stimuli in the gustatory cortex (GC) of alert rats before and after associative learning. We found that, in untrained rats, the majority of GC neurons were modulated by a single modality. Upon learning, both prevalence of cross-modal responsive neurons and their breadth of tuning increased, leading to a greater overlap of representations. Altogether, our results show that the gustatory cortex represents cross-modal stimuli according to their sensory identity, and that learning changes the overlap of cross-modal representations.

Knowledge synthesis with maps of neural connectivity.

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Tallis, Marcelo; Thompson, Richard; Russ, Thomas A; Burns, Gully A P C

2011-01-01

This paper describes software for neuroanatomical knowledge synthesis based on neural connectivity data. This software supports a mature methodology developed since the early 1990s. Over this time, the Swanson laboratory at USC has generated an account of the neural connectivity of the sub-structures of the hypothalamus, amygdala, septum, hippocampus, and bed nucleus of the stria terminalis. This is based on neuroanatomical data maps drawn into a standard brain atlas by experts. In earlier work, we presented an application for visualizing and comparing anatomical macro connections using the Swanson third edition atlas as a framework for accurate registration. Here we describe major improvements to the NeuARt application based on the incorporation of a knowledge representation of experimental design. We also present improvements in the interface and features of the data mapping components within a unified web-application. As a step toward developing an accurate sub-regional account of neural connectivity, we provide navigational access between the data maps and a semantic representation of area-to-area connections that they support. We do so based on an approach called "Knowledge Engineering from Experimental Design" (KEfED) model that is based on experimental variables. We have extended the underlying KEfED representation of tract-tracing experiments by incorporating the definition of a neuronanatomical data map as a measurement variable in the study design. This paper describes the software design of a web-application that allows anatomical data sets to be described within a standard experimental context and thus indexed by non-spatial experimental design features.

Knowledge synthesis with maps of neural connectivity

Directory of Open Access Journals (Sweden)

Marcelo eTallis

2011-11-01

Full Text Available This paper describes software for neuroanatomical knowledge synthesis based on high-quality neural connectivity data. This software supports a mature neuroanatomical methodology developed since the early 1990s. Over this time, the Swanson laboratory at USC has generated an account of the neural connectivity of the sub-structures of the hypothalamus, amygdala, septum, hippocampus and bed nucleus of the stria terminalis. This is based on neuroanatomical data maps drawn into a standard brain atlas by experts. In earlier work, we presented an application for visualizing and comparing anatomical macroconnections using the Swanson 3rd edition atlas as a framework for accurate registration. Here we describe major improvements to the NeuARt application based on the incorporation of a knowledge representation of experimental design. We also present improvements in the interface and features of the neuroanatomical data mapping components within a unified web-application. As a step towards developing an accurate sub-regional account of neural connectivity, we provide navigational access between the neuroanatomical data maps and a semantic representation of area-to-area connections that they support. We do so based on an approach called ’Knowledge Engineering from Experimental Design’ (KEfED model that is based on experimental variables. We have extended the underlying KEfED representation of tract-tracing experiments by incorporating the definition of a neuronanatomical data map as a measurement variable in the study design. This paper describes the software design of a web application that allows anatomical data sets to be described within a standard experimental context and thus incorporated with non-spatial data sets.

Neural Activity Patterns in the Human Brain Reflect Tactile Stickiness Perception

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Kim, Junsuk; Yeon, Jiwon; Ryu, Jaekyun; Park, Jang-Yeon; Chung, Soon-Cheol; Kim, Sung-Phil

2017-01-01

Our previous human fMRI study found brain activations correlated with tactile stickiness perception using the uni-variate general linear model (GLM) (Yeon et al., 2017). Here, we conducted an in-depth investigation on neural correlates of sticky sensations by employing a multivoxel pattern analysis (MVPA) on the same dataset. In particular, we statistically compared multi-variate neural activities in response to the three groups of sticky stimuli: A supra-threshold group including a set of sticky stimuli that evoked vivid sticky perception; an infra-threshold group including another set of sticky stimuli that barely evoked sticky perception; and a sham group including acrylic stimuli with no physically sticky property. Searchlight MVPAs were performed to search for local activity patterns carrying neural information of stickiness perception. Similar to the uni-variate GLM results, significant multi-variate neural activity patterns were identified in postcentral gyrus, subcortical (basal ganglia and thalamus), and insula areas (insula and adjacent areas). Moreover, MVPAs revealed that activity patterns in posterior parietal cortex discriminated the perceptual intensities of stickiness, which was not present in the uni-variate analysis. Next, we applied a principal component analysis (PCA) to the voxel response patterns within identified clusters so as to find low-dimensional neural representations of stickiness intensities. Follow-up clustering analyses clearly showed separate neural grouping configurations between the Supra- and Infra-threshold groups. Interestingly, this neural categorization was in line with the perceptual grouping pattern obtained from the psychophysical data. Our findings thus suggest that different stickiness intensities would elicit distinct neural activity patterns in the human brain and may provide a neural basis for the perception and categorization of tactile stickiness. PMID:28936171

Not my future? Core values and the neural representation of future events.

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Brosch, Tobias; Stussi, Yoann; Desrichard, Olivier; Sander, David

2018-06-01

Individuals with pronounced self-transcendence values have been shown to put greater weight on the long-term consequences of their actions when making decisions. Using functional magnetic resonance imaging, we investigated the neural mechanisms underlying the evaluation of events occurring several decades in the future as well as the role of core values in these processes. Thirty-six participants viewed a series of events, consisting of potential consequences of climate change, which could occur in the near future (around 2030), and thus would be experienced by the participants themselves, or in the far future (around 2080). We observed increased activation in anterior VMPFC (BA11), a region involved in encoding the personal significance of future events, when participants were envisioning far future events, demonstrating for the first time that the role of the VMPFC in future projection extends to the time scale of decades. Importantly, this activation increase was observed only in participants with pronounced self-transcendence values measured by self-report questionnaire, as shown by a statistically significant interaction of temporal distance and value structure. These findings suggest that future projection mechanisms are modulated by self-transcendence values to allow for a more extensive simulation of far future events. Consistent with this, these participants reported similar concern ratings for near and far future events, whereas participants with pronounced self-enhancement values were more concerned about near future events. Our findings provide a neural substrate for the tendency of individuals with pronounced self-transcendence values to consider the long-term consequences of their actions.

Emerging Object Representations in the Visual System Predict Reaction Times for Categorization

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Ritchie, J. Brendan; Tovar, David A.; Carlson, Thomas A.

2015-01-01

Recognizing an object takes just a fraction of a second, less than the blink of an eye. Applying multivariate pattern analysis, or “brain decoding”, methods to magnetoencephalography (MEG) data has allowed researchers to characterize, in high temporal resolution, the emerging representation of object categories that underlie our capacity for rapid recognition. Shortly after stimulus onset, object exemplars cluster by category in a high-dimensional activation space in the brain. In this emerging activation space, the decodability of exemplar category varies over time, reflecting the brain’s transformation of visual inputs into coherent category representations. How do these emerging representations relate to categorization behavior? Recently it has been proposed that the distance of an exemplar representation from a categorical boundary in an activation space is critical for perceptual decision-making, and that reaction times should therefore correlate with distance from the boundary. The predictions of this distance hypothesis have been born out in human inferior temporal cortex (IT), an area of the brain crucial for the representation of object categories. When viewed in the context of a time varying neural signal, the optimal time to “read out” category information is when category representations in the brain are most decodable. Here, we show that the distance from a decision boundary through activation space, as measured using MEG decoding methods, correlates with reaction times for visual categorization during the period of peak decodability. Our results suggest that the brain begins to read out information about exemplar category at the optimal time for use in choice behaviour, and support the hypothesis that the structure of the representation for objects in the visual system is partially constitutive of the decision process in recognition. PMID:26107634

Prior Knowledge about Objects Determines Neural Color Representation in Human Visual Cortex.

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Vandenbroucke, A R E; Fahrenfort, J J; Meuwese, J D I; Scholte, H S; Lamme, V A F

2016-04-01

To create subjective experience, our brain must translate physical stimulus input by incorporating prior knowledge and expectations. For example, we perceive color and not wavelength information, and this in part depends on our past experience with colored objects ( Hansen et al. 2006; Mitterer and de Ruiter 2008). Here, we investigated the influence of object knowledge on the neural substrates underlying subjective color vision. In a functional magnetic resonance imaging experiment, human subjects viewed a color that lay midway between red and green (ambiguous with respect to its distance from red and green) presented on either typical red (e.g., tomato), typical green (e.g., clover), or semantically meaningless (nonsense) objects. Using decoding techniques, we could predict whether subjects viewed the ambiguous color on typical red or typical green objects based on the neural response of veridical red and green. This shift of neural response for the ambiguous color did not occur for nonsense objects. The modulation of neural responses was observed in visual areas (V3, V4, VO1, lateral occipital complex) involved in color and object processing, as well as frontal areas. This demonstrates that object memory influences wavelength information relatively early in the human visual system to produce subjective color vision. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Natural language acquisition in large scale neural semantic networks

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Ealey, Douglas

This thesis puts forward the view that a purely signal- based approach to natural language processing is both plausible and desirable. By questioning the veracity of symbolic representations of meaning, it argues for a unified, non-symbolic model of knowledge representation that is both biologically plausible and, potentially, highly efficient. Processes to generate a grounded, neural form of this model-dubbed the semantic filter-are discussed. The combined effects of local neural organisation, coincident with perceptual maturation, are used to hypothesise its nature. This theoretical model is then validated in light of a number of fundamental neurological constraints and milestones. The mechanisms of semantic and episodic development that the model predicts are then used to explain linguistic properties, such as propositions and verbs, syntax and scripting. To mimic the growth of locally densely connected structures upon an unbounded neural substrate, a system is developed that can grow arbitrarily large, data- dependant structures composed of individual self- organising neural networks. The maturational nature of the data used results in a structure in which the perception of concepts is refined by the networks, but demarcated by subsequent structure. As a consequence, the overall structure shows significant memory and computational benefits, as predicted by the cognitive and neural models. Furthermore, the localised nature of the neural architecture also avoids the increasing error sensitivity and redundancy of traditional systems as the training domain grows. The semantic and episodic filters have been demonstrated to perform as well, or better, than more specialist networks, whilst using significantly larger vocabularies, more complex sentence forms and more natural corpora.

Short-term plasticity as a neural mechanism supporting memory and attentional functions.

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Jääskeläinen, Iiro P; Ahveninen, Jyrki; Andermann, Mark L; Belliveau, John W; Raij, Tommi; Sams, Mikko

2011-11-08

Based on behavioral studies, several relatively distinct perceptual and cognitive functions have been defined in cognitive psychology such as sensory memory, short-term memory, and selective attention. Here, we review evidence suggesting that some of these functions may be supported by shared underlying neuronal mechanisms. Specifically, we present, based on an integrative review of the literature, a hypothetical model wherein short-term plasticity, in the form of transient center-excitatory and surround-inhibitory modulations, constitutes a generic processing principle that supports sensory memory, short-term memory, involuntary attention, selective attention, and perceptual learning. In our model, the size and complexity of receptive fields/level of abstraction of neural representations, as well as the length of temporal receptive windows, increases as one steps up the cortical hierarchy. Consequently, the type of input (bottom-up vs. top down) and the level of cortical hierarchy that the inputs target, determine whether short-term plasticity supports purely sensory vs. semantic short-term memory or attentional functions. Furthermore, we suggest that rather than discrete memory systems, there are continuums of memory representations from short-lived sensory ones to more abstract longer-duration representations, such as those tapped by behavioral studies of short-term memory. Copyright © 2011 Elsevier B.V. All rights reserved.

Kernel Temporal Differences for Neural Decoding

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Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2015-01-01

We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504

An interpretable LSTM neural network for autoregressive exogenous model

OpenAIRE

Guo, Tian; Lin, Tao; Lu, Yao

2018-01-01

In this paper, we propose an interpretable LSTM recurrent neural network, i.e., multi-variable LSTM for time series with exogenous variables. Currently, widely used attention mechanism in recurrent neural networks mostly focuses on the temporal aspect of data and falls short of characterizing variable importance. To this end, our multi-variable LSTM equipped with tensorized hidden states is developed to learn variable specific representations, which give rise to both temporal and variable lev...

Representation of linguistic form and function in recurrent neural networks

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Kadar, Akos; Chrupala, Grzegorz; Alishahi, Afra

2017-01-01

We present novel methods for analyzing the activation patterns of recurrent neural networks from a linguistic point of view and explore the types of linguistic structure they learn. As a case study, we use a standard standalone language model, and a multi-task gated recurrent network architecture

Oscillatory neural representations in the sensory thalamus predict neuropathic pain relief by deep brain stimulation.

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Huang, Yongzhi; Green, Alexander L; Hyam, Jonathan; Fitzgerald, James; Aziz, Tipu Z; Wang, Shouyan

2018-01-01

Understanding the function of sensory thalamic neural activity is essential for developing and improving interventions for neuropathic pain. However, there is a lack of investigation of the relationship between sensory thalamic oscillations and pain relief in patients with neuropathic pain. This study aims to identify the oscillatory neural characteristics correlated with pain relief induced by deep brain stimulation (DBS), and develop a quantitative model to predict pain relief by integrating characteristic measures of the neural oscillations. Measures of sensory thalamic local field potentials (LFPs) in thirteen patients with neuropathic pain were screened in three dimensional feature space according to the rhythm, balancing, and coupling neural behaviours, and correlated with pain relief. An integrated approach based on principal component analysis (PCA) and multiple regression analysis is proposed to integrate the multiple measures and provide a predictive model. This study reveals distinct thalamic rhythms of theta, alpha, high beta and high gamma oscillations correlating with pain relief. The balancing and coupling measures between these neural oscillations were also significantly correlated with pain relief. The study enriches the series research on the function of thalamic neural oscillations in neuropathic pain and relief, and provides a quantitative approach for predicting pain relief by DBS using thalamic neural oscillations. Copyright © 2017 Elsevier Inc. All rights reserved.

Brain-to-text: Decoding spoken phrases from phone representations in the brain

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Christian eHerff

2015-06-01

Full Text Available It has long been speculated whether communication between humans and machines based on natural speech related cortical activity is possible. Over the past decade, studies have suggested that it is feasible to recognize isolated aspects of speech from neural signals, such as auditory features, phones or one of a few isolated words. However, until now it remained an unsolved challenge to decode continuously spoken speech from the neural substrate associated with speech and language processing. Here, we show for the first time that continuously spoken speech can be decoded into the expressed words from intracranial electrocorticographic (ECoG recordings. Specifically, we implemented a system, which we call Brain-To-Text that models single phones, employs techniques from automatic speech recognition (ASR, and thereby transforms brain activity while speaking into the corresponding textual representation. Our results demonstrate that our system achieved word error rates as low as 25% and phone error rates below 50%. Additionally, our approach contributes to the current understanding of the neural basis of continuous speech production by identifying those cortical regions that hold substantial information about individual phones. In conclusion, the Brain-To-Text system described in this paper represents an important step towards human-machine communication based on imagined speech.

Neural mechanisms of order information processing in working memory

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Barbara Dolenc

2013-11-01

Full Text Available The ability to encode and maintain the exact order of short sequences of stimuli or events is often crucial to our ability for effective high-order planning. However, it is not yet clear which neural mechanisms underpin this process. Several studies suggest that in comparison with item recognition temporal order coding activates prefrontal and parietal brain regions. Results of various studies tend to favour the hypothesis that the order of the stimuli is represented and encoded on several stages, from primacy and recency estimates to the exact position of the item in a sequence. Different brain regions play a different role in this process. Dorsolateral prefrontal cortex has a more general role in attention, while the premotor cortex is more involved in the process of information grouping. Parietal lobe and hippocampus also play a significant role in order processing as they enable the representation of distance. Moreover, order maintenance is associated with the existence of neural oscillators that operate at different frequencies. Electrophysiological studies revealed that theta and alpha oscillations play an important role in the maintenance of temporal order information. Those EEG oscillations are differentially associated with processes that support the maintenance of order information and item recognition. Various studies suggest a link between prefrontal areas and memory for temporal order, implying that EEG neural oscillations in the prefrontal cortex may play a role in the maintenance of information on temporal order.

More than Skin Deep: Body Representation beyond Primary Somatosensory Cortex

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Longo, Matthew R.; Azanon, Elena; Haggard, Patrick

2010-01-01

The neural circuits underlying initial sensory processing of somatic information are relatively well understood. In contrast, the processes that go beyond primary somatosensation to create more abstract representations related to the body are less clear. In this review, we focus on two classes of higher-order processing beyond Somatosensation.…

Rich Representations with Exposed Semantics for Deep Visual Reasoning

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2016-06-01

of a relationship between visual recognition, associative processing, and episodic memory and provides important clues into the neural mechanism...provides critical evidence of a relationship between visual recognition, associative processing, and episodic memory and provides important clues into...From - To) ;run.- ~01~ Final!Technical 4. TITLE AND SUBTITLE Sa. CONTRACT NUMBER Rich Representations with Exposed Semantics for Deep Visual

Recurrent Neural Network Approach Based on the Integral Representation of the Drazin Inverse.

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Stanimirović, Predrag S; Živković, Ivan S; Wei, Yimin

2015-10-01

In this letter, we present the dynamical equation and corresponding artificial recurrent neural network for computing the Drazin inverse for arbitrary square real matrix, without any restriction on its eigenvalues. Conditions that ensure the stability of the defined recurrent neural network as well as its convergence toward the Drazin inverse are considered. Several illustrative examples present the results of computer simulations.

Differential neural representation of oral ethanol by central taste-sensitive neurons in ethanol-preferring and genetically heterogeneous rats.

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Lemon, Christian H; Wilson, David M; Brasser, Susan M

2011-12-01

In randomly bred rats, orally applied ethanol stimulates neural substrates for appetitive sweet taste. To study associations between ethanol's oral sensory characteristics and genetically mediated ethanol preference, we made electrophysiological recordings of oral responses (spike density) by taste-sensitive nucleus tractus solitarii neurons in anesthetized selectively bred ethanol-preferring (P) rats and their genetically heterogeneous Wistar (W) control strain. Stimuli (25 total) included ethanol [3%, 5%, 10%, 15%, 25%, and 40% (vol/vol)], a sucrose series (0.01, 0.03, 0.1, 0.3, 0.5, and 1 M), and other sweet, salt, acidic, and bitter stimuli; 50 P and 39 W neurons were sampled. k-means clustering applied to the sucrose response series identified cells showing high (S(1)) or relatively low (S(0)) sensitivity to sucrose. A three-way factorial analysis revealed that activity to ethanol was influenced by a neuron's sensitivity to sucrose, ethanol concentration, and rat line (P = 0.01). Ethanol produced concentration-dependent responses in S(1) neurons that were larger than those in S(0) cells. Although responses to ethanol by S(1) cells did not differ between lines, neuronal firing rates to ethanol in S(0) cells increased across concentration only in P rats. Correlation and multivariate analyses revealed that ethanol evoked responses in W neurons that were strongly and selectively associated with activity to sweet stimuli, whereas responses to ethanol by P neurons were not easily associated with activity to representative sweet, sodium salt, acidic, or bitter stimuli. These findings show differential central neural representation of oral ethanol between genetically heterogeneous rats and P rats genetically selected to prefer alcohol.

Visual perception from the perspective of a representational, non-reductionistic, level-dependent account of perception and conscious awareness

DEFF Research Database (Denmark)

Overgaard, Morten; Mogensen, Jesper

2014-01-01

This article proposes a new model to interpret seemingly conflicting evidence concerning the correlation of consciousness and neural processes. Based on an analysis of research of blindsight and subliminal perception, the reorganization of elementary functions and consciousness framework suggests......’ of analysis in a ‘situational algorithmic strategy’ that reflects the general state of an individual. We argue that conscious experience is intrinsically related to representations that are available to guide behaviour. From this perspective, we find that blindsight and subliminal perception can be explained...

Representational Classroom Practices that Contribute to Students' Conceptual and Representational Understanding of Chemical Bonding

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Hilton, Annette; Nichols, Kim

2011-11-01

Understanding bonding is fundamental to success in chemistry. A number of alternative conceptions related to chemical bonding have been reported in the literature. Research suggests that many alternative conceptions held by chemistry students result from previous teaching; if teachers are explicit in the use of representations and explain their content-specific forms and functions, this might be avoided. The development of an understanding of and ability to use multiple representations is crucial to students' understanding of chemical bonding. This paper draws on data from a larger study involving two Year 11 chemistry classes (n = 27, n = 22). It explores the contribution of explicit instruction about multiple representations to students' understanding and representation of chemical bonding. The instructional strategies were documented using audio-recordings and the teacher-researcher's reflection journal. Pre-test-post-test comparisons showed an improvement in conceptual understanding and representational competence. Analysis of the students' texts provided further evidence of the students' ability to use multiple representations to explain macroscopic phenomena on the molecular level. The findings suggest that explicit instruction about representational form and function contributes to the enhancement of representational competence and conceptual understanding of bonding in chemistry. However, the scaffolding strategies employed by the teacher play an important role in the learning process. This research has implications for professional development enhancing teachers' approaches to these aspects of instruction around chemical bonding.

Learning of Multimodal Representations With Random Walks on the Click Graph.

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Wu, Fei; Lu, Xinyan; Song, Jun; Yan, Shuicheng; Zhang, Zhongfei Mark; Rui, Yong; Zhuang, Yueting

2016-02-01

In multimedia information retrieval, most classic approaches tend to represent different modalities of media in the same feature space. With the click data collected from the users' searching behavior, existing approaches take either one-to-one paired data (text-image pairs) or ranking examples (text-query-image and/or image-query-text ranking lists) as training examples, which do not make full use of the click data, particularly the implicit connections among the data objects. In this paper, we treat the click data as a large click graph, in which vertices are images/text queries and edges indicate the clicks between an image and a query. We consider learning a multimodal representation from the perspective of encoding the explicit/implicit relevance relationship between the vertices in the click graph. By minimizing both the truncated random walk loss as well as the distance between the learned representation of vertices and their corresponding deep neural network output, the proposed model which is named multimodal random walk neural network (MRW-NN) can be applied to not only learn robust representation of the existing multimodal data in the click graph, but also deal with the unseen queries and images to support cross-modal retrieval. We evaluate the latent representation learned by MRW-NN on a public large-scale click log data set Clickture and further show that MRW-NN achieves much better cross-modal retrieval performance on the unseen queries/images than the other state-of-the-art methods.

Neural substrates for semantic memory of familiar songs: is there an interface between lyrics and melodies?

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Saito, Yoko; Ishii, Kenji; Sakuma, Naoko; Kawasaki, Keiichi; Oda, Keiichi; Mizusawa, Hidehiro

2012-01-01

Findings on song perception and song production have increasingly suggested that common but partially distinct neural networks exist for processing lyrics and melody. However, the neural substrates of song recognition remain to be investigated. The purpose of this study was to examine the neural substrates involved in the accessing "song lexicon" as corresponding to a representational system that might provide links between the musical and phonological lexicons using positron emission tomography (PET). We exposed participants to auditory stimuli consisting of familiar and unfamiliar songs presented in three ways: sung lyrics (song), sung lyrics on a single pitch (lyrics), and the sung syllable 'la' on original pitches (melody). The auditory stimuli were designed to have equivalent familiarity to participants, and they were recorded at exactly the same tempo. Eleven right-handed nonmusicians participated in four conditions: three familiarity decision tasks using song, lyrics, and melody and a sound type decision task (control) that was designed to engage perceptual and prelexical processing but not lexical processing. The contrasts (familiarity decision tasks versus control) showed no common areas of activation between lyrics and melody. This result indicates that essentially separate neural networks exist in semantic memory for the verbal and melodic processing of familiar songs. Verbal lexical processing recruited the left fusiform gyrus and the left inferior occipital gyrus, whereas melodic lexical processing engaged the right middle temporal sulcus and the bilateral temporo-occipital cortices. Moreover, we found that song specifically activated the left posterior inferior temporal cortex, which may serve as an interface between verbal and musical representations in order to facilitate song recognition.

Understanding representations in design

DEFF Research Database (Denmark)

Bødker, Susanne

1998-01-01

Representing computer applications and their use is an important aspect of design. In various ways, designers need to externalize design proposals and present them to other designers, users, or managers. This article deals with understanding design representations and the work they do in design....... The article is based on a series of theoretical concepts coming out of studies of scientific and other work practices and on practical experiences from design of computer applications. The article presents alternatives to the ideas that design representations are mappings of present or future work situations...... and computer applications. It suggests that representations are primarily containers of ideas and that representation is situated at the same time as representations are crossing boundaries between various design and use activities. As such, representations should be carriers of their own contexts regarding...

Neural representation of anxiety and personality during exposure to anxiety-provoking and neutral scenes from scary movies.

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Straube, Thomas; Preissler, Sandra; Lipka, Judith; Hewig, Johannes; Mentzel, Hans-Joachim; Miltner, Wolfgang H R

2010-01-01

Some people search for intense sensations such as being scared by frightening movies while others do not. The brain mechanisms underlying such inter-individual differences are not clear. Testing theoretical models, we investigated neural correlates of anxiety and the personality trait sensation seeking in 40 subjects who watched threatening and neutral scenes from scary movies during functional magnetic resonance imaging. Threat versus neutral scenes induced increased activation in anterior cingulate cortex, insula, thalamus, and visual areas. Movie-induced anxiety correlated positively with activation in dorsomedial prefrontal cortex, indicating a role for this area in the subjective experience of being scared. Sensation seeking-scores correlated positively with brain activation to threat versus neutral scenes in visual areas and in thalamus and anterior insula, i.e. regions involved in the induction and representation of arousal states. For the insula and thalamus, these outcomes were partly due to an inverse relation between sensation seeking scores and brain activation during neutral film clips. These results support models predicting cerebral hypoactivation in high sensation seekers during neutral stimulation, which may be compensated by more intense sensations such as watching scary movies. 2009 Wiley-Liss, Inc.

Psychological and neural mechanisms of experimental extinction: a selective review.

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Delamater, Andrew R; Westbrook, R Frederick

2014-02-01

The present review examines key psychological concepts in the study of experimental extinction and implications these have for an understanding of the underlying neurobiology of extinction learning. We suggest that many of the signature characteristics of extinction learning (spontaneous recovery, renewal, reinstatement, rapid reacquisition) can be accommodated by the standard associative learning theory assumption that extinction results in partial erasure of the original learning together with new inhibitory learning. Moreover, we consider recent behavioral and neural evidence that supports the partial erasure view of extinction, but also note shortcomings in our understanding of extinction circuits as these relate to the negative prediction error concept. Recent work suggests that common prediction error and stimulus-specific prediction error terms both may be required to explain neural plasticity both in acquisition and extinction learning. In addition, we suggest that many issues in the content of extinction learning have not been fully addressed in current research, but that neurobiological approaches should be especially helpful in addressing such issues. These include questions about the nature of extinction learning (excitatory CS-No US, inhibitory CS-US learning, occasion setting processes), especially as this relates to studies of the micro-circuitry of extinction, as well as its representational content (sensory, motivational, response). An additional understudied problem in extinction research is the role played by attention processes and their underlying neural networks, although some research and theory converge on the idea that extinction is accompanied by attention decrements (i.e., habituation-like processes). Copyright © 2013 Elsevier Inc. All rights reserved.

Assessment of Multivariate Neural Time Series by Phase Synchrony Clustering in a Time-Frequency-Topography Representation

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M. A. Porta-Garcia

2018-01-01

Full Text Available Most EEG phase synchrony measures are of bivariate nature. Those that are multivariate focus on producing global indices of the synchronization state of the system. Thus, better descriptions of spatial and temporal local interactions are still in demand. A framework for characterization of phase synchrony relationships between multivariate neural time series is presented, applied either in a single epoch or over an intertrial assessment, relying on a proposed clustering algorithm, termed Multivariate Time Series Clustering by Phase Synchrony, which generates fuzzy clusters for each multivalued time sample and thereupon obtains hard clusters according to a circular variance threshold; such cluster modes are then depicted in Time-Frequency-Topography representations of synchrony state beyond mere global indices. EEG signals from P300 Speller sessions of four subjects were analyzed, obtaining useful insights of synchrony patterns related to the ERP and even revealing steady-state artifacts at 7.6 Hz. Further, contrast maps of Levenshtein Distance highlight synchrony differences between ERP and no-ERP epochs, mainly at delta and theta bands. The framework, which is not limited to one synchrony measure, allows observing dynamics of phase changes and interactions among channels and can be applied to analyze other cognitive states rather than ERP versus no ERP.

COMMUNICATION Designing a somatosensory neural prosthesis: percepts evoked by different patterns of thalamic stimulation

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Heming, Ethan; Sanden, Andrew; Kiss, Zelma H. T.

2010-12-01

Although major advances have been made in the development of motor prostheses, fine motor control requires intuitive somatosensory feedback. Here we explored whether a thalamic site for a somatosensory neural prosthetic could provide natural somatic sensation to humans. Different patterns of electrical stimulation (obtained from thalamic spike trains) were applied in patients undergoing deep brain stimulation surgery. Changes in pattern produced different sensations, while preserving somatotopic representation. While most percepts were reported as 'unnatural', some stimulations produced more 'natural' sensations than others. However, the additional patterns did not elicit more 'natural' percepts than high-frequency (333 Hz) electrical stimulation. These features suggest that despite some limitations, the thalamus may be a feasible site for a somatosensory neural prosthesis and different stimulation patterns may be useful in its development.

Neural Networks as a Tool for Georadar Data Processing

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Szymczyk Piotr

2015-12-01

Full Text Available In this article a new neural network based method for automatic classification of ground penetrating radar (GPR traces is proposed. The presented approach is based on a new representation of GPR signals by polynomials approximation. The coefficients of the polynomial (the feature vector are neural network inputs for automatic classification of a special kind of geologic structure—a sinkhole. The analysis and results show that the classifier can effectively distinguish sinkholes from other geologic structures.

A survey of visual preprocessing and shape representation techniques

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Olshausen, Bruno A.

1988-01-01

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention).

Neural Pattern Similarity in the Left IFG and Fusiform Is Associated with Novel Word Learning

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Jing Qu

2017-08-01

Full Text Available Previous studies have revealed that greater neural pattern similarity across repetitions is associated with better subsequent memory. In this study, we used an artificial language training paradigm and representational similarity analysis to examine whether neural pattern similarity across repetitions before training was associated with post-training behavioral performance. Twenty-four native Chinese speakers were trained to learn a logographic artificial language for 12 days and behavioral performance was recorded using the word naming and picture naming tasks. Participants were scanned while performing a passive viewing task before training, after 4-day training and after 12-day training. Results showed that pattern similarity in the left pars opercularis (PO and fusiform gyrus (FG before training was negatively associated with reaction time (RT in both word naming and picture naming tasks after training. These results suggest that neural pattern similarity is an effective neurofunctional predictor of novel word learning in addition to word memory.

Neural Pattern Similarity in the Left IFG and Fusiform Is Associated with Novel Word Learning

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Qu, Jing; Qian, Liu; Chen, Chuansheng; Xue, Gui; Li, Huiling; Xie, Peng; Mei, Leilei

2017-01-01

Previous studies have revealed that greater neural pattern similarity across repetitions is associated with better subsequent memory. In this study, we used an artificial language training paradigm and representational similarity analysis to examine whether neural pattern similarity across repetitions before training was associated with post-training behavioral performance. Twenty-four native Chinese speakers were trained to learn a logographic artificial language for 12 days and behavioral performance was recorded using the word naming and picture naming tasks. Participants were scanned while performing a passive viewing task before training, after 4-day training and after 12-day training. Results showed that pattern similarity in the left pars opercularis (PO) and fusiform gyrus (FG) before training was negatively associated with reaction time (RT) in both word naming and picture naming tasks after training. These results suggest that neural pattern similarity is an effective neurofunctional predictor of novel word learning in addition to word memory. PMID:28878640

Hybrid Histogram Descriptor: A Fusion Feature Representation for Image Retrieval.

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Feng, Qinghe; Hao, Qiaohong; Chen, Yuqi; Yi, Yugen; Wei, Ying; Dai, Jiangyan

2018-06-15

Currently, visual sensors are becoming increasingly affordable and fashionable, acceleratingly the increasing number of image data. Image retrieval has attracted increasing interest due to space exploration, industrial, and biomedical applications. Nevertheless, designing effective feature representation is acknowledged as a hard yet fundamental issue. This paper presents a fusion feature representation called a hybrid histogram descriptor (HHD) for image retrieval. The proposed descriptor comprises two histograms jointly: a perceptually uniform histogram which is extracted by exploiting the color and edge orientation information in perceptually uniform regions; and a motif co-occurrence histogram which is acquired by calculating the probability of a pair of motif patterns. To evaluate the performance, we benchmarked the proposed descriptor on RSSCN7, AID, Outex-00013, Outex-00014 and ETHZ-53 datasets. Experimental results suggest that the proposed descriptor is more effective and robust than ten recent fusion-based descriptors under the content-based image retrieval framework. The computational complexity was also analyzed to give an in-depth evaluation. Furthermore, compared with the state-of-the-art convolutional neural network (CNN)-based descriptors, the proposed descriptor also achieves comparable performance, but does not require any training process.

The Neural Representation of Goal-Directed Actions and Outcomes in the Ventral Striatum's Olfactory Tubercle

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Gadziola, Marie A.

2016-01-01

The ventral striatum is critical for evaluating reward information and the initiation of goal-directed behaviors. The many cellular, afferent, and efferent similarities between the ventral striatum's nucleus accumbens and olfactory tubercle (OT) suggests the distributed involvement of neurons within the ventral striatopallidal complex in motivated behaviors. Although the nucleus accumbens has an established role in representing goal-directed actions and their outcomes, it is not known whether this function is localized within the nucleus accumbens or distributed also within the OT. Answering such a fundamental question will expand our understanding of the neural mechanisms underlying motivated behaviors. Here we address whether the OT encodes natural reinforcers and serves as a substrate for motivational information processing. In recordings from mice engaged in a novel water-motivated instrumental task, we report that OT neurons modulate their firing rate during initiation and progression of the instrumental licking behavior, with some activity being internally generated and preceding the first lick. We further found that as motivational drive decreases throughout a session, the activity of OT neurons is enhanced earlier relative to the behavioral action. Additionally, OT neurons discriminate the types and magnitudes of fluid reinforcers. Together, these data suggest that the processing of reward information and the orchestration of goal-directed behaviors is a global principle of the ventral striatum and have important implications for understanding the neural systems subserving addiction and mood disorders. SIGNIFICANCE STATEMENT Goal-directed behaviors are widespread among animals and underlie complex behaviors ranging from food intake, social behavior, and even pathological conditions, such as gambling and drug addiction. The ventral striatum is a neural system critical for evaluating reward information and the initiation of goal-directed behaviors. Here we

ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation.

Science.gov (United States)

Hohman, Fred; Hodas, Nathan; Chau, Duen Horng

2017-05-01

Deep learning is the driving force behind many recent technologies; however, deep neural networks are often viewed as "black-boxes" due to their internal complexity that is hard to understand. Little research focuses on helping people explore and understand the relationship between a user's data and the learned representations in deep learning models. We present our ongoing work, ShapeShop, an interactive system for visualizing and understanding what semantics a neural network model has learned. Built using standard web technologies, ShapeShop allows users to experiment with and compare deep learning models to help explore the robustness of image classifiers.

ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation

Energy Technology Data Exchange (ETDEWEB)

Hohman, Frederick M.; Hodas, Nathan O.; Chau, Duen Horng

2017-05-30

Deep learning is the driving force behind many recent technologies; however, deep neural networks are often viewed as “black-boxes” due to their internal complexity that is hard to understand. Little research focuses on helping people explore and understand the relationship between a user’s data and the learned representations in deep learning models. We present our ongoing work, ShapeShop, an interactive system for visualizing and understanding what semantics a neural network model has learned. Built using standard web technologies, ShapeShop allows users to experiment with and compare deep learning models to help explore the robustness of image classifiers.

Combinatorial structures and processing in neural blackboard architectures

NARCIS (Netherlands)

van der Velde, Frank; van der Velde, Frank; de Kamps, Marc; Besold, Tarek R.; d'Avila Garcez, Artur; Marcus, Gary F.; Miikkulainen, Risto

2015-01-01

We discuss and illustrate Neural Blackboard Architectures (NBAs) as the basis for variable binding and combinatorial processing the brain. We focus on the NBA for sentence structure. NBAs are based on the notion that conceptual representations are in situ, hence cannot be copied or transported.

Neural systems language: a formal modeling language for the systematic description, unambiguous communication, and automated digital curation of neural connectivity.

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Brown, Ramsay A; Swanson, Larry W

2013-09-01

Systematic description and the unambiguous communication of findings and models remain among the unresolved fundamental challenges in systems neuroscience. No common descriptive frameworks exist to describe systematically the connective architecture of the nervous system, even at the grossest level of observation. Furthermore, the accelerating volume of novel data generated on neural connectivity outpaces the rate at which this data is curated into neuroinformatics databases to synthesize digitally systems-level insights from disjointed reports and observations. To help address these challenges, we propose the Neural Systems Language (NSyL). NSyL is a modeling language to be used by investigators to encode and communicate systematically reports of neural connectivity from neuroanatomy and brain imaging. NSyL engenders systematic description and communication of connectivity irrespective of the animal taxon described, experimental or observational technique implemented, or nomenclature referenced. As a language, NSyL is internally consistent, concise, and comprehensible to both humans and computers. NSyL is a promising development for systematizing the representation of neural architecture, effectively managing the increasing volume of data on neural connectivity and streamlining systems neuroscience research. Here we present similar precedent systems, how NSyL extends existing frameworks, and the reasoning behind NSyL's development. We explore NSyL's potential for balancing robustness and consistency in representation by encoding previously reported assertions of connectivity from the literature as examples. Finally, we propose and discuss the implications of a framework for how NSyL will be digitally implemented in the future to streamline curation of experimental results and bridge the gaps among anatomists, imagers, and neuroinformatics databases. Copyright © 2013 Wiley Periodicals, Inc.

The representation of order information in auditory-verbal short-term memory.

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Kalm, Kristjan; Norris, Dennis

2014-05-14

Here we investigate how order information is represented in auditory-verbal short-term memory (STM). We used fMRI and a serial recall task to dissociate neural activity patterns representing the phonological properties of the items stored in STM from the patterns representing their order. For this purpose, we analyzed fMRI activity patterns elicited by different item sets and different orderings of those items. These fMRI activity patterns were compared with the predictions made by positional and chaining models of serial order. The positional models encode associations between items and their positions in a sequence, whereas the chaining models encode associations between successive items and retain no position information. We show that a set of brain areas in the postero-dorsal stream of auditory processing store associations between items and order as predicted by a positional model. The chaining model of order representation generates a different pattern similarity prediction, which was shown to be inconsistent with the fMRI data. Our results thus favor a neural model of order representation that stores item codes, position codes, and the mapping between them. This study provides the first fMRI evidence for a specific model of order representation in the human brain. Copyright © 2014 the authors 0270-6474/14/346879-08$15.00/0.

Particle identification with neural networks using a rotational invariant moment representation

Science.gov (United States)

Sinkus, Ralph; Voss, Thomas

1997-02-01

A feed-forward neural network is used to identify electromagnetic particles based upon their showering properties within a segmented calorimeter. A preprocessing procedure is applied to the spatial energy distribution of the particle shower in order to account for the varying geometry of the calorimeter. The novel feature is the expansion of the energy distribution in terms of moments of the so-called Zernike functions which are invariant under rotation. The distributions of moments exhibit very different scales, thus the multidimensional input distribution for the neural network is transformed via a principal component analysis and rescaled by its respective variances to ensure input values of the order of one. This increases the sensitivity of the network and thus results in better performance in identifying and separating electromagnetic from hadronic particles, especially at low energies.

Particle identification with neural networks using a rotational invariant moment representation

International Nuclear Information System (INIS)

Sinkus, R.

1997-01-01

A feed-forward neural network is used to identify electromagnetic particles based upon their showering properties within a segmented calorimeter. A preprocessing procedure is applied to the spatial energy distribution of the particle shower in order to account for the varying geometry of the calorimeter. The novel feature is the expansion of the energy distribution in terms of moments of the so-called Zernike functions which are invariant under rotation. The distributions of moments exhibit very different scales, thus the multidimensional input distribution for the neural network is transformed via a principal component analysis and rescaled by its respective variances to ensure input values of the order of one. This increases the sensitivity of the network and thus results in better performance in identifying and separating electromagnetic from hadronic particles, especially at low energies. (orig.)

Differential representation of liver proteins in obese human subjects suggests novel biomarkers and promising targets for drug development in obesity.

Science.gov (United States)

Caira, Simonetta; Iannelli, Antonio; Sciarrillo, Rosaria; Picariello, Gianluca; Renzone, Giovanni; Scaloni, Andrea; Addeo, Pietro

2017-12-01

The proteome of liver biopsies from human obese (O) subjects has been compared to those of nonobese (NO) subjects using two-dimensional gel electrophoresis (2-DE). Differentially represented proteins were identified by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS)-based peptide mass fingerprinting (PMF) and nanoflow-liquid chromatography coupled to electrospray-tandem mass spectrometry (nLC-ESI-MS/MS). Overall, 61 gene products common to all of the liver biopsies were identified within 65 spots, among which 25 ones were differently represented between O and NO subjects. In particular, over-representation of short-chain acyl-CoA dehydrogenase, Δ(3,5)-Δ(2,4)dienoyl-CoA isomerase, acetyl-CoA acetyltransferase, glyoxylate reductase/hydroxypyruvate reductase, fructose-biphosphate aldolase B, peroxiredoxin I, protein DJ-1, catalase, α- and β-hemoglobin subunits, 3-mercaptopyruvate S-transferase, calreticulin, aminoacylase 1, phenazine biosynthesis-like domain-containing protein and a form of fatty acid-binding protein, together with downrepresentation of glutamate dehydrogenase, glutathione S-transferase A1, S-adenosylmethionine synthase 1A and a form of apolipoprotein A-I, was associated with the obesity condition. Some of these metabolic enzymes and antioxidant proteins have already been identified as putative diagnostic markers of liver dysfunction in animal models of steatosis or obesity, suggesting additional investigations on their role in these syndromes. Their differential representation in human liver was suggestive of their consideration as obesity human biomarkers and for the development of novel antiobesity drugs.

Specific neural traces for intonational discourse categories as revealed by human-evoked potentials.

Science.gov (United States)

Borràs-Comes, Joan; Costa-Faidella, Jordi; Prieto, Pilar; Escera, Carles

2012-04-01

The neural representation of segmental and tonal phonological distinctions has been shown by means of the MMN ERP, yet this is not the case for intonational discourse contrasts. In Catalan, a rising-falling intonational sequence can be perceived as a statement or as a counterexpectational question, depending exclusively on the size of the pitch range interval of the rising movement. We tested here, using the MMN, whether such categorical distinctions elicited distinct neurophysiological patterns of activity, supporting their specific neural representation. From a behavioral identification experiment, we set the boundary between the two categories and defined four stimuli across the continuum. Although the physical distance between each pair of stimuli was kept constant, the central pair represented an across-category contrast, whereas the other pairs represented within-category contrasts. These four auditory stimuli were contrasted by pairs in three different oddball blocks. The mean amplitude of the MMN was larger for the across-category contrast, suggesting that intonational contrasts in the target language can be encoded automatically in the auditory cortex. These results are in line with recent findings in other fields of linguistics, showing that, when a boundary between categories is crossed, the MMN response is not just larger but rather includes a separate subcomponent.

Neural encoding of the speech envelope by children with developmental dyslexia.

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Power, Alan J; Colling, Lincoln J; Mead, Natasha; Barnes, Lisa; Goswami, Usha

2016-09-01

Developmental dyslexia is consistently associated with difficulties in processing phonology (linguistic sound structure) across languages. One view is that dyslexia is characterised by a cognitive impairment in the "phonological representation" of word forms, which arises long before the child presents with a reading problem. Here we investigate a possible neural basis for developmental phonological impairments. We assess the neural quality of speech encoding in children with dyslexia by measuring the accuracy of low-frequency speech envelope encoding using EEG. We tested children with dyslexia and chronological age-matched (CA) and reading-level matched (RL) younger children. Participants listened to semantically-unpredictable sentences in a word report task. The sentences were noise-vocoded to increase reliance on envelope cues. Envelope reconstruction for envelopes between 0 and 10Hz showed that the children with dyslexia had significantly poorer speech encoding in the 0-2Hz band compared to both CA and RL controls. These data suggest that impaired neural encoding of low frequency speech envelopes, related to speech prosody, may underpin the phonological deficit that causes dyslexia across languages. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Using neural networks to describe tracer correlations

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D. J. Lary

2004-01-01

Full Text Available Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and methane volume mixing ratio (v.m.r.. In this study a neural network using Quickprop learning and one hidden layer with eight nodes was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. Such as the dataset from the Halogen Occultation Experiment (HALOE which has continuously observed CH4  (but not N2O from 1991 till the present. The neural network Fortran code used is available for download.

Effects of sport expertise on representational momentum during timing control.

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Nakamoto, Hiroki; Mori, Shiro; Ikudome, Sachi; Unenaka, Satoshi; Imanaka, Kuniyasu

2015-04-01

Sports involving fast visual perception require players to compensate for delays in neural processing of visual information. Memory for the final position of a moving object is distorted forward along its path of motion (i.e., "representational momentum," RM). This cognitive extrapolation of visual perception might compensate for the neural delay in interacting appropriately with a moving object. The present study examined whether experienced batters cognitively extrapolate the location of a fast-moving object and whether this extrapolation is associated with coincident timing control. Nine expert and nine novice baseball players performed a prediction motion task in which a target moved from one end of a straight 400-cm track at a constant velocity. In half of the trials, vision was suddenly occluded when the target reached the 200-cm point (occlusion condition). Participants had to press a button concurrently with the target arrival at the end of the track and verbally report their subjective assessment of the first target-occluded position. Experts showed larger RM magnitude (cognitive extrapolation) than did novices in the occlusion condition. RM magnitude and timing errors were strongly correlated in the fast velocity condition in both experts and novices, whereas in the slow velocity condition, a significant correlation appeared only in experts. This suggests that experts can cognitively extrapolate the location of a moving object according to their anticipation and, as a result, potentially circumvent neural processing delays. This process might be used to control response timing when interacting with moving objects.

P1-24: Neural Representation of Gloss in the Macaque Inferior Temporal Cortex

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Akiko Nishio

2012-10-01

Full Text Available The variation of the appearance such as gloss provides one of the most important information for object recognition. However, little is known about the neural mechanisms related to the perception of gloss. We examined whether the neurons in the inferior temporal (IT cortex of the monkeys are coding gloss of objects. We made visual stimuli which have various surface reflectance properties, and tested responses of IT neurons to these stimuli while a monkey was performing a visual fixation task. We found that there exist neurons in the lower bank of the superior temporal sulcus that selectively responded to specific stimuli. The selectivity was largely maintained when the object shape or illumination condition was changed. In contrast, neural selectivity was lost when the pixels of objects were randomly rearranged. In the former manipulation of the stimuli, gloss perceptions were maintained, whereas in the latter manipulation, gloss perception was dramatically changed. These results indicate that these IT neurons selectively responded to gloss, not to the irrelevant local image features or average luminance or color. Next, to understand how the responses of gloss selective neurons are related to perceived gloss, responses of gloss selective neurons were mapped in perceptual gloss space in which glossiness changes uniformly. I found that responses of most gloss selective neurons can be explained by linear combinations of two parameters that are shown to be important for gloss perception. This result suggests that the responses of gloss selective neurons of IT cortex are closely related to gloss perception.

Multiple neural states of representation in short-term memory? It's a matter of attention.

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Larocque, Joshua J; Lewis-Peacock, Jarrod A; Postle, Bradley R

2014-01-01

Short-term memory (STM) refers to the capacity-limited retention of information over a brief period of time, and working memory (WM) refers to the manipulation and use of that information to guide behavior. In recent years it has become apparent that STM and WM interact and overlap with other cognitive processes, including attention (the selection of a subset of information for further processing) and long-term memory (LTM-the encoding and retention of an effectively unlimited amount of information for a much longer period of time). Broadly speaking, there have been two classes of memory models: systems models, which posit distinct stores for STM and LTM (Atkinson and Shiffrin, 1968; Baddeley and Hitch, 1974); and state-based models, which posit a common store with different activation states corresponding to STM and LTM (Cowan, 1995; McElree, 1996; Oberauer, 2002). In this paper, we will focus on state-based accounts of STM. First, we will consider several theoretical models that postulate, based on considerable behavioral evidence, that information in STM can exist in multiple representational states. We will then consider how neural data from recent studies of STM can inform and constrain these theoretical models. In the process we will highlight the inferential advantage of multivariate, information-based analyses of neuroimaging data (fMRI and electroencephalography (EEG)) over conventional activation-based analysis approaches (Postle, in press). We will conclude by addressing lingering questions regarding the fractionation of STM, highlighting differences between the attention to information vs. the retention of information during brief memory delays.

Multiple neural states of representation in short-term memory? It's a matter of attention

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Joshua J Larocque

2014-01-01

Full Text Available Short-term memory (STM refers to the capacity-limited retention of information over a brief period of time, and working memory (WM refers to the manipulation and use of that information to guide behavior. In recent years it has become apparent that STM and WM interact and overlap with other cognitive processes, including attention (the selection of a subset of information for further processing and long-term memory (LTM – the encoding and retention of an effectively unlimited amount of information for a much longer period of time. Broadly speaking, there have been two classes of memory models: systems models, which posit distinct stores for STM and LTM (Atkinson & Shiffrin, 1968; Baddeley & Hitch, 1974; and state-based models, which posit a common store with different activation states corresponding to STM and LTM (Cowan, 1995; McElree, 1996; Oberauer, 2002. In this paper, we will focus on state-based accounts of STM. First, we will consider several theoretical models that postulate, based on considerable behavioral evidence, that information in STM can exist in multiple representational states. We will then consider how neural data from recent studies of STM can inform and constrain these theoretical models. In the process we will highlight the inferential advantage of multivariate, information-based analyses of neuroimaging data (fMRI and EEG over conventional activation-based analysis approaches (Postle, in press. We will conclude by addressing lingering questions regarding the fractionation of STM, highlighting differences between the attention to information vs. the retention of information during brief memory delays.

The neural basis of speech sound discrimination from infancy to adulthood

OpenAIRE

Partanen, Eino

2013-01-01

Rapid processing of speech is facilitated by neural representations of native language phonemes. However, some disorders and developmental conditions, such as developmental dyslexia, can hamper the development of these neural memory traces, leading to language delays and poor academic achievement. While the early identification of such deficits is paramount so that interventions can be started as early as possible, there is currently no systematically used ecologically valid paradigm for the ...

The effectiveness of cognitive- behavior therapy on illness representations of multiple-sclerosis and improving their emotional states

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Farhad Hazhir

2012-01-01

Full Text Available Background: Illness representations (based on Leventhal's model are associated with chronic illness outcomes. It has been suggested that targeting these cognitive components improves illness outcomes. Multiple sclerosis is a common disorder between neural and immune systems that creates physical and psychological consequences. There are few pre psychological trails on these patients. The aim of this study was to determine effectiveness of cognitive-behavior therapy on altering illness representations and improving emotional states of the patients.Methods: By using a randomized controlled trial design, among 52 selected patients, 35 volunteers randomly were allocated into intervention and control groups. An extensive interventional cognitive behavior therapy based package was conducted to intervention group in 10 weekly sessions. The control group stayed in waiting list and participated in 5 group meeting sessions. (IPQR and (DASS-42 psychological scales were administered, Leven and T statistical tests were applied for dat analysis.Results: The results showed positive changes in four illness representation components of patients including illness (identity, consequences, coherence and personal control. Associated improvement occurred in depression, anxiety, stress and emotional representations.Conclusion: Mooney and Padeskey's theoretically based cognitive-behavior therapy, is effective on illness representations modification and improving emotional states of the patients. The findings are less similar to Goodman's trial on Systemic Lupus Erythematosus patients and more similar to Petrie's trail on cardiac patients.

Neural modeling of prefrontal executive function

Energy Technology Data Exchange (ETDEWEB)

Levine, D.S. [Univ. of Texas, Arlington, TX (United States)

1996-12-31

Brain executive function is based in a distributed system whereby prefrontal cortex is interconnected with other cortical. and subcortical loci. Executive function is divided roughly into three interacting parts: affective guidance of responses; linkage among working memory representations; and forming complex behavioral schemata. Neural network models of each of these parts are reviewed and fit into a preliminary theoretical framework.

Deciphering Neural Codes of Memory during Sleep

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Chen, Zhe; Wilson, Matthew A.

2017-01-01

Memories of experiences are stored in the cerebral cortex. Sleep is critical for consolidating hippocampal memory of wake experiences into the neocortex. Understanding representations of neural codes of hippocampal-neocortical networks during sleep would reveal important circuit mechanisms on memory consolidation, and provide novel insights into memory and dreams. Although sleep-associated ensemble spike activity has been investigated, identifying the content of memory in sleep remains challenging. Here, we revisit important experimental findings on sleep-associated memory (i.e., neural activity patterns in sleep that reflect memory processing) and review computational approaches for analyzing sleep-associated neural codes (SANC). We focus on two analysis paradigms for sleep-associated memory, and propose a new unsupervised learning framework (“memory first, meaning later”) for unbiased assessment of SANC. PMID:28390699

Brain correlates of mathematical competence in processing mathematical representations

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Roland H. Grabner

2011-11-01

Full Text Available The ability to extract numerical information from different representation formats (e.g., equations, tables, or diagrams is a key component of mathematical competence but little is known about its neural correlate. Previous studies comparing mathematically less and more competent adults have focused on mental arithmetic and reported differences in left angular gyrus activity which were interpreted to reflect differential reliance on arithmetic fact retrieval during problem solving. The aim of the present functional magnetic resonance imaging (fMRI study was to investigate the brain correlates of mathematical competence in a task requiring the processing of typical mathematical representations. Twenty-eight adults of lower and higher mathematical competence worked on a representation matching task in which they had to evaluate whether the numerical information of a symbolic equation matches that of a bar chart. Two task conditions without and one condition with arithmetic demands were administered. Both competence groups performed equally well in the non-arithmetic conditions and only differed in accuracy in the condition requiring calculation. Activation contrasts between the groups revealed consistently stronger left angular gyrus activation in the more competent individuals across all three task conditions. The finding of competence-related activation differences independently of arithmetic demands suggests that more and less competent individuals differ in a cognitive process other than arithmetic fact retrieval. Specifically, it is argued that the stronger left angular gyrus activity in the more competent adults may reflect their higher proficiency in processing mathematical symbols. Moreover, the study demonstrates competence-related parietal activation differences that were not accompanied by differential experimental performance.

An Ensemble of Neural Networks for Stock Trading Decision Making

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Chang, Pei-Chann; Liu, Chen-Hao; Fan, Chin-Yuan; Lin, Jun-Lin; Lai, Chih-Ming

Stock turning signals detection are very interesting subject arising in numerous financial and economic planning problems. In this paper, Ensemble Neural Network system with Intelligent Piecewise Linear Representation for stock turning points detection is presented. The Intelligent piecewise linear representation method is able to generate numerous stocks turning signals from the historic data base, then Ensemble Neural Network system will be applied to train the pattern and retrieve similar stock price patterns from historic data for training. These turning signals represent short-term and long-term trading signals for selling or buying stocks from the market which are applied to forecast the future turning points from the set of test data. Experimental results demonstrate that the hybrid system can make a significant and constant amount of profit when compared with other approaches using stock data available in the market.

The protection of warranties and representations

International Nuclear Information System (INIS)

Spence, C.D.; Thusoo, N.

1999-01-01

Most acquisition contracts within the oil and gas industry consist of representations and warranties. The legal distinction between representations and warranties was explained as follows: a representation is a statement of fact made by the representor before making the contract, but a warranty is a statement of fact which forms part of the terms of the contract. The paper outlines the nature of a representation or warranty and explains why certain warranties are not given. The protection offered by representations and warranties in breach of contract cases is also explained. Suggestions are offered for increasing protection by representations and warranties. 22 refs

From sensation to percept: the neural signature of auditory event-related potentials.

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Joos, Kathleen; Gilles, Annick; Van de Heyning, Paul; De Ridder, Dirk; Vanneste, Sven

2014-05-01

An external auditory stimulus induces an auditory sensation which may lead to a conscious auditory perception. Although the sensory aspect is well known, it is still a question how an auditory stimulus results in an individual's conscious percept. To unravel the uncertainties concerning the neural correlates of a conscious auditory percept, event-related potentials may serve as a useful tool. In the current review we mainly wanted to shed light on the perceptual aspects of auditory processing and therefore we mainly focused on the auditory late-latency responses. Moreover, there is increasing evidence that perception is an active process in which the brain searches for the information it expects to be present, suggesting that auditory perception requires the presence of both bottom-up, i.e. sensory and top-down, i.e. prediction-driven processing. Therefore, the auditory evoked potentials will be interpreted in the context of the Bayesian brain model, in which the brain predicts which information it expects and when this will happen. The internal representation of the auditory environment will be verified by sensation samples of the environment (P50, N100). When this incoming information violates the expectation, it will induce the emission of a prediction error signal (Mismatch Negativity), activating higher-order neural networks and inducing the update of prior internal representations of the environment (P300). Copyright © 2014 Elsevier Ltd. All rights reserved.

Directing spatial attention to locations within remembered and imagined mental representations

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Simon G Gosling

2013-04-01

Full Text Available Spatial attention enables us to enhance the processing of items at target locations, at the expense of items presented at irrelevant locations. Many studies have explored the neural correlates of these spatial biases using event-related potentials. More recently some studies have shown that these ERP correlates are also present when subjects search visual short-term memory. This suggests firstly that this type of mental representation retains a spatial organisation that is based upon that of the original percept, and secondly that these attentional biases are flexible and can act to modulate remembered as well as perceptual representations. We aimed to test whether it was necessary for subjects to have actually seen the memoranda at those spatial locations, or whether simply imagining the spatial layout was sufficient to elicit the spatial attention effects. On some trials subjects performed a ‘visual’ search of an array held in visual short-term memory, and upon other trials subjects imagined the items at those spatial locations. We found ERP markers of spatial attention in both the memory-search and the imagery-search conditions. However, there were differences between the conditions, the effect in the memory-search began earlier and included posterior electrode sites. By contrast the ERP effect in the imagery-search condition was apparent only over fronto-central electrode sites and emerged slightly later. Nonetheless, our data demonstrate that it is not necessary for subjects to have ever seen the items at spatial locations for neural markers of spatial attention to be elicited; searching an imaginary spatial layout also triggers spatially-specific attention effects in the ERP data.

Morphing between expressions dissociates continuous from categorical representations of facial expression in the human brain

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Harris, Richard J.; Young, Andrew W.; Andrews, Timothy J.

2012-01-01

Whether the brain represents facial expressions as perceptual continua or as emotion categories remains controversial. Here, we measured the neural response to morphed images to directly address how facial expressions of emotion are represented in the brain. We found that face-selective regions in the posterior superior temporal sulcus and the amygdala responded selectively to changes in facial expression, independent of changes in identity. We then asked whether the responses in these regions reflected categorical or continuous neural representations of facial expression. Participants viewed images from continua generated by morphing between faces posing different expressions such that the expression could be the same, could involve a physical change but convey the same emotion, or could differ by the same physical amount but be perceived as two different emotions. We found that the posterior superior temporal sulcus was equally sensitive to all changes in facial expression, consistent with a continuous representation. In contrast, the amygdala was only sensitive to changes in expression that altered the perceived emotion, demonstrating a more categorical representation. These results offer a resolution to the controversy about how facial expression is processed in the brain by showing that both continuous and categorical representations underlie our ability to extract this important social cue. PMID:23213218

SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs.

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Jamil Ahmad

Full Text Available Medical image collections contain a wealth of information which can assist radiologists and medical experts in diagnosis and disease detection for making well-informed decisions. However, this objective can only be realized if efficient access is provided to semantically relevant cases from the ever-growing medical image repositories. In this paper, we present an efficient method for representing medical images by incorporating visual saliency and deep features obtained from a fine-tuned convolutional neural network (CNN pre-trained on natural images. Saliency detector is employed to automatically identify regions of interest like tumors, fractures, and calcified spots in images prior to feature extraction. Neuronal activation features termed as neural codes from different CNN layers are comprehensively studied to identify most appropriate features for representing radiographs. This study revealed that neural codes from the last fully connected layer of the fine-tuned CNN are found to be the most suitable for representing medical images. The neural codes extracted from the entire image and salient part of the image are fused to obtain the saliency-injected neural codes (SiNC descriptor which is used for indexing and retrieval. Finally, locality sensitive hashing techniques are applied on the SiNC descriptor to acquire short binary codes for allowing efficient retrieval in large scale image collections. Comprehensive experimental evaluations on the radiology images dataset reveal that the proposed framework achieves high retrieval accuracy and efficiency for scalable image retrieval applications and compares favorably with existing approaches.

Neural substrates for semantic memory of familiar songs: is there an interface between lyrics and melodies?

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Yoko Saito

Full Text Available Findings on song perception and song production have increasingly suggested that common but partially distinct neural networks exist for processing lyrics and melody. However, the neural substrates of song recognition remain to be investigated. The purpose of this study was to examine the neural substrates involved in the accessing "song lexicon" as corresponding to a representational system that might provide links between the musical and phonological lexicons using positron emission tomography (PET. We exposed participants to auditory stimuli consisting of familiar and unfamiliar songs presented in three ways: sung lyrics (song, sung lyrics on a single pitch (lyrics, and the sung syllable 'la' on original pitches (melody. The auditory stimuli were designed to have equivalent familiarity to participants, and they were recorded at exactly the same tempo. Eleven right-handed nonmusicians participated in four conditions: three familiarity decision tasks using song, lyrics, and melody and a sound type decision task (control that was designed to engage perceptual and prelexical processing but not lexical processing. The contrasts (familiarity decision tasks versus control showed no common areas of activation between lyrics and melody. This result indicates that essentially separate neural networks exist in semantic memory for the verbal and melodic processing of familiar songs. Verbal lexical processing recruited the left fusiform gyrus and the left inferior occipital gyrus, whereas melodic lexical processing engaged the right middle temporal sulcus and the bilateral temporo-occipital cortices. Moreover, we found that song specifically activated the left posterior inferior temporal cortex, which may serve as an interface between verbal and musical representations in order to facilitate song recognition.

Commonalities and differences in the neural representations of English, Portuguese, and Mandarin sentences: When knowledge of the brain-language mappings for two languages is better than one.

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Yang, Ying; Wang, Jing; Bailer, Cyntia; Cherkassky, Vladimir; Just, Marcel Adam

2017-12-01

This study extended cross-language semantic decoding (based on a concept's fMRI signature) to the decoding of sentences across three different languages (English, Portuguese and Mandarin). A classifier was trained on either the mapping between words and activation patterns in one language or the mappings in two languages (using an equivalent amount of training data), and then tested on its ability to decode the semantic content of a third language. The model trained on two languages was reliably more accurate than a classifier trained on one language for all three pairs of languages. This two-language advantage was selective to abstract concept domains such as social interactions and mental activity. Representational Similarity Analyses (RSA) of the inter-sentence neural similarities resulted in similar clustering of sentences in all the three languages, indicating a shared neural concept space among languages. These findings identify semantic domains that are common across these three languages versus those that are more language or culture-specific. Copyright © 2017 Elsevier Inc. All rights reserved.

Response of neural reward regions to food cues in autism spectrum disorders

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Cascio Carissa J

2012-05-01

Full Text Available Abstract Background One hypothesis for the social deficits that characterize autism spectrum disorders (ASD is diminished neural reward response to social interaction and attachment. Prior research using established monetary reward paradigms as a test of non-social reward to compare with social reward may involve confounds in the ability of individuals with ASD to utilize symbolic representation of money and the abstraction required to interpret monetary gains. Thus, a useful addition to our understanding of neural reward circuitry in ASD includes a characterization of the neural response to primary rewards. Method We asked 17 children with ASD and 18 children without ASD to abstain from eating for at least four hours before an MRI scan in which they viewed images of high-calorie foods. We assessed the neural reward network for increases in the blood oxygenation level dependent (BOLD signal in response to the food images Results We found very similar patterns of increased BOLD signal to these images in the two groups; both groups showed increased BOLD signal in the bilateral amygdala, as well as in the nucleus accumbens, orbitofrontal cortex, and insula. Direct group comparisons revealed that the ASD group showed a stronger response to food cues in bilateral insula along the anterior-posterior gradient and in the anterior cingulate cortex than the control group, whereas there were no neural reward regions that showed higher activation for controls than for ASD. Conclusion These results suggest that neural response to primary rewards is not diminished but in fact shows an aberrant enhancement in children with ASD.

Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations

OpenAIRE

Harradon, Michael; Druce, Jeff; Ruttenberg, Brian

2018-01-01

Deep neural networks are complex and opaque. As they enter application in a variety of important and safety critical domains, users seek methods to explain their output predictions. We develop an approach to explaining deep neural networks by constructing causal models on salient concepts contained in a CNN. We develop methods to extract salient concepts throughout a target network by using autoencoders trained to extract human-understandable representations of network activations. We then bu...

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

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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.

Preserving information in neural transmission.

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Sincich, Lawrence C; Horton, Jonathan C; Sharpee, Tatyana O

2009-05-13

Along most neural pathways, the spike trains transmitted from one neuron to the next are altered. In the process, neurons can either achieve a more efficient stimulus representation, or extract some biologically important stimulus parameter, or succeed at both. We recorded the inputs from single retinal ganglion cells and the outputs from connected lateral geniculate neurons in the macaque to examine how visual signals are relayed from retina to cortex. We found that geniculate neurons re-encoded multiple temporal stimulus features to yield output spikes that carried more information about stimuli than was available in each input spike. The coding transformation of some relay neurons occurred with no decrement in information rate, despite output spike rates that averaged half the input spike rates. This preservation of transmitted information was achieved by the short-term summation of inputs that geniculate neurons require to spike. A reduced model of the retinal and geniculate visual responses, based on two stimulus features and their associated nonlinearities, could account for >85% of the total information available in the spike trains and the preserved information transmission. These results apply to neurons operating on a single time-varying input, suggesting that synaptic temporal integration can alter the temporal receptive field properties to create a more efficient representation of visual signals in the thalamus than the retina.

Training data representation in a neural based robot position estimation system

International Nuclear Information System (INIS)

Taraglio, S.; Di Fonzo, F.; Burrascano, P.

1997-03-01

The vision subsystem of an autonomous vehicle is studies. It is based on a multi layer perceptron that uses TV images to estimate the position of the vehicle. A comparative study of the effects of output data representation and input data processing is presented and discussed

Neural Networks

International Nuclear Information System (INIS)

Smith, Patrick I.

2003-01-01

Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

Selective Attention to Auditory Memory Neurally Enhances Perceptual Precision.

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Lim, Sung-Joo; Wöstmann, Malte; Obleser, Jonas

2015-12-09

Selective attention to a task-relevant stimulus facilitates encoding of that stimulus into a working memory representation. It is less clear whether selective attention also improves the precision of a stimulus already represented in memory. Here, we investigate the behavioral and neural dynamics of selective attention to representations in auditory working memory (i.e., auditory objects) using psychophysical modeling and model-based analysis of electroencephalographic signals. Human listeners performed a syllable pitch discrimination task where two syllables served as to-be-encoded auditory objects. Valid (vs neutral) retroactive cues were presented during retention to allow listeners to selectively attend to the to-be-probed auditory object in memory. Behaviorally, listeners represented auditory objects in memory more precisely (expressed by steeper slopes of a psychometric curve) and made faster perceptual decisions when valid compared to neutral retrocues were presented. Neurally, valid compared to neutral retrocues elicited a larger frontocentral sustained negativity in the evoked potential as well as enhanced parietal alpha/low-beta oscillatory power (9-18 Hz) during memory retention. Critically, individual magnitudes of alpha oscillatory power (7-11 Hz) modulation predicted the degree to which valid retrocues benefitted individuals' behavior. Our results indicate that selective attention to a specific object in auditory memory does benefit human performance not by simply reducing memory load, but by actively engaging complementary neural resources to sharpen the precision of the task-relevant object in memory. Can selective attention improve the representational precision with which objects are held in memory? And if so, what are the neural mechanisms that support such improvement? These issues have been rarely examined within the auditory modality, in which acoustic signals change and vanish on a milliseconds time scale. Introducing a new auditory memory

Neural imaginaries and clinical epistemology: Rhetorically mapping the adolescent brain in the clinical encounter.

Science.gov (United States)

Buchbinder, Mara

2015-10-01

The social work of brain images has taken center stage in recent theorizing of the intersections between neuroscience and society. However, neuroimaging is only one of the discursive modes through which public representations of neurobiology travel. This article adopts an expanded view toward the social implications of neuroscientific thinking to examine how neural imaginaries are constructed in the absence of visual evidence. Drawing on ethnographic fieldwork conducted over 18 months (2008-2009) in a United States multidisciplinary pediatric pain clinic, I examine the pragmatic clinical work undertaken to represent ambiguous symptoms in neurobiological form. Focusing on one physician, I illustrate how, by rhetorically mapping the brain as a therapeutic tool, she engaged in a distinctive form of representation that I call neural imagining. In shifting my focus away from the purely material dimensions of brain images, I juxtapose the cultural work of brain scanning technologies with clinical neural imaginaries in which the teenage brain becomes a space of possibility, not to map things as they are, but rather, things as we hope they might be. These neural imaginaries rely upon a distinctive clinical epistemology that privileges the creative work of the imagination over visualization technologies in revealing the truths of the body. By creating a therapeutic space for adolescents to exercise their imaginative faculties and a discursive template for doing so, neural imagining relocates adolescents' agency with respect to epistemologies of bodily knowledge and the role of visualization practices therein. In doing so, it provides a more hopeful alternative to the dominant popular and scientific representations of the teenage brain that view it primarily through the lens of pathology. Copyright © 2014 Elsevier Ltd. All rights reserved.

Bidirectional Joint Representation Learning with Symmetrical Deep Neural Networks for Multimodal and Crossmodal Applications

OpenAIRE

Vukotic , Vedran; Raymond , Christian; Gravier , Guillaume

2016-01-01

International audience; Common approaches to problems involving multiple modalities (classification, retrieval, hyperlinking, etc.) are early fusion of the initial modalities and crossmodal translation from one modality to the other. Recently, deep neural networks, especially deep autoencoders, have proven promising both for crossmodal translation and for early fusion via multimodal embedding. In this work, we propose a flexible cross-modal deep neural network architecture for multimodal and ...

Neural predictors of sensorimotor adaptation rate and savings.

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Cassady, Kaitlin; Ruitenberg, Marit; Koppelmans, Vincent; Reuter-Lorenz, Patricia; De Dios, Yiri; Gadd, Nichole; Wood, Scott; Riascos Castenada, Roy; Kofman, Igor; Bloomberg, Jacob; Mulavara, Ajitkumar; Seidler, Rachael

2018-04-01

In this study, we investigate whether individual variability in the rate of visuomotor adaptation and multiday savings is associated with differences in regional gray matter volume and resting-state functional connectivity. Thirty-four participants performed a manual adaptation task during two separate test sessions, on average 9 days apart. Functional connectivity strength between sensorimotor, dorsal cingulate, and temporoparietal regions of the brain was found to predict the rate of learning during the early phase of the adaptation task. In contrast, default mode network connectivity strength was found to predict both the rate of learning during the late adaptation phase and savings. As for structural predictors, greater gray matter volume in temporoparietal and occipital regions predicted faster early learning, whereas greater gray matter volume in superior posterior regions of the cerebellum predicted faster late learning. These findings suggest that the offline neural predictors of early adaptation may facilitate the cognitive aspects of sensorimotor adaptation, supported by the involvement of temporoparietal and cingulate networks. The offline neural predictors of late adaptation and savings, including the default mode network and the cerebellum, likely support the storage and modification of newly acquired sensorimotor representations. © 2017 Wiley Periodicals, Inc.

Differential dorsal and ventral medial prefrontal representations of the implicit self modulated by individualism and collectivism: An fMRI study.

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Harada, Tokiko; Li, Zhang; Chiao, Joan Y

2010-01-01

Individualism and collectivism, or self-construal style, refer to cultural values that influence how people think about themselves and their relation to the social and physical environment. Recent neuroimaging evidence suggests that cultural values of individualism and collectivism dynamically modulate neural response within cortical midline structures, such as the medial prefrontal cortex (MPFC) and posterior cingulate cortex (PCC), during explicit self-evaluation. However, it remains unknown whether cultural priming modulates neural response during self-evaluation due to explicit task demands. Here we investigated how cultural priming of self-construal style affects neural activity within cortical midline structures during implicit self-evaluation in bicultural individuals. Results indicate that ventral MPFC showed relatively less deactivation during implicit evaluation of both self- and father-relevant information as compared to control condition (e.g., information of an unfamiliar person), irrespective of cultural priming. By contrast, dorsal MPFC showed relatively less deactivation during implicit evaluation of father-relevant information, but not self-relevant information, as compared to control condition, only when they were primed with individualism. Furthermore, dorsal MPFC showed relatively less deactivation during implicit evaluation of father-relevant information as compared to self-relevant condition only when they were primed with individualism. Hence, our results indicate that cultural priming modulates neural response within dorsal, but not ventral, portions of MPFC in a stimulus-driven rather than task-driven manner. More broadly, these findings suggest that cultural values dynamically shape neural representations during the evaluation, rather than the detection, of self-relevant information.

Neural Correlates of Visual Short-term Memory Dissociate between Fragile and Working Memory Representations.

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Vandenbroucke, Annelinde R E; Sligte, Ilja G; de Vries, Jade G; Cohen, Michael X; Lamme, Victor A F

2015-12-01

Evidence is accumulating that the classic two-stage model of visual STM (VSTM), comprising iconic memory (IM) and visual working memory (WM), is incomplete. A third memory stage, termed fragile VSTM (FM), seems to exist in between IM and WM [Vandenbroucke, A. R. E., Sligte, I. G., & Lamme, V. A. F. Manipulations of attention dissociate fragile visual STM from visual working memory. Neuropsychologia, 49, 1559-1568, 2011; Sligte, I. G., Scholte, H. S., & Lamme, V. A. F. Are there multiple visual STM stores? PLoS One, 3, e1699, 2008]. Although FM can be distinguished from IM using behavioral and fMRI methods, the question remains whether FM is a weak expression of WM or a separate form of memory with its own neural signature. Here, we tested whether FM and WM in humans are supported by dissociable time-frequency features of EEG recordings. Participants performed a partial-report change detection task, from which individual differences in FM and WM capacity were estimated. These individual FM and WM capacities were correlated with time-frequency characteristics of the EEG signal before and during encoding and maintenance of the memory display. FM capacity showed negative alpha correlations over peri-occipital electrodes, whereas WM capacity was positively related, suggesting increased visual processing (lower alpha) to be related to FM capacity. Furthermore, FM capacity correlated with an increase in theta power over central electrodes during preparation and processing of the memory display, whereas WM did not. In addition to a difference in visual processing characteristics, a positive relation between gamma power and FM capacity was observed during both preparation and maintenance periods of the task. On the other hand, we observed that theta-gamma coupling was negatively correlated with FM capacity, whereas it was slightly positively correlated with WM. These data show clear differences in the neural substrates of FM versus WM and suggest that FM depends more on

Neural overlap of L1 and L2 semantic representations in speech : A decoding approach

NARCIS (Netherlands)

Van De Putte, Eowyn; De Baene, W.; Brass, Marcel; Duyck, Wouter

2017-01-01

Although research has now converged towards a consensus that both languages of a bilingual are represented in at least partly shared systems for language comprehension, it remains unclear whether both languages are represented in the same neural populations for production. We investigated the neural

Similar representations of emotions across faces and voices.

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Kuhn, Lisa Katharina; Wydell, Taeko; Lavan, Nadine; McGettigan, Carolyn; Garrido, Lúcia

2017-09-01

[Correction Notice: An Erratum for this article was reported in Vol 17(6) of Emotion (see record 2017-18585-001). In the article, the copyright attribution was incorrectly listed and the Creative Commons CC-BY license disclaimer was incorrectly omitted from the author note. The correct copyright is "© 2017 The Author(s)" and the omitted disclaimer is below. All versions of this article have been corrected. "This article has been published under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Copyright for this article is retained by the author(s). Author(s) grant(s) the American Psychological Association the exclusive right to publish the article and identify itself as the original publisher."] Emotions are a vital component of social communication, carried across a range of modalities and via different perceptual signals such as specific muscle contractions in the face and in the upper respiratory system. Previous studies have found that emotion recognition impairments after brain damage depend on the modality of presentation: recognition from faces may be impaired whereas recognition from voices remains preserved, and vice versa. On the other hand, there is also evidence for shared neural activation during emotion processing in both modalities. In a behavioral study, we investigated whether there are shared representations in the recognition of emotions from faces and voices. We used a within-subjects design in which participants rated the intensity of facial expressions and nonverbal vocalizations for each of the 6 basic emotion labels. For each participant and each modality, we then computed a representation matrix with the intensity ratings of each emotion. These matrices allowed us to examine the patterns of confusions between emotions and to characterize the representations

Deep neural networks for texture classification-A theoretical analysis.

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Basu, Saikat; Mukhopadhyay, Supratik; Karki, Manohar; DiBiano, Robert; Ganguly, Sangram; Nemani, Ramakrishna; Gayaka, Shreekant

2018-01-01

We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Neural network representation and learning of mappings and their derivatives

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White, Halbert; Hornik, Kurt; Stinchcombe, Maxwell; Gallant, A. Ronald

1991-01-01

Discussed here are recent theorems proving that artificial neural networks are capable of approximating an arbitrary mapping and its derivatives as accurately as desired. This fact forms the basis for further results establishing the learnability of the desired approximations, using results from non-parametric statistics. These results have potential applications in robotics, chaotic dynamics, control, and sensitivity analysis. An example involving learning the transfer function and its derivatives for a chaotic map is discussed.

A Drone Remote Sensing for Virtual Reality Simulation System for Forest Fires: Semantic Neural Network Approach

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Narasimha Rao, Gudikandhula; Jagadeeswara Rao, Peddada; Duvvuru, Rajesh

2016-09-01

Wild fires have significant impact on atmosphere and lives. The demand of predicting exact fire area in forest may help fire management team by using drone as a robot. These are flexible, inexpensive and elevated-motion remote sensing systems that use drones as platforms are important for substantial data gaps and supplementing the capabilities of manned aircraft and satellite remote sensing systems. In addition, powerful computational tools are essential for predicting certain burned area in the duration of a forest fire. The reason of this study is to built up a smart system based on semantic neural networking for the forecast of burned areas. The usage of virtual reality simulator is used to support the instruction process of fire fighters and all users for saving of surrounded wild lives by using a naive method Semantic Neural Network System (SNNS). Semantics are valuable initially to have a enhanced representation of the burned area prediction and better alteration of simulation situation to the users. In meticulous, consequences obtained with geometric semantic neural networking is extensively superior to other methods. This learning suggests that deeper investigation of neural networking in the field of forest fires prediction could be productive.

Neural dichotomy of word concreteness: a view from functional neuroimaging.

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Kumar, Uttam

2016-02-01

Our perception about the representation and processing of concrete and abstract concepts is based on the fact that concrete words are highly imagined and remembered faster than abstract words. In order to explain the processing differences between abstract and concrete concepts, various theories have been proposed, yet there is no unanimous consensus about its neural implication. The present study investigated the processing of concrete and abstract words during an orthography judgment task (implicit semantic processing) using functional magnetic resonance imaging to validate the involvement of the neural regions. Relative to non-words, both abstract and concrete words show activation in the regions of bilateral hemisphere previously associated with semantic processing. The common areas (conjunction analyses) observed for abstract and concrete words are bilateral inferior frontal gyrus (BA 44/45), left superior parietal (BA 7), left fusiform gyrus and bilateral middle occipital. The additional areas for abstract words were noticed in bilateral superior temporal and bilateral middle temporal region, whereas no distinct region was noticed for concrete words. This suggests that words with abstract concepts recruit additional language regions in the brain.

Oscillations, neural computations and learning during wake and sleep.

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Penagos, Hector; Varela, Carmen; Wilson, Matthew A

2017-06-01

Learning and memory theories consider sleep and the reactivation of waking hippocampal neural patterns to be crucial for the long-term consolidation of memories. Here we propose that precisely coordinated representations across brain regions allow the inference and evaluation of causal relationships to train an internal generative model of the world. This training starts during wakefulness and strongly benefits from sleep because its recurring nested oscillations may reflect compositional operations that facilitate a hierarchical processing of information, potentially including behavioral policy evaluations. This suggests that an important function of sleep activity is to provide conditions conducive to general inference, prediction and insight, which contribute to a more robust internal model that underlies generalization and adaptive behavior. Copyright © 2017 Elsevier Ltd. All rights reserved.

The neural basis of love as a subliminal prime: an event-related functional magnetic resonance imaging study.

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Ortigue, S; Bianchi-Demicheli, F; Hamilton, A F de C; Grafton, S T

2007-07-01

Throughout the ages, love has been defined as a motivated and goal-directed mechanism with explicit and implicit mechanisms. Recent evidence demonstrated that the explicit representation of love recruits subcorticocortical pathways mediating reward, emotion, and motivation systems. However, the neural basis of the implicit (unconscious) representation of love remains unknown. To assess this question, we combined event-related functional magnetic resonance imaging (fMRI) with a behavioral subliminal priming paradigm embedded in a lexical decision task. In this task, the name of either a beloved partner, a neutral friend, or a passionate hobby was subliminally presented before a target stimulus (word, nonword, or blank), and participants were required to decide if the target was a word or not. Behavioral results showed that subliminal presentation of either a beloved's name (love prime) or a passion descriptor (passion prime) enhanced reaction times in a similar fashion. Subliminal presentation of a friend's name (friend prime) did not show any beneficial effects. Functional results showed that subliminal priming with a beloved's name (as opposed to either a friend's name or a passion descriptor) specifically recruited brain areas involved in abstract representations of others and the self, in addition to motivation circuits shared with other sources of passion. More precisely, love primes recruited the fusiform and angular gyri. Our findings suggest that love, as a subliminal prime, involves a specific neural network that surpasses a dopaminergic-motivation system.

A continuous-time neural model for sequential action.

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Kachergis, George; Wyatte, Dean; O'Reilly, Randall C; de Kleijn, Roy; Hommel, Bernhard

2014-11-05

Action selection, planning and execution are continuous processes that evolve over time, responding to perceptual feedback as well as evolving top-down constraints. Existing models of routine sequential action (e.g. coffee- or pancake-making) generally fall into one of two classes: hierarchical models that include hand-built task representations, or heterarchical models that must learn to represent hierarchy via temporal context, but thus far lack goal-orientedness. We present a biologically motivated model of the latter class that, because it is situated in the Leabra neural architecture, affords an opportunity to include both unsupervised and goal-directed learning mechanisms. Moreover, we embed this neurocomputational model in the theoretical framework of the theory of event coding (TEC), which posits that actions and perceptions share a common representation with bidirectional associations between the two. Thus, in this view, not only does perception select actions (along with task context), but actions are also used to generate perceptions (i.e. intended effects). We propose a neural model that implements TEC to carry out sequential action control in hierarchically structured tasks such as coffee-making. Unlike traditional feedforward discrete-time neural network models, which use static percepts to generate static outputs, our biological model accepts continuous-time inputs and likewise generates non-stationary outputs, making short-timescale dynamic predictions. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

The neural subjective frame: from bodily signals to perceptual consciousness.

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Park, Hyeong-Dong; Tallon-Baudry, Catherine

2014-05-05

The report 'I saw the stimulus' operationally defines visual consciousness, but where does the 'I' come from? To account for the subjective dimension of perceptual experience, we introduce the concept of the neural subjective frame. The neural subjective frame would be based on the constantly updated neural maps of the internal state of the body and constitute a neural referential from which first person experience can be created. We propose to root the neural subjective frame in the neural representation of visceral information which is transmitted through multiple anatomical pathways to a number of target sites, including posterior insula, ventral anterior cingulate cortex, amygdala and somatosensory cortex. We review existing experimental evidence showing that the processing of external stimuli can interact with visceral function. The neural subjective frame is a low-level building block of subjective experience which is not explicitly experienced by itself which is necessary but not sufficient for perceptual experience. It could also underlie other types of subjective experiences such as self-consciousness and emotional feelings. Because the neural subjective frame is tightly linked to homeostatic regulations involved in vigilance, it could also make a link between state and content consciousness.

Look out for strangers! Sustained neural activity during visual working memory maintenance of other-race faces is modulated by implicit racial prejudice

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Sessa, Paola; Tomelleri, Silvia; Luria, Roy; Castelli, Luigi; Reynolds, Michael

2012-01-01

We tested the ability of white participants to encode and retain over a brief period of time information about the identity of white and black people, using faces as stimuli in a standard change detection task and tracking neural activity using electroencephalography. Neural responses recorded over the posterior parietal cortex reflecting visual working memory activity increased in amplitude as a function of the number of faces that had to be maintained in memory. Critically, these memory-related neural responses varied as a function of participants’ implicit racial prejudice toward black people. High-prejudiced participants encoded black people faces with a lower degree of precision compared to low-prejudiced participants, suggesting that the class of mental operations affected by implicit racial prejudice includes basic cognitive mechanisms underpinning the encoding and maintenance of faces’ visual representations in visual working memory. PMID:21768206

Suggestion-Induced Modulation of Semantic Priming during Functional Magnetic Resonance Imaging

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Ulrich, Martin; Kiefer, Markus; Bongartz, Walter; Grön, Georg; Hoenig, Klaus

2015-01-01

Using functional magnetic resonance imaging during a primed visual lexical decision task, we investigated the neural and functional mechanisms underlying modulations of semantic word processing through hypnotic suggestions aimed at altering lexical processing of primes. The priming task was to discriminate between target words and pseudowords presented 200 ms after the prime word which was semantically related or unrelated to the target. In a counterbalanced study design, each participant performed the task once at normal wakefulness and once after the administration of hypnotic suggestions to perceive the prime as a meaningless symbol of a foreign language. Neural correlates of priming were defined as significantly lower activations upon semantically related compared to unrelated trials. We found significant suggestive treatment-induced reductions in neural priming, albeit irrespective of the degree of suggestibility. Neural priming was attenuated upon suggestive treatment compared with normal wakefulness in brain regions supporting automatic (fusiform gyrus) and controlled semantic processing (superior and middle temporal gyri, pre- and postcentral gyri, and supplementary motor area). Hence, suggestions reduced semantic word processing by conjointly dampening both automatic and strategic semantic processes. PMID:25923740

Suggestion-Induced Modulation of Semantic Priming during Functional Magnetic Resonance Imaging.

Directory of Open Access Journals (Sweden)

Martin Ulrich

Full Text Available Using functional magnetic resonance imaging during a primed visual lexical decision task, we investigated the neural and functional mechanisms underlying modulations of semantic word processing through hypnotic suggestions aimed at altering lexical processing of primes. The priming task was to discriminate between target words and pseudowords presented 200 ms after the prime word which was semantically related or unrelated to the target. In a counterbalanced study design, each participant performed the task once at normal wakefulness and once after the administration of hypnotic suggestions to perceive the prime as a meaningless symbol of a foreign language. Neural correlates of priming were defined as significantly lower activations upon semantically related compared to unrelated trials. We found significant suggestive treatment-induced reductions in neural priming, albeit irrespective of the degree of suggestibility. Neural priming was attenuated upon suggestive treatment compared with normal wakefulness in brain regions supporting automatic (fusiform gyrus and controlled semantic processing (superior and middle temporal gyri, pre- and postcentral gyri, and supplementary motor area. Hence, suggestions reduced semantic word processing by conjointly dampening both automatic and strategic semantic processes.

Optimal Hierarchical Modular Topologies for Producing Limited Sustained Activation of Neural Networks

OpenAIRE

Kaiser, Marcus; Hilgetag, Claus C.

2010-01-01

An essential requirement for the representation of functional patterns in complex neural networks, such as the mammalian cerebral cortex, is the existence of stable regimes of network activation, typically arising from a limited parameter range. In this range of limited sustained activity (LSA), the activity of neural populations in the network persists between the extremes of either quickly dying out or activating the whole network. Hierarchical modular networks were previously found to show...

Multiple neural states of representation in short-term memory? It’s a matter of attention

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LaRocque, Joshua J.; Lewis-Peacock, Jarrod A.; Postle, Bradley R.

2014-01-01

Short-term memory (STM) refers to the capacity-limited retention of information over a brief period of time, and working memory (WM) refers to the manipulation and use of that information to guide behavior. In recent years it has become apparent that STM and WM interact and overlap with other cognitive processes, including attention (the selection of a subset of information for further processing) and long-term memory (LTM—the encoding and retention of an effectively unlimited amount of information for a much longer period of time). Broadly speaking, there have been two classes of memory models: systems models, which posit distinct stores for STM and LTM (Atkinson and Shiffrin, 1968; Baddeley and Hitch, 1974); and state-based models, which posit a common store with different activation states corresponding to STM and LTM (Cowan, 1995; McElree, 1996; Oberauer, 2002). In this paper, we will focus on state-based accounts of STM. First, we will consider several theoretical models that postulate, based on considerable behavioral evidence, that information in STM can exist in multiple representational states. We will then consider how neural data from recent studies of STM can inform and constrain these theoretical models. In the process we will highlight the inferential advantage of multivariate, information-based analyses of neuroimaging data (fMRI and electroencephalography (EEG)) over conventional activation-based analysis approaches (Postle, in press). We will conclude by addressing lingering questions regarding the fractionation of STM, highlighting differences between the attention to information vs. the retention of information during brief memory delays. PMID:24478671

Concepts in context: Processing mental state concepts with internal or external focus involves different neural systems

Science.gov (United States)

Oosterwijk, Suzanne; Mackey, Scott; Wilson-Mendenhall, Christine; Winkielman, Piotr; Paulus, Martin P.

2015-01-01

According to embodied cognition theories concepts are contextually-situated and grounded in neural systems that produce experiential states. This view predicts that processing mental state concepts recruits neural regions associated with different aspects of experience depending on the context in which people understand a concept. This neuroimaging study tested this prediction using a set of sentences that described emotional (e.g., fear, joy) and non-emotional (e.g., thinking, hunger) mental states with internal focus (i.e. focusing on bodily sensations and introspection) or external focus (i.e. focusing on expression and action). Consistent with our predictions, data suggested that the inferior frontal gyrus, a region associated with action representation, was engaged more by external than internal sentences. By contrast, the ventromedial prefrontal cortex, a region associated with the generation of internal states, was engaged more by internal emotion sentences than external sentence categories. Similar patterns emerged when we examined the relationship between neural activity and independent ratings of sentence focus. Furthermore, ratings of emotion were associated with activation in the medial prefrontal cortex, whereas ratings of activity were associated with activation in the inferior frontal gyrus. These results suggest that mental state concepts are represented in a dynamic way, using context-relevant interoceptive and sensorimotor resources. PMID:25748274

Using Neural Pattern Classifiers to Quantify the Modularity of Conflict–Control Mechanisms in the Human Brain

Science.gov (United States)

Jiang, Jiefeng; Egner, Tobias

2014-01-01

Resolving conflicting sensory and motor representations is a core function of cognitive control, but it remains uncertain to what degree control over different sources of conflict is implemented by shared (domain general) or distinct (domain specific) neural resources. Behavioral data suggest conflict–control to be domain specific, but results from neuroimaging studies have been ambivalent. Here, we employed multivoxel pattern analyses that can decode a brain region's informational content, allowing us to distinguish incidental activation overlap from actual shared information processing. We trained independent sets of “searchlight” classifiers on functional magnetic resonance imaging data to decode control processes associated with stimulus-conflict (Stroop task) and ideomotor-conflict (Simon task). Quantifying the proportion of domain-specific searchlights (capable of decoding only one type of conflict) and domain-general searchlights (capable of decoding both conflict types) in each subject, we found both domain-specific and domain-general searchlights, though the former were more common. When mapping anatomical loci of these searchlights across subjects, neural substrates of stimulus- and ideomotor-specific conflict–control were found to be anatomically consistent across subjects, whereas the substrates of domain-general conflict–control were not. Overall, these findings suggest a hybrid neural architecture of conflict–control that entails both modular (domain specific) and global (domain general) components. PMID:23402762

Neural Language Processing in Adolescent First-Language Learners: Longitudinal Case Studies in American Sign Language.

Science.gov (United States)

Ferjan Ramirez, Naja; Leonard, Matthew K; Davenport, Tristan S; Torres, Christina; Halgren, Eric; Mayberry, Rachel I

2016-03-01

One key question in neurolinguistics is the extent to which the neural processing system for language requires linguistic experience during early life to develop fully. We conducted a longitudinal anatomically constrained magnetoencephalography (aMEG) analysis of lexico-semantic processing in 2 deaf adolescents who had no sustained language input until 14 years of age, when they became fully immersed in American Sign Language. After 2 to 3 years of language, the adolescents' neural responses to signed words were highly atypical, localizing mainly to right dorsal frontoparietal regions and often responding more strongly to semantically primed words (Ferjan Ramirez N, Leonard MK, Torres C, Hatrak M, Halgren E, Mayberry RI. 2014. Neural language processing in adolescent first-language learners. Cereb Cortex. 24 (10): 2772-2783). Here, we show that after an additional 15 months of language experience, the adolescents' neural responses remained atypical in terms of polarity. While their responses to less familiar signed words still showed atypical localization patterns, the localization of responses to highly familiar signed words became more concentrated in the left perisylvian language network. Our findings suggest that the timing of language experience affects the organization of neural language processing; however, even in adolescence, language representation in the human brain continues to evolve with experience. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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.

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

Learning representations for the early detection of sepsis with deep neural networks.

Science.gov (United States)

Kam, Hye Jin; Kim, Ha Young

2017-10-01

Sepsis is one of the leading causes of death in intensive care unit patients. Early detection of sepsis is vital because mortality increases as the sepsis stage worsens. This study aimed to develop detection models for the early stage of sepsis using deep learning methodologies, and to compare the feasibility and performance of the new deep learning methodology with those of the regression method with conventional temporal feature extraction. Study group selection adhered to the InSight model. The results of the deep learning-based models and the InSight model were compared. With deep feedforward networks, the area under the ROC curve (AUC) of the models were 0.887 and 0.915 for the InSight and the new feature sets, respectively. For the model with the combined feature set, the AUC was the same as that of the basic feature set (0.915). For the long short-term memory model, only the basic feature set was applied and the AUC improved to 0.929 compared with the existing 0.887 of the InSight model. The contributions of this paper can be summarized in three ways: (i) improved performance without feature extraction using domain knowledge, (ii) verification of feature extraction capability of deep neural networks through comparison with reference features, and (iii) improved performance with feedforward neural networks using long short-term memory, a neural network architecture that can learn sequential patterns. Copyright © 2017 Elsevier Ltd. All rights reserved.

Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches.

Science.gov (United States)

Crichton, Gamal; Guo, Yufan; Pyysalo, Sampo; Korhonen, Anna

2018-05-21

Link prediction in biomedical graphs has several important applications including predicting Drug-Target Interactions (DTI), Protein-Protein Interaction (PPI) prediction and Literature-Based Discovery (LBD). It can be done using a classifier to output the probability of link formation between nodes. Recently several works have used neural networks to create node representations which allow rich inputs to neural classifiers. Preliminary works were done on this and report promising results. However they did not use realistic settings like time-slicing, evaluate performances with comprehensive metrics or explain when or why neural network methods outperform. We investigated how inputs from four node representation algorithms affect performance of a neural link predictor on random- and time-sliced biomedical graphs of real-world sizes (∼ 6 million edges) containing information relevant to DTI, PPI and LBD. We compared the performance of the neural link predictor to those of established baselines and report performance across five metrics. In random- and time-sliced experiments when the neural network methods were able to learn good node representations and there was a negligible amount of disconnected nodes, those approaches outperformed the baselines. In the smallest graph (∼ 15,000 edges) and in larger graphs with approximately 14% disconnected nodes, baselines such as Common Neighbours proved a justifiable choice for link prediction. At low recall levels (∼ 0.3) the approaches were mostly equal, but at higher recall levels across all nodes and average performance at individual nodes, neural network approaches were superior. Analysis showed that neural network methods performed well on links between nodes with no previous common neighbours; potentially the most interesting links. Additionally, while neural network methods benefit from large amounts of data, they require considerable amounts of computational resources to utilise them. Our results indicate

Foundations of children's numerical and mathematical skills: the roles of symbolic and nonsymbolic representations of numerical magnitude.

Science.gov (United States)

Lyons, Ian M; Ansari, Daniel

2015-01-01

Numerical and mathematical skills are critical predictors of academic success. The last three decades have seen a substantial growth in our understanding of how the human mind and brain represent and process numbers. In particular, research has shown that we share with animals the ability to represent numerical magnitude (the total number of items in a set) and that preverbal infants can process numerical magnitude. Further research has shown that similar processing signatures characterize numerical magnitude processing across species and developmental time. These findings suggest that an approximate system for nonsymbolic (e.g., dot arrays) numerical magnitude representation serves as the basis for the acquisition of cultural, symbolic (e.g., Arabic numerals) representations of numerical magnitude. This chapter explores this hypothesis by reviewing studies that have examined the relation between individual differences in nonsymbolic numerical magnitude processing and symbolic math abilities (e.g., arithmetic). Furthermore, we examine the extent to which the available literature provides strong evidence for a link between symbolic and nonsymbolic representations of numerical magnitude at the behavioral and neural levels of analysis. We conclude that claims that symbolic number abilities are grounded in the approximate system for the nonsymbolic representation of numerical magnitude are not strongly supported by the available evidence. Alternative models and future research directions are discussed. © 2015 Elsevier Inc. All rights reserved.

Dissociating neural variability related to stimulus quality and response times in perceptual decision-making.

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Bode, Stefan; Bennett, Daniel; Sewell, David K; Paton, Bryan; Egan, Gary F; Smith, Philip L; Murawski, Carsten

2018-03-01

According to sequential sampling models, perceptual decision-making is based on accumulation of noisy evidence towards a decision threshold. The speed with which a decision is reached is determined by both the quality of incoming sensory information and random trial-by-trial variability in the encoded stimulus representations. To investigate those decision dynamics at the neural level, participants made perceptual decisions while functional magnetic resonance imaging (fMRI) was conducted. On each trial, participants judged whether an image presented under conditions of high, medium, or low visual noise showed a piano or a chair. Higher stimulus quality (lower visual noise) was associated with increased activation in bilateral medial occipito-temporal cortex and ventral striatum. Lower stimulus quality was related to stronger activation in posterior parietal cortex (PPC) and dorsolateral prefrontal cortex (DLPFC). When stimulus quality was fixed, faster response times were associated with a positive parametric modulation of activation in medial prefrontal and orbitofrontal cortex, while slower response times were again related to more activation in PPC, DLPFC and insula. Our results suggest that distinct neural networks were sensitive to the quality of stimulus information, and to trial-to-trial variability in the encoded stimulus representations, but that reaching a decision was a consequence of their joint activity. Copyright © 2018 Elsevier Ltd. All rights reserved.

Hierarchical Representation Learning for Kinship Verification.

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Kohli, Naman; Vatsa, Mayank; Singh, Richa; Noore, Afzel; Majumdar, Angshul

2017-01-01

Kinship verification has a number of applications such as organizing large collections of images and recognizing resemblances among humans. In this paper, first, a human study is conducted to understand the capabilities of human mind and to identify the discriminatory areas of a face that facilitate kinship-cues. The visual stimuli presented to the participants determine their ability to recognize kin relationship using the whole face as well as specific facial regions. The effect of participant gender and age and kin-relation pair of the stimulus is analyzed using quantitative measures such as accuracy, discriminability index d' , and perceptual information entropy. Utilizing the information obtained from the human study, a hierarchical kinship verification via representation learning (KVRL) framework is utilized to learn the representation of different face regions in an unsupervised manner. We propose a novel approach for feature representation termed as filtered contractive deep belief networks (fcDBN). The proposed feature representation encodes relational information present in images using filters and contractive regularization penalty. A compact representation of facial images of kin is extracted as an output from the learned model and a multi-layer neural network is utilized to verify the kin accurately. A new WVU kinship database is created, which consists of multiple images per subject to facilitate kinship verification. The results show that the proposed deep learning framework (KVRL-fcDBN) yields the state-of-the-art kinship verification accuracy on the WVU kinship database and on four existing benchmark data sets. Furthermore, kinship information is used as a soft biometric modality to boost the performance of face verification via product of likelihood ratio and support vector machine based approaches. Using the proposed KVRL-fcDBN framework, an improvement of over 20% is observed in the performance of face verification.

A feed forward neural network for classification of bull's-eye myocardial perfusion images

International Nuclear Information System (INIS)

Hamilton, D.; Riley, P.J.; Miola, U.J.; Amro, A.A.

1995-01-01

Identification of hypoperfused areas in myocardial perfusion single-photon emission tomography studies can be aided by bull's-eye representation of raw counts, lesion extent and lesion severity, the latter two being produced by comparison of the raw bull's-eye data with a normal data base. An artificial intelligence technique which is presently becoming widely popular and which is particularly suitable for pattern recognition is that of artificial neural network. We have studied the ability of feed forward neural networks to extract patterns from bull's-eye data by assessing their capability to predict lesion presence without direct comparison with a normal data base. Studies were undertaken on both simulation data and on real stress-rest data obtained from 410 male patients undergoing routine thallium-201 myocardial perfusion scintigraphy. The ability of trained neural networks to predict lesion presence was quantified by calculating the areas under receiver operating characteristic curves. Figures as high as 0.96 for non-preclassified patient data were obtained, corresponding to an accuracy of 92%. The results demonstrate that neural networks can accurately classify patterns from bull's-eye myocardial perfusion images and detect the presence of hypoperfused areas without the need for comparison with a normal data base. Preliminary work suggests that this technique could be used to study perfusion patterns in the myocardium and their correlation with clinical parameters. (orig.)

Dynamic neural architecture for social knowledge retrieval.

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Wang, Yin; Collins, Jessica A; Koski, Jessica; Nugiel, Tehila; Metoki, Athanasia; Olson, Ingrid R

2017-04-18

Social behavior is often shaped by the rich storehouse of biographical information that we hold for other people. In our daily life, we rapidly and flexibly retrieve a host of biographical details about individuals in our social network, which often guide our decisions as we navigate complex social interactions. Even abstract traits associated with an individual, such as their political affiliation, can cue a rich cascade of person-specific knowledge. Here, we asked whether the anterior temporal lobe (ATL) serves as a hub for a distributed neural circuit that represents person knowledge. Fifty participants across two studies learned biographical information about fictitious people in a 2-d training paradigm. On day 3, they retrieved this biographical information while undergoing an fMRI scan. A series of multivariate and connectivity analyses suggest that the ATL stores abstract person identity representations. Moreover, this region coordinates interactions with a distributed network to support the flexible retrieval of person attributes. Together, our results suggest that the ATL is a central hub for representing and retrieving person knowledge.

Internal representation of task rules by recurrent dynamics: the importance of the diversity of neural responses

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Mattia Rigotti

2010-10-01

Full Text Available Neural activity of behaving animals, especially in the prefrontal cortex, is highly heterogeneous, with selective responses to diverse aspects of the executed task. We propose a general model of recurrent neural networks that perform complex rule-based tasks, and we show that the diversity of neuronal responses plays a fundamental role when the behavioral responses are context dependent. Specifically, we found that when the inner mental states encoding the task rules are represented by stable patterns of neural activity (attractors of the neural dynamics, the neurons must be selective for combinations of sensory stimuli and inner mental states. Such mixed selectivity is easily obtained by neurons that connect with random synaptic strengths both to the recurrent network and to neurons encoding sensory inputs. The number of randomly connected neurons needed to solve a task is on average only three times as large as the number of neurons needed in a network designed ad hoc. Moreover, the number of needed neurons grows only linearly with the number of task-relevant events and mental states, provided that each neuron responds to a large proportion of events (dense/distributed coding. A biologically realistic implementation of the model captures several aspects of the activity recorded from monkeys performing context dependent tasks. Our findings explain the importance of the diversity of neural responses and provide us with simple and general principles for designing attractor neural networks that perform complex computation.

Neural activity predicts attitude change in cognitive dissonance.

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van Veen, Vincent; Krug, Marie K; Schooler, Jonathan W; Carter, Cameron S

2009-11-01

When our actions conflict with our prior attitudes, we often change our attitudes to be more consistent with our actions. This phenomenon, known as cognitive dissonance, is considered to be one of the most influential theories in psychology. However, the neural basis of this phenomenon is unknown. Using a Solomon four-group design, we scanned participants with functional MRI while they argued that the uncomfortable scanner environment was nevertheless a pleasant experience. We found that cognitive dissonance engaged the dorsal anterior cingulate cortex and anterior insula; furthermore, we found that the activation of these regions tightly predicted participants' subsequent attitude change. These effects were not observed in a control group. Our findings elucidate the neural representation of cognitive dissonance, and support the role of the anterior cingulate cortex in detecting cognitive conflict and the neural prediction of attitude change.

Shared neural coding for social hierarchy and reward value in primate amygdala.

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Munuera, Jérôme; Rigotti, Mattia; Salzman, C Daniel

2018-03-01

The social brain hypothesis posits that dedicated neural systems process social information. In support of this, neurophysiological data have shown that some brain regions are specialized for representing faces. It remains unknown, however, whether distinct anatomical substrates also represent more complex social variables, such as the hierarchical rank of individuals within a social group. Here we show that the primate amygdala encodes the hierarchical rank of individuals in the same neuronal ensembles that encode the rewards associated with nonsocial stimuli. By contrast, orbitofrontal and anterior cingulate cortices lack strong representations of hierarchical rank while still representing reward values. These results challenge the conventional view that dedicated neural systems process social information. Instead, information about hierarchical rank-which contributes to the assessment of the social value of individuals within a group-is linked in the amygdala to representations of rewards associated with nonsocial stimuli.

Hemispheric asymmetry of liking for representational and abstract paintings.

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Nadal, Marcos; Schiavi, Susanna; Cattaneo, Zaira

2017-10-13

Although the neural correlates of the appreciation of aesthetic qualities have been the target of much research in the past decade, few experiments have explored the hemispheric asymmetries in underlying processes. In this study, we used a divided visual field paradigm to test for hemispheric asymmetries in men and women's preference for abstract and representational artworks. Both male and female participants liked representational paintings more when presented in the right visual field, whereas preference for abstract paintings was unaffected by presentation hemifield. We hypothesize that this result reflects a facilitation of the sort of visual processes relevant to laypeople's liking for art-specifically, local processing of highly informative object features-when artworks are presented in the right visual field, given the left hemisphere's advantage in processing such features.

Neural signatures of economic parameters during decision-making: a functional MRI (FMRI), electroencephalography (EEG) and autonomic monitoring study.

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Minati, Ludovico; Grisoli, Marina; Franceschetti, Silvana; Epifani, Francesca; Granvillano, Alice; Medford, Nick; Harrison, Neil A; Piacentini, Sylvie; Critchley, Hugo D

2012-01-01

Adaptive behaviour requires an ability to obtain rewards by choosing between different risky options. Financial gambles can be used to study effective decision-making experimentally, and to distinguish processes involved in choice option evaluation from outcome feedback and other contextual factors. Here, we used a paradigm where participants evaluated 'mixed' gambles, each presenting a potential gain and a potential loss and an associated variable outcome probability. We recorded neural responses using autonomic monitoring, electroencephalography (EEG) and functional neuroimaging (fMRI), and used a univariate, parametric design to test for correlations with the eleven economic parameters that varied across gambles, including expected value (EV) and amount magnitude. Consistent with behavioural economic theory, participants were risk-averse. Gamble evaluation generated detectable autonomic responses, but only weak correlations with outcome uncertainty were found, suggesting that peripheral autonomic feedback does not play a major role in this task. Long-latency stimulus-evoked EEG potentials were sensitive to expected gain and expected value, while alpha-band power reflected expected loss and amount magnitude, suggesting parallel representations of distinct economic qualities in cortical activation and central arousal. Neural correlates of expected value representation were localized using fMRI to ventromedial prefrontal cortex, while the processing of other economic parameters was associated with distinct patterns across lateral prefrontal, cingulate, insula and occipital cortices including default-mode network and early visual areas. These multimodal data provide complementary evidence for distributed substrates of choice evaluation across multiple, predominantly cortical, brain systems wherein distinct regions are preferentially attuned to specific economic features. Our findings extend biologically-plausible models of risky decision-making while providing

Neural network determination of parton distributions: the nonsinglet case

International Nuclear Information System (INIS)

Del Debbio, Luigi; Forte, Stefano; Latorre, Jose I.; Piccione, Andrea; Rojo, Joan

2007-01-01

We provide a determination of the isotriplet quark distribution from available deep-inelastic data using neural networks. We give a general introduction to the neural network approach to parton distributions, which provides a solution to the problem of constructing a faithful and unbiased probability distribution of parton densities based on available experimental information. We discuss in detail the techniques which are necessary in order to construct a Monte Carlo representation of the data, to construct and evolve neural parton distributions, and to train them in such a way that the correct statistical features of the data are reproduced. We present the results of the application of this method to the determination of the nonsinglet quark distribution up to next-to-next-to-leading order, and compare them with those obtained using other approaches

A conceptual lemon: theta burst stimulation to the left anterior temporal lobe untangles object representation and its canonical color.

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Chiou, Rocco; Sowman, Paul F; Etchell, Andrew C; Rich, Anina N

2014-05-01

Object recognition benefits greatly from our knowledge of typical color (e.g., a lemon is usually yellow). Most research on object color knowledge focuses on whether both knowledge and perception of object color recruit the well-established neural substrates of color vision (the V4 complex). Compared with the intensive investigation of the V4 complex, we know little about where and how neural mechanisms beyond V4 contribute to color knowledge. The anterior temporal lobe (ATL) is thought to act as a "hub" that supports semantic memory by integrating different modality-specific contents into a meaningful entity at a supramodal conceptual level, making it a good candidate zone for mediating the mappings between object attributes. Here, we explore whether the ATL is critical for integrating typical color with other object attributes (object shape and name), akin to its role in combining nonperceptual semantic representations. In separate experimental sessions, we applied TMS to disrupt neural processing in the left ATL and a control site (the occipital pole). Participants performed an object naming task that probes color knowledge and elicits a reliable color congruency effect as well as a control quantity naming task that also elicits a cognitive congruency effect but involves no conceptual integration. Critically, ATL stimulation eliminated the otherwise robust color congruency effect but had no impact on the numerical congruency effect, indicating a selective disruption of object color knowledge. Neither color nor numerical congruency effects were affected by stimulation at the control occipital site, ruling out nonspecific effects of cortical stimulation. Our findings suggest that the ATL is involved in the representation of object concepts that include their canonical colors.

A model for integrating elementary neural functions into delayed-response behavior.

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Thomas Gisiger

2006-04-01

Full Text Available It is well established that various cortical regions can implement a wide array of neural processes, yet the mechanisms which integrate these processes into behavior-producing, brain-scale activity remain elusive. We propose that an important role in this respect might be played by executive structures controlling the traffic of information between the cortical regions involved. To illustrate this hypothesis, we present a neural network model comprising a set of interconnected structures harboring stimulus-related activity (visual representation, working memory, and planning, and a group of executive units with task-related activity patterns that manage the information flowing between them. The resulting dynamics allows the network to perform the dual task of either retaining an image during a delay (delayed-matching to sample task, or recalling from this image another one that has been associated with it during training (delayed-pair association task. The model reproduces behavioral and electrophysiological data gathered on the inferior temporal and prefrontal cortices of primates performing these same tasks. It also makes predictions on how neural activity coding for the recall of the image associated with the sample emerges and becomes prospective during the training phase. The network dynamics proves to be very stable against perturbations, and it exhibits signs of scale-invariant organization and cooperativity. The present network represents a possible neural implementation for active, top-down, prospective memory retrieval in primates. The model suggests that brain activity leading to performance of cognitive tasks might be organized in modular fashion, simple neural functions becoming integrated into more complex behavior by executive structures harbored in prefrontal cortex and/or basal ganglia.

A model for integrating elementary neural functions into delayed-response behavior.

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Gisiger, Thomas; Kerszberg, Michel

2006-04-01

It is well established that various cortical regions can implement a wide array of neural processes, yet the mechanisms which integrate these processes into behavior-producing, brain-scale activity remain elusive. We propose that an important role in this respect might be played by executive structures controlling the traffic of information between the cortical regions involved. To illustrate this hypothesis, we present a neural network model comprising a set of interconnected structures harboring stimulus-related activity (visual representation, working memory, and planning), and a group of executive units with task-related activity patterns that manage the information flowing between them. The resulting dynamics allows the network to perform the dual task of either retaining an image during a delay (delayed-matching to sample task), or recalling from this image another one that has been associated with it during training (delayed-pair association task). The model reproduces behavioral and electrophysiological data gathered on the inferior temporal and prefrontal cortices of primates performing these same tasks. It also makes predictions on how neural activity coding for the recall of the image associated with the sample emerges and becomes prospective during the training phase. The network dynamics proves to be very stable against perturbations, and it exhibits signs of scale-invariant organization and cooperativity. The present network represents a possible neural implementation for active, top-down, prospective memory retrieval in primates. The model suggests that brain activity leading to performance of cognitive tasks might be organized in modular fashion, simple neural functions becoming integrated into more complex behavior by executive structures harbored in prefrontal cortex and/or basal ganglia.

Order-based representation in random networks of cortical neurons.

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Goded Shahaf

2008-11-01

Full Text Available The wide range of time scales involved in neural excitability and synaptic transmission might lead to ongoing change in the temporal structure of responses to recurring stimulus presentations on a trial-to-trial basis. This is probably the most severe biophysical constraint on putative time-based primitives of stimulus representation in neuronal networks. Here we show that in spontaneously developing large-scale random networks of cortical neurons in vitro the order in which neurons are recruited following each stimulus is a naturally emerging representation primitive that is invariant to significant temporal changes in spike times. With a relatively small number of randomly sampled neurons, the information about stimulus position is fully retrievable from the recruitment order. The effective connectivity that makes order-based representation invariant to time warping is characterized by the existence of stations through which activity is required to pass in order to propagate further into the network. This study uncovers a simple invariant in a noisy biological network in vitro; its applicability under in vivo constraints remains to be seen.

Analysis of the atmospheric 7Be radioactivity by neural network

International Nuclear Information System (INIS)

Moroz, Z.; Myslek-Laurikainen, B.; Matul, M.; Mikolajewski, S.; Preibisz, Z.; Trzaskowaka, H.; Kownacki, C.

2002-01-01

Computational methods of artificial intelligence (neural networks) and modern signal processing (wavelet decomposition were applied for the analysis of atmospheric 7 Be radioactivity data. Measurements were performed each week during 1994-2001 in the sampling station placed at Swider Geophysical Observatory. Raw data as well as those averaged over 4 and 10 weeks were used. Ability of the neural networks for the purpose of representation, interpolation and prediction was tested. The corresponding relative errors are calculated. Next, the time sequences were decomposed using the wavelet method and eight components of different time scales were obtained. Neural networks were applied separately to each of those components. Application of such analysis and their possible extensions useful for the construction of phenomeno-logical models of atmospheric radioactivity are discussed. (author)

Neural correlates of appetite and hunger-related evaluative judgments.

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Richard M Piech

2009-08-01

Full Text Available How much we desire a meal depends on both the constituent foods and how hungry we are, though not every meal becomes more desirable with increasing hunger. The brain therefore needs to be able to integrate hunger and meal properties to compute the correct incentive value of a meal. The present study investigated the functional role of the amygdala and the orbitofrontal cortex in mediating hunger and dish attractiveness. Furthermore, it explored neural responses to dish descriptions particularly susceptible to value-increase following fasting. We instructed participants to rate how much they wanted food menu items while they were either hungry or sated, and compared the rating differences in these states. Our results point to the representation of food value in the amygdala, and to an integration of attractiveness with hunger level in the orbitofrontal cortex. Dishes particularly desirable during hunger activated the thalamus and the insula. Our results specify the functions of evaluative structures in the context of food attractiveness, and point to a complex neural representation of dish qualities which contribute to state-dependent value.

Character-level neural network for biomedical named entity recognition.

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Gridach, Mourad

2017-06-01

Biomedical named entity recognition (BNER), which extracts important named entities such as genes and proteins, is a challenging task in automated systems that mine knowledge in biomedical texts. The previous state-of-the-art systems required large amounts of task-specific knowledge in the form of feature engineering, lexicons and data pre-processing to achieve high performance. In this paper, we introduce a novel neural network architecture that benefits from both word- and character-level representations automatically, by using a combination of bidirectional long short-term memory (LSTM) and conditional random field (CRF) eliminating the need for most feature engineering tasks. We evaluate our system on two datasets: JNLPBA corpus and the BioCreAtIvE II Gene Mention (GM) corpus. We obtained state-of-the-art performance by outperforming the previous systems. To the best of our knowledge, we are the first to investigate the combination of deep neural networks, CRF, word embeddings and character-level representation in recognizing biomedical named entities. Copyright © 2017 Elsevier Inc. All rights reserved.

Group Analysis in FieldTrip of Time-Frequency Responses: A Pipeline for Reproducibility at Every Step of Processing, Going From Individual Sensor Space Representations to an Across-Group Source Space Representation

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Lau M. Andersen

2018-05-01

Full Text Available An important aim of an analysis pipeline for magnetoencephalographic (MEG data is that it allows for the researcher spending maximal effort on making the statistical comparisons that will answer his or her questions. The example question being answered here is whether the so-called beta rebound differs between novel and repeated stimulations. Two analyses are presented: going from individual sensor space representations to, respectively, an across-group sensor space representation and an across-group source space representation. The data analyzed are neural responses to tactile stimulations of the right index finger in a group of 20 healthy participants acquired from an Elekta Neuromag System. The processing steps covered for the first analysis are MaxFiltering the raw data, defining, preprocessing and epoching the data, cleaning the data, finding and removing independent components related to eye blinks, eye movements and heart beats, calculating participants' individual evoked responses by averaging over epoched data and subsequently removing the average response from single epochs, calculating a time-frequency representation and baselining it with non-stimulation trials and finally calculating a grand average, an across-group sensor space representation. The second analysis starts from the grand average sensor space representation and after identification of the beta rebound the neural origin is imaged using beamformer source reconstruction. This analysis covers reading in co-registered magnetic resonance images, segmenting the data, creating a volume conductor, creating a forward model, cutting out MEG data of interest in the time and frequency domains, getting Fourier transforms and estimating source activity with a beamformer model where power is expressed relative to MEG data measured during periods of non-stimulation. Finally, morphing the source estimates onto a common template and performing group-level statistics on the data are

Reference frames for spatial frequency in face representation differ in the temporal visual cortex and amygdala.

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Inagaki, Mikio; Fujita, Ichiro

2011-07-13

Social communication in nonhuman primates and humans is strongly affected by facial information from other individuals. Many cortical and subcortical brain areas are known to be involved in processing facial information. However, how the neural representation of faces differs across different brain areas remains unclear. Here, we demonstrate that the reference frame for spatial frequency (SF) tuning of face-responsive neurons differs in the temporal visual cortex and amygdala in monkeys. Consistent with psychophysical properties for face recognition, temporal cortex neurons were tuned to image-based SFs (cycles/image) and showed viewing distance-invariant representation of face patterns. On the other hand, many amygdala neurons were influenced by retina-based SFs (cycles/degree), a characteristic that is useful for social distance computation. The two brain areas also differed in the luminance contrast sensitivity of face-responsive neurons; amygdala neurons sharply reduced their responses to low luminance contrast images, while temporal cortex neurons maintained the level of their responses. From these results, we conclude that different types of visual processing in the temporal visual cortex and the amygdala contribute to the construction of the neural representations of faces.

Religious beliefs influence neural substrates of self-reflection in Tibetans.

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Wu, Yanhong; Wang, Cheng; He, Xi; Mao, Lihua; Zhang, Li

2010-06-01

Previous transcultural neuroimaging studies have shown that the neural substrates of self-reflection can be shaped by different cultures. There are few studies, however, on the neural activity of self-reflection where religion is viewed as a form of cultural expression. The present study examined the self-processing of two Chinese ethnic groups (Han and Tibetan) to investigate the significant role of religion on the functional anatomy of self-representation. We replicated the previous results in Han participants with the ventral medial prefrontal cortex and left anterior cingulate cortex showing stronger activation in self-processing when compared with other-processing conditions. However, no typical self-reference pattern was identified in Tibetan participants on behavioral or neural levels. This could be explained by the minimal subjective sense of 'I-ness' in Tibetan Buddhists. Our findings lend support to the presumed role of culture and religion in shaping the neural substrate of self.

Love flows downstream: mothers' and children's neural representation similarity in perceiving distress of self and family.

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Lee, Tae-Ho; Qu, Yang; Telzer, Eva H

2017-12-01

The current study aimed to capture empathy processing in an interpersonal context. Mother-adolescent dyads (N = 22) each completed an empathy task during fMRI, in which they imagined the target person in distressing scenes as either themselves or their family (i.e. child for the mother, mother for the child). Using multi-voxel pattern approach, we compared neural pattern similarity for the self and family conditions and found that mothers showed greater perceptual similarity between self and child in the fusiform face area (FFA), representing high self-child overlap, whereas adolescents showed significantly less self-mother overlap. Adolescents' pattern similarity was dependent upon family relationship quality, such that they showed greater self-mother overlap with higher relationship quality, whereas mothers' pattern similarity was independent of relationship quality. Furthermore, adolescents' perceptual similarity in the FFA was associated with increased social brain activation (e.g. temporal parietal junction). Mediation analyses indicated that high relationship quality was associated with greater social brain activation, which was mediated by greater self-mother overlap in the FFA. Our findings suggest that adolescents show more distinct neural patterns in perceiving their own vs their mother's distress, and such distinction is sensitive to mother-child relationship quality. In contrast, mothers' perception for their own and child's distress is highly similar and unconditional. © The Author (2017). Published by Oxford University Press.

Representation of cognitive reappraisal goals in frontal gamma oscillations.

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Kang, Jae-Hwan; Jeong, Ji Woon; Kim, Hyun Taek; Kim, Sang Hee; Kim, Sung-Phil

2014-01-01

Recently, numerous efforts have been made to understand the neural mechanisms underlying cognitive regulation of emotion, such as cognitive reappraisal. Many studies have reported that cognitive control of emotion induces increases in neural activity of the control system, including the prefrontal cortex and the dorsal anterior cingulate cortex, and increases or decreases (depending upon the regulation goal) in neural activity of the appraisal system, including the amygdala and the insula. It has been hypothesized that information about regulation goals needs to be processed through interactions between the control and appraisal systems in order to support cognitive reappraisal. However, how this information is represented in the dynamics of cortical activity remains largely unknown. To address this, we investigated temporal changes in gamma band activity (35-55 Hz) in human electroencephalograms during a cognitive reappraisal task that was comprised of three reappraisal goals: to decease, maintain, or increase emotional responses modulated by affect-laden pictures. We examined how the characteristics of gamma oscillations, such as spectral power and large-scale phase synchronization, represented cognitive reappraisal goals. We found that left frontal gamma power decreased, was sustained, or increased when the participants suppressed, maintained, or amplified their emotions, respectively. This change in left frontal gamma power appeared during an interval of 1926 to 2453 ms after stimulus onset. We also found that the number of phase-synchronized pairs of gamma oscillations over the entire brain increased when participants regulated their emotions compared to when they maintained their emotions. These results suggest that left frontal gamma power may reflect cortical representation of emotional states modulated by cognitive reappraisal goals and gamma phase synchronization across whole brain regions may reflect emotional regulatory efforts to achieve these goals

Knowledge extraction from evolving spiking neural networks with rank order population coding.

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Soltic, Snjezana; Kasabov, Nikola

2010-12-01

This paper demonstrates how knowledge can be extracted from evolving spiking neural networks with rank order population coding. Knowledge discovery is a very important feature of intelligent systems. Yet, a disproportionally small amount of research is centered on the issue of knowledge extraction from spiking neural networks which are considered to be the third generation of artificial neural networks. The lack of knowledge representation compatibility is becoming a major detriment to end users of these networks. We show that a high-level knowledge can be obtained from evolving spiking neural networks. More specifically, we propose a method for fuzzy rule extraction from an evolving spiking network with rank order population coding. The proposed method was used for knowledge discovery on two benchmark taste recognition problems where the knowledge learnt by an evolving spiking neural network was extracted in the form of zero-order Takagi-Sugeno fuzzy IF-THEN rules.

Internal measuring models in trained neural networks for parameter estimation from images

NARCIS (Netherlands)

Feng, Tian-Jin; Feng, T.J.; Houkes, Z.; Korsten, Maarten J.; Spreeuwers, Lieuwe Jan

1992-01-01

The internal representations of 'learned' knowledge in neural networks are still poorly understood, even for backpropagation networks. The paper discusses a possible interpretation of learned knowledge of a network trained for parameter estimation from images. The outputs of the hidden layer are the

Identification and integration of sensory modalities: Neural basis and relation to consciousness

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Pennartz, C.M.A.

2009-01-01

A key question in studying consciousness is how neural operations in the brain can identify streams of sensory input as belonging to distinct modalities, which contributes to the representation of qualitatively different experiences. The basis for identification of modalities is proposed to be

Automatic Seismic-Event Classification with Convolutional Neural Networks.

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Bueno Rodriguez, A.; Titos Luzón, M.; Garcia Martinez, L.; Benitez, C.; Ibáñez, J. M.

2017-12-01

Active volcanoes exhibit a wide range of seismic signals, providing vast amounts of unlabelled volcano-seismic data that can be analyzed through the lens of artificial intelligence. However, obtaining high-quality labelled data is time-consuming and expensive. Deep neural networks can process data in their raw form, compute high-level features and provide a better representation of the input data distribution. These systems can be deployed to classify seismic data at scale, enhance current early-warning systems and build extensive seismic catalogs. In this research, we aim to classify spectrograms from seven different seismic events registered at "Volcán de Fuego" (Colima, Mexico), during four eruptive periods. Our approach is based on convolutional neural networks (CNNs), a sub-type of deep neural networks that can exploit grid structure from the data. Volcano-seismic signals can be mapped into a grid-like structure using the spectrogram: a representation of the temporal evolution in terms of time and frequency. Spectrograms were computed from the data using Hamming windows with 4 seconds length, 2.5 seconds overlapping and 128 points FFT resolution. Results are compared to deep neural networks, random forest and SVMs. Experiments show that CNNs can exploit temporal and frequency information, attaining a classification accuracy of 93%, similar to deep networks 91% but outperforming SVM and random forest. These results empirically show that CNNs are powerful models to classify a wide range of volcano-seismic signals, and achieve good generalization. Furthermore, volcano-seismic spectrograms contains useful discriminative information for the CNN, as higher layers of the network combine high-level features computed for each frequency band, helping to detect simultaneous events in time. Being at the intersection of deep learning and geophysics, this research enables future studies of how CNNs can be used in volcano monitoring to accurately determine the detection and

Global exponential stability of uncertain fuzzy BAM neural networks with time-varying delays

International Nuclear Information System (INIS)

Syed Ali, M.; Balasubramaniam, P.

2009-01-01

In this paper, the Takagi-Sugeno (TS) fuzzy model representation is extended to the stability analysis for uncertain Bidirectional Associative Memory (BAM) neural networks with time-varying delays using linear matrix inequality (LMI) theory. A novel LMI-based stability criterion is obtained by LMI optimization algorithms to guarantee the exponential stability of uncertain BAM neural networks with time-varying delays which are represented by TS fuzzy models. Finally, the proposed stability conditions are demonstrated with numerical examples.

Monitoring nuclear reactor systems using neural networks and fuzzy logic

International Nuclear Information System (INIS)

Ikonomopoulos, A.; Tsoukalas, L.H.; Uhrig, R.E.; Mullens, J.A.

1992-01-01

A new approach is presented that demonstrates the potential of trained artificial neural networks (ANNs) as generators of membership functions for the purpose of monitoring nuclear reactor systems. ANN's provide a complex-to-simple mapping of reactor parameters in a process analogous to that of measurement. Through such virtual measurements the value of parameters with operational significance, e.g., control-valve-disk-position, valve-line-up-or performance can be determined. In the methodology presented the output of virtual measuring device is a set of membership functions which independently represent different states of the system. Utilizing a fuzzy logic representation offers the advantage of describing the state of the system in a condensed form, developed through linguistic descriptions and convenient for application in monitoring, diagnostics and generally control algorithms. The developed methodology is applied to the problem of measuring the disk position of the secondary flow control is clearly demonstrated as well as a method for selecting the actual output. The results suggest that it is possible to construct virtual measuring devices through artificial neural networks mapping dynamic time series to a set of membership functions and thus enhance the capability of monitoring systems

Medical Concept Normalization in Social Media Posts with Recurrent Neural Networks.

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Tutubalina, Elena; Miftahutdinov, Zulfat; Nikolenko, Sergey; Malykh, Valentin

2018-06-12

Text mining of scientific libraries and social media has already proven itself as a reliable tool for drug repurposing and hypothesis generation. The task of mapping a disease mention to a concept in a controlled vocabulary, typically to the standard thesaurus in the Unified Medical Language System (UMLS), is known as medical concept normalization. This task is challenging due to the differences in the use of medical terminology between health care professionals and social media texts coming from the lay public. To bridge this gap, we use sequence learning with recurrent neural networks and semantic representation of one- or multi-word expressions: we develop end-to-end architectures directly tailored to the task, including bidirectional Long Short-Term Memory, Gated Recurrent Units with an attention mechanism, and additional semantic similarity features based on UMLS. Our evaluation against a standard benchmark shows that recurrent neural networks improve results over an effective baseline for classification based on convolutional neural networks. A qualitative examination of mentions discovered in a dataset of user reviews collected from popular online health information platforms as well as a quantitative evaluation both show improvements in the semantic representation of health-related expressions in social media. Copyright © 2018. Published by Elsevier Inc.

Application of the minimum fuel neural network to music signals

DEFF Research Database (Denmark)

Harbo, Anders La-Cour

2004-01-01

) for finding sparse representations of music signals. This method is a set of two ordinary differential equations. We argue that the most important parameter for optimal use of this method is the discretization step size, and we demonstrate that this can be a priori determined. This significantly speeds up......Finding an optimal representation of a signal in an over-complete dictionary is often quite difficult. Since general results in this field are not very application friendly it truly helps to specify the framework as much as possible. We investigate the method Minimum Fuel Neural Network (MFNN...

Shared Representations and the Translation Process

DEFF Research Database (Denmark)

Schaeffer, Moritz; Carl, Michael

2015-01-01

The purpose of the present chapter is to investigate automated processing during translation. We provide evidence from a translation priming study which suggests that translation involves activation of shared lexico-semantic and syntactical representations, i.e., the activation of features of both...... source and target language items which share one single cognitive representation. We argue that activation of shared representations facilitates automated processing. The chapter revises the literal translation hypothesis and the monitor model (Ivir 1981; Toury 1995; Tirkkonen-Condit 2005), and re...

Shared Representations and the Translation Process

DEFF Research Database (Denmark)

Schaeffer, Moritz; Carl, Michael

2013-01-01

The purpose of the present paper is to investigate automated processing during translation. We provide evidence from a translation priming study which suggests that translation involves activation of shared lexico-semantic and syntactical representations, i.e., the activation of features of both...... source and target language items which share one single cognitive representation. We argue that activation of shared representations facilitates automated processing. The paper revises the literal translation hypothesis and the monitor model (Ivir 1981; Toury 1995; Tirkkonen-Condit 2005), and re...

Experience with adults shapes multisensory representation of social familiarity in the brain of a songbird.

Directory of Open Access Journals (Sweden)

Isabelle George

Full Text Available Social animals learn to perceive their social environment, and their social skills and preferences are thought to emerge from greater exposure to and hence familiarity with some social signals rather than others. Familiarity appears to be tightly linked to multisensory integration. The ability to differentiate and categorize familiar and unfamiliar individuals and to build a multisensory representation of known individuals emerges from successive social interactions, in particular with adult, experienced models. In different species, adults have been shown to shape the social behavior of young by promoting selective attention to multisensory cues. The question of what representation of known conspecifics adult-deprived animals may build therefore arises. Here we show that starlings raised with no experience with adults fail to develop a multisensory representation of familiar and unfamiliar starlings. Electrophysiological recordings of neuronal activity throughout the primary auditory area of these birds, while they were exposed to audio-only or audiovisual familiar and unfamiliar cues, showed that visual stimuli did, as in wild-caught starlings, modulate auditory responses but that, unlike what was observed in wild-caught birds, this modulation was not influenced by familiarity. Thus, adult-deprived starlings seem to fail to discriminate between familiar and unfamiliar individuals. This suggests that adults may shape multisensory representation of known individuals in the brain, possibly by focusing the young's attention on relevant, multisensory cues. Multisensory stimulation by experienced, adult models may thus be ubiquitously important for the development of social skills (and of the neural properties underlying such skills in a variety of species.

Using neural pattern classifiers to quantify the modularity of conflict-control mechanisms in the human brain.

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Jiang, Jiefeng; Egner, Tobias

2014-07-01

Resolving conflicting sensory and motor representations is a core function of cognitive control, but it remains uncertain to what degree control over different sources of conflict is implemented by shared (domain general) or distinct (domain specific) neural resources. Behavioral data suggest conflict-control to be domain specific, but results from neuroimaging studies have been ambivalent. Here, we employed multivoxel pattern analyses that can decode a brain region's informational content, allowing us to distinguish incidental activation overlap from actual shared information processing. We trained independent sets of "searchlight" classifiers on functional magnetic resonance imaging data to decode control processes associated with stimulus-conflict (Stroop task) and ideomotor-conflict (Simon task). Quantifying the proportion of domain-specific searchlights (capable of decoding only one type of conflict) and domain-general searchlights (capable of decoding both conflict types) in each subject, we found both domain-specific and domain-general searchlights, though the former were more common. When mapping anatomical loci of these searchlights across subjects, neural substrates of stimulus- and ideomotor-specific conflict-control were found to be anatomically consistent across subjects, whereas the substrates of domain-general conflict-control were not. Overall, these findings suggest a hybrid neural architecture of conflict-control that entails both modular (domain specific) and global (domain general) components. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Neural Mechanism for Mirrored Self-face Recognition.

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Sugiura, Motoaki; Miyauchi, Carlos Makoto; Kotozaki, Yuka; Akimoto, Yoritaka; Nozawa, Takayuki; Yomogida, Yukihito; Hanawa, Sugiko; Yamamoto, Yuki; Sakuma, Atsushi; Nakagawa, Seishu; Kawashima, Ryuta

2015-09-01

Self-face recognition in the mirror is considered to involve multiple processes that integrate 2 perceptual cues: temporal contingency of the visual feedback on one's action (contingency cue) and matching with self-face representation in long-term memory (figurative cue). The aim of this study was to examine the neural bases of these processes by manipulating 2 perceptual cues using a "virtual mirror" system. This system allowed online dynamic presentations of real-time and delayed self- or other facial actions. Perception-level processes were identified as responses to only a single perceptual cue. The effect of the contingency cue was identified in the cuneus. The regions sensitive to the figurative cue were subdivided by the response to a static self-face, which was identified in the right temporal, parietal, and frontal regions, but not in the bilateral occipitoparietal regions. Semantic- or integration-level processes, including amodal self-representation and belief validation, which allow modality-independent self-recognition and the resolution of potential conflicts between perceptual cues, respectively, were identified in distinct regions in the right frontal and insular cortices. The results are supportive of the multicomponent notion of self-recognition and suggest a critical role for contingency detection in the co-emergence of self-recognition and empathy in infants. © The Author 2014. Published by Oxford University Press.

High level cognitive information processing in neural networks

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Barnden, John A.; Fields, Christopher A.

1992-01-01

Two related research efforts were addressed: (1) high-level connectionist cognitive modeling; and (2) local neural circuit modeling. The goals of the first effort were to develop connectionist models of high-level cognitive processes such as problem solving or natural language understanding, and to understand the computational requirements of such models. The goals of the second effort were to develop biologically-realistic model of local neural circuits, and to understand the computational behavior of such models. In keeping with the nature of NASA's Innovative Research Program, all the work conducted under the grant was highly innovative. For instance, the following ideas, all summarized, are contributions to the study of connectionist/neural networks: (1) the temporal-winner-take-all, relative-position encoding, and pattern-similarity association techniques; (2) the importation of logical combinators into connection; (3) the use of analogy-based reasoning as a bridge across the gap between the traditional symbolic paradigm and the connectionist paradigm; and (4) the application of connectionism to the domain of belief representation/reasoning. The work on local neural circuit modeling also departs significantly from the work of related researchers. In particular, its concentration on low-level neural phenomena that could support high-level cognitive processing is unusual within the area of biological local circuit modeling, and also serves to expand the horizons of the artificial neural net field.

Neural correlates related to action observation in expert archers.

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Kim, Yang-Tae; Seo, Jee-Hye; Song, Hui-Jin; Yoo, Done-Sik; Lee, Hui Joong; Lee, Jongmin; Lee, Gunyoung; Kwon, Eunjin; Kim, Jin Goo; Chang, Yongmin

2011-10-01

A growing body of evidence suggests that activity of the mirror neuron system is dependent on the observer's motor experience of a given action. It remains unclear, however, whether activity of the mirror neuron system is also associated with the observer's motor experience in sports game. Therefore, the aim of the present study is to investigate differences in activation of the mirror neuron system during action observation between experts and non-archer control subjects. We used video of Western-style archery in which participants were asked to watch the archery movements. Hyperactivation of the premotor and inferior parietal cortex in expert archers relative to non-archer control subjects suggests that the human mirror neuron system could contain and expand representations of the motor repertoire. The fact that dorsomedial prefrontal cortex was more active in expert archers than in non-archer control subjects indicates a spontaneous engagement of theory of mind in experts when watching video of Western-style archery. Compared with the non-archer control subjects, expert archers showed greater activation in the neural system in regions associated with episodic recall from familiar and meaningful information, including the cingulate cortex, retrosplenial cortex, and parahippocampal gyrus. The results demonstrate that expertise effects stimulate brain activity not only in the mirror neuron system but also in the neural networks related to theory of mind and episodic memory. Copyright © 2011 Elsevier B.V. All rights reserved.

A Gloss Composition and Context Clustering Based Distributed Word Sense Representation Model

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Tao Chen

2015-08-01

Full Text Available In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word senses. This paper presents a novel approach which addresses these limitations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned representations outperform the publicly available embeddings on half of the metrics in the word similarity task, 6 out of 13 sub tasks in the analogical reasoning task, and gives the best overall accuracy in the word sense effect classification task, which shows the effectiveness of our proposed distributed distribution learning model.

The neural basis of precise visual short-term memory for complex recognisable objects.

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Veldsman, Michele; Mitchell, Daniel J; Cusack, Rhodri

2017-10-01

Recent evidence suggests that visual short-term memory (VSTM) capacity estimated using simple objects, such as colours and oriented bars, may not generalise well to more naturalistic stimuli. More visual detail can be stored in VSTM when complex, recognisable objects are maintained compared to simple objects. It is not yet known if it is recognisability that enhances memory precision, nor whether maintenance of recognisable objects is achieved with the same network of brain regions supporting maintenance of simple objects. We used a novel stimulus generation method to parametrically warp photographic images along a continuum, allowing separate estimation of the precision of memory representations and the number of items retained. The stimulus generation method was also designed to create unrecognisable, though perceptually matched, stimuli, to investigate the impact of recognisability on VSTM. We adapted the widely-used change detection and continuous report paradigms for use with complex, photographic images. Across three functional magnetic resonance imaging (fMRI) experiments, we demonstrated greater precision for recognisable objects in VSTM compared to unrecognisable objects. This clear behavioural advantage was not the result of recruitment of additional brain regions, or of stronger mean activity within the core network. Representational similarity analysis revealed greater variability across item repetitions in the representations of recognisable, compared to unrecognisable complex objects. We therefore propose that a richer range of neural representations support VSTM for complex recognisable objects. Copyright © 2017 Elsevier Inc. All rights reserved.

The neural network z-vertex trigger for the Belle II detector

Energy Technology Data Exchange (ETDEWEB)

Skambraks, Sebastian; Neuhaus, Sara [Technische Universitaet Muenchen (Germany); Chen, Yang; Kiesling, Christian [Max-Planck-Institut fuer Physik, Muenchen (Germany); Collaboration: Belle II-Collaboration

2016-07-01

We present a neural network based first level track trigger for the upcoming Belle II detector at the high luminosity SuperKEKB flavor factory. Using hit and drift time information from the Central Drift Chamber (CDC), neural networks estimate the z-coordinates of single track vertex positions. Especially beam induced background, with vertices outside of the interaction region, can clearly be rejected. This allows to relax the track trigger conditions and thus enhances the efficiency for events with a low track multiplicity. In the CDC trigger pipeline, the preceding 2D pattern recognition enables a unique per track input representation and a sectorization of the track parameter phase space. The precise z-vertices are then estimated by an ensemble of sector-specific local expert neural networks. After an introduction to the neural trigger system, the benefits of an improved 3D pattern recognition are discussed.

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

Science.gov (United States)

2017-01-01

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. Availability: weilab.math.msu.edu/TDL/ PMID:28749969

The Functional Role of Neural Oscillations in Non-Verbal Emotional Communication.

Science.gov (United States)

Symons, Ashley E; El-Deredy, Wael; Schwartze, Michael; Kotz, Sonja A

2016-01-01

Effective interpersonal communication depends on the ability to perceive and interpret nonverbal emotional expressions from multiple sensory modalities. Current theoretical models propose that visual and auditory emotion perception involves a network of brain regions including the primary sensory cortices, the superior temporal sulcus (STS), and orbitofrontal cortex (OFC). However, relatively little is known about how the dynamic interplay between these regions gives rise to the perception of emotions. In recent years, there has been increasing recognition of the importance of neural oscillations in mediating neural communication within and between functional neural networks. Here we review studies investigating changes in oscillatory activity during the perception of visual, auditory, and audiovisual emotional expressions, and aim to characterize the functional role of neural oscillations in nonverbal emotion perception. Findings from the reviewed literature suggest that theta band oscillations most consistently differentiate between emotional and neutral expressions. While early theta synchronization appears to reflect the initial encoding of emotionally salient sensory information, later fronto-central theta synchronization may reflect the further integration of sensory information with internal representations. Additionally, gamma synchronization reflects facilitated sensory binding of emotional expressions within regions such as the OFC, STS, and, potentially, the amygdala. However, the evidence is more ambiguous when it comes to the role of oscillations within the alpha and beta frequencies, which vary as a function of modality (or modalities), presence or absence of predictive information, and attentional or task demands. Thus, the synchronization of neural oscillations within specific frequency bands mediates the rapid detection, integration, and evaluation of emotional expressions. Moreover, the functional coupling of oscillatory activity across multiples

When mirroring is both simple and smart: How mimicry can be embodied, adaptive, and non-representational

Directory of Open Access Journals (Sweden)

Evan Walker Carr

2014-07-01

Full Text Available The concept of mirroring has become rather ubiquitous. One of the most fundamental empirical and theoretical debates within research on mirroring concerns the role of mental representations: While some models argue that higher-order representational mechanisms underpin most cases of mirroring, other models argue that they only moderate a primarily non-representational process. As such, even though research on mirroring—along with its neural substrates, including the putative mirror neuron system—has grown tremendously, so too has confusion about what it actually means to mirror. Using recent research on spontaneous imitation, we argue that flexible mirroring effects can be fully embodied and dynamic—even in the absence of higher-order mental representations. We propose that mirroring can simply reflect an adaptive integration and utilization of cues obtained from the brain, body, and environment, which is especially evident within the social context. Such a view offers reconciliation among both representational and non-representational frameworks in cognitive neuroscience, which will facilitate revised interpretations of modern (and seemingly divergent findings on when and how these embodied mirroring responses are employed.

Character Recognition Using Genetically Trained Neural Networks

Energy Technology Data Exchange (ETDEWEB)

Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

1998-10-01

Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfid recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the

Analysis of infant cry through weighted linear prediction cepstral coefficients and Probabilistic Neural Network.

Science.gov (United States)

Hariharan, M; Chee, Lim Sin; Yaacob, Sazali

2012-06-01

Acoustic analysis of infant cry signals has been proven to be an excellent tool in the area of automatic detection of pathological status of an infant. This paper investigates the application of parameter weighting for linear prediction cepstral coefficients (LPCCs) to provide the robust representation of infant cry signals. Three classes of infant cry signals were considered such as normal cry signals, cry signals from deaf babies and babies with asphyxia. A Probabilistic Neural Network (PNN) is suggested to classify the infant cry signals into normal and pathological cries. PNN is trained with different spread factor or smoothing parameter to obtain better classification accuracy. The experimental results demonstrate that the suggested features and classification algorithms give very promising classification accuracy of above 98% and it expounds that the suggested method can be used to help medical professionals for diagnosing pathological status of an infant from cry signals.

Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.

Science.gov (United States)

Kriegeskorte, Nikolaus

2015-11-24

Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.

Empirical modeling of nuclear power plants using neural networks

International Nuclear Information System (INIS)

Parlos, A.G.; Atiya, A.; Chong, K.T.

1991-01-01

A summary of a procedure for nonlinear identification of process dynamics encountered in nuclear power plant components is presented in this paper using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the nonlinear structure for system identification. In the overall identification process, the feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of time-dependent system nonlinearities. The standard backpropagation learning algorithm is modified and is used to train the proposed hybrid network in a supervised manner. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The nonlinear response of a representative steam generator is predicted using a neural network and is compared to the response obtained from a sophisticated physical model during both high- and low-power operation. The transient responses compare well, though further research is warranted for training and testing of recurrent neural networks during more severe operational transients and accident scenarios

A Novel Method of Case Representation and Retrieval in CBR for E-Learning

Science.gov (United States)

Khamparia, Aditya; Pandey, Babita

2017-01-01

In this paper we have discussed a novel method which has been developed for representation and retrieval of cases in case based reasoning (CBR) as a part of e-learning system which is based on various student features. In this approach we have integrated Artificial Neural Network (ANN) with Data mining (DM) and CBR. ANN is used to find the…

Pre-attentive, context-specific representation of fear memory in the auditory cortex of rat.

Directory of Open Access Journals (Sweden)

Akihiro Funamizu

Full Text Available Neural representation in the auditory cortex is rapidly modulated by both top-down attention and bottom-up stimulus properties, in order to improve perception in a given context. Learning-induced, pre-attentive, map plasticity has been also studied in the anesthetized cortex; however, little attention has been paid to rapid, context-dependent modulation. We hypothesize that context-specific learning leads to pre-attentively modulated, multiplex representation in the auditory cortex. Here, we investigate map plasticity in the auditory cortices of anesthetized rats conditioned in a context-dependent manner, such that a conditioned stimulus (CS of a 20-kHz tone and an unconditioned stimulus (US of a mild electrical shock were associated only under a noisy auditory context, but not in silence. After the conditioning, although no distinct plasticity was found in the tonotopic map, tone-evoked responses were more noise-resistive than pre-conditioning. Yet, the conditioned group showed a reduced spread of activation to each tone with noise, but not with silence, associated with a sharpening of frequency tuning. The encoding accuracy index of neurons showed that conditioning deteriorated the accuracy of tone-frequency representations in noisy condition at off-CS regions, but not at CS regions, suggesting that arbitrary tones around the frequency of the CS were more likely perceived as the CS in a specific context, where CS was associated with US. These results together demonstrate that learning-induced plasticity in the auditory cortex occurs in a context-dependent manner.

Pre-attentive, context-specific representation of fear memory in the auditory cortex of rat.

Science.gov (United States)

Funamizu, Akihiro; Kanzaki, Ryohei; Takahashi, Hirokazu

2013-01-01

Neural representation in the auditory cortex is rapidly modulated by both top-down attention and bottom-up stimulus properties, in order to improve perception in a given context. Learning-induced, pre-attentive, map plasticity has been also studied in the anesthetized cortex; however, little attention has been paid to rapid, context-dependent modulation. We hypothesize that context-specific learning leads to pre-attentively modulated, multiplex representation in the auditory cortex. Here, we investigate map plasticity in the auditory cortices of anesthetized rats conditioned in a context-dependent manner, such that a conditioned stimulus (CS) of a 20-kHz tone and an unconditioned stimulus (US) of a mild electrical shock were associated only under a noisy auditory context, but not in silence. After the conditioning, although no distinct plasticity was found in the tonotopic map, tone-evoked responses were more noise-resistive than pre-conditioning. Yet, the conditioned group showed a reduced spread of activation to each tone with noise, but not with silence, associated with a sharpening of frequency tuning. The encoding accuracy index of neurons showed that conditioning deteriorated the accuracy of tone-frequency representations in noisy condition at off-CS regions, but not at CS regions, suggesting that arbitrary tones around the frequency of the CS were more likely perceived as the CS in a specific context, where CS was associated with US. These results together demonstrate that learning-induced plasticity in the auditory cortex occurs in a context-dependent manner.

Motor memory is encoded as a gain-field combination of intrinsic and extrinsic action representations.

Science.gov (United States)

Brayanov, Jordan B; Press, Daniel Z; Smith, Maurice A

2012-10-24

Actions can be planned in either an intrinsic (body-based) reference frame or an extrinsic (world-based) frame, and understanding how the internal representations associated with these frames contribute to the learning of motor actions is a key issue in motor control. We studied the internal representation of this learning in human subjects by analyzing generalization patterns across an array of different movement directions and workspaces after training a visuomotor rotation in a single movement direction in one workspace. This provided a dense sampling of the generalization function across intrinsic and extrinsic reference frames, which allowed us to dissociate intrinsic and extrinsic representations and determine the manner in which they contributed to the motor memory for a trained action. A first experiment showed that the generalization pattern reflected a memory that was intermediate between intrinsic and extrinsic representations. A second experiment showed that this intermediate representation could not arise from separate intrinsic and extrinsic learning. Instead, we find that the representation of learning is based on a gain-field combination of local representations in intrinsic and extrinsic coordinates. This gain-field representation generalizes between actions by effectively computing similarity based on the (Mahalanobis) distance across intrinsic and extrinsic coordinates and is in line with neural recordings showing mixed intrinsic-extrinsic representations in motor and parietal cortices.

SORN: a self-organizing recurrent neural network

Directory of Open Access Journals (Sweden)

Andreea Lazar

2009-10-01

Full Text Available Understanding the dynamics of recurrent neural networks is crucial for explaining how the brain processes information. In the neocortex, a range of different plasticity mechanisms are shaping recurrent networks into effective information processing circuits that learn appropriate representations for time-varying sensory stimuli. However, it has been difficult to mimic these abilities in artificial neural network models. Here we introduce SORN, a self-organizing recurrent network. It combines three distinct forms of local plasticity to learn spatio-temporal patterns in its input while maintaining its dynamics in a healthy regime suitable for learning. The SORN learns to encode information in the form of trajectories through its high-dimensional state space reminiscent of recent biological findings on cortical coding. All three forms of plasticity are shown to be essential for the network's success.

Sign Language Recognition System using Neural Network for Digital Hardware Implementation

International Nuclear Information System (INIS)

Vargas, Lorena P; Barba, Leiner; Torres, C O; Mattos, L

2011-01-01

This work presents an image pattern recognition system using neural network for the identification of sign language to deaf people. The system has several stored image that show the specific symbol in this kind of language, which is employed to teach a multilayer neural network using a back propagation algorithm. Initially, the images are processed to adapt them and to improve the performance of discriminating of the network, including in this process of filtering, reduction and elimination noise algorithms as well as edge detection. The system is evaluated using the signs without including movement in their representation.

Neural markers of opposite-sex bias in face processing

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Alice Mado eProverbio

2010-10-01

Full Text Available Some behavioral and neuroimaging studies suggest that adults prefer to view attractive faces of the opposite sex more than attractive faces of the same sex. However, unlike the other-race face effect (ORE; Caldara et al., 2004, little is known regarding the existence of an opposite-/same-sex bias in face processing. In this study, the faces of 130 attractive male and female adults were foveally presented to 40 heterosexual university students (20 men and 20 women who were engaged in a secondary perceptual task (landscape detection. The automatic processing of face gender was investigated by recording ERPs from 128 scalp sites. Neural markers of opposite- vs. same-sex bias in face processing included larger and earlier centro-parietal N400s in response to faces of the opposite sex and a larger late positivity (LP to same-sex faces. Analysis of intra-cortical neural generators (swLORETA showed that facial processing-related (FG, BA37, BA20/21 and emotion-related brain areas (the right parahippocampal gyrus, BA35; uncus, BA36/38; and the cingulate gyrus, BA24 had higher activations in response to opposite- than same-sex faces. The results of this analysis, along with data obtained from ERP recordings, support the hypothesis that both genders process opposite-sex faces differently than same-sex faces. The data also suggest a hemispheric asymmetry in the processing of opposite-/same-sex faces, with the right hemisphere involved in processing same-sex faces and the left hemisphere involved in processing faces of the opposite sex. The data support previous literature suggesting a right lateralization for the representation of self-image and body awareness.

Neural markers of opposite-sex bias in face processing.

Science.gov (United States)

Proverbio, Alice Mado; Riva, Federica; Martin, Eleonora; Zani, Alberto

2010-01-01

Some behavioral and neuroimaging studies suggest that adults prefer to view attractive faces of the opposite sex more than attractive faces of the same sex. However, unlike the other-race face effect (Caldara et al., 2004), little is known regarding the existence of an opposite-/same-sex bias in face processing. In this study, the faces of 130 attractive male and female adults were foveally presented to 40 heterosexual university students (20 men and 20 women) who were engaged in a secondary perceptual task (landscape detection). The automatic processing of face gender was investigated by recording ERPs from 128 scalp sites. Neural markers of opposite- vs. same-sex bias in face processing included larger and earlier centro-parietal N400s in response to faces of the opposite sex and a larger late positivity (LP) to same-sex faces. Analysis of intra-cortical neural generators (swLORETA) showed that facial processing-related (FG, BA37, BA20/21) and emotion-related brain areas (the right parahippocampal gyrus, BA35; uncus, BA36/38; and the cingulate gyrus, BA24) had higher activations in response to opposite- than same-sex faces. The results of this analysis, along with data obtained from ERP recordings, support the hypothesis that both genders process opposite-sex faces differently than same-sex faces. The data also suggest a hemispheric asymmetry in the processing of opposite-/same-sex faces, with the right hemisphere involved in processing same-sex faces and the left hemisphere involved in processing faces of the opposite sex. The data support previous literature suggesting a right lateralization for the representation of self-image and body awareness.

Smart-phone based electrocardiogram wavelet decomposition and neural network classification

International Nuclear Information System (INIS)

Jannah, N; Hadjiloucas, S; Hwang, F; Galvão, R K H

2013-01-01

This paper discusses ECG classification after parametrizing the ECG waveforms in the wavelet domain. The aim of the work is to develop an accurate classification algorithm that can be used to diagnose cardiac beat abnormalities detected using a mobile platform such as smart-phones. Continuous time recurrent neural network classifiers are considered for this task. Records from the European ST-T Database are decomposed in the wavelet domain using discrete wavelet transform (DWT) filter banks and the resulting DWT coefficients are filtered and used as inputs for training the neural network classifier. Advantages of the proposed methodology are the reduced memory requirement for the signals which is of relevance to mobile applications as well as an improvement in the ability of the neural network in its generalization ability due to the more parsimonious representation of the signal to its inputs.

Boosted Jet Tagging with Jet-Images and Deep Neural Networks

International Nuclear Information System (INIS)

Kagan, Michael; Oliveira, Luke de; Mackey, Lester; Nachman, Benjamin; Schwartzman, Ariel

2016-01-01

Building on the jet-image based representation of high energy jets, we develop computer vision based techniques for jet tagging through the use of deep neural networks. Jet-images enabled the connection between jet substructure and tagging with the fields of computer vision and image processing. We show how applying such techniques using deep neural networks can improve the performance to identify highly boosted W bosons with respect to state-of-the-art substructure methods. In addition, we explore new ways to extract and visualize the discriminating features of different classes of jets, adding a new capability to understand the physics within jets and to design more powerful jet tagging methods

Usage of semantic representations in recognition memory.

Science.gov (United States)

Nishiyama, Ryoji; Hirano, Tetsuji; Ukita, Jun

2017-11-01

Meanings of words facilitate false acceptance as well as correct rejection of lures in recognition memory tests, depending on the experimental context. This suggests that semantic representations are both directly and indirectly (i.e., mediated by perceptual representations) used in remembering. Studies using memory conjunction errors (MCEs) paradigms, in which the lures consist of component parts of studied words, have reported semantic facilitation of rejection of the lures. However, attending to components of the lures could potentially cause this. Therefore, we investigated whether semantic overlap of lures facilitates MCEs using Japanese Kanji words in which a whole-word image is more concerned in reading. Experiments demonstrated semantic facilitation of MCEs in a delayed recognition test (Experiment 1), and in immediate recognition tests in which participants were prevented from using phonological or orthographic representations (Experiment 2), and the salient effect on individuals with high semantic memory capacities (Experiment 3). Additionally, analysis of the receiver operating characteristic suggested that this effect is attributed to familiarity-based memory judgement and phantom recollection. These findings indicate that semantic representations can be directly used in remembering, even when perceptual representations of studied words are available.

Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision.

Science.gov (United States)

Shi, Junxing; Wen, Haiguang; Zhang, Yizhen; Han, Kuan; Liu, Zhongming

2018-05-01

The human visual cortex extracts both spatial and temporal visual features to support perception and guide behavior. Deep convolutional neural networks (CNNs) provide a computational framework to model cortical representation and organization for spatial visual processing, but unable to explain how the brain processes temporal information. To overcome this limitation, we extended a CNN by adding recurrent connections to different layers of the CNN to allow spatial representations to be remembered and accumulated over time. The extended model, or the recurrent neural network (RNN), embodied a hierarchical and distributed model of process memory as an integral part of visual processing. Unlike the CNN, the RNN learned spatiotemporal features from videos to enable action recognition. The RNN better predicted cortical responses to natural movie stimuli than the CNN, at all visual areas, especially those along the dorsal stream. As a fully observable model of visual processing, the RNN also revealed a cortical hierarchy of temporal receptive window, dynamics of process memory, and spatiotemporal representations. These results support the hypothesis of process memory, and demonstrate the potential of using the RNN for in-depth computational understanding of dynamic natural vision. © 2018 Wiley Periodicals, Inc.

Application of artificial neural networks to evaluate weld defects of nuclear components

International Nuclear Information System (INIS)

Amin, E.S.

2007-01-01

Artificial neural networks (ANNs) are computational representations based on the biological neural architecture of the brain. ANNs have been successfully applied to a wide range of engineering and scientific applications, such as signal, image processing and data analysis. Although Radiographic testing is widely used for welding defects, it is unsuccessful in identifying some welding defects because of the nature of image formation and quality. Neoteric algorithms have been used for the purpose of weld defects identifications in radiographic images to replace the expert knowledge. The application of artificial neural networks in noise detection of radiographic films is used. Radial Basis (RB) and learning vector quantization (LVQ) were applied. The method shows good performance in weld defects recognition and classification problems.

Learning, memory, and the role of neural network architecture.

Directory of Open Access Journals (Sweden)

Ann M Hermundstad

2011-06-01

Full Text Available The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems.

Interactions between visual working memory representations.

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Bae, Gi-Yeul; Luck, Steven J

2017-11-01

We investigated whether the representations of different objects are maintained independently in working memory or interact with each other. Observers were shown two sequentially presented orientations and required to reproduce each orientation after a delay. The sequential presentation minimized perceptual interactions so that we could isolate interactions between memory representations per se. We found that similar orientations were repelled from each other whereas dissimilar orientations were attracted to each other. In addition, when one of the items was given greater attentional priority by means of a cue, the representation of the high-priority item was not influenced very much by the orientation of the low-priority item, but the representation of the low-priority item was strongly influenced by the orientation of the high-priority item. This indicates that attention modulates the interactions between working memory representations. In addition, errors in the reported orientations of the two objects were positively correlated under some conditions, suggesting that representations of distinct objects may become grouped together in memory. Together, these results demonstrate that working-memory representations are not independent but instead interact with each other in a manner that depends on attentional priority.

Amodal Semantic Representations Depend on both Anterior Temporal Lobes: Evidence from Repetitive Transcranial Magnetic Stimulation

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Pobric, Gorana; Jefferies, Elizabeth; Ralph, Matthew A. Lambon

2010-01-01

The key question of how the brain codes the meaning of words and pictures is the focus of vigorous debate. Is there a "semantic hub" in the temporal poles where these different inputs converge to form amodal conceptual representations? Alternatively, are there distinct neural circuits that underpin our comprehension of pictures and words?…

Neural and Computational Mechanisms of Action Processing: Interaction between Visual and Motor Representations.

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Giese, Martin A; Rizzolatti, Giacomo

2015-10-07

Action recognition has received enormous interest in the field of neuroscience over the last two decades. In spite of this interest, the knowledge in terms of fundamental neural mechanisms that provide constraints for underlying computations remains rather limited. This fact stands in contrast with a wide variety of speculative theories about how action recognition might work. This review focuses on new fundamental electrophysiological results in monkeys, which provide constraints for the detailed underlying computations. In addition, we review models for action recognition and processing that have concrete mathematical implementations, as opposed to conceptual models. We think that only such implemented models can be meaningfully linked quantitatively to physiological data and have a potential to narrow down the many possible computational explanations for action recognition. In addition, only concrete implementations allow judging whether postulated computational concepts have a feasible implementation in terms of realistic neural circuits. Copyright © 2015 Elsevier Inc. All rights reserved.

Remarks on the history of the terms "object representation" and "self representation".

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May, Ulrike

2005-01-01

This paper reconstructs the history of the term "object representation" and "self representation". It seeks to show that "Objektrepräsentanz" was introduced by Fenichel in 1926, following on from Radó, in order to be able to integrate identification (and the superego) into metapsychology. Freud himself never used "Objektrepräsentanz". Fenichel's pioneering role is not discernible in the English literature mainly because of the diverging approaches used in the translation of this term (object representative versus object representation). It is generally acknowledged that "self representation" was first used by Hartmann but this paper suggests that it actually played a more crucial role in Jacobson's work than it did in Hartmann's. In addition, this paper sees the terms of self and object representation as a reflection of the paradigm change in the 1920s that ensued after the publication of Freud's "The Ego and the Id". In tracing the history of the terms, the significance of the Berlin Psychoanalytical Institute in the 1920s emerges as do the Berlin roots of the works written in the USA in the 1950s and 1960s by Edith Jacobson. She received her analytical training in Berlin. Fenichel was her analyst, Radó was one of her teachers, and she was closely involved with the work of her fellow analysts there.

A Self-Organizing Incremental Neural Network based on local distribution learning.

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Xing, Youlu; Shi, Xiaofeng; Shen, Furao; Zhou, Ke; Zhao, Jinxi

2016-12-01

In this paper, we propose an unsupervised incremental learning neural network based on local distribution learning, which is called Local Distribution Self-Organizing Incremental Neural Network (LD-SOINN). The LD-SOINN combines the advantages of incremental learning and matrix learning. It can automatically discover suitable nodes to fit the learning data in an incremental way without a priori knowledge such as the structure of the network. The nodes of the network store rich local information regarding the learning data. The adaptive vigilance parameter guarantees that LD-SOINN is able to add new nodes for new knowledge automatically and the number of nodes will not grow unlimitedly. While the learning process continues, nodes that are close to each other and have similar principal components are merged to obtain a concise local representation, which we call a relaxation data representation. A denoising process based on density is designed to reduce the influence of noise. Experiments show that the LD-SOINN performs well on both artificial and real-word data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Enhancing business intelligence by means of suggestive reviews.

Science.gov (United States)

Qazi, Atika; Raj, Ram Gopal; Tahir, Muhammad; Cambria, Erik; Syed, Karim Bux Shah

2014-01-01

Appropriate identification and classification of online reviews to satisfy the needs of current and potential users pose a critical challenge for the business environment. This paper focuses on a specific kind of reviews: the suggestive type. Suggestions have a significant influence on both consumers' choices and designers' understanding and, hence, they are key for tasks such as brand positioning and social media marketing. The proposed approach consists of three main steps: (1) classify comparative and suggestive sentences; (2) categorize suggestive sentences into different types, either explicit or implicit locutions; (3) perform sentiment analysis on the classified reviews. A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. Experimental results for all three tasks are obtained on a dataset of mobile phone reviews and demonstrate that extending a bag-of-words representation with suggestive and comparative patterns is ideal for distinguishing suggestive sentences. In particular, it is observed that classifying suggestive sentences into implicit and explicit locutions works best when using a mixed sequential rule feature representation. Sentiment analysis achieves maximum performance when employing additional preprocessing in the form of negation handling and target masking, combined with sentiment lexicons.

Body-specific motor imagery of hand actions: neural evidence from right- and left-handers

Directory of Open Access Journals (Sweden)

Roel M Willems

2009-11-01

Full Text Available If motor imagery uses neural structures involved in action execution, then the neural correlates of imagining an action should differ between individuals who tend to execute the action differently. Here we report fMRI data showing that motor imagery is influenced by the way people habitually perform motor actions with their particular bodies; that is, motor imagery is ‘body-specific’ (Casasanto, 2009. During mental imagery for complex hand actions, activation of cortical areas involved in motor planning and execution was left-lateralized in right-handers but right-lateralized in left-handers. We conclude that motor imagery involves the generation of an action plan that is grounded in the participant’s motor habits, not just an abstract representation at the level of the action’s goal. People with different patterns of motor experience form correspondingly different neurocognitive representations of imagined actions.

Learning-induced neural plasticity of speech processing before birth.

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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.

Neural basis of feature-based contextual effects on visual search behavior

Directory of Open Access Journals (Sweden)

Kelly eShen

2012-01-01

Full Text Available Searching for a visual object is known to be adaptable to context, and it is thought to result from the selection of neural representations distributed on a visual salience map, wherein stimulus-driven and goal-directed signals are combined. Here we investigated the neural basis of this adaptability by recording superior colliculus (SC neurons while three female rhesus monkeys (Macaca mulatta searched with saccadic eye movements for a target presented in an array of visual stimuli whose feature composition varied from trial to trial. We found that sensory-motor activity associated with distracters was enhanced or suppressed depending on the search array composition and that it corresponded to the monkey's search strategy, as assessed by the distribution of the occasional errant saccades. This feature-related modulation occurred independently from the saccade goal and facilitated the process of saccade target selection. We also observed feature-related enhancement in the activity associated with distracters that had been the search target during the previous session. Consistent with recurrent processing, both feature-related neuronal modulations occurred more than 60 ms after the onset of the visually evoked responses, and their near coincidence with the time of saccade target selection suggests that they are integral to this process. These results suggest that SC neuronal activity is shaped by the visual context as dictated by both stimulus-driven and goal-directed signals. Given the close proximity of the SC to the motor circuit, our findings suggest a direct link between perception and action and no need for distinct salience and motor maps.

The Emergence and Representation of Knowledge about Social and Nonsocial Hierarchies

Science.gov (United States)

Kumaran, Dharshan; Melo, Hans Ludwig; Duzel, Emrah

2012-01-01

Summary Primates are remarkably adept at ranking each other within social hierarchies, a capacity that is critical to successful group living. Surprisingly little, however, is understood about the neurobiology underlying this quintessential aspect of primate cognition. In our experiment, participants first acquired knowledge about a social and a nonsocial hierarchy and then used this information to guide investment decisions. We found that neural activity in the amygdala tracked the development of knowledge about a social, but not a nonsocial, hierarchy. Further, structural variations in amygdala gray matter volume accounted for interindividual differences in social transitivity performance. Finally, the amygdala expressed a neural signal selectively coding for social rank, whose robustness predicted the influence of rank on participants’ investment decisions. In contrast, we observed that the linear structure of both social and nonsocial hierarchies was represented at a neural level in the hippocampus. Our study implicates the amygdala in the emergence and representation of knowledge about social hierarchies and distinguishes the domain-general contribution of the hippocampus. PMID:23141075

HWNet v2: An Efficient Word Image Representation for Handwritten Documents

OpenAIRE

Krishnan, Praveen; Jawahar, C. V.

2018-01-01

We present a framework for learning efficient holistic representation for handwritten word images. The proposed method uses a deep convolutional neural network with traditional classification loss. The major strengths of our work lie in: (i) the efficient usage of synthetic data to pre-train a deep network, (ii) an adapted version of ResNet-34 architecture with region of interest pooling (referred as HWNet v2) which learns discriminative features with variable sized word images, and (iii) rea...

A review of the neural and behavioral consequences for unitizing emotional and neutral information

Directory of Open Access Journals (Sweden)

Brendan eMurray

2013-05-01

Full Text Available A special type of association, called a unitization, is formed when pieces of information are encoded as a single representation in memory (e.g., shirt and blue are encoded as a blue shirt; Graf & Schacter, 1989 and typically are later reactivated in memory as a single unit, allowing access to the features of multiple related stimuli at once (Bader et al., 2010; Diana et al., 2011. This review examines the neural processes supporting memory for unitizations and how the emotional content of the material may influence unitization. Although associative binding is typically reliant on hippocampal processes and supported by recollection, the first part of this review will present evidence to suggest that when two items are unitized into a single representation, memory for those bound items may be accomplished on the basis of familiarity and without reliance on the hippocampus. The second part of this review discusses how emotion may affect the processes that give rise to unitizations. Emotional information typically receives a mnemonic benefit over neutral information, but the literature is mixed on whether the presence of emotional information impedes or enhances the associative binding of neutral information (reviewed by Mather, 2007. It has been suggested that the way the emotional and neutral details are related together may be critical to whether the neutral details are enhanced or impeded (Mather, 2007; Mather & Sutherland, 2011. We focus on whether emotional arousal aides or inhibits the creation of a unitized representation, presenting preliminary data and future directions to test empirically the effects of forming and retrieving emotional and neutral unitizations.

Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models.

Science.gov (United States)

Khaligh-Razavi, Seyed-Mahdi; Henriksson, Linda; Kay, Kendrick; Kriegeskorte, Nikolaus

2017-02-01

Studies of the primate visual system have begun to test a wide range of complex computational object-vision models. Realistic models have many parameters, which in practice cannot be fitted using the limited amounts of brain-activity data typically available. Task performance optimization (e.g. using backpropagation to train neural networks) provides major constraints for fitting parameters and discovering nonlinear representational features appropriate for the task (e.g. object classification). Model representations can be compared to brain representations in terms of the representational dissimilarities they predict for an image set. This method, called representational similarity analysis (RSA), enables us to test the representational feature space as is (fixed RSA) or to fit a linear transformation that mixes the nonlinear model features so as to best explain a cortical area's representational space (mixed RSA). Like voxel/population-receptive-field modelling, mixed RSA uses a training set (different stimuli) to fit one weight per model feature and response channel (voxels here), so as to best predict the response profile across images for each response channel. We analysed response patterns elicited by natural images, which were measured with functional magnetic resonance imaging (fMRI). We found that early visual areas were best accounted for by shallow models, such as a Gabor wavelet pyramid (GWP). The GWP model performed similarly with and without mixing, suggesting that the original features already approximated the representational space, obviating the need for mixing. However, a higher ventral-stream visual representation (lateral occipital region) was best explained by the higher layers of a deep convolutional network and mixing of its feature set was essential for this model to explain the representation. We suspect that mixing was essential because the convolutional network had been trained to discriminate a set of 1000 categories, whose frequencies

Sparse distributed representation of odors in a large-scale olfactory bulb circuit.

Directory of Open Access Journals (Sweden)

Yuguo Yu

Full Text Available In the olfactory bulb, lateral inhibition mediated by granule cells has been suggested to modulate the timing of mitral cell firing, thereby shaping the representation of input odorants. Current experimental techniques, however, do not enable a clear study of how the mitral-granule cell network sculpts odor inputs to represent odor information spatially and temporally. To address this critical step in the neural basis of odor recognition, we built a biophysical network model of mitral and granule cells, corresponding to 1/100th of the real system in the rat, and used direct experimental imaging data of glomeruli activated by various odors. The model allows the systematic investigation and generation of testable hypotheses of the functional mechanisms underlying odor representation in the olfactory bulb circuit. Specifically, we demonstrate that lateral inhibition emerges within the olfactory bulb network through recurrent dendrodendritic synapses when constrained by a range of balanced excitatory and inhibitory conductances. We find that the spatio-temporal dynamics of lateral inhibition plays a critical role in building the glomerular-related cell clusters observed in experiments, through the modulation of synaptic weights during odor training. Lateral inhibition also mediates the development of sparse and synchronized spiking patterns of mitral cells related to odor inputs within the network, with the frequency of these synchronized spiking patterns also modulated by the sniff cycle.

Neural networks underlying language and social cognition during self-other processing in Autism spectrum disorders.

Science.gov (United States)

Kana, Rajesh K; Sartin, Emma B; Stevens, Carl; Deshpande, Hrishikesh D; Klein, Christopher; Klinger, Mark R; Klinger, Laura Grofer

2017-07-28

The social communication impairments defining autism spectrum disorders (ASD) may be built upon core deficits in perspective-taking, language processing, and self-other representation. Self-referential processing entails the ability to incorporate self-awareness, self-judgment, and self-memory in information processing. Very few studies have examined the neural bases of integrating self-other representation and semantic processing in individuals with ASD. The main objective of this functional MRI study is to examine the role of language and social brain networks in self-other processing in young adults with ASD. Nineteen high-functioning male adults with ASD and 19 age-sex-and-IQ-matched typically developing (TD) control participants made "yes" or "no" judgments of whether an adjective, presented visually, described them (self) or their favorite teacher (other). Both ASD and TD participants showed significantly increased activity in the medial prefrontal cortex (MPFC) during self and other processing relative to letter search. Analyses of group differences revealed significantly reduced activity in left inferior frontal gyrus (LIFG), and left inferior parietal lobule (LIPL) in ASD participants, relative to TD controls. ASD participants also showed significantly weaker functional connectivity of the anterior cingulate cortex (ACC) with several brain areas while processing self-related words. The LIFG and IPL are important regions functionally at the intersection of language and social roles; reduced recruitment of these regions in ASD participants may suggest poor level of semantic and social processing. In addition, poor connectivity of the ACC may suggest the difficulty in meeting the linguistic and social demands of this task in ASD. Overall, this study provides new evidence of the altered recruitment of the neural networks underlying language and social cognition in ASD. Published by Elsevier Ltd.

Artificial limb representation in amputees.

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van den Heiligenberg, Fiona M Z; Orlov, Tanya; Macdonald, Scott N; Duff, Eugene P; Henderson Slater, David; Beckmann, Christian F; Johansen-Berg, Heidi; Culham, Jody C; Makin, Tamar R

2018-05-01

The human brain contains multiple hand-selective areas, in both the sensorimotor and visual systems. Could our brain repurpose neural resources, originally developed for supporting hand function, to represent and control artificial limbs? We studied individuals with congenital or acquired hand-loss (hereafter one-handers) using functional MRI. We show that the more one-handers use an artificial limb (prosthesis) in their everyday life, the stronger visual hand-selective areas in the lateral occipitotemporal cortex respond to prosthesis images. This was found even when one-handers were presented with images of active prostheses that share the functionality of the hand but not necessarily its visual features (e.g. a 'hook' prosthesis). Further, we show that daily prosthesis usage determines large-scale inter-network communication across hand-selective areas. This was demonstrated by increased resting state functional connectivity between visual and sensorimotor hand-selective areas, proportional to the intensiveness of everyday prosthesis usage. Further analysis revealed a 3-fold coupling between prosthesis activity, visuomotor connectivity and usage, suggesting a possible role for the motor system in shaping use-dependent representation in visual hand-selective areas, and/or vice versa. Moreover, able-bodied control participants who routinely observe prosthesis usage (albeit less intensively than the prosthesis users) showed significantly weaker associations between degree of prosthesis observation and visual cortex activity or connectivity. Together, our findings suggest that altered daily motor behaviour facilitates prosthesis-related visual processing and shapes communication across hand-selective areas. This neurophysiological substrate for prosthesis embodiment may inspire rehabilitation approaches to improve usage of existing substitutionary devices and aid implementation of future assistive and augmentative technologies.

Vietnamese Document Representation and Classification

Science.gov (United States)

Nguyen, Giang-Son; Gao, Xiaoying; Andreae, Peter

Vietnamese is very different from English and little research has been done on Vietnamese document classification, or indeed, on any kind of Vietnamese language processing, and only a few small corpora are available for research. We created a large Vietnamese text corpus with about 18000 documents, and manually classified them based on different criteria such as topics and styles, giving several classification tasks of different difficulty levels. This paper introduces a new syllable-based document representation at the morphological level of the language for efficient classification. We tested the representation on our corpus with different classification tasks using six classification algorithms and two feature selection techniques. Our experiments show that the new representation is effective for Vietnamese categorization, and suggest that best performance can be achieved using syllable-pair document representation, an SVM with a polynomial kernel as the learning algorithm, and using Information gain and an external dictionary for feature selection.

Joint Multi-scale Convolution Neural Network for Scene Classification of High Resolution Remote Sensing Imagery

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ZHENG Zhuo

2018-05-01

Full Text Available High resolution remote sensing imagery scene classification is important for automatic complex scene recognition, which is the key technology for military and disaster relief, etc. In this paper, we propose a novel joint multi-scale convolution neural network (JMCNN method using a limited amount of image data for high resolution remote sensing imagery scene classification. Different from traditional convolutional neural network, the proposed JMCNN is an end-to-end training model with joint enhanced high-level feature representation, which includes multi-channel feature extractor, joint multi-scale feature fusion and Softmax classifier. Multi-channel and scale convolutional extractors are used to extract scene middle features, firstly. Then, in order to achieve enhanced high-level feature representation in a limit dataset, joint multi-scale feature fusion is proposed to combine multi-channel and scale features using two feature fusions. Finally, enhanced high-level feature representation can be used for classification by Softmax. Experiments were conducted using two limit public UCM and SIRI datasets. Compared to state-of-the-art methods, the JMCNN achieved improved performance and great robustness with average accuracies of 89.3% and 88.3% on the two datasets.

A Cheap Linear Attention Mechanism with Fast Lookups and Fixed-Size Representations

OpenAIRE

de Brébisson, Alexandre; Vincent, Pascal

2016-01-01

The softmax content-based attention mechanism has proven to be very beneficial in many applications of recurrent neural networks. Nevertheless it suffers from two major computational limitations. First, its computations for an attention lookup scale linearly in the size of the attended sequence. Second, it does not encode the sequence into a fixed-size representation but instead requires to memorize all the hidden states. These two limitations restrict the use of the softmax attention mechani...

In Situ Representations and Access Consciousness in Neural Blackboard or Workspace Architectures

OpenAIRE

Frank van der Velde

2018-01-01

Phenomenal theories of consciousness assert that consciousness is based on specific neural correlates in the brain, which can be separated from all cognitive functions we can perform. If so, the search for robot consciousness seems to be doomed. By contrast, theories of functional or access consciousness assert that consciousness can be studied only with forms of cognitive access, given by cognitive processes. Consequently, consciousness and cognitive access cannot be fully dissociated. Here,...

Towards representation of a perceptual color manifold using associative memory for color constancy.

Science.gov (United States)

Seow, Ming-Jung; Asari, Vijayan K

2009-01-01

In this paper, we propose the concept of a manifold of color perception through empirical observation that the center-surround properties of images in a perceptually similar environment define a manifold in the high dimensional space. Such a manifold representation can be learned using a novel recurrent neural network based learning algorithm. Unlike the conventional recurrent neural network model in which the memory is stored in an attractive fixed point at discrete locations in the state space, the dynamics of the proposed learning algorithm represent memory as a nonlinear line of attraction. The region of convergence around the nonlinear line is defined by the statistical characteristics of the training data. This learned manifold can then be used as a basis for color correction of the images having different color perception to the learned color perception. Experimental results show that the proposed recurrent neural network learning algorithm is capable of color balance the lighting variations in images captured in different environments successfully.

The semantic representation of prejudice and stereotypes.

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Bhatia, Sudeep

2017-07-01

We use a theory of semantic representation to study prejudice and stereotyping. Particularly, we consider large datasets of newspaper articles published in the United States, and apply latent semantic analysis (LSA), a prominent model of human semantic memory, to these datasets to learn representations for common male and female, White, African American, and Latino names. LSA performs a singular value decomposition on word distribution statistics in order to recover word vector representations, and we find that our recovered representations display the types of biases observed in human participants using tasks such as the implicit association test. Importantly, these biases are strongest for vector representations with moderate dimensionality, and weaken or disappear for representations with very high or very low dimensionality. Moderate dimensional LSA models are also the best at learning race, ethnicity, and gender-based categories, suggesting that social category knowledge, acquired through dimensionality reduction on word distribution statistics, can facilitate prejudiced and stereotyped associations. Copyright © 2017 Elsevier B.V. All rights reserved.

Neural correlates of HIV risk feelings.

Science.gov (United States)

Häcker, Frank E K; Schmälzle, Ralf; Renner, Britta; Schupp, Harald T

2015-04-01

Field studies on HIV risk perception suggest that people rely on impressions they have about the safety of their partner. The present fMRI study investigated the neural correlates of the intuitive perception of risk. First, during an implicit condition, participants viewed a series of unacquainted persons and performed a task unrelated to HIV risk. In the following explicit condition, participants evaluated the HIV risk for each presented person. Contrasting responses for high and low HIV risk revealed that risky stimuli evoked enhanced activity in the anterior insula and medial prefrontal regions, which are involved in salience processing and frequently activated by threatening and negative affect-related stimuli. Importantly, neural regions responding to explicit HIV risk judgments were also enhanced in the implicit condition, suggesting a neural mechanism for intuitive impressions of riskiness. Overall, these findings suggest the saliency network as neural correlate for the intuitive sensing of risk. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Measuring the representational space of music with fMRI: a case study with Sting.

Science.gov (United States)

Levitin, Daniel J; Grafton, Scott T

2016-12-01

Functional brain imaging has revealed much about the neuroanatomical substrates of higher cognition, including music, language, learning, and memory. The technique lends itself to studying of groups of individuals. In contrast, the nature of expert performance is typically studied through the examination of exceptional individuals using behavioral case studies and retrospective biography. Here, we combined fMRI and the study of an individual who is a world-class expert musician and composer in order to better understand the neural underpinnings of his music perception and cognition, in particular, his mental representations for music. We used state of the art multivoxel pattern analysis (MVPA) and representational dissimilarity analysis (RDA) in a fixed set of brain regions to test three exploratory hypotheses with the musician Sting: (1) Composing would recruit neutral structures that are both unique and distinguishable from other creative acts, such as composing prose or visual art; (2) listening and imagining music would recruit similar neural regions, indicating that musical memory shares anatomical substrates with music listening; (3) the MVPA and RDA results would help us to map the representational space for music, revealing which musical pieces and genres are perceived to be similar in the musician's mental models for music. Our hypotheses were confirmed. The act of composing, and even of imagining elements of the composed piece separately, such as melody and rhythm, activated a similar cluster of brain regions, and were distinct from prose and visual art. Listened and imagined music showed high similarity, and in addition, notable similarity/dissimilarity patterns emerged among the various pieces used as stimuli: Muzak and Top 100/Pop songs were far from all other musical styles in Mahalanobis distance (Euclidean representational space), whereas jazz, R&B, tango and rock were comparatively close. Closer inspection revealed principaled explanations for the

Nonlinear identification of process dynamics using neural networks

International Nuclear Information System (INIS)

Parlos, A.G.; Atiya, A.F.; Chong, K.T.

1992-01-01

In this paper the nonlinear identification of process dynamics encountered in nuclear power plant components is addressed, in an input-output sense, using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the model structure to be identified. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard backpropagation learning algorithm is modified, and it is used for the supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The response of representative steam generator is predicted using a neural network, and it is compared to the response obtained from a sophisticated computer model based on first principles. The transient responses compare well, although further research is warranted to determine the predictive capabilities of these networks during more severe operational transients and accident scenarios

Adaptive control using neural networks and approximate models.

Science.gov (United States)

Narendra, K S; Mukhopadhyay, S

1997-01-01

The NARMA model is an exact representation of the input-output behavior of finite-dimensional nonlinear discrete-time dynamical systems in a neighborhood of the equilibrium state. However, it is not convenient for purposes of adaptive control using neural networks due to its nonlinear dependence on the control input. Hence, quite often, approximate methods are used for realizing the neural controllers to overcome computational complexity. In this paper, we introduce two classes of models which are approximations to the NARMA model, and which are linear in the control input. The latter fact substantially simplifies both the theoretical analysis as well as the practical implementation of the controller. Extensive simulation studies have shown that the neural controllers designed using the proposed approximate models perform very well, and in many cases even better than an approximate controller designed using the exact NARMA model. In view of their mathematical tractability as well as their success in simulation studies, a case is made in this paper that such approximate input-output models warrant a detailed study in their own right.

A hierarachical data structure representation for fusing multisensor information

Energy Technology Data Exchange (ETDEWEB)

Maren, A.J. [Tennessee Univ., Tullahoma, TN (United States). Space Inst.; Pap, R.M.; Harston, C.T. [Accurate Automation Corp., Chattanooga, TN (United States)

1989-12-31

A major problem with MultiSensor Information Fusion (MSIF) is establishing the level of processing at which information should be fused. Current methodologies, whether based on fusion at the data element, segment/feature, or symbolic levels, are each inadequate for robust MSIF. Data-element fusion has problems with coregistration. Attempts to fuse information using the features of segmented data relies on a Presumed similarity between the segmentation characteristics of each data stream. Symbolic-level fusion requires too much advance processing (including object identification) to be useful. MSIF systems need to operate in real-time, must perform fusion using a variety of sensor types, and should be effective across a wide range of operating conditions or deployment environments. We address this problem through developing a new representation level which facilitates matching and information fusion. The Hierarchical Data Structure (HDS) representation, created using a multilayer, cooperative/competitive neural network, meets this need. The HDS is an intermediate representation between the raw or smoothed data stream and symbolic interpretation of the data. it represents the structural organization of the data. Fused HDSs will incorporate information from multiple sensors. Their knowledge-rich structure aids top-down scene interpretation via both model matching and knowledge-based region interpretation.

A hierarachical data structure representation for fusing multisensor information

Energy Technology Data Exchange (ETDEWEB)

Maren, A.J. (Tennessee Univ., Tullahoma, TN (United States). Space Inst.); Pap, R.M.; Harston, C.T. (Accurate Automation Corp., Chattanooga, TN (United States))

1989-01-01

A major problem with MultiSensor Information Fusion (MSIF) is establishing the level of processing at which information should be fused. Current methodologies, whether based on fusion at the data element, segment/feature, or symbolic levels, are each inadequate for robust MSIF. Data-element fusion has problems with coregistration. Attempts to fuse information using the features of segmented data relies on a Presumed similarity between the segmentation characteristics of each data stream. Symbolic-level fusion requires too much advance processing (including object identification) to be useful. MSIF systems need to operate in real-time, must perform fusion using a variety of sensor types, and should be effective across a wide range of operating conditions or deployment environments. We address this problem through developing a new representation level which facilitates matching and information fusion. The Hierarchical Data Structure (HDS) representation, created using a multilayer, cooperative/competitive neural network, meets this need. The HDS is an intermediate representation between the raw or smoothed data stream and symbolic interpretation of the data. it represents the structural organization of the data. Fused HDSs will incorporate information from multiple sensors. Their knowledge-rich structure aids top-down scene interpretation via both model matching and knowledge-based region interpretation.

Experience-driven formation of parts-based representations in a model of layered visual memory

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Jenia Jitsev

2009-09-01

Full Text Available Growing neuropsychological and neurophysiological evidence suggests that the visual cortex uses parts-based representations to encode, store and retrieve relevant objects. In such a scheme, objects are represented as a set of spatially distributed local features, or parts, arranged in stereotypical fashion. To encode the local appearance and to represent the relations between the constituent parts, there has to be an appropriate memory structure formed by previous experience with visual objects. Here, we propose a model how a hierarchical memory structure supporting efficient storage and rapid recall of parts-based representations can be established by an experience-driven process of self-organization. The process is based on the collaboration of slow bidirectional synaptic plasticity and homeostatic unit activity regulation, both running at the top of fast activity dynamics with winner-take-all character modulated by an oscillatory rhythm. These neural mechanisms lay down the basis for cooperation and competition between the distributed units and their synaptic connections. Choosing human face recognition as a test task, we show that, under the condition of open-ended, unsupervised incremental learning, the system is able to form memory traces for individual faces in a parts-based fashion. On a lower memory layer the synaptic structure is developed to represent local facial features and their interrelations, while the identities of different persons are captured explicitly on a higher layer. An additional property of the resulting representations is the sparseness of both the activity during the recall and the synaptic patterns comprising the memory traces.

Neural correlates and neural computations in posterior parietal cortex during perceptual decision-making

Directory of Open Access Journals (Sweden)

Alexander eHuk

2012-10-01

Full Text Available A recent line of work has found remarkable success in relating perceptual decision-making and the spiking activity in the macaque lateral intraparietal area (LIP. In this review, we focus on questions about the neural computations in LIP that are not answered by demonstrations of neural correlates of psychological processes. We highlight three areas of limitations in our current understanding of the precise neural computations that might underlie neural correlates of decisions: (1 empirical questions not yet answered by existing data; (2 implementation issues related to how neural circuits could actually implement the mechanisms suggested by both physiology and psychology; and (3 ecological constraints related to the use of well-controlled laboratory tasks and whether they provide an accurate window on sensorimotor computation. These issues motivate the adoption of a more general encoding-decoding framework that will be fruitful for more detailed contemplation of how neural computations in LIP relate to the formation of perceptual decisions.

Two-dimensional shape recognition using oriented-polar representation

Science.gov (United States)

Hu, Neng-Chung; Yu, Kuo-Kan; Hsu, Yung-Li

1997-10-01

To deal with such a problem as object recognition of position, scale, and rotation invariance (PSRI), we utilize some PSRI properties of images obtained from objects, for example, the centroid of the image. The corresponding position of the centroid to the boundary of the image is invariant in spite of rotation, scale, and translation of the image. To obtain the information of the image, we use the technique similar to Radon transform, called the oriented-polar representation of a 2D image. In this representation, two specific points, the centroid and the weighted mean point, are selected to form an initial ray, then the image is sampled with N angularly equispaced rays departing from the initial rays. Each ray contains a number of intersections and the distance information obtained from the centroid to the intersections. The shape recognition algorithm is based on the least total error of these two items of information. Together with a simple noise removal and a typical backpropagation neural network, this algorithm is simple, but the PSRI is achieved with a high recognition rate.

Exploring the Structure of Spatial Representations

Science.gov (United States)

Madl, Tamas; Franklin, Stan; Chen, Ke; Trappl, Robert; Montaldi, Daniela

2016-01-01

It has been suggested that the map-like representations that support human spatial memory are fragmented into sub-maps with local reference frames, rather than being unitary and global. However, the principles underlying the structure of these ‘cognitive maps’ are not well understood. We propose that the structure of the representations of navigation space arises from clustering within individual psychological spaces, i.e. from a process that groups together objects that are close in these spaces. Building on the ideas of representational geometry and similarity-based representations in cognitive science, we formulate methods for learning dissimilarity functions (metrics) characterizing participants’ psychological spaces. We show that these learned metrics, together with a probabilistic model of clustering based on the Bayesian cognition paradigm, allow prediction of participants’ cognitive map structures in advance. Apart from insights into spatial representation learning in human cognition, these methods could facilitate novel computational tools capable of using human-like spatial concepts. We also compare several features influencing spatial memory structure, including spatial distance, visual similarity and functional similarity, and report strong correlations between these dimensions and the grouping probability in participants’ spatial representations, providing further support for clustering in spatial memory. PMID:27347681

Abnormal neural responses to social exclusion in schizophrenia.

Directory of Open Access Journals (Sweden)

Victoria B Gradin

Full Text Available Social exclusion is an influential concept in politics, mental health and social psychology. Studies on healthy subjects have implicated the medial prefrontal cortex (mPFC, a region involved in emotional and social information processing, in neural responses to social exclusion. Impairments in social interactions are common in schizophrenia and are associated with reduced quality of life. Core symptoms such as delusions usually have a social content. However little is known about the neural underpinnings of social abnormalities. The aim of this study was to investigate the neural substrates of social exclusion in schizophrenia. Patients with schizophrenia and healthy controls underwent fMRI while participating in a popular social exclusion paradigm. This task involves passing a 'ball' between the participant and two cartoon representations of other subjects. The extent of social exclusion (ball not being passed to the participant was parametrically varied throughout the task. Replicating previous findings, increasing social exclusion activated the mPFC in controls. In contrast, patients with schizophrenia failed to modulate mPFC responses with increasing exclusion. Furthermore, the blunted response to exclusion correlated with increased severity of positive symptoms. These data support the hypothesis that the neural response to social exclusion differs in schizophrenia, highlighting the mPFC as a potential substrate of impaired social interactions.

A Sparse Auto Encoder Deep Process Neural Network Model and its Application

Directory of Open Access Journals (Sweden)

Xu Shaohua

2017-01-01

Full Text Available Aiming at the problem of time-varying signal pattern classification, a sparse auto-encoder deep process neural network (SAE-DPNN is proposed. The input of SAE-DPNN is time-varying process signal and the output is pattern category. It combines the time-varying signal classification method of process neural network (PNN and the data feature extraction and hierarchical sparse representation mechanism of sparse automatic encoder (SAE. Based on the feedforward PNN model, SAE-DPNN is constructed by stacking the process neurons, SAE network and softmax classifier. It can maintain the time-sequence and structure of the input signal, express and synthesize the process distribution characteristics of multidimensional time-varying signals and their combinations. SAE-DPNN improves the identification of complex features and distinguishes between different types of signals, realizes the direct classification of time-varying signals. In this paper, the feature extraction and representation mechanism of time-varying signal in SAE-DPNN are analyzed, and a specific learning algorithm is given. The experimental results verify the effectiveness of the model and algorithm.

Enhancing Business Intelligence by Means of Suggestive Reviews

Science.gov (United States)

Qazi, Atika

2014-01-01

Appropriate identification and classification of online reviews to satisfy the needs of current and potential users pose a critical challenge for the business environment. This paper focuses on a specific kind of reviews: the suggestive type. Suggestions have a significant influence on both consumers' choices and designers' understanding and, hence, they are key for tasks such as brand positioning and social media marketing. The proposed approach consists of three main steps: (1) classify comparative and suggestive sentences; (2) categorize suggestive sentences into different types, either explicit or implicit locutions; (3) perform sentiment analysis on the classified reviews. A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. Experimental results for all three tasks are obtained on a dataset of mobile phone reviews and demonstrate that extending a bag-of-words representation with suggestive and comparative patterns is ideal for distinguishing suggestive sentences. In particular, it is observed that classifying suggestive sentences into implicit and explicit locutions works best when using a mixed sequential rule feature representation. Sentiment analysis achieves maximum performance when employing additional preprocessing in the form of negation handling and target masking, combined with sentiment lexicons. PMID:25054188

Enhancing Business Intelligence by Means of Suggestive Reviews

Directory of Open Access Journals (Sweden)

Atika Qazi

2014-01-01

Full Text Available Appropriate identification and classification of online reviews to satisfy the needs of current and potential users pose a critical challenge for the business environment. This paper focuses on a specific kind of reviews: the suggestive type. Suggestions have a significant influence on both consumers’ choices and designers’ understanding and, hence, they are key for tasks such as brand positioning and social media marketing. The proposed approach consists of three main steps: (1 classify comparative and suggestive sentences; (2 categorize suggestive sentences into different types, either explicit or implicit locutions; (3 perform sentiment analysis on the classified reviews. A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. Experimental results for all three tasks are obtained on a dataset of mobile phone reviews and demonstrate that extending a bag-of-words representation with suggestive and comparative patterns is ideal for distinguishing suggestive sentences. In particular, it is observed that classifying suggestive sentences into implicit and explicit locutions works best when using a mixed sequential rule feature representation. Sentiment analysis achieves maximum performance when employing additional preprocessing in the form of negation handling and target masking, combined with sentiment lexicons.

Integrating piecewise linear representation and ensemble neural network for stock price prediction

OpenAIRE

Asaduzzaman, Md.; Shahjahan, Md.; Ahmed, Fatema Johera; Islam, Md. Monirul; Murase, Kazuyuki

2014-01-01

Stock Prices are considered to be very dynamic and susceptible to quick changes because of the underlying nature of the financial domain, and in part because of the interchange between known parameters and unknown factors. Of late, several researchers have used Piecewise Linear Representation (PLR) to predict the stock market pricing. However, some improvements are needed to avoid the appropriate threshold of the trading decision, choosing the input index as well as improving the overall perf...

A neural link between affective understanding and interpersonal attraction

Science.gov (United States)

Anders, Silke; de Jong, Roos; Beck, Christian; Haynes, John-Dylan; Ethofer, Thomas

2016-01-01

Being able to comprehend another person’s intentions and emotions is essential for successful social interaction. However, it is currently unknown whether the human brain possesses a neural mechanism that attracts people to others whose mental states they can easily understand. Here we show that the degree to which a person feels attracted to another person can change while they observe the other’s affective behavior, and that these changes depend on the observer’s confidence in having correctly understood the other’s affective state. At the neural level, changes in interpersonal attraction were predicted by activity in the reward system of the observer’s brain. Importantly, these effects were specific to individual observer–target pairs and could not be explained by a target’s general attractiveness or expressivity. Furthermore, using multivoxel pattern analysis (MVPA), we found that neural activity in the reward system of the observer’s brain varied as a function of how well the target’s affective behavior matched the observer’s neural representation of the underlying affective state: The greater the match, the larger the brain’s intrinsic reward signal. Taken together, these findings provide evidence that reward-related neural activity during social encounters signals how well an individual’s “neural vocabulary” is suited to infer another person’s affective state, and that this intrinsic reward might be a source of changes in interpersonal attraction. PMID:27044071

Choosing the rules: distinct and overlapping frontoparietal representations of task rules for perceptual decisions.

Science.gov (United States)

Zhang, Jiaxiang; Kriegeskorte, Nikolaus; Carlin, Johan D; Rowe, James B

2013-07-17

Behavior is governed by rules that associate stimuli with responses and outcomes. Human and monkey studies have shown that rule-specific information is widely represented in the frontoparietal cortex. However, it is not known how establishing a rule under different contexts affects its neural representation. Here, we use event-related functional MRI (fMRI) and multivoxel pattern classification methods to investigate the human brain's mechanisms of establishing and maintaining rules for multiple perceptual decision tasks. Rules were either chosen by participants or specifically instructed to them, and the fMRI activation patterns representing rule-specific information were compared between these contexts. We show that frontoparietal regions differ in the properties of their rule representations during active maintenance before execution. First, rule-specific information maintained in the dorsolateral and medial frontal cortex depends on the context in which it was established (chosen vs specified). Second, rule representations maintained in the ventrolateral frontal and parietal cortex are independent of the context in which they were established. Furthermore, we found that the rule-specific coding maintained in anticipation of stimuli may change with execution of the rule: representations in context-independent regions remain invariant from maintenance to execution stages, whereas rule representations in context-dependent regions do not generalize to execution stage. The identification of distinct frontoparietal systems with context-independent and context-dependent task rule representations, and the distinction between anticipatory and executive rule representations, provide new insights into the functional architecture of goal-directed behavior.

Quantum Entanglement in Neural Network States

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Dong-Ling Deng

2017-05-01

Full Text Available Machine learning, one of today’s most rapidly growing interdisciplinary fields, promises an unprecedented perspective for solving intricate quantum many-body problems. Understanding the physical aspects of the representative artificial neural-network states has recently become highly desirable in the applications of machine-learning techniques to quantum many-body physics. In this paper, we explore the data structures that encode the physical features in the network states by studying the quantum entanglement properties, with a focus on the restricted-Boltzmann-machine (RBM architecture. We prove that the entanglement entropy of all short-range RBM states satisfies an area law for arbitrary dimensions and bipartition geometry. For long-range RBM states, we show by using an exact construction that such states could exhibit volume-law entanglement, implying a notable capability of RBM in representing quantum states with massive entanglement. Strikingly, the neural-network representation for these states is remarkably efficient, in the sense that the number of nonzero parameters scales only linearly with the system size. We further examine the entanglement properties of generic RBM states by randomly sampling the weight parameters of the RBM. We find that their averaged entanglement entropy obeys volume-law scaling, and the meantime strongly deviates from the Page entropy of the completely random pure states. We show that their entanglement spectrum has no universal part associated with random matrix theory and bears a Poisson-type level statistics. Using reinforcement learning, we demonstrate that RBM is capable of finding the ground state (with power-law entanglement of a model Hamiltonian with a long-range interaction. In addition, we show, through a concrete example of the one-dimensional symmetry-protected topological cluster states, that the RBM representation may also be used as a tool to analytically compute the entanglement spectrum. Our

Classification of hadith into positive suggestion, negative suggestion, and information

Science.gov (United States)

Faraby, Said Al; Riviera Rachmawati Jasin, Eliza; Kusumaningrum, Andina; Adiwijaya

2018-03-01

As one of the Muslim life guidelines, based on the meaning of its sentence(s), a hadith can be viewed as a suggestion for doing something, or a suggestion for not doing something, or just information without any suggestion. In this paper, we tried to classify the Bahasa translation of hadith into the three categories using machine learning approach. We tried stemming and stopword removal in preprocessing, and TF-IDF of unigram, bigram, and trigram as the extracted features. As the classifier, we compared between SVM and Neural Network. Since the categories are new, so in order to compare the results of the previous pipelines, we created a baseline classifier using simple rule-based string matching technique. The rule-based algorithm conditions on the occurrence of words such as “janganlah, sholatlah, and so on” to determine the category. The baseline method achieved F1-Score of 0.69, while the best F1-Score from the machine learning approach was 0.88, and it was produced by SVM model with the linear kernel.

Learning in Artificial Neural Systems

Science.gov (United States)

Matheus, Christopher J.; Hohensee, William E.

1987-01-01

This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined.

Visual Representations of DNA Replication: Middle Grades Students' Perceptions and Interpretations

Science.gov (United States)

Patrick, Michelle D.; Carter, Glenda; Wiebe, Eric N.

2005-01-01

Visual representations play a critical role in the communication of science concepts for scientists and students alike. However, recent research suggests that novice students experience difficulty extracting relevant information from representations. This study examined students' interpretations of visual representations of DNA replication. Each…

View-Independent Working Memory Representations of Artificial Shapes in Prefrontal and Posterior Regions of the Human Brain.

Science.gov (United States)

Christophel, Thomas B; Allefeld, Carsten; Endisch, Christian; Haynes, John-Dylan

2017-05-13

Traditional views of visual working memory postulate that memorized contents are stored in dorsolateral prefrontal cortex using an adaptive and flexible code. In contrast, recent studies proposed that contents are maintained by posterior brain areas using codes akin to perceptual representations. An important question is whether this reflects a difference in the level of abstraction between posterior and prefrontal representations. Here, we investigated whether neural representations of visual working memory contents are view-independent, as indicated by rotation-invariance. Using functional magnetic resonance imaging and multivariate pattern analyses, we show that when subjects memorize complex shapes, both posterior and frontal brain regions maintain the memorized contents using a rotation-invariant code. Importantly, we found the representations in frontal cortex to be localized to the frontal eye fields rather than dorsolateral prefrontal cortices. Thus, our results give evidence for the view-independent storage of complex shapes in distributed representations across posterior and frontal brain regions. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

The ART of representation: Memory reduction and noise tolerance in a neural network vision system

Science.gov (United States)

Langley, Christopher S.

The Feature Cerebellar Model Arithmetic Computer (FCMAC) is a multiple-input-single-output neural network that can provide three-degree-of-freedom (3-DOF) pose estimation for a robotic vision system. The FCMAC provides sufficient accuracy to enable a manipulator to grasp an object from an arbitrary pose within its workspace. The network learns an appearance-based representation of an object by storing coarsely quantized feature patterns. As all unique patterns are encoded, the network size grows uncontrollably. A new architecture is introduced herein, which combines the FCMAC with an Adaptive Resonance Theory (ART) network. The ART module categorizes patterns observed during training into a set of prototypes that are used to build the FCMAC. As a result, the network no longer grows without bound, but constrains itself to a user-specified size. Pose estimates remain accurate since the ART layer tends to discard the least relevant information first. The smaller network performs recall faster, and in some cases is better for generalization, resulting in a reduction of error at recall time. The ART-Under-Constraint (ART-C) algorithm is extended to include initial filling with randomly selected patterns (referred to as ART-F). In experiments using a real-world data set, the new network performed equally well using less than one tenth the number of coarse patterns as a regular FCMAC. The FCMAC is also extended to include real-valued input activations. As a result, the network can be tuned to reject a variety of types of noise in the image feature detection. A quantitative analysis of noise tolerance was performed using four synthetic noise algorithms, and a qualitative investigation was made using noisy real-world image data. In validation experiments, the FCMAC system outperformed Radial Basis Function (RBF) networks for the 3-DOF problem, and had accuracy comparable to that of Principal Component Analysis (PCA) and superior to that of Shape Context Matching (SCM), both

Studying the mechanisms of the Somatic Marker Hypothesis in Spiking Neural Networks (SNN

Directory of Open Access Journals (Sweden)

Manuel GONZÁLEZ

2013-07-01

Full Text Available Normal 0 21 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} In this paper, a mechanism of emotional bias in decision making is studied using Spiking Neural Networks to simulate the associative and recurrent networks involved. The results obtained are along the lines of those proposed by A. Damasio as part of the Somatic Marker Hypothesis, in particular, that, in absence of emotional input, the decision making is driven by the rational input alone. Appropriate representations for the Objective and Emotional Values are also suggested, provided a spike representation (code of the information.

Studying the mechanisms of the Somatic Marker Hypothesis in Spiking Neural Networks (SNN

Directory of Open Access Journals (Sweden)

Alejandro JIMÉNEZ-RODRÍGUEZ

2012-09-01

Full Text Available Normal 0 21 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} In this paper, a mechanism of emotional bias in decision making is studied using Spiking Neural Networks to simulate the associative and recurrent networks involved. The results obtained are along the lines of those proposed by A. Damasio as part of the Somatic Marker Hypothesis, in particular, that, in absence of emotional input, the decision making is driven by the rational input alone. Appropriate representations for the Objective and Emotional Values are also suggested, provided a spike representation (code of the information.

Early and late changes in the distal forelimb representation of the supplementary motor area after injury to frontal motor areas in the squirrel monkey.

Science.gov (United States)

Eisner-Janowicz, Ines; Barbay, Scott; Hoover, Erica; Stowe, Ann M; Frost, Shawn B; Plautz, Erik J; Nudo, Randolph J

2008-09-01

Neuroimaging studies in stroke survivors have suggested that adaptive plasticity occurs following stroke. However, the complex temporal dynamics of neural reorganization after injury make the interpretation of functional imaging studies equivocal. In the present study in adult squirrel monkeys, intracortical microstimulation (ICMS) techniques were used to monitor changes in representational maps of the distal forelimb in the supplementary motor area (SMA) after a unilateral ischemic infarct of primary motor (M1) and premotor distal forelimb representations (DFLs). In each animal, ICMS maps were derived at early (3 wk) and late (13 wk) postinfarct stages. Lesions resulted in severe deficits in motor abilities on a reach and retrieval task. Limited behavioral recovery occurred and plateaued at 3 wk postinfarct. At both early and late postinfarct stages, distal forelimb movements could still be evoked by ICMS in SMA at low current levels. However, the size of the SMA DFL changed after the infarct. In particular, wrist-forearm representations enlarged significantly between early and late stages, attaining a size substantially larger than the preinfarct area. At the late postinfarct stage, the expansion in the SMA DFL area was directly proportional to the absolute size of the lesion. The motor performance scores were positively correlated to the absolute size of the SMA DFL at the late postinfarct stage. Together, these data suggest that, at least in squirrel monkeys, descending output from M1 and dorsal and ventral premotor cortices is not necessary for SMA representations to be maintained and that SMA motor output maps undergo delayed increases in representational area after damage to other motor areas. Finally, the role of SMA in recovery of function after such lesions remains unclear because behavioral recovery appears to precede neurophysiological map changes.

Resting-state brain activity in the motor cortex reflects task-induced activity: A multi-voxel pattern analysis.

Science.gov (United States)

Kusano, Toshiki; Kurashige, Hiroki; Nambu, Isao; Moriguchi, Yoshiya; Hanakawa, Takashi; Wada, Yasuhiro; Osu, Rieko

2015-08-01

It has been suggested that resting-state brain activity reflects task-induced brain activity patterns. In this study, we examined whether neural representations of specific movements can be observed in the resting-state brain activity patterns of motor areas. First, we defined two regions of interest (ROIs) to examine brain activity associated with two different behavioral tasks. Using multi-voxel pattern analysis with regularized logistic regression, we designed a decoder to detect voxel-level neural representations corresponding to the tasks in each ROI. Next, we applied the decoder to resting-state brain activity. We found that the decoder discriminated resting-state neural activity with accuracy comparable to that associated with task-induced neural activity. The distribution of learned weighted parameters for each ROI was similar for resting-state and task-induced activities. Large weighted parameters were mainly located on conjunctive areas. Moreover, the accuracy of detection was higher than that for a decoder whose weights were randomly shuffled, indicating that the resting-state brain activity includes multi-voxel patterns similar to the neural representation for the tasks. Therefore, these results suggest that the neural representation of resting-state brain activity is more finely organized and more complex than conventionally considered.

Neural Network Models for Free Radical Polymerization of Methyl Methacrylate

International Nuclear Information System (INIS)

Curteanu, S.; Leon, F.; Galea, D.

2003-01-01

In this paper, a neural network modeling of the batch bulk methyl methacrylate polymerization is performed. To obtain conversion, number and weight average molecular weights, three neural networks were built. Each was a multilayer perception with one or two hidden layers. The choice of network topology, i.e. the number of hidden layers and the number of neurons in these layers, was based on achieving a compromise between precision and complexity. Thus, it was intended to have an error as small as possible at the end of back-propagation training phases, while using a network with reduced complexity. The performances of the networks were evaluated by comparing network predictions with training data, validation data (which were not uses for training), and with the results of a mechanistic model. The accurate predictions of neural networks for monomer conversion, number average molecular weight and weight average molecular weight proves that this modeling methodology gives a good representation and generalization of the batch bulk methyl methacrylate polymerization. (author)

Neural Integration of Information Specifying Human Structure from Form, Motion, and Depth

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Jackson, Stuart; Blake, Randolph

2010-01-01

Recent computational models of biological motion perception operate on ambiguous two-dimensional representations of the body (e.g., snapshots, posture templates) and contain no explicit means for disambiguating the three-dimensional orientation of a perceived human figure. Are there neural mechanisms in the visual system that represent a moving human figure’s orientation in three dimensions? To isolate and characterize the neural mechanisms mediating perception of biological motion, we used an adaptation paradigm together with bistable point-light (PL) animations whose perceived direction of heading fluctuates over time. After exposure to a PL walker with a particular stereoscopically defined heading direction, observers experienced a consistent aftereffect: a bistable PL walker, which could be perceived in the adapted orientation or reversed in depth, was perceived predominantly reversed in depth. A phase-scrambled adaptor produced no aftereffect, yet when adapting and test walkers differed in size or appeared on opposite sides of fixation aftereffects did occur. Thus, this heading direction aftereffect cannot be explained by local, disparity-specific motion adaptation, and the properties of scale and position invariance imply higher-level origins of neural adaptation. Nor is disparity essential for producing adaptation: when suspended on top of a stereoscopically defined, rotating globe, a context-disambiguated “globetrotter” was sufficient to bias the bistable walker’s direction, as were full-body adaptors. In sum, these results imply that the neural signals supporting biomotion perception integrate information on the form, motion, and three-dimensional depth orientation of the moving human figure. Models of biomotion perception should incorporate mechanisms to disambiguate depth ambiguities in two-dimensional body representations. PMID:20089892

The Laplacian spectrum of neural networks

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de Lange, Siemon C.; de Reus, Marcel A.; van den Heuvel, Martijn P.

2014-01-01

The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks. Many popular graph metrics, however, encode average properties of individual network elements. Complementing these “conventional” graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network's structure directly at a systems level, without referring to individual nodes or connections. In this paper, the Laplacian spectra of the macroscopic anatomical neuronal networks of the macaque and cat, and the microscopic network of the Caenorhabditis elegans were examined. Consistent with conventional graph metrics, analysis of the Laplacian spectra revealed an integrative community structure in neural brain networks. Extending previous findings of overlap of network attributes across species, similarity of the Laplacian spectra across the cat, macaque and C. elegans neural networks suggests a certain level of consistency in the overall architecture of the anatomical neural networks of these species. Our results further suggest a specific network class for neural networks, distinct from conceptual small-world and scale-free models as well as several empirical networks. PMID:24454286

Gender in facial representations: a contrast-based study of adaptation within and between the sexes.

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Oruç, Ipek; Guo, Xiaoyue M; Barton, Jason J S

2011-01-18

Face aftereffects are proving to be an effective means of examining the properties of face-specific processes in the human visual system. We examined the role of gender in the neural representation of faces using a contrast-based adaptation method. If faces of different genders share the same representational face space, then adaptation to a face of one gender should affect both same- and different-gender faces. Further, if these aftereffects differ in magnitude, this may indicate distinct gender-related factors in the organization of this face space. To control for a potential confound between physical similarity and gender, we used a Bayesian ideal observer and human discrimination data to construct a stimulus set in which pairs of different-gender faces were equally dissimilar as same-gender pairs. We found that the recognition of both same-gender and different-gender faces was suppressed following a brief exposure of 100 ms. Moreover, recognition was more suppressed for test faces of a different-gender than those of the same-gender as the adaptor, despite the equivalence in physical and psychophysical similarity. Our results suggest that male and female faces likely occupy the same face space, allowing transfer of aftereffects between the genders, but that there are special properties that emerge along gender-defining dimensions of this space.

Classification of time-series images using deep convolutional neural networks

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Hatami, Nima; Gavet, Yann; Debayle, Johan

2018-04-01

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.

Neural systems underlying aversive conditioning in humans with primary and secondary reinforcers

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Mauricio R Delgado

2011-05-01

Full Text Available Money is a secondary reinforcer commonly used across a range of disciplines in experimental paradigms investigating reward learning and decision-making. The effectiveness of monetary reinforcers during aversive learning and its neural basis, however, remains a topic of debate. Specifically, it is unclear if the initial acquisition of aversive representations of monetary losses depends on similar neural systems as more traditional aversive conditioning that involves primary reinforcers. This study contrasts the efficacy of a biologically defined primary reinforcer (shock and a socially defined secondary reinforcer (money during aversive learning and its associated neural circuitry. During a two-part experiment, participants first played a gambling game where wins and losses were based on performance to gain an experimental bank. Participants were then exposed to two separate aversive conditioning sessions. In one session, a primary reinforcer (mild shock served as an unconditioned stimulus (US and was paired with one of two colored squares, the conditioned stimuli (CS+ and CS-, respectively. In another session, a secondary reinforcer (loss of money served as the US and was paired with one of two different CS. Skin conductance responses were greater for CS+ compared to CS- trials irrespective of type of reinforcer. Neuroimaging results revealed that the striatum, a region typically linked with reward-related processing, was found to be involved in the acquisition of aversive conditioned response irrespective of reinforcer type. In contrast, the amygdala was involved during aversive conditioning with primary reinforcers, as suggested by both an exploratory fMRI analysis and a follow-up case study with a patient with bilateral amygdala damage. Taken together, these results suggest that learning about potential monetary losses may depend on reinforcement learning related systems, rather than on typical structures involved in more biologically based

Studying Action Representation in Children via Motor Imagery

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Gabbard, Carl

2009-01-01

The use of motor imagery is a widely used experimental paradigm for the study of cognitive aspects of action planning and control in adults. Furthermore, there are indications that motor imagery provides a window into the process of action representation. These notions complement internal model theory suggesting that such representations allow…

Neural cell adhesion molecule, NCAM, regulates thalamocortical axon pathfinding and the organization of the cortical somatosensory representation in mouse

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Enriquez-Barreto, Lilian; Palazzetti, Cecilia; Brennaman, Leann H.; Maness, Patricia F.; Fairén, Alfonso

2012-01-01

To study the potential role of neural cell adhesion molecule (NCAM) in the development of thalamocortical (TC) axon topography, wild type, and NCAM null mutant mice were analyzed for NCAM expression, projection, and targeting of TC afferents within the somatosensory area of the neocortex. Here we report that NCAM and its α-2,8-linked polysialic acid (PSA) are expressed in developing TC axons during projection to the neocortex. Pathfinding of TC axons in wild type and null mutant mice was mapped using anterograde DiI labeling. At embryonic day E16.5, null mutant mice displayed misguided TC axons in the dorsal telencephalon, but not in the ventral telencephalon, an intermediate target that initially sorts TC axons toward correct neocortical areas. During the early postnatal period, rostrolateral TC axons within the internal capsule along the ventral telencephalon adopted distorted trajectories in the ventral telencephalon and failed to reach the neocortex in NCAM null mutant animals. NCAM null mutants showed abnormal segregation of layer IV barrels in a restricted portion of the somatosensory cortex. As shown by Nissl and cytochrome oxidase staining, barrels of the anterolateral barrel subfield (ALBSF) and the most distal barrels of the posteromedial barrel subfield (PMBSF) did not segregate properly in null mutant mice. These results indicate a novel role for NCAM in axonal pathfinding and topographic sorting of TC axons, which may be important for the function of specific territories of sensory representation in the somatosensory cortex. PMID:22723769

Parametric Jominy profiles predictor based on neural networks

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Valentini, R.

2005-12-01

Full Text Available The paper presents a method for the prediction of the Jominy hardness profiles of steels for microalloyed Boron steel which is based on neural networks. The Jominy profile has been parameterized and the parameters, which are a sort of "compact representation" of the profile itself, are linked to the steel chemical composition through a neural network. Numerical results are presented and discussed.

El trabajo presenta un método de estimación de perfiles de dureza Jominy para aceros microaleados al boro basado en redes neuronales. Los parámetros de perfil Jominy, que constituyen una especie de "representación compacta" del perfil mismo, son determinados y puestos en relación con la composición química del acero mediante una red neuronal. Los resultados numéricos son expuestos y discutidos.

Naturalising Representational Content

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Shea, Nicholas

2014-01-01

This paper sets out a view about the explanatory role of representational content and advocates one approach to naturalising content – to giving a naturalistic account of what makes an entity a representation and in virtue of what it has the content it does. It argues for pluralism about the metaphysics of content and suggests that a good strategy is to ask the content question with respect to a variety of predictively successful information processing models in experimental psychology and cognitive neuroscience; and hence that data from psychology and cognitive neuroscience should play a greater role in theorising about the nature of content. Finally, the contours of the view are illustrated by drawing out and defending a surprising consequence: that individuation of vehicles of content is partly externalist. PMID:24563661

Financial Incentives Differentially Regulate Neural Processing of Positive and Negative Emotions during Value-Based Decision-Making

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Anne M. Farrell

2018-02-01

Full Text Available Emotional and economic incentives often conflict in decision environments. To make economically desirable decisions then, deliberative neural processes must be engaged to regulate automatic emotional reactions. In this functional magnetic resonance imaging (fMRI study, we evaluated how fixed wage (FW incentives and performance-based (PB financial incentives, in which pay is proportional to outcome, differentially regulate positive and negative emotional reactions to hypothetical colleagues that conflicted with the economics of available alternatives. Neural activity from FW to PB incentive contexts decreased for positive emotional stimuli but increased for negative stimuli in middle temporal, insula, and medial prefrontal regions. In addition, PB incentives further induced greater responses to negative than positive emotional decisions in the frontal and anterior cingulate regions involved in emotion regulation. Greater response to positive than negative emotional features in these regions also correlated with lower frequencies of economically desirable choices. Our findings suggest that whereas positive emotion regulation involves a reduction of responses in valence representation regions, negative emotion regulation additionally engages brain regions for deliberative processing and signaling of incongruous events.

Financial Incentives Differentially Regulate Neural Processing of Positive and Negative Emotions during Value-Based Decision-Making.

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Farrell, Anne M; Goh, Joshua O S; White, Brian J

2018-01-01

Emotional and economic incentives often conflict in decision environments. To make economically desirable decisions then, deliberative neural processes must be engaged to regulate automatic emotional reactions. In this functional magnetic resonance imaging (fMRI) study, we evaluated how fixed wage (FW) incentives and performance-based (PB) financial incentives, in which pay is proportional to outcome, differentially regulate positive and negative emotional reactions to hypothetical colleagues that conflicted with the economics of available alternatives. Neural activity from FW to PB incentive contexts decreased for positive emotional stimuli but increased for negative stimuli in middle temporal, insula, and medial prefrontal regions. In addition, PB incentives further induced greater responses to negative than positive emotional decisions in the frontal and anterior cingulate regions involved in emotion regulation. Greater response to positive than negative emotional features in these regions also correlated with lower frequencies of economically desirable choices. Our findings suggest that whereas positive emotion regulation involves a reduction of responses in valence representation regions, negative emotion regulation additionally engages brain regions for deliberative processing and signaling of incongruous events.

Oscillations and Sparsening of Odor Representations in the Mushroom Body

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Perez-Orive, Javier; Mazor, Ofer; Turner, Glenn C.; Cassenaer, Stijn; Wilson, Rachel I.; Laurent, Gilles

2002-07-01

In the insect olfactory system, oscillatory synchronization is functionally relevant and reflects the coherent activation of dynamic neural assemblies. We examined the role of such oscillatory synchronization in information transfer between networks in this system. The antennal lobe is the obligatory relay for olfactory afferent signals and generates oscillatory output. The mushroom body is responsible for formation and retrieval of olfactory and other memories. The format of odor representations differs significantly across these structures. Whereas representations are dense, dynamic, and seemingly redundant in the antennal lobe, they are sparse and carried by more selective neurons in the mushroom body. This transformation relies on a combination of oscillatory dynamics and intrinsic and circuit properties that act together to selectively filter and synthesize the output from the antennal lobe. These results provide direct support for the functional relevance of correlation codes and shed some light on the role of oscillatory synchronization in sensory networks.

Dissociating the neural correlates of intra-item and inter-item working-memory binding.

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Carinne Piekema

Full Text Available BACKGROUND: Integration of information streams into a unitary representation is an important task of our cognitive system. Within working memory, the medial temporal lobe (MTL has been conceptually linked to the maintenance of bound representations. In a previous fMRI study, we have shown that the MTL is indeed more active during working-memory maintenance of spatial associations as compared to non-spatial associations or single items. There are two explanations for this result, the mere presence of the spatial component activates the MTL, or the MTL is recruited to bind associations between neurally non-overlapping representations. METHODOLOGY/PRINCIPAL FINDINGS: The current fMRI study investigates this issue further by directly comparing intrinsic intra-item binding (object/colour, extrinsic intra-item binding (object/location, and inter-item binding (object/object. The three binding conditions resulted in differential activation of brain regions. Specifically, we show that the MTL is important for establishing extrinsic intra-item associations and inter-item associations, in line with the notion that binding of information processed in different brain regions depends on the MTL. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that different forms of working-memory binding rely on specific neural structures. In addition, these results extend previous reports indicating that the MTL is implicated in working-memory maintenance, challenging the classic distinction between short-term and long-term memory systems.

Associative representational plasticity in the auditory cortex: A synthesis of two disciplines

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Weinberger, Norman M.

2013-01-01

Historically, sensory systems have been largely ignored as potential loci of information storage in the neurobiology of learning and memory. They continued to be relegated to the role of “sensory analyzers” despite consistent findings of associatively induced enhancement of responses in primary sensory cortices to behaviorally important signal stimuli, such as conditioned stimuli (CS), during classical conditioning. This disregard may have been promoted by the fact that the brain was interrogated using only one or two stimuli, e.g., a CS+ sometimes with a CS−, providing little insight into the specificity of neural plasticity. This review describes a novel approach that synthesizes the basic experimental designs of the experimental psychology of learning with that of sensory neurophysiology. By probing the brain with a large stimulus set before and after learning, this unified method has revealed that associative processes produce highly specific changes in the receptive fields of cells in the primary auditory cortex (A1). This associative representational plasticity (ARP) selectively facilitates responses to tonal CSs at the expense of other frequencies, producing tuning shifts toward and to the CS and expanded representation of CS frequencies in the tonotopic map of A1. ARPs have the major characteristics of associative memory: They are highly specific, discriminative, rapidly acquired, exhibit consolidation over hours and days, and can be retained indefinitely. Evidence to date suggests that ARPs encode the level of acquired behavioral importance of stimuli. The nucleus basalis cholinergic system is sufficient both for the induction of ARPs and the induction of specific auditory memory. Investigation of ARPs has attracted workers with diverse backgrounds, often resulting in behavioral approaches that yield data that are difficult to interpret. The advantages of studying associative representational plasticity are emphasized, as is the need for greater

Synthesis of recurrent neural networks for dynamical system simulation.

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Trischler, Adam P; D'Eleuterio, Gabriele M T

2016-08-01

We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time. Copyright © 2016 Elsevier Ltd. All rights reserved.

Hierarchical modeling of molecular energies using a deep neural network

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Lubbers, Nicholas; Smith, Justin S.; Barros, Kipton

2018-06-01

We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network—a composition of many nonlinear transformations—acting on a representation of the molecule. HIP-NN achieves the state-of-the-art performance on a dataset of 131k ground state organic molecules and predicts energies with 0.26 kcal/mol mean absolute error. With minimal tuning, our model is also competitive on a dataset of molecular dynamics trajectories. In addition to enabling accurate energy predictions, the hierarchical structure of HIP-NN helps to identify regions of model uncertainty.

Developmental and Architectural Principles of the Lateral-line Neural Map

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Hernan eLopez-Schier

2013-03-01

Full Text Available The transmission and central representation of sensory cues through the accurate construction of neural maps is essential for animals to react to environmental stimuli. Structural diversity of sensorineural maps along a continuum between discrete- and continuous-map architectures can influence behavior. The mechanosensory lateral line of fishes and amphibians, for example, detects complex hydrodynamics occurring around the animal body. It. It triggers innate fast escape reactions but also modulates complex navigation behaviors that require constant knowledge about the environment. The aim of this article is to summarize recent work in the zebrafish that has shed light on the development and structure of the lateralis neural map, which is helping to understand how individual sensory modalities generate appropriate behavioral responses to the sensory context.

Shape perception simultaneously up- and downregulates neural activity in the primary visual cortex.

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Kok, Peter; de Lange, Floris P

2014-07-07

An essential part of visual perception is the grouping of local elements (such as edges and lines) into coherent shapes. Previous studies have shown that this grouping process modulates neural activity in the primary visual cortex (V1) that is signaling the local elements [1-4]. However, the nature of this modulation is controversial. Some studies find that shape perception reduces neural activity in V1 [2, 5, 6], while others report increased V1 activity during shape perception [1, 3, 4, 7-10]. Neurocomputational theories that cast perception as a generative process [11-13] propose that feedback connections carry predictions (i.e., the generative model), while feedforward connections signal the mismatch between top-down predictions and bottom-up inputs. Within this framework, the effect of feedback on early visual cortex may be either enhancing or suppressive, depending on whether the feedback signal is met by congruent bottom-up input. Here, we tested this hypothesis by quantifying the spatial profile of neural activity in V1 during the perception of illusory shapes using population receptive field mapping. We find that shape perception concurrently increases neural activity in regions of V1 that have a receptive field on the shape but do not receive bottom-up input and suppresses activity in regions of V1 that receive bottom-up input that is predicted by the shape. These effects were not modulated by task requirements. Together, these findings suggest that shape perception changes lower-order sensory representations in a highly specific and automatic manner, in line with theories that cast perception in terms of hierarchical generative models. Copyright © 2014 Elsevier Ltd. All rights reserved.

Neural crest contributions to the lamprey head

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McCauley, David W.; Bronner-Fraser, Marianne

2003-01-01

The neural crest is a vertebrate-specific cell population that contributes to the facial skeleton and other derivatives. We have performed focal DiI injection into the cranial neural tube of the developing lamprey in order to follow the migratory pathways of discrete groups of cells from origin to destination and to compare neural crest migratory pathways in a basal vertebrate to those of gnathostomes. The results show that the general pathways of cranial neural crest migration are conserved throughout the vertebrates, with cells migrating in streams analogous to the mandibular and hyoid streams. Caudal branchial neural crest cells migrate ventrally as a sheet of cells from the hindbrain and super-pharyngeal region of the neural tube and form a cylinder surrounding a core of mesoderm in each pharyngeal arch, similar to that seen in zebrafish and axolotl. In addition to these similarities, we also uncovered important differences. Migration into the presumptive caudal branchial arches of the lamprey involves both rostral and caudal movements of neural crest cells that have not been described in gnathostomes, suggesting that barriers that constrain rostrocaudal movement of cranial neural crest cells may have arisen after the agnathan/gnathostome split. Accordingly, neural crest cells from a single axial level contributed to multiple arches and there was extensive mixing between populations. There was no apparent filling of neural crest derivatives in a ventral-to-dorsal order, as has been observed in higher vertebrates, nor did we find evidence of a neural crest contribution to cranial sensory ganglia. These results suggest that migratory constraints and additional neural crest derivatives arose later in gnathostome evolution.

The common neural bases between sexual desire and love: a multilevel kernel density fMRI analysis.

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Cacioppo, Stephanie; Bianchi-Demicheli, Francesco; Frum, Chris; Pfaus, James G; Lewis, James W

2012-04-01

One of the most difficult dilemmas in relationship science and couple therapy concerns the interaction between sexual desire and love. As two mental states of intense longing for union with others, sexual desire and love are, in fact, often difficult to disentangle from one another. The present review aims to help understand the differences and similarities between these two mental states using a comprehensive statistical meta-analyses of all functional magnetic resonance imaging (fMRI) studies on sexual desire and love. Systematic retrospective review of pertinent neuroimaging literature. Review of published literature on fMRI studies illustrating brain regions associated with love and sexual desire to date. Sexual desire and love not only show differences but also recruit a striking common set of brain areas that mediate somatosensory integration, reward expectation, and social cognition. More precisely, a significant posterior-to-anterior insular pattern appears to track sexual desire and love progressively. This specific pattern of activation suggests that love builds upon a neural circuit for emotions and pleasure, adding regions associated with reward expectancy, habit formation, and feature detection. In particular, the shared activation within the insula, with a posterior-to-anterior pattern, from desire to love, suggests that love grows out of and is a more abstract representation of the pleasant sensorimotor experiences that characterize desire. From these results, one may consider desire and love on a spectrum that evolves from integrative representations of affective visceral sensations to an ultimate representation of feelings incorporating mechanisms of reward expectancy and habit learning. © 2012 International Society for Sexual Medicine.

Parameter estimation in space systems using recurrent neural networks

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Parlos, Alexander G.; Atiya, Amir F.; Sunkel, John W.

1991-01-01

The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.

EEG source reconstruction evidence for the noun-verb neural dissociation along semantic dimensions.

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Zhao, Bin; Dang, Jianwu; Zhang, Gaoyan

2017-09-17

One of the long-standing issues in neurolinguistic research is about the neural basis of word representation, concerning whether grammatical classification or semantic difference causes the neural dissociation of brain activity patterns when processing different word categories, especially nouns and verbs. To disentangle this puzzle, four orthogonalized word categories in Chinese: unambiguous nouns (UN), unambiguous verbs (UV), ambiguous words with noun-biased semantics (AN), and ambiguous words with verb-biased semantics (AV) were adopted in an auditory task for recording electroencephalographic (EEG) signals from 128 electrodes on the scalps of twenty-two subjects. With the advanced current density reconstruction (CDR) algorithm and the constraint of standardized low-resolution electromagnetic tomography, the spatiotemporal brain dynamics of word processing were explored with the results that in multiple time periods including P1 (60-90ms), N1 (100-140ms), P200 (150-250ms) and N400 (350-450ms), noun-verb dissociation over the parietal-occipital and frontal-central cortices appeared not only between the UN-UV grammatical classes but also between the grammatically identical but semantically different AN-AV pairs. The apparent semantic dissociation within one grammatical class strongly suggests that the semantic difference rather than grammatical classification could be interpreted as the origin of the noun-verb neural dissociation. Our results also revealed that semantic dissociation occurs from an early stage and repeats in multiple phases, thus supporting a functionally hierarchical word processing mechanism. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Digital and optical shape representation and pattern recognition; Proceedings of the Meeting, Orlando, FL, Apr. 4-6, 1988

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Juday, Richard D. (Editor)

1988-01-01

The present conference discusses topics in pattern-recognition correlator architectures, digital stereo systems, geometric image transformations and their applications, topics in pattern recognition, filter algorithms, object detection and classification, shape representation techniques, and model-based object recognition methods. Attention is given to edge-enhancement preprocessing using liquid crystal TVs, massively-parallel optical data base management, three-dimensional sensing with polar exponential sensor arrays, the optical processing of imaging spectrometer data, hybrid associative memories and metric data models, the representation of shape primitives in neural networks, and the Monte Carlo estimation of moment invariants for pattern recognition.

Self-teaching neural network learns difficult reactor control problem

International Nuclear Information System (INIS)

Jouse, W.C.

1989-01-01

A self-teaching neural network used as an adaptive controller quickly learns to control an unstable reactor configuration. The network models the behavior of a human operator. It is trained by allowing it to operate the reactivity control impulsively. It is punished whenever either the power or fuel temperature stray outside technical limits. Using a simple paradigm, the network constructs an internal representation of the punishment and of the reactor system. The reactor is constrained to small power orbits

Cortical Representations of Speech in a Multitalker Auditory Scene.

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Puvvada, Krishna C; Simon, Jonathan Z

2017-09-20

The ability to parse a complex auditory scene into perceptual objects is facilitated by a hierarchical auditory system. Successive stages in the hierarchy transform an auditory scene of multiple overlapping sources, from peripheral tonotopically based representations in the auditory nerve, into perceptually distinct auditory-object-based representations in the auditory cortex. Here, using magnetoencephalography recordings from men and women, we investigate how a complex acoustic scene consisting of multiple speech sources is represented in distinct hierarchical stages of the auditory cortex. Using systems-theoretic methods of stimulus reconstruction, we show that the primary-like areas in the auditory cortex contain dominantly spectrotemporal-based representations of the entire auditory scene. Here, both attended and ignored speech streams are represented with almost equal fidelity, and a global representation of the full auditory scene with all its streams is a better candidate neural representation than that of individual streams being represented separately. We also show that higher-order auditory cortical areas, by contrast, represent the attended stream separately and with significantly higher fidelity than unattended streams. Furthermore, the unattended background streams are more faithfully represented as a single unsegregated background object rather than as separated objects. Together, these findings demonstrate the progression of the representations and processing of a complex acoustic scene up through the hierarchy of the human auditory cortex. SIGNIFICANCE STATEMENT Using magnetoencephalography recordings from human listeners in a simulated cocktail party environment, we investigate how a complex acoustic scene consisting of multiple speech sources is represented in separate hierarchical stages of the auditory cortex. We show that the primary-like areas in the auditory cortex use a dominantly spectrotemporal-based representation of the entire auditory

Monitoring nuclear reactor systems using neural networks and fuzzy logic

International Nuclear Information System (INIS)

Ikonomopoulos, A.; Tsoukalas, L.H.; Uhrig, R.E.; Mullens, J.A.

1991-01-01

A new approach is presented that demonstrates the potential of trained artificial neural networks (ANNs) as generators of membership functions for the purpose of monitoring nuclear reactor systems. ANN's provide a complex-to-simple mapping of reactor parameters in a process analogous to that of measurement. Through such ''virtual measurements'' the value of parameters with operational significance, e.g., control-valve-disk-position, valve-line-up or performance can be determined. In the methodology presented the output of a virtual measuring device is a set of membership functions which independently represent different states of the system. Utilizing a fuzzy logic representation offers the advantage of describing the state of the system in a condensed form, developed through linguistic descriptions and convenient for application in monitoring, diagnostics and generally control algorithms. The developed methodology is applied to the problem of measuring the disk position of the secondary flow control valve of an experimental reactor using data obtained during a start-up. The enhanced noise tolerance of the methodology is clearly demonstrated as well as a method for selecting the actual output. The results suggest that it is possible to construct virtual measuring devices through artificial neural networks mapping dynamic time series to a set of membership functions and thus enhance the capability of monitoring systems. 8 refs., 11 figs., 1 tab

Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

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Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep

Improved Collaborative Representation Classifier Based on l2-Regularized for Human Action Recognition

Directory of Open Access Journals (Sweden)

Shirui Huo

2017-01-01

Full Text Available Human action recognition is an important recent challenging task. Projecting depth images onto three depth motion maps (DMMs and extracting deep convolutional neural network (DCNN features are discriminant descriptor features to characterize the spatiotemporal information of a specific action from a sequence of depth images. In this paper, a unified improved collaborative representation framework is proposed in which the probability that a test sample belongs to the collaborative subspace of all classes can be well defined and calculated. The improved collaborative representation classifier (ICRC based on l2-regularized for human action recognition is presented to maximize the likelihood that a test sample belongs to each class, then theoretical investigation into ICRC shows that it obtains a final classification by computing the likelihood for each class. Coupled with the DMMs and DCNN features, experiments on depth image-based action recognition, including MSRAction3D and MSRGesture3D datasets, demonstrate that the proposed approach successfully using a distance-based representation classifier achieves superior performance over the state-of-the-art methods, including SRC, CRC, and SVM.

Diffeomorphism Group Representations in Relativistic Quantum Field Theory

Energy Technology Data Exchange (ETDEWEB)

Goldin, Gerald A. [Rutgers Univ., Piscataway, NJ (United States); Sharp, David H. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-12-20

We explore the role played by the di eomorphism group and its unitary representations in relativistic quantum eld theory. From the quantum kinematics of particles described by representations of the di eomorphism group of a space-like surface in an inertial reference frame, we reconstruct the local relativistic neutral scalar eld in the Fock representation. An explicit expression for the free Hamiltonian is obtained in terms of the Lie algebra generators (mass and momentum densities). We suggest that this approach can be generalized to elds whose quanta are spatially extended objects.

Effects of microgravity on muscle and cerebral cortex: a suggested interaction

Science.gov (United States)

D'Amelio, F.; Fox, R. A.; Wu, L. C.; Daunton, N. G.; Corcoran, M. L.

The ``slow'' antigravity muscle adductor longus was studied in rats after 14 days of spaceflight (SF). The techniques employed included standard methods for light microscopy, neural cell adhesion molecule (N-CAM) immunocytochemistry and electron microscopy. Light and electron microscopy revealed myofiber atrophy, segmental necrosis and regenerative myofibers. Regenerative myofibers were N-CAM immunoreactive (N-CAM-IR). The neuromuscular junctions showed axon terminals with a decrease or absence of synaptic vesicles, degenerative changes, vacant axonal spaces and changes suggestive of axonal sprouting. No alterations of muscle spindles was seen either by light or electron microscopy. These observations suggest that muscle regeneration and denervation and synaptic remodeling at the level of the neuromuscular junction may take place during spaceflight. In a separate study, GABA immunoreactivity (GABA-IR) was evaluated at the level of the hindlimb representation of the rat somatosensory cortex after 14 days of hindlimb unloading by tail suspension (``simulated'' microgravity). A reduction in number of GABA-immunoreactive cells with respect to the control animals was observed in layer Va and Vb. GABA-IR terminals were also reduced in the same layers, particularly those terminals surrounding the soma and apical dendrites of pyramidal cells in layer Vb. On the basis of previous morphological and behavioral studies of the neuromuscular system after spaceflight and hindlimb suspension it is suggested that after limb unloading there are alterations of afferent signaling and feedback information from intramuscular receptors to the cerebral cortex due to modifications in the reflex organization of hindlimb muscle groups. We propose that the changes observed in GABA immunoreactivity of cells and terminals is an expression of changes in their modulatory activity to compensate for the alterations in the afferent information.

Specific and Nonspecific Neural Activity during Selective Processing of Visual Representations in Working Memory

Science.gov (United States)

Oh, Hwamee; Leung, Hoi-Chung

2010-01-01

In this fMRI study, we investigated prefrontal cortex (PFC) and visual association regions during selective information processing. We recorded behavioral responses and neural activity during a delayed recognition task with a cue presented during the delay period. A specific cue ("Face" or "Scene") was used to indicate which one of the two…

Noradrenergic modulation of neural erotic stimulus perception.

Science.gov (United States)

Graf, Heiko; Wiegers, Maike; Metzger, Coraline Danielle; Walter, Martin; Grön, Georg; Abler, Birgit

2017-09-01

We recently investigated neuromodulatory effects of the noradrenergic agent reboxetine and the dopamine receptor affine amisulpride in healthy subjects on dynamic erotic stimulus processing. Whereas amisulpride left sexual functions and neural activations unimpaired, we observed detrimental activations under reboxetine within the caudate nucleus corresponding to motivational components of sexual behavior. However, broadly impaired subjective sexual functioning under reboxetine suggested effects on further neural components. We now investigated the same sample under these two agents with static erotic picture stimulation as alternative stimulus presentation mode to potentially observe further neural treatment effects of reboxetine. 19 healthy males were investigated under reboxetine, amisulpride and placebo for 7 days each within a double-blind cross-over design. During fMRI static erotic picture were presented with preceding anticipation periods. Subjective sexual functions were assessed by a self-reported questionnaire. Neural activations were attenuated within the caudate nucleus, putamen, ventral striatum, the pregenual and anterior midcingulate cortex and in the orbitofrontal cortex under reboxetine. Subjective diminished sexual arousal under reboxetine was correlated with attenuated neural reactivity within the posterior insula. Again, amisulpride left neural activations along with subjective sexual functioning unimpaired. Neither reboxetine nor amisulpride altered differential neural activations during anticipation of erotic stimuli. Our results verified detrimental effects of noradrenergic agents on neural motivational but also emotional and autonomic components of sexual behavior. Considering the overlap of neural network alterations with those evoked by serotonergic agents, our results suggest similar neuromodulatory effects of serotonergic and noradrenergic agents on common neural pathways relevant for sexual behavior. Copyright © 2017 Elsevier B.V. and

Chondroitin sulfate effects on neural stem cell differentiation.

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Canning, David R; Brelsford, Natalie R; Lovett, Neil W

2016-01-01

We have investigated the role chondroitin sulfate has on cell interactions during neural plate formation in the early chick embryo. Using tissue culture isolates from the prospective neural plate, we have measured neural gene expression profiles associated with neural stem cell differentiation. Removal of chondroitin sulfate from stage 4 neural plate tissue leads to altered associations of N-cadherin-positive neural progenitors and causes changes in the normal sequence of neural marker gene expression. Absence of chondroitin sulfate in the neural plate leads to reduced Sox2 expression and is accompanied by an increase in the expression of anterior markers of neural regionalization. Results obtained in this study suggest that the presence of chondroitin sulfate in the anterior chick embryo is instrumental in maintaining cells in the neural precursor state.

PatterNet: a system to learn compact physical design pattern representations for pattern-based analytics

Science.gov (United States)

Lutich, Andrey

2017-07-01

This research considers the problem of generating compact vector representations of physical design patterns for analytics purposes in semiconductor patterning domain. PatterNet uses a deep artificial neural network to learn mapping of physical design patterns to a compact Euclidean hyperspace. Distances among mapped patterns in this space correspond to dissimilarities among patterns defined at the time of the network training. Once the mapping network has been trained, PatterNet embeddings can be used as feature vectors with standard machine learning algorithms, and pattern search, comparison, and clustering become trivial problems. PatterNet is inspired by the concepts developed within the framework of generative adversarial networks as well as the FaceNet. Our method facilitates a deep neural network (DNN) to learn directly the compact representation by supplying it with pairs of design patterns and dissimilarity among these patterns defined by a user. In the simplest case, the dissimilarity is represented by an area of the XOR of two patterns. Important to realize that our PatterNet approach is very different to the methods developed for deep learning on image data. In contrast to "conventional" pictures, the patterns in the CAD world are the lists of polygon vertex coordinates. The method solely relies on the promise of deep learning to discover internal structure of the incoming data and learn its hierarchical representations. Artificial intelligence arising from the combination of PatterNet and clustering analysis very precisely follows intuition of patterning/optical proximity correction experts paving the way toward human-like and human-friendly engineering tools.

Qualitative aspects of representational competence among college chemistry students: Multiple representations and their role in the understanding of ideal gases

Science.gov (United States)

Madden, Sean Patrick

difficult type of representation for students to interpret. Most subjects scored higher on representational competence when engaged in creating graphs and sketches than when evaluating provided representations. This study suggests that students may benefit from an instruction that emphasizes heuristic use of multiple representations in chemistry problem solving. An instructional strategy that makes use of a variety of representations and requires students to create their own representations may have measurable benefits to chemistry students.

Two takes on the social brain: a comparison of theory of mind tasks.

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Gobbini, Maria Ida; Koralek, Aaron C; Bryan, Ronald E; Montgomery, Kimberly J; Haxby, James V

2007-11-01

We compared two tasks that are widely used in research on mentalizing--false belief stories and animations of rigid geometric shapes that depict social interactions--to investigate whether the neural systems that mediate the representation of others' mental states are consistent across these tasks. Whereas false belief stories activated primarily the anterior paracingulate cortex (APC), the posterior cingulate cortex/precuneus (PCC/PC), and the temporo-parietal junction (TPJ)--components of the distributed neural system for theory of mind (ToM)--the social animations activated an extensive region along nearly the full extent of the superior temporal sulcus, including a locus in the posterior superior temporal sulcus (pSTS), as well as the frontal operculum and inferior parietal lobule (IPL)--components of the distributed neural system for action understanding--and the fusiform gyrus. These results suggest that the representation of covert mental states that may predict behavior and the representation of intentions that are implied by perceived actions involve distinct neural systems. These results show that the TPJ and the pSTS play dissociable roles in mentalizing and are parts of different distributed neural systems. Because the social animations do not depict articulated body movements, these results also highlight that the perception of the kinematics of actions is not necessary to activate the mirror neuron system, suggesting that this system plays a general role in the representation of intentions and goals of actions. Furthermore, these results suggest that the fusiform gyrus plays a general role in the representation of visual stimuli that signify agency, independent of visual form.

The Weyl approach to the representation theory of reflection equation algebra

International Nuclear Information System (INIS)

Saponov, P A

2004-01-01

The present paper deals with the representation theory of reflection equation algebra, connected to a Hecke type R-matrix. Up to some reasonable additional conditions, the R-matrix is arbitrary (not necessary originating from quantum groups). We suggest a universal method for constructing finite dimensional irreducible representations in the framework of the Weyl approach well known in the representation theory of classical Lie groups and algebras. With this method a series of irreducible modules is constructed. The modules are parametrized by Young diagrams. The spectrum of central elements s k Tr q L k is calculated in the single-row and single-column representations. A rule for the decomposition of the tensor product of modules into a direct sum of irreducible components is also suggested

An improved advertising CTR prediction approach based on the fuzzy deep neural network.

Science.gov (United States)

Jiang, Zilong; Gao, Shu; Li, Mingjiang

2018-01-01

Combining a deep neural network with fuzzy theory, this paper proposes an advertising click-through rate (CTR) prediction approach based on a fuzzy deep neural network (FDNN). In this approach, fuzzy Gaussian-Bernoulli restricted Boltzmann machine (FGBRBM) is first applied to input raw data from advertising datasets. Next, fuzzy restricted Boltzmann machine (FRBM) is used to construct the fuzzy deep belief network (FDBN) with the unsupervised method layer by layer. Finally, fuzzy logistic regression (FLR) is utilized for modeling the CTR. The experimental results show that the proposed FDNN model outperforms several baseline models in terms of both data representation capability and robustness in advertising click log datasets with noise.

Neural Evidence for a Distinction between Short-Term Memory and the Focus of Attention

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Lewis-Peacock, Jarrod A.; Drysdale, Andrew T.; Oberauer, Klaus; Postle, Bradley R.

2012-01-01

It is widely assumed that the short-term retention of information is accomplished via maintenance of an active neural trace. However, we demonstrate that memory can be preserved across a brief delay despite the apparent loss of sustained representations. Delay period activity may, in fact, reflect the focus of attention, rather than STM. We…

Population-wide distributions of neural activity during perceptual decision-making

Science.gov (United States)

Machens, Christian

2018-01-01

Cortical activity involves large populations of neurons, even when it is limited to functionally coherent areas. Electrophysiological recordings, on the other hand, involve comparatively small neural ensembles, even when modern-day techniques are used. Here we review results which have started to fill the gap between these two scales of inquiry, by shedding light on the statistical distributions of activity in large populations of cells. We put our main focus on data recorded in awake animals that perform simple decision-making tasks and consider statistical distributions of activity throughout cortex, across sensory, associative, and motor areas. We transversally review the complexity of these distributions, from distributions of firing rates and metrics of spike-train structure, through distributions of tuning to stimuli or actions and of choice signals, and finally the dynamical evolution of neural population activity and the distributions of (pairwise) neural interactions. This approach reveals shared patterns of statistical organization across cortex, including: (i) long-tailed distributions of activity, where quasi-silence seems to be the rule for a majority of neurons; that are barely distinguishable between spontaneous and active states; (ii) distributions of tuning parameters for sensory (and motor) variables, which show an extensive extrapolation and fragmentation of their representations in the periphery; and (iii) population-wide dynamics that reveal rotations of internal representations over time, whose traces can be found both in stimulus-driven and internally generated activity. We discuss how these insights are leading us away from the notion of discrete classes of cells, and are acting as powerful constraints on theories and models of cortical organization and population coding. PMID:23123501

Neural Global Pattern Similarity Underlies True and False Memories.

Science.gov (United States)

Ye, Zhifang; Zhu, Bi; Zhuang, Liping; Lu, Zhonglin; Chen, Chuansheng; Xue, Gui

2016-06-22

The neural processes giving rise to human memory strength signals remain poorly understood. Inspired by formal computational models that posit a central role of global matching in memory strength, we tested a novel hypothesis that the strengths of both true and false memories arise from the global similarity of an item's neural activation pattern during retrieval to that of all the studied items during encoding (i.e., the encoding-retrieval neural global pattern similarity [ER-nGPS]). We revealed multiple ER-nGPS signals that carried distinct information and contributed differentially to true and false memories: Whereas the ER-nGPS in the parietal regions reflected semantic similarity and was scaled with the recognition strengths of both true and false memories, ER-nGPS in the visual cortex contributed solely to true memory. Moreover, ER-nGPS differences between the parietal and visual cortices were correlated with frontal monitoring processes. By combining computational and neuroimaging approaches, our results advance a mechanistic understanding of memory strength in recognition. What neural processes give rise to memory strength signals, and lead to our conscious feelings of familiarity? Using fMRI, we found that the memory strength of a given item depends not only on how it was encoded during learning, but also on the similarity of its neural representation with other studied items. The global neural matching signal, mainly in the parietal lobule, could account for the memory strengths of both studied and unstudied items. Interestingly, a different global matching signal, originated from the visual cortex, could distinguish true from false memories. The findings reveal multiple neural mechanisms underlying the memory strengths of events registered in the brain. Copyright © 2016 the authors 0270-6474/16/366792-11$15.00/0.

Evolved Representation and Computational Creativity

Directory of Open Access Journals (Sweden)

Ashraf Fouad Hafez Ismail

2001-01-01

Full Text Available Advances in science and technology have influenced designing activity in architecture throughout its history. Observing the fundamental changes to architectural designing due to the substantial influences of the advent of the computing era, we now witness our design environment gradually changing from conventional pencil and paper to digital multi-media. Although designing is considered to be a unique human activity, there has always been a great dependency on design aid tools. One of the greatest aids to architectural design, amongst the many conventional and widely accepted computational tools, is the computer-aided object modeling and rendering tool, commonly known as a CAD package. But even though conventional modeling tools have provided designers with fast and precise object handling capabilities that were not available in the pencil-and-paper age, they normally show weaknesses and limitations in covering the whole design process.In any kind of design activity, the design worked on has to be represented in some way. For a human designer, designs are for example represented using models, drawings, or verbal descriptions. If a computer is used for design work, designs are usually represented by groups of pixels (paintbrush programs, lines and shapes (general-purpose CAD programs or higher-level objects like ‘walls’ and ‘rooms’ (purpose-specific CAD programs.A human designer usually has a large number of representations available, and can use the representation most suitable for what he or she is working on. Humans can also introduce new representations and thereby represent objects that are not part of the world they experience with their sensory organs, for example vector representations of four and five dimensional objects. In design computing on the other hand, the representation or representations used have to be explicitly defined. Many different representations have been suggested, often optimized for specific design domains

Nicotine deprivation elevates neural representation of smoking-related cues in object-sensitive visual cortex: a proof of concept study.

Science.gov (United States)

Havermans, Anne; van Schayck, Onno C P; Vuurman, Eric F P M; Riedel, Wim J; van den Hurk, Job

2017-08-01

In the current study, we use functional magnetic resonance imaging (fMRI) and multi-voxel pattern analysis (MVPA) to investigate whether tobacco addiction biases basic visual processing in favour of smoking-related images. We hypothesize that the neural representation of smoking-related stimuli in the lateral occipital complex (LOC) is elevated after a period of nicotine deprivation compared to a satiated state, but that this is not the case for object categories unrelated to smoking. Current smokers (≥10 cigarettes a day) underwent two fMRI scanning sessions: one after 10 h of nicotine abstinence and the other one after smoking ad libitum. Regional blood oxygenated level-dependent (BOLD) response was measured while participants were presented with 24 blocks of 8 colour-matched pictures of cigarettes, pencils or chairs. The functional data of 10 participants were analysed through a pattern classification approach. In bilateral LOC clusters, the classifier was able to discriminate between patterns of activity elicited by visually similar smoking-related (cigarettes) and neutral objects (pencils) above empirically estimated chance levels only during deprivation (mean = 61.0%, chance (permutations) = 50.0%, p = .01) but not during satiation (mean = 53.5%, chance (permutations) = 49.9%, ns.). For all other stimulus contrasts, there was no difference in discriminability between the deprived and satiated conditions. The discriminability between smoking and non-smoking visual objects was elevated in object-selective brain region LOC after a period of nicotine abstinence. This indicates that attention bias likely affects basic visual object processing.

Social discounting involves modulation of neural value signals by temporoparietal junction

Science.gov (United States)

Strombach, Tina; Weber, Bernd; Hangebrauk, Zsofia; Kenning, Peter; Karipidis, Iliana I.; Tobler, Philippe N.; Kalenscher, Tobias

2015-01-01

Most people are generous, but not toward everyone alike: generosity usually declines with social distance between individuals, a phenomenon called social discounting. Despite the pervasiveness of social discounting, social distance between actors has been surprisingly neglected in economic theory and neuroscientific research. We used functional magnetic resonance imaging (fMRI) to study the neural basis of this process to understand the neural underpinnings of social decision making. Participants chose between selfish and generous alternatives, yielding either a large reward for the participant alone, or smaller rewards for the participant and another individual at a particular social distance. We found that generous choices engaged the temporoparietal junction (TPJ). In particular, the TPJ activity was scaled to the social-distance–dependent conflict between selfish and generous motives during prosocial choice, consistent with ideas that the TPJ promotes generosity by facilitating overcoming egoism bias. Based on functional coupling data, we propose and provide evidence for a biologically plausible neural model according to which the TPJ supports social discounting by modulating basic neural value signals in the ventromedial prefrontal cortex to incorporate social-distance–dependent other-regarding preferences into an otherwise exclusively own-reward value representation. PMID:25605887

A universal multilingual weightless neural network tagger via quantitative linguistics.

Science.gov (United States)

Carneiro, Hugo C C; Pedreira, Carlos E; França, Felipe M G; Lima, Priscila M V

2017-07-01

In the last decade, given the availability of corpora in several distinct languages, research on multilingual part-of-speech tagging started to grow. Amongst the novelties there is mWANN-Tagger (multilingual weightless artificial neural network tagger), a weightless neural part-of-speech tagger capable of being used for mostly-suffix-oriented languages. The tagger was subjected to corpora in eight languages of quite distinct natures and had a remarkable accuracy with very low sample deviation in every one of them, indicating the robustness of weightless neural systems for part-of-speech tagging tasks. However, mWANN-Tagger needed to be tuned for every new corpus, since each one required a different parameter configuration. For mWANN-Tagger to be truly multilingual, it should be usable for any new language with no need of parameter tuning. This article proposes a study that aims to find a relation between the lexical diversity of a language and the parameter configuration that would produce the best performing mWANN-Tagger instance. Preliminary analyses suggested that a single parameter configuration may be applied to the eight aforementioned languages. The mWANN-Tagger instance produced by this configuration was as accurate as the language-dependent ones obtained through tuning. Afterwards, the weightless neural tagger was further subjected to new corpora in languages that range from very isolating to polysynthetic ones. The best performing instances of mWANN-Tagger are again the ones produced by the universal parameter configuration. Hence, mWANN-Tagger can be applied to new corpora with no need of parameter tuning, making it a universal multilingual part-of-speech tagger. Further experiments with Universal Dependencies treebanks reveal that mWANN-Tagger may be extended and that it has potential to outperform most state-of-the-art part-of-speech taggers if better word representations are provided. Copyright © 2017 Elsevier Ltd. All rights reserved.

P1-24: Neural Representation of Gloss in the Macaque Inferior Temporal Cortex

OpenAIRE

Akiko Nishio; Naokazu Goda; Hidehiko Komatsu

2012-01-01

The variation of the appearance such as gloss provides one of the most important information for object recognition. However, little is known about the neural mechanisms related to the perception of gloss. We examined whether the neurons in the inferior temporal (IT) cortex of the monkeys are coding gloss of objects. We made visual stimuli which have various surface reflectance properties, and tested responses of IT neurons to these stimuli while a monkey was performing a visual fixation task...

In silico Interrogation of Insect Central Complex Suggests Computational Roles for the Ellipsoid Body in Spatial Navigation