Spike-timing-based computation in sound localization.

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Dan F M Goodman

2010-11-01

Full Text Available Spike timing is precise in the auditory system and it has been argued that it conveys information about auditory stimuli, in particular about the location of a sound source. However, beyond simple time differences, the way in which neurons might extract this information is unclear and the potential computational advantages are unknown. The computational difficulty of this task for an animal is to locate the source of an unexpected sound from two monaural signals that are highly dependent on the unknown source signal. In neuron models consisting of spectro-temporal filtering and spiking nonlinearity, we found that the binaural structure induced by spatialized sounds is mapped to synchrony patterns that depend on source location rather than on source signal. Location-specific synchrony patterns would then result in the activation of location-specific assemblies of postsynaptic neurons. We designed a spiking neuron model which exploited this principle to locate a variety of sound sources in a virtual acoustic environment using measured human head-related transfer functions. The model was able to accurately estimate the location of previously unknown sounds in both azimuth and elevation (including front/back discrimination in a known acoustic environment. We found that multiple representations of different acoustic environments could coexist as sets of overlapping neural assemblies which could be associated with spatial locations by Hebbian learning. The model demonstrates the computational relevance of relative spike timing to extract spatial information about sources independently of the source signal.

Exit from Synchrony in Joint Improvised Motion.

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Assi Dahan

Full Text Available Motion synchrony correlates with effective and well-rated human interaction. However, people do not remain locked in synchrony; Instead, they repeatedly enter and exit synchrony. In many important interactions, such as therapy, marriage and parent-infant communication, it is the ability to exit and then re-enter synchrony that is thought to build strong relationship. The phenomenon of entry into zero-phase synchrony is well-studied experimentally and in terms of mathematical modeling. In contrast, exit-from-synchrony is under-studied. Here, we focus on human motion coordination, and examine the exit-from-synchrony phenomenon using experimental data from the mirror game paradigm, in which people perform joint improvised motion, and from human tracking of computer-generated stimuli. We present a mathematical mechanism that captures aspects of exit-from-synchrony in human motion. The mechanism adds a random motion component when the accumulated velocity error between the players is small. We introduce this mechanism to several models for human coordinated motion, including the widely studied HKB model, and the predictor-corrector model of Noy, Dekel and Alon. In all models, the new mechanism produces realistic simulated behavior when compared to experimental data from the mirror game and from tracking of computer generated stimuli, including repeated entry and exit from zero-phase synchrony that generates a complexity of motion similar to that of human players. We hope that these results can inform future research on exit-from-synchrony, to better understand the dynamics of coordinated action of people and to enhance human-computer and human-robot interaction.

Axonal propagation of simple and complex spikes in cerebellar Purkinje neurons.

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Khaliq, Zayd M; Raman, Indira M

2005-01-12

In cerebellar Purkinje neurons, the reliability of propagation of high-frequency simple spikes and spikelets of complex spikes is likely to regulate inhibition of Purkinje target neurons. To test the extent to which a one-to-one correspondence exists between somatic and axonal spikes, we made dual somatic and axonal recordings from Purkinje neurons in mouse cerebellar slices. Somatic action potentials were recorded with a whole-cell pipette, and the corresponding axonal signals were recorded extracellularly with a loose-patch pipette. Propagation of spontaneous and evoked simple spikes was highly reliable. At somatic firing rates of approximately 200 spikes/sec, 375 Hz during somatic hyperpolarizations that silenced spontaneous firing to approximately 150 Hz during spontaneous activity. The probability of propagation of individual spikelets could be described quantitatively as a saturating function of spikelet amplitude, rate of rise, or preceding interspike interval. The results suggest that ion channels of Purkinje axons are adapted to produce extremely short refractory periods and that brief bursts of forward-propagating action potentials generated by complex spikes may contribute transiently to inhibition of postsynaptic neurons.

An Overview of Bayesian Methods for Neural Spike Train Analysis

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Zhe Chen

2013-01-01

Full Text Available Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.

Neural Correlates of Temporal Complexity and Synchrony during Audiovisual Correspondence Detection.

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Baumann, Oliver; Vromen, Joyce M G; Cheung, Allen; McFadyen, Jessica; Ren, Yudan; Guo, Christine C

2018-01-01

We often perceive real-life objects as multisensory cues through space and time. A key challenge for audiovisual integration is to match neural signals that not only originate from different sensory modalities but also that typically reach the observer at slightly different times. In humans, complex, unpredictable audiovisual streams lead to higher levels of perceptual coherence than predictable, rhythmic streams. In addition, perceptual coherence for complex signals seems less affected by increased asynchrony between visual and auditory modalities than for simple signals. Here, we used functional magnetic resonance imaging to determine the human neural correlates of audiovisual signals with different levels of temporal complexity and synchrony. Our study demonstrated that greater perceptual asynchrony and lower signal complexity impaired performance in an audiovisual coherence-matching task. Differences in asynchrony and complexity were also underpinned by a partially different set of brain regions. In particular, our results suggest that, while regions in the dorsolateral prefrontal cortex (DLPFC) were modulated by differences in memory load due to stimulus asynchrony, areas traditionally thought to be involved in speech production and recognition, such as the inferior frontal and superior temporal cortex, were modulated by the temporal complexity of the audiovisual signals. Our results, therefore, indicate specific processing roles for different subregions of the fronto-temporal cortex during audiovisual coherence detection.

Synchrony in Dyadic Psychotherapy Sessions

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Ramseyer, Fabian; Tschacher, Wolfgang

Synchrony is a multi-faceted concept used in diverse domains such as physics, biology, and the social sciences. This chapter reviews some of the evidence of nonverbal synchrony in human communication, with a main focus on the role of synchrony in the psychotherapeutic setting. Nonverbal synchrony describes coordinated behavior of patient and therapist. Its association with empathy, rapport and the therapeutic relationship has been pointed out repeatedly, yet close evaluation of empirical studies suggests that the evidence remains inconclusive. Particularly in naturalistic studies, research with quantitative measures of synchrony is still lacking. We introduce a new empirical approach for the study of synchrony in psychotherapies under field conditions: Motion Energy Analysis (MEA). This is a video-based algorithm that quantifies the amount of movement in freely definable regions of interest. Our statistical analysis detects synchrony on a global level, irrespective of the specific body parts moving. Synchrony thus defined can be considered as a general measure of movement coordination between interacting individuals. Data from a sequence of N = 21 therapy sessions taken from one psychotherapy dyad shows a high positive relationship between synchrony and the therapeutic bond. Nonverbal synchrony can thus be considered a promising concept for research on the therapeutic alliance. Further areas of application are discussed.

Inferior Olive HCN1 Channels Coordinate Synaptic Integration and Complex Spike Timing

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Derek L.F. Garden

2018-02-01

Full Text Available Cerebellar climbing-fiber-mediated complex spikes originate from neurons in the inferior olive (IO, are critical for motor coordination, and are central to theories of cerebellar learning. Hyperpolarization-activated cyclic-nucleotide-gated (HCN channels expressed by IO neurons have been considered as pacemaker currents important for oscillatory and resonant dynamics. Here, we demonstrate that in vitro, network actions of HCN1 channels enable bidirectional glutamatergic synaptic responses, while local actions of HCN1 channels determine the timing and waveform of synaptically driven action potentials. These roles are distinct from, and may complement, proposed pacemaker functions of HCN channels. We find that in behaving animals HCN1 channels reduce variability in the timing of cerebellar complex spikes, which serve as a readout of IO spiking. Our results suggest that spatially distributed actions of HCN1 channels enable the IO to implement network-wide rules for synaptic integration that modulate the timing of cerebellar climbing fiber signals.

Spatial synchrony in cisco recruitment

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Myers, Jared T.; Yule, Daniel L.; Jones, Michael L.; Ahrenstorff, Tyler D.; Hrabik, Thomas R.; Claramunt, Randall M.; Ebener, Mark P.; Berglund, Eric K.

2015-01-01

We examined the spatial scale of recruitment variability for disparate cisco (Coregonus artedi) populations in the Great Lakes (n = 8) and Minnesota inland lakes (n = 4). We found that the scale of synchrony was approximately 400 km when all available data were utilized; much greater than the 50-km scale suggested for freshwater fish populations in an earlier global analysis. The presence of recruitment synchrony between Great Lakes and inland lake cisco populations supports the hypothesis that synchronicity is driven by climate and not dispersal. We also found synchrony in larval densities among three Lake Superior populations separated by 25–275 km, which further supports the hypothesis that broad-scale climatic factors are the cause of spatial synchrony. Among several candidate climate variables measured during the period of larval cisco emergence, maximum wind speeds exhibited the most similar spatial scale of synchrony to that observed for cisco. Other factors, such as average water temperatures, exhibited synchrony on broader spatial scales, which suggests they could also be contributing to recruitment synchrony. Our results provide evidence that abiotic factors can induce synchronous patterns of recruitment for populations of cisco inhabiting waters across a broad geographic range, and show that broad-scale synchrony of recruitment can occur in freshwater fish populations as well as those from marine systems.

Differential contribution of demographic rate synchrony to population synchrony in barn swallows.

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Schaub, Michael; von Hirschheydt, Johann; Grüebler, Martin U

2015-11-01

Populations of many species show temporally synchronous dynamics over some range, mostly caused by spatial autocorrelation of the environment that affects demographic rates. Synchronous fluctuation of a demographic rate is a necessary, but not sufficient condition for population synchrony because population growth is differentially sensitive to variation in demographic rates. Little is known about the relative effects of demographic rates to population synchrony, because it is rare that all demographic rates from several populations are known. We develop a hierarchical integrated population model with which all relevant demographic rates from all study populations can be estimated and apply it to demographic data of barn swallows Hirundo rustica from nine sites that were between 19 and 224 km apart from each other. We decompose the variation of the population growth and of the demographic rates (apparent survival, components of productivity, immigration) into global and local temporal components using random effects which allowed the estimation of synchrony of these rates. The barn swallow populations fluctuated synchronously, but less so than most demographic rates. The highest synchrony showed the probability of double brooding, while fledging success was highly asynchronous. Apparent survival, immigration and total productivity achieved intermediate levels of synchrony. The growth of all populations was most sensitive to changes in immigration and adult apparent survival, and both of them contributed to the observed temporal variation of population growth rates. Using a simulation model, we show that immigration and apparent survival of juveniles and adults were able to induce population synchrony, but not components of local productivity due to their low population growth rate sensitivity. Immigrants are mostly first-time breeders, and consequently, their number depends on the productivity of neighbouring populations. Since total productivity was synchronized

Spiking Regularity and Coherence in Complex Hodgkin–Huxley Neuron Networks

International Nuclear Information System (INIS)

Zhi-Qiang, Sun; Ping, Xie; Wei, Li; Peng-Ye, Wang

2010-01-01

We study the effects of the strength of coupling between neurons on the spiking regularity and coherence in a complex network with randomly connected Hodgkin–Huxley neurons driven by colored noise. It is found that for the given topology realization and colored noise correlation time, there exists an optimal strength of coupling, at which the spiking regularity of the network reaches the best level. Moreover, when the temporal regularity reaches the best level, the spatial coherence of the system has already increased to a relatively high level. In addition, for the given number of neurons and noise correlation time, the values of average regularity and spatial coherence at the optimal strength of coupling are nearly independent of the topology realization. Furthermore, there exists an optimal value of colored noise correlation time at which the spiking regularity can reach its best level. These results may be helpful for understanding of the real neuron world. (cross-disciplinary physics and related areas of science and technology)

Cooperative Learning and Interpersonal Synchrony.

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Vink, Roy; Wijnants, Maarten L; Cillessen, Antonius H N; Bosman, Anna M T

2017-04-01

Cooperative learning has been shown to result in better task performance, compared to individual and competitive learning, and can lead to positive social effects. However, potential working mechanisms at a micro level remain unexplored. One potential working mechanism might be the level of interpersonal synchrony between cooperating individuals. It has been shown that increased levels of interpersonal synchrony are related to better cognitive performance (e.g., increased memory). Social factors also appear to be affected by the level of interpersonal synchrony, with more interpersonal synchrony leading to increased likeability. In the present study, interpersonal synchrony of postural sway and its relation to task performance and social factors (i.e., popularity, social acceptance, and likeability) was examined. To test this, 183 dyads performed a tangram task while each child stood on a Nintendo Wii Balance Board that recorded their postural sway. The results showed that lower levels of interpersonal synchrony were related to better task performance and those dyads who were on average more popular synchronized more. These results contradict previous findings. It is suggested that for task performance, a more loosely coupled system is better than a synchronized system. In terms of social competence, dyad popularity was associated with more interpersonal synchrony.

Prosocial Consequences of Interpersonal Synchrony

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2016-01-01

Abstract. The capacity to establish interpersonal synchrony is fundamental to human beings because it constitutes the basis for social connection and understanding. Interpersonal synchrony refers to instances when the movements or sensations of two or more people overlap in time and form. Recently, the causal influence of interpersonal synchrony on prosociality has been established through experiments. The current meta-analysis is the first to synthesize these isolated and sometimes contradictory experiments. We meta-analyzed 60 published and unpublished experiments that compared an interpersonal synchrony condition with at least one control condition. The results reveal a medium effect of interpersonal synchrony on prosociality with regard to both attitudes and behaviors. Furthermore, experimenter effects and intentionality moderate these effects. We discuss the strengths and limitations of our analysis, as well as its practical implications, and we suggest avenues for future research. PMID:28105388

Role of LAMP1 Binding and pH Sensing by the Spike Complex of Lassa Virus.

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Cohen-Dvashi, Hadas; Israeli, Hadar; Shani, Orly; Katz, Aliza; Diskin, Ron

2016-11-15

To effectively infect cells, Lassa virus needs to switch in an endosomal compartment from its primary receptor, α-dystroglycan, to a protein termed LAMP1. A unique histidine triad on the surface of the receptor-binding domain from the glycoprotein spike complex of Lassa virus is important for LAMP1 binding. Here we investigate mutated spikes that have an impaired ability to interact with LAMP1 and show that although LAMP1 is important for efficient infectivity, it is not required for spike-mediated membrane fusion per se Our studies reveal important regulatory roles for histidines from the triad in sensing acidic pH and preventing premature spike triggering. We further show that LAMP1 requires a positively charged His230 residue to engage with the spike complex and that LAMP1 binding promotes membrane fusion. These results elucidate the molecular role of LAMP1 binding during Lassa virus cell entry and provide new insights into how pH is sensed by the spike. Lassa virus is a devastating disease-causing agent in West Africa, with a significant yearly death toll and severe long-term complications associated with its infection in survivors. In recent years, we learned that Lassa virus needs to switch receptors in a pH-dependent manner to efficiently infect cells, but neither the molecular mechanisms that allow switching nor the actual effects of switching were known. Here we investigate the activity of the viral spike complex after abrogation of its ability to switch receptors. These studies inform us about the role of switching receptors and provide new insights into how the spike senses acidic pH. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

A novel technique for phase synchrony measurement from the complex motor imaginary potential of combined body and limb action

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Zhou, Zhong-xing; Wan, Bai-kun; Ming, Dong; Qi, Hong-zhi

2010-08-01

In this study, we proposed and evaluated the use of the empirical mode decomposition (EMD) technique combined with phase synchronization analysis to investigate the human brain synchrony of the supplementary motor area (SMA) and primary motor area (M1) during complex motor imagination of combined body and limb action. We separated the EEG data of the SMA and M1 into intrinsic mode functions (IMFs) using the EMD method and determined the characteristic IMFs by power spectral density (PSD) analysis. Thereafter, the instantaneous phases of the characteristic IMFs were obtained by the Hilbert transformation, and the single-trial phase-locking value (PLV) features for brain synchrony measurement between the SMA and M1 were investigated separately. The classification performance suggests that the proposed approach is effective for phase synchronization analysis and is promising for the application of a brain-computer interface in motor nerve reconstruction of the lower limbs.

One in the Dance: Musical Correlates of Group Synchrony in a Real-World Club Environment.

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Melissa Ellamil

Full Text Available Previous research on interpersonal synchrony has mainly investigated small groups in isolated laboratory settings, which may not fully reflect the complex and dynamic interactions of real-life social situations. The present study expands on this by examining group synchrony across a large number of individuals in a naturalistic environment. Smartphone acceleration measures were recorded from participants during a music set in a dance club and assessed to identify how group movement synchrony covaried with various features of the music. In an evaluation of different preprocessing and analysis methods, giving more weight to front-back movement provided the most sensitive and reliable measure of group synchrony. During the club music set, group synchrony of torso movement was most strongly associated with pulsations that approximate walking rhythm (100-150 beats per minute. Songs with higher real-world play counts were also correlated with greater group synchrony. Group synchrony thus appears to be constrained by familiarity of the movement (walking action and rhythm and of the music (song popularity. These findings from a real-world, large-scale social and musical setting can guide the development of methods for capturing and examining collective experiences in the laboratory and for effectively linking them to synchrony across people in daily life.

One in the Dance: Musical Correlates of Group Synchrony in a Real-World Club Environment.

Science.gov (United States)

Ellamil, Melissa; Berson, Joshua; Wong, Jen; Buckley, Louis; Margulies, Daniel S

2016-01-01

Previous research on interpersonal synchrony has mainly investigated small groups in isolated laboratory settings, which may not fully reflect the complex and dynamic interactions of real-life social situations. The present study expands on this by examining group synchrony across a large number of individuals in a naturalistic environment. Smartphone acceleration measures were recorded from participants during a music set in a dance club and assessed to identify how group movement synchrony covaried with various features of the music. In an evaluation of different preprocessing and analysis methods, giving more weight to front-back movement provided the most sensitive and reliable measure of group synchrony. During the club music set, group synchrony of torso movement was most strongly associated with pulsations that approximate walking rhythm (100-150 beats per minute). Songs with higher real-world play counts were also correlated with greater group synchrony. Group synchrony thus appears to be constrained by familiarity of the movement (walking action and rhythm) and of the music (song popularity). These findings from a real-world, large-scale social and musical setting can guide the development of methods for capturing and examining collective experiences in the laboratory and for effectively linking them to synchrony across people in daily life.

Spiking neural network for recognizing spatiotemporal sequences of spikes

International Nuclear Information System (INIS)

Jin, Dezhe Z.

2004-01-01

Sensory neurons in many brain areas spike with precise timing to stimuli with temporal structures, and encode temporally complex stimuli into spatiotemporal spikes. How the downstream neurons read out such neural code is an important unsolved problem. In this paper, we describe a decoding scheme using a spiking recurrent neural network. The network consists of excitatory neurons that form a synfire chain, and two globally inhibitory interneurons of different types that provide delayed feedforward and fast feedback inhibition, respectively. The network signals recognition of a specific spatiotemporal sequence when the last excitatory neuron down the synfire chain spikes, which happens if and only if that sequence was present in the input spike stream. The recognition scheme is invariant to variations in the intervals between input spikes within some range. The computation of the network can be mapped into that of a finite state machine. Our network provides a simple way to decode spatiotemporal spikes with diverse types of neurons

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

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Zenke, Friedemann; Ganguli, Surya

2018-04-13

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.

Nonverbal synchrony and affect in dyadic interactions

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Wolfgang eTschacher

2014-11-01

Full Text Available In an experiment on dyadic social interaction, we invited participants to verbal interactions in cooperative, competitive, and 'fun task' conditions. We focused on the link between interactants' affectivity and their nonverbal synchrony, and explored which further variables contributed to affectivity: interactants' personality traits, sex, and the prescribed interaction tasks. Nonverbal synchrony was quantified by the coordination of interactants' body movement, using an automated video-analysis algorithm (Motion Energy Analysis, MEA. Traits were assessed with standard questionnaires of personality, attachment, interactional style, psychopathology and interpersonal reactivity. We included 168 previously unacquainted individuals who were randomly allocated to same-sex dyads (84 females, 84 males, mean age 27.3 years. Dyads discussed four topics of general interest drawn from an urn of eight topics, and finally engaged in a fun interaction. Each interaction lasted five minutes. In between interactions, participants repeatedly assessed their affect. Using hierarchical linear modeling, we found moderate to strong effect sizes for synchrony to occur, especially in competitive and fun task conditions. Positive affect was associated positively with synchrony, negative affect was associated negatively. As for causal direction, data supported the interpretation that synchrony entailed affect rather than vice versa. The link between nonverbal synchrony and affect was strongest in female dyads. The findings extend previous reports of synchrony and mimicry associated with emotion in relationships and suggest a possible mechanism of the synchrony-affect correlation.

Quality level of a treatment by Cyberknife with Synchrony; Niveau de qualite d'un traitement par le CyberKnife avec Synchrony

Energy Technology Data Exchange (ETDEWEB)

Khawaja, M.S.A.; Noel, A. [CRAN UMR 7039, CNRS, 54 - Vandoeuvre-les-Nancy (France); Centre Alexis-Vautrin, 54 - Vandoeuvre-les-Nancy (France); Nancy-1 Univ., 54 (France); Marchesi, V. [Centre Alexis-Vautrin, 54 - Vandoeuvre-les-Nancy (France); Wolf, D. [INPL CRAN UMR 7039, CNRS, 54 - Vandoeuvre-les-Nancy (France)

2009-10-15

The Cyberknife and its Synchrony associated subsystem represent a very reliable mean to treat the tumors of soft tissues moving with respiration. It allows to guarantee a high quality of treatment because of sub-millimetric accuracy whatever be the complexity degree of the patient respiration cycle. (N.C.)

A complex-valued firing-rate model that approximates the dynamics of spiking networks.

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Evan S Schaffer

2013-10-01

Full Text Available Firing-rate models provide an attractive approach for studying large neural networks because they can be simulated rapidly and are amenable to mathematical analysis. Traditional firing-rate models assume a simple form in which the dynamics are governed by a single time constant. These models fail to replicate certain dynamic features of populations of spiking neurons, especially those involving synchronization. We present a complex-valued firing-rate model derived from an eigenfunction expansion of the Fokker-Planck equation and apply it to the linear, quadratic and exponential integrate-and-fire models. Despite being almost as simple as a traditional firing-rate description, this model can reproduce firing-rate dynamics due to partial synchronization of the action potentials in a spiking model, and it successfully predicts the transition to spike synchronization in networks of coupled excitatory and inhibitory neurons.

A complex-valued firing-rate model that approximates the dynamics of spiking networks.

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Schaffer, Evan S; Ostojic, Srdjan; Abbott, L F

2013-10-01

Firing-rate models provide an attractive approach for studying large neural networks because they can be simulated rapidly and are amenable to mathematical analysis. Traditional firing-rate models assume a simple form in which the dynamics are governed by a single time constant. These models fail to replicate certain dynamic features of populations of spiking neurons, especially those involving synchronization. We present a complex-valued firing-rate model derived from an eigenfunction expansion of the Fokker-Planck equation and apply it to the linear, quadratic and exponential integrate-and-fire models. Despite being almost as simple as a traditional firing-rate description, this model can reproduce firing-rate dynamics due to partial synchronization of the action potentials in a spiking model, and it successfully predicts the transition to spike synchronization in networks of coupled excitatory and inhibitory neurons.

Tonal synchrony in mother-infant interaction based on harmonic and pentatonic series.

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Van Puyvelde, Martine; Vanfleteren, Pol; Loots, Gerrit; Deschuyffeleer, Sara; Vinck, Bart; Jacquet, Wolfgang; Verhelst, Werner

2010-12-01

This study reports the occurrence of 'tonal synchrony' as a new dimension of early mother-infant interaction synchrony. The findings are based on a tonal and temporal analysis of vocal interactions between 15 mothers and their 3-month-old infants during 5 min of free-play in a laboratory setting. In total, 558 vocal exchanges were identified and analysed, of which 84% reflected harmonic or pentatonic series. Another 10% of the exchanges contained absolute and/or relative pitch and/or interval imitations. The total durations of dyads being in tonal synchrony were normally distributed (M=3.71, SD=2.44). Vocalisations based on harmonic series appeared organised around the major triad, containing significantly more simple frequency ratios (octave, fifth and third) than complex ones (non-major triad tones). Tonal synchrony and its characteristics are discussed in relation to infant-directed speech, communicative musicality, pre-reflective communication and its impact on the quality of early mother-infant interaction and child's development. Copyright © 2010 Elsevier Inc. All rights reserved.

Complexity optimization and high-throughput low-latency hardware implementation of a multi-electrode spike-sorting algorithm.

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Dragas, Jelena; Jackel, David; Hierlemann, Andreas; Franke, Felix

2015-03-01

Reliable real-time low-latency spike sorting with large data throughput is essential for studies of neural network dynamics and for brain-machine interfaces (BMIs), in which the stimulation of neural networks is based on the networks' most recent activity. However, the majority of existing multi-electrode spike-sorting algorithms are unsuited for processing high quantities of simultaneously recorded data. Recording from large neuronal networks using large high-density electrode sets (thousands of electrodes) imposes high demands on the data-processing hardware regarding computational complexity and data transmission bandwidth; this, in turn, entails demanding requirements in terms of chip area, memory resources and processing latency. This paper presents computational complexity optimization techniques, which facilitate the use of spike-sorting algorithms in large multi-electrode-based recording systems. The techniques are then applied to a previously published algorithm, on its own, unsuited for large electrode set recordings. Further, a real-time low-latency high-performance VLSI hardware architecture of the modified algorithm is presented, featuring a folded structure capable of processing the activity of hundreds of neurons simultaneously. The hardware is reconfigurable “on-the-fly” and adaptable to the nonstationarities of neuronal recordings. By transmitting exclusively spike time stamps and/or spike waveforms, its real-time processing offers the possibility of data bandwidth and data storage reduction.

Interactional synchrony in chimpanzees: Examination through a finger-tapping experiment.

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Yu, Lira; Tomonaga, Masaki

2015-05-11

Humans often unconsciously coordinate behaviour with that of others in daily life. This interpersonal coordination, including mimicry and interactional synchrony, has been suggested to play a fundamental role in social interaction. If this coordinative behavior is socially adaptive, it may be shared with other highly social animal species. The current study targeted chimpanzees, which phylogenetically are the closest living relatives of humans and live in complex social groups, and examined whether interactional synchrony would emerge in pairs of chimpanzees when auditory information about a partner's movement was provided. A finger-tapping task was introduced via touch panels to elicit repetitive and rhythmic movement from each chimpanzee. We found that one of four chimpanzees produced significant changes in both tapping tempo and timing of the tapping relative to its partner's tap when auditory sounds were provided. Although the current results may have limitations in generalizing to chimpanzees as a species, we suggest that a finger-tapping task is one potential method to investigate interactional synchrony in chimpanzees under a laboratory setup.

Improved SpikeProp for Using Particle Swarm Optimization

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Falah Y. H. Ahmed

2013-01-01

Full Text Available A spiking neurons network encodes information in the timing of individual spike times. A novel supervised learning rule for SpikeProp is derived to overcome the discontinuities introduced by the spiking thresholding. This algorithm is based on an error-backpropagation learning rule suited for supervised learning of spiking neurons that use exact spike time coding. The SpikeProp is able to demonstrate the spiking neurons that can perform complex nonlinear classification in fast temporal coding. This study proposes enhancements of SpikeProp learning algorithm for supervised training of spiking networks which can deal with complex patterns. The proposed methods include the SpikeProp particle swarm optimization (PSO and angle driven dependency learning rate. These methods are presented to SpikeProp network for multilayer learning enhancement and weights optimization. Input and output patterns are encoded as spike trains of precisely timed spikes, and the network learns to transform the input trains into target output trains. With these enhancements, our proposed methods outperformed other conventional neural network architectures.

Eyeblink Synchrony in Multimodal Human-Android Interaction.

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Tatsukawa, Kyohei; Nakano, Tamami; Ishiguro, Hiroshi; Yoshikawa, Yuichiro

2016-12-23

As the result of recent progress in technology of communication robot, robots are becoming an important social partner for humans. Behavioral synchrony is understood as an important factor in establishing good human-robot relationships. In this study, we hypothesized that biasing a human's attitude toward a robot changes the degree of synchrony between human and robot. We first examined whether eyeblinks were synchronized between a human and an android in face-to-face interaction and found that human listeners' eyeblinks were entrained to android speakers' eyeblinks. This eyeblink synchrony disappeared when the android speaker spoke while looking away from the human listeners but was enhanced when the human participants listened to the speaking android while touching the android's hand. These results suggest that eyeblink synchrony reflects a qualitative state in human-robot interactions.

Span: spike pattern association neuron for learning spatio-temporal spike patterns.

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Mohemmed, Ammar; Schliebs, Stefan; Matsuda, Satoshi; Kasabov, Nikola

2012-08-01

Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-temporal information. However, due to their inherent complexity, the formulation of efficient supervised learning algorithms for SNN is difficult and remains an important problem in the research area. This article presents SPAN - a spiking neuron that is able to learn associations of arbitrary spike trains in a supervised fashion allowing the processing of spatio-temporal information encoded in the precise timing of spikes. The idea of the proposed algorithm is to transform spike trains during the learning phase into analog signals so that common mathematical operations can be performed on them. Using this conversion, it is possible to apply the well-known Widrow-Hoff rule directly to the transformed spike trains in order to adjust the synaptic weights and to achieve a desired input/output spike behavior of the neuron. In the presented experimental analysis, the proposed learning algorithm is evaluated regarding its learning capabilities, its memory capacity, its robustness to noisy stimuli and its classification performance. Differences and similarities of SPAN regarding two related algorithms, ReSuMe and Chronotron, are discussed.

A novel role of dendritic gap junction and mechanisms underlying its interaction with thalamocortical conductance in fast spiking inhibitory neurons

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Sun Qian-Quan

2009-10-01

Full Text Available Abstract Background Little is known about the roles of dendritic gap junctions (GJs of inhibitory interneurons in modulating temporal properties of sensory induced responses in sensory cortices. Electrophysiological dual patch-clamp recording and computational simulation methods were used in combination to examine a novel role of GJs in sensory mediated feed-forward inhibitory responses in barrel cortex layer IV and its underlying mechanisms. Results Under physiological conditions, excitatory post-junctional potentials (EPJPs interact with thalamocortical (TC inputs within an unprecedented few milliseconds (i.e. over 200 Hz to enhance the firing probability and synchrony of coupled fast-spiking (FS cells. Dendritic GJ coupling allows fourfold increase in synchrony and a significant enhancement in spike transmission efficacy in excitatory spiny stellate cells. The model revealed the following novel mechanisms: 1 rapid capacitive current (Icap underlies the activation of voltage-gated sodium channels; 2 there was less than 2 milliseconds in which the Icap underlying TC input and EPJP was coupled effectively; 3 cells with dendritic GJs had larger input conductance and smaller membrane response to weaker inputs; 4 synchrony in inhibitory networks by GJ coupling leads to reduced sporadic lateral inhibition and increased TC transmission efficacy. Conclusion Dendritic GJs of neocortical inhibitory networks can have very powerful effects in modulating the strength and the temporal properties of sensory induced feed-forward inhibitory and excitatory responses at a very high frequency band (>200 Hz. Rapid capacitive currents are identified as main mechanisms underlying interaction between two transient synaptic conductances.

Visual Orientation and Directional Selectivity through Thalamic Synchrony

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Stanley, Garrett B.; Jin, Jianzhong; Wang, Yushi; Desbordes, Gaëlle; Wang, Qi; Black, Michael J.; Alonso, Jose-Manuel

2012-01-01

Thalamic neurons respond to visual scenes by generating synchronous spike trains on the timescale of 10 – 20 ms that are very effective at driving cortical targets. Here we demonstrate that this synchronous activity contains unexpectedly rich information about fundamental properties of visual stimuli. We report that the occurrence of synchronous firing of cat thalamic cells with highly overlapping receptive fields is strongly sensitive to the orientation and the direction of motion of the visual stimulus. We show that this stimulus selectivity is robust, remaining relatively unchanged under different contrasts and temporal frequencies (stimulus velocities). A computational analysis based on an integrate-and-fire model of the direct thalamic input to a layer 4 cortical cell reveals a strong correlation between the degree of thalamic synchrony and the nonlinear relationship between cortical membrane potential and the resultant firing rate. Together, these findings suggest a novel population code in the synchronous firing of neurons in the early visual pathway that could serve as the substrate for establishing cortical representations of the visual scene. PMID:22745507

Decoding spatiotemporal spike sequences via the finite state automata dynamics of spiking neural networks

International Nuclear Information System (INIS)

Jin, Dezhe Z

2008-01-01

Temporally complex stimuli are encoded into spatiotemporal spike sequences of neurons in many sensory areas. Here, we describe how downstream neurons with dendritic bistable plateau potentials can be connected to decode such spike sequences. Driven by feedforward inputs from the sensory neurons and controlled by feedforward inhibition and lateral excitation, the neurons transit between UP and DOWN states of the membrane potentials. The neurons spike only in the UP states. A decoding neuron spikes at the end of an input to signal the recognition of specific spike sequences. The transition dynamics is equivalent to that of a finite state automaton. A connection rule for the networks guarantees that any finite state automaton can be mapped into the transition dynamics, demonstrating the equivalence in computational power between the networks and finite state automata. The decoding mechanism is capable of recognizing an arbitrary number of spatiotemporal spike sequences, and is insensitive to the variations of the spike timings in the sequences

A Simple Deep Learning Method for Neuronal Spike Sorting

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Yang, Kai; Wu, Haifeng; Zeng, Yu

2017-10-01

Spike sorting is one of key technique to understand brain activity. With the development of modern electrophysiology technology, some recent multi-electrode technologies have been able to record the activity of thousands of neuronal spikes simultaneously. The spike sorting in this case will increase the computational complexity of conventional sorting algorithms. In this paper, we will focus spike sorting on how to reduce the complexity, and introduce a deep learning algorithm, principal component analysis network (PCANet) to spike sorting. The introduced method starts from a conventional model and establish a Toeplitz matrix. Through the column vectors in the matrix, we trains a PCANet, where some eigenvalue vectors of spikes could be extracted. Finally, support vector machine (SVM) is used to sort spikes. In experiments, we choose two groups of simulated data from public databases availably and compare this introduced method with conventional methods. The results indicate that the introduced method indeed has lower complexity with the same sorting errors as the conventional methods.

Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting.

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Werner, Thilo; Vianello, Elisa; Bichler, Olivier; Garbin, Daniele; Cattaert, Daniel; Yvert, Blaise; De Salvo, Barbara; Perniola, Luca

2016-01-01

In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision.

The Subjective Sensation of Synchrony: An Experimental Study

KAUST Repository

Llobera, Joan; Charbonnier, Caecilia; Chagué , Sylvain; Preissmann, Delphine; Antonietti, Jean-Philippe; Ansermet, Franç ois; Magistretti, Pierre J.

2016-01-01

People performing actions together have a natural tendency to synchronize their behavior. Consistently, people doing a task together build internal representations not only of their actions and goals, but also of the other people performing the task. However, little is known about which are the behavioral mechanisms and the psychological factors affecting the subjective sensation of synchrony, or “connecting” with someone else. In this work, we sought to find which factors induce the subjective sensation of synchrony, combining motion capture data and psychological measures. Our results show that the subjective sensation of synchrony is affected by performance quality together with task category, and time. Psychological factors such as empathy and negative subjective affects also correlate with the subjective sensation of synchrony. However, when people estimate synchrony as seen from a third person perspective, their psychological factors do not affect the accuracy of the estimation. We suggest that to feel this sensation it is necessary to, first, have a good joint performance and, second, to assume the existence of an attention monitoring mechanism that reports that the attention of both participants (self and other) is focused on the task.

The Subjective Sensation of Synchrony: An Experimental Study

KAUST Repository

Llobera, Joan

2016-02-12

People performing actions together have a natural tendency to synchronize their behavior. Consistently, people doing a task together build internal representations not only of their actions and goals, but also of the other people performing the task. However, little is known about which are the behavioral mechanisms and the psychological factors affecting the subjective sensation of synchrony, or “connecting” with someone else. In this work, we sought to find which factors induce the subjective sensation of synchrony, combining motion capture data and psychological measures. Our results show that the subjective sensation of synchrony is affected by performance quality together with task category, and time. Psychological factors such as empathy and negative subjective affects also correlate with the subjective sensation of synchrony. However, when people estimate synchrony as seen from a third person perspective, their psychological factors do not affect the accuracy of the estimation. We suggest that to feel this sensation it is necessary to, first, have a good joint performance and, second, to assume the existence of an attention monitoring mechanism that reports that the attention of both participants (self and other) is focused on the task.

Influences on and measures of unintentional group synchrony

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Melissa Ellamil

2016-11-01

Full Text Available Many instances of large-scale coordination occur in real-life social situations without the explicit awareness of the individuals involved. While the majority of research to date has examined dyadic interactions – those between two individuals – during intentional or deliberate coordination, the present review surveys the handful of recent studies investigating behavioral and physiological synchrony across groups of more than two people when coordination was not an explicit goal. Both minimal (e.g., visual information, shared location and naturalistic (e.g., choir singing part, family relationship group interactions appear to promote unintentional group synchrony although they have so far only been studied separately. State differences in unintentional group synchrony, or the relative presence of coordination in various conditions, have tended to be assessed differently, such as using correlation-type relationships, compared to its temporal dynamics, or changes over time in the degree of coordination, which appear to be best captured using phase differences. Simultaneously evaluating behavioral, physiological, and social responses as well systematically comparing different synchrony measures could further our understanding of the influences on and measures of group synchrony, allowing us to move away from studying individual persons responding to static laboratory stimuli and towards investigating collective experiences in natural, dynamic social interactions.

Impairment of decision making and disruption of synchrony between basolateral amygdala and anterior cingulate cortex in the maternally separated rat.

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Cao, Bing; Wang, Jun; Zhang, Xu; Yang, Xiangwei; Poon, David Chun-Hei; Jelfs, Beth; Chan, Rosa H M; Wu, Justin Che-Yuen; Li, Ying

2016-12-01

There is considerable evidence to suggest early life experiences, such as maternal separation (MS), play a role in the prevalence of emotional dysregulation and cognitive impairment. At the same time, optimal decision making requires functional integrity between the amygdala and anterior cingulate cortex (ACC), and any dysfunction of this system is believed to induce decision-making deficits. However, the impact of MS on decision-making behavior and the underlying neurophysiological mechanisms have not been thoroughly studied. As such, we consider the impact of MS on the emotional and cognitive functions of rats by employing the open-field test, elevated plus-maze test, and rat gambling task (RGT). Using multi-channel recordings from freely behaving rats, we assessed the effects of MS on the large scale synchrony between the basolateral amygdala (BLA) and the ACC; while also characterizing the relationship between neural spiking activity and the ongoing oscillations in theta frequency band across the BLA and ACC. The results indicated that the MS rats demonstrated anxiety-like behavior. While the RGT showed a decrease in the percentage of good decision-makers, and an increase in the percentage of poor decision-makers. Electrophysiological data revealed an increase in the total power in the theta band of the LFP in the BLA and a decrease in theta power in the ACC in MS rats. MS was also found to disrupt the spike-field coherence of the ACC single unit spiking activity to the ongoing theta oscillations in the BLA and interrupt the synchrony in the BLA-ACC pathway. We provide specific evidence that MS leads to decision-making deficits that are accompanied by alteration of the theta band LFP in the BLA-ACC circuitries and disruption of the neural network integrity. These observations may help revise fundamental notions regarding neurophysiological biomarkers to treat cognitive impairment induced by early life stress. Copyright © 2016 Elsevier Inc. All rights

Hysteresis in audiovisual synchrony perception.

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Jean-Rémy Martin

Full Text Available The effect of stimulation history on the perception of a current event can yield two opposite effects, namely: adaptation or hysteresis. The perception of the current event thus goes in the opposite or in the same direction as prior stimulation, respectively. In audiovisual (AV synchrony perception, adaptation effects have primarily been reported. Here, we tested if perceptual hysteresis could also be observed over adaptation in AV timing perception by varying different experimental conditions. Participants were asked to judge the synchrony of the last (test stimulus of an AV sequence with either constant or gradually changing AV intervals (constant and dynamic condition, respectively. The onset timing of the test stimulus could be cued or not (prospective vs. retrospective condition, respectively. We observed hysteretic effects for AV synchrony judgments in the retrospective condition that were independent of the constant or dynamic nature of the adapted stimuli; these effects disappeared in the prospective condition. The present findings suggest that knowing when to estimate a stimulus property has a crucial impact on perceptual simultaneity judgments. Our results extend beyond AV timing perception, and have strong implications regarding the comparative study of hysteresis and adaptation phenomena.

Fluid and flexible minds: Intelligence reflects synchrony in the brain’s intrinsic network architecture

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Michael A. Ferguson

2017-06-01

Full Text Available Human intelligence has been conceptualized as a complex system of dissociable cognitive processes, yet studies investigating the neural basis of intelligence have typically emphasized the contributions of discrete brain regions or, more recently, of specific networks of functionally connected regions. Here we take a broader, systems perspective in order to investigate whether intelligence is an emergent property of synchrony within the brain’s intrinsic network architecture. Using a large sample of resting-state fMRI and cognitive data (n = 830, we report that the synchrony of functional interactions within and across distributed brain networks reliably predicts fluid and flexible intellectual functioning. By adopting a whole-brain, systems-level approach, we were able to reliably predict individual differences in human intelligence by characterizing features of the brain’s intrinsic network architecture. These findings hold promise for the eventual development of neural markers to predict changes in intellectual function that are associated with neurodevelopment, normal aging, and brain disease. In our study, we aimed to understand how individual differences in intellectual functioning are reflected in the intrinsic network architecture of the human brain. We applied statistical methods, known as spectral decompositions, in order to identify individual differences in the synchronous patterns of spontaneous brain activity that reliably predict core aspects of human intelligence. The synchrony of brain activity at rest across multiple discrete neural networks demonstrated positive relationships with fluid intelligence. In contrast, global synchrony within the brain’s network architecture reliably, and inversely, predicted mental flexibility, a core facet of intellectual functioning. The multinetwork systems approach described here represents a methodological and conceptual extension of earlier efforts that related differences in

Rising climate variability and synchrony in North Pacific ecosystems

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Black, Bryan

2017-04-01

Rising climate variability and synchrony in North Pacific ecosystems Evidence is growing that climate variability of the northeast Pacific Ocean has increased over the last century, culminating in such events as the record-breaking El Niño years 1983, 1998, and 2016 and the unusually persistent 2014/15 North Pacific Ocean heat wave known as "The Blob." Of particular concern is that rising variability could increase synchrony within and among North Pacific ecosystems, which could reduce the diversity of biological responses to climate (i.e. the "portfolio effect"), diminish resilience, and leave populations more prone to extirpation. To test this phenomenon, we use a network of multidecadal fish otolith growth-increment chronologies that were strongly correlated to records of winter (Jan-Mar) sea level. These biological and physical datasets spanned the California Current through the Gulf of Alaska. Synchrony was quantified as directional changes in running (31-year window) mean pairwise correlation within sea level and then within otolith time series. Synchrony in winter sea level at the nine stations with the longest records has increased by more than 40% over the 1950-2015 interval. Likewise, synchrony among the eight longest otolith chronologies has increased more than 100% over a comparable time period. These directional changes in synchrony are highly unlikely due to chance alone, as confirmed by comparing trends in observed data to those in simulated data (n = 10,000 iterations) with time series of identical number, length, and autocorrelation. Ultimately, this trend in rising synchrony may be linked to increased impacts of the El Niño Southern Oscillation (ENSO) on mid-latitude ecosystems of North America, and may therefore reflect a much broader, global-scale signature.

Computational modeling of distinct neocortical oscillations driven by cell-type selective optogenetic drive: Separable resonant circuits controlled by low-threshold spiking and fast-spiking interneurons

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Dorea Vierling-Claassen

2010-11-01

Full Text Available Selective optogenetic drive of fast spiking interneurons (FS leads to enhanced local field potential (LFP power across the traditional gamma frequency band (20-80Hz; Cardin et al., 2009. In contrast, drive to regular-spiking pyramidal cells (RS enhances power at lower frequencies, with a peak at 8 Hz. The first result is consistent with previous computational studies emphasizing the role of FS and the time constant of GABAA synaptic inhibition in gamma rhythmicity. However, the same theoretical models do not typically predict low-frequency LFP enhancement with RS drive. To develop hypotheses as to how the same network can support these contrasting behaviors, we constructed a biophysically principled network model of primary somatosensory neocortex containing FS, RS and low-threshold-spiking (LTS interneurons. Cells were modeled with detailed cell anatomy and physiology, multiple dendritic compartments, and included active somatic and dendritic ionic currents. Consistent with prior studies, the model demonstrated gamma resonance during FS drive, dependent on the time-constant of GABAA inhibition induced by synchronous FS activity. Lower frequency enhancement during RS drive was replicated only on inclusion of an inhibitory LTS population, whose activation was critically dependent on RS synchrony and evoked longer-lasting inhibition. Our results predict that differential recruitment of FS and LTS inhibitory populations is essential to the observed cortical dynamics and may provide a means for amplifying the natural expression of distinct oscillations in normal cortical processing.

The Effect of Different Phases of Synchrony on Pain Threshold in a Drumming Task

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Philip Sullivan

2017-06-01

Full Text Available Behavioral synchrony has been linked to endorphin activity (Cohen et al., 2010; Sullivan and Rickers, 2013; Sullivan et al., 2014; Tarr et al., 2015, 2016; Weinstein et al., 2016. This has been called the synchrony effect. Synchrony has two dominant phases of movement; in-phase and anti-phase. The majority of research investigating synchrony’s effect on endorphin activity has focused on in-phase synchrony following vigorous activities. The only research to investigate the effects of anti-phase synchrony on endorphin activity found that anti-phase synchronized rowing did not produce the synchrony effect (Sullivan et al., 2014. Anti-phase synchrony, however, is counter-intuitive to the sport of rowing and may have interfered with the synchrony effect. This study investigated the effect of anti-phase synchrony on endorphin activity in a different task (i.e., drumming. University students (n = 30 were asked to drum solo and in in-phase and anti-phase pairs for 3 min. Pain threshold was assessed as an indirect indicator of endorphin activity prior to and following the task. Although the in-phase synchrony effect was not found, a repeated measures ANOVA found that there was a significant difference in pain threshold change among the three conditions [F(2,24 = 4.10, = 0.255, p < 0.05. Post hoc t-tests showed that the anti-phase condition had a significantly greater pain threshold change than both the solo and in-phase conditions at p < 0.05. This is the first time that anti-phase synchrony has been shown to produce the synchrony effect. Because anti-phase drumming may have required more attention between partners than in-phase synchrony, it may have affected self-other merging (Tarr et al., 2014. These results support Tarr et al.’s (2014 model that multiple mechanisms account for the effect of synchrony on pain threshold, and suggest that different characteristics of the activity may influence the synchrony effect.

Pallidal gap junctions-triggers of synchrony in Parkinson's disease?

NARCIS (Netherlands)

Schwab, B.C.; Heida, T.; Zhao, Y.; Gils, S.A. van; Wezel, R.J.A. van

2014-01-01

Although increased synchrony of the neural activity in the basal ganglia may underlie the motor deficiencies exhibited in Parkinson's disease (PD), how this synchrony arises, propagates through the basal ganglia, and changes under dopamine replacement remains unknown. Gap junctions could play a

Pallidal gap junctions - Triggers of synchrony in Parkinson's disease?

NARCIS (Netherlands)

Schwab, B.C.; Heida, Tjitske; Zhao, Yan; van Gils, Stephanus A.; van Wezel, Richard Jack Anton

2014-01-01

Although increased synchrony of the neural activity in the basal ganglia may underlie the motor deficiencies exhibited in Parkinson's disease (PD), how this synchrony arises, propagates through the basal ganglia, and changes under dopamine replacement remains unknown. Gap junctions could play a

Impact of spike train autostructure on probability distribution of joint spike events.

Science.gov (United States)

Pipa, Gordon; Grün, Sonja; van Vreeswijk, Carl

2013-05-01

The discussion whether temporally coordinated spiking activity really exists and whether it is relevant has been heated over the past few years. To investigate this issue, several approaches have been taken to determine whether synchronized events occur significantly above chance, that is, whether they occur more often than expected if the neurons fire independently. Most investigations ignore or destroy the autostructure of the spiking activity of individual cells or assume Poissonian spiking as a model. Such methods that ignore the autostructure can significantly bias the coincidence statistics. Here, we study the influence of the autostructure on the probability distribution of coincident spiking events between tuples of mutually independent non-Poisson renewal processes. In particular, we consider two types of renewal processes that were suggested as appropriate models of experimental spike trains: a gamma and a log-normal process. For a gamma process, we characterize the shape of the distribution analytically with the Fano factor (FFc). In addition, we perform Monte Carlo estimations to derive the full shape of the distribution and the probability for false positives if a different process type is assumed as was actually present. We also determine how manipulations of such spike trains, here dithering, used for the generation of surrogate data change the distribution of coincident events and influence the significance estimation. We find, first, that the width of the coincidence count distribution and its FFc depend critically and in a nontrivial way on the detailed properties of the structure of the spike trains as characterized by the coefficient of variation CV. Second, the dependence of the FFc on the CV is complex and mostly nonmonotonic. Third, spike dithering, even if as small as a fraction of the interspike interval, can falsify the inference on coordinated firing.

Statistical detection of EEG synchrony using empirical bayesian inference.

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Archana K Singh

Full Text Available There is growing interest in understanding how the brain utilizes synchronized oscillatory activity to integrate information across functionally connected regions. Computing phase-locking values (PLV between EEG signals is a popular method for quantifying such synchronizations and elucidating their role in cognitive tasks. However, high-dimensionality in PLV data incurs a serious multiple testing problem. Standard multiple testing methods in neuroimaging research (e.g., false discovery rate, FDR suffer severe loss of power, because they fail to exploit complex dependence structure between hypotheses that vary in spectral, temporal and spatial dimension. Previously, we showed that a hierarchical FDR and optimal discovery procedures could be effectively applied for PLV analysis to provide better power than FDR. In this article, we revisit the multiple comparison problem from a new Empirical Bayes perspective and propose the application of the local FDR method (locFDR; Efron, 2001 for PLV synchrony analysis to compute FDR as a posterior probability that an observed statistic belongs to a null hypothesis. We demonstrate the application of Efron's Empirical Bayes approach for PLV synchrony analysis for the first time. We use simulations to validate the specificity and sensitivity of locFDR and a real EEG dataset from a visual search study for experimental validation. We also compare locFDR with hierarchical FDR and optimal discovery procedures in both simulation and experimental analyses. Our simulation results showed that the locFDR can effectively control false positives without compromising on the power of PLV synchrony inference. Our results from the application locFDR on experiment data detected more significant discoveries than our previously proposed methods whereas the standard FDR method failed to detect any significant discoveries.

Statistical detection of EEG synchrony using empirical bayesian inference.

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Singh, Archana K; Asoh, Hideki; Takeda, Yuji; Phillips, Steven

2015-01-01

There is growing interest in understanding how the brain utilizes synchronized oscillatory activity to integrate information across functionally connected regions. Computing phase-locking values (PLV) between EEG signals is a popular method for quantifying such synchronizations and elucidating their role in cognitive tasks. However, high-dimensionality in PLV data incurs a serious multiple testing problem. Standard multiple testing methods in neuroimaging research (e.g., false discovery rate, FDR) suffer severe loss of power, because they fail to exploit complex dependence structure between hypotheses that vary in spectral, temporal and spatial dimension. Previously, we showed that a hierarchical FDR and optimal discovery procedures could be effectively applied for PLV analysis to provide better power than FDR. In this article, we revisit the multiple comparison problem from a new Empirical Bayes perspective and propose the application of the local FDR method (locFDR; Efron, 2001) for PLV synchrony analysis to compute FDR as a posterior probability that an observed statistic belongs to a null hypothesis. We demonstrate the application of Efron's Empirical Bayes approach for PLV synchrony analysis for the first time. We use simulations to validate the specificity and sensitivity of locFDR and a real EEG dataset from a visual search study for experimental validation. We also compare locFDR with hierarchical FDR and optimal discovery procedures in both simulation and experimental analyses. Our simulation results showed that the locFDR can effectively control false positives without compromising on the power of PLV synchrony inference. Our results from the application locFDR on experiment data detected more significant discoveries than our previously proposed methods whereas the standard FDR method failed to detect any significant discoveries.

Rising synchrony controls western North American ecosystems

Science.gov (United States)

Black, Bryan A.; van der Sleen, Peter; Di Lorenzo, Emanuele; Griffin, Daniel; Sydeman, William J.; Dunham, Jason B.; Rykaczewski, Ryan R.; Garcia-Reyes, Marisol; Safeeq, Mohammad; Arismendi, Ivan; Bograd, Steven J.

2018-01-01

Along the western margin of North America, the winter expression of the North Pacific High (NPH) strongly influences interannual variability in coastal upwelling, storm track position, precipitation, and river discharge. Coherence among these factors induces covariance among physical and biological processes across adjacent marine and terrestrial ecosystems. Here, we show that over the past century the degree and spatial extent of this covariance (synchrony) has substantially increased, and is coincident with rising variance in the winter NPH. Furthermore, centuries‐long blue oak (Quercus douglasii) growth chronologies sensitive to the winter NPH provide robust evidence that modern levels of synchrony are among the highest observed in the context of the last 250 years. These trends may ultimately be linked to changing impacts of the El Niño Southern Oscillation on mid‐latitude ecosystems of North America. Such a rise in synchrony may destabilize ecosystems, expose populations to higher risks of extinction, and is thus a concern given the broad biological relevance of winter climate to biological systems.

Job Stress and Dyadic Synchrony in Police Marriages: A Preliminary Investigation

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ROBERTS, NICOLE A.; LEONARD, RACHEL C.; BUTLER, EMILY A.; LEVENSON, ROBERT W.; KANTER, JONATHAN W.

2015-01-01

Despite reports documenting adverse effects of stress on police marriages, few empirical studies focus on actual emotional behaviors of officers and spouses. In this preliminary investigation, 17 male police officers and their nonpolice wives completed daily stress diaries for 1 week and then participated in a laboratory-based discussion about their respective days. Conversations were video-recorded and coded for specific emotional behaviors reflecting hostility and affection, which are strong predictors of marital outcomes. We examined associations between officers’ job stress (per diaries and the Police Stress Survey) and couples’ emotional behavior (mean levels and behavioral synchrony) using a dyadic repeated measures design capitalizing on the large number of observations available for each couple (1020 observations). When officers reported more job stress, they showed less hostility, less synchrony with their wives’ hostility, and more synchrony with their wives’ affection; their wives showed greater synchrony with officers’ hostility and less synchrony with officers’ affection. Therefore, for officers, greater job stress was associated with less behavioral negativity, potentially less attunement to wives’ negativity, but potentially greater attunement to wives’ affection—perhaps a compensatory strategy or attempt to buffer their marriage from stress. These attempts may be less effective, however, if, as our synchrony findings may suggest, wives are focusing on officers’ hostility rather than affection. Although it will be important to replicate these results given the small sample, our findings reveal that patterns of behavioral synchrony may be a key means to better understand how job stress exacts a toll on police marriages. PMID:23763686

Multi-scale approach to radiation damage induced by ion beams: complex DNA damage and effects of thermal spikes

International Nuclear Information System (INIS)

Surdutovich, E.; Yakubovich, A.V.; Solov'yov, A.V.; Surdutovich, E.; Yakubovich, A.V.; Solov'yov, A.V.

2010-01-01

We present the latest advances of the multi-scale approach to radiation damage caused by irradiation of a tissue with energetic ions and report the calculations of complex DNA damage and the effects of thermal spikes on biomolecules. The multi-scale approach aims to quantify the most important physical, chemical, and biological phenomena taking place during and following irradiation with ions and provide a better means for clinically-necessary calculations with adequate accuracy. We suggest a way of quantifying the complex clustered damage, one of the most important features of the radiation damage caused by ions. This quantification allows the studying of how the clusterization of DNA lesions affects the lethality of damage. We discuss the first results of molecular dynamics simulations of ubiquitin in the environment of thermal spikes, predicted to occur in tissue for a short time after an ion's passage in the vicinity of the ions' tracks. (authors)

Synchronous Spike Patterns in Macaque Motor Cortex during an Instructed-Delay Reach-to-Grasp Task.

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Torre, Emiliano; Quaglio, Pietro; Denker, Michael; Brochier, Thomas; Riehle, Alexa; Grün, Sonja

2016-08-10

The computational role of spike time synchronization at millisecond precision among neurons in the cerebral cortex is hotly debated. Studies performed on data of limited size provided experimental evidence that low-order correlations occur in relation to behavior. Advances in electrophysiological technology to record from hundreds of neurons simultaneously provide the opportunity to observe coordinated spiking activity of larger populations of cells. We recently published a method that combines data mining and statistical evaluation to search for significant patterns of synchronous spikes in massively parallel spike trains (Torre et al., 2013). The method solves the computational and multiple testing problems raised by the high dimensionality of the data. In the current study, we used our method on simultaneous recordings from two macaque monkeys engaged in an instructed-delay reach-to-grasp task to determine the emergence of spike synchronization in relation to behavior. We found a multitude of synchronous spike patterns aligned in both monkeys along a preferential mediolateral orientation in brain space. The occurrence of the patterns is highly specific to behavior, indicating that different behaviors are associated with the synchronization of different groups of neurons ("cell assemblies"). However, pooled patterns that overlap in neuronal composition exhibit no specificity, suggesting that exclusive cell assemblies become active during different behaviors, but can recruit partly identical neurons. These findings are consistent across multiple recording sessions analyzed across the two monkeys. Neurons in the brain communicate via electrical impulses called spikes. How spikes are coordinated to process information is still largely unknown. Synchronous spikes are effective in triggering a spike emission in receiving neurons and have been shown to occur in relation to behavior in a number of studies on simultaneous recordings of few neurons. We recently published

Measuring Group Synchrony: A Cluster-Phase Method for Analyzing Multivariate Movement Time-Series

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Michael eRichardson

2012-10-01

Full Text Available A new method for assessing group synchrony is introduced as being potentially useful for objectively determining degree of group cohesiveness or entitativity. The cluster-phase method of Frank and Richardson (2010 was used to analyze movement data from the rocking chair movements of six-member groups who rocked their chairs while seated in a circle facing the center. In some trials group members had no information about others’ movements (their eyes were shut or they had their eyes open and gazed at a marker in the center of the group. As predicted, the group level synchrony measure was able to distinguish between situations where synchrony would have been possible and situations where it would be impossible. Moreover, other aspects of the analysis illustrated how the cluster phase measures can be used to determine the type of patterning of group synchrony, and, when integrated with multi-level modeling, can be used to examine individual-level differences in synchrony and dyadic level synchrony as well.

Measurements of spatial population synchrony: influence of time series transformations.

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Chevalier, Mathieu; Laffaille, Pascal; Ferdy, Jean-Baptiste; Grenouillet, Gaël

2015-09-01

Two mechanisms have been proposed to explain spatial population synchrony: dispersal among populations, and the spatial correlation of density-independent factors (the "Moran effect"). To identify which of these two mechanisms is driving spatial population synchrony, time series transformations (TSTs) of abundance data have been used to remove the signature of one mechanism, and highlight the effect of the other. However, several issues with TSTs remain, and to date no consensus has emerged about how population time series should be handled in synchrony studies. Here, by using 3131 time series involving 34 fish species found in French rivers, we computed several metrics commonly used in synchrony studies to determine whether a large-scale climatic factor (temperature) influenced fish population dynamics at the regional scale, and to test the effect of three commonly used TSTs (detrending, prewhitening and a combination of both) on these metrics. We also tested whether the influence of TSTs on time series and population synchrony levels was related to the features of the time series using both empirical and simulated time series. For several species, and regardless of the TST used, we evidenced a Moran effect on freshwater fish populations. However, these results were globally biased downward by TSTs which reduced our ability to detect significant signals. Depending on the species and the features of the time series, we found that TSTs could lead to contradictory results, regardless of the metric considered. Finally, we suggest guidelines on how population time series should be processed in synchrony studies.

Synchrony - Cyberknife Respiratory Compensation Technology

International Nuclear Information System (INIS)

Ozhasoglu, Cihat; Saw, Cheng B.; Chen Hungcheng; Burton, Steven; Komanduri, Krishna; Yue, Ning J.; Huq, Saiful M.; Heron, Dwight E.

2008-01-01

Studies of organs in the thorax and abdomen have shown that these organs can move as much as 40 mm due to respiratory motion. Without compensation for this motion during the course of external beam radiation therapy, the dose coverage to target may be compromised. On the other hand, if compensation of this motion is by expansion of the margin around the target, a significant volume of normal tissue may be unnecessarily irradiated. In hypofractionated regimens, the issue of respiratory compensation becomes an important factor and is critical in single-fraction extracranial radiosurgery applications. CyberKnife is an image-guided radiosurgery system that consists of a 6-MV LINAC mounted to a robotic arm coupled through a control loop to a digital diagnostic x-ray imaging system. The robotic arm can point the beam anywhere in space with 6 degrees of freedom, without being constrained to a conventional isocenter. The CyberKnife has been recently upgraded with a real-time respiratory tracking and compensation system called Synchrony. Using external markers in conjunction with diagnostic x-ray images, Synchrony helps guide the robotic arm to move the radiation beam in real time such that the beam always remains aligned with the target. With the aid of Synchrony, the tumor motion can be tracked in three-dimensional space, and the motion-induced dosimetric change to target can be minimized with a limited margin. The working principles, advantages, limitations, and our clinical experience with this new technology will be discussed

A universal order parameter for synchrony in networks of limit cycle oscillators

Science.gov (United States)

Schröder, Malte; Timme, Marc; Witthaut, Dirk

2017-07-01

We analyze the properties of order parameters measuring synchronization and phase locking in complex oscillator networks. First, we review network order parameters previously introduced and reveal several shortcomings: none of the introduced order parameters capture all transitions from incoherence over phase locking to full synchrony for arbitrary, finite networks. We then introduce an alternative, universal order parameter that accurately tracks the degree of partial phase locking and synchronization, adapting the traditional definition to account for the network topology and its influence on the phase coherence of the oscillators. We rigorously prove that this order parameter is strictly monotonously increasing with the coupling strength in the phase locked state, directly reflecting the dynamic stability of the network. Furthermore, it indicates the onset of full phase locking by a diverging slope at the critical coupling strength. The order parameter may find applications across systems where different types of synchrony are possible, including biological networks and power grids.

Enhancing "theory of mind" through behavioral synchrony.

Science.gov (United States)

Baimel, Adam; Severson, Rachel L; Baron, Andrew S; Birch, Susan A J

2015-01-01

Theory of mind refers to the abilities underlying the capacity to reason about one's own and others' mental states. This ability is critical for predicting and making sense of the actions of others, is essential for efficient communication, fosters social learning, and provides the foundation for empathic concern. Clearly, there is incredible value in fostering theory of mind. Unfortunately, despite being the focus of a wealth of research over the last 40 years relatively little is known about specific strategies for fostering social perspective taking abilities. We provide a discussion of the rationale for applying one specific strategy for fostering efficient theory of mind-that of engaging in "behavioral synchrony" (i.e., the act of keeping together in time with others). Culturally evolved collective rituals involving synchronous actions have long been held to act as social glue. Specifically, here we present how behavioral synchrony tunes our minds for reasoning about other minds in the process of fostering social coordination and cooperation, and propose that we can apply behavioral synchrony as a tool for enhancing theory of mind.

Learning Universal Computations with Spikes

Science.gov (United States)

Thalmeier, Dominik; Uhlmann, Marvin; Kappen, Hilbert J.; Memmesheimer, Raoul-Martin

2016-01-01

Providing the neurobiological basis of information processing in higher animals, spiking neural networks must be able to learn a variety of complicated computations, including the generation of appropriate, possibly delayed reactions to inputs and the self-sustained generation of complex activity patterns, e.g. for locomotion. Many such computations require previous building of intrinsic world models. Here we show how spiking neural networks may solve these different tasks. Firstly, we derive constraints under which classes of spiking neural networks lend themselves to substrates of powerful general purpose computing. The networks contain dendritic or synaptic nonlinearities and have a constrained connectivity. We then combine such networks with learning rules for outputs or recurrent connections. We show that this allows to learn even difficult benchmark tasks such as the self-sustained generation of desired low-dimensional chaotic dynamics or memory-dependent computations. Furthermore, we show how spiking networks can build models of external world systems and use the acquired knowledge to control them. PMID:27309381

Multineuronal Spike Sequences Repeat with Millisecond Precision

Directory of Open Access Journals (Sweden)

Koki eMatsumoto

2013-06-01

Full Text Available Cortical microcircuits are nonrandomly wired by neurons. As a natural consequence, spikes emitted by microcircuits are also nonrandomly patterned in time and space. One of the prominent spike organizations is a repetition of fixed patterns of spike series across multiple neurons. However, several questions remain unsolved, including how precisely spike sequences repeat, how the sequences are spatially organized, how many neurons participate in sequences, and how different sequences are functionally linked. To address these questions, we monitored spontaneous spikes of hippocampal CA3 neurons ex vivo using a high-speed functional multineuron calcium imaging technique that allowed us to monitor spikes with millisecond resolution and to record the location of spiking and nonspiking neurons. Multineuronal spike sequences were overrepresented in spontaneous activity compared to the statistical chance level. Approximately 75% of neurons participated in at least one sequence during our observation period. The participants were sparsely dispersed and did not show specific spatial organization. The number of sequences relative to the chance level decreased when larger time frames were used to detect sequences. Thus, sequences were precise at the millisecond level. Sequences often shared common spikes with other sequences; parts of sequences were subsequently relayed by following sequences, generating complex chains of multiple sequences.

Audiovisual Temporal Recalibration for Speech in Synchrony Perception and Speech Identification

Science.gov (United States)

Asakawa, Kaori; Tanaka, Akihiro; Imai, Hisato

We investigated whether audiovisual synchrony perception for speech could change after observation of the audiovisual temporal mismatch. Previous studies have revealed that audiovisual synchrony perception is re-calibrated after exposure to a constant timing difference between auditory and visual signals in non-speech. In the present study, we examined whether this audiovisual temporal recalibration occurs at the perceptual level even for speech (monosyllables). In Experiment 1, participants performed an audiovisual simultaneity judgment task (i.e., a direct measurement of the audiovisual synchrony perception) in terms of the speech signal after observation of the speech stimuli which had a constant audiovisual lag. The results showed that the “simultaneous” responses (i.e., proportion of responses for which participants judged the auditory and visual stimuli to be synchronous) at least partly depended on exposure lag. In Experiment 2, we adopted the McGurk identification task (i.e., an indirect measurement of the audiovisual synchrony perception) to exclude the possibility that this modulation of synchrony perception was solely attributable to the response strategy using stimuli identical to those of Experiment 1. The characteristics of the McGurk effect reported by participants depended on exposure lag. Thus, it was shown that audiovisual synchrony perception for speech could be modulated following exposure to constant lag both in direct and indirect measurement. Our results suggest that temporal recalibration occurs not only in non-speech signals but also in monosyllabic speech at the perceptual level.

Psychosocial effects of perceived emotional synchrony in collective gatherings.

Science.gov (United States)

Páez, Dario; Rimé, Bernard; Basabe, Nekane; Wlodarczyk, Anna; Zumeta, Larraitz

2015-05-01

In a classic theory, Durkheim (1912) predicted that because of the social sharing of emotion they generate, collective gatherings bring participants to a stage of collective effervescence in which they experience a sense of union with others and a feeling of empowerment accompanied by positive affect. This would lead them to leave the collective situation with a renewed sense of confidence in life and in social institutions. A century after Durkheim's predictions of these effects, though, they remained untested as a whole. This article reports 4 studies, 2 correlational, 1 semilongitudinal, and 1 experimental, assessing the positive effects of participation in either positively valenced (folkloric marches) or negatively valenced (protest demonstrations) collective gatherings. Results confirmed that collective gatherings consistently strengthened collective identity, identity fusion, and social integration, as well as enhancing personal and collective self-esteem and efficacy, positive affect, and positive social beliefs among participants. In line with a central tenet of the theory, emotional communion, or perceived emotional synchrony with others mediated these effects. Higher perceived emotional synchrony was associated with stronger emotional reactions, stronger social support, and higher endorsement of social beliefs and values. Participation in symbolic collective gatherings also particularly reinforced identity fusion when perceived emotional synchrony was high. The respective contributions of perceived emotional synchrony and flow, or optimal experience, were also assessed. Whereas perceived emotional synchrony emerged as strongly related to the various social outcomes, flow was observed to be related first to collective efficacy and self-esteem, and thus, to encompass mainly empowerment effects. (c) 2015 APA, all rights reserved).

Cytoplasmic tail of coronavirus spike protein has intracellular

Indian Academy of Sciences (India)

https://www.ias.ac.in/article/fulltext/jbsc/042/02/0231-0244. Keywords. Coronavirus spike protein trafficking; cytoplasmic tail signal; endoplasmic reticulum–Golgi intermediate complex; lysosome. Abstract. Intracellular trafficking and localization studies of spike protein from SARS and OC43 showed that SARS spikeprotein is ...

Spiking and bursting patterns of fractional-order Izhikevich model

Science.gov (United States)

Teka, Wondimu W.; Upadhyay, Ranjit Kumar; Mondal, Argha

2018-03-01

Bursting and spiking oscillations play major roles in processing and transmitting information in the brain through cortical neurons that respond differently to the same signal. These oscillations display complex dynamics that might be produced by using neuronal models and varying many model parameters. Recent studies have shown that models with fractional order can produce several types of history-dependent neuronal activities without the adjustment of several parameters. We studied the fractional-order Izhikevich model and analyzed different kinds of oscillations that emerge from the fractional dynamics. The model produces a wide range of neuronal spike responses, including regular spiking, fast spiking, intrinsic bursting, mixed mode oscillations, regular bursting and chattering, by adjusting only the fractional order. Both the active and silent phase of the burst increase when the fractional-order model further deviates from the classical model. For smaller fractional order, the model produces memory dependent spiking activity after the pulse signal turned off. This special spiking activity and other properties of the fractional-order model are caused by the memory trace that emerges from the fractional-order dynamics and integrates all the past activities of the neuron. On the network level, the response of the neuronal network shifts from random to scale-free spiking. Our results suggest that the complex dynamics of spiking and bursting can be the result of the long-term dependence and interaction of intracellular and extracellular ionic currents.

Methodological Advances for Detecting Physiological Synchrony During Dyadic Interactions

OpenAIRE

McAssey, M.P.; Helm, J.; Hsieh, F.; Sbarra, D.; Ferrer, E.

2011-01-01

A defining feature of many physiological systems is their synchrony and reciprocal influence. An important challenge, however, is how to measure such features. This paper presents two new approaches for identifying synchrony between the physiological signals of individuals in dyads. The approaches are adaptations of two recently-developed techniques, depending on the nature of the physiological time series. For respiration and thoracic impedance, signals that are measured continuously, we use...

Sound Rhythms Are Encoded by Postinhibitory Rebound Spiking in the Superior Paraolivary Nucleus

Science.gov (United States)

Felix, Richard A.; Fridberger, Anders; Leijon, Sara; Berrebi, Albert S.; Magnusson, Anna K.

2013-01-01

The superior paraolivary nucleus (SPON) is a prominent structure in the auditory brainstem. In contrast to the principal superior olivary nuclei with identified roles in processing binaural sound localization cues, the role of the SPON in hearing is not well understood. A combined in vitro and in vivo approach was used to investigate the cellular properties of SPON neurons in the mouse. Patch-clamp recordings in brain slices revealed that brief and well timed postinhibitory rebound spiking, generated by the interaction of two subthreshold-activated ion currents, is a hallmark of SPON neurons. The Ih current determines the timing of the rebound, whereas the T-type Ca2+ current boosts the rebound to spike threshold. This precisely timed rebound spiking provides a physiological explanation for the sensitivity of SPON neurons to sinusoidally amplitude-modulated (SAM) tones in vivo, where peaks in the sound envelope drive inhibitory inputs and SPON neurons fire action potentials during the waveform troughs. Consistent with this notion, SPON neurons display intrinsic tuning to frequency-modulated sinusoidal currents (1–15Hz) in vitro and discharge with strong synchrony to SAMs with modulation frequencies between 1 and 20 Hz in vivo. The results of this study suggest that the SPON is particularly well suited to encode rhythmic sound patterns. Such temporal periodicity information is likely important for detection of communication cues, such as the acoustic envelopes of animal vocalizations and speech signals. PMID:21880918

Synchrony and Physiological Arousal Increase Cohesion and Cooperation in Large Naturalistic Groups.

Science.gov (United States)

Jackson, Joshua Conrad; Jong, Jonathan; Bilkey, David; Whitehouse, Harvey; Zollmann, Stefanie; McNaughton, Craig; Halberstadt, Jamin

2018-01-09

Separate research streams have identified synchrony and arousal as two factors that might contribute to the effects of human rituals on social cohesion and cooperation. But no research has manipulated these variables in the field to investigate their causal - and potentially interactive - effects on prosocial behaviour. Across four experimental sessions involving large samples of strangers, we manipulated the synchronous and physiologically arousing affordances of a group marching task within a sports stadium. We observed participants' subsequent movement, grouping, and cooperation via a camera hidden in the stadium's roof. Synchrony and arousal both showed main effects, predicting larger groups, tighter clustering, and more cooperative behaviour in a free-rider dilemma. Synchrony and arousal also interacted on measures of clustering and cooperation such that synchrony only encouraged closer clustering-and encouraged greater cooperation-when paired with physiological arousal. The research helps us understand why synchrony and arousal often co-occur in rituals around the world. It also represents the first use of real-time spatial tracking as a precise and naturalistic method of simulating collective rituals.

Temporal Synchrony Detection and Associations with Language in Young Children with ASD

Directory of Open Access Journals (Sweden)

Elena Patten

2014-01-01

Full Text Available Temporally synchronous audio-visual stimuli serve to recruit attention and enhance learning, including language learning in infants. Although few studies have examined this effect on children with autism, it appears that the ability to detect temporal synchrony between auditory and visual stimuli may be impaired, particularly given social-linguistic stimuli delivered via oral movement and spoken language pairings. However, children with autism can detect audio-visual synchrony given nonsocial stimuli (objects dropping and their corresponding sounds. We tested whether preschool children with autism could detect audio-visual synchrony given video recordings of linguistic stimuli paired with movement of related toys in the absence of faces. As a group, children with autism demonstrated the ability to detect audio-visual synchrony. Further, the amount of time they attended to the synchronous condition was positively correlated with receptive language. Findings suggest that object manipulations may enhance multisensory processing in linguistic contexts. Moreover, associations between synchrony detection and language development suggest that better processing of multisensory stimuli may guide and direct attention to communicative events thus enhancing linguistic development.

Nonverbal synchrony of head- and body-movement in psychotherapy: different signals have different associations with outcome

Directory of Open Access Journals (Sweden)

Fabian eRamseyer

2014-09-01

Full Text Available Objective: The coordination of patient’s and therapist’s bodily movement – nonverbal synchrony – has been empirically shown to be associated with psychotherapy outcome. This finding was based on dynamic movement patterns of the whole body. The present paper is a new analysis of an existing dataset (Ramseyer & Tschacher, 2011, which extends previous findings by differentiating movements pertaining to head and upper-body regions. Method: In a sample of 70 patients (37 female, 33 male treated at an outpatient psychotherapy clinic, we quantified nonverbal synchrony with an automated objective video-analysis algorithm (Motion Energy Analysis, MEA. Head- and body-synchrony was quantified during the initial 15 minutes of video-recorded therapy sessions. Micro-outcome was assessed with self-report post-session questionnaires provided by patients and their therapists. Macro-outcome was measured with questionnaires that quantified attainment of treatment goals and changes in experiencing and behavior at the end of therapy. Results: The differentiation of head- and body-synchrony showed that these two facets of motor coordination were differentially associated with outcome. Head-synchrony predicted global outcome of therapy, while body-synchrony did not, and body-synchrony predicted session outcome, while head-synchrony did not. Conclusions: The results pose an important amendment to previous findings, which showed that nonverbal synchrony embodied both outcome and interpersonal variables of psychotherapy dyads. The separation of head- and body-synchrony suggested that distinct mechanisms may operate in these two regions: Head-synchrony embodied phenomena with a long temporal extension (overall therapy success, while body-synchrony embodied phenomena of a more immediate nature (session-level success. More explorations with fine-grained analyses of synchronized phenomena in nonverbal behavior may shed additional light on the embodiment of

Nested synchrony – a novel cross-scale interaction among neuronal oscillations

Directory of Open Access Journals (Sweden)

Simo eMonto

2012-09-01

Full Text Available Neuronal interactions form the basis for our brain function, and oscillations and synchrony are the principal candidates for mediating them in the cortical networks. Phase synchrony, where oscillatory neuronal ensembles directly synchronize their phases, enables precise integration between separated brain regions. However, it is unclear how neuronal interactions are dynamically coordinated in space and over time. Cross-scale effects have been proposed to be responsible for linking levels of processing hierarchy and to regulate neuronal dynamics. Most notably, nested oscillations, where the phase of a neuronal oscillation modulates the amplitude of a faster one, may locally integrate neuronal activities in distinct frequency bands. Yet, hierarchical control of inter-areal synchrony could provide a more comprehensive view to the dynamical structure of oscillatory interdependencies in the human brain.In this study, the notion of nested oscillations is extended to a cross-frequency and inter-areal model of oscillatory interactions. In this model, the phase of a slower oscillation modulates inter-areal synchrony in a higher frequency band. This would allow cross-scale integration of global interactions and, thus, offers a mechanism for binding distributed neuronal activities.We show that inter-areal phase synchrony can be modulated by the phase of a slower neuronal oscillation using magnetoencephalography. This effect is the most pronounced at frequencies below 35 Hz. Importantly, changes in oscillation amplitudes did not explain the findings. We expect that the novel cross-frequency interaction could offer new ways to understand the flexible but accurate dynamic organization of ongoing neuronal oscillations and synchrony.

A new supervised learning algorithm for spiking neurons.

Science.gov (United States)

Xu, Yan; Zeng, Xiaoqin; Zhong, Shuiming

2013-06-01

The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.

Dance on cortex: enhanced theta synchrony in experts when watching a dance piece.

Science.gov (United States)

Poikonen, Hanna; Toiviainen, Petri; Tervaniemi, Mari

2018-03-01

When watching performing arts, a wide and complex network of brain processes emerge. These processes can be shaped by professional expertise. When compared to laymen, dancers have enhanced processes in observation of short dance movement and listening to music. But how do the cortical processes differ in musicians and dancers when watching an audio-visual dance performance? In our study, we presented the participants long excerpts from the contemporary dance choreography of Carmen. During multimodal movement of a dancer, theta phase synchrony over the fronto-central electrodes was stronger in dancers when compared to musicians and laymen. In addition, alpha synchrony was decreased in all groups during large rapid movement when compared to nearly motionless parts of the choreography. Our results suggest an enhanced cortical communication in dancers when watching dance and, further, that this enhancement is rather related to multimodal, cognitive and emotional processes than to simple observation of dance movement. © 2018 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Supervised Learning Based on Temporal Coding in Spiking Neural Networks.

Science.gov (United States)

Mostafa, Hesham

2017-08-01

Gradient descent training techniques are remarkably successful in training analog-valued artificial neural networks (ANNs). Such training techniques, however, do not transfer easily to spiking networks due to the spike generation hard nonlinearity and the discrete nature of spike communication. We show that in a feedforward spiking network that uses a temporal coding scheme where information is encoded in spike times instead of spike rates, the network input-output relation is differentiable almost everywhere. Moreover, this relation is piecewise linear after a transformation of variables. Methods for training ANNs thus carry directly to the training of such spiking networks as we show when training on the permutation invariant MNIST task. In contrast to rate-based spiking networks that are often used to approximate the behavior of ANNs, the networks we present spike much more sparsely and their behavior cannot be directly approximated by conventional ANNs. Our results highlight a new approach for controlling the behavior of spiking networks with realistic temporal dynamics, opening up the potential for using these networks to process spike patterns with complex temporal information.

Voltage-spike analysis for a free-running parallel inverter

Science.gov (United States)

Lee, F. C. Y.; Wilson, T. G.

1974-01-01

Unwanted and sometimes damaging high-amplitude voltage spikes occur during each half cycle in many transistor saturable-core inverters at the moment when the core saturates and the transistors switch. The analysis shows that spikes are an intrinsic characteristic of certain types of inverters even with negligible leakage inductance and purely resistive load. The small but unavoidable after-saturation inductance of the saturable-core transformer plays an essential role in creating these undesired thigh-voltage spikes. State-plane analysis provides insight into the complex interaction between core and transistors, and shows the circuit parameters upon which the magnitude of these spikes depends.

Audiovisual Temporal Processing and Synchrony Perception in the Rat.

Science.gov (United States)

Schormans, Ashley L; Scott, Kaela E; Vo, Albert M Q; Tyker, Anna; Typlt, Marei; Stolzberg, Daniel; Allman, Brian L

2016-01-01

Extensive research on humans has improved our understanding of how the brain integrates information from our different senses, and has begun to uncover the brain regions and large-scale neural activity that contributes to an observer's ability to perceive the relative timing of auditory and visual stimuli. In the present study, we developed the first behavioral tasks to assess the perception of audiovisual temporal synchrony in rats. Modeled after the parameters used in human studies, separate groups of rats were trained to perform: (1) a simultaneity judgment task in which they reported whether audiovisual stimuli at various stimulus onset asynchronies (SOAs) were presented simultaneously or not; and (2) a temporal order judgment task in which they reported whether they perceived the auditory or visual stimulus to have been presented first. Furthermore, using in vivo electrophysiological recordings in the lateral extrastriate visual (V2L) cortex of anesthetized rats, we performed the first investigation of how neurons in the rat multisensory cortex integrate audiovisual stimuli presented at different SOAs. As predicted, rats ( n = 7) trained to perform the simultaneity judgment task could accurately (~80%) identify synchronous vs. asynchronous (200 ms SOA) trials. Moreover, the rats judged trials at 10 ms SOA to be synchronous, whereas the majority (~70%) of trials at 100 ms SOA were perceived to be asynchronous. During the temporal order judgment task, rats ( n = 7) perceived the synchronous audiovisual stimuli to be "visual first" for ~52% of the trials, and calculation of the smallest timing interval between the auditory and visual stimuli that could be detected in each rat (i.e., the just noticeable difference (JND)) ranged from 77 ms to 122 ms. Neurons in the rat V2L cortex were sensitive to the timing of audiovisual stimuli, such that spiking activity was greatest during trials when the visual stimulus preceded the auditory by 20-40 ms. Ultimately, given

Comparison of spike-sorting algorithms for future hardware implementation.

Science.gov (United States)

Gibson, Sarah; Judy, Jack W; Markovic, Dejan

2008-01-01

Applications such as brain-machine interfaces require hardware spike sorting in order to (1) obtain single-unit activity and (2) perform data reduction for wireless transmission of data. Such systems must be low-power, low-area, high-accuracy, automatic, and able to operate in real time. Several detection and feature extraction algorithms for spike sorting are described briefly and evaluated in terms of accuracy versus computational complexity. The nonlinear energy operator method is chosen as the optimal spike detection algorithm, being most robust over noise and relatively simple. The discrete derivatives method [1] is chosen as the optimal feature extraction method, maintaining high accuracy across SNRs with a complexity orders of magnitude less than that of traditional methods such as PCA.

Causal Inference and Explaining Away in a Spiking Network

Science.gov (United States)

Moreno-Bote, Rubén; Drugowitsch, Jan

2015-01-01

While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification. PMID:26621426

Synchrony in Psychotherapy: A Review and an Integrative Framework for the Therapeutic Alliance

Science.gov (United States)

Koole, Sander L.; Tschacher, Wolfgang

2016-01-01

During psychotherapy, patient and therapist tend to spontaneously synchronize their vocal pitch, bodily movements, and even their physiological processes. In the present article, we consider how this pervasive phenomenon may shed new light on the therapeutic relationship– or alliance– and its role within psychotherapy. We first review clinical research on the alliance and the multidisciplinary area of interpersonal synchrony. We then integrate both literatures in the Interpersonal Synchrony (In-Sync) model of psychotherapy. According to the model, the alliance is grounded in the coupling of patient and therapist’s brains. Because brains do not interact directly, movement synchrony may help to establish inter-brain coupling. Inter-brain coupling may provide patient and therapist with access to another’s internal states, which facilitates common understanding and emotional sharing. Over time, these interpersonal exchanges may improve patients’ emotion-regulatory capacities and related therapeutic outcomes. We discuss the empirical assessment of interpersonal synchrony and review preliminary research on synchrony in psychotherapy. Finally, we summarize our main conclusions and consider the broader implications of viewing psychotherapy as the product of two interacting brains. PMID:27378968

Synchrony in Psychotherapy: A Review and an Integrative Framework for the Therapeutic Alliance.

Science.gov (United States)

Koole, Sander L; Tschacher, Wolfgang

2016-01-01

During psychotherapy, patient and therapist tend to spontaneously synchronize their vocal pitch, bodily movements, and even their physiological processes. In the present article, we consider how this pervasive phenomenon may shed new light on the therapeutic relationship- or alliance- and its role within psychotherapy. We first review clinical research on the alliance and the multidisciplinary area of interpersonal synchrony. We then integrate both literatures in the Interpersonal Synchrony (In-Sync) model of psychotherapy. According to the model, the alliance is grounded in the coupling of patient and therapist's brains. Because brains do not interact directly, movement synchrony may help to establish inter-brain coupling. Inter-brain coupling may provide patient and therapist with access to another's internal states, which facilitates common understanding and emotional sharing. Over time, these interpersonal exchanges may improve patients' emotion-regulatory capacities and related therapeutic outcomes. We discuss the empirical assessment of interpersonal synchrony and review preliminary research on synchrony in psychotherapy. Finally, we summarize our main conclusions and consider the broader implications of viewing psychotherapy as the product of two interacting brains.

Spectral components of cytosolic [Ca2+] spiking in neurons

DEFF Research Database (Denmark)

Kardos, J; Szilágyi, N; Juhász, G

1998-01-01

. Delayed complex responses of large [Ca2+]c spiking observed in cells from a different set of cultures were synthesized by a set of frequencies within the range 0.018-0.117 Hz. Differential frequency patterns are suggested as characteristics of the [Ca2+]c spiking responses of neurons under different...

Cerebellar Nuclear Neurons Use Time and Rate Coding to Transmit Purkinje Neuron Pauses.

Science.gov (United States)

Sudhakar, Shyam Kumar; Torben-Nielsen, Benjamin; De Schutter, Erik

2015-12-01

Neurons of the cerebellar nuclei convey the final output of the cerebellum to their targets in various parts of the brain. Within the cerebellum their direct upstream connections originate from inhibitory Purkinje neurons. Purkinje neurons have a complex firing pattern of regular spikes interrupted by intermittent pauses of variable length. How can the cerebellar nucleus process this complex input pattern? In this modeling study, we investigate different forms of Purkinje neuron simple spike pause synchrony and its influence on candidate coding strategies in the cerebellar nuclei. That is, we investigate how different alignments of synchronous pauses in synthetic Purkinje neuron spike trains affect either time-locking or rate-changes in the downstream nuclei. We find that Purkinje neuron synchrony is mainly represented by changes in the firing rate of cerebellar nuclei neurons. Pause beginning synchronization produced a unique effect on nuclei neuron firing, while the effect of pause ending and pause overlapping synchronization could not be distinguished from each other. Pause beginning synchronization produced better time-locking of nuclear neurons for short length pauses. We also characterize the effect of pause length and spike jitter on the nuclear neuron firing. Additionally, we find that the rate of rebound responses in nuclear neurons after a synchronous pause is controlled by the firing rate of Purkinje neurons preceding it.

Cerebellar Nuclear Neurons Use Time and Rate Coding to Transmit Purkinje Neuron Pauses

Science.gov (United States)

Sudhakar, Shyam Kumar; Torben-Nielsen, Benjamin; De Schutter, Erik

2015-01-01

Neurons of the cerebellar nuclei convey the final output of the cerebellum to their targets in various parts of the brain. Within the cerebellum their direct upstream connections originate from inhibitory Purkinje neurons. Purkinje neurons have a complex firing pattern of regular spikes interrupted by intermittent pauses of variable length. How can the cerebellar nucleus process this complex input pattern? In this modeling study, we investigate different forms of Purkinje neuron simple spike pause synchrony and its influence on candidate coding strategies in the cerebellar nuclei. That is, we investigate how different alignments of synchronous pauses in synthetic Purkinje neuron spike trains affect either time-locking or rate-changes in the downstream nuclei. We find that Purkinje neuron synchrony is mainly represented by changes in the firing rate of cerebellar nuclei neurons. Pause beginning synchronization produced a unique effect on nuclei neuron firing, while the effect of pause ending and pause overlapping synchronization could not be distinguished from each other. Pause beginning synchronization produced better time-locking of nuclear neurons for short length pauses. We also characterize the effect of pause length and spike jitter on the nuclear neuron firing. Additionally, we find that the rate of rebound responses in nuclear neurons after a synchronous pause is controlled by the firing rate of Purkinje neurons preceding it. PMID:26630202

Spatial synchrony of a highly endemic fish Assemblage (Segredo Reservoir, Iguaçu River, Paraná State, Brazil

Directory of Open Access Journals (Sweden)

W. M. Domingues

Full Text Available In this study, patterns of spatial synchrony in population fluctuations (cross-correlation of an endemic fish assemblage of a Neotropical reservoir (Segredo Reservoir, Iguaçu River, Paraná State, Brazil were reported. First, the level of population synchrony for 20 species was estimated. Second, population synchrony was correlated, using the Mantel test, with geographical distances among sites (n = 11 and also environmental synchrony (temperature. Nine species presented significant correlations between spatial synchrony and geographic distances (Astyanax sp. b, Astyanax sp. c, Pimelodus sp., Hoplias malabaricus, Crenicichla iguassuensis, Hypostomus derbyi, Hypostomus myersi, Rhamdia branneri, and R. voulezi. Considering the ecology of the species and the significant relationship between population and environmental synchronies, it seems that environmental stochasticity is the most plausible hypothesis in explaining the observed synchrony patterns.

Synchrony of physiological activity during mother-child interaction: moderation by maternal history of major depressive disorder.

Science.gov (United States)

Woody, Mary L; Feurer, Cope; Sosoo, Effua E; Hastings, Paul D; Gibb, Brandon E

2016-07-01

Family environment plays an important role in the intergenerational transmission of major depressive disorder (MDD), but less is known about how day-to-day mother-child interactions may be disrupted in families with a history of MDD. Disruptions in mother-child synchrony, the dynamic and convergent exchange of physiological and behavioral cues during interactions, may be one important risk factor. Although maternal MDD is associated with a lack of mother-child synchrony at the behavioral level, no studies have examined the impact of maternal MDD on physiological synchrony. Therefore, this study examined whether maternal history of MDD moderates mother-child physiological synchrony [measured via respiratory sinus arrhythmia (RSA)] during positive and negative discussions. Children aged 7-11 years and mothers with either a history of MDD during the child's lifetime (n = 44) or no lifetime diagnosis of any mood disorder (n = 50) completed positive and negative discussion tasks while RSA was continuously recorded for both child and mother. Results indicated significant between-dyad and within-dyad group differences in physiological synchrony during positive and negative discussions. Between-dyad analyses revealed evidence of synchrony only among never depressed dyads, among whom higher average mother RSA during both discussions was associated with higher average child RSA. Within-dyad analyses revealed that never depressed dyads displayed positive synchrony (RSA concordance), whereas dyads with a history of maternal MDD displayed negative synchrony (RSA discordance) during the negative discussion and that the degree of negative synchrony exhibited during the negative discussion was associated with mothers' and children's levels of sadness. These results provide preliminary evidence that physiological synchrony is disrupted in families with a history of maternal MDD and may be a potential risk factor for the intergenerational transmission of depression. © 2016

A psychophysical investigation of differences between synchrony and temporal order judgments.

Science.gov (United States)

Love, Scott A; Petrini, Karin; Cheng, Adam; Pollick, Frank E

2013-01-01

Synchrony judgments involve deciding whether cues to an event are in synch or out of synch, while temporal order judgments involve deciding which of the cues came first. When the cues come from different sensory modalities these judgments can be used to investigate multisensory integration in the temporal domain. However, evidence indicates that that these two tasks should not be used interchangeably as it is unlikely that they measure the same perceptual mechanism. The current experiment further explores this issue across a variety of different audiovisual stimulus types. Participants were presented with 5 audiovisual stimulus types, each at 11 parametrically manipulated levels of cue asynchrony. During separate blocks, participants had to make synchrony judgments or temporal order judgments. For some stimulus types many participants were unable to successfully make temporal order judgments, but they were able to make synchrony judgments. The mean points of subjective simultaneity for synchrony judgments were all video-leading, while those for temporal order judgments were all audio-leading. In the within participants analyses no correlation was found across the two tasks for either the point of subjective simultaneity or the temporal integration window. Stimulus type influenced how the two tasks differed; nevertheless, consistent differences were found between the two tasks regardless of stimulus type. Therefore, in line with previous work, we conclude that synchrony and temporal order judgments are supported by different perceptual mechanisms and should not be interpreted as being representative of the same perceptual process.

The transfer function of neuron spike.

Science.gov (United States)

Palmieri, Igor; Monteiro, Luiz H A; Miranda, Maria D

2015-08-01

The mathematical modeling of neuronal signals is a relevant problem in neuroscience. The complexity of the neuron behavior, however, makes this problem a particularly difficult task. Here, we propose a discrete-time linear time-invariant (LTI) model with a rational function in order to represent the neuronal spike detected by an electrode located in the surroundings of the nerve cell. The model is presented as a cascade association of two subsystems: one that generates an action potential from an input stimulus, and one that represents the medium between the cell and the electrode. The suggested approach employs system identification and signal processing concepts, and is dissociated from any considerations about the biophysical processes of the neuronal cell, providing a low-complexity alternative to model the neuronal spike. The model is validated by using in vivo experimental readings of intracellular and extracellular signals. A computational simulation of the model is presented in order to assess its proximity to the neuronal signal and to observe the variability of the estimated parameters. The implications of the results are discussed in the context of spike sorting. Copyright © 2015 Elsevier Ltd. All rights reserved.

Nonverbal Synchrony in Psychotherapy: Coordinated Body Movement Reflects Relationship Quality and Outcome

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Ramseyer, Fabian; Tschacher, Wolfgang

2011-01-01

Objective: The authors quantified nonverbal synchrony--the coordination of patient's and therapist's movement--in a random sample of same-sex psychotherapy dyads. The authors contrasted nonverbal synchrony in these dyads with a control condition and assessed its association with session-level and overall psychotherapy outcome. Method: Using an…

Audio-visual synchrony and feature-selective attention co-amplify early visual processing.

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Keitel, Christian; Müller, Matthias M

2016-05-01

Our brain relies on neural mechanisms of selective attention and converging sensory processing to efficiently cope with rich and unceasing multisensory inputs. One prominent assumption holds that audio-visual synchrony can act as a strong attractor for spatial attention. Here, we tested for a similar effect of audio-visual synchrony on feature-selective attention. We presented two superimposed Gabor patches that differed in colour and orientation. On each trial, participants were cued to selectively attend to one of the two patches. Over time, spatial frequencies of both patches varied sinusoidally at distinct rates (3.14 and 3.63 Hz), giving rise to pulse-like percepts. A simultaneously presented pure tone carried a frequency modulation at the pulse rate of one of the two visual stimuli to introduce audio-visual synchrony. Pulsed stimulation elicited distinct time-locked oscillatory electrophysiological brain responses. These steady-state responses were quantified in the spectral domain to examine individual stimulus processing under conditions of synchronous versus asynchronous tone presentation and when respective stimuli were attended versus unattended. We found that both, attending to the colour of a stimulus and its synchrony with the tone, enhanced its processing. Moreover, both gain effects combined linearly for attended in-sync stimuli. Our results suggest that audio-visual synchrony can attract attention to specific stimulus features when stimuli overlap in space.

Lateral Information Processing by Spiking Neurons: A Theoretical Model of the Neural Correlate of Consciousness

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Marc Ebner

2011-01-01

Full Text Available Cognitive brain functions, for example, sensory perception, motor control and learning, are understood as computation by axonal-dendritic chemical synapses in networks of integrate-and-fire neurons. Cognitive brain functions may occur either consciously or nonconsciously (on “autopilot”. Conscious cognition is marked by gamma synchrony EEG, mediated largely by dendritic-dendritic gap junctions, sideways connections in input/integration layers. Gap-junction-connected neurons define a sub-network within a larger neural network. A theoretical model (the “conscious pilot” suggests that as gap junctions open and close, a gamma-synchronized subnetwork, or zone moves through the brain as an executive agent, converting nonconscious “auto-pilot” cognition to consciousness, and enhancing computation by coherent processing and collective integration. In this study we implemented sideways “gap junctions” in a single-layer artificial neural network to perform figure/ground separation. The set of neurons connected through gap junctions form a reconfigurable resistive grid or sub-network zone. In the model, outgoing spikes are temporally integrated and spatially averaged using the fixed resistive grid set up by neurons of similar function which are connected through gap-junctions. This spatial average, essentially a feedback signal from the neuron's output, determines whether particular gap junctions between neurons will open or close. Neurons connected through open gap junctions synchronize their output spikes. We have tested our gap-junction-defined sub-network in a one-layer neural network on artificial retinal inputs using real-world images. Our system is able to perform figure/ground separation where the laterally connected sub-network of neurons represents a perceived object. Even though we only show results for visual stimuli, our approach should generalize to other modalities. The system demonstrates a moving sub-network zone of

SPAN: spike pattern association neuron for learning spatio-temporal sequences

OpenAIRE

Mohemmed, A; Schliebs, S; Matsuda, S; Kasabov, N

2012-01-01

Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-temporal information. However, due to their inherent complexity, the formulation of efficient supervised learning algorithms for SNN is difficult and remains an important problem in the research area. This article presents SPAN — a spiking neuron that is able to learn associations of arbitrary spike trains in a supervised fashion allowing the processing of spatio-temporal information encoded in the prec...

Dynamic binding of visual features by neuronal/stimulus synchrony.

Science.gov (United States)

Iwabuchi, A

1998-05-01

When people see a visual scene, certain parts of the visual scene are treated as belonging together and we regard them as a perceptual unit, which is called a "figure". People focus on figures, and the remaining parts of the scene are disregarded as "ground". In Gestalt psychology this process is called "figure-ground segregation". According to current perceptual psychology, a figure is formed by binding various visual features in a scene, and developments in neuroscience have revealed that there are many feature-encoding neurons, which respond to such features specifically. It is not known, however, how the brain binds different features of an object into a coherent visual object representation. Recently, the theory of binding by neuronal synchrony, which argues that feature binding is dynamically mediated by neuronal synchrony of feature-encoding neurons, has been proposed. This review article portrays the problem of figure-ground segregation and features binding, summarizes neurophysiological and psychophysical experiments and theory relevant to feature binding by neuronal/stimulus synchrony, and suggests possible directions for future research on this topic.

A Psychophysical Investigation of Differences between Synchrony and Temporal Order Judgments

Science.gov (United States)

Love, Scott A.; Petrini, Karin; Cheng, Adam; Pollick, Frank E.

2013-01-01

Background Synchrony judgments involve deciding whether cues to an event are in synch or out of synch, while temporal order judgments involve deciding which of the cues came first. When the cues come from different sensory modalities these judgments can be used to investigate multisensory integration in the temporal domain. However, evidence indicates that that these two tasks should not be used interchangeably as it is unlikely that they measure the same perceptual mechanism. The current experiment further explores this issue across a variety of different audiovisual stimulus types. Methodology/Principal Findings Participants were presented with 5 audiovisual stimulus types, each at 11 parametrically manipulated levels of cue asynchrony. During separate blocks, participants had to make synchrony judgments or temporal order judgments. For some stimulus types many participants were unable to successfully make temporal order judgments, but they were able to make synchrony judgments. The mean points of subjective simultaneity for synchrony judgments were all video-leading, while those for temporal order judgments were all audio-leading. In the within participants analyses no correlation was found across the two tasks for either the point of subjective simultaneity or the temporal integration window. Conclusions Stimulus type influenced how the two tasks differed; nevertheless, consistent differences were found between the two tasks regardless of stimulus type. Therefore, in line with previous work, we conclude that synchrony and temporal order judgments are supported by different perceptual mechanisms and should not be interpreted as being representative of the same perceptual process. PMID:23349971

A psychophysical investigation of differences between synchrony and temporal order judgments.

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Scott A Love

Full Text Available BACKGROUND: Synchrony judgments involve deciding whether cues to an event are in synch or out of synch, while temporal order judgments involve deciding which of the cues came first. When the cues come from different sensory modalities these judgments can be used to investigate multisensory integration in the temporal domain. However, evidence indicates that that these two tasks should not be used interchangeably as it is unlikely that they measure the same perceptual mechanism. The current experiment further explores this issue across a variety of different audiovisual stimulus types. METHODOLOGY/PRINCIPAL FINDINGS: Participants were presented with 5 audiovisual stimulus types, each at 11 parametrically manipulated levels of cue asynchrony. During separate blocks, participants had to make synchrony judgments or temporal order judgments. For some stimulus types many participants were unable to successfully make temporal order judgments, but they were able to make synchrony judgments. The mean points of subjective simultaneity for synchrony judgments were all video-leading, while those for temporal order judgments were all audio-leading. In the within participants analyses no correlation was found across the two tasks for either the point of subjective simultaneity or the temporal integration window. CONCLUSIONS: Stimulus type influenced how the two tasks differed; nevertheless, consistent differences were found between the two tasks regardless of stimulus type. Therefore, in line with previous work, we conclude that synchrony and temporal order judgments are supported by different perceptual mechanisms and should not be interpreted as being representative of the same perceptual process.

Pressurized water reactor iodine spiking behavior under power transient conditions

International Nuclear Information System (INIS)

Ho, J.C.

1992-01-01

The most accepted theory explaining the cause of pressurized water reactor iodine spiking is steam formation and condensation in damaged fuel rods. The phase transformation of the primary coolant from water to steam and back again is believed to cause the iodine spiking phenomenon. But due to the complex nature of the phenomenon, a comprehensive model of the behavior has not yet been successfully developed. This paper presents a new model based on an empirical approach, which gives a first-order estimation of the peak iodine spiking magnitude. Based on the proposed iodine spiking model, it is apparent that it is feasible to derive a correlation using the plant operating data base to monitor and control the peak iodine spiking magnitude

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.

Serial Spike Time Correlations Affect Probability Distribution of Joint Spike Events.

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Shahi, Mina; van Vreeswijk, Carl; Pipa, Gordon

2016-01-01

Detecting the existence of temporally coordinated spiking activity, and its role in information processing in the cortex, has remained a major challenge for neuroscience research. Different methods and approaches have been suggested to test whether the observed synchronized events are significantly different from those expected by chance. To analyze the simultaneous spike trains for precise spike correlation, these methods typically model the spike trains as a Poisson process implying that the generation of each spike is independent of all the other spikes. However, studies have shown that neural spike trains exhibit dependence among spike sequences, such as the absolute and relative refractory periods which govern the spike probability of the oncoming action potential based on the time of the last spike, or the bursting behavior, which is characterized by short epochs of rapid action potentials, followed by longer episodes of silence. Here we investigate non-renewal processes with the inter-spike interval distribution model that incorporates spike-history dependence of individual neurons. For that, we use the Monte Carlo method to estimate the full shape of the coincidence count distribution and to generate false positives for coincidence detection. The results show that compared to the distributions based on homogeneous Poisson processes, and also non-Poisson processes, the width of the distribution of joint spike events changes. Non-renewal processes can lead to both heavy tailed or narrow coincidence distribution. We conclude that small differences in the exact autostructure of the point process can cause large differences in the width of a coincidence distribution. Therefore, manipulations of the autostructure for the estimation of significance of joint spike events seem to be inadequate.

iSpike: a spiking neural interface for the iCub robot

International Nuclear Information System (INIS)

Gamez, D; Fidjeland, A K; Lazdins, E

2012-01-01

This paper presents iSpike: a C++ library that interfaces between spiking neural network simulators and the iCub humanoid robot. It uses a biologically inspired approach to convert the robot’s sensory information into spikes that are passed to the neural network simulator, and it decodes output spikes from the network into motor signals that are sent to control the robot. Applications of iSpike range from embodied models of the brain to the development of intelligent robots using biologically inspired spiking neural networks. iSpike is an open source library that is available for free download under the terms of the GPL. (paper)

Interplay of intrinsic and synaptic conductances in the generation of high-frequency oscillations in interneuronal networks with irregular spiking.

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Fabiano Baroni

2014-05-01

Full Text Available High-frequency oscillations (above 30 Hz have been observed in sensory and higher-order brain areas, and are believed to constitute a general hallmark of functional neuronal activation. Fast inhibition in interneuronal networks has been suggested as a general mechanism for the generation of high-frequency oscillations. Certain classes of interneurons exhibit subthreshold oscillations, but the effect of this intrinsic neuronal property on the population rhythm is not completely understood. We study the influence of intrinsic damped subthreshold oscillations in the emergence of collective high-frequency oscillations, and elucidate the dynamical mechanisms that underlie this phenomenon. We simulate neuronal networks composed of either Integrate-and-Fire (IF or Generalized Integrate-and-Fire (GIF neurons. The IF model displays purely passive subthreshold dynamics, while the GIF model exhibits subthreshold damped oscillations. Individual neurons receive inhibitory synaptic currents mediated by spiking activity in their neighbors as well as noisy synaptic bombardment, and fire irregularly at a lower rate than population frequency. We identify three factors that affect the influence of single-neuron properties on synchronization mediated by inhibition: i the firing rate response to the noisy background input, ii the membrane potential distribution, and iii the shape of Inhibitory Post-Synaptic Potentials (IPSPs. For hyperpolarizing inhibition, the GIF IPSP profile (factor iii exhibits post-inhibitory rebound, which induces a coherent spike-mediated depolarization across cells that greatly facilitates synchronous oscillations. This effect dominates the network dynamics, hence GIF networks display stronger oscillations than IF networks. However, the restorative current in the GIF neuron lowers firing rates and narrows the membrane potential distribution (factors i and ii, respectively, which tend to decrease synchrony. If inhibition is shunting instead

Interplay of intrinsic and synaptic conductances in the generation of high-frequency oscillations in interneuronal networks with irregular spiking.

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Baroni, Fabiano; Burkitt, Anthony N; Grayden, David B

2014-05-01

High-frequency oscillations (above 30 Hz) have been observed in sensory and higher-order brain areas, and are believed to constitute a general hallmark of functional neuronal activation. Fast inhibition in interneuronal networks has been suggested as a general mechanism for the generation of high-frequency oscillations. Certain classes of interneurons exhibit subthreshold oscillations, but the effect of this intrinsic neuronal property on the population rhythm is not completely understood. We study the influence of intrinsic damped subthreshold oscillations in the emergence of collective high-frequency oscillations, and elucidate the dynamical mechanisms that underlie this phenomenon. We simulate neuronal networks composed of either Integrate-and-Fire (IF) or Generalized Integrate-and-Fire (GIF) neurons. The IF model displays purely passive subthreshold dynamics, while the GIF model exhibits subthreshold damped oscillations. Individual neurons receive inhibitory synaptic currents mediated by spiking activity in their neighbors as well as noisy synaptic bombardment, and fire irregularly at a lower rate than population frequency. We identify three factors that affect the influence of single-neuron properties on synchronization mediated by inhibition: i) the firing rate response to the noisy background input, ii) the membrane potential distribution, and iii) the shape of Inhibitory Post-Synaptic Potentials (IPSPs). For hyperpolarizing inhibition, the GIF IPSP profile (factor iii)) exhibits post-inhibitory rebound, which induces a coherent spike-mediated depolarization across cells that greatly facilitates synchronous oscillations. This effect dominates the network dynamics, hence GIF networks display stronger oscillations than IF networks. However, the restorative current in the GIF neuron lowers firing rates and narrows the membrane potential distribution (factors i) and ii), respectively), which tend to decrease synchrony. If inhibition is shunting instead of

Spike Neural Models Part II: Abstract Neural Models

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Johnson, Melissa G.

2018-02-01

Full Text Available Neurons are complex cells that require a lot of time and resources to model completely. In spiking neural networks (SNN though, not all that complexity is required. Therefore simple, abstract models are often used. These models save time, use less computer resources, and are easier to understand. This tutorial presents two such models: Izhikevich's model, which is biologically realistic in the resulting spike trains but not in the parameters, and the Leaky Integrate and Fire (LIF model which is not biologically realistic but does quickly and easily integrate input to produce spikes. Izhikevich's model is based on Hodgkin-Huxley's model but simplified such that it uses only two differentiation equations and four parameters to produce various realistic spike patterns. LIF is based on a standard electrical circuit and contains one equation. Either of these two models, or any of the many other models in literature can be used in a SNN. Choosing a neural model is an important task that depends on the goal of the research and the resources available. Once a model is chosen, network decisions such as connectivity, delay, and sparseness, need to be made. Understanding neural models and how they are incorporated into the network is the first step in creating a SNN.

Efficient transmission of subthreshold signals in complex networks of spiking neurons.

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Torres, Joaquin J; Elices, Irene; Marro, J

2015-01-01

We investigate the efficient transmission and processing of weak, subthreshold signals in a realistic neural medium in the presence of different levels of the underlying noise. Assuming Hebbian weights for maximal synaptic conductances--that naturally balances the network with excitatory and inhibitory synapses--and considering short-term synaptic plasticity affecting such conductances, we found different dynamic phases in the system. This includes a memory phase where population of neurons remain synchronized, an oscillatory phase where transitions between different synchronized populations of neurons appears and an asynchronous or noisy phase. When a weak stimulus input is applied to each neuron, increasing the level of noise in the medium we found an efficient transmission of such stimuli around the transition and critical points separating different phases for well-defined different levels of stochasticity in the system. We proved that this intriguing phenomenon is quite robust, as it occurs in different situations including several types of synaptic plasticity, different type and number of stored patterns and diverse network topologies, namely, diluted networks and complex topologies such as scale-free and small-world networks. We conclude that the robustness of the phenomenon in different realistic scenarios, including spiking neurons, short-term synaptic plasticity and complex networks topologies, make very likely that it could also occur in actual neural systems as recent psycho-physical experiments suggest.

Efficient transmission of subthreshold signals in complex networks of spiking neurons.

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Joaquin J Torres

Full Text Available We investigate the efficient transmission and processing of weak, subthreshold signals in a realistic neural medium in the presence of different levels of the underlying noise. Assuming Hebbian weights for maximal synaptic conductances--that naturally balances the network with excitatory and inhibitory synapses--and considering short-term synaptic plasticity affecting such conductances, we found different dynamic phases in the system. This includes a memory phase where population of neurons remain synchronized, an oscillatory phase where transitions between different synchronized populations of neurons appears and an asynchronous or noisy phase. When a weak stimulus input is applied to each neuron, increasing the level of noise in the medium we found an efficient transmission of such stimuli around the transition and critical points separating different phases for well-defined different levels of stochasticity in the system. We proved that this intriguing phenomenon is quite robust, as it occurs in different situations including several types of synaptic plasticity, different type and number of stored patterns and diverse network topologies, namely, diluted networks and complex topologies such as scale-free and small-world networks. We conclude that the robustness of the phenomenon in different realistic scenarios, including spiking neurons, short-term synaptic plasticity and complex networks topologies, make very likely that it could also occur in actual neural systems as recent psycho-physical experiments suggest.

Hatching synchrony in birds

OpenAIRE

Tippeltová, Zuzana

2011-01-01

This bachelor thesis is about hatching synchrony in birds. Generally, among birds there are two types of hatching - asynchronous and synchronous- and the type of hatching is primarily determined by the time of the onset of incubation. In many bird species, including most precocial ones, incubation does not begin until the last egg has been laid, which results in hatching of all the eggs within a few hours. In synchronously-hatched broods, all the chicks are about the same age. Thus no single ...

Early development of synchrony in cortical activations in the human.

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Koolen, N; Dereymaeker, A; Räsänen, O; Jansen, K; Vervisch, J; Matic, V; Naulaers, G; De Vos, M; Van Huffel, S; Vanhatalo, S

2016-05-13

Early intermittent cortical activity is thought to play a crucial role in the growth of neuronal network development, and large scale brain networks are known to provide the basis for higher brain functions. Yet, the early development of the large scale synchrony in cortical activations is unknown. Here, we tested the hypothesis that the early intermittent cortical activations seen in the human scalp EEG show a clear developmental course during the last trimester of pregnancy, the period of intensive growth of cortico-cortical connections. We recorded scalp EEG from altogether 22 premature infants at post-menstrual age between 30 and 44 weeks, and the early cortical synchrony was quantified using recently introduced activation synchrony index (ASI). The developmental correlations of ASI were computed for individual EEG signals as well as anatomically and mathematically defined spatial subgroups. We report two main findings. First, we observed a robust and statistically significant increase in ASI in all cortical areas. Second, there were significant spatial gradients in the synchrony in fronto-occipital and left-to-right directions. These findings provide evidence that early cortical activity is increasingly synchronized across the neocortex. The ASI-based metrics introduced in our work allow direct translational comparison to in vivo animal models, as well as hold promise for implementation as a functional developmental biomarker in future research on human neonates. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Emotional lability and affective synchrony in borderline personality disorder.

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Schoenleber, Michelle; Berghoff, Christopher R; Tull, Matthew T; DiLillo, David; Messman-Moore, Terri; Gratz, Kim L

2016-07-01

Extant research on emotional lability in borderline personality disorder (BPD) has focused almost exclusively on lability of individual emotions or emotion types, with limited research considering how different types of emotions shift together over time. Thus, this study examined the temporal dynamics of emotion in BPD at the level of both individual emotions (i.e., self-conscious emotions [SCE], anger, and anxiety) and mixed emotions (i.e., synchrony between emotions). One hundred forty-four women from the community completed a diagnostic interview and laboratory study involving 5 emotion induction tasks (each of which was preceded and followed by a 5-min resting period or neutral task). State ratings of SCE, anger, and anxiety were provided at 14 time points (before and after each laboratory task and resting period). Hierarchical linear modeling results indicate that women with BPD reported greater mean levels of SCE and Anxiety (but not Anger), and greater lability of Anxiety. Women with BPD also exhibited greater variability in lability of all 3 emotions (suggestive of within-group differences in the relevance of lability to BPD). Results also revealed synchrony (i.e., positive relations) between each possible pair of emotions, regardless of BPD status. Follow-up regression analyses suggest the importance of accounting for lability when examining the role of synchrony in BPD, as the relation of SCE-Anger synchrony to BPD symptom severity was moderated by Anger and SCE lability. Specifically, synchronous changes in SCE and Anger were associated with greater BPD symptom severity when large shifts in SCE were paired with minor shifts in Anger. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Climate-induced trends in predator–prey synchrony differ across life-history stages of an anadromous salmonid

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Bell, Donovan A.; Kovach, Ryan; Vulstek, Scott C.; Joyce, John E.; Tallmon, David A.

2017-01-01

Differential climate-induced shifts in phenology can create mismatches between predators and prey, but few studies have examined predator–prey mismatch across multiple life-history stages. We used long-term data from a warming stream with shifting salmonid migration timings to quantify intra-annual migration synchrony between predatory Dolly Varden (Salvelinus malma) and Pacific salmon prey and examined how predator–prey synchrony has been influenced by climate change. We demonstrate that Dolly Varden have become increasingly mismatched with spring downstream migrations of abundant pink salmon (Oncorhynchus gorbuscha) juveniles. However, Dolly Varden have remained matched with fall upstream migrations of spawning Pacific salmon, including coho (Oncorhynchus kisutch), sockeye (Oncorhynchus nerka), and pink salmon. Downstream predator–prey migration synchrony decreased over time and with higher temperatures, particularly with pink salmon. In contrast, upstream migration synchrony was temporally stable and increased with rising temperatures. Differing trends in Dolly Varden predator–prey synchrony may be explained by the direct use of salmon to cue upstream migration, but not downstream migration. Overall, we show that climate change can have differing impacts on predator–prey synchrony across life-history stages.

Temporal Correlations and Neural Spike Train Entropy

International Nuclear Information System (INIS)

Schultz, Simon R.; Panzeri, Stefano

2001-01-01

Sampling considerations limit the experimental conditions under which information theoretic analyses of neurophysiological data yield reliable results. We develop a procedure for computing the full temporal entropy and information of ensembles of neural spike trains, which performs reliably for limited samples of data. This approach also yields insight to the role of correlations between spikes in temporal coding mechanisms. The method, when applied to recordings from complex cells of the monkey primary visual cortex, results in lower rms error information estimates in comparison to a 'brute force' approach

Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex.

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Laureline Logiaco

2015-08-01

Full Text Available The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70-200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys' behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators.

Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex.

Science.gov (United States)

Logiaco, Laureline; Quilodran, René; Procyk, Emmanuel; Arleo, Angelo

2015-08-01

The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC) of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70-200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys' behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators.

Infanticide and within-clutch competition select for reproductive synchrony in a cooperative bird.

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Riehl, Christina

2016-08-01

Reproduction among members of social animal groups is often highly synchronized, but neither the selective advantages nor the proximate causes of synchrony are fully understood. Here I investigate the evolution of hatching synchrony in the Greater Ani (Crotophaga major), a communally nesting bird in which several unrelated females contribute eggs to a large, shared clutch. Hatching synchrony is variable, ranging from complete synchrony to moderate asynchrony, and is determined by the onset of incubation of the communal clutch. Data from a 10-year field study indicate that individual reproductive success is highest in synchronous groups, and that nestlings that hatch in the middle of the hatching sequence are most likely to survive. Nestling mortality is high in asynchronous clutches because early-hatching nestlings are more likely to be killed by adult group members, whereas late-hatching nestlings are more likely to starve due competition with their older nest-mates. Therefore, the timing of hatching appears to be under stabilizing selection from infanticide and resource competition acting in concert. These results provide empirical support for models predicting that synchrony may evolve as an adaptive counter-strategy to infanticide, and they highlight the importance of competition in shaping the timing of reproduction in social groups. © 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.

Nonverbal Synchrony in Social Interactions of Patients with Schizophrenia Indicates Socio-Communicative Deficits.

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Zeno Kupper

Full Text Available Disordered interpersonal communication can be a serious problem in schizophrenia. Recent advances in computer-based measures allow reliable and objective quantification of nonverbal behavior. Research using these novel measures has shown that objective amounts of body and head movement in patients with schizophrenia during social interactions are closely related to the symptom profiles of these patients. In addition to and above mere amounts of movement, the degree of synchrony, or imitation, between patients and normal interactants may be indicative of core deficits underlying various problems in domains related to interpersonal communication, such as symptoms, social competence, and social functioning.Nonverbal synchrony was assessed objectively using Motion Energy Analysis (MEA in 378 brief, videotaped role-play scenes involving 27 stabilized outpatients diagnosed with paranoid-type schizophrenia.Low nonverbal synchrony was indicative of symptoms, low social competence, impaired social functioning, and low self-evaluation of competence. These relationships remained largely significant when correcting for the amounts of patients' movement. When patients showed reduced imitation of their interactants' movements, negative symptoms were likely to be prominent. Conversely, positive symptoms were more prominent in patients when their interaction partners' imitation of their movements was reduced.Nonverbal synchrony can be an objective and sensitive indicator of the severity of patients' problems. Furthermore, quantitative analysis of nonverbal synchrony may provide novel insights into specific relationships between symptoms, cognition, and core communicative problems in schizophrenia.

Training spiking neural networks to associate spatio-temporal input-output spike patterns

OpenAIRE

Mohemmed, A; Schliebs, S; Matsuda, S; Kasabov, N

2013-01-01

In a previous work (Mohemmed et al., Method for training a spiking neuron to associate input–output spike trains) [1] we have proposed a supervised learning algorithm based on temporal coding to train a spiking neuron to associate input spatiotemporal spike patterns to desired output spike patterns. The algorithm is based on the conversion of spike trains into analogue signals and the application of the Widrow–Hoff learning rule. In this paper we present a mathematical formulation of the prop...

Network periodic solutions: patterns of phase-shift synchrony

International Nuclear Information System (INIS)

Golubitsky, Martin; Wang, Yunjiao; Romano, David

2012-01-01

We prove the rigid phase conjecture of Stewart and Parker. It then follows from previous results (of Stewart and Parker and our own) that rigid phase-shifts in periodic solutions on a transitive network are produced by a cyclic symmetry on a quotient network. More precisely, let X(t) = (x 1 (t), ..., x n (t)) be a hyperbolic T-periodic solution of an admissible system on an n-node network. Two nodes c and d are phase-related if there exists a phase-shift θ cd in [0, 1) such that x d (t) = x c (t + θ cd T). The conjecture states that if phase relations persist under all small admissible perturbations (that is, the phase relations are rigid), then for each pair of phase-related cells, their input signals are also phase-related to the same phase-shift. For a transitive network, rigid phase relations can also be described abstractly as a Z m permutation symmetry of a quotient network. We discuss how patterns of phase-shift synchrony lead to rigid synchrony, rigid phase synchrony, and rigid multirhythms, and we show that for each phase pattern there exists an admissible system with a periodic solution with that phase pattern. Finally, we generalize the results to nontransitive networks where we show that the symmetry that generates rigid phase-shifts occurs on an extension of a quotient network

Spike persistence and normalization in benign epilepsy with centrotemporal spikes - Implications for management.

Science.gov (United States)

Kim, Hunmin; Kim, Soo Yeon; Lim, Byung Chan; Hwang, Hee; Chae, Jong-Hee; Choi, Jieun; Kim, Ki Joong; Dlugos, Dennis J

2018-05-10

This study was performed 1) to determine the timing of spike normalization in patients with benign epilepsy with centrotemporal spikes (BECTS); 2) to identify relationships between age of seizure onset, age of spike normalization, years of spike persistence and treatment; and 3) to assess final outcomes between groups of patients with or without spikes at the time of medication tapering. Retrospective analysis of BECTS patients confirmed by clinical data, including age of onset, seizure semiology and serial electroencephalography (EEG) from diagnosis to remission. Age at spike normalization, years of spike persistence, and time of treatment onset to spike normalization were assessed. Final seizure and EEG outcome were compared between the groups with or without spikes at the time of AED tapering. One hundred and thirty-four patients were included. Mean age at seizure onset was 7.52 ± 2.11 years. Mean age at spike normalization was 11.89 ± 2.11 (range: 6.3-16.8) years. Mean time of treatment onset to spike normalization was 4.11 ± 2.13 (range: 0.24-10.08) years. Younger age of seizure onset was correlated with longer duration of spike persistence (r = -0.41, p < 0.001). In treated patients, spikes persisted for 4.1 ± 1.95 years, compared with 2.9 ± 1.97 years in untreated patients. No patients had recurrent seizures after AED was discontinued, regardless of the presence/absence of spikes at time of AED tapering. Years of spike persistence was longer in early onset BECTS patients. Treatment with AEDs did not shorten years of spike persistence. Persistence of spikes at time of treatment withdrawal was not associated with seizure recurrence. Copyright © 2018 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

Neural synchrony in cortical networks: history, concept and current status

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Peter Uhlhaas

2009-07-01

Full Text Available Following the discovery of context-dependent synchronization of oscillatory neuronal responses in the visual system, the role of neural synchrony in cortical networks has been expanded to provide a general mechanism for the coordination of distributed neural activity patterns. In the current paper, we present an update of the status of this hypothesis through summarizing recent results from our laboratory that suggest important new insights regarding the mechanisms, function and relevance of this phenomenon. In the first part, we present recent results derived from animal experiments and mathematical simulations that provide novel explanations and mechanisms for zero and nero-zero phase lag synchronization. In the second part, we shall discuss the role of neural synchrony for expectancy during perceptual organization and its role in conscious experience. This will be followed by evidence that indicates that in addition to supporting conscious cognition, neural synchrony is abnormal in major brain disorders, such as schizophrenia and autism spectrum disorders. We conclude this paper with suggestions for further research as well as with critical issues that need to be addressed in future studies.

Mother-adolescent physiological synchrony in naturalistic settings: within-family cortisol associations and moderators.

Science.gov (United States)

Papp, Lauren M; Pendry, Patricia; Adam, Emma K

2009-12-01

In this study, the authors examined parent-adolescent cortisol associations in 45 families with adolescent children (24 girls; M age = 15.78 years, SD = 1.44 years). Family members' salivary cortisol levels were measured seven times a day on 2 typical weekdays. Family members provided reports of demographic and health variables, and adolescents rated parent-child relationship characteristics. After accounting for the effects of time of day and relevant demographic and health control variables on cortisol levels, hierarchical linear models indicated the presence of significant covariation over time in mother-adolescent cortisol (i.e., physiological synchrony). Furthermore, moderating tests revealed that mother-adolescent cortisol synchrony was strengthened among dyads characterized by mothers and adolescents spending more time together, and in families rated higher on levels of parent-youth shared activities and parental monitoring or supervision. Analysis of momentary characteristics indicated that maternal presence at the time of cortisol sampling lowered adolescent cortisol levels but did not account for mother-adolescent cortisol synchrony. Within-family physiological synchrony was amplified in momentary contexts of elevated maternal negative affect and elevated adolescent negative affect.

The Mechanisms of Repetitive Spike Generation in an Axonless Retinal Interneuron

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Mark S. Cembrowski

2012-02-01

Full Text Available Several types of retinal interneurons exhibit spikes but lack axons. One such neuron is the AII amacrine cell, in which spikes recorded at the soma exhibit small amplitudes (5 ms. Here, we used electrophysiological recordings and computational analysis to examine the mechanisms underlying this atypical spiking. We found that somatic spikes likely represent large, brief action potential-like events initiated in a single, electrotonically distal dendritic compartment. In this same compartment, spiking undergoes slow modulation, likely by an M-type K conductance. The structural correlate of this compartment is a thin neurite that extends from the primary dendritic tree: local application of TTX to this neurite, or excision of it, eliminates spiking. Thus, the physiology of the axonless AII is much more complex than would be anticipated from morphological descriptions and somatic recordings; in particular, the AII possesses a single dendritic structure that controls its firing pattern.

Measuring Multimodal Synchrony for Human-Computer Interaction

NARCIS (Netherlands)

Reidsma, Dennis; Nijholt, Antinus; Tschacher, Wolfgang; Ramseyer, Fabian; Sourin, A.

2010-01-01

Nonverbal synchrony is an important and natural element in human-human interaction. It can also play various roles in human-computer interaction. In particular this is the case in the interaction between humans and the virtual humans that inhabit our cyberworlds. Virtual humans need to adapt their

An Association between Auditory-Visual Synchrony Processing and Reading Comprehension: Behavioral and Electrophysiological Evidence.

Science.gov (United States)

Mossbridge, Julia; Zweig, Jacob; Grabowecky, Marcia; Suzuki, Satoru

2017-03-01

The perceptual system integrates synchronized auditory-visual signals in part to promote individuation of objects in cluttered environments. The processing of auditory-visual synchrony may more generally contribute to cognition by synchronizing internally generated multimodal signals. Reading is a prime example because the ability to synchronize internal phonological and/or lexical processing with visual orthographic processing may facilitate encoding of words and meanings. Consistent with this possibility, developmental and clinical research has suggested a link between reading performance and the ability to compare visual spatial/temporal patterns with auditory temporal patterns. Here, we provide converging behavioral and electrophysiological evidence suggesting that greater behavioral ability to judge auditory-visual synchrony (Experiment 1) and greater sensitivity of an electrophysiological marker of auditory-visual synchrony processing (Experiment 2) both predict superior reading comprehension performance, accounting for 16% and 25% of the variance, respectively. These results support the idea that the mechanisms that detect auditory-visual synchrony contribute to reading comprehension.

ViSAPy: a Python tool for biophysics-based generation of virtual spiking activity for evaluation of spike-sorting algorithms.

Science.gov (United States)

Hagen, Espen; Ness, Torbjørn V; Khosrowshahi, Amir; Sørensen, Christina; Fyhn, Marianne; Hafting, Torkel; Franke, Felix; Einevoll, Gaute T

2015-04-30

New, silicon-based multielectrodes comprising hundreds or more electrode contacts offer the possibility to record spike trains from thousands of neurons simultaneously. This potential cannot be realized unless accurate, reliable automated methods for spike sorting are developed, in turn requiring benchmarking data sets with known ground-truth spike times. We here present a general simulation tool for computing benchmarking data for evaluation of spike-sorting algorithms entitled ViSAPy (Virtual Spiking Activity in Python). The tool is based on a well-established biophysical forward-modeling scheme and is implemented as a Python package built on top of the neuronal simulator NEURON and the Python tool LFPy. ViSAPy allows for arbitrary combinations of multicompartmental neuron models and geometries of recording multielectrodes. Three example benchmarking data sets are generated, i.e., tetrode and polytrode data mimicking in vivo cortical recordings and microelectrode array (MEA) recordings of in vitro activity in salamander retinas. The synthesized example benchmarking data mimics salient features of typical experimental recordings, for example, spike waveforms depending on interspike interval. ViSAPy goes beyond existing methods as it includes biologically realistic model noise, synaptic activation by recurrent spiking networks, finite-sized electrode contacts, and allows for inhomogeneous electrical conductivities. ViSAPy is optimized to allow for generation of long time series of benchmarking data, spanning minutes of biological time, by parallel execution on multi-core computers. ViSAPy is an open-ended tool as it can be generalized to produce benchmarking data or arbitrary recording-electrode geometries and with various levels of complexity. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Neuronal spike sorting based on radial basis function neural networks

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Taghavi Kani M

2011-02-01

Full Text Available "nBackground: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.

A motion capture study to measure the feeling of synchrony in romantic couples and in professional musicians

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Delphine Preissmann

2016-10-01

Full Text Available The feeling of synchrony is fundamental for most social activities and prosocial behaviors. However, little is known about the behavioral correlates of this feeling and its modulation by intergroup differences. We previously showed that the subjective feeling of synchrony in subjects involved in a mirror imitation task was modulated by objective behavioral measures, as well as contextual factors such as task difficulty and duration of the task performance. In the present study, we extended our methodology to investigate possible interindividual differences. We hypothesized that being in a romantic relationship or being a professional musician can modulate both implicit and explicit synchronisation and the feeling of synchrony as well as the ability to detect synchrony from a third person perspective. Contrary to our hypothesis, we did not find significant differences between people in a romantic relationship and control subjects. However, we observed differences between musicians and control subjects. For the implicit synchrony (spontaneous synchronization during walking, the results revealed that musicians that had never met before spontaneously synchronized their movements earlier among themselves than control subjects, but not better than people sharing a romantic relationship. Moreover, in explicit behavioral synchronisation tasks (mirror game, musicians reported earlier feeling of synchrony and had less speed errors than control subjects. This was in interaction with tasks difficulty as these differences appeared only in tasks with intermediate difficulty. Finally, when subjects had to judge synchrony from a third person perspective, musicians had a better performance to identify if they were present or not in the videos. Taken together, our results suggest that being a professional musician can play a role in the feeling of synchrony and its underlying mechanisms.

Brief Report: A Pilot Study of Parent-Child Biobehavioral Synchrony in Autism Spectrum Disorder

Science.gov (United States)

Baker, Jason K.; Fenning, Rachel M.; Howland, Mariann A.; Baucom, Brian R.; Moffitt, Jacquelyn; Erath, Stephen A.

2015-01-01

The theory of biobehavioral synchrony proposes that the predictive power of parent-child attunement likely lies in the manner with which behaviors are aligned with relevant biological processes. Symptoms of autism spectrum disorder (ASD) may challenge the formation of behavioral and physiological synchrony, but maintenance of such parent-child…

Interpersonal synchrony enhanced through 20 Hz phase-coupled dual brain stimulation

Science.gov (United States)

Knoblich, Günther; Dunne, Laura; Keller, Peter E.

2017-01-01

Abstract Synchronous movement is a key component of social behavior in several species including humans. Recent theories have suggested a link between interpersonal synchrony of brain oscillations and interpersonal movement synchrony. The present study investigated this link. Using transcranial alternating current stimulation (tACS) applied over the left motor cortex, we induced beta band (20 Hz) oscillations in pairs of individuals who both performed a finger-tapping task with the right hand. In-phase or anti-phase oscillations were delivered during a preparatory period prior to movement and while the tapping task was performed. In-phase 20 Hz stimulation enhanced interpersonal movement synchrony, compared with anti-phase or sham stimulation, particularly for the initial taps following the preparatory period. This was confirmed in an analysis comparing real vs pseudo pair surrogate data. No enhancement was observed for stimulation frequencies of 2 Hz (matching the target movement frequency) or 10 Hz (alpha band). Thus, phase-coupling of beta band neural oscillations across two individuals’ (resting) motor cortices supports the interpersonal alignment of sensorimotor processes that regulate rhythmic action initiation, thereby facilitating the establishment of synchronous movement. Phase-locked dual brain stimulation provides a promising method to study causal effects of interpersonal brain synchrony on social, sensorimotor and cognitive processes. PMID:28119510

POPULATION SYNCHRONY WITHIN AND AMONG LEPIDOPTERA SPECIES IN RELATION TO WEATHER, PHYLOGENY, AND LARVEL PHENOLOGY

Science.gov (United States)

1. The population dynamics of native herbivore species in central Appalachian deciduous forests were studied by analysing patterns of synchrony among intra- and interspecific populations and weather. 2. Spatial synchrony of 10 Lepidoptera species and three weather variables (min...

The Second Spiking Threshold: Dynamics of Laminar Network Spiking in the Visual Cortex

DEFF Research Database (Denmark)

Forsberg, Lars E.; Bonde, Lars H.; Harvey, Michael A.

2016-01-01

and moving visual stimuli from the spontaneous ongoing spiking state, in all layers and zones of areas 17 and 18 indicating that the second threshold is a property of the network. Spontaneous and evoked spiking, thus can easily be distinguished. In addition, the trajectories of spontaneous ongoing states......Most neurons have a threshold separating the silent non-spiking state and the state of producing temporal sequences of spikes. But neurons in vivo also have a second threshold, found recently in granular layer neurons of the primary visual cortex, separating spontaneous ongoing spiking from...... visually evoked spiking driven by sharp transients. Here we examine whether this second threshold exists outside the granular layer and examine details of transitions between spiking states in ferrets exposed to moving objects. We found the second threshold, separating spiking states evoked by stationary...

A Motion Capture Study to Measure the Feeling of Synchrony in Romantic Couples and in Professional Musicians

KAUST Repository

Preissmann, Delphine

2016-10-27

The feeling of synchrony is fundamental for most social activities and prosocial behaviors. However, little is known about the behavioral correlates of this feeling and its modulation by intergroup differences. We previously showed that the subjective feeling of synchrony in subjects involved in a mirror imitation task was modulated by objective behavioral measures, as well as contextual factors such as task difficulty and duration of the task performance. In the present study, we extended our methodology to investigate possible interindividual differences. We hypothesized that being in a romantic relationship or being a professional musician can modulate both implicit and explicit synchronization and the feeling of synchrony as well as the ability to detect synchrony from a third person perspective. Contrary to our hypothesis, we did not find significant differences between people in a romantic relationship and control subjects. However, we observed differences between musicians and control subjects. For the implicit synchrony (spontaneous synchronization during walking), the results revealed that musicians that had never met before spontaneously synchronized their movements earlier among themselves than control subjects, but not better than people sharing a romantic relationship. Moreover, in explicit behavioral synchronization tasks (mirror game), musicians reported earlier feeling of synchrony and had less speed errors than control subjects. This was in interaction with tasks difficulty as these differences appeared only in tasks with intermediate difficulty. Finally, when subjects had to judge synchrony from a third person perspective, musicians had a better performance to identify if they were present or not in the videos. Taken together, our results suggest that being a professional musician can play a role in the feeling of synchrony and its underlying mechanisms. © 2016 Preissmann, Charbonnier, Chagué, Antonietti, Llobera, Ansermet and Magistretti.

A joint behavioral and emotive analysis of synchrony in music therapy of children with autism spectrum disorders

Directory of Open Access Journals (Sweden)

Paola Venuti

2016-12-01

Full Text Available Background Synchrony is an essential component of interactive exchanges. In mother-infant interaction, synchrony underlies reciprocity and emotive regulation. A severe lack of synchrony is indeed a core issue within the communication and interaction deficit that characterizes autism spectrum disorders (ASD in accordance with the DSM-5 classification. Based on emerging evidence that music therapy can improve the communication and regulation ability in children with ASD, we aim to verify quantitatively whether: 1 children with ASD improve synchrony with their therapist during music therapy sessions, and 2 this ability persists in different structured contexts. Participants and procedure Twenty-five children, aged from 4 to 6 years (M = 57.80, SD = 16.70, with an autistic disorder diagnosis based on DSM IV-TR and the Autism Diagnostic Observation Schedule (ADOS, participated in the study. An observational tool for coding behaviors and emotive states of synchrony (Child Behavioral and Emotional status Code [CBEC] and Adult Behavioral and Emotional status Code [ABEC] was applied in video recorded sessions of improvisational music therapy (IMT for the subject-therapist pair. For each subject, we considered the 20 central minutes of the first, tenth and twentieth session of IMT. To verify the persistence of effect in a different context with a different adult, we administered and coded the interactive ADOS section (anticipation of a routine with objects applied after session 20 of therapy. Results During the IMT cycle, the amount of synchronic activity increases, with a significant difference from Session 1 to Session 20 in behavioral synchrony and emotional attunement. Also, the increase of synchrony is confirmed at the end of the therapy cycle as measured by an interactive ADOS section. Conclusions Synchrony is an effective indicator of efficacy for music therapy in children with ASD, in particular to evaluate the expansion of positive emotive

Electricity market price spike analysis by a hybrid data model and feature selection technique

International Nuclear Information System (INIS)

Amjady, Nima; Keynia, Farshid

2010-01-01

In a competitive electricity market, energy price forecasting is an important activity for both suppliers and consumers. For this reason, many techniques have been proposed to predict electricity market prices in the recent years. However, electricity price is a complex volatile signal owning many spikes. Most of electricity price forecast techniques focus on the normal price prediction, while price spike forecast is a different and more complex prediction process. Price spike forecasting has two main aspects: prediction of price spike occurrence and value. In this paper, a novel technique for price spike occurrence prediction is presented composed of a new hybrid data model, a novel feature selection technique and an efficient forecast engine. The hybrid data model includes both wavelet and time domain variables as well as calendar indicators, comprising a large candidate input set. The set is refined by the proposed feature selection technique evaluating both relevancy and redundancy of the candidate inputs. The forecast engine is a probabilistic neural network, which are fed by the selected candidate inputs of the feature selection technique and predict price spike occurrence. The efficiency of the whole proposed method for price spike occurrence forecasting is evaluated by means of real data from the Queensland and PJM electricity markets. (author)

Geographic variation in forest composition and precipitation predict the synchrony of forest insect outbreaks

Science.gov (United States)

Kyle J. Haynes; Andrew M. Liebhold; Ottar N. Bjørnstad; Andrew J. Allstadt; Randall S. Morin

2018-01-01

Evaluating the causes of spatial synchrony in population dynamics in nature is notoriously difficult due to a lack of data and appropriate statistical methods. Here, we use a recently developed method, a multivariate extension of the local indicators of spatial autocorrelation statistic, to map geographic variation in the synchrony of gypsy moth outbreaks. Regression...

Deep Spiking Networks

NARCIS (Netherlands)

O'Connor, P.; Welling, M.

2016-01-01

We introduce an algorithm to do backpropagation on a spiking network. Our network is "spiking" in the sense that our neurons accumulate their activation into a potential over time, and only send out a signal (a "spike") when this potential crosses a threshold and the neuron is reset. Neurons only

Active Drumming Experience Increases Infants' Sensitivity to Audiovisual Synchrony during Observed Drumming Actions.

Science.gov (United States)

Gerson, Sarah A; Schiavio, Andrea; Timmers, Renee; Hunnius, Sabine

2015-01-01

In the current study, we examined the role of active experience on sensitivity to multisensory synchrony in six-month-old infants in a musical context. In the first of two experiments, we trained infants to produce a novel multimodal effect (i.e., a drum beat) and assessed the effects of this training, relative to no training, on their later perception of the synchrony between audio and visual presentation of the drumming action. In a second experiment, we then contrasted this active experience with the observation of drumming in order to test whether observation of the audiovisual effect was as effective for sensitivity to multimodal synchrony as active experience. Our results indicated that active experience provided a unique benefit above and beyond observational experience, providing insights on the embodied roots of (early) music perception and cognition.

A method for decoding the neurophysiological spike-response transform.

Science.gov (United States)

Stern, Estee; García-Crescioni, Keyla; Miller, Mark W; Peskin, Charles S; Brezina, Vladimir

2009-11-15

Many physiological responses elicited by neuronal spikes-intracellular calcium transients, synaptic potentials, muscle contractions-are built up of discrete, elementary responses to each spike. However, the spikes occur in trains of arbitrary temporal complexity, and each elementary response not only sums with previous ones, but can itself be modified by the previous history of the activity. A basic goal in system identification is to characterize the spike-response transform in terms of a small number of functions-the elementary response kernel and additional kernels or functions that describe the dependence on previous history-that will predict the response to any arbitrary spike train. Here we do this by developing further and generalizing the "synaptic decoding" approach of Sen et al. (1996). Given the spike times in a train and the observed overall response, we use least-squares minimization to construct the best estimated response and at the same time best estimates of the elementary response kernel and the other functions that characterize the spike-response transform. We avoid the need for any specific initial assumptions about these functions by using techniques of mathematical analysis and linear algebra that allow us to solve simultaneously for all of the numerical function values treated as independent parameters. The functions are such that they may be interpreted mechanistically. We examine the performance of the method as applied to synthetic data. We then use the method to decode real synaptic and muscle contraction transforms.

Group Rhythmic Synchrony and Attention in Children

Directory of Open Access Journals (Sweden)

Alexander K Khalil

2013-09-01

Full Text Available Synchrony, or the coordinated processing of time, is an often-overlooked yet critical context for human interaction. This study tests the relationship between the ability to synchronize rhythmically in a group setting with the ability to attend in 102 elementary schoolchildren. Impairments in temporal processing have frequently been shown to exist in clinical populations with learning disorders, particularly those with Attention Deficit Hyperactivity Disorder (ADHD. Based on this evidence, we hypothesized that the ability to synchronize rhythmically in a group setting—an instance of the type of temporal processing necessary for successful interaction and learning—would be correlated with the ability to attend across the continuum of the population. A music class is an ideal setting for the study of interpersonal timing. In order to measure synchrony in this context, we constructed instruments that allowed the recording and measurement of individual rhythmic performance. The SWAN teacher questionnaire was used as a measurement of attentional behavior. We find that the ability to synchronize with others in a group music class can predict a child’s attentional behavior.

Decoding spikes in a spiking neuronal network

Energy Technology Data Exchange (ETDEWEB)

Feng Jianfeng [Department of Informatics, University of Sussex, Brighton BN1 9QH (United Kingdom); Ding, Mingzhou [Department of Mathematics, Florida Atlantic University, Boca Raton, FL 33431 (United States)

2004-06-04

We investigate how to reliably decode the input information from the output of a spiking neuronal network. A maximum likelihood estimator of the input signal, together with its Fisher information, is rigorously calculated. The advantage of the maximum likelihood estimation over the 'brute-force rate coding' estimate is clearly demonstrated. It is pointed out that the ergodic assumption in neuroscience, i.e. a temporal average is equivalent to an ensemble average, is in general not true. Averaging over an ensemble of neurons usually gives a biased estimate of the input information. A method on how to compensate for the bias is proposed. Reconstruction of dynamical input signals with a group of spiking neurons is extensively studied and our results show that less than a spike is sufficient to accurately decode dynamical inputs.

Decoding spikes in a spiking neuronal network

International Nuclear Information System (INIS)

Feng Jianfeng; Ding, Mingzhou

2004-01-01

We investigate how to reliably decode the input information from the output of a spiking neuronal network. A maximum likelihood estimator of the input signal, together with its Fisher information, is rigorously calculated. The advantage of the maximum likelihood estimation over the 'brute-force rate coding' estimate is clearly demonstrated. It is pointed out that the ergodic assumption in neuroscience, i.e. a temporal average is equivalent to an ensemble average, is in general not true. Averaging over an ensemble of neurons usually gives a biased estimate of the input information. A method on how to compensate for the bias is proposed. Reconstruction of dynamical input signals with a group of spiking neurons is extensively studied and our results show that less than a spike is sufficient to accurately decode dynamical inputs

Active Drumming Experience Increases Infants' Sensitivity to Audiovisual Synchrony during Observed Drumming Actions.

Directory of Open Access Journals (Sweden)

Sarah A Gerson

Full Text Available In the current study, we examined the role of active experience on sensitivity to multisensory synchrony in six-month-old infants in a musical context. In the first of two experiments, we trained infants to produce a novel multimodal effect (i.e., a drum beat and assessed the effects of this training, relative to no training, on their later perception of the synchrony between audio and visual presentation of the drumming action. In a second experiment, we then contrasted this active experience with the observation of drumming in order to test whether observation of the audiovisual effect was as effective for sensitivity to multimodal synchrony as active experience. Our results indicated that active experience provided a unique benefit above and beyond observational experience, providing insights on the embodied roots of (early music perception and cognition.

Active Drumming Experience Increases Infants’ Sensitivity to Audiovisual Synchrony during Observed Drumming Actions

Science.gov (United States)

Timmers, Renee; Hunnius, Sabine

2015-01-01

In the current study, we examined the role of active experience on sensitivity to multisensory synchrony in six-month-old infants in a musical context. In the first of two experiments, we trained infants to produce a novel multimodal effect (i.e., a drum beat) and assessed the effects of this training, relative to no training, on their later perception of the synchrony between audio and visual presentation of the drumming action. In a second experiment, we then contrasted this active experience with the observation of drumming in order to test whether observation of the audiovisual effect was as effective for sensitivity to multimodal synchrony as active experience. Our results indicated that active experience provided a unique benefit above and beyond observational experience, providing insights on the embodied roots of (early) music perception and cognition. PMID:26111226

Effect of Phase Response Curve Skew on Synchronization with and without Conduction Delays

Directory of Open Access Journals (Sweden)

Carmen eCanavier

2013-12-01

Full Text Available A central problem in cortical processing including sensory binding and attentional gating is how neurons can synchronize their responses with zero or near-zero time lag. For a spontaneously firing neuron, an input from another neuron can delay or advance the next spike by different amounts depending upon the timing of the input relative to the previous spike. This information constitutes the phase response curve (PRC. We present a simple graphical method for determining the effect of PRC shape on synchronization tendencies and illustrate it using type 1 PRCs, which consist entirely of advances (delays in response to excitation (inhibition. We obtained the following generic solutions for type 1 PRCs, which include the pulse coupled leaky integrate and fire model. For pairs with mutual excitation, exact synchrony can be stable for strong coupling because of the stabilizing effect of the causal limit region of the PRC in which an input triggers a spike immediately upon arrival. However, synchrony is unstable for short delays, because delayed inputs arrive during a refractory period and cannot trigger an immediate spike. Right skew destabilizes antiphase and enables modes with time lags that grow as the conduction delay is increased. Therefore, right skew favors near-synchrony at short conduction delays and a gradual transition between synchrony and antiphase for pairs coupled by mutual excitation. For pairs with mutual inhibition, zero time lag synchrony is stable for conduction delays ranging from zero to a substantial fraction of the period for pairs. However, for right skew there is a preferred antiphase mode at short delays. In contrast to mutual excitation, left skew destabilizes antiphase for mutual inhibition so that synchrony dominates at short delays as well. These pairwise synchronization tendencies constrain the synchronization properties of neurons embedded in larger networks.

On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions.

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Schmitt, Michael

2004-09-01

We study networks of spiking neurons that use the timing of pulses to encode information. Nonlinear interactions model the spatial groupings of synapses on the neural dendrites and describe the computations performed at local branches. Within a theoretical framework of learning we analyze the question of how many training examples these networks must receive to be able to generalize well. Bounds for this sample complexity of learning can be obtained in terms of a combinatorial parameter known as the pseudodimension. This dimension characterizes the computational richness of a neural network and is given in terms of the number of network parameters. Two types of feedforward architectures are considered: constant-depth networks and networks of unconstrained depth. We derive asymptotically tight bounds for each of these network types. Constant depth networks are shown to have an almost linear pseudodimension, whereas the pseudodimension of general networks is quadratic. Networks of spiking neurons that use temporal coding are becoming increasingly more important in practical tasks such as computer vision, speech recognition, and motor control. The question of how well these networks generalize from a given set of training examples is a central issue for their successful application as adaptive systems. The results show that, although coding and computation in these networks is quite different and in many cases more powerful, their generalization capabilities are at least as good as those of traditional neural network models.

The Second Spiking Threshold: Dynamics of Laminar Network Spiking in the Visual Cortex

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Forsberg, Lars E.; Bonde, Lars H.; Harvey, Michael A.; Roland, Per E.

2016-01-01

Most neurons have a threshold separating the silent non-spiking state and the state of producing temporal sequences of spikes. But neurons in vivo also have a second threshold, found recently in granular layer neurons of the primary visual cortex, separating spontaneous ongoing spiking from visually evoked spiking driven by sharp transients. Here we examine whether this second threshold exists outside the granular layer and examine details of transitions between spiking states in ferrets exposed to moving objects. We found the second threshold, separating spiking states evoked by stationary and moving visual stimuli from the spontaneous ongoing spiking state, in all layers and zones of areas 17 and 18 indicating that the second threshold is a property of the network. Spontaneous and evoked spiking, thus can easily be distinguished. In addition, the trajectories of spontaneous ongoing states were slow, frequently changing direction. In single trials, sharp as well as smooth and slow transients transform the trajectories to be outward directed, fast and crossing the threshold to become evoked. Although the speeds of the evolution of the evoked states differ, the same domain of the state space is explored indicating uniformity of the evoked states. All evoked states return to the spontaneous evoked spiking state as in a typical mono-stable dynamical system. In single trials, neither the original spiking rates, nor the temporal evolution in state space could distinguish simple visual scenes. PMID:27582693

Utilizing time-frequency amplitude and phase synchrony measure to assess feedback processing in a gambling task.

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Watts, Adreanna T M; Tootell, Anne V; Fix, Spencer T; Aviyente, Selin; Bernat, Edward M

2018-04-29

The neurophysiological mechanisms involved in the evaluation of performance feedback have been widely studied in the ERP literature over the past twenty years, but understanding has been limited by the use of traditional time-domain amplitude analytic approaches. Gambling outcome valence has been identified as an important factor modulating event-related potential (ERP) components, most notably the feedback negativity (FN). Recent work employing time-frequency analysis has shown that processes indexed by the FN are confounded in the time-domain and can be better represented as separable feedback-related processes in the theta (3-7 Hz) and delta (0-3 Hz) frequency bands. In addition to time-frequency amplitude analysis, phase synchrony measures have begun to further our understanding of performance evaluation by revealing how feedback information is processed within and between various brain regions. The current study aimed to provide an integrative assessment of time-frequency amplitude, inter-trial phase synchrony, and inter-channel phase synchrony changes following monetary feedback in a gambling task. Results revealed that time-frequency amplitude activity explained separable loss and gain processes confounded in the time-domain. Furthermore, phase synchrony measures explained unique variance above and beyond amplitude measures and demonstrated enhanced functional integration between medial prefrontal and bilateral frontal, motor, and occipital regions for loss relative to gain feedback. These findings demonstrate the utility of assessing time-frequency amplitude, inter-trial phase synchrony, and inter-channel phase synchrony together to better elucidate the neurophysiology of feedback processing. Copyright © 2017. Published by Elsevier B.V.

More than reflections: Empathy in motivational interviewing includes language style synchrony between therapist and client

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Lord, Sarah Peregrine; Sheng, Elisa; Imel, Zac E.; Baer, John; Atkins, David C.

2016-01-01

Empathy is a basic psychological process that involves the development of synchrony in dyads. It is also a foundational ingredient in specific, evidence-based behavioral treatments like motivational interviewing (MI). Ratings of therapist empathy typically rely on a gestalt, “felt sense” of therapist understanding and the presence of specific verbal behaviors like reflective listening. These ratings do not provide a direct test of psychological processes like behavioral synchrony that are theorized to be an important component of empathy in psychotherapy. To explore a new objective indicator of empathy, we hypothesized that synchrony in language style (i.e., matching how statements are phrased) between client and therapists would predict gestalt ratings of empathy over and above the contribution of reflections. We analyzed 122 MI transcripts with high and low empathy ratings based on the Motivational Interviewing Treatment Integrity (MITI) global rating scale. Linguistic inquiry and word count was used to estimate language style synchrony (LSS) of adjacent client and therapist talk turns. High empathy sessions showed greater LSS across 11 language style categories compared to low empathy sessions (p empathy vs. low empathy sessions (d = 0.62). Regression analyses showed that LSS was predictive of empathy ratings over and above reflection counts; a 1 SD increase in LSS is associated with 2.4 times increase in the odds of a high empathy rating, controlling for therapist reflections (odds ratio = 2.4, 95% CI: 1.36, 4.24, p empathy ratings are related to synchrony in language style, over and above synchrony of content as measured by therapist reflections. Novel indicators of therapist empathy may have implications for the study of MI process as well as the training of therapists. PMID:25892166

Implementing Signature Neural Networks with Spiking Neurons.

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Carrillo-Medina, José Luis; Latorre, Roberto

2016-01-01

Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence

Spatio-temporal patterns of event-related potentials related to audiovisual synchrony judgments in older adults.

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Chan, Yu Man; Pianta, Michael Julian; Bode, Stefan; McKendrick, Allison Maree

2017-07-01

Older adults have altered perception of the relative timing between auditory and visual stimuli, even when stimuli are scaled to equate detectability. To help understand why, this study investigated the neural correlates of audiovisual synchrony judgments in older adults using electroencephalography (EEG). Fourteen younger (18-32 year old) and 16 older (61-74 year old) adults performed an audiovisual synchrony judgment task on flash-pip stimuli while EEG was recorded. All participants were assessed to have healthy vision and hearing for their age. Observers responded to whether audiovisual pairs were perceived as synchronous or asynchronous via a button press. The results showed that the onset of predictive sensory information for synchrony judgments was not different between groups. Channels over auditory areas contributed more to this predictive sensory information than visual areas. The spatial-temporal profile of the EEG activity also indicates that older adults used different resources to maintain a similar level of performance in audiovisual synchrony judgments compared with younger adults. Copyright © 2017 Elsevier Inc. All rights reserved.

Dynamic ErbB4 Activity in Hippocampal-Prefrontal Synchrony and Top-Down Attention in Rodents.

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Tan, Zhibing; Robinson, Heath L; Yin, Dong-Min; Liu, Yu; Liu, Fang; Wang, Hongsheng; Lin, Thiri W; Xing, Guanglin; Gan, Lin; Xiong, Wen-Cheng; Mei, Lin

2018-04-18

Top-down attention is crucial for meaningful behaviors and impaired in various mental disorders. However, its underpinning regulatory mechanisms are poorly understood. We demonstrate that the hippocampal-prefrontal synchrony associates with levels of top-down attention. Both attention and synchrony are reduced in mutant mice of ErbB4, a receptor of neuregulin-1. We used chemical genetic and optogenetic approaches to inactivate ErbB4 kinase and ErbB4+ interneurons, respectively, both of which reduce gamma-aminobutyric acid (GABA) activity. Such inhibitions in the hippocampus impair both hippocampal-prefrontal synchrony and top-down attention, whereas those in the prefrontal cortex alter attention, but not synchrony. These observations identify a role of ErbB4-dependent GABA activity in the hippocampus in synchronizing the hippocampal-prefrontal pathway and demonstrate that acute, dynamic ErbB4 signaling is required to command top-down attention. Because both neuregulin-1 and ErbB4 are susceptibility genes of schizophrenia and major depression, our study contributes to a better understanding of these disorders. VIDEO ABSTRACT. Copyright © 2018 Elsevier Inc. All rights reserved.

Automatic EEG spike detection.

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Harner, Richard

2009-10-01

Since the 1970s advances in science and technology during each succeeding decade have renewed the expectation of efficient, reliable automatic epileptiform spike detection (AESD). But even when reinforced with better, faster tools, clinically reliable unsupervised spike detection remains beyond our reach. Expert-selected spike parameters were the first and still most widely used for AESD. Thresholds for amplitude, duration, sharpness, rise-time, fall-time, after-coming slow waves, background frequency, and more have been used. It is still unclear which of these wave parameters are essential, beyond peak-peak amplitude and duration. Wavelet parameters are very appropriate to AESD but need to be combined with other parameters to achieve desired levels of spike detection efficiency. Artificial Neural Network (ANN) and expert-system methods may have reached peak efficiency. Support Vector Machine (SVM) technology focuses on outliers rather than centroids of spike and nonspike data clusters and should improve AESD efficiency. An exemplary spike/nonspike database is suggested as a tool for assessing parameters and methods for AESD and is available in CSV or Matlab formats from the author at brainvue@gmail.com. Exploratory Data Analysis (EDA) is presented as a graphic method for finding better spike parameters and for the step-wise evaluation of the spike detection process.

Population spikes in cortical networks during different functional states.

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Shirley eMark

2012-07-01

Full Text Available Brain computational challenges vary between behavioral states. Engaged animals react according to incoming sensory information, while in relaxed and sleeping states consolidation of the learned information is believed to take place. Different states are characterized by different forms of cortical activity. We study a possible neuronal mechanism for generating these diverse dynamics and suggest their possible functional significance. Previous studies demonstrated that brief synchronized increase in a neural firing (Population Spikes can be generated in homogenous recurrent neural networks with short-term synaptic depression. Here we consider more realistic networks with clustered architecture. We show that the level of synchronization in neural activity can be controlled smoothly by network parameters. The network shifts from asynchronous activity to a regime in which clusters synchronized separately, then, the synchronization between the clusters increases gradually to fully synchronized state. We examine the effects of different synchrony levels on the transmission of information by the network. We find that the regime of intermediate synchronization is preferential for the flow of information between sparsely connected areas. Based on these results, we suggest that the regime of intermediate synchronization corresponds to engaged behavioral state of the animal, while global synchronization is exhibited during relaxed and sleeping states.

Cell synchrony techniques. I. A comparison of methods

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Grdina, D.J.; Meistrich, M.L.; Meyn, R.E.; Johnson, T.S.; White, R.A.

1984-01-01

Selected cell synchrony techniques, as applied to asynchronous populations of Chinese hamster ovary (CHO) cells, have been compared. Aliquots from the same culture of exponentially growing cells were synchronized using mitotic selection, mitotic selection and hydroxyurea block, centrifugal elutriation, or an EPICS V cell sorter. Sorting of cells was achieved after staining cells with Hoechst 33258. After syncronization by the various methods the relative distribution of cells in G/sub 1/, S, or G/sub 2/ + M phases of the cell cycle was determined by flow cytometry. Fractions of synchronized cells obtained from each method were replated and allowed to progress through a second cell cycle. Mitotic selection gave rise to relatively pure and unperturbed early G/sub 1/ phase cells. While cell synchrony rapidly dispersed with time, cells progressed through the cell cycle in 12 hr. Sorting with the EPIC V on the modal G/sub 1/ peak yielded a relatively pure but heterogeneous G/sub 1/ population (i.e. early to late G/sub 1/). Again, synchrony dispersed with time, but cell-cycle progression required 14 hr. With centrifugal elutriation, several different cell populations synchronized throughout the cell cycle could be rapidly obtained with a purity comparable to mitotic selection and cell sorting. It was concluded that, either alone or in combination with blocking agents such as hydroxyurea, elutriation and mitotic selection were both excellent methods for synchronizing CHO cells. Cell sorting exhibited limitations in sample size and time required for synchronizing CHO cells. Its major advantage would be its ability to isolate cell populations unique with respect to selected cellular parameters. 19 references, 9 figures.

Accounting for sampling error when inferring population synchrony from time-series data: a Bayesian state-space modelling approach with applications.

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Hugues Santin-Janin

Full Text Available BACKGROUND: Data collected to inform time variations in natural population size are tainted by sampling error. Ignoring sampling error in population dynamics models induces bias in parameter estimators, e.g., density-dependence. In particular, when sampling errors are independent among populations, the classical estimator of the synchrony strength (zero-lag correlation is biased downward. However, this bias is rarely taken into account in synchrony studies although it may lead to overemphasizing the role of intrinsic factors (e.g., dispersal with respect to extrinsic factors (the Moran effect in generating population synchrony as well as to underestimating the extinction risk of a metapopulation. METHODOLOGY/PRINCIPAL FINDINGS: The aim of this paper was first to illustrate the extent of the bias that can be encountered in empirical studies when sampling error is neglected. Second, we presented a space-state modelling approach that explicitly accounts for sampling error when quantifying population synchrony. Third, we exemplify our approach with datasets for which sampling variance (i has been previously estimated, and (ii has to be jointly estimated with population synchrony. Finally, we compared our results to those of a standard approach neglecting sampling variance. We showed that ignoring sampling variance can mask a synchrony pattern whatever its true value and that the common practice of averaging few replicates of population size estimates poorly performed at decreasing the bias of the classical estimator of the synchrony strength. CONCLUSION/SIGNIFICANCE: The state-space model used in this study provides a flexible way of accurately quantifying the strength of synchrony patterns from most population size data encountered in field studies, including over-dispersed count data. We provided a user-friendly R-program and a tutorial example to encourage further studies aiming at quantifying the strength of population synchrony to account for

Time-delay-induced phase-transition to synchrony in coupled bursting neurons

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Adhikari, Bhim Mani; Prasad, Awadhesh; Dhamala, Mukeshwar

2011-06-01

Signal transmission time delays in a network of nonlinear oscillators are known to be responsible for a variety of interesting dynamic behaviors including phase-flip transitions leading to synchrony or out of synchrony. Here, we uncover that phase-flip transitions are general phenomena and can occur in a network of coupled bursting neurons with a variety of coupling types. The transitions are marked by nonlinear changes in both temporal and phase-space characteristics of the coupled system. We demonstrate these phase-transitions with Hindmarsh-Rose and Leech-Heart interneuron models and discuss the implications of these results in understanding collective dynamics of bursting neurons in the brain.

SpikeTemp: An Enhanced Rank-Order-Based Learning Approach for Spiking Neural Networks With Adaptive Structure.

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Wang, Jinling; Belatreche, Ammar; Maguire, Liam P; McGinnity, Thomas Martin

2017-01-01

This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order-based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and, thus, reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark data sets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order-based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms, and the results demonstrate the ability of SpikeTemp to classify different data sets after just one presentation of the training samples with comparable classification performance.

A novel automated spike sorting algorithm with adaptable feature extraction.

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Bestel, Robert; Daus, Andreas W; Thielemann, Christiane

2012-10-15

To study the electrophysiological properties of neuronal networks, in vitro studies based on microelectrode arrays have become a viable tool for analysis. Although in constant progress, a challenging task still remains in this area: the development of an efficient spike sorting algorithm that allows an accurate signal analysis at the single-cell level. Most sorting algorithms currently available only extract a specific feature type, such as the principal components or Wavelet coefficients of the measured spike signals in order to separate different spike shapes generated by different neurons. However, due to the great variety in the obtained spike shapes, the derivation of an optimal feature set is still a very complex issue that current algorithms struggle with. To address this problem, we propose a novel algorithm that (i) extracts a variety of geometric, Wavelet and principal component-based features and (ii) automatically derives a feature subset, most suitable for sorting an individual set of spike signals. Thus, there is a new approach that evaluates the probability distribution of the obtained spike features and consequently determines the candidates most suitable for the actual spike sorting. These candidates can be formed into an individually adjusted set of spike features, allowing a separation of the various shapes present in the obtained neuronal signal by a subsequent expectation maximisation clustering algorithm. Test results with simulated data files and data obtained from chick embryonic neurons cultured on microelectrode arrays showed an excellent classification result, indicating the superior performance of the described algorithm approach. Copyright © 2012 Elsevier B.V. All rights reserved.

What Iconic Gesture Fragments Reveal about Gesture-Speech Integration: When Synchrony Is Lost, Memory Can Help

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Obermeier, Christian; Holle, Henning; Gunter, Thomas C.

2011-01-01

The present series of experiments explores several issues related to gesture-speech integration and synchrony during sentence processing. To be able to more precisely manipulate gesture-speech synchrony, we used gesture fragments instead of complete gestures, thereby avoiding the usual long temporal overlap of gestures with their coexpressive…

Changes in Purkinje cell simple spike encoding of reach kinematics during adaption to a mechanical perturbation.

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Hewitt, Angela L; Popa, Laurentiu S; Ebner, Timothy J

2015-01-21

The cerebellum is essential in motor learning. At the cellular level, changes occur in both the simple spike and complex spike firing of Purkinje cells. Because simple spike discharge reflects the main output of the cerebellar cortex, changes in simple spike firing likely reflect the contribution of the cerebellum to the adapted behavior. Therefore, we investigated in Rhesus monkeys how the representation of arm kinematics in Purkinje cell simple spike discharge changed during adaptation to mechanical perturbations of reach movements. Monkeys rapidly adapted to a novel assistive or resistive perturbation along the direction of the reach. Adaptation consisted of matching the amplitude and timing of the perturbation to minimize its effect on the reach. In a majority of Purkinje cells, simple spike firing recorded before and during adaptation demonstrated significant changes in position, velocity, and acceleration sensitivity. The timing of the simple spike representations change within individual cells, including shifts in predictive versus feedback signals. At the population level, feedback-based encoding of position increases early in learning and velocity decreases. Both timing changes reverse later in learning. The complex spike discharge was only weakly modulated by the perturbations, demonstrating that the changes in simple spike firing can be independent of climbing fiber input. In summary, we observed extensive alterations in individual Purkinje cell encoding of reach kinematics, although the movements were nearly identical in the baseline and adapted states. Therefore, adaption to mechanical perturbation of a reaching movement is accompanied by widespread modifications in the simple spike encoding. Copyright © 2015 the authors 0270-6474/15/351106-19$15.00/0.

Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays.

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Mena, Gonzalo E; Grosberg, Lauren E; Madugula, Sasidhar; Hottowy, Paweł; Litke, Alan; Cunningham, John; Chichilnisky, E J; Paninski, Liam

2017-11-01

Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible.

Synaptic energy drives the information processing mechanisms in spiking neural networks.

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El Laithy, Karim; Bogdan, Martin

2014-04-01

Flow of energy and free energy minimization underpins almost every aspect of naturally occurring physical mechanisms. Inspired by this fact this work establishes an energy-based framework that spans the multi-scale range of biological neural systems and integrates synaptic dynamic, synchronous spiking activity and neural states into one consistent working paradigm. Following a bottom-up approach, a hypothetical energy function is proposed for dynamic synaptic models based on the theoretical thermodynamic principles and the Hopfield networks. We show that a synapse exposes stable operating points in terms of its excitatory postsynaptic potential as a function of its synaptic strength. We postulate that synapses in a network operating at these stable points can drive this network to an internal state of synchronous firing. The presented analysis is related to the widely investigated temporal coherent activities (cell assemblies) over a certain range of time scales (binding-by-synchrony). This introduces a novel explanation of the observed (poly)synchronous activities within networks regarding the synaptic (coupling) functionality. On a network level the transitions from one firing scheme to the other express discrete sets of neural states. The neural states exist as long as the network sustains the internal synaptic energy.

Dissociable effects of dopamine on neuronal firing rate and synchrony in the dorsal striatum

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John M Burkhardt

2009-10-01

Full Text Available Previous studies showed that dopamine depletion leads to both changes in firing rate and in neuronal synchrony in the basal ganglia. Since dopamine D1 and D2 receptors are preferentially expressed in striatonigral and striatopallidal medium spiny neurons, respectively, we investigated the relative contribution of lack of D1 and/or D2-type receptor activation to the changes in striatal firing rate and synchrony observed after dopamine depletion. Similar to what was observed after dopamine depletion, co-administration of D1 and D2 antagonists to mice chronically implanted with multielectrode arrays in the striatum caused significant changes in firing rate, power of the local field potential (LFP oscillations, and synchrony measured by the entrainment of neurons to striatal local field potentials. However, although blockade of either D1 or D2 type receptors produced similarly severe akinesia, the effects on neural activity differed. Blockade of D2 receptors affected the firing rate of medium spiny neurons and the power of the LFP oscillations substantially, but it did not affect synchrony to the same extent. In contrast, D1 blockade affected synchrony dramatically, but had less substantial effects on firing rate and LFP power. Furthermore, there was no consistent relation between neurons changing firing rate and changing LFP entrainment after dopamine blockade. Our results suggest that the changes in rate and entrainment to the LFP observed in medium spiny neurons after dopamine depletion are somewhat dissociable, and that lack of D1- or D2-type receptor activation can exert independent yet interactive pathological effects during the progression of Parkinson’s disease.

Measure of synchrony in the activity of intrinsic cardiac neurons

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Longpré, Jean-Philippe; Salavatian, Siamak; Jacquemet, Vincent; Beaumont, Eric; Armour, J Andrew; Ardell, Jeffrey L

2014-01-01

Recent multielectrode array recordings in ganglionated plexi of canine atria have opened the way to the study of population dynamics of intrinsic cardiac neurons. These data provide critical insights into the role of local processing that these ganglia play in the regulation of cardiac function. Low firing rates, marked non-stationarity, interplay with the cardiovascular and pulmonary systems and artifacts generated by myocardial activity create new constraints not present in brain recordings for which almost all neuronal analysis techniques have been developed. We adapted and extended the jitter-based synchrony index (SI) to (1) provide a robust and computationally efficient tool for assessing the level and statistical significance of SI between cardiac neurons, (2) estimate the bias on SI resulting from neuronal activity possibly hidden in myocardial artifacts, (3) quantify the synchrony or anti-synchrony between neuronal activity and the phase in the cardiac and respiratory cycles. The method was validated on firing time series from a total of 98 individual neurons identified in 8 dog experiments. SI ranged from −0.14 to 0.66, with 23 pairs of neurons with SI > 0.1. The estimated bias due to artifacts was typically <1%. Strongly cardiovascular- and pulmonary-related neurons (SI > 0.5) were found. Results support the use of jitter-based SI in the context of intrinsic cardiac neurons. (paper)

Collective Efficacy in Sports and Physical Activities: Perceived Emotional Synchrony and Shared Flow

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Zumeta, Larraitz N.; Oriol, Xavier; Telletxea, Saioa; Amutio, Alberto; Basabe, Nekane

2016-01-01

This cross-sectional study analyzes the relationship between collective efficacy and two psychosocial processes involved in collective sport-physical activities. It argues that in-group identification and fusion with the group will affect collective efficacy (CE). A sample of 276 university students answered different scales regarding their participation in collective physical and sport activities. Multiple-mediation analyses showed that shared flow and perceived emotional synchrony mediate the relationship between in-group identification and CE, whereas the relationship between identity fusion and CE was only mediated by perceived emotional synchrony. Results suggest that both psychosocial processes explain the positive effects of in-group identification and identity fusion with the group in collective efficacy. Specifically, the role of perceived emotional synchrony in explaining the positive effects of participation in collective sport-physical activities is underlined. In sum, this study highlights the utility of collective actions and social identities to explain the psychosocial processes related to collective efficacy in physical and sports activities. Finally, practical implications are discussed. PMID:26779077

Automatic spike sorting using tuning information.

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Ventura, Valérie

2009-09-01

Current spike sorting methods focus on clustering neurons' characteristic spike waveforms. The resulting spike-sorted data are typically used to estimate how covariates of interest modulate the firing rates of neurons. However, when these covariates do modulate the firing rates, they provide information about spikes' identities, which thus far have been ignored for the purpose of spike sorting. This letter describes a novel approach to spike sorting, which incorporates both waveform information and tuning information obtained from the modulation of firing rates. Because it efficiently uses all the available information, this spike sorter yields lower spike misclassification rates than traditional automatic spike sorters. This theoretical result is verified empirically on several examples. The proposed method does not require additional assumptions; only its implementation is different. It essentially consists of performing spike sorting and tuning estimation simultaneously rather than sequentially, as is currently done. We used an expectation-maximization maximum likelihood algorithm to implement the new spike sorter. We present the general form of this algorithm and provide a detailed implementable version under the assumptions that neurons are independent and spike according to Poisson processes. Finally, we uncover a systematic flaw of spike sorting based on waveform information only.

Reliability of MEG source imaging of anterior temporal spikes: analysis of an intracranially characterized spike focus.

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Wennberg, Richard; Cheyne, Douglas

2014-05-01

To assess the reliability of MEG source imaging (MSI) of anterior temporal spikes through detailed analysis of the localization and orientation of source solutions obtained for a large number of spikes that were separately confirmed by intracranial EEG to be focally generated within a single, well-characterized spike focus. MSI was performed on 64 identical right anterior temporal spikes from an anterolateral temporal neocortical spike focus. The effects of different volume conductors (sphere and realistic head model), removal of noise with low frequency filters (LFFs) and averaging multiple spikes were assessed in terms of the reliability of the source solutions. MSI of single spikes resulted in scattered dipole source solutions that showed reasonable reliability for localization at the lobar level, but only for solutions with a goodness-of-fit exceeding 80% using a LFF of 3 Hz. Reliability at a finer level of intralobar localization was limited. Spike averaging significantly improved the reliability of source solutions and averaging 8 or more spikes reduced dependency on goodness-of-fit and data filtering. MSI performed on topographically identical individual spikes from an intracranially defined classical anterior temporal lobe spike focus was limited by low reliability (i.e., scattered source solutions) in terms of fine, sublobar localization within the ipsilateral temporal lobe. Spike averaging significantly improved reliability. MSI performed on individual anterior temporal spikes is limited by low reliability. Reduction of background noise through spike averaging significantly improves the reliability of MSI solutions. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Hardware implementation of stochastic spiking neural networks.

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Rosselló, Josep L; Canals, Vincent; Morro, Antoni; Oliver, Antoni

2012-08-01

Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature. The advantage of the proposed implementation is that it is fully digital and therefore can be massively implemented in Field Programmable Gate Arrays. The high computational capabilities of the proposed model are demonstrated by the study of both feed-forward and recurrent networks that are able to implement high-speed signal filtering and to solve complex systems of linear equations.

Application of cross-correlated delay shift rule in spiking neural networks for interictal spike detection.

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Lilin Guo; Zhenzhong Wang; Cabrerizo, Mercedes; Adjouadi, Malek

2016-08-01

This study proposes a Cross-Correlated Delay Shift (CCDS) supervised learning rule to train neurons with associated spatiotemporal patterns to classify spike patterns. The objective of this study was to evaluate the feasibility of using the CCDS rule to automate the detection of interictal spikes in electroencephalogram (EEG) data on patients with epilepsy. Encoding is the initial yet essential step for spiking neurons to process EEG patterns. A new encoding method is utilized to convert the EEG signal into spike patterns. The simulation results show that the proposed algorithm identified 69 spikes out of 82 spikes, or 84% detection rate, which is quite high considering the subtleties of interictal spikes and the tediousness of monitoring long EEG records. This CCDS rule is also benchmarked by ReSuMe on the same task.

MicroRNA-276 promotes egg-hatching synchrony by up-regulating brm in locusts

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He, Jing; Chen, Qianquan; Wei, Yuanyuan; Jiang, Feng; Yang, Meiling; Hao, Shuguang; Guo, Xiaojiao; Chen, Dahua; Kang, Le

2016-01-01

Developmental synchrony, the basis of uniform swarming, migration, and sexual maturation, is an important strategy for social animals to adapt to variable environments. However, the molecular mechanisms underlying developmental synchrony are largely unexplored. The migratory locust exhibits polyphenism between gregarious and solitarious individuals, with the former displaying more synchronous sexual maturation and migration than the latter. Here, we found that the egg-hatching time of gregarious locusts was more uniform compared with solitarious locusts and that microRNA-276 (miR-276) was expressed significantly higher in both ovaries and eggs of gregarious locusts than in solitarious locusts. Interestingly, inhibiting miR-276 in gregarious females and overexpressing it in solitarious females, respectively, caused more heterochronic and synchronous hatching of progeny eggs. Moreover, miR-276 directly targeted a transcription coactivator gene, brahma (brm), resulting in its up-regulation. Knockdown of brm not only resulted in asynchronous egg hatching in gregarious locusts but also impaired the miR-276–induced synchronous egg hatching in solitarious locusts. Mechanistically, miR-276 mediated brm activation in a manner that depended on the secondary structure of brm, namely, a stem-loop around the binding site of miR-276. Collectively, our results unravel a mechanism by which miR-276 enhances brm expression to promote developmental synchrony and provide insight into regulation of developmental homeostasis and population sustaining that are closely related to biological synchrony. PMID:26729868

Neuromolecular Imaging Shows Temporal Synchrony Patterns between Serotonin and Movement within Neuronal Motor Circuits in the Brain

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Patricia A. Broderick

2013-06-01

Full Text Available The present discourse links the electrical and chemical properties of the brain with neurotransmitters and movement behaviors to further elucidate strategies to diagnose and treat brain disease. Neuromolecular imaging (NMI, based on electrochemical principles, is used to detect serotonin in nerve terminals (dorsal and ventral striata and somatodendrites (ventral tegmentum of reward/motor mesocorticolimbic and nigrostriatal brain circuits. Neuronal release of serotonin is detected at the same time and in the same animal, freely moving and unrestrained, while open-field behaviors are monitored via infrared photobeams. The purpose is to emphasize the unique ability of NMI and the BRODERICK PROBE® biosensors to empirically image a pattern of temporal synchrony, previously reported, for example, in Aplysia using central pattern generators (CPGs, serotonin and cerebral peptide-2. Temporal synchrony is reviewed within the context of the literature on central pattern generators, neurotransmitters and movement disorders. Specifically, temporal synchrony data are derived from studies on psychostimulant behavior with and without cocaine while at the same time and continuously, serotonin release in motor neurons within basal ganglia, is detected. The results show that temporal synchrony between the neurotransmitter, serotonin and natural movement occurs when the brain is NOT injured via, e.g., trauma, addictive drugs or psychiatric illness. In striking contrast, in the case of serotonin and cocaine-induced psychostimulant behavior, a different form of synchrony and also asynchrony can occur. Thus, the known dysfunctional movement behavior produced by cocaine may well be related to the loss of temporal synchrony, the loss of the ability to match serotonin in brain with motor activity. The empirical study of temporal synchrony patterns in humans and animals may be more relevant to the dynamics of motor circuits and movement behaviors than are studies of

Neuromolecular Imaging Shows Temporal Synchrony Patterns between Serotonin and Movement within Neuronal Motor Circuits in the Brain.

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Broderick, Patricia A

2013-06-21

The present discourse links the electrical and chemical properties of the brain with neurotransmitters and movement behaviors to further elucidate strategies to diagnose and treat brain disease. Neuromolecular imaging (NMI), based on electrochemical principles, is used to detect serotonin in nerve terminals (dorsal and ventral striata) and somatodendrites (ventral tegmentum) of reward/motor mesocorticolimbic and nigrostriatal brain circuits. Neuronal release of serotonin is detected at the same time and in the same animal, freely moving and unrestrained, while open-field behaviors are monitored via infrared photobeams. The purpose is to emphasize the unique ability of NMI and the BRODERICK PROBE® biosensors to empirically image a pattern of temporal synchrony, previously reported, for example, in Aplysia using central pattern generators (CPGs), serotonin and cerebral peptide-2. Temporal synchrony is reviewed within the context of the literature on central pattern generators, neurotransmitters and movement disorders. Specifically, temporal synchrony data are derived from studies on psychostimulant behavior with and without cocaine while at the same time and continuously, serotonin release in motor neurons within basal ganglia, is detected. The results show that temporal synchrony between the neurotransmitter, serotonin and natural movement occurs when the brain is NOT injured via, e.g., trauma, addictive drugs or psychiatric illness. In striking contrast, in the case of serotonin and cocaine-induced psychostimulant behavior, a different form of synchrony and also asynchrony can occur. Thus, the known dysfunctional movement behavior produced by cocaine may well be related to the loss of temporal synchrony, the loss of the ability to match serotonin in brain with motor activity. The empirical study of temporal synchrony patterns in humans and animals may be more relevant to the dynamics of motor circuits and movement behaviors than are studies of static parameters

Spike rate and spike timing contributions to coding taste quality information in rat periphery

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Vernon eLawhern

2011-05-01

Full Text Available There is emerging evidence that individual sensory neurons in the rodent brain rely on temporal features of the discharge pattern to code differences in taste quality information. In contrast, in-vestigations of individual sensory neurons in the periphery have focused on analysis of spike rate and mostly disregarded spike timing as a taste quality coding mechanism. The purpose of this work was to determine the contribution of spike timing to taste quality coding by rat geniculate ganglion neurons using computational methods that have been applied successfully in other sys-tems. We recorded the discharge patterns of narrowly-tuned and broadly-tuned neurons in the rat geniculate ganglion to representatives of the five basic taste qualities. We used mutual in-formation to determine significant responses and the van Rossum metric to characterize their temporal features. While our findings show that spike timing contributes a significant part of the message, spike rate contributes the largest portion of the message relayed by afferent neurons from rat fungiform taste buds to the brain. Thus, spike rate and spike timing together are more effective than spike rate alone in coding stimulus quality information to a single basic taste in the periphery for both narrowly-tuned specialist and broadly-tuned generalist neurons.

Efficient computation in networks of spiking neurons: simulations and theory

International Nuclear Information System (INIS)

Natschlaeger, T.

1999-01-01

One of the most prominent features of biological neural systems is that individual neurons communicate via short electrical pulses, the so called action potentials or spikes. In this thesis we investigate possible mechanisms which can in principle explain how complex computations in spiking neural networks (SNN) can be performed very fast, i.e. within a few 10 milliseconds. Some of these models are based on the assumption that relevant information is encoded by the timing of individual spikes (temporal coding). We will also discuss a model which is based on a population code and still is able to perform fast complex computations. In their natural environment biological neural systems have to process signals with a rich temporal structure. Hence it is an interesting question how neural systems process time series. In this context we explore possible links between biophysical characteristics of single neurons (refractory behavior, connectivity, time course of postsynaptic potentials) and synapses (unreliability, dynamics) on the one hand and possible computations on times series on the other hand. Furthermore we describe a general model of computation that exploits dynamic synapses. This model provides a general framework for understanding how neural systems process time-varying signals. (author)

Nicotine-Mediated ADP to Spike Transition: Double Spiking in Septal Neurons.

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Kodirov, Sodikdjon A; Wehrmeister, Michael; Colom, Luis

2016-04-01

The majority of neurons in lateral septum (LS) are electrically silent at resting membrane potential. Nicotine transiently excites a subset of neurons and occasionally leads to long lasting bursting activity upon longer applications. We have observed simultaneous changes in frequencies and amplitudes of spontaneous action potentials (AP) in the presence of nicotine. During the prolonged exposure, nicotine increased numbers of spikes within a burst. One of the hallmarks of nicotine effects was the occurrences of double spikes (known also as bursting). Alignment of 51 spontaneous spikes, triggered upon continuous application of nicotine, revealed that the slope of after-depolarizing potential gradually increased (1.4 vs. 3 mV/ms) and neuron fired the second AP, termed as double spiking. A transition from a single AP to double spikes increased the amplitude of after-hyperpolarizing potential. The amplitude of the second (premature) AP was smaller compared to the first one, and this correlation persisted in regard to their duration (half-width). A similar bursting activity in the presence of nicotine, to our knowledge, has not been reported previously in the septal structure in general and in LS in particular.

Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting.

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Paraskevopoulou, Sivylla E; Wu, Di; Eftekhar, Amir; Constandinou, Timothy G

2014-09-30

This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

Supervised learning in spiking neural networks with FORCE training.

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Nicola, Wilten; Clopath, Claudia

2017-12-20

Populations of neurons display an extraordinary diversity in the behaviors they affect and display. Machine learning techniques have recently emerged that allow us to create networks of model neurons that display behaviors of similar complexity. Here we demonstrate the direct applicability of one such technique, the FORCE method, to spiking neural networks. We train these networks to mimic dynamical systems, classify inputs, and store discrete sequences that correspond to the notes of a song. Finally, we use FORCE training to create two biologically motivated model circuits. One is inspired by the zebra finch and successfully reproduces songbird singing. The second network is motivated by the hippocampus and is trained to store and replay a movie scene. FORCE trained networks reproduce behaviors comparable in complexity to their inspired circuits and yield information not easily obtainable with other techniques, such as behavioral responses to pharmacological manipulations and spike timing statistics.

Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks.

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Gardner, Brian; Sporea, Ioana; Grüning, André

2015-12-01

Information encoding in the nervous system is supported through the precise spike timings of neurons; however, an understanding of the underlying processes by which such representations are formed in the first place remains an open question. Here we examine how multilayered networks of spiking neurons can learn to encode for input patterns using a fully temporal coding scheme. To this end, we introduce a new supervised learning rule, MultilayerSpiker, that can train spiking networks containing hidden layer neurons to perform transformations between spatiotemporal input and output spike patterns. The performance of the proposed learning rule is demonstrated in terms of the number of pattern mappings it can learn, the complexity of network structures it can be used on, and its classification accuracy when using multispike-based encodings. In particular, the learning rule displays robustness against input noise and can generalize well on an example data set. Our approach contributes to both a systematic understanding of how computations might take place in the nervous system and a learning rule that displays strong technical capability.

Collective efficacy in sports and physical activities: perceived emotional synchrony and shared flow

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Larraitz Nerea Zumeta

2016-01-01

Full Text Available This cross-sectional study analyzes the relationship between collective efficacy and two psychosocial processes involved in collective sport-physical activities. It argues that in-group identification and fusion with the group will affect collective efficacy (CE. A sample of 276 university students answered different scales regarding their participation in collective physical and sport activities. Multiple-mediation analyses showed that shared flow and perceived emotional synchrony mediate the relationship between in-group identification and CE, whereas the relationship between identity fusion and CE was only mediated by perceived emotional synchrony. Results suggest that both psychosocial processes explain the positive effects of in-group identification and identity fusion with the group in collective efficacy. Especially, the role of perceived emotional synchrony in explaining the positive effects of participation in collective sport-physical activities is underlined. In sum, this study remarks the utility of collective actions and social identities to explain the psychosocial processes related to collective efficacy in physical and sports activities. Finally, practical implications are discussed.

Prior Knowledge Facilitates Mutual Gaze Convergence and Head Nodding Synchrony in Face-to-face Communication.

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Thepsoonthorn, C; Yokozuka, T; Miura, S; Ogawa, K; Miyake, Y

2016-12-02

As prior knowledge is claimed to be an essential key to achieve effective education, we are interested in exploring whether prior knowledge enhances communication effectiveness. To demonstrate the effects of prior knowledge, mutual gaze convergence and head nodding synchrony are observed as indicators of communication effectiveness. We conducted an experiment on lecture task between lecturer and student under 2 conditions: prior knowledge and non-prior knowledge. The students in prior knowledge condition were provided the basic information about the lecture content and were assessed their understanding by the experimenter before starting the lecture while the students in non-prior knowledge had none. The result shows that the interaction in prior knowledge condition establishes significantly higher mutual gaze convergence (t(15.03) = 6.72, p < 0.0001; α = 0.05, n = 20) and head nodding synchrony (t(16.67) = 1.83, p = 0.04; α = 0.05, n = 19) compared to non-prior knowledge condition. This study reveals that prior knowledge facilitates mutual gaze convergence and head nodding synchrony. Furthermore, the interaction with and without prior knowledge can be evaluated by measuring or observing mutual gaze convergence and head nodding synchrony.

Impact of adaptation currents on synchronization of coupled exponential integrate-and-fire neurons.

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Josef Ladenbauer

Full Text Available The ability of spiking neurons to synchronize their activity in a network depends on the response behavior of these neurons as quantified by the phase response curve (PRC and on coupling properties. The PRC characterizes the effects of transient inputs on spike timing and can be measured experimentally. Here we use the adaptive exponential integrate-and-fire (aEIF neuron model to determine how subthreshold and spike-triggered slow adaptation currents shape the PRC. Based on that, we predict how synchrony and phase locked states of coupled neurons change in presence of synaptic delays and unequal coupling strengths. We find that increased subthreshold adaptation currents cause a transition of the PRC from only phase advances to phase advances and delays in response to excitatory perturbations. Increased spike-triggered adaptation currents on the other hand predominantly skew the PRC to the right. Both adaptation induced changes of the PRC are modulated by spike frequency, being more prominent at lower frequencies. Applying phase reduction theory, we show that subthreshold adaptation stabilizes synchrony for pairs of coupled excitatory neurons, while spike-triggered adaptation causes locking with a small phase difference, as long as synaptic heterogeneities are negligible. For inhibitory pairs synchrony is stable and robust against conduction delays, and adaptation can mediate bistability of in-phase and anti-phase locking. We further demonstrate that stable synchrony and bistable in/anti-phase locking of pairs carry over to synchronization and clustering of larger networks. The effects of adaptation in aEIF neurons on PRCs and network dynamics qualitatively reflect those of biophysical adaptation currents in detailed Hodgkin-Huxley-based neurons, which underscores the utility of the aEIF model for investigating the dynamical behavior of networks. Our results suggest neuronal spike frequency adaptation as a mechanism synchronizing low frequency

The role of temporal synchrony as a binding cue for visual persistence in early visual areas: an fMRI study.

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Wong, Yvonne J; Aldcroft, Adrian J; Large, Mary-Ellen; Culham, Jody C; Vilis, Tutis

2009-12-01

We examined the role of temporal synchrony-the simultaneous appearance of visual features-in the perceptual and neural processes underlying object persistence. When a binding cue (such as color or motion) momentarily exposes an object from a background of similar elements, viewers remain aware of the object for several seconds before it perceptually fades into the background, a phenomenon known as object persistence. We showed that persistence from temporal stimulus synchrony, like that arising from motion and color, is associated with activation in the lateral occipital (LO) area, as measured by functional magnetic resonance imaging. We also compared the distribution of occipital cortex activity related to persistence to that of iconic visual memory. Although activation related to iconic memory was largely confined to LO, activation related to object persistence was present across V1 to LO, peaking in V3 and V4, regardless of the binding cue (temporal synchrony, motion, or color). Although persistence from motion cues was not associated with higher activation in the MT+ motion complex, persistence from color cues was associated with increased activation in V4. Taken together, these results demonstrate that although persistence is a form of visual memory, it relies on neural mechanisms different from those of iconic memory. That is, persistence not only activates LO in a cue-independent manner, it also recruits visual areas that may be necessary to maintain binding between object elements.

Naturalistic music and dance: Cortical phase synchrony in musicians and dancers.

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Poikonen, Hanna; Toiviainen, Petri; Tervaniemi, Mari

2018-01-01

Expertise in music has been investigated for decades and the results have been applied not only in composition, performance and music education, but also in understanding brain plasticity in a larger context. Several studies have revealed a strong connection between auditory and motor processes and listening to and performing music, and music imagination. Recently, as a logical next step in music and movement, the cognitive and affective neurosciences have been directed towards expertise in dance. To understand the versatile and overlapping processes during artistic stimuli, such as music and dance, it is necessary to study them with continuous naturalistic stimuli. Thus, we used long excerpts from the contemporary dance piece Carmen presented with and without music to professional dancers, musicians, and laymen in an EEG laboratory. We were interested in the cortical phase synchrony within each participant group over several frequency bands during uni- and multimodal processing. Dancers had strengthened theta and gamma synchrony during music relative to silence and silent dance, whereas the presence of music decreased systematically the alpha and beta synchrony in musicians. Laymen were the only group of participants with significant results related to dance. Future studies are required to understand whether these results are related to some other factor (such as familiarity to the stimuli), or if our results reveal a new point of view to dance observation and expertise.

Naturalistic music and dance: Cortical phase synchrony in musicians and dancers

Science.gov (United States)

Toiviainen, Petri; Tervaniemi, Mari

2018-01-01

Expertise in music has been investigated for decades and the results have been applied not only in composition, performance and music education, but also in understanding brain plasticity in a larger context. Several studies have revealed a strong connection between auditory and motor processes and listening to and performing music, and music imagination. Recently, as a logical next step in music and movement, the cognitive and affective neurosciences have been directed towards expertise in dance. To understand the versatile and overlapping processes during artistic stimuli, such as music and dance, it is necessary to study them with continuous naturalistic stimuli. Thus, we used long excerpts from the contemporary dance piece Carmen presented with and without music to professional dancers, musicians, and laymen in an EEG laboratory. We were interested in the cortical phase synchrony within each participant group over several frequency bands during uni- and multimodal processing. Dancers had strengthened theta and gamma synchrony during music relative to silence and silent dance, whereas the presence of music decreased systematically the alpha and beta synchrony in musicians. Laymen were the only group of participants with significant results related to dance. Future studies are required to understand whether these results are related to some other factor (such as familiarity to the stimuli), or if our results reveal a new point of view to dance observation and expertise. PMID:29672597

Mechanisms of social synchrony between circadian activity rhythms in cohabiting marmosets.

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Bessa, Zoélia Camila Moura; Melo, Paula Rocha De; Gonçalves, Bruno S B; Azevedo, Carolina V M De

2018-01-26

In marmosets, social synchrony between circadian profiles of activity is stronger in animals that cohabit in a family. The activity of three breeding pairs was recorded by actiwatches to investigate the mechanisms involved in the synchrony between the circadian activity profiles during cohabitation in marmoset reproductive pairs. The dyads were submitted to LD 12:12 (21 days) and LL: 1) cohabitation (24 days), 2) removal of the cage mate (20 days), 3) reintroduction of the mate into the cage of the 1 st situation (30 days) and 4) removal of the cage mate (7 days). Next, they were rejoined and maintained in LD 12:12 (11 days). In conditions involving cohabitation of pair, the general and maximum correlation indexes between circadian profiles were higher in cage mates compared to animals of the same or different sex with which they maintain only acoustic and olfactive contact. This strong synchrony between rhythms was accompanied by a stable phase relationship at the activity onset and offset, with identical circadian periods between mates. When the pairs were separated, there was a break in stability in the phase relationships between activity profiles with different circadian periods and a greater phase angle difference between rhythms of cage mates. During separation, two females and one male progressively anticipated the activity onset and offset in a phase similar to that in previous conditions, expressing entrainment to the mate. During the first reintroduction, two pairs exhibited signs of masking in rhythm. Although modulation in the rhythm of some animals has been observed through acoustic cues from animals outside the colony, we suggest that cohabitation favors strong synchrony between the circadian activity profiles of marmoset reproductive pairs involving synchronization by entrainment and masking. Further studies in the absence of external social cues are necessary to clarify the role of these mechanisms on social synchronization in marmosets.

Epileptic encephalopathy with continuous spike-waves during sleep: the need for transition from childhood to adulthood medical care appears to be related to etiology.

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de Saint-Martin, Anne; Rudolf, Gabrielle; Seegmuller, Caroline; Valenti-Hirsch, Maria Paola; Hirsch, Edouard

2014-08-01

Epileptic encephalopathy with continuous diffuse spike-waves during slow-wave sleep (ECSWS) presents clinically with infrequent nocturnal focal seizures, atypical absences related to secondary bilateral synchrony, negative myoclonia, and atonic and rare generalized tonic-clonic seizures. The unique electroencephalography (EEG) pattern found in ECSWS consists of continuous, diffuse, bilateral spike-waves during slow-wave sleep. Despite the eventual disappearance of clinical seizures and EEG abnormalities by adolescence, the prognosis is guarded in most cases because of neuropsychological and behavioral deficits. ECSWS has a heterogeneous etiology (genetic, structural, and unknown). Because epilepsy and electroencephalography (EEG) abnormalities in epileptic encephalopathy with continuous diffuse spike-waves during slow-wave sleep (ECSWS) are self-limited and age related, the need for ongoing medical care and transition to adult care might be questioned. For adolescents in whom etiology remains unknown (possibly genetic) and who experience the disappearance of seizures and EEG abnormalities, there is rarely need for long-term neurologic follow-up, because often a relatively normal cognitive and social evolution follows. However, the majority of patients with structural and possibly "genetic syndromic" etiologies will have persistent cognitive deficits and will need suitable socioeducative care. Therefore, the transition process in ECSWS will depend mainly on etiology and its related features (epileptic active phase duration, and cognitive and behavioral evolution) and revolve around neuropsychological and social support rather than medical and pharmacologic follow-up. Wiley Periodicals, Inc. © 2014 International League Against Epilepsy.

Predicting Spike Occurrence and Neuronal Responsiveness from LFPs in Primary Somatosensory Cortex

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Storchi, Riccardo; Zippo, Antonio G.; Caramenti, Gian Carlo; Valente, Maurizio; Biella, Gabriele E. M.

2012-01-01

Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role. PMID:22586452

Local Variation of Hashtag Spike Trains and Popularity in Twitter

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Sanlı, Ceyda; Lambiotte, Renaud

2015-01-01

We draw a parallel between hashtag time series and neuron spike trains. In each case, the process presents complex dynamic patterns including temporal correlations, burstiness, and all other types of nonstationarity. We propose the adoption of the so-called local variation in order to uncover salient dynamical properties, while properly detrending for the time-dependent features of a signal. The methodology is tested on both real and randomized hashtag spike trains, and identifies that popular hashtags present regular and so less bursty behavior, suggesting its potential use for predicting online popularity in social media. PMID:26161650

Effects of flowering phenology and synchrony on the reproductive success of a long-flowering shrub

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Rodríguez-Pérez, Javier; Traveset, Anna

2016-01-01

Flowering phenology and synchrony with biotic and abiotic resources are crucial traits determining the reproductive success in insect-pollinated plants. In seasonal climates, plants flowering for long periods should assure reproductive success when resources are more predictable. In this work, we evaluated the relationship between flowering phenology and synchrony and reproductive success in Hypericum balearicum, a shrub flowering all year round but mainly during spring and summer. We studied two contrasting localities (differing mostly in rainfall) during 3 years, and at different biological scales spanning from localities to individual flowers and fruits. We first monitored (monthly) flowering phenology and reproductive success (fruit and seed set) of plants, and assessed whether in the locality with higher rainfall plants had longer flowering phenology and synchrony and relatively higher reproductive success within or outside the flowering peak. Secondly, we censused pollinators on H. balearicum individuals and measured reproductive success along the flowering peak of each locality to test for an association between (i) richness and abundance of pollinators and (ii) fruit and seed set, and seed weight. We found that most flowers (∼90 %) and the highest fruit set (∼70 %) were produced during the flowering peak of each locality. Contrary to expectations, plants in the locality with lower rainfall showed more relaxed flowering phenology and synchrony and set more fruits outside the flowering peak. During the flowering peak of each locality, the reproductive success of early-flowering individuals depended on a combination of both pollinator richness and abundance and rainfall; by contrast, reproductive success of late-flowering individuals was most dependent on rainfall. Plant species flowering for long periods in seasonal climates, thus, appear to be ideal organisms to understand how flowering phenology and synchrony match with biotic and abiotic resources, and

Detection of bursts in neuronal spike trains by the mean inter-spike interval method

Institute of Scientific and Technical Information of China (English)

Lin Chen; Yong Deng; Weihua Luo; Zhen Wang; Shaoqun Zeng

2009-01-01

Bursts are electrical spikes firing with a high frequency, which are the most important property in synaptic plasticity and information processing in the central nervous system. However, bursts are difficult to identify because bursting activities or patterns vary with phys-iological conditions or external stimuli. In this paper, a simple method automatically to detect bursts in spike trains is described. This method auto-adaptively sets a parameter (mean inter-spike interval) according to intrinsic properties of the detected burst spike trains, without any arbitrary choices or any operator judgrnent. When the mean value of several successive inter-spike intervals is not larger than the parameter, a burst is identified. By this method, bursts can be automatically extracted from different bursting patterns of cultured neurons on multi-electrode arrays, as accurately as by visual inspection. Furthermore, significant changes of burst variables caused by electrical stimulus have been found in spontaneous activity of neuronal network. These suggest that the mean inter-spike interval method is robust for detecting changes in burst patterns and characteristics induced by environmental alterations.

Information transmission with spiking Bayesian neurons

International Nuclear Information System (INIS)

Lochmann, Timm; Deneve, Sophie

2008-01-01

Spike trains of cortical neurons resulting from repeatedpresentations of a stimulus are variable and exhibit Poisson-like statistics. Many models of neural coding therefore assumed that sensory information is contained in instantaneous firing rates, not spike times. Here, we ask how much information about time-varying stimuli can be transmitted by spiking neurons with such input and output variability. In particular, does this variability imply spike generation to be intrinsically stochastic? We consider a model neuron that estimates optimally the current state of a time-varying binary variable (e.g. presence of a stimulus) by integrating incoming spikes. The unit signals its current estimate to other units with spikes whenever the estimate increased by a fixed amount. As shown previously, this computation results in integrate and fire dynamics with Poisson-like output spike trains. This output variability is entirely due to the stochastic input rather than noisy spike generation. As a result such a deterministic neuron can transmit most of the information about the time varying stimulus. This contrasts with a standard model of sensory neurons, the linear-nonlinear Poisson (LNP) model which assumes that most variability in output spike trains is due to stochastic spike generation. Although it yields the same firing statistics, we found that such noisy firing results in the loss of most information. Finally, we use this framework to compare potential effects of top-down attention versus bottom-up saliency on information transfer with spiking neurons

Kinetics of fast short-term depression are matched to spike train statistics to reduce noise.

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Khanbabaie, Reza; Nesse, William H; Longtin, Andre; Maler, Leonard

2010-06-01

Short-term depression (STD) is observed at many synapses of the CNS and is important for diverse computations. We have discovered a form of fast STD (FSTD) in the synaptic responses of pyramidal cells evoked by stimulation of their electrosensory afferent fibers (P-units). The dynamics of the FSTD are matched to the mean and variance of natural P-unit discharge. FSTD exhibits switch-like behavior in that it is immediately activated with stimulus intervals near the mean interspike interval (ISI) of P-units (approximately 5 ms) and recovers immediately after stimulation with the slightly longer intervals (>7.5 ms) that also occur during P-unit natural and evoked discharge patterns. Remarkably, the magnitude of evoked excitatory postsynaptic potentials appear to depend only on the duration of the previous ISI. Our theoretical analysis suggests that FSTD can serve as a mechanism for noise reduction. Because the kinetics of depression are as fast as the natural spike statistics, this role is distinct from previously ascribed functional roles of STD in gain modulation, synchrony detection or as a temporal filter.

In Sync and in Control: A Meta-Analysis of Parent-Child Positive Behavioral Synchrony and Youth Self-Regulation.

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Davis, Molly; Bilms, Joanie; Suveg, Cynthia

2017-12-01

A growing body of research has highlighted the connection between parent-child positive behavioral synchrony and youth self-regulation; however, this association has yet to be the focus of a meta-analytic review. Therefore, the present meta-analysis aimed to estimate the magnitude of the relation between parent-child positive behavioral synchrony and youth self-regulation and to identify moderator variables that can explain the variability in the degree of this association across the extant literature. A thorough literature search of two major databases, in addition to scanning the reference sections of relevant articles, yielded a total of 10 peer-reviewed articles (24 effect sizes, 658 children) that were eligible for inclusion in the current meta-analysis. Results from the overall mean effect size calculation using a random-effects model indicated that parent-child positive behavioral synchrony was significantly, positively correlated with youth self-regulation and the effect size was medium. Children's ages at the time of synchrony and self-regulation measurements, as well as parent gender, served as significant moderator variables. Findings from the present meta-analysis can help to refine existing theoretical models on the role of the parent-child relationship in youth adjustment. Prevention and intervention efforts may benefit from an increased emphasis on building parent-child positive behavioral synchrony to promote youth self-regulation and thus children's overall well-being. © 2016 Family Process Institute.

Barbed micro-spikes for micro-scale biopsy

Science.gov (United States)

Byun, Sangwon; Lim, Jung-Min; Paik, Seung-Joon; Lee, Ahra; Koo, Kyo-in; Park, Sunkil; Park, Jaehong; Choi, Byoung-Doo; Seo, Jong Mo; Kim, Kyung-ah; Chung, Hum; Song, Si Young; Jeon, Doyoung; Cho, Dongil

2005-06-01

Single-crystal silicon planar micro-spikes with protruding barbs are developed for micro-scale biopsy and the feasibility of using the micro-spike as a micro-scale biopsy tool is evaluated for the first time. The fabrication process utilizes a deep silicon etch to define the micro-spike outline, resulting in protruding barbs of various shapes. Shanks of the fabricated micro-spikes are 3 mm long, 100 µm thick and 250 µm wide. Barbs protruding from micro-spike shanks facilitate the biopsy procedure by tearing off and retaining samples from target tissues. Micro-spikes with barbs successfully extracted tissue samples from the small intestines of the anesthetized pig, whereas micro-spikes without barbs failed to obtain a biopsy sample. Parylene coating can be applied to improve the biocompatibility of the micro-spike without deteriorating the biopsy function of the micro-spike. In addition, to show that the biopsy with the micro-spike can be applied to tissue analysis, samples obtained by micro-spikes were examined using immunofluorescent staining. Nuclei and F-actin of cells which are extracted by the micro-spike from a transwell were clearly visualized by immunofluorescent staining.

Altered gene synchrony suggests a combined hormone-mediated dysregulated state in major depression.

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Chris Gaiteri

2010-04-01

Full Text Available Coordinated gene transcript levels across tissues (denoted "gene synchrony" reflect converging influences of genetic, biochemical and environmental factors; hence they are informative of the biological state of an individual. So could brain gene synchrony also integrate the multiple factors engaged in neuropsychiatric disorders and reveal underlying pathologies? Using bootstrapped Pearson correlation for transcript levels for the same genes across distinct brain areas, we report robust gene transcript synchrony between the amygdala and cingulate cortex in the human postmortem brain of normal control subjects (n = 14; Control/Permutated data, p<0.000001. Coordinated expression was confirmed across distinct prefrontal cortex areas in a separate cohort (n = 19 subjects and affected different gene sets, potentially reflecting regional network- and function-dependent transcriptional programs. Genewise regional transcript coordination was independent of age-related changes and array technical parameters. Robust shifts in amygdala-cingulate gene synchrony were observed in subjects with major depressive disorder (MDD, denoted here "depression" (n = 14; MDD/Permutated data, p<0.000001, significantly affecting between 100 and 250 individual genes (10-30% false discovery rate. Biological networks and signal transduction pathways corresponding to the identified gene set suggested putative dysregulated functions for several hormone-type factors previously implicated in depression (insulin, interleukin-1, thyroid hormone, estradiol and glucocorticoids; p<0.01 for association with depression-related networks. In summary, we showed that coordinated gene expression across brain areas may represent a novel molecular probe for brain structure/function that is sensitive to disease condition, suggesting the presence of a distinct and integrated hormone-mediated corticolimbic homeostatic, although maladaptive and pathological, state in major depression.

The Effect of Delayed Visual Feedback on Synchrony Perception in a Tapping Task

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Mirjam Keetels

2011-10-01

Full Text Available Sensory events following a motor action are, within limits, interpreted as a causal consequence of those actions. For example, the clapping of the hands is initiated by the motor system, but subsequently visual, auditory, and tactile information is provided and processed. In the present study we examine the effect of temporal disturbances in this chain of motor-sensory events. Participants are instructed to tap a surface with their finger in synchrony with a chain of 20 sound clicks (ISI 750 ms. We examined the effect of additional visual information on this ‘tap-sound’-synchronization task. During tapping, subjects will see a video of their own tapping hand on a screen in front of them. The video can either be in synchrony with the tap (real-time recording, or can be slightly delayed (∼40–160 ms. In a control condition, no video is provided. We explore whether ‘tap-sound’ synchrony will be shifted as a function of the delayed visual feedback. Results will provide fundamental insights into how the brain preserves a causal interpretation of motor actions and their sensory consequences.

A Case of Hand Waving: Action Synchrony and Person Perception

Science.gov (United States)

Macrae, C. Neil; Duffy, Oonagh K.; Miles, Lynden K.; Lawrence, Julie

2008-01-01

While previous research has demonstrated that people's movements can become coordinated during social interaction, little is known about the cognitive consequences of behavioral synchrony. Given intimate links between the systems that regulate perception and action, we hypothesized that the synchronization of movements during a dyadic interaction…

Bayesian population decoding of spiking neurons.

Science.gov (United States)

Gerwinn, Sebastian; Macke, Jakob; Bethge, Matthias

2009-01-01

The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. However, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here, we study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise. We derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus we thus obtain an estimate of the uncertainty as well. Furthermore, we derive a 'spike-by-spike' online decoding scheme that recursively updates the posterior with the arrival of each new spike. We use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as well as neural populations.

Bayesian population decoding of spiking neurons

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Sebastian Gerwinn

2009-10-01

Full Text Available The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. However, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here, we study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise. We derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus we thus obtain an estimate of the uncertainty as well. Furthermore, we derive a `spike-by-spike' online decoding scheme that recursively updates the posterior with the arrival of each new spike. We use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as well as neural populations.

Genetic influences on phase synchrony of brain oscillations supporting response inhibition.

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Müller, Viktor; Anokhin, Andrey P; Lindenberger, Ulman

2017-05-01

Phase synchronization of neuronal oscillations is a fundamental mechanism underlying cognitive processing and behavior, including context-dependent response production and inhibition. Abnormalities in neural synchrony can lead to abnormal information processing and contribute to cognitive and behavioral deficits in neuropsychiatric disorders. However, little is known about genetic and environmental contributions to individual differences in cortical oscillatory dynamics underlying response inhibition. This study examined heritability of event-related phase synchronization of brain oscillations in 302 young female twins including 94 MZ and 57 DZ pairs performing a cued Go/No-Go version of the Continuous Performance Test (CPT). We used the Phase Locking Index (PLI) to assess inter-trial phase clustering (synchrony) in several frequency bands in two time intervals after stimulus onset (0-300 and 301-600ms). Response inhibition (i.e., successful response suppression in No-Go trials) was characterized by a transient increase in phase synchronization of delta- and theta-band oscillations in the fronto-central midline region. Genetic analysis showed significant heritability of the phase locking measures related to response inhibition, with 30 to 49% of inter-individual variability being accounted for by genetic factors. This is the first study providing evidence for heritability of task-related neural synchrony. The present results suggest that PLI can serve as an indicator of genetically transmitted individual differences in neural substrates of response inhibition. Copyright © 2016 Elsevier B.V. All rights reserved.

Maternal depression and anxiety, social synchrony, and infant regulation of negative and positive emotions.

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Granat, Adi; Gadassi, Reuma; Gilboa-Schechtman, Eva; Feldman, Ruth

2017-02-01

Maternal postpartum depression (PPD) exerts long-term negative effects on infants; yet the mechanisms by which PPD disrupts emotional development are not fully clear. Utilizing an extreme-case design, 971 women reported symptoms of depression and anxiety following childbirth and 215 high and low on depressive symptomatology reported again at 6 months. Of these, mothers diagnosed with major depressive disorder (n = 22), anxiety disorders (n = 19), and controls (n = 59) were visited at 9 months. Mother-infant interaction was microcoded for maternal and infant's social behavior and synchrony. Infant negative and positive emotional expression and self-regulation were tested in 4 emotion-eliciting paradigms: anger with mother, anger with stranger, joy with mother, and joy with stranger. Infants of depressed mothers displayed less social gaze and more gaze aversion. Gaze and touch synchrony were lowest for depressed mothers, highest for anxious mothers, and midlevel among controls. Infants of control and anxious mothers expressed less negative affect with mother compared with stranger; however, maternal presence failed to buffer negative affect in the depressed group. Maternal depression chronicity predicted increased self-regulatory behavior during joy episodes, and touch synchrony moderated the effects of PPD on infant self-regulation. Findings describe subtle microlevel processes by which maternal depression across the postpartum year disrupts the development of infant emotion regulation and suggest that diminished social synchrony, low differentiation of attachment and nonattachment contexts, and increased self-regulation during positive moments may chart pathways for the cross-generational transfer of emotional maladjustment from depressed mothers to their infants. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Reduction in Cortical Gamma Synchrony during Depolarized State of Slow Wave Activity in Mice

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EUNJIN eHWANG

2013-12-01

Full Text Available EEG gamma band oscillations have been proposed to account for the neural synchronization crucial for perceptual integration. While increased gamma power and synchronization is generally observed during cognitive tasks performed during wake, several studies have additionally reported increased gamma power during sleep or anesthesia, raising questions about the characteristics of gamma oscillation during impaired consciousness and its role in conscious processing. Phase-amplitude modulation has been observed between slow wave activity (SWA, 0.5–4 Hz and gamma oscillations during ketamine/xylazine anesthesia or sleep, showing increased gamma activity corresponding to the depolarized (ON state of SWA. Here we divided gamma activity into its ON and OFF (hyperpolarized state components based on the phase of SWA induced by ketamine/xylazine anesthesia and compared their power and synchrony with wake state levels in mice. We further investigated the state-dependent changes in both gamma power and synchrony across primary motor and primary somatosensory cortical regions and their interconnected thalamic regions throughout anesthesia and recovery. As observed previously, gamma power was as high as during wake specifically during the ON state of SWA. However, the synchrony of this gamma activity between somatosensory-motor cortical regions was significantly reduced compared to the baseline wake state. In addition, the somatosensory-motor cortical synchrony of gamma oscillations was reduced and restored in an anesthetic state-dependent manner, reflecting the changing depth of anesthesia. Our results provide evidence that during anesthesia changes in long-range information integration between cortical regions might be more critical for changes in consciousness than changes in local gamma oscillatory power.

Complex transitions between spike, burst or chaos synchronization states in coupled neurons with coexisting bursting patterns

International Nuclear Information System (INIS)

Gu Hua-Guang; Chen Sheng-Gen; Li Yu-Ye

2015-01-01

We investigated the synchronization dynamics of a coupled neuronal system composed of two identical Chay model neurons. The Chay model showed coexisting period-1 and period-2 bursting patterns as a parameter and initial values are varied. We simulated multiple periodic and chaotic bursting patterns with non-(NS), burst phase (BS), spike phase (SS), complete (CS), and lag synchronization states. When the coexisting behavior is near period-2 bursting, the transitions of synchronization states of the coupled system follows very complex transitions that begins with transitions between BS and SS, moves to transitions between CS and SS, and to CS. Most initial values lead to the CS state of period-2 bursting while only a few lead to the CS state of period-1 bursting. When the coexisting behavior is near period-1 bursting, the transitions begin with NS, move to transitions between SS and BS, to transitions between SS and CS, and then to CS. Most initial values lead to the CS state of period-1 bursting but a few lead to the CS state of period-2 bursting. The BS was identified as chaos synchronization. The patterns for NS and transitions between BS and SS are insensitive to initial values. The patterns for transitions between CS and SS and the CS state are sensitive to them. The number of spikes per burst of non-CS bursting increases with increasing coupling strength. These results not only reveal the initial value- and parameter-dependent synchronization transitions of coupled systems with coexisting behaviors, but also facilitate interpretation of various bursting patterns and synchronization transitions generated in the nervous system with weak coupling strength. (paper)

Patient-ventilator trigger dys-synchrony: a common phenomenon with important implications

OpenAIRE

MacIntyre, Neil

2013-01-01

Patient-ventilator trigger dys-synchronies are common with the use of assisted forms of mechanical ventilatory support, including non-invasive mechanical ventilatory support (NIV). Future system designs need to address this in order to improve the effectiveness of NIV.

ORGANIC MATTER AND CRUDE PROTEIN DEGRADATION SYNCHRONY IN DIETS SELECTED BY RANGE GOATS.

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Rafael Ramírez Orduña

2010-09-01

Full Text Available The study was carried out with the aim to asses the synchrony of organic matter and crude protein degradation in the rumen of diets selected by range goats through two years. Five esophageal cannulated adult male goats were used to collect extrusa samples during summer (August 9–13 and autumn (November 29 –December 3 of 2006, winter (February 20 – 24, spring (April 29 –May 5, summer (September 10–15 and autumn (December 4–8 of 2007 and winter (February 20 – 25 and spring (May 9 –13 of 2008. Extrusa samples were subjected to chemical analysis to determine organic matter (OM, crude protein (CP in situ and in vitro true digestibility of dry matter. OM and CP intake were estimated by total fecal collection. Effective extent of degradation of the OM and CP was calculated hourly and total 24 hours. From the hourly quantity of OM and CP degraded, a synchrony index of CP to OM was calculated, and from the total 24 hours degradation, degraded organic matter intake and crude protein intake were also estimated. Sampling date was the main effect that determined the variation of diet OM and CP degradation parameters. Degraded crude protein intake as a proportion of degraded OM was affected by sampling date and was correlated to rainfall. During winter of the first year degraded crude protein intake was below the requirements for maintenance or to promote growth for range goats weighing 40 kg. Even though, synchrony index between OM and CP degradation was affected by sampling date goats maintained a high synchrony index throughout the years.

Functioning within a relationship : Mother-infant synchrony and infant sleep

NARCIS (Netherlands)

de Graag, Jolien A.; Cox, Ralf F. A.; Hasselman, Fred; Jansen, Jarno; de Weerth, Carolina

The aim of this study was to investigate the coupling of the biological system of infant sleep and the social system of mother-infant synchrony. Before birth and shortly after birth the systems appear to be connected, but it is unclear whether this remains the case over time. This study therefore

Slowing and Loss of Complexity in Alzheimer's EEG: Two Sides of the Same Coin?

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Justin Dauwels

2011-01-01

Full Text Available Medical studies have shown that EEG of Alzheimer's disease (AD patients is “slower” (i.e., contains more low-frequency power and is less complex compared to age-matched healthy subjects. The relation between those two phenomena has not yet been studied, and they are often silently assumed to be independent. In this paper, it is shown that both phenomena are strongly related. Strong correlation between slowing and loss of complexity is observed in two independent EEG datasets: (1 EEG of predementia patients (a.k.a. Mild Cognitive Impairment; MCI and control subjects; (2 EEG of mild AD patients and control subjects. The two data sets are from different patients, different hospitals and obtained through different recording systems. The paper also investigates the potential of EEG slowing and loss of EEG complexity as indicators of AD onset. In particular, relative power and complexity measures are used as features to classify the MCI and MiAD patients versus age-matched control subjects. When combined with two synchrony measures (Granger causality and stochastic event synchrony, classification rates of 83% (MCI and 98% (MiAD are obtained. By including the compression ratios as features, slightly better classification rates are obtained than with relative power and synchrony measures alone.

Century-scale Changes in Environmental Synchrony and Variability and their Effects on Populations of Birds and Reproduction of Trees

Science.gov (United States)

Koenig, W.

2016-12-01

The ecological impacts of modern global climate change are detectable in a wide variety of phenomena ranging from shifts in species ranges to changes in community composition and human disease dynamics. Thus far, however, little attention has been given to temporal changes in environmental spatial synchrony-the coincident change in abundance or value across the landscape-or environmental variability, despite the importance of these factors as drivers of population rescue and extinction and reproductive dynamics of both animal and plant populations. We quantified spatial synchrony of widespread North American wintering birds species using Audubon Christmas Bird Counts over the past 50 years and seed set variability (mast fruiting) among trees over the past century and found that both spatial synchrony of the birds and seed set variability have significantly increased over these time periods. The first of these results was mirrored by significant increases in spatial synchrony of mean maximum air temperature across North America, primarily during the summer, while the second is consistent with the hypothesis that climate change is resulting in greater seed set variability. These findings suggest the potential for temporal changes in envioronmental synchrony and variability to be affecting a wide range of ecological phenomena by influencing the probability of population rescue and extinction and by affecting ecosystem processes that rely on the resource pulses provided by mast fruiting plants.

REGIONAL DYNAMICS OF WETLAND-BREEDING FROGS AND TOADS: TURNOVER AND SYNCHRONY

Science.gov (United States)

We used data from a statewide frog monitoring network to investigate population turnover and synchrony in eight wetland-breeding species. We found that subpopulations at many sites turn over frequently, with breeding choruses absent or undetectable in most years. Frequencies of d...

Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns.

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Qiang Yu

Full Text Available A new learning rule (Precise-Spike-Driven (PSD Synaptic Plasticity is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns.

Science.gov (United States)

Yu, Qiang; Tang, Huajin; Tan, Kay Chen; Li, Haizhou

2013-01-01

A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

Mother-infant circadian rhythm: development of individual patterns and dyadic synchrony.

Science.gov (United States)

Thomas, Karen A; Burr, Robert L; Spieker, Susan; Lee, Jungeun; Chen, Jessica

2014-12-01

Mutual circadian rhythm is an early and essential component in the development of maternal-infant physiological synchrony. The aim of this to examine the longitudinal pattern of maternal-infant circadian rhythm and rhythm synchrony as measured by rhythm parameters. In-home dyadic actigraphy monitoring at infant age 4, 8, and 12 weeks. Forty-three healthy mother-infant pairs. Circadian parameters derived from cosinor and non-parametric analysis including mesor, magnitude, acrophase, L5 and M10 midpoints (midpoint of lowest 5 and highest 10h of activity), amplitude, interdaily stability (IS), and intradaily variability (IV). Mothers experienced early disruption of circadian rhythm, with re-establishment of rhythm over time. Significant time effects were noted in increasing maternal magnitude, amplitude, and IS and decreasing IV (pcircadian pattern with significant time effects for increasing mesor, magnitude, amplitude, L5, IS, and IV (pcircadian rhythm. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Effects of Spike Anticipation on the Spiking Dynamics of Neural Networks

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Daniel ede Santos-Sierra

2015-11-01

Full Text Available Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc. Though the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions cite{Pyragas}, where the slave neuron is able to anticipate in time the behaviour of the master one. In this paper we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI, one of the main features of the neural response associated to the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh-Rose neurons.

Effects of Spike Anticipation on the Spiking Dynamics of Neural Networks.

Science.gov (United States)

de Santos-Sierra, Daniel; Sanchez-Jimenez, Abel; Garcia-Vellisca, Mariano A; Navas, Adrian; Villacorta-Atienza, Jose A

2015-01-01

Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc. Though, the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions (Pyragiene and Pyragas, 2013), where the slave neuron is able to anticipate in time the behavior of the master one. In this paper, we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI), one of the main features of the neural response associated with the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh-Rose neurons.

Coronavirus spike-receptor interactions

NARCIS (Netherlands)

Mou, H.

2015-01-01

Coronaviruses cause important diseases in humans and animals. Coronavirus infection starts with the virus binding with its spike proteins to molecules present on the surface of host cells that act as receptors. This spike-receptor interaction is highly specific and determines the virus’ cell, tissue

Hyper-Transcranial Alternating Current Stimulation: Experimental Manipulation of Inter-Brain Synchrony

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Caroline Szymanski

2017-11-01

Full Text Available We walk together, we watch together, we win together: Interpersonally coordinated actions are omnipresent in everyday life, yet the associated neural mechanisms are not well understood. Available evidence suggests that the synchronization of oscillatory activity across brains may provide a mechanism for the temporal alignment of actions between two or more individuals. In an attempt to provide a direct test of this hypothesis, we applied transcranial alternating current stimulation simultaneously to two individuals (hyper-tACS who were asked to drum in synchrony at a set pace. Thirty-eight female-female dyads performed the dyadic drumming in the course of 3 weeks under three different hyper-tACS stimulation conditions: same-phase-same-frequency; different-phase-different-frequency; sham. Based on available evidence and theoretical considerations, stimulation was applied over right frontal and parietal sites in the theta frequency range. We predicted that same-phase-same-frequency stimulation would improve interpersonal action coordination, expressed as the degree of synchrony in dyadic drumming, relative to the other two conditions. Contrary to expectations, both the same-phase-same-frequency and the different-phase-different-frequency conditions were associated with greater dyadic drumming asynchrony relative to the sham condition. No influence of hyper-tACS on behavioral performance was seen when participants were asked to drum separately in synchrony to a metronome. Individual and dyad preferred drumming tempo was also unaffected by hyper-tACS. We discuss limitations of the present version of the hyper-tACS paradigm, and suggest avenues for future research.

Hyper-Transcranial Alternating Current Stimulation: Experimental Manipulation of Inter-Brain Synchrony.

Science.gov (United States)

Szymanski, Caroline; Müller, Viktor; Brick, Timothy R; von Oertzen, Timo; Lindenberger, Ulman

2017-01-01

We walk together, we watch together, we win together: Interpersonally coordinated actions are omnipresent in everyday life, yet the associated neural mechanisms are not well understood. Available evidence suggests that the synchronization of oscillatory activity across brains may provide a mechanism for the temporal alignment of actions between two or more individuals. In an attempt to provide a direct test of this hypothesis, we applied transcranial alternating current stimulation simultaneously to two individuals (hyper-tACS) who were asked to drum in synchrony at a set pace. Thirty-eight female-female dyads performed the dyadic drumming in the course of 3 weeks under three different hyper-tACS stimulation conditions: same-phase-same-frequency; different-phase-different-frequency; sham. Based on available evidence and theoretical considerations, stimulation was applied over right frontal and parietal sites in the theta frequency range. We predicted that same-phase-same-frequency stimulation would improve interpersonal action coordination, expressed as the degree of synchrony in dyadic drumming, relative to the other two conditions. Contrary to expectations, both the same-phase-same-frequency and the different-phase-different-frequency conditions were associated with greater dyadic drumming asynchrony relative to the sham condition. No influence of hyper-tACS on behavioral performance was seen when participants were asked to drum separately in synchrony to a metronome. Individual and dyad preferred drumming tempo was also unaffected by hyper-tACS. We discuss limitations of the present version of the hyper-tACS paradigm, and suggest avenues for future research.

Spike-based population coding and working memory.

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Martin Boerlin

2011-02-01

Full Text Available Compelling behavioral evidence suggests that humans can make optimal decisions despite the uncertainty inherent in perceptual or motor tasks. A key question in neuroscience is how populations of spiking neurons can implement such probabilistic computations. In this article, we develop a comprehensive framework for optimal, spike-based sensory integration and working memory in a dynamic environment. We propose that probability distributions are inferred spike-per-spike in recurrently connected networks of integrate-and-fire neurons. As a result, these networks can combine sensory cues optimally, track the state of a time-varying stimulus and memorize accumulated evidence over periods much longer than the time constant of single neurons. Importantly, we propose that population responses and persistent working memory states represent entire probability distributions and not only single stimulus values. These memories are reflected by sustained, asynchronous patterns of activity which make relevant information available to downstream neurons within their short time window of integration. Model neurons act as predictive encoders, only firing spikes which account for new information that has not yet been signaled. Thus, spike times signal deterministically a prediction error, contrary to rate codes in which spike times are considered to be random samples of an underlying firing rate. As a consequence of this coding scheme, a multitude of spike patterns can reliably encode the same information. This results in weakly correlated, Poisson-like spike trains that are sensitive to initial conditions but robust to even high levels of external neural noise. This spike train variability reproduces the one observed in cortical sensory spike trains, but cannot be equated to noise. On the contrary, it is a consequence of optimal spike-based inference. In contrast, we show that rate-based models perform poorly when implemented with stochastically spiking neurons.

Consensus-Based Sorting of Neuronal Spike Waveforms.

Science.gov (United States)

Fournier, Julien; Mueller, Christian M; Shein-Idelson, Mark; Hemberger, Mike; Laurent, Gilles

2016-01-01

Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained "ground truth" data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data.

Visually Evoked Spiking Evolves While Spontaneous Ongoing Dynamics Persist

Science.gov (United States)

Huys, Raoul; Jirsa, Viktor K.; Darokhan, Ziauddin; Valentiniene, Sonata; Roland, Per E.

2016-01-01

Neurons in the primary visual cortex spontaneously spike even when there are no visual stimuli. It is unknown whether the spiking evoked by visual stimuli is just a modification of the spontaneous ongoing cortical spiking dynamics or whether the spontaneous spiking state disappears and is replaced by evoked spiking. This study of laminar recordings of spontaneous spiking and visually evoked spiking of neurons in the ferret primary visual cortex shows that the spiking dynamics does not change: the spontaneous spiking as well as evoked spiking is controlled by a stable and persisting fixed point attractor. Its existence guarantees that evoked spiking return to the spontaneous state. However, the spontaneous ongoing spiking state and the visual evoked spiking states are qualitatively different and are separated by a threshold (separatrix). The functional advantage of this organization is that it avoids the need for a system reorganization following visual stimulation, and impedes the transition of spontaneous spiking to evoked spiking and the propagation of spontaneous spiking from layer 4 to layers 2–3. PMID:26778982

Cognition, attention et conscience : la synchronie dans l’esprit

OpenAIRE

Ward, Lawrence

2011-01-01

Professeur à British Columbia University (Vancouver, Canada), invité par l’Assemblée des professeurs à l’initiative des Prs Alain Berthoz et Stanislas Dehaene, a donné en mai 2010, quatre conférences intitulées « Cognition, attention et conscience : la synchronie dans l’esprit »

Time Resolution Dependence of Information Measures for Spiking Neurons: Scaling and Universality

Directory of Open Access Journals (Sweden)

James P Crutchfield

2015-08-01

Full Text Available The mutual information between stimulus and spike-train response is commonly used to monitor neural coding efficiency, but neuronal computation broadly conceived requires more refined and targeted information measures of input-output joint processes. A first step towards that larger goal is todevelop information measures for individual output processes, including information generation (entropy rate, stored information (statisticalcomplexity, predictable information (excess entropy, and active information accumulation (bound information rate. We calculate these for spike trains generated by a variety of noise-driven integrate-and-fire neurons as a function of time resolution and for alternating renewal processes. We show that their time-resolution dependence reveals coarse-grained structural properties of interspike interval statistics; e.g., $tau$-entropy rates that diverge less quickly than the firing rate indicate interspike interval correlations. We also find evidence that the excess entropy and regularized statistical complexity of different types of integrate-and-fire neurons are universal in the continuous-time limit in the sense that they do not depend on mechanism details. This suggests a surprising simplicity in the spike trains generated by these model neurons. Interestingly, neurons with gamma-distributed ISIs and neurons whose spike trains are alternating renewal processes do not fall into the same universality class. These results lead to two conclusions. First, the dependence of information measures on time resolution reveals mechanistic details about spike train generation. Second, information measures can be used as model selection tools for analyzing spike train processes.

Spike voltage topography in temporal lobe epilepsy.

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Asadi-Pooya, Ali A; Asadollahi, Marjan; Shimamoto, Shoichi; Lorenzo, Matthew; Sperling, Michael R

2016-07-15

We investigated the voltage topography of interictal spikes in patients with temporal lobe epilepsy (TLE) to see whether topography was related to etiology for TLE. Adults with TLE, who had epilepsy surgery for drug-resistant seizures from 2011 until 2014 at Jefferson Comprehensive Epilepsy Center were selected. Two groups of patients were studied: patients with mesial temporal sclerosis (MTS) on MRI and those with other MRI findings. The voltage topography maps of the interictal spikes at the peak were created using BESA software. We classified the interictal spikes as polar, basal, lateral, or others. Thirty-four patients were studied, from which the characteristics of 340 spikes were investigated. The most common type of spike orientation was others (186 spikes; 54.7%), followed by lateral (146; 42.9%), polar (5; 1.5%), and basal (3; 0.9%). Characteristics of the voltage topography maps of the spikes between the two groups of patients were somewhat different. Five spikes in patients with MTS had polar orientation, but none of the spikes in patients with other MRI findings had polar orientation (odds ratio=6.98, 95% confidence interval=0.38 to 127.38; p=0.07). Scalp topographic mapping of interictal spikes has the potential to offer different information than visual inspection alone. The present results do not allow an immediate clinical application of our findings; however, detecting a polar spike in a patient with TLE may increase the possibility of mesial temporal sclerosis as the underlying etiology. Copyright © 2016 Elsevier B.V. All rights reserved.

Brain-to-brain synchrony in parent-child dyads and the relationship with emotion regulation revealed by fNIRS-based hyperscanning.

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Reindl, Vanessa; Gerloff, Christian; Scharke, Wolfgang; Konrad, Kerstin

2018-05-25

Parent-child synchrony, the coupling of behavioral and biological signals during social contact, may fine-tune the child's brain circuitries associated with emotional bond formation and the child's development of emotion regulation. Here, we examined the neurobiological underpinnings of these processes by measuring parent's and child's prefrontal neural activity concurrently with functional near-infrared spectroscopy hyperscanning. Each child played both a cooperative and a competitive game with the parent, mostly the mother, as well as an adult stranger. During cooperation, parent's and child's brain activities synchronized in the dorsolateral prefrontal and frontopolar cortex (FPC), which was predictive for their cooperative performance in subsequent trials. No significant brain-to-brain synchrony was observed in the conditions parent-child competition, stranger-child cooperation and stranger-child competition. Furthermore, parent-child compared to stranger-child brain-to-brain synchrony during cooperation in the FPC mediated the association between the parent's and the child's emotion regulation, as assessed by questionnaires. Thus, we conclude that brain-to-brain synchrony may represent an underlying neural mechanism of the emotional connection between parent and child, which is linked to the child's development of adaptive emotion regulation. Future studies may uncover whether brain-to-brain synchrony can serve as a neurobiological marker of the dyad's socio-emotional interaction, which is sensitive to risk conditions, and can be modified by interventions. Copyright © 2018. Published by Elsevier Inc.

Rebound spiking in layer II medial entorhinal cortex stellate cells: Possible mechanism of grid cell function

Science.gov (United States)

Shay, Christopher F.; Ferrante, Michele; Chapman, G. William; Hasselmo, Michael E.

2015-01-01

Rebound spiking properties of medial entorhinal cortex (mEC) stellate cells induced by inhibition may underlie their functional properties in awake behaving rats, including the temporal phase separation of distinct grid cells and differences in grid cell firing properties. We investigated rebound spiking properties using whole cell patch recording in entorhinal slices, holding cells near spiking threshold and delivering sinusoidal inputs, superimposed with realistic inhibitory synaptic inputs to test the capacity of cells to selectively respond to specific phases of inhibitory input. Stellate cells showed a specific phase range of hyperpolarizing inputs that elicited spiking, but non-stellate cells did not show phase specificity. In both cell types, the phase range of spiking output occurred between the peak and subsequent descending zero crossing of the sinusoid. The phases of inhibitory inputs that induced spikes shifted earlier as the baseline sinusoid frequency increased, while spiking output shifted to later phases. Increases in magnitude of the inhibitory inputs shifted the spiking output to earlier phases. Pharmacological blockade of h-current abolished the phase selectivity of hyperpolarizing inputs eliciting spikes. A network computational model using cells possessing similar rebound properties as found in vitro produces spatially periodic firing properties resembling grid cell firing when a simulated animal moves along a linear track. These results suggest that the ability of mEC stellate cells to fire rebound spikes in response to a specific range of phases of inhibition could support complex attractor dynamics that provide completion and separation to maintain spiking activity of specific grid cell populations. PMID:26385258

Left ventricular synchrony assessed by phase analysis of gated myocardial perfusion SPECT imaging in healthy subjects

International Nuclear Information System (INIS)

Wang Yuetao; Wang Jianfeng; Yang Minfu; Niu Rong

2013-01-01

Objective: To investigate the value of Cedars-Sinai quantitative gated SPECT (QGS) phase analysis for left ventricular synchrony assessment in healthy subjects. Methods: Seventy-four healthy subjects (41 males, 33 females,average age: (60±13) years) underwent both rest and exercise 99 Tc m -MIBI G-MPI. QGS software was used to analyze the reconstructed rest gated SPECT images automatically, and then the parameters of left ventricular synchrony including phase bandwidth (BW) and phase standard deviation (SD) were obtained. The influences of gender and age (age<60 years, n=36; age ≥ 60 years, n=38) on left ventricular systolic synchronicity were analyzed. The phase angle for original segmental contraction was measured to determine the onset of the ventricular contraction using 17-segment model. Forty healthy subjects were selected by simple random sampling method to evaluate the intra-observer and interobserver repeatability of QGS phase analysis software. Two-sample t test and linear correlation analysis were used to analyze the data. Results: The BW and SD of left ventricular in healthy subjects were (37.22 ±11.71)°, (11.84±5.39)° respectively. Comparisons between male and female for BW and SD yielded no statistical significance (BW: (36.00±9.70)°, (38.73±13.84)°; SD: (11.88±5.56)°, (11.79±5.26)°; t=0.96 and-0.07, both P>0.05); whereas the older subjects (age≥60 years) had larger BW than the others (age<60 years ; (39.95± 12.65)°, (34.33± 10.00)°; t=-2.11, P<0.05) and no statistical significance was shown for SD between the two age groups ((11.18±4.31)°, (12.54±6.33)°; t=1.08, P>0.05). Of the 74 subjects, the mechanical activation started from the ventricular base to apex in 54 subjects (73%), and from apex to base in only 20 subjects (27%). High repeatability of phase analysis was observed for both intra-observer and inter-observer (r=0.867-0.906, all P<0.001). Conclusions: Good left ventricular segmental synchrony is shown in healthy

Absolute spike frequency as a predictor of surgical outcome in temporal lobe epilepsy.

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Ngo, Ly; Sperling, Michael R; Skidmore, Christopher; Mintzer, Scott; Nei, Maromi

2017-04-01

Frequent interictal epileptiform abnormalities may correlate with poor prognosis after temporal lobe resection for refractory epilepsy. To date, studies have focused on limited resections such as selective amygdalohippocampectomy and apical temporal lobectomy without hippocampectomy. However, it is unclear whether the frequency of spikes predicts outcome after standard anterior temporal lobectomy. Preoperative scalp video-EEG monitoring data from patients who subsequently underwent anterior temporal lobectomy over a three year period and were followed for at least one year were reviewed for the frequency of interictal epileptiform abnormalities. Surgical outcome for those patients with frequent spikes (>60/h) was compared with those with less frequent spikes. Additionally, spike frequency was evaluated as a continuous variable and correlated with outcome to determine if increased spike frequency correlated with worse outcome, as assessed by modified Engel Class outcome. Forty-seven patients (18 men, 29 women; mean age 40 years at surgery) were included. Forty-six patients had standard anterior temporal lobectomy (24 right, 22 left) and one had a modified left temporal lobectomy. There was no significant difference in seizure outcome between those with frequent (57% Class I) vs. those with less frequent (58% Class I) spikes. Increased spike frequency did not correlate with worse outcome. Greater than 20 complex partial seizures/month and generalized tonic-clonic seizures within one year of surgery correlated with worse outcome. This study suggests that absolute spike frequency does not predict seizure outcome after anterior temporal lobectomy unlike in selective procedures, and should not be used as a prognostic factor in this population. Copyright © 2017 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Reduced orienting to audiovisual synchrony in infancy predicts autism diagnosis at 3 years of age.

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Falck-Ytter, Terje; Nyström, Pär; Gredebäck, Gustaf; Gliga, Teodora; Bölte, Sven

2018-01-23

Effective multisensory processing develops in infancy and is thought to be important for the perception of unified and multimodal objects and events. Previous research suggests impaired multisensory processing in autism, but its role in the early development of the disorder is yet uncertain. Here, using a prospective longitudinal design, we tested whether reduced visual attention to audiovisual synchrony is an infant marker of later-emerging autism diagnosis. We studied 10-month-old siblings of children with autism using an eye tracking task previously used in studies of preschoolers. The task assessed the effect of manipulations of audiovisual synchrony on viewing patterns while the infants were observing point light displays of biological motion. We analyzed the gaze data recorded in infancy according to diagnostic status at 3 years of age (DSM-5). Ten-month-old infants who later received an autism diagnosis did not orient to audiovisual synchrony expressed within biological motion. In contrast, both infants at low-risk and high-risk siblings without autism at follow-up had a strong preference for this type of information. No group differences were observed in terms of orienting to upright biological motion. This study suggests that reduced orienting to audiovisual synchrony within biological motion is an early sign of autism. The findings support the view that poor multisensory processing could be an important antecedent marker of this neurodevelopmental condition. © 2018 Association for Child and Adolescent Mental Health.

STICK: Spike Time Interval Computational Kernel, a Framework for General Purpose Computation Using Neurons, Precise Timing, Delays, and Synchrony.

Science.gov (United States)

Lagorce, Xavier; Benosman, Ryad

2015-11-01

There has been significant research over the past two decades in developing new platforms for spiking neural computation. Current neural computers are primarily developed to mimic biology. They use neural networks, which can be trained to perform specific tasks to mainly solve pattern recognition problems. These machines can do more than simulate biology; they allow us to rethink our current paradigm of computation. The ultimate goal is to develop brain-inspired general purpose computation architectures that can breach the current bottleneck introduced by the von Neumann architecture. This work proposes a new framework for such a machine. We show that the use of neuron-like units with precise timing representation, synaptic diversity, and temporal delays allows us to set a complete, scalable compact computation framework. The framework provides both linear and nonlinear operations, allowing us to represent and solve any function. We show usability in solving real use cases from simple differential equations to sets of nonlinear differential equations leading to chaotic attractors.

Speaker detection for conversational robots using synchrony between audio and video

NARCIS (Netherlands)

Noulas, A.; Englebienne, G.; Terwijn, B.; Kröse, B.; Hanheide, M.; Zender, H.

2010-01-01

This paper compares different methods for detecting the speaking person when multiple persons are interacting with a robot. We evaluate the state-of-the-art speaker detection methods on the iCat robot. These methods use the synchrony between audio and video to locate the most probable speaker. We

Spiking Neural P Systems with Communication on Request.

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Pan, Linqiang; Păun, Gheorghe; Zhang, Gexiang; Neri, Ferrante

2017-12-01

Spiking Neural [Formula: see text] Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural [Formula: see text] systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these [Formula: see text] systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron. [Formula: see text]In the present work, a novel communication strategy among neurons of Spiking Neural [Formula: see text] Systems is proposed. In the resulting models, called Spiking Neural [Formula: see text] Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural [Formula: see text] systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron). [Formula: see text]The Spiking Neural [Formula: see text] Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems.

Self-control with spiking and non-spiking neural networks playing games.

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Christodoulou, Chris; Banfield, Gaye; Cleanthous, Aristodemos

2010-01-01

Self-control can be defined as choosing a large delayed reward over a small immediate reward, while precommitment is the making of a choice with the specific aim of denying oneself future choices. Humans recognise that they have self-control problems and attempt to overcome them by applying precommitment. Problems in exercising self-control, suggest a conflict between cognition and motivation, which has been linked to competition between higher and lower brain functions (representing the frontal lobes and the limbic system respectively). This premise of an internal process conflict, lead to a behavioural model being proposed, based on which, we implemented a computational model for studying and explaining self-control through precommitment behaviour. Our model consists of two neural networks, initially non-spiking and then spiking ones, representing the higher and lower brain systems viewed as cooperating for the benefit of the organism. The non-spiking neural networks are of simple feed forward multilayer type with reinforcement learning, one with selective bootstrap weight update rule, which is seen as myopic, representing the lower brain and the other with the temporal difference weight update rule, which is seen as far-sighted, representing the higher brain. The spiking neural networks are implemented with leaky integrate-and-fire neurons with learning based on stochastic synaptic transmission. The differentiating element between the two brain centres in this implementation is based on the memory of past actions determined by an eligibility trace time constant. As the structure of the self-control problem can be likened to the Iterated Prisoner's Dilemma (IPD) game in that cooperation is to defection what self-control is to impulsiveness or what compromising is to insisting, we implemented the neural networks as two players, learning simultaneously but independently, competing in the IPD game. With a technique resembling the precommitment effect, whereby the

Memristors Empower Spiking Neurons With Stochasticity

KAUST Repository

Al-Shedivat, Maruan

2015-06-01

Recent theoretical studies have shown that probabilistic spiking can be interpreted as learning and inference in cortical microcircuits. This interpretation creates new opportunities for building neuromorphic systems driven by probabilistic learning algorithms. However, such systems must have two crucial features: 1) the neurons should follow a specific behavioral model, and 2) stochastic spiking should be implemented efficiently for it to be scalable. This paper proposes a memristor-based stochastically spiking neuron that fulfills these requirements. First, the analytical model of the memristor is enhanced so it can capture the behavioral stochasticity consistent with experimentally observed phenomena. The switching behavior of the memristor model is demonstrated to be akin to the firing of the stochastic spike response neuron model, the primary building block for probabilistic algorithms in spiking neural networks. Furthermore, the paper proposes a neural soma circuit that utilizes the intrinsic nondeterminism of memristive switching for efficient spike generation. The simulations and analysis of the behavior of a single stochastic neuron and a winner-take-all network built of such neurons and trained on handwritten digits confirm that the circuit can be used for building probabilistic sampling and pattern adaptation machinery in spiking networks. The findings constitute an important step towards scalable and efficient probabilistic neuromorphic platforms. © 2011 IEEE.

Comparing motor performance, praxis, coordination, and interpersonal synchrony between children with and without Autism Spectrum Disorder (ASD).

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Kaur, Maninderjit; M Srinivasan, Sudha; N Bhat, Anjana

2018-01-01

Children with Autism Spectrum Disorder (ASD) have basic motor impairments in balance, gait, and coordination as well as autism-specific impairments in praxis/motor planning and interpersonal synchrony. Majority of the current literature focuses on isolated motor behaviors or domains. Additionally, the relationship between cognition, symptom severity, and motor performance in ASD is unclear. We used a comprehensive set of measures to compare gross and fine motor, praxis/imitation, motor coordination, and interpersonal synchrony skills across three groups of children between 5 and 12 years of age: children with ASD with high IQ (HASD), children with ASD with low IQ (LASD), and typically developing (TD) children. We used the Bruininks-Oseretsky Test of Motor Proficiency and the Bilateral Motor Coordination subtest of the Sensory Integration and Praxis Tests to assess motor performance and praxis skills respectively. Children were also examined while performing simple and complex rhythmic upper and lower limb actions on their own (solo context) and with a social partner (social context). Both ASD groups had lower gross and fine motor scores, greater praxis errors in total and within various error types, lower movement rates, greater movement variability, and weaker interpersonal synchrony compared to the TD group. In addition, the LASD group had lower gross motor scores and greater mirroring errors compared to the HASD group. Overall, a variety of motor impairments are present across the entire spectrum of children with ASD, regardless of their IQ scores. Both, fine and gross motor performance significantly correlated with IQ but not with autism severity; however, praxis errors (mainly, total, overflow, and rhythmicity) strongly correlated with autism severity and not IQ. Our study findings highlight the need for clinicians and therapists to include motor evaluations and interventions in the standard-of-care of children with ASD and for the broader autism community to

Volitional enhancement of firing synchrony and oscillation by neuronal operant conditioning: interaction with neurorehabilitation and brain-machine interface.

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Sakurai, Yoshio; Song, Kichan; Tachibana, Shota; Takahashi, Susumu

2014-01-01

In this review, we focus on neuronal operant conditioning in which increments in neuronal activities are directly rewarded without behaviors. We discuss the potential of this approach to elucidate neuronal plasticity for enhancing specific brain functions and its interaction with the progress in neurorehabilitation and brain-machine interfaces. The key to-be-conditioned activities that this paper emphasizes are synchronous and oscillatory firings of multiple neurons that reflect activities of cell assemblies. First, we introduce certain well-known studies on neuronal operant conditioning in which conditioned enhancements of neuronal firing were reported in animals and humans. These studies demonstrated the feasibility of volitional control over neuronal activity. Second, we refer to the recent studies on operant conditioning of synchrony and oscillation of neuronal activities. In particular, we introduce a recent study showing volitional enhancement of oscillatory activity in monkey motor cortex and our study showing selective enhancement of firing synchrony of neighboring neurons in rat hippocampus. Third, we discuss the reasons for emphasizing firing synchrony and oscillation in neuronal operant conditioning, the main reason being that they reflect the activities of cell assemblies, which have been suggested to be basic neuronal codes representing information in the brain. Finally, we discuss the interaction of neuronal operant conditioning with neurorehabilitation and brain-machine interface (BMI). We argue that synchrony and oscillation of neuronal firing are the key activities required for developing both reliable neurorehabilitation and high-performance BMI. Further, we conclude that research of neuronal operant conditioning, neurorehabilitation, BMI, and system neuroscience will produce findings applicable to these interrelated fields, and neuronal synchrony and oscillation can be a common important bridge among all of them.

DMN Operational Synchrony Relates to Self-Consciousness: Evidence from Patients in Vegetative and Minimally Conscious States.

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Fingelkurts, Andrew A; Fingelkurts, Alexander A; Bagnato, Sergio; Boccagni, Cristina; Galardi, Giuseppe

2012-01-01

The default mode network (DMN) has been consistently activated across a wide variety of self-related tasks, leading to a proposal of the DMN's role in self-related processing. Indeed, there is limited fMRI evidence that the functional connectivity within the DMN may underlie a phenomenon referred to as self-awareness. At the same time, none of the known studies have explicitly investigated neuronal functional interactions among brain areas that comprise the DMN as a function of self-consciousness loss. To fill this gap, EEG operational synchrony analysis [1, 2] was performed in patients with severe brain injuries in vegetative and minimally conscious states to study the strength of DMN operational synchrony as a function of self-consciousness expression. We demonstrated that the strength of DMN EEG operational synchrony was smallest or even absent in patients in vegetative state, intermediate in patients in minimally conscious state and highest in healthy fully self-conscious subjects. At the same time the process of ecoupling of operations performed by neuronal assemblies that comprise the DMN was highest in patients in vegetative state, intermediate in patients in minimally conscious state and minimal in healthy fully self-conscious subjects. The DMN's frontal EEG operational module had the strongest decrease in operational synchrony strength as a function of selfconsciousness loss, when compared with the DMN's posterior modules. Based on these results it is suggested that the strength of DMN functional connectivity could mediate the strength of self-consciousness expression. The observed alterations similarly occurred across EEG alpha, beta1 and beta2 frequency oscillations. Presented results suggest that the EEG operational synchrony within DMN may provide an objective and accurate measure for the assessment of signs of self-(un)consciousness in these challenging patient populations. This method therefore, may complement the current diagnostic procedures for

Mercury in mercury(II)-spiked soils is highly susceptible to plant bioaccumulation.

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Hlodák, Michal; Urík, Martin; Matúš, Peter; Kořenková, Lucia

2016-01-01

Heavy metal phytotoxicity assessments usually use soluble metal compounds in spiked soils to evaluate metal bioaccumulation, growth inhibition and adverse effects on physiological parameters. However, exampling mercury phytotoxicity for barley (Hordeum vulgare) this paper highlights unsuitability of this experimental approach. Mercury(II) in spiked soils is extremely bioavailable, and there experimentally determined bioaccumulation is significantly higher compared to reported mercury bioaccumulation efficiency from soils collected from mercury-polluted areas. Our results indicate this is not affected by soil sorption capacity, thus soil ageing and formation of more stable mercuric complexes with soil fractions is necessary for reasonable metal phytotoxicity assessments.

Non-orthogonally transitive G2 spike solution

International Nuclear Information System (INIS)

Lim, Woei Chet

2015-01-01

We generalize the orthogonally transitive (OT) G 2 spike solution to the non-OT G 2 case. This is achieved by applying Geroch’s transformation on a Kasner seed. The new solution contains two more parameters than the OT G 2 spike solution. Unlike the OT G 2 spike solution, the new solution always resolves its spike. (fast track communication)

Audiovisual Speech Synchrony Measure: Application to Biometrics

Directory of Open Access Journals (Sweden)

Gérard Chollet

2007-01-01

Full Text Available Speech is a means of communication which is intrinsically bimodal: the audio signal originates from the dynamics of the articulators. This paper reviews recent works in the field of audiovisual speech, and more specifically techniques developed to measure the level of correspondence between audio and visual speech. It overviews the most common audio and visual speech front-end processing, transformations performed on audio, visual, or joint audiovisual feature spaces, and the actual measure of correspondence between audio and visual speech. Finally, the use of synchrony measure for biometric identity verification based on talking faces is experimented on the BANCA database.

Wavelet analysis of epileptic spikes

Science.gov (United States)

Latka, Miroslaw; Was, Ziemowit; Kozik, Andrzej; West, Bruce J.

2003-05-01

Interictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after long-term monitoring became common in investigation of epileptic patients. The traditional definition of a spike is based on its amplitude, duration, sharpness, and emergence from its background. However, spike detection systems built solely around this definition are not reliable due to the presence of numerous transients and artifacts. We use wavelet transform to analyze the properties of EEG manifestations of epilepsy. We demonstrate that the behavior of wavelet transform of epileptic spikes across scales can constitute the foundation of a relatively simple yet effective detection algorithm.

Wavelet analysis of epileptic spikes

CERN Document Server

Latka, M; Kozik, A; West, B J; Latka, Miroslaw; Was, Ziemowit; Kozik, Andrzej; West, Bruce J.

2003-01-01

Interictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous, pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after long-term monitoring became common in investigation of epileptic patients. The traditional definition of a spike is based on its amplitude, duration, sharpness, and emergence from its background. However, spike detection systems built solely around this definition are not reliable due to the presence of numerous transients and artifacts. We use wavelet transform to analyze the properties of EEG manifestations of epilepsy. We demonstrate that the behavior of wavelet transform of epileptic spikes across scales can constitute the foundation of a relatively simple yet effective detection algorithm.

Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

Science.gov (United States)

Pecevski, Dejan; Buesing, Lars; Maass, Wolfgang

2011-01-01

An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows (“explaining away”) and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons. PMID:22219717

Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons.

Directory of Open Access Journals (Sweden)

Dejan Pecevski

2011-12-01

Full Text Available An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows ("explaining away" and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons.

Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons.

Science.gov (United States)

Pecevski, Dejan; Buesing, Lars; Maass, Wolfgang

2011-12-01

An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows ("explaining away") and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons.

Chaos and reliability in balanced spiking networks with temporal drive.

Science.gov (United States)

Lajoie, Guillaume; Lin, Kevin K; Shea-Brown, Eric

2013-05-01

Biological information processing is often carried out by complex networks of interconnected dynamical units. A basic question about such networks is that of reliability: If the same signal is presented many times with the network in different initial states, will the system entrain to the signal in a repeatable way? Reliability is of particular interest in neuroscience, where large, complex networks of excitatory and inhibitory cells are ubiquitous. These networks are known to autonomously produce strongly chaotic dynamics-an obvious threat to reliability. Here, we show that such chaos persists in the presence of weak and strong stimuli, but that even in the presence of chaos, intermittent periods of highly reliable spiking often coexist with unreliable activity. We elucidate the local dynamical mechanisms involved in this intermittent reliability, and investigate the relationship between this phenomenon and certain time-dependent attractors arising from the dynamics. A conclusion is that chaotic dynamics do not have to be an obstacle to precise spike responses, a fact with implications for signal coding in large networks.

Note on the coefficient of variations of neuronal spike trains.

Science.gov (United States)

Lengler, Johannes; Steger, Angelika

2017-08-01

It is known that many neurons in the brain show spike trains with a coefficient of variation (CV) of the interspike times of approximately 1, thus resembling the properties of Poisson spike trains. Computational studies have been able to reproduce this phenomenon. However, the underlying models were too complex to be examined analytically. In this paper, we offer a simple model that shows the same effect but is accessible to an analytic treatment. The model is a random walk model with a reflecting barrier; we give explicit formulas for the CV in the regime of excess inhibition. We also analyze the effect of probabilistic synapses in our model and show that it resembles previous findings that were obtained by simulation.

Motor control by precisely timed spike patterns

DEFF Research Database (Denmark)

Srivastava, Kyle H; Holmes, Caroline M; Vellema, Michiel

2017-01-01

whether the information in spike timing actually plays a role in brain function. By examining the activity of individual motor units (the muscle fibers innervated by a single motor neuron) and manipulating patterns of activation of these neurons, we provide both correlative and causal evidence......A fundamental problem in neuroscience is understanding how sequences of action potentials ("spikes") encode information about sensory signals and motor outputs. Although traditional theories assume that this information is conveyed by the total number of spikes fired within a specified time...... interval (spike rate), recent studies have shown that additional information is carried by the millisecond-scale timing patterns of action potentials (spike timing). However, it is unknown whether or how subtle differences in spike timing drive differences in perception or behavior, leaving it unclear...

Perceived synchrony for realistic and dynamic audiovisual events.

Science.gov (United States)

Eg, Ragnhild; Behne, Dawn M

2015-01-01

In well-controlled laboratory experiments, researchers have found that humans can perceive delays between auditory and visual signals as short as 20 ms. Conversely, other experiments have shown that humans can tolerate audiovisual asynchrony that exceeds 200 ms. This seeming contradiction in human temporal sensitivity can be attributed to a number of factors such as experimental approaches and precedence of the asynchronous signals, along with the nature, duration, location, complexity and repetitiveness of the audiovisual stimuli, and even individual differences. In order to better understand how temporal integration of audiovisual events occurs in the real world, we need to close the gap between the experimental setting and the complex setting of everyday life. With this work, we aimed to contribute one brick to the bridge that will close this gap. We compared perceived synchrony for long-running and eventful audiovisual sequences to shorter sequences that contain a single audiovisual event, for three types of content: action, music, and speech. The resulting windows of temporal integration showed that participants were better at detecting asynchrony for the longer stimuli, possibly because the long-running sequences contain multiple corresponding events that offer audiovisual timing cues. Moreover, the points of subjective simultaneity differ between content types, suggesting that the nature of a visual scene could influence the temporal perception of events. An expected outcome from this type of experiment was the rich variation among participants' distributions and the derived points of subjective simultaneity. Hence, the designs of similar experiments call for more participants than traditional psychophysical studies. Heeding this caution, we conclude that existing theories on multisensory perception are ready to be tested on more natural and representative stimuli.

Neuronal synchrony: peculiarity and generality.

Science.gov (United States)

Nowotny, Thomas; Huerta, Ramon; Rabinovich, Mikhail I

2008-09-01

Synchronization in neuronal systems is a new and intriguing application of dynamical systems theory. Why are neuronal systems different as a subject for synchronization? (1) Neurons in themselves are multidimensional nonlinear systems that are able to exhibit a wide variety of different activity patterns. Their "dynamical repertoire" includes regular or chaotic spiking, regular or chaotic bursting, multistability, and complex transient regimes. (2) Usually, neuronal oscillations are the result of the cooperative activity of many synaptically connected neurons (a neuronal circuit). Thus, it is necessary to consider synchronization between different neuronal circuits as well. (3) The synapses that implement the coupling between neurons are also dynamical elements and their intrinsic dynamics influences the process of synchronization or entrainment significantly. In this review we will focus on four new problems: (i) the synchronization in minimal neuronal networks with plastic synapses (synchronization with activity dependent coupling), (ii) synchronization of bursts that are generated by a group of nonsymmetrically coupled inhibitory neurons (heteroclinic synchronization), (iii) the coordination of activities of two coupled neuronal networks (partial synchronization of small composite structures), and (iv) coarse grained synchronization in larger systems (synchronization on a mesoscopic scale). (c) 2008 American Institute of Physics.

Disruption in neural phase synchrony is related to identification of inattentional deafness in real-world setting.

Science.gov (United States)

Callan, Daniel E; Gateau, Thibault; Durantin, Gautier; Gonthier, Nicolas; Dehais, Frédéric

2018-06-01

Individuals often have reduced ability to hear alarms in real world situations (e.g., anesthesia monitoring, flying airplanes) when attention is focused on another task, sometimes with devastating consequences. This phenomenon is called inattentional deafness and usually occurs under critical high workload conditions. It is difficult to simulate the critical nature of these tasks in the laboratory. In this study, dry electroencephalography is used to investigate inattentional deafness in real flight while piloting an airplane. The pilots participating in the experiment responded to audio alarms while experiencing critical high workload situations. It was found that missed relative to detected alarms were marked by reduced stimulus evoked phase synchrony in theta and alpha frequencies (6-14 Hz) from 120 to 230 ms poststimulus onset. Correlation of alarm detection performance with intertrial coherence measures of neural phase synchrony showed different frequency and time ranges for detected and missed alarms. These results are consistent with selective attentional processes actively disrupting oscillatory coherence in sensory networks not involved with the primary task (piloting in this case) under critical high load conditions. This hypothesis is corroborated by analyses of flight parameters showing greater maneuvering associated with difficult phases of flight occurring during missed alarms. Our results suggest modulation of neural oscillation is a general mechanism of attention utilizing enhancement of phase synchrony to sharpen alarm perception during successful divided attention, and disruption of phase synchrony in brain networks when attentional demands of the primary task are great, such as in the case of inattentional deafness. © 2018 Wiley Periodicals, Inc.

Generalized activity equations for spiking neural network dynamics

Directory of Open Access Journals (Sweden)

Michael A Buice

2013-11-01

Full Text Available Much progress has been made in uncovering the computational capabilities of spiking neural networks. However, spiking neurons will always be more expensive to simulate compared to rate neurons because of the inherent disparity in time scales - the spike duration time is much shorter than the inter-spike time, which is much shorter than any learning time scale. In numerical analysis, this is a classic stiff problem. Spiking neurons are also much more difficult to study analytically. One possible approach to making spiking networks more tractable is to augment mean field activity models with some information about spiking correlations. For example, such a generalized activity model could carry information about spiking rates and correlations between spikes self-consistently. Here, we will show how this can be accomplished by constructing a complete formal probabilistic description of the network and then expanding around a small parameter such as the inverse of the number of neurons in the network. The mean field theory of the system gives a rate-like description. The first order terms in the perturbation expansion keep track of covariances.

Augmented brain function by coordinated reset stimulation with slowly varying sequences

OpenAIRE

Magteld eZeitler; Peter A. Tass; Peter A. Tass; Peter A. Tass

2015-01-01

Several brain disorders are characterized by abnormally strong neuronal synchrony. Coordinated Reset (CR) stimulation was developed to selectively counteract abnormal neuronal synchrony by desynchronization. For this, phase resetting stimuli are delivered to different subpopulations in a timely coordinated way. In neural networks with spike timing-dependent plasticity CR stimulation may eventually lead to an anti-kindling, i.e. an unlearning of abnormal synaptic connectivity and abnormal sync...

Augmented brain function by coordinated reset stimulation with slowly varying sequences

OpenAIRE

Zeitler, Magteld; Tass, Peter A.

2015-01-01

Several brain disorders are characterized by abnormally strong neuronal synchrony. Coordinated Reset (CR) stimulation was developed to selectively counteract abnormal neuronal synchrony by desynchronization. For this, phase resetting stimuli are delivered to different subpopulations in a timely coordinated way. In neural networks with spike timing-dependent plasticity CR stimulation may eventually lead to an anti-kindling, i.e., an unlearning of abnormal synaptic connectivity and abnormal syn...

Feature extraction using extrema sampling of discrete derivatives for spike sorting in implantable upper-limb neural prostheses.

Science.gov (United States)

Zamani, Majid; Demosthenous, Andreas

2014-07-01

Next generation neural interfaces for upper-limb (and other) prostheses aim to develop implantable interfaces for one or more nerves, each interface having many neural signal channels that work reliably in the stump without harming the nerves. To achieve real-time multi-channel processing it is important to integrate spike sorting on-chip to overcome limitations in transmission bandwidth. This requires computationally efficient algorithms for feature extraction and clustering suitable for low-power hardware implementation. This paper describes a new feature extraction method for real-time spike sorting based on extrema analysis (namely positive peaks and negative peaks) of spike shapes and their discrete derivatives at different frequency bands. Employing simulation across different datasets, the accuracy and computational complexity of the proposed method are assessed and compared with other methods. The average classification accuracy of the proposed method in conjunction with online sorting (O-Sort) is 91.6%, outperforming all the other methods tested with the O-Sort clustering algorithm. The proposed method offers a better tradeoff between classification error and computational complexity, making it a particularly strong choice for on-chip spike sorting.

Event-driven processing for hardware-efficient neural spike sorting

Science.gov (United States)

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.

A Low Cost VLSI Architecture for Spike Sorting Based on Feature Extraction with Peak Search.

Science.gov (United States)

Chang, Yuan-Jyun; Hwang, Wen-Jyi; Chen, Chih-Chang

2016-12-07

The goal of this paper is to present a novel VLSI architecture for spike sorting with high classification accuracy, low area costs and low power consumption. A novel feature extraction algorithm with low computational complexities is proposed for the design of the architecture. In the feature extraction algorithm, a spike is separated into two portions based on its peak value. The area of each portion is then used as a feature. The algorithm is simple to implement and less susceptible to noise interference. Based on the algorithm, a novel architecture capable of identifying peak values and computing spike areas concurrently is proposed. To further accelerate the computation, a spike can be divided into a number of segments for the local feature computation. The local features are subsequently merged with the global ones by a simple hardware circuit. The architecture can also be easily operated in conjunction with the circuits for commonly-used spike detection algorithms, such as the Non-linear Energy Operator (NEO). The architecture has been implemented by an Application-Specific Integrated Circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture is well suited for real-time multi-channel spike detection and feature extraction requiring low hardware area costs, low power consumption and high classification accuracy.

Epileptiform spike detection via convolutional neural networks

DEFF Research Database (Denmark)

Johansen, Alexander Rosenberg; Jin, Jing; Maszczyk, Tomasz

2016-01-01

The EEG of epileptic patients often contains sharp waveforms called "spikes", occurring between seizures. Detecting such spikes is crucial for diagnosing epilepsy. In this paper, we develop a convolutional neural network (CNN) for detecting spikes in EEG of epileptic patients in an automated...

Statistical properties of superimposed stationary spike trains.

Science.gov (United States)

Deger, Moritz; Helias, Moritz; Boucsein, Clemens; Rotter, Stefan

2012-06-01

The Poisson process is an often employed model for the activity of neuronal populations. It is known, though, that superpositions of realistic, non- Poisson spike trains are not in general Poisson processes, not even for large numbers of superimposed processes. Here we construct superimposed spike trains from intracellular in vivo recordings from rat neocortex neurons and compare their statistics to specific point process models. The constructed superimposed spike trains reveal strong deviations from the Poisson model. We find that superpositions of model spike trains that take the effective refractoriness of the neurons into account yield a much better description. A minimal model of this kind is the Poisson process with dead-time (PPD). For this process, and for superpositions thereof, we obtain analytical expressions for some second-order statistical quantities-like the count variability, inter-spike interval (ISI) variability and ISI correlations-and demonstrate the match with the in vivo data. We conclude that effective refractoriness is the key property that shapes the statistical properties of the superposition spike trains. We present new, efficient algorithms to generate superpositions of PPDs and of gamma processes that can be used to provide more realistic background input in simulations of networks of spiking neurons. Using these generators, we show in simulations that neurons which receive superimposed spike trains as input are highly sensitive for the statistical effects induced by neuronal refractoriness.

Simple networks for spike-timing-based computation, with application to olfactory processing.

Science.gov (United States)

Brody, Carlos D; Hopfield, J J

2003-03-06

Spike synchronization across neurons can be selective for the situation where neurons are driven at similar firing rates, a "many are equal" computation. This can be achieved in the absence of synaptic interactions between neurons, through phase locking to a common underlying oscillatory potential. Based on this principle, we instantiate an algorithm for robust odor recognition into a model network of spiking neurons whose main features are taken from known properties of biological olfactory systems. Here, recognition of odors is signaled by spike synchronization of specific subsets of "mitral cells." This synchronization is highly odor selective and invariant to a wide range of odor concentrations. It is also robust to the presence of strong distractor odors, thus allowing odor segmentation within complex olfactory scenes. Information about odors is encoded in both the identity of glomeruli activated above threshold (1 bit of information per glomerulus) and in the analog degree of activation of the glomeruli (approximately 3 bits per glomerulus).

Spiking Neurons for Analysis of Patterns

Science.gov (United States)

Huntsberger, Terrance

2008-01-01

Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological

The Omega-Infinity Limit of Single Spikes

CERN Document Server

Axenides, Minos; Linardopoulos, Georgios

A new infinite-size limit of strings in RxS2 is presented. The limit is obtained from single spike strings by letting their angular velocity omega become infinite. We derive the energy-momenta relation of omega-infinity single spikes as their linear velocity v-->1 and their angular momentum J-->1. Generally, the v-->1, J-->1 limit of single spikes is singular and has to be excluded from the spectrum and be studied separately. We discover that the dispersion relation of omega-infinity single spikes contains logarithms in the limit J-->1. This result is somewhat surprising, since the logarithmic behavior in the string spectra is typically associated with their motion in non-compact spaces such as AdS. Omega-infinity single spikes seem to completely cover the surface of the 2-sphere they occupy, so that they may essentially be viewed as some sort of "brany strings". A proof of the sphere-filling property of omega-infinity single spikes is given in the appendix.

The simplest problem in the collective dynamics of neural networks: is synchrony stable?

International Nuclear Information System (INIS)

Timme, Marc; Wolf, Fred

2008-01-01

For spiking neural networks we consider the stability problem of global synchrony, arguably the simplest non-trivial collective dynamics in such networks. We find that even this simplest dynamical problem—local stability of synchrony—is non-trivial to solve and requires novel methods for its solution. In particular, the discrete mode of pulsed communication together with the complicated connectivity of neural interaction networks requires a non-standard approach. The dynamics in the vicinity of the synchronous state is determined by a multitude of linear operators, in contrast to a single stability matrix in conventional linear stability theory. This unusual property qualitatively depends on network topology and may be neglected for globally coupled homogeneous networks. For generic networks, however, the number of operators increases exponentially with the size of the network. We present methods to treat this multi-operator problem exactly. First, based on the Gershgorin and Perron–Frobenius theorems, we derive bounds on the eigenvalues that provide important information about the synchronization process but are not sufficient to establish the asymptotic stability or instability of the synchronous state. We then present a complete analysis of asymptotic stability for topologically strongly connected networks using simple graph-theoretical considerations. For inhibitory interactions between dissipative (leaky) oscillatory neurons the synchronous state is stable, independent of the parameters and the network connectivity. These results indicate that pulse-like interactions play a profound role in network dynamical systems, and in particular in the dynamics of biological synchronization, unless the coupling is homogeneous and all-to-all. The concepts introduced here are expected to also facilitate the exact analysis of more complicated dynamical network states, for instance the irregular balanced activity in cortical neural networks

Stress-Induced Impairment of a Working Memory Task: Role of Spiking Rate and Spiking History Predicted Discharge

Science.gov (United States)

Devilbiss, David M.; Jenison, Rick L.; Berridge, Craig W.

2012-01-01

Stress, pervasive in society, contributes to over half of all work place accidents a year and over time can contribute to a variety of psychiatric disorders including depression, schizophrenia, and post-traumatic stress disorder. Stress impairs higher cognitive processes, dependent on the prefrontal cortex (PFC) and that involve maintenance and integration of information over extended periods, including working memory and attention. Substantial evidence has demonstrated a relationship between patterns of PFC neuron spiking activity (action-potential discharge) and components of delayed-response tasks used to probe PFC-dependent cognitive function in rats and monkeys. During delay periods of these tasks, persistent spiking activity is posited to be essential for the maintenance of information for working memory and attention. However, the degree to which stress-induced impairment in PFC-dependent cognition involves changes in task-related spiking rates or the ability for PFC neurons to retain information over time remains unknown. In the current study, spiking activity was recorded from the medial PFC of rats performing a delayed-response task of working memory during acute noise stress (93 db). Spike history-predicted discharge (SHPD) for PFC neurons was quantified as a measure of the degree to which ongoing neuronal discharge can be predicted by past spiking activity and reflects the degree to which past information is retained by these neurons over time. We found that PFC neuron discharge is predicted by their past spiking patterns for nearly one second. Acute stress impaired SHPD, selectively during delay intervals of the task, and simultaneously impaired task performance. Despite the reduction in delay-related SHPD, stress increased delay-related spiking rates. These findings suggest that neural codes utilizing SHPD within PFC networks likely reflects an additional important neurophysiological mechanism for maintenance of past information over time. Stress

Spikes and matter inhomogeneities in massless scalar field models

International Nuclear Information System (INIS)

Coley, A A; Lim, W C

2016-01-01

We shall discuss the general relativistic generation of spikes in a massless scalar field or stiff perfect fluid model. We first investigate orthogonally transitive (OT) G 2 stiff fluid spike models both heuristically and numerically, and give a new exact OT G 2 stiff fluid spike solution. We then present a new two-parameter family of non-OT G 2 stiff fluid spike solutions, obtained by the generalization of non-OT G 2 vacuum spike solutions to the stiff fluid case by applying Geroch’s transformation on a Jacobs seed. The dynamics of these new stiff fluid spike solutions is qualitatively different from that of the vacuum spike solutions in that the matter (stiff fluid) feels the spike directly and the stiff fluid spike solution can end up with a permanent spike. We then derive the evolution equations of non-OT G 2 stiff fluid models, including a second perfect fluid, in full generality, and briefly discuss some of their qualitative properties and their potential numerical analysis. Finally, we discuss how a fluid, and especially a stiff fluid or massless scalar field, affects the physics of the generation of spikes. (paper)

Synchrony-desynchrony in the tripartite model of fear: Predicting treatment outcome in clinically phobic children.

Science.gov (United States)

Benoit Allen, Kristy; Allen, Ben; Austin, Kristin E; Waldron, Jonathan C; Ollendick, Thomas H

2015-08-01

The tripartite model of fear posits that the fear response entails three loosely coupled components: subjective distress, behavioral avoidance, and physiological arousal. The concept of synchrony vs. desynchrony describes the degree to which changes in the activation of these components vary together (synchrony), independently, or inversely (both forms of desynchrony) over time. The present study assessed synchrony-desynchrony and its relationship to treatment outcome in a sample of 98 children with specific phobias both prior to and 1 week after receiving one-session treatment, a 3 h cognitive-behavioral intervention. The results suggest an overall pattern of desynchronous change whereby youth improved on behavioral avoidance and subjective distress following treatment, but their level of cardiovascular reactivity remained stable. However, we found evidence that synchronous change on the behavioral avoidance and subjective distress components was related to better treatment outcome, whereas desynchronous change on these components was related to poorer treatment outcome. These findings suggest that a fuller understanding of the three response systems and their interrelations in phobic youth may assist us in the assessment and treatment of these disorders, potentially leading to a more person-centered approach and eventually to enhanced treatment outcomes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Spiking neural P systems with multiple channels.

Science.gov (United States)

Peng, Hong; Yang, Jinyu; Wang, Jun; Wang, Tao; Sun, Zhang; Song, Xiaoxiao; Luo, Xiaohui; Huang, Xiangnian

2017-11-01

Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices. Copyright © 2017 Elsevier Ltd. All rights reserved.

Spike-timing dependent plasticity in the striatum

Directory of Open Access Journals (Sweden)

Elodie Fino

2010-06-01

Full Text Available The striatum is the major input nucleus of basal ganglia, an ensemble of interconnected sub-cortical nuclei associated with fundamental processes of action-selection and procedural learning and memory. The striatum receives afferents from the cerebral cortex and the thalamus. In turn, it relays the integrated information towards the basal ganglia output nuclei through which it operates a selected activation of behavioral effectors. The striatal output neurons, the GABAergic medium-sized spiny neurons (MSNs, are in charge of the detection and integration of behaviorally relevant information. This property confers to the striatum the ability to extract relevant information from the background noise and select cognitive-motor sequences adapted to environmental stimuli. As long-term synaptic efficacy changes are believed to underlie learning and memory, the corticostriatal long-term plasticity provides a fundamental mechanism for the function of the basal ganglia in procedural learning. Here, we reviewed the different forms of spike-timing dependent plasticity (STDP occurring at corticostriatal synapses. Most of the studies have focused on MSNs and their ability to develop long-term plasticity. Nevertheless, the striatal interneurons (the fast-spiking GABAergic, the NO synthase and cholinergic interneurons also receive monosynaptic afferents from the cortex and tightly regulated corticostriatal information processing. Therefore, it is important to take into account the variety of striatal neurons to fully understand the ability of striatum to develop long-term plasticity. Corticostriatal STDP with various spike-timing dependence have been observed depending on the neuronal sub-populations and experimental conditions. This complexity highlights the extraordinary potentiality in term of plasticity of the corticostriatal pathway.

A Parallel Supercomputer Implementation of a Biological Inspired Neural Network and its use for Pattern Recognition

International Nuclear Information System (INIS)

De Ladurantaye, Vincent; Lavoie, Jean; Bergeron, Jocelyn; Parenteau, Maxime; Lu Huizhong; Pichevar, Ramin; Rouat, Jean

2012-01-01

A parallel implementation of a large spiking neural network is proposed and evaluated. The neural network implements the binding by synchrony process using the Oscillatory Dynamic Link Matcher (ODLM). Scalability, speed and performance are compared for 2 implementations: Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA) running on clusters of multicore supercomputers and NVIDIA graphical processing units respectively. A global spiking list that represents at each instant the state of the neural network is described. This list indexes each neuron that fires during the current simulation time so that the influence of their spikes are simultaneously processed on all computing units. Our implementation shows a good scalability for very large networks. A complex and large spiking neural network has been implemented in parallel with success, thus paving the road towards real-life applications based on networks of spiking neurons. MPI offers a better scalability than CUDA, while the CUDA implementation on a GeForce GTX 285 gives the best cost to performance ratio. When running the neural network on the GTX 285, the processing speed is comparable to the MPI implementation on RQCHP's Mammouth parallel with 64 notes (128 cores).

High reproductive synchrony of Acropora (Anthozoa: Scleractinia) in the Gulf of Aqaba, Red Sea

KAUST Repository

Bouwmeester, Jessica

2015-01-05

Coral spawning in the northern Gulf of Aqaba has been reported to be asynchronous, making it almost unique when compared to other regions in the world. Here, we document the reproductive condition of Acropora corals in early June 2014 in Dahab, in the Gulf of Aqaba, 125 km south of previous studies conducted in Eilat, Israel. Seventy-eight percent of Acropora colonies from 14 species had mature eggs, indicating that most colonies will spawn on or around the June full moon, with a very high probability of multi-species synchronous spawning. Given the proximity to Eilat, we predict that a comparable sampling protocol would detect similar levels of reproductive synchrony throughout the Gulf of Aqaba consistent with the hypothesis that high levels of spawning synchrony are a feature of all speciose coral assemblages.

Analysis of infant cortical synchrony is constrained by the number of recording electrodes and the recording montage.

Science.gov (United States)

Tokariev, Anton; Vanhatalo, Sampsa; Palva, J Matias

2016-01-01

To assess how the recording montage in the neonatal EEG influences the detection of cortical source signals and their phase interactions. Scalp EEG was simulated by forward modeling 20-200 simultaneously active sources covering the cortical surface of a realistic neonatal head model. We assessed systematically how the number of scalp electrodes (11-85), analysis montage, or the size of cortical sources affect the detection of cortical phase synchrony. Statistical metrics were developed for quantifying the resolution and reliability of the montages. The findings converge to show that an increase in the number of recording electrodes leads to a systematic improvement in the detection of true cortical phase synchrony. While there is always a ceiling effect with respect to discernible cortical details, we show that the average and Laplacian montages exhibit superior specificity and sensitivity as compared to other conventional montages. Reliability in assessing true neonatal cortical synchrony is directly related to the choice of EEG recording and analysis configurations. Because of the high conductivity of the neonatal skull, the conventional neonatal EEG recordings are spatially far too sparse for pertinent studies, and this loss of information cannot be recovered by re-montaging during analysis. Future neonatal EEG studies will need prospective planning of recording configuration to allow analysis of spatial details required by each study question. Our findings also advice about the level of details in brain synchrony that can be studied with existing datasets or by using conventional EEG recordings. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Geomagnetic spikes on the core-mantle boundary

Science.gov (United States)

Davies, C. J.; Constable, C.

2017-12-01

Extreme variations of Earth's magnetic field occurred in the Levantine region around 1000 BC, where the field intensity rose and fell by a factor of 2-3 over a short time and confined spatial region. There is presently no coherent link between this intensity spike and the generating processes in Earth's liquid core. Here we test the attribution of a surface spike to a flux patch visible on the core-mantle boundary (CMB), calculating geometric and energetic bounds on resulting surface geomagnetic features. We show that the Levantine intensity high must span at least 60 degrees in longitude. Models providing the best trade-off between matching surface spike intensity, minimizing L1 and L2 misfit to the available data and satisfying core energy constraints produce CMB spikes 8-22 degrees wide with peak values of O(100) mT. We propose that the Levantine spike grew in place before migrating northward and westward, contributing to the growth of the axial dipole field seen in Holocene field models. Estimates of Ohmic dissipation suggest that diffusive processes, which are often neglected, likely govern the ultimate decay of geomagnetic spikes. Using these results, we search for the presence of spike-like features in geodynamo simulations.

Long-Term Trends and Temporal Synchrony in Plankton Richness, Diversity and Biomass Driven by Re-Oligotrophication and Climate across 17 Danish Lakes

Directory of Open Access Journals (Sweden)

Korhan Özkan

2016-09-01

Full Text Available A two-decade (1989–2008 time series of lake phyto- and zooplankton, water characteristics and climate in 17 Danish lakes was analysed to examine the long term changes and the effects of lake restoration efforts. The analyses of the pair-wise correlations across time series revealed a strong synchrony in climatic variables among the lakes. A significant, but weak increase in air temperature was observed and resulted in a corresponding increase in surface water temperature only in summer. Lake physico-chemical variables had weaker synchrony than climatic variables. Synchrony in water temperature and stratification was stronger than lake chemistry as the former is mostly affected by atmospheric energy flux. Synchrony in the taxonomic richness of the plankton groups and phytoplankton biomass was apparent, to a similar degree as observed for lake chemistry. The synchrony and the temporal trends in lake chemistry and plankton were more pronounced for the lakes with strong re-oligotrophication. Phytoplankton biomass decreased and plankton richness increased in these lakes, with a shift from Chlorophyta dominance towards more heterogeneous phytoplankton communities. Notably, a widespread significant positive trend in plankton richness was observed not only in lakes with strong re-oligotrophication but across all lakes. The widespread increase in plankton richness coincided with widespread decrease in phosphate and total nitrogen concentrations, as well as with the trends in climate indicating a likely joint effect of nutrient reduction and climate in driving lake plankton. However, temporal changes and synchrony as well as the recovery of richness and composition of lake plankton more coherently corresponded with the nutrient loading reduction across the Danish landscape, while the role of climate control of the lake plankton was less pronounced.

Bursts generate a non-reducible spike-pattern code

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Hugo G Eyherabide

2009-05-01

Full Text Available On the single-neuron level, precisely timed spikes can either constitute firing-rate codes or spike-pattern codes that utilize the relative timing between consecutive spikes. There has been little experimental support for the hypothesis that such temporal patterns contribute substantially to information transmission. Using grasshopper auditory receptors as a model system, we show that correlations between spikes can be used to represent behaviorally relevant stimuli. The correlations reflect the inner structure of the spike train: a succession of burst-like patterns. We demonstrate that bursts with different spike counts encode different stimulus features, such that about 20% of the transmitted information corresponds to discriminating between different features, and the remaining 80% is used to allocate these features in time. In this spike-pattern code, the "what" and the "when" of the stimuli are encoded in the duration of each burst and the time of burst onset, respectively. Given the ubiquity of burst firing, we expect similar findings also for other neural systems.

Stochastic Variational Learning in Recurrent Spiking Networks

Directory of Open Access Journals (Sweden)

Danilo eJimenez Rezende

2014-04-01

Full Text Available The ability to learn and perform statistical inference with biologically plausible recurrent network of spiking neurons is an important step towards understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators conveying information about ``novelty on a statistically rigorous ground.Simulations show that our model is able to learn bothstationary and non-stationary patterns of spike trains.We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

Stochastic variational learning in recurrent spiking networks.

Science.gov (United States)

Jimenez Rezende, Danilo; Gerstner, Wulfram

2014-01-01

The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators) conveying information about "novelty" on a statistically rigorous ground. Simulations show that our model is able to learn both stationary and non-stationary patterns of spike trains. We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

A Low Cost VLSI Architecture for Spike Sorting Based on Feature Extraction with Peak Search

Directory of Open Access Journals (Sweden)

Yuan-Jyun Chang

2016-12-01

Full Text Available The goal of this paper is to present a novel VLSI architecture for spike sorting with high classification accuracy, low area costs and low power consumption. A novel feature extraction algorithm with low computational complexities is proposed for the design of the architecture. In the feature extraction algorithm, a spike is separated into two portions based on its peak value. The area of each portion is then used as a feature. The algorithm is simple to implement and less susceptible to noise interference. Based on the algorithm, a novel architecture capable of identifying peak values and computing spike areas concurrently is proposed. To further accelerate the computation, a spike can be divided into a number of segments for the local feature computation. The local features are subsequently merged with the global ones by a simple hardware circuit. The architecture can also be easily operated in conjunction with the circuits for commonly-used spike detection algorithms, such as the Non-linear Energy Operator (NEO. The architecture has been implemented by an Application-Specific Integrated Circuit (ASIC with 90-nm technology. Comparisons to the existing works show that the proposed architecture is well suited for real-time multi-channel spike detection and feature extraction requiring low hardware area costs, low power consumption and high classification accuracy.

Gamma synchrony: towards a translational biomarker for the treatment resistant symptoms of schizophrenia

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Gandal, Michael J.; Edgar, J. Christopher; Klook, Kerstin; Siegel, Steven J.

2011-01-01

The lack of efficacy for antipsychotics with respect to negative symptoms and cognitive deficits is a significant obstacle for the treatment of schizophrenia. Developing new drugs to target these symptoms requires appropriate neural biomarkers that can be investigated in model organisms, be used to track treatment response, and provide insight into pathophysiological disease mechanisms. A growing body of evidence indicates that neural oscillations in the gamma frequency range (30–80 Hz) are disturbed in schizophrenia. Gamma synchrony has been shown to mediate a host of sensory and cognitive functions, including perceptual encoding, selective attention, salience, and working memory – neurocognitive processes that are dysfunctional in schizophrenia and largely refractory to treatment. This review summarizes the current state of clinical literature with respect to gamma band responses (GBRs) in schizophrenia, focusing on resting and auditory paradigms. Next, preclinical studies of schizophrenia that have investigated gamma band activity are reviewed to gain insight into neural mechanisms associated with these deficits. We conclude that abnormalities in gamma synchrony are ubiquitous in schizophrenia and likely reflect an elevation in baseline cortical gamma synchrony (‘noise’) coupled with reduced stimulus-evoked GBRs (‘signal’). Such a model likely reflects hippocampal and cortical dysfunction, as well as reduced glutamatergic signaling with downstream GABAergic deficits, but is probably less influenced by dopaminergic abnormalities implicated in schizophrenia. Finally, we propose that analogous signal-to-noise deficits in the flow of cortical information in preclinical models are useful targets for the development of new drugs that target the treatment-resistant symptoms of schizophrenia. PMID:21349276

Temporal Pattern of Online Communication Spike Trains in Spreading a Scientific Rumor: How Often, Who Interacts with Whom?

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Ceyda eSanli

2015-09-01

Full Text Available We study complex time series (spike trains of online user communication while spreading messages about the discovery of the Higgs boson in Twitter. We focus on online social interactions among users such as retweet, mention, and reply, and construct different types of active (performing an action and passive (receiving an action spike trains for each user. The spike trains are analyzed by means of local variation, to quantify the temporal behavior of active and passive users, as a function of their activity and popularity. We show that the active spike trains are bursty, independently of their activation frequency. For passive spike trains, in contrast, the local variation of popular users presents uncorrelated (Poisson random dynamics. We further characterize the correlations of the local variation in different interactions. We obtain high values of correlation, and thus consistent temporal behavior, between retweets and mentions, but only for popular users, indicating that creating online attention suggests an alignment in the dynamics of the two interactions.

Inferring oscillatory modulation in neural spike trains.

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Arai, Kensuke; Kass, Robert E

2017-10-01

Oscillations are observed at various frequency bands in continuous-valued neural recordings like the electroencephalogram (EEG) and local field potential (LFP) in bulk brain matter, and analysis of spike-field coherence reveals that spiking of single neurons often occurs at certain phases of the global oscillation. Oscillatory modulation has been examined in relation to continuous-valued oscillatory signals, and independently from the spike train alone, but behavior or stimulus triggered firing-rate modulation, spiking sparseness, presence of slow modulation not locked to stimuli and irregular oscillations with large variability in oscillatory periods, present challenges to searching for temporal structures present in the spike train. In order to study oscillatory modulation in real data collected under a variety of experimental conditions, we describe a flexible point-process framework we call the Latent Oscillatory Spike Train (LOST) model to decompose the instantaneous firing rate in biologically and behaviorally relevant factors: spiking refractoriness, event-locked firing rate non-stationarity, and trial-to-trial variability accounted for by baseline offset and a stochastic oscillatory modulation. We also extend the LOST model to accommodate changes in the modulatory structure over the duration of the experiment, and thereby discover trial-to-trial variability in the spike-field coherence of a rat primary motor cortical neuron to the LFP theta rhythm. Because LOST incorporates a latent stochastic auto-regressive term, LOST is able to detect oscillations when the firing rate is low, the modulation is weak, and when the modulating oscillation has a broad spectral peak.

Spatial synchrony propagates through a forest food web via consumer-resource interactions

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Kyle J. ​Haynes; Andrew M. Liebhold; Todd M. Fearer; Guiming Wang; Gary W. Norman; Derek M. Johnson

2009-01-01

In many study systems, populations fluctuate synchronously across large regions. Several mechanisms have been advanced to explain this, but their importance in nature is often uncertain. Theoretical studies suggest that spatial synchrony initiated in one species through Moran effects may propagate among trophically linked species, but evidence for this in nature is...

Variation in Population Synchrony in a Multi-Species Seabird Community: Response to Changes in Predator Abundance.

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Gail S Robertson

Full Text Available Ecologically similar sympatric species, subject to typical environmental conditions, may be expected to exhibit synchronous temporal fluctuations in demographic parameters, while populations of dissimilar species might be expected to show less synchrony. Previous studies have tested for synchrony in different populations of single species, and those including data from more than one species have compared fluctuations in only one demographic parameter. We tested for synchrony in inter-annual changes in breeding population abundance and productivity among four tern species on Coquet Island, northeast England. We also examined how manipulation of one independent environmental variable (predator abundance influenced temporal changes in ecologically similar and dissimilar tern species. Changes in breeding abundance and productivity of ecologically similar species (Arctic Sterna paradisaea, Common S. hirundo and Roseate Terns S. dougallii were synchronous with one another over time, but not with a species with different foraging and breeding behaviour (Sandwich Terns Thalasseus sandvicensis. With respect to changes in predator abundance, there was no clear pattern. Roseate Tern abundance was negatively correlated with that of large gulls breeding on the island from 1975 to 2013, while Common Tern abundance was positively correlated with number of large gulls, and no significant correlations were found between large gull and Arctic and Sandwich Tern populations. Large gull abundance was negatively correlated with productivity of Arctic and Common Terns two years later, possibly due to predation risk after fledging, while no correlation with Roseate Tern productivity was found. The varying effect of predator abundance is most likely due to specific differences in the behaviour and ecology of even these closely-related species. Examining synchrony in multi-species assemblages improves our understanding of how whole communities react to long-term changes

Motor correlates of models of secondary bilateral synchrony and multiple epileptic foci

Czech Academy of Sciences Publication Activity Database

Jiruška, Přemysl; Prokš, J.; Otáhal, Jakub; Mareš, Pavel

2007-01-01

Roč. 16, č. 7 (2007), s. 627-635 ISSN 1059-1311 R&D Projects: GA ČR(CZ) GA309/03/0770; GA ČR GA304/05/2582 Institutional research plan: CEZ:AV0Z5011922 Keywords : epileptic foci * secondary bilateral synchrony * neocortex Subject RIV: ED - Physiology Impact factor: 1.815, year: 2007

Motif statistics and spike correlations in neuronal networks

International Nuclear Information System (INIS)

Hu, Yu; Shea-Brown, Eric; Trousdale, James; Josić, Krešimir

2013-01-01

Motifs are patterns of subgraphs of complex networks. We studied the impact of such patterns of connectivity on the level of correlated, or synchronized, spiking activity among pairs of cells in a recurrent network of integrate and fire neurons. For a range of network architectures, we find that the pairwise correlation coefficients, averaged across the network, can be closely approximated using only three statistics of network connectivity. These are the overall network connection probability and the frequencies of two second order motifs: diverging motifs, in which one cell provides input to two others, and chain motifs, in which two cells are connected via a third intermediary cell. Specifically, the prevalence of diverging and chain motifs tends to increase correlation. Our method is based on linear response theory, which enables us to express spiking statistics using linear algebra, and a resumming technique, which extrapolates from second order motifs to predict the overall effect of coupling on network correlation. Our motif-based results seek to isolate the effect of network architecture perturbatively from a known network state. (paper)

Stimulus Sensitivity of a Spiking Neural Network Model

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Chevallier, Julien

2018-02-01

Some recent papers relate the criticality of complex systems to their maximal capacity of information processing. In the present paper, we consider high dimensional point processes, known as age-dependent Hawkes processes, which have been used to model spiking neural networks. Using mean-field approximation, the response of the network to a stimulus is computed and we provide a notion of stimulus sensitivity. It appears that the maximal sensitivity is achieved in the sub-critical regime, yet almost critical for a range of biologically relevant parameters.

Structural (operational) synchrony of EEG alpha activity during an auditory memory task.

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Fingelkurts, Andrew; Fingelkurts, Alexander; Krause, Christina; Kaplan, Alexander; Borisov, Sergei; Sams, Mikko

2003-09-01

Memory paradigms are often used in psycho-physiological experiments in order to understand the neural basis underlying cognitive processes. One of the fundamental problems encountered in memory research is how specific and complementary cortical structures interact with each other during episodic encoding and retrieval. A key aspect of the research described below was estimating the coupling of rapid transition processes (in terms of EEG description) which occur in separate cortical areas rather than estimating the routine phase-frequency synchrony in terms of correlation and coherency. It is assumed that these rapid transition processes in the EEG amplitude correspond to the "switching on/off" of brain elemental operations. By making a quantitative estimate of the EEG structural synchrony of alpha-band power between different EEG channels, it was shown that short-term memory has the emergent property of a multiregional neuronal network, and is not the product of strictly hierarchical processing based on convergence through association regions. Moreover, it was demonstrated that the dynamic temporal structure of alpha activity is strongly correlated to the dynamic structure of working memory.

Developmental synchrony of thalamocortical circuits in the neonatal brain.

Science.gov (United States)

Poh, Joann S; Li, Yue; Ratnarajah, Nagulan; Fortier, Marielle V; Chong, Yap-Seng; Kwek, Kenneth; Saw, Seang-Mei; Gluckman, Peter D; Meaney, Michael J; Qiu, Anqi

2015-08-01

The thalamus is a deep gray matter structure and consists of axonal fibers projecting to the entire cortex, which provide the anatomical support for its sensorimotor and higher-level cognitive functions. There is limited in vivo evidence on the normal thalamocortical development, especially in early life. In this study, we aimed to investigate the developmental patterns of the cerebral cortex, the thalamic substructures, and their connectivity with the cortex in the first few weeks of the postnatal brain. We hypothesized that there is developmental synchrony of the thalamus, its cortical projections, and corresponding target cortical structures. We employed diffusion tensor imaging (DTI) and divided the thalamus into five substructures respectively connecting to the frontal, precentral, postcentral, temporal, and parietal and occipital cortex. T2-weighted magnetic resonance imaging (MRI) was used to measure cortical thickness. We found age-related increases in cortical thickness of bilateral frontal cortex and left temporal cortex in the early postnatal brain. We also found that the development of the thalamic substructures was synchronized with that of their respective thalamocortical connectivity in the first few weeks of the postnatal life. In particular, the right thalamo-frontal substructure had the fastest growth in the early postnatal brain. Our study suggests that the distinct growth patterns of the thalamic substructures are in synchrony with those of the cortex in early life, which may be critical for the development of the cortical and subcortical functional specialization. Copyright © 2015 Elsevier Inc. All rights reserved.

Linking investment spikes and productivity growth

NARCIS (Netherlands)

Geylani, P.C.; Stefanou, S.E.

2013-01-01

We investigate the relationship between productivity growth and investment spikes using Census Bureau’s plant-level dataset for the U.S. food manufacturing industry. There are differences in productivity growth and investment spike patterns across different sub-industries and food manufacturing

Synchrony in the snowshoe hare cycle in Northwestern North America, 1970-2012

Science.gov (United States)

C.J. Krebs; K. Kielland; J.P Bryant; M. O' Donoghue; F. Doyle; C. McIntyre; D. DiFolco; N. Berg; S. Carriere; R. Boonstra; S. Boutin; A. J. Kenney; D. G. Reid; K. Bodony; J. Putera; H. K. Timm; T. Burke.

2013-01-01

Snowshoe hares (Lepus americanus Erxleben, 1777) fluctuate in 9–10 year cycles throughout much of their North American range. Regional synchrony has been assumed to be the rule for these cycles, so that hare populations in virtually all of northwestern North America have been assumed to be in phase. We gathered qualitative and quantitative data on...

Solving constraint satisfaction problems with networks of spiking neurons

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Zeno eJonke

2016-03-01

Full Text Available Network of neurons in the brain apply – unlike processors in our current generation ofcomputer hardware – an event-based processing strategy, where short pulses (spikes areemitted sparsely by neurons to signal the occurrence of an event at a particular point intime. Such spike-based computations promise to be substantially more power-efficient thantraditional clocked processing schemes. However it turned out to be surprisingly difficult todesign networks of spiking neurons that can solve difficult computational problems on the levelof single spikes (rather than rates of spikes. We present here a new method for designingnetworks of spiking neurons via an energy function. Furthermore we show how the energyfunction of a network of stochastically firing neurons can be shaped in a quite transparentmanner by composing the networks of simple stereotypical network motifs. We show that thisdesign approach enables networks of spiking neurons to produce approximate solutions todifficult (NP-hard constraint satisfaction problems from the domains of planning/optimizationand verification/logical inference. The resulting networks employ noise as a computationalresource. Nevertheless the timing of spikes (rather than just spike rates plays an essential rolein their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines and Gibbs sampling.

Surfing a spike wave down the ventral stream.

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VanRullen, Rufin; Thorpe, Simon J

2002-10-01

Numerous theories of neural processing, often motivated by experimental observations, have explored the computational properties of neural codes based on the absolute or relative timing of spikes in spike trains. Spiking neuron models and theories however, as well as their experimental counterparts, have generally been limited to the simulation or observation of isolated neurons, isolated spike trains, or reduced neural populations. Such theories would therefore seem inappropriate to capture the properties of a neural code relying on temporal spike patterns distributed across large neuronal populations. Here we report a range of computer simulations and theoretical considerations that were designed to explore the possibilities of one such code and its relevance for visual processing. In a unified framework where the relation between stimulus saliency and spike relative timing plays the central role, we describe how the ventral stream of the visual system could process natural input scenes and extract meaningful information, both rapidly and reliably. The first wave of spikes generated in the retina in response to a visual stimulation carries information explicitly in its spatio-temporal structure: the most salient information is represented by the first spikes over the population. This spike wave, propagating through a hierarchy of visual areas, is regenerated at each processing stage, where its temporal structure can be modified by (i). the selectivity of the cortical neurons, (ii). lateral interactions and (iii). top-down attentional influences from higher order cortical areas. The resulting model could account for the remarkable efficiency and rapidity of processing observed in the primate visual system.

Dynamics and Synchrony of Pancreatic beta-cells and Islets

DEFF Research Database (Denmark)

Pedersen, Morten Gram

2006-01-01

description of these processes and their interactions would provide important input in the search for a better treatment of the disease. The thesis describes several aspects of mathematical modeling of beta-cells relevant for the understanding of glucose stimulated insulin secretion. It consists...... and the synchronized behavior of many coupled beta-cells as well as to the synchrony of islets. Rather than developing new biophysical models, the thesis investigates existing models, their integration and simplifications, and analyzed the corresponding dynamics, in order to use these models for investigating...

Coherence and phase synchrony analyses of EEG signals in Mild Cognitive Impairment (MCI): A study of functional brain connectivity

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Handayani, Nita; Haryanto, Freddy; Khotimah, Siti Nurul; Arif, Idam; Taruno, Warsito Purwo

2018-03-01

This paper presents an EEG study for coherence and phase synchrony in mild cognitive impairment (MCI) subjects. MCI is characterized by cognitive decline, which is an early stage of Alzheimer's disease (AD). AD is a neurodegenerative disorder with symptoms such as memory loss and cognitive impairment. EEG coherence is a statistical measure of correlation between signals from electrodes spatially separated on the scalp. The magnitude of phase synchrony is expressed in the phase locking value (PLV), a statistical measure of neuronal connectivity in the human brain. Brain signals were recorded using an Emotiv Epoc 14-channel wireless EEG at a sampling frequency of 128 Hz. In this study, we used 22 elderly subjects consisted of 10 MCI subjects and 12 healthy subjects as control group. The coherence between each electrode pair was measured for all frequency bands (delta, theta, alpha and beta). In the MCI subjects, the value of coherence and phase synchrony was generally lower than in the healthy subjects especially in the beta frequency. A decline of intrahemisphere coherence in the MCI subjects occurred in the left temporo-parietal-occipital region. The pattern of decline in MCI coherence is associated with decreased cholinergic connectivity along the path that connects the temporal, occipital, and parietal areas of the brain to the frontal area of the brain. EEG coherence and phase synchrony are able to distinguish persons who suffer AD in the early stages from healthy elderly subjects.

Spike timing precision of neuronal circuits.

Science.gov (United States)

Kilinc, Deniz; Demir, Alper

2018-04-17

Spike timing is believed to be a key factor in sensory information encoding and computations performed by the neurons and neuronal circuits. However, the considerable noise and variability, arising from the inherently stochastic mechanisms that exist in the neurons and the synapses, degrade spike timing precision. Computational modeling can help decipher the mechanisms utilized by the neuronal circuits in order to regulate timing precision. In this paper, we utilize semi-analytical techniques, which were adapted from previously developed methods for electronic circuits, for the stochastic characterization of neuronal circuits. These techniques, which are orders of magnitude faster than traditional Monte Carlo type simulations, can be used to directly compute the spike timing jitter variance, power spectral densities, correlation functions, and other stochastic characterizations of neuronal circuit operation. We consider three distinct neuronal circuit motifs: Feedback inhibition, synaptic integration, and synaptic coupling. First, we show that both the spike timing precision and the energy efficiency of a spiking neuron are improved with feedback inhibition. We unveil the underlying mechanism through which this is achieved. Then, we demonstrate that a neuron can improve on the timing precision of its synaptic inputs, coming from multiple sources, via synaptic integration: The phase of the output spikes of the integrator neuron has the same variance as that of the sample average of the phases of its inputs. Finally, we reveal that weak synaptic coupling among neurons, in a fully connected network, enables them to behave like a single neuron with a larger membrane area, resulting in an improvement in the timing precision through cooperation.

Neuro-Inspired Spike-Based Motion: From Dynamic Vision Sensor to Robot Motor Open-Loop Control through Spike-VITE

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Fernando Perez-Peña

2013-11-01

Full Text Available In this paper we present a complete spike-based architecture: from a Dynamic Vision Sensor (retina to a stereo head robotic platform. The aim of this research is to reproduce intended movements performed by humans taking into account as many features as possible from the biological point of view. This paper fills the gap between current spike silicon sensors and robotic actuators by applying a spike processing strategy to the data flows in real time. The architecture is divided into layers: the retina, visual information processing, the trajectory generator layer which uses a neuroinspired algorithm (SVITE that can be replicated into as many times as DoF the robot has; and finally the actuation layer to supply the spikes to the robot (using PFM. All the layers do their tasks in a spike-processing mode, and they communicate each other through the neuro-inspired AER protocol. The open-loop controller is implemented on FPGA using AER interfaces developed by RTC Lab. Experimental results reveal the viability of this spike-based controller. Two main advantages are: low hardware resources (2% of a Xilinx Spartan 6 and power requirements (3.4 W to control a robot with a high number of DoF (up to 100 for a Xilinx Spartan 6. It also evidences the suitable use of AER as a communication protocol between processing and actuation.

Neuro-Inspired Spike-Based Motion: From Dynamic Vision Sensor to Robot Motor Open-Loop Control through Spike-VITE

Science.gov (United States)

Perez-Peña, Fernando; Morgado-Estevez, Arturo; Linares-Barranco, Alejandro; Jimenez-Fernandez, Angel; Gomez-Rodriguez, Francisco; Jimenez-Moreno, Gabriel; Lopez-Coronado, Juan

2013-01-01

In this paper we present a complete spike-based architecture: from a Dynamic Vision Sensor (retina) to a stereo head robotic platform. The aim of this research is to reproduce intended movements performed by humans taking into account as many features as possible from the biological point of view. This paper fills the gap between current spike silicon sensors and robotic actuators by applying a spike processing strategy to the data flows in real time. The architecture is divided into layers: the retina, visual information processing, the trajectory generator layer which uses a neuroinspired algorithm (SVITE) that can be replicated into as many times as DoF the robot has; and finally the actuation layer to supply the spikes to the robot (using PFM). All the layers do their tasks in a spike-processing mode, and they communicate each other through the neuro-inspired AER protocol. The open-loop controller is implemented on FPGA using AER interfaces developed by RTC Lab. Experimental results reveal the viability of this spike-based controller. Two main advantages are: low hardware resources (2% of a Xilinx Spartan 6) and power requirements (3.4 W) to control a robot with a high number of DoF (up to 100 for a Xilinx Spartan 6). It also evidences the suitable use of AER as a communication protocol between processing and actuation. PMID:24264330

Active drumming experience increases infants' sensitivity to audiovisual synchrony during observed drumming actions

NARCIS (Netherlands)

Gerson, S.A.; Schiavio, A.A.R.; Timmers, R.; Hunnius, S.

2015-01-01

In the current study, we examined the role of active experience on sensitivity to multisensory synchrony in six-month-old infants in a musical context. In the first of two experiments, we trained infants to produce a novel multimodal effect (i.e., a drum beat) and assessed the effects of this

Spike Pattern Structure Influences Synaptic Efficacy Variability Under STDP and Synaptic Homeostasis. II: Spike Shuffling Methods on LIF Networks

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Zedong Bi

2016-08-01

Full Text Available Synapses may undergo variable changes during plasticity because of the variability of spike patterns such as temporal stochasticity and spatial randomness. Here, we call the variability of synaptic weight changes during plasticity to be efficacy variability. In this paper, we investigate how four aspects of spike pattern statistics (i.e., synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations influence the efficacy variability under pair-wise additive spike-timing dependent plasticity (STDP and synaptic homeostasis (the mean strength of plastic synapses into a neuron is bounded, by implementing spike shuffling methods onto spike patterns self-organized by a network of excitatory and inhibitory leaky integrate-and-fire (LIF neurons. With the increase of the decay time scale of the inhibitory synaptic currents, the LIF network undergoes a transition from asynchronous state to weak synchronous state and then to synchronous bursting state. We first shuffle these spike patterns using a variety of methods, each designed to evidently change a specific pattern statistics; and then investigate the change of efficacy variability of the synapses under STDP and synaptic homeostasis, when the neurons in the network fire according to the spike patterns before and after being treated by a shuffling method. In this way, we can understand how the change of pattern statistics may cause the change of efficacy variability. Our results are consistent with those of our previous study which implements spike-generating models on converging motifs. We also find that burstiness/regularity is important to determine the efficacy variability under asynchronous states, while heterogeneity of cross-correlations is the main factor to cause efficacy variability when the network moves into synchronous bursting states (the states observed in epilepsy.

Joint Probability-Based Neuronal Spike Train Classification

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Yan Chen

2009-01-01

Full Text Available Neuronal spike trains are used by the nervous system to encode and transmit information. Euclidean distance-based methods (EDBMs have been applied to quantify the similarity between temporally-discretized spike trains and model responses. In this study, using the same discretization procedure, we developed and applied a joint probability-based method (JPBM to classify individual spike trains of slowly adapting pulmonary stretch receptors (SARs. The activity of individual SARs was recorded in anaesthetized, paralysed adult male rabbits, which were artificially-ventilated at constant rate and one of three different volumes. Two-thirds of the responses to the 600 stimuli presented at each volume were used to construct three response models (one for each stimulus volume consisting of a series of time bins, each with spike probabilities. The remaining one-third of the responses where used as test responses to be classified into one of the three model responses. This was done by computing the joint probability of observing the same series of events (spikes or no spikes, dictated by the test response in a given model and determining which probability of the three was highest. The JPBM generally produced better classification accuracy than the EDBM, and both performed well above chance. Both methods were similarly affected by variations in discretization parameters, response epoch duration, and two different response alignment strategies. Increasing bin widths increased classification accuracy, which also improved with increased observation time, but primarily during periods of increasing lung inflation. Thus, the JPBM is a simple and effective method performing spike train classification.

Fitting neuron models to spike trains

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Cyrille eRossant

2011-02-01

Full Text Available Computational modeling is increasingly used to understand the function of neural circuitsin systems neuroscience.These studies require models of individual neurons with realisticinput-output properties.Recently, it was found that spiking models can accurately predict theprecisely timed spike trains produced by cortical neurons in response tosomatically injected currents,if properly fitted. This requires fitting techniques that are efficientand flexible enough to easily test different candidate models.We present a generic solution, based on the Brian simulator(a neural network simulator in Python, which allowsthe user to define and fit arbitrary neuron models to electrophysiological recordings.It relies on vectorization and parallel computing techniques toachieve efficiency.We demonstrate its use on neural recordings in the barrel cortex andin the auditory brainstem, and confirm that simple adaptive spiking modelscan accurately predict the response of cortical neurons. Finally, we show how a complexmulticompartmental model can be reduced to a simple effective spiking model.

Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE

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Pietro Quaglio

2017-05-01

Full Text Available Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs. STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons. In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST. We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE analysis.

Constructing Precisely Computing Networks with Biophysical Spiking Neurons.

Science.gov (United States)

Schwemmer, Michael A; Fairhall, Adrienne L; Denéve, Sophie; Shea-Brown, Eric T

2015-07-15

While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates. In equivalent spiking implementations, firing is assumed to be random such that averaging across populations of neurons recovers the rate-based approach. Recently, however, Denéve and colleagues have suggested that the spiking behavior of neurons may be fundamental to how neuronal networks compute, with precise spike timing determined by each neuron's contribution to producing the desired output (Boerlin and Denéve, 2011; Boerlin et al., 2013). By postulating that each neuron fires to reduce the error in the network's output, it was demonstrated that linear computations can be performed by networks of integrate-and-fire neurons that communicate through instantaneous synapses. This left open, however, the possibility that realistic networks, with conductance-based neurons with subthreshold nonlinearity and the slower timescales of biophysical synapses, may not fit into this framework. Here, we show how the spike-based approach can be extended to biophysically plausible networks. We then show that our network reproduces a number of key features of cortical networks including irregular and Poisson-like spike times and a tight balance between excitation and inhibition. Lastly, we discuss how the behavior of our model scales with network size or with the number of neurons "recorded" from a larger computing network. These results significantly increase the biological plausibility of the spike-based approach to network computation. We derive a network of neurons with standard spike-generating currents and synapses with realistic timescales that computes based upon the principle that the precise timing of each spike is important for the computation. We then show that our network reproduces a number of key features of cortical networks

Automated analysis of calcium spiking profiles with CaSA software: two case studies from root-microbe symbioses.

Science.gov (United States)

Russo, Giulia; Spinella, Salvatore; Sciacca, Eva; Bonfante, Paola; Genre, Andrea

2013-12-26

Repeated oscillations in intracellular calcium (Ca2+) concentration, known as Ca2+ spiking signals, have been described in plants for a limited number of cellular responses to biotic or abiotic stimuli and most notably the common symbiotic signaling pathway (CSSP) which mediates the recognition by their plant hosts of two endosymbiotic microbes, arbuscular mycorrhizal (AM) fungi and nitrogen fixing rhizobia. The detailed analysis of the complexity and variability of the Ca2+ spiking patterns which have been revealed in recent studies requires both extensive datasets and sophisticated statistical tools. As a contribution, we have developed automated Ca2+ spiking analysis (CaSA) software that performs i) automated peak detection, ii) statistical analyses based on the detected peaks, iii) autocorrelation analysis of peak-to-peak intervals to highlight major traits in the spiking pattern.We have evaluated CaSA in two experimental studies. In the first, CaSA highlighted unpredicted differences in the spiking patterns induced in Medicago truncatula root epidermal cells by exudates of the AM fungus Gigaspora margarita as a function of the phosphate concentration in the growth medium of both host and fungus. In the second study we compared the spiking patterns triggered by either AM fungal or rhizobial symbiotic signals. CaSA revealed the existence of different patterns in signal periodicity, which are thought to contribute to the so-called Ca2+ signature. We therefore propose CaSA as a useful tool for characterizing oscillatory biological phenomena such as Ca2+ spiking.

Analysis of bidirectional pattern synchrony of concentration-secretion pairs: implementation in the human testicular and adrenal axes.

Science.gov (United States)

Liu, Peter Y; Pincus, Steven M; Keenan, Daniel M; Roelfsema, Ferdinand; Veldhuis, Johannes D

2005-02-01

The hypothalamo-pituitary-testicular and hypothalamo-pituitary-adrenal axes are prototypical coupled neuroendocrine systems. In the present study, we contrasted in vivo linkages within and between these two axes using methods without linearity assumptions. We examined 11 young (21-31 yr) and 8 older (62-74 yr) men who underwent frequent (every 2.5 min) blood sampling overnight for paired measurement of LH and testosterone and 35 adults (17 women and 18 men; 26-77 yr old) who underwent adrenocorticotropic hormone (ACTH) and cortisol measurements every 10 min for 24 h. To mirror physiological interactions, hormone secretion was first deconvolved from serial concentrations with a waveform-independent biexponential elimination model. Feedforward synchrony, feedback synchrony, and the difference in feedforward-feedback synchrony were quantified by the cross-approximate entropy (X-ApEn) statistic. These were applied in a forward (LH concentration template, examining pattern recurrence in testosterone secretion), reverse (testosterone concentration template, examining pattern recurrence in LH secretion), and differential (forward minus reverse) manner, respectively. Analogous concentration-secretion X-ApEn estimates were calculated from ACTH-cortisol pairs. X-ApEn, a scale- and model-independent measure of pattern reproducibility, disclosed 1) greater testosterone-LH feedback coordination than LH-testosterone feedforward synchrony in healthy men and significant and symmetric erosion of both feedforward and feedback linkages with aging; 2) more synchronous ACTH concentration-dependent feedforward than feedback drive of cortisol secretion, independent of gender and age; and 3) enhanced detection of bidirectional physiological regulation by in vivo pairwise concentration-secretion compared with concentration-concentration analyses. The linking of relevant biological input to output signals and vice versa should be useful in the dissection of the reciprocal control of

Solving Constraint Satisfaction Problems with Networks of Spiking Neurons.

Science.gov (United States)

Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang

2016-01-01

Network of neurons in the brain apply-unlike processors in our current generation of computer hardware-an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However, it turns out to be surprisingly difficult to design networks of spiking neurons that can solve difficult computational problems on the level of single spikes, rather than rates of spikes. We present here a new method for designing networks of spiking neurons via an energy function. Furthermore, we show how the energy function of a network of stochastically firing neurons can be shaped in a transparent manner by composing the networks of simple stereotypical network motifs. We show that this design approach enables networks of spiking neurons to produce approximate solutions to difficult (NP-hard) constraint satisfaction problems from the domains of planning/optimization and verification/logical inference. The resulting networks employ noise as a computational resource. Nevertheless, the timing of spikes plays an essential role in their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling.

Recent progress in multi-electrode spike sorting methods.

Science.gov (United States)

Lefebvre, Baptiste; Yger, Pierre; Marre, Olivier

2016-11-01

In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing algorithms. Algorithms need to solve a source separation problem, also known as spike sorting. However, these new devices challenge the classical way to do spike sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike sorting algorithms. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multineuron spike train analysis with R-convolution linear combination kernel.

Science.gov (United States)

Tezuka, Taro

2018-06-01

A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Sequentially switching cell assemblies in random inhibitory networks of spiking neurons in the striatum.

Science.gov (United States)

Ponzi, Adam; Wickens, Jeff

2010-04-28

The striatum is composed of GABAergic medium spiny neurons with inhibitory collaterals forming a sparse random asymmetric network and receiving an excitatory glutamatergic cortical projection. Because the inhibitory collaterals are sparse and weak, their role in striatal network dynamics is puzzling. However, here we show by simulation of a striatal inhibitory network model composed of spiking neurons that cells form assemblies that fire in sequential coherent episodes and display complex identity-temporal spiking patterns even when cortical excitation is simply constant or fluctuating noisily. Strongly correlated large-scale firing rate fluctuations on slow behaviorally relevant timescales of hundreds of milliseconds are shown by members of the same assembly whereas members of different assemblies show strong negative correlation, and we show how randomly connected spiking networks can generate this activity. Cells display highly irregular spiking with high coefficients of variation, broadly distributed low firing rates, and interspike interval distributions that are consistent with exponentially tailed power laws. Although firing rates vary coherently on slow timescales, precise spiking synchronization is absent in general. Our model only requires the minimal but striatally realistic assumptions of sparse to intermediate random connectivity, weak inhibitory synapses, and sufficient cortical excitation so that some cells are depolarized above the firing threshold during up states. Our results are in good qualitative agreement with experimental studies, consistent with recently determined striatal anatomy and physiology, and support a new view of endogenously generated metastable state switching dynamics of the striatal network underlying its information processing operations.

An improved model to simulate pressurized water reactor iodine spiking behavior under power transient conditions

International Nuclear Information System (INIS)

Ho, J.C.

2004-01-01

Among those theories to interpret the PWR iodine spiking behaviors, the most accepted concept is based on steam formation and condensation in damaged fuel rods. Due to the complex nature of the phenomenon, a comprehensive model of the iodine behavior has not yet been successfully developed. In 1992 a new empirical model was introduced to establish a correlation with the operating parameters. The comparison results of the predicted iodine-131 equivalent activity value with the operating radiochemistry database was off by 23%. This paper presents an improved model. Although it is still an empirical model which also gives a first order estimation of the peak iodine spiking magnitude, the deviation between prediction and measurement was reduced to ∼7%. It is believed that this improved model can be used for better prediction and control of the iodine spiking magnitude resulted from failed fuel rods during power transients or plant shutdown. (author)

Comparison of electrodialytic removal of Cu from spiked kaolinite, spiked soil and industrially polluted soil

DEFF Research Database (Denmark)

Ottosen, Lisbeth M.; Lepkova, Katarina; Kubal, Martin

2006-01-01

Electrokinetic remediation methods for removal of heavy metals from polluted soils have been subjected for quite intense research during the past years since these methods are well suitable for fine-grained soils where other remediation methods fail. Electrodialytic remediation is an electrokinetic...... remediation method which is based on applying an electric DC field and the use of ion exchange membranes that ensures the main transport of heavy metals to be out of the pollutes soil. An experimental investigation was made with electrodialytic removal of Cu from spiked kaolinite, spiked soil and industrially...... polluted soil under the same operational conditions (constant current density 0.2 mA/cm2 and duration 28 days). The results of the present paper show that caution must be taken when generalising results obtained in spiked kaolinite to remediation of industrially polluted soils, as it was shown...

Toxicity of nickel-spiked freshwater sediments to benthic invertebrates-Spiking methodology, species sensitivity, and nickel bioavailability

Science.gov (United States)

Besser, John M.; Brumbaugh, William G.; Kemble, Nile E.; Ivey, Chris D.; Kunz, James L.; Ingersoll, Christopher G.; Rudel, David

2011-01-01

This report summarizes data from studies of the toxicity and bioavailability of nickel in nickel-spiked freshwater sediments. The goal of these studies was to generate toxicity and chemistry data to support development of broadly applicable sediment quality guidelines for nickel. The studies were conducted as three tasks, which are presented here as three chapters: Task 1, Development of methods for preparation and toxicity testing of nickel-spiked freshwater sediments; Task 2, Sensitivity of benthic invertebrates to toxicity of nickel-spiked freshwater sediments; and Task 3, Effect of sediment characteristics on nickel bioavailability. Appendices with additional methodological details and raw chemistry and toxicity data for the three tasks are available online at http://pubs.usgs.gov/sir/2011/5225/downloads/.

Amplification of asynchronous inhibition-mediated synchronization by feedback in recurrent networks.

Directory of Open Access Journals (Sweden)

Sashi Marella

2010-02-01

Full Text Available Synchronization of 30-80 Hz oscillatory activity of the principle neurons in the olfactory bulb (mitral cells is believed to be important for odor discrimination. Previous theoretical studies of these fast rhythms in other brain areas have proposed that principle neuron synchrony can be mediated by short-latency, rapidly decaying inhibition. This phasic inhibition provides a narrow time window for the principle neurons to fire, thus promoting synchrony. However, in the olfactory bulb, the inhibitory granule cells produce long lasting, small amplitude, asynchronous and aperiodic inhibitory input and thus the narrow time window that is required to synchronize spiking does not exist. Instead, it has been suggested that correlated output of the granule cells could serve to synchronize uncoupled mitral cells through a mechanism called "stochastic synchronization", wherein the synchronization arises through correlation of inputs to two neural oscillators. Almost all work on synchrony due to correlations presumes that the correlation is imposed and fixed. Building on theory and experiments that we and others have developed, we show that increased synchrony in the mitral cells could produce an increase in granule cell activity for those granule cells that share a synchronous group of mitral cells. Common granule cell input increases the input correlation to the mitral cells and hence their synchrony by providing a positive feedback loop in correlation. Thus we demonstrate the emergence and temporal evolution of input correlation in recurrent networks with feedback. We explore several theoretical models of this idea, ranging from spiking models to an analytically tractable model.

A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations

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Jan eHahne

2015-09-01

Full Text Available Contemporary simulators for networks of point and few-compartment model neurons come with a plethora of ready-to-use neuron and synapse models and support complex network topologies. Recent technological advancements have broadened the spectrum of application further to the efficient simulation of brain-scale networks on supercomputers. In distributed network simulations the amount of spike data that accrues per millisecond and process is typically low, such that a common optimization strategy is to communicate spikes at relatively long intervals, where the upper limit is given by the shortest synaptic transmission delay in the network. This approach is well-suited for simulations that employ only chemical synapses but it has so far impeded the incorporation of gap-junction models, which require instantaneous neuronal interactions. Here, we present a numerical algorithm based on a waveform-relaxation technique which allows for network simulations with gap junctions in a way that is compatible with the delayed communication strategy. Using a reference implementation in the NEST simulator, we demonstrate that the algorithm and the required data structures can be smoothly integrated with existing code such that they complement the infrastructure for spiking connections. To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy...

Synchrony-optimized networks of non-identical Kuramoto oscillators

International Nuclear Information System (INIS)

Brede, Markus

2008-01-01

In this Letter we discuss a method for generating synchrony-optimized coupling architectures of Kuramoto oscillators with a heterogeneous distribution of native frequencies. The method allows us to relate the properties of the coupling network to its synchronizability. These relations were previously only established from a linear stability analysis of the identical oscillator case. We further demonstrate that the heterogeneity in the oscillator population produces heterogeneity in the optimal coupling network as well. Two rules for enhancing the synchronizability of a given network by a suitable placement of oscillators are given: (i) native frequencies of adjacent oscillators must be anti-correlated and (ii) frequency magnitudes should positively correlate with the degree of the node they are placed at

Visually Evoked Spiking Evolves While Spontaneous Ongoing Dynamics Persist

DEFF Research Database (Denmark)

Huys, Raoul; Jirsa, Viktor K; Darokhan, Ziauddin

2016-01-01

attractor. Its existence guarantees that evoked spiking return to the spontaneous state. However, the spontaneous ongoing spiking state and the visual evoked spiking states are qualitatively different and are separated by a threshold (separatrix). The functional advantage of this organization...

Multichannel interictal spike activity detection using time-frequency entropy measure.

Science.gov (United States)

Thanaraj, Palani; Parvathavarthini, B

2017-06-01

Localization of interictal spikes is an important clinical step in the pre-surgical assessment of pharmacoresistant epileptic patients. The manual selection of interictal spike periods is cumbersome and involves a considerable amount of analysis workload for the physician. The primary focus of this paper is to automate the detection of interictal spikes for clinical applications in epilepsy localization. The epilepsy localization procedure involves detection of spikes in a multichannel EEG epoch. Therefore, a multichannel Time-Frequency (T-F) entropy measure is proposed to extract features related to the interictal spike activity. Least squares support vector machine is used to train the proposed feature to classify the EEG epochs as either normal or interictal spike period. The proposed T-F entropy measure, when validated with epilepsy dataset of 15 patients, shows an interictal spike classification accuracy of 91.20%, sensitivity of 100% and specificity of 84.23%. Moreover, the area under the curve of Receiver Operating Characteristics plot of 0.9339 shows the superior classification performance of the proposed T-F entropy measure. The results of this paper show a good spike detection accuracy without any prior information about the spike morphology.

Physical and Relational Aggression in Young Children: The Role of Mother-Child Interactional Synchrony

Science.gov (United States)

Ambrose, Holly N.; Menna, Rosanne

2013-01-01

This study examined the relationships between the quality of parent-child interactions, specifically interactional synchrony (IS), and physical and relational aggression in young children. Seventy-three children (3-6 years; 44 males, 29 females) and their mothers participated in this study. The children's level of aggression was assessed through…

Multi-species spawning synchrony within scleractinian coral assemblages in the Red Sea

KAUST Repository

Bouwmeester, Jessica; Baird, Andrew Hamilton; Chen, C. J.; Guest, James R.; Vicentuan, Kareen C.; Berumen, Michael L.

2014-01-01

Early work on coral reproduction in the far northern Red Sea suggested that the spawning times of ecologically abundant species did not overlap, unlike on the Great Barrier Reef where many species spawn with high synchrony. In contrast, recent work in the northern and central Red Sea indicates a high degree of synchrony in the reproductive condition of Acropora species: over 90 % of species sampled in April/May contain mature gametes. However, it has yet to be determined when most Acropora release their gametes. In addition, there is a lack of data for other ecologically important scleractinian species such as merulinids and poritids. Here, we document the date and time of spawning for 51 species in the central Red Sea over three consecutive years, and the month of spawning for an additional 17 species inferred from the presence of mature gametes. Spawning occurs on nights around the full moon, the spawning season lasts at least 4 months from April until July, and observations are consistent with the few other records from the Red Sea. The number of Acropora species spawning was highest in April with 13 species spawning two nights before the full moon in 2011, 13 species spawning on the night of the full moon in 2012, and eight species spawning four nights after the full moon in 2013. The total number of species spawning was high in April, May, and June and involved 15–19 species per month in 2012. Only four species spawned in July 2012. Few regions worldwide have been similarly sampled and include the Philippines, Okinawa in Japan, and Palau, where spawning patterns are very similar to those in the central Red Sea and where corals spawn on nights around the full moon over a period of 3–4 months. In particular, in all four locations, Acropora are among the first species to spawn. Our results add to a growing body of evidence indicating that multi-species spawning synchrony is a feature of all speciose coral assemblages.

Multi-species spawning synchrony within scleractinian coral assemblages in the Red Sea

KAUST Repository

Bouwmeester, Jessica

2014-09-21

Early work on coral reproduction in the far northern Red Sea suggested that the spawning times of ecologically abundant species did not overlap, unlike on the Great Barrier Reef where many species spawn with high synchrony. In contrast, recent work in the northern and central Red Sea indicates a high degree of synchrony in the reproductive condition of Acropora species: over 90 % of species sampled in April/May contain mature gametes. However, it has yet to be determined when most Acropora release their gametes. In addition, there is a lack of data for other ecologically important scleractinian species such as merulinids and poritids. Here, we document the date and time of spawning for 51 species in the central Red Sea over three consecutive years, and the month of spawning for an additional 17 species inferred from the presence of mature gametes. Spawning occurs on nights around the full moon, the spawning season lasts at least 4 months from April until July, and observations are consistent with the few other records from the Red Sea. The number of Acropora species spawning was highest in April with 13 species spawning two nights before the full moon in 2011, 13 species spawning on the night of the full moon in 2012, and eight species spawning four nights after the full moon in 2013. The total number of species spawning was high in April, May, and June and involved 15–19 species per month in 2012. Only four species spawned in July 2012. Few regions worldwide have been similarly sampled and include the Philippines, Okinawa in Japan, and Palau, where spawning patterns are very similar to those in the central Red Sea and where corals spawn on nights around the full moon over a period of 3–4 months. In particular, in all four locations, Acropora are among the first species to spawn. Our results add to a growing body of evidence indicating that multi-species spawning synchrony is a feature of all speciose coral assemblages.

ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains

Science.gov (United States)

Canova, Carlos; Denker, Michael; Gerstein, George; Helias, Moritz

2016-01-01

With the ability to observe the activity from large numbers of neurons simultaneously using modern recording technologies, the chance to identify sub-networks involved in coordinated processing increases. Sequences of synchronous spike events (SSEs) constitute one type of such coordinated spiking that propagates activity in a temporally precise manner. The synfire chain was proposed as one potential model for such network processing. Previous work introduced a method for visualization of SSEs in massively parallel spike trains, based on an intersection matrix that contains in each entry the degree of overlap of active neurons in two corresponding time bins. Repeated SSEs are reflected in the matrix as diagonal structures of high overlap values. The method as such, however, leaves the task of identifying these diagonal structures to visual inspection rather than to a quantitative analysis. Here we present ASSET (Analysis of Sequences of Synchronous EvenTs), an improved, fully automated method which determines diagonal structures in the intersection matrix by a robust mathematical procedure. The method consists of a sequence of steps that i) assess which entries in the matrix potentially belong to a diagonal structure, ii) cluster these entries into individual diagonal structures and iii) determine the neurons composing the associated SSEs. We employ parallel point processes generated by stochastic simulations as test data to demonstrate the performance of the method under a wide range of realistic scenarios, including different types of non-stationarity of the spiking activity and different correlation structures. Finally, the ability of the method to discover SSEs is demonstrated on complex data from large network simulations with embedded synfire chains. Thus, ASSET represents an effective and efficient tool to analyze massively parallel spike data for temporal sequences of synchronous activity. PMID:27420734

Training Deep Spiking Neural Networks Using Backpropagation.

Science.gov (United States)

Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

2016-01-01

Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

Spike Code Flow in Cultured Neuronal Networks.

Science.gov (United States)

Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime; Kamimura, Takuya; Yagi, Yasushi; Mizuno-Matsumoto, Yuko; Chen, Yen-Wei

2016-01-01

We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of "1101" and "1011," which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the "maximum cross-correlations" among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. Thus, the analysis suggested that local codes of approximately constant shape propagated and conveyed information across the network. Hence, the codes can serve as visible and trackable marks of propagating spike waves as well as evaluating information flow in the neuronal network.

Grain price spikes and beggar-thy-neighbor policy responses

DEFF Research Database (Denmark)

Jensen, Hans Grinsted; Anderson, Kym

2017-01-01

When prices spike in international grain markets, national governments often reduce the extent to which that spike affects their domestic food markets. Those actions exacerbate the price spike and international welfare transfer associated with that terms of trade change. Several recent analyses...

Systematic Regional Variations in Purkinje Cell Spiking Patterns

Science.gov (United States)

Xiao, Jianqiang; Cerminara, Nadia L.; Kotsurovskyy, Yuriy; Aoki, Hanako; Burroughs, Amelia; Wise, Andrew K.; Luo, Yuanjun; Marshall, Sarah P.; Sugihara, Izumi; Apps, Richard; Lang, Eric J.

2014-01-01

In contrast to the uniform anatomy of the cerebellar cortex, molecular and physiological studies indicate that significant differences exist between cortical regions, suggesting that the spiking activity of Purkinje cells (PCs) in different regions could also show distinct characteristics. To investigate this possibility we obtained extracellular recordings from PCs in different zebrin bands in crus IIa and vermis lobules VIII and IX in anesthetized rats in order to compare PC firing characteristics between zebrin positive (Z+) and negative (Z−) bands. In addition, we analyzed recordings from PCs in the A2 and C1 zones of several lobules in the posterior lobe, which largely contain Z+ and Z− PCs, respectively. In both datasets significant differences in simple spike (SS) activity were observed between cortical regions. Specifically, Z− and C1 PCs had higher SS firing rates than Z+ and A2 PCs, respectively. The irregularity of SS firing (as assessed by measures of interspike interval distribution) was greater in Z+ bands in both absolute and relative terms. The results regarding systematic variations in complex spike (CS) activity were less consistent, suggesting that while real differences can exist, they may be sensitive to other factors than the cortical location of the PC. However, differences in the interactions between SSs and CSs, including the post-CS pause in SSs and post-pause modulation of SSs, were also consistently observed between bands. Similar, though less strong trends were observed in the zonal recordings. These systematic variations in spontaneous firing characteristics of PCs between zebrin bands in vivo, raises the possibility that fundamental differences in information encoding exist between cerebellar cortical regions. PMID:25144311

Systematic regional variations in Purkinje cell spiking patterns.

Directory of Open Access Journals (Sweden)

Jianqiang Xiao

Full Text Available In contrast to the uniform anatomy of the cerebellar cortex, molecular and physiological studies indicate that significant differences exist between cortical regions, suggesting that the spiking activity of Purkinje cells (PCs in different regions could also show distinct characteristics. To investigate this possibility we obtained extracellular recordings from PCs in different zebrin bands in crus IIa and vermis lobules VIII and IX in anesthetized rats in order to compare PC firing characteristics between zebrin positive (Z+ and negative (Z- bands. In addition, we analyzed recordings from PCs in the A2 and C1 zones of several lobules in the posterior lobe, which largely contain Z+ and Z- PCs, respectively. In both datasets significant differences in simple spike (SS activity were observed between cortical regions. Specifically, Z- and C1 PCs had higher SS firing rates than Z+ and A2 PCs, respectively. The irregularity of SS firing (as assessed by measures of interspike interval distribution was greater in Z+ bands in both absolute and relative terms. The results regarding systematic variations in complex spike (CS activity were less consistent, suggesting that while real differences can exist, they may be sensitive to other factors than the cortical location of the PC. However, differences in the interactions between SSs and CSs, including the post-CS pause in SSs and post-pause modulation of SSs, were also consistently observed between bands. Similar, though less strong trends were observed in the zonal recordings. These systematic variations in spontaneous firing characteristics of PCs between zebrin bands in vivo, raises the possibility that fundamental differences in information encoding exist between cerebellar cortical regions.

Spike sorting for polytrodes: a divide and conquer approach

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Nicholas V. Swindale

2014-02-01

Full Text Available In order to determine patterns of neural activity, spike signals recorded by extracellular electrodes have to be clustered (sorted with the aim of ensuring that each cluster represents all the spikes generated by an individual neuron. Many methods for spike sorting have been proposed but few are easily applicable to recordings from polytrodes which may have 16 or more recording sites. As with tetrodes, these are spaced sufficiently closely that signals from single neurons will usually be recorded on several adjacent sites. Although this offers a better chance of distinguishing neurons with similarly shaped spikes, sorting is difficult in such cases because of the high dimensionality of the space in which the signals must be classified. This report details a method for spike sorting based on a divide and conquer approach. Clusters are initially formed by assigning each event to the channel on which it is largest. Each channel-based cluster is then sub-divided into as many distinct clusters as possible. These are then recombined on the basis of pairwise tests into a final set of clusters. Pairwise tests are also performed to establish how distinct each cluster is from the others. A modified gradient ascent clustering (GAC algorithm is used to do the clustering. The method can sort spikes with minimal user input in times comparable to real time for recordings lasting up to 45 minutes. Our results illustrate some of the difficulties inherent in spike sorting, including changes in spike shape over time. We show that some physiologically distinct units may have very similar spike shapes. We show that RMS measures of spike shape similarity are not sensitive enough to discriminate clusters that can otherwise be separated by principal components analysis. Hence spike sorting based on least-squares matching to templates may be unreliable. Our methods should be applicable to tetrodes and scaleable to larger multi-electrode arrays (MEAs.

Introduction to spiking neural networks: Information processing, learning and applications.

Science.gov (United States)

Ponulak, Filip; Kasinski, Andrzej

2011-01-01

The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.

Spiking irregularity and frequency modulate the behavioral report of single-neuron stimulation.

Science.gov (United States)

Doron, Guy; von Heimendahl, Moritz; Schlattmann, Peter; Houweling, Arthur R; Brecht, Michael

2014-02-05

The action potential activity of single cortical neurons can evoke measurable sensory effects, but it is not known how spiking parameters and neuronal subtypes affect the evoked sensations. Here, we examined the effects of spike train irregularity, spike frequency, and spike number on the detectability of single-neuron stimulation in rat somatosensory cortex. For regular-spiking, putative excitatory neurons, detectability increased with spike train irregularity and decreasing spike frequencies but was not affected by spike number. Stimulation of single, fast-spiking, putative inhibitory neurons led to a larger sensory effect compared to regular-spiking neurons, and the effect size depended only on spike irregularity. An ideal-observer analysis suggests that, under our experimental conditions, rats were using integration windows of a few hundred milliseconds or more. Our data imply that the behaving animal is sensitive to single neurons' spikes and even to their temporal patterning. Copyright © 2014 Elsevier Inc. All rights reserved.

Acrolein inhalation alters myocardial synchrony and performance at and below exposure concentrations that cause ventilatory responses

Science.gov (United States)

Acrolein is an irritating aldehyde generated during combustion of organic compounds. Altered autonomic activity has been documented following acrolein inhalation, possibly impacting myocardial synchrony and function. Given the ubiquitous nature of acrolein in the environment, we ...

Error-backpropagation in temporally encoded networks of spiking neurons

NARCIS (Netherlands)

S.M. Bohte (Sander); J.A. La Poutré (Han); J.N. Kok (Joost)

2000-01-01

textabstractFor a network of spiking neurons that encodes information in the timing of individual spike-times, we derive a supervised learning rule, emph{SpikeProp, akin to traditional error-backpropagation and show how to overcome the discontinuities introduced by thresholding. With this algorithm,

Spike Pattern Structure Influences Synaptic Efficacy Variability Under STDP and Synaptic Homeostasis. I: Spike Generating Models on Converging Motifs

Directory of Open Access Journals (Sweden)

Zedong eBi

2016-02-01

Full Text Available In neural systems, synaptic plasticity is usually driven by spike trains. Due to the inherent noises of neurons and synapses as well as the randomness of connection details, spike trains typically exhibit variability such as spatial randomness and temporal stochasticity, resulting in variability of synaptic changes under plasticity, which we call efficacy variability. How the variability of spike trains influences the efficacy variability of synapses remains unclear. In this paper, we try to understand this influence under pair-wise additive spike-timing dependent plasticity (STDP when the mean strength of plastic synapses into a neuron is bounded (synaptic homeostasis. Specifically, we systematically study, analytically and numerically, how four aspects of statistical features, i.e. synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations, as well as their interactions influence the efficacy variability in converging motifs (simple networks in which one neuron receives from many other neurons. Neurons (including the post-synaptic neuron in a converging motif generate spikes according to statistical models with tunable parameters. In this way, we can explicitly control the statistics of the spike patterns, and investigate their influence onto the efficacy variability, without worrying about the feedback from synaptic changes onto the dynamics of the post-synaptic neuron. We separate efficacy variability into two parts: the drift part (DriftV induced by the heterogeneity of change rates of different synapses, and the diffusion part (DiffV induced by weight diffusion caused by stochasticity of spike trains. Our main findings are: (1 synchronous firing and burstiness tend to increase DiffV, (2 heterogeneity of rates induces DriftV when potentiation and depression in STDP are not balanced, and (3 heterogeneity of cross-correlations induces DriftV together with heterogeneity of rates. We anticipate our

Mimickers of generalized spike and wave discharges.

Science.gov (United States)

Azzam, Raed; Bhatt, Amar B

2014-06-01

Overinterpretation of benign EEG variants is a common problem that can lead to the misdiagnosis of epilepsy. We review four normal patterns that mimic generalized spike and wave discharges: phantom spike-and-wave, hyperventilation hypersynchrony, hypnagogic/ hypnopompic hypersynchrony, and mitten patterns.

A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy.

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Guo, Lilin; Wang, Zhenzhong; Cabrerizo, Mercedes; Adjouadi, Malek

2017-05-01

This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timing of spikes. Unlike the Remote Supervised Method (ReSuMe), synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. The CCDS rule is both biologically plausible and computationally efficient. The properties of this learning rule are investigated extensively through experimental evaluations in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. Results presented show that the CCDS learning method achieves learning accuracy and learning speed comparable with ReSuMe, but improves classification accuracy when compared to both the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. The merit of CCDS rule is further validated on a practical example involving the automated detection of interictal spikes in EEG records of patients with epilepsy. Results again show that with proper encoding, the CCDS rule achieves good recognition performance.

Unsupervised spike sorting based on discriminative subspace learning.

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Keshtkaran, Mohammad Reza; Yang, Zhi

2014-01-01

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separability in the learned subspace than in the subspace obtained by principal component analysis or wavelet transform.

Multiplexed Spike Coding and Adaptation in the Thalamus

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Rebecca A. Mease

2017-05-01

Full Text Available High-frequency “burst” clusters of spikes are a generic output pattern of many neurons. While bursting is a ubiquitous computational feature of different nervous systems across animal species, the encoding of synaptic inputs by bursts is not well understood. We find that bursting neurons in the rodent thalamus employ “multiplexing” to differentially encode low- and high-frequency stimulus features associated with either T-type calcium “low-threshold” or fast sodium spiking events, respectively, and these events adapt differently. Thus, thalamic bursts encode disparate information in three channels: (1 burst size, (2 burst onset time, and (3 precise spike timing within bursts. Strikingly, this latter “intraburst” encoding channel shows millisecond-level feature selectivity and adapts across statistical contexts to maintain stable information encoded per spike. Consequently, calcium events both encode low-frequency stimuli and, in parallel, gate a transient window for high-frequency, adaptive stimulus encoding by sodium spike timing, allowing bursts to efficiently convey fine-scale temporal information.

Effect of heavy metals on pH buffering capacity and solubility of Ca, Mg, K, and P in non-spiked and heavy metal-spiked soils.

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Najafi, Sarvenaz; Jalali, Mohsen

2016-06-01

In many parts of the world, soil acidification and heavy metal contamination has become a serious concern due to the adverse effects on chemical properties of soil and crop yield. The aim of this study was to investigate the effect of pH (in the range of 1 to 3 units above and below the native pH of soils) on calcium (Ca), magnesium (Mg), potassium (K), and phosphorus (P) solubility in non-spiked and heavy metal-spiked soil samples. Spiked samples were prepared by cadmium (Cd), copper (Cu), nickel (Ni), and zinc (Zn) as chloride salts and incubating soils for 40 days. The pH buffering capacity (pHBC) of each sample was determined by plotting the amount of H(+) or OH(-) added (mmol kg(-1)) versus the related pH value. The pHBC of soils ranged from 47.1 to 1302.5 mmol kg(-1) for non-spiked samples and from 45.0 to 1187.4 mmol kg(-1) for spiked soil samples. The pHBC values were higher in soil 2 (non-spiked and spiked) which had higher calcium carbonate content. The results indicated the presence of heavy metals in soils generally decreased the solution pH and pHBC values in spiked samples. In general, solubility of Ca, Mg, and K decreased with increasing equilibrium pH of non-spiked and spiked soil samples. In the case of P, increasing the pH to about 7, decreased the solubility in all soils but further increase of pH from 7, enhanced P solubility. The solubility trends and values for Ca, Mg, and K did not differed significantly in non-spiked and spiked samples. But in the case of P, a reduction in solubility was observed in heavy metal-spiked soils. The information obtained in this study can be useful to make better estimation of the effects of soil pollutants on anion and cation solubility from agricultural and environmental viewpoints.

The Mutation Frequency in Different Spike Categories in Barley

DEFF Research Database (Denmark)

Frydenberg, O.; Doll, Hans; Sandfær, J.

1964-01-01

After gamma irradiation of barley seeds, a comparison has been made between the chlorophyll-mutant frequencies in X1 spikes that had multicellular bud meristems in the seeds at the time of treatment (denoted as pre-formed spikes) and X1 spikes having no recognizable meristems at the time...

Bio-inspired spiking neural network for nonlinear systems control.

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Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

2018-08-01

Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Two sides of a coin: host-plant synchrony fitness trade-offs in the population dynamics of the western spruce budworm.

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Régnière, Jacques; Nealis, Vincent G

2018-02-01

Conifer-feeding budworms emerge from overwintering sites as small larvae in early spring, several days before budburst, and mine old needles. These early-emerging larvae suffer considerable mortality during this foraging period as they disperse in search of available, current-year buds. Once buds flush, surviving budworms construct feeding shelters and must complete maturation before fresh host foliage senesces and lignifies later in the summer. Late-developing larvae suffer greater mortality and survivors have lower fecundity when feeding on older foliage. Thus, there is a seasonal trade-off in fitness associated with host synchrony: early-emerging budworms have a greater risk of mortality during spring dispersal but gain better access to the most nutritious foliage, while, on the other hand, late-emerging larvae incur a lower risk during the initial foraging period but must contend with rapidly diminishing resource quality at the end of the feeding period. We investigate the balance that results from these early-season and late-season synchrony fitness trade-offs using the concept of the phenological window. Parameters associated with the variation in the phenological window are used to estimate generational fitness as a function of host-plant synchrony. Because defoliation modifies these relationships, it is also included in the analysis. We show that fitness trade-offs characterizing the phenological window result in a robust synchrony relationship between budworm and host plant over a wide geographic range in southern British Columbia, Canada. © 2016 Her Majesty the Queen in Right of Canada Insect Science © 2016 Institute of Zoology, Chinese Academy of Sciences.

Orthobunyavirus ultrastructure and the curious tripodal glycoprotein spike.

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Thomas A Bowden

Full Text Available The genus Orthobunyavirus within the family Bunyaviridae constitutes an expanding group of emerging viruses, which threaten human and animal health. Despite the medical importance, little is known about orthobunyavirus structure, a prerequisite for understanding virus assembly and entry. Here, using electron cryo-tomography, we report the ultrastructure of Bunyamwera virus, the prototypic member of this genus. Whilst Bunyamwera virions are pleomorphic in shape, they display a locally ordered lattice of glycoprotein spikes. Each spike protrudes 18 nm from the viral membrane and becomes disordered upon introduction to an acidic environment. Using sub-tomogram averaging, we derived a three-dimensional model of the trimeric pre-fusion glycoprotein spike to 3-nm resolution. The glycoprotein spike consists mainly of the putative class-II fusion glycoprotein and exhibits a unique tripod-like arrangement. Protein-protein contacts between neighbouring spikes occur at membrane-proximal regions and intra-spike contacts at membrane-distal regions. This trimeric assembly deviates from previously observed fusion glycoprotein arrangements, suggesting a greater than anticipated repertoire of viral fusion glycoprotein oligomerization. Our study provides evidence of a pH-dependent conformational change that occurs during orthobunyaviral entry into host cells and a blueprint for the structure of this group of emerging pathogens.

Independent component analysis separates spikes of different origin in the EEG.

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Urrestarazu, Elena; Iriarte, Jorge; Artieda, Julio; Alegre, Manuel; Valencia, Miguel; Viteri, César

2006-02-01

Independent component analysis (ICA) is a novel system that finds independent sources in recorded signals. Its usefulness in separating epileptiform activity of different origin has not been determined. The goal of this study was to demonstrate that ICA is useful for separating different spikes using samples of EEG of patients with focal epilepsy. Digital EEG samples from four patients with focal epilepsy were included. The patients had temporal (n = 2), centrotemporal (n = 1) or frontal spikes (n = 1). Twenty-six samples with two (or more) spikes from two different patients were created. The selection of the two spikes for each mixed EEG was performed randomly, trying to have all the different combinations and rejecting the mixture of two spikes from the same patient. Two different examiners studied the EEGs using ICA with JADE paradigm in Matlab platform, trying to separate and to identify the spikes. They agreed in the correct separation of the spikes in 24 of the 26 samples, classifying the spikes as frontal, temporal or centrotemporal, left or right sided. The demonstration of the possibility of detecting different artificially mixed spikes confirms that ICA may be useful in separating spikes or other elements in real EEGs.

Spike Bursts from an Excitable Optical System

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Rios Leite, Jose R.; Rosero, Edison J.; Barbosa, Wendson A. S.; Tredicce, Jorge R.

Diode Lasers with double optical feedback are shown to present power drop spikes with statistical distribution controllable by the ratio of the two feedback times. The average time between spikes and the variance within long time series are studied. The system is shown to be excitable and present bursting of spikes created with specific feedback time ratios and strength. A rate equation model, extending the Lang-Kobayashi single feedback for semiconductor lasers proves to match the experimental observations. Potential applications to construct network to mimic neural systems having controlled bursting properties in each unit will be discussed. Brazilian Agency CNPQ.

Responses of Purkinje cells in the oculomotor vermis of monkeys during smooth pursuit eye movements and saccades: comparison with floccular complex.

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Raghavan, Ramanujan T; Lisberger, Stephen G

2017-08-01

We recorded the responses of Purkinje cells in the oculomotor vermis during smooth pursuit and saccadic eye movements. Our goal was to characterize the responses in the vermis using approaches that would allow direct comparisons with responses of Purkinje cells in another cerebellar area for pursuit, the floccular complex. Simple-spike firing of vermis Purkinje cells is direction selective during both pursuit and saccades, but the preferred directions are sufficiently independent so that downstream circuits could decode signals to drive pursuit and saccades separately. Complex spikes also were direction selective during pursuit, and almost all Purkinje cells showed a peak in the probability of complex spikes during the initiation of pursuit in at least one direction. Unlike the floccular complex, the preferred directions for simple spikes and complex spikes were not opposite. The kinematics of smooth eye movement described the simple-spike responses of vermis Purkinje cells well. Sensitivities were similar to those in the floccular complex for eye position and considerably lower for eye velocity and acceleration. The kinematic relations were quite different for saccades vs. pursuit, supporting the idea that the contributions from the vermis to each kind of movement could contribute independently in downstream areas. Finally, neither the complex-spike nor the simple-spike responses of vermis Purkinje cells were appropriate to support direction learning in pursuit. Complex spikes were not triggered reliably by an instructive change in target direction; simple-spike responses showed very small amounts of learning. We conclude that the vermis plays a different role in pursuit eye movements compared with the floccular complex. NEW & NOTEWORTHY The midline oculomotor cerebellum plays a different role in smooth pursuit eye movements compared with the lateral, floccular complex and appears to be much less involved in direction learning in pursuit. The output from the

Spike Code Flow in Cultured Neuronal Networks

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Shinichi Tamura

2016-01-01

Full Text Available We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of “1101” and “1011,” which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the “maximum cross-correlations” among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. Thus, the analysis suggested that local codes of approximately constant shape propagated and conveyed information across the network. Hence, the codes can serve as visible and trackable marks of propagating spike waves as well as evaluating information flow in the neuronal network.

A Motion Capture Study to Measure the Feeling of Synchrony in Romantic Couples and in Professional Musicians

KAUST Repository

Preissmann, Delphine; Charbonnier, Caecilia; Chagué , Sylvain; Antonietti, Jean Philippe; Llobera, Joan; Ansermet, Francois; Magistretti, Pierre J.

2016-01-01

The feeling of synchrony is fundamental for most social activities and prosocial behaviors. However, little is known about the behavioral correlates of this feeling and its modulation by intergroup differences. We previously showed

Interaction of synchronized dynamics in cortex and basal ganglia in Parkinson's disease.

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Ahn, Sungwoo; Zauber, S Elizabeth; Worth, Robert M; Witt, Thomas; Rubchinsky, Leonid L

2015-09-01

Parkinson's disease pathophysiology is marked by increased oscillatory and synchronous activity in the beta frequency band in cortical and basal ganglia circuits. This study explores the functional connections between synchronized dynamics of cortical areas and synchronized dynamics of subcortical areas in Parkinson's disease. We simultaneously recorded neuronal units (spikes) and local field potentials (LFP) from subthalamic nucleus (STN) and electroencephalograms (EEGs) from the scalp in parkinsonian patients, and analysed the correlation between the time courses of the spike-LFP synchronization and inter-electrode EEG synchronization. We found the (non-invasively obtained) time course of the synchrony strength between EEG electrodes and the (invasively obtained) time course of the synchrony between spiking units and LFP in STN to be weakly, but significantly, correlated with each other. This correlation is largest for the bilateral motor EEG synchronization, followed by bilateral frontal EEG synchronization. Our observations suggest that there may be multiple functional modes by which the cortical and basal ganglia circuits interact with each other in Parkinson's disease: not only may synchronization be observed between some areas in cortex and the basal ganglia, but also synchronization within cortex and within basal ganglia may be related, suggesting potentially a more global functional interaction. More coherent dynamics in one brain region may modulate or activate the dynamics of another brain region in a more powerful way, causing correlations between changes in synchrony strength in the two regions. © 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons.

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Taherkhani, Aboozar; Belatreche, Ammar; Li, Yuhua; Maguire, Liam P

2015-12-01

Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe.

Noise exposure alters long-term neural firing rates and synchrony in primary auditory and rostral belt cortices following bimodal stimulation.

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Takacs, Joseph D; Forrest, Taylor J; Basura, Gregory J

2017-12-01

We previously demonstrated that bimodal stimulation (spinal trigeminal nucleus [Sp5] paired with best frequency tone) altered neural tone-evoked and spontaneous firing rates (SFRs) in primary auditory cortex (A1) 15 min after pairing in guinea pigs with and without noise-induced tinnitus. Neural responses were enhanced (+10 ms) or suppressed (0 ms) based on the bimodal pairing interval. Here we investigated whether bimodal stimulation leads to long-term (up to 2 h) changes in tone-evoked and SFRs and neural synchrony (correlate of tinnitus) and if the long-term bimodal effects are altered following noise exposure. To obviate the effects of permanent hearing loss on the results, firing rates and neural synchrony were measured three weeks following unilateral (left ear) noise exposure and a temporary threshold shift. Simultaneous extra-cellular single-unit recordings were made from contralateral (to noise) A1 and dorsal rostral belt (RB); an associative auditory cortical region thought to influence A1, before and after bimodal stimulation (pairing intervals of 0 ms; simultaneous Sp5-tone and +10 ms; Sp5 precedes tone). Sixty and 120 min after 0 ms pairing tone-evoked and SFRs were suppressed in sham A1; an effect only preserved 120 min following pairing in noise. Stimulation at +10 ms only affected SFRs 120 min after pairing in sham and noise-exposed A1. Within sham RB, pairing at 0 and +10 ms persistently suppressed tone-evoked and SFRs, while 0 ms pairing in noise markedly enhanced tone-evoked and SFRs up to 2 h. Together, these findings suggest that bimodal stimulation has long-lasting effects in A1 that also extend to the associative RB that is altered by noise and may have persistent implications for how noise damaged brains process multi-sensory information. Moreover, prior to bimodal stimulation, noise damage increased neural synchrony in A1, RB and between A1 and RB neurons. Bimodal stimulation led to persistent changes in neural synchrony in

Sensitivity to audio-visual synchrony and its relation to language abilities in children with and without ASD.

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Righi, Giulia; Tenenbaum, Elena J; McCormick, Carolyn; Blossom, Megan; Amso, Dima; Sheinkopf, Stephen J

2018-04-01

Autism Spectrum Disorder (ASD) is often accompanied by deficits in speech and language processing. Speech processing relies heavily on the integration of auditory and visual information, and it has been suggested that the ability to detect correspondence between auditory and visual signals helps to lay the foundation for successful language development. The goal of the present study was to examine whether young children with ASD show reduced sensitivity to temporal asynchronies in a speech processing task when compared to typically developing controls, and to examine how this sensitivity might relate to language proficiency. Using automated eye tracking methods, we found that children with ASD failed to demonstrate sensitivity to asynchronies of 0.3s, 0.6s, or 1.0s between a video of a woman speaking and the corresponding audio track. In contrast, typically developing children who were language-matched to the ASD group, were sensitive to both 0.6s and 1.0s asynchronies. We also demonstrated that individual differences in sensitivity to audiovisual asynchronies and individual differences in orientation to relevant facial features were both correlated with scores on a standardized measure of language abilities. Results are discussed in the context of attention to visual language and audio-visual processing as potential precursors to language impairment in ASD. Autism Res 2018, 11: 645-653. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. Speech processing relies heavily on the integration of auditory and visual information, and it has been suggested that the ability to detect correspondence between auditory and visual signals helps to lay the foundation for successful language development. The goal of the present study was to explore whether children with ASD process audio-visual synchrony in ways comparable to their typically developing peers, and the relationship between preference for synchrony and language ability. Results showed that

Semantic congruency but not temporal synchrony enhances long-term memory performance for audio-visual scenes.

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Meyerhoff, Hauke S; Huff, Markus

2016-04-01

Human long-term memory for visual objects and scenes is tremendous. Here, we test how auditory information contributes to long-term memory performance for realistic scenes. In a total of six experiments, we manipulated the presentation modality (auditory, visual, audio-visual) as well as semantic congruency and temporal synchrony between auditory and visual information of brief filmic clips. Our results show that audio-visual clips generally elicit more accurate memory performance than unimodal clips. This advantage even increases with congruent visual and auditory information. However, violations of audio-visual synchrony hardly have any influence on memory performance. Memory performance remained intact even with a sequential presentation of auditory and visual information, but finally declined when the matching tracks of one scene were presented separately with intervening tracks during learning. With respect to memory performance, our results therefore show that audio-visual integration is sensitive to semantic congruency but remarkably robust against asymmetries between different modalities.

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

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Runchun Mark Wang

2015-05-01

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

Comparison of Classifier Architectures for Online Neural Spike Sorting.

Science.gov (United States)

Saeed, Maryam; Khan, Amir Ali; Kamboh, Awais Mehmood

2017-04-01

High-density, intracranial recordings from micro-electrode arrays need to undergo Spike Sorting in order to associate the recorded neuronal spikes to particular neurons. This involves spike detection, feature extraction, and classification. To reduce the data transmission and power requirements, on-chip real-time processing is becoming very popular. However, high computational resources are required for classifiers in on-chip spike-sorters, making scalability a great challenge. In this review paper, we analyze several popular classifiers to propose five new hardware architectures using the off-chip training with on-chip classification approach. These include support vector classification, fuzzy C-means classification, self-organizing maps classification, moving-centroid K-means classification, and Cosine distance classification. The performance of these architectures is analyzed in terms of accuracy and resource requirement. We establish that the neural networks based Self-Organizing Maps classifier offers the most viable solution. A spike sorter based on the Self-Organizing Maps classifier, requires only 7.83% of computational resources of the best-reported spike sorter, hierarchical adaptive means, while offering a 3% better accuracy at 7 dB SNR.

Automatic fitting of spiking neuron models to electrophysiological recordings

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Cyrille Rossant

2010-03-01

Full Text Available Spiking models can accurately predict the spike trains produced by cortical neurons in response to somatically injected currents. Since the specific characteristics of the model depend on the neuron, a computational method is required to fit models to electrophysiological recordings. The fitting procedure can be very time consuming both in terms of computer simulations and in terms of code writing. We present algorithms to fit spiking models to electrophysiological data (time-varying input and spike trains that can run in parallel on graphics processing units (GPUs. The model fitting library is interfaced with Brian, a neural network simulator in Python. If a GPU is present it uses just-in-time compilation to translate model equations into optimized code. Arbitrary models can then be defined at script level and run on the graphics card. This tool can be used to obtain empirically validated spiking models of neurons in various systems. We demonstrate its use on public data from the INCF Quantitative Single-Neuron Modeling 2009 competition by comparing the performance of a number of neuron spiking models.

STDP-based spiking deep convolutional neural networks for object recognition.

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Kheradpisheh, Saeed Reza; Ganjtabesh, Mohammad; Thorpe, Simon J; Masquelier, Timothée

2018-03-01

Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity in an unsupervised manner. These studies, however, used relatively shallow architectures, and only one layer was trainable. Another line of research has demonstrated - using rate-based neural networks trained with back-propagation - that having many layers increases the recognition robustness, an approach known as deep learning. We thus designed a deep SNN, comprising several convolutional (trainable with STDP) and pooling layers. We used a temporal coding scheme where the most strongly activated neurons fire first, and less activated neurons fire later or not at all. The network was exposed to natural images. Thanks to STDP, neurons progressively learned features corresponding to prototypical patterns that were both salient and frequent. Only a few tens of examples per category were required and no label was needed. After learning, the complexity of the extracted features increased along the hierarchy, from edge detectors in the first layer to object prototypes in the last layer. Coding was very sparse, with only a few thousands spikes per image, and in some cases the object category could be reasonably well inferred from the activity of a single higher-order neuron. More generally, the activity of a few hundreds of such neurons contained robust category information, as demonstrated using a classifier on Caltech 101, ETH-80, and MNIST databases. We also demonstrate the superiority of STDP over other unsupervised techniques such as random crops (HMAX) or auto-encoders. Taken together, our results suggest that the combination of STDP with latency coding may be a key to understanding the way that the primate visual system learns, its remarkable processing speed and its low energy consumption. These mechanisms are also interesting for artificial vision systems, particularly for hardware

Multiscale synchrony behaviors of paired financial time series by 3D multi-continuum percolation

Science.gov (United States)

Wang, M.; Wang, J.; Wang, B. T.

2018-02-01

Multiscale synchrony behaviors and nonlinear dynamics of paired financial time series are investigated, in an attempt to study the cross correlation relationships between two stock markets. A random stock price model is developed by a new system called three-dimensional (3D) multi-continuum percolation system, which is utilized to imitate the formation mechanism of price dynamics and explain the nonlinear behaviors found in financial time series. We assume that the price fluctuations are caused by the spread of investment information. The cluster of 3D multi-continuum percolation represents the cluster of investors who share the same investment attitude. In this paper, we focus on the paired return series, the paired volatility series, and the paired intrinsic mode functions which are decomposed by empirical mode decomposition. A new cross recurrence quantification analysis is put forward, combining with multiscale cross-sample entropy, to investigate the multiscale synchrony of these paired series from the proposed model. The corresponding research is also carried out for two China stock markets as comparison.

Scaling of spiking and humping in keyhole welding

Energy Technology Data Exchange (ETDEWEB)

Wei, P S; Chuang, K C [Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan (China); DebRoy, T [Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA 16802 (United States); Ku, J S, E-mail: pswei@mail.nsysu.edu.tw, E-mail: cielo.zhuang@gmail.com, E-mail: rtd1@psu.edu, E-mail: jsku@mail.nsysu.edu.tw [Institute of Materials Science and Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan (China)

2011-06-22

Spiking, rippling and humping seriously reduce the strength of welds. The effects of beam focusing, volatile alloying element concentration and welding velocity on spiking, coarse rippling and humping in keyhole mode electron-beam welding are examined through scale analysis. Although these defects have been studied in the past, the mechanisms for their formation are not fully understood. This work relates the average amplitudes of spikes to fusion zone depth for the welding of Al 6061, SS 304 and carbon steel, and Al 5083. The scale analysis introduces welding and melting efficiencies and an appropriate power distribution to account for the focusing effects, and the energy which is reflected and escapes through the keyhole opening to the surroundings. The frequency of humping and spiking can also be predicted from the scale analysis. The analysis also reveals the interrelation between coarse rippling and humping. The data and the mechanistic findings reported in this study are useful for understanding and preventing spiking and humping during keyhole mode electron and laser beam welding.

A metric space approach to the information capacity of spike trains

OpenAIRE

HOUGHTON, CONOR JAMES; GILLESPIE, JAMES

2010-01-01

PUBLISHED Classical information theory can be either discrete or continuous, corresponding to discrete or continuous random variables. However, although spike times in a spike train are described by continuous variables, the information content is usually calculated using discrete information theory. This is because the number of spikes, and hence, the number of variables, varies from spike train to spike train, making the continuous theory difficult to apply.It is possible to avoid ...

Clustering predicts memory performance in networks of spiking and non-spiking neurons

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Weiliang eChen

2011-03-01

Full Text Available The problem we address in this paper is that of finding effective and parsimonious patterns of connectivity in sparse associative memories. This problem must be addressed in real neuronal systems, so that results in artificial systems could throw light on real systems. We show that there are efficient patterns of connectivity and that these patterns are effective in models with either spiking or non-spiking neurons. This suggests that there may be some underlying general principles governing good connectivity in such networks. We also show that the clustering of the network, measured by Clustering Coefficient, has a strong linear correlation to the performance of associative memory. This result is important since a purely static measure of network connectivity appears to determine an important dynamic property of the network.

Performance evaluation of PCA-based spike sorting algorithms.

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Adamos, Dimitrios A; Kosmidis, Efstratios K; Theophilidis, George

2008-09-01

Deciphering the electrical activity of individual neurons from multi-unit noisy recordings is critical for understanding complex neural systems. A widely used spike sorting algorithm is being evaluated for single-electrode nerve trunk recordings. The algorithm is based on principal component analysis (PCA) for spike feature extraction. In the neuroscience literature it is generally assumed that the use of the first two or most commonly three principal components is sufficient. We estimate the optimum PCA-based feature space by evaluating the algorithm's performance on simulated series of action potentials. A number of modifications are made to the open source nev2lkit software to enable systematic investigation of the parameter space. We introduce a new metric to define clustering error considering over-clustering more favorable than under-clustering as proposed by experimentalists for our data. Both the program patch and the metric are available online. Correlated and white Gaussian noise processes are superimposed to account for biological and artificial jitter in the recordings. We report that the employment of more than three principal components is in general beneficial for all noise cases considered. Finally, we apply our results to experimental data and verify that the sorting process with four principal components is in agreement with a panel of electrophysiology experts.

In-reactor creep of zirconium alloys by thermal spikes

International Nuclear Information System (INIS)

Ibrahim, E.F.

1975-01-01

The size and duration of thermal spikes from fast neutrons have been calculated for zirconium alloys, showing that spikes up to 1.8 nm radius may exist for 2 x 10 -11 s at greater than melting point, at 570K ambient temperature. Creep rates have been calculated assuming that the elastic strain from the applied stress relaxes in the volume of the spikes (by preferential loop alignment or modification of an existing dislocation network). The calculated rates are consistent with strain rates observed in long term tests-in-reactor, if spike lifetimes are 2 to 2.5 x 10 -11 s. (Auth.)

Automated spike preparation system for Isotope Dilution Mass Spectrometry (IDMS)

International Nuclear Information System (INIS)

Maxwell, S.L. III; Clark, J.P.

1990-01-01

Isotope Dilution Mass Spectrometry (IDMS) is a method frequently employed to measure dissolved, irradiated nuclear materials. A known quantity of a unique isotope of the element to be measured (referred to as the ''spike'') is added to the solution containing the analyte. The resulting solution is chemically purified then analyzed by mass spectrometry. By measuring the magnitude of the response for each isotope and the response for the ''unique spike'' then relating this to the known quantity of the ''spike'', the quantity of the nuclear material can be determined. An automated spike preparation system was developed at the Savannah River Site (SRS) to dispense spikes for use in IDMS analytical methods. Prior to this development, technicians weighed each individual spike manually to achieve the accuracy required. This procedure was time-consuming and subjected the master stock solution to evaporation. The new system employs a high precision SMI Model 300 Unipump dispenser interfaced with an electronic balance and a portable Epson HX-20 notebook computer to automate spike preparation

Spike-threshold adaptation predicted by membrane potential dynamics in vivo.

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Bertrand Fontaine

2014-04-01

Full Text Available Neurons encode information in sequences of spikes, which are triggered when their membrane potential crosses a threshold. In vivo, the spiking threshold displays large variability suggesting that threshold dynamics have a profound influence on how the combined input of a neuron is encoded in the spiking. Threshold variability could be explained by adaptation to the membrane potential. However, it could also be the case that most threshold variability reflects noise and processes other than threshold adaptation. Here, we investigated threshold variation in auditory neurons responses recorded in vivo in barn owls. We found that spike threshold is quantitatively predicted by a model in which the threshold adapts, tracking the membrane potential at a short timescale. As a result, in these neurons, slow voltage fluctuations do not contribute to spiking because they are filtered by threshold adaptation. More importantly, these neurons can only respond to input spikes arriving together on a millisecond timescale. These results demonstrate that fast adaptation to the membrane potential captures spike threshold variability in vivo.

Patterns of Geographic Synchrony in Growth and Reproduction of Oaks Within California and Beyond

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Walter D. Koenig; Johannes M.H. Knops

1997-01-01

We measured patterns of spatial synchrony in growth and reproduction by oaks using direct acorn surveys, published data on acorn production, and tree-ring chronologies. The two data sets involving acorn production both indicate that acorn crops are detectably synchronous over areas of at least 500 to 1,000 km not only within individual species but among species that...

An Unsupervised Online Spike-Sorting Framework.

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Knieling, Simeon; Sridharan, Kousik S; Belardinelli, Paolo; Naros, Georgios; Weiss, Daniel; Mormann, Florian; Gharabaghi, Alireza

2016-08-01

Extracellular neuronal microelectrode recordings can include action potentials from multiple neurons. To separate spikes from different neurons, they can be sorted according to their shape, a procedure referred to as spike-sorting. Several algorithms have been reported to solve this task. However, when clustering outcomes are unsatisfactory, most of them are difficult to adjust to achieve the desired results. We present an online spike-sorting framework that uses feature normalization and weighting to maximize the distinctiveness between different spike shapes. Furthermore, multiple criteria are applied to either facilitate or prevent cluster fusion, thereby enabling experimenters to fine-tune the sorting process. We compare our method to established unsupervised offline (Wave_Clus (WC)) and online (OSort (OS)) algorithms by examining their performance in sorting various test datasets using two different scoring systems (AMI and the Adamos metric). Furthermore, we evaluate sorting capabilities on intra-operative recordings using established quality metrics. Compared to WC and OS, our algorithm achieved comparable or higher scores on average and produced more convincing sorting results for intra-operative datasets. Thus, the presented framework is suitable for both online and offline analysis and could substantially improve the quality of microelectrode-based data evaluation for research and clinical application.

Single-trial estimation of stimulus and spike-history effects on time-varying ensemble spiking activity of multiple neurons: a simulation study

International Nuclear Information System (INIS)

Shimazaki, Hideaki

2013-01-01

Neurons in cortical circuits exhibit coordinated spiking activity, and can produce correlated synchronous spikes during behavior and cognition. We recently developed a method for estimating the dynamics of correlated ensemble activity by combining a model of simultaneous neuronal interactions (e.g., a spin-glass model) with a state-space method (Shimazaki et al. 2012 PLoS Comput Biol 8 e1002385). This method allows us to estimate stimulus-evoked dynamics of neuronal interactions which is reproducible in repeated trials under identical experimental conditions. However, the method may not be suitable for detecting stimulus responses if the neuronal dynamics exhibits significant variability across trials. In addition, the previous model does not include effects of past spiking activity of the neurons on the current state of ensemble activity. In this study, we develop a parametric method for simultaneously estimating the stimulus and spike-history effects on the ensemble activity from single-trial data even if the neurons exhibit dynamics that is largely unrelated to these effects. For this goal, we model ensemble neuronal activity as a latent process and include the stimulus and spike-history effects as exogenous inputs to the latent process. We develop an expectation-maximization algorithm that simultaneously achieves estimation of the latent process, stimulus responses, and spike-history effects. The proposed method is useful to analyze an interaction of internal cortical states and sensory evoked activity

The electric potential of tripolar spikes

Energy Technology Data Exchange (ETDEWEB)

Nocera, L. [Theoretical Plasma Physics, IPCF-CNR, Via Moruzzi 1, I-56124 Pisa (Italy)

2010-02-22

We present an analytical formula for the waveform of the electric potential associated with a tripolar spike in a plasma. This formula is based on the construction and on the subsequent solution of a differential equation for the waveform. We work out this equation as a direct consequence of the morphological and functional properties of the observed waveform, without making any reference to the velocity distributions of the electrons and of the ions which sustain the spike. In the approximation of small potential amplitudes, we solve this equation by quadrature. In particular, in the second order approximation, the solution of this equation is given in terms of elementary functions. This analytical solution is able to reproduce the potential waveforms associated with electron holes, ion holes, monotonic and nonmonotonic double layers and tripolar spikes, in excellent agreement with observations.

The electric potential of tripolar spikes

International Nuclear Information System (INIS)

Nocera, L.

2010-01-01

We present an analytical formula for the waveform of the electric potential associated with a tripolar spike in a plasma. This formula is based on the construction and on the subsequent solution of a differential equation for the waveform. We work out this equation as a direct consequence of the morphological and functional properties of the observed waveform, without making any reference to the velocity distributions of the electrons and of the ions which sustain the spike. In the approximation of small potential amplitudes, we solve this equation by quadrature. In particular, in the second order approximation, the solution of this equation is given in terms of elementary functions. This analytical solution is able to reproduce the potential waveforms associated with electron holes, ion holes, monotonic and nonmonotonic double layers and tripolar spikes, in excellent agreement with observations.

Noisy Spiking in Visual Area V2 of Amblyopic Monkeys.

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Wang, Ye; Zhang, Bin; Tao, Xiaofeng; Wensveen, Janice M; Smith, Earl L; Chino, Yuzo M

2017-01-25

Interocular decorrelation of input signals in developing visual cortex can cause impaired binocular vision and amblyopia. Although increased intrinsic noise is thought to be responsible for a range of perceptual deficits in amblyopic humans, the neural basis for the elevated perceptual noise in amblyopic primates is not known. Here, we tested the idea that perceptual noise is linked to the neuronal spiking noise (variability) resulting from developmental alterations in cortical circuitry. To assess spiking noise, we analyzed the contrast-dependent dynamics of spike counts and spiking irregularity by calculating the square of the coefficient of variation in interspike intervals (CV 2 ) and the trial-to-trial fluctuations in spiking, or mean matched Fano factor (m-FF) in visual area V2 of monkeys reared with chronic monocular defocus. In amblyopic neurons, the contrast versus response functions and the spike count dynamics exhibited significant deviations from comparable data for normal monkeys. The CV 2 was pronounced in amblyopic neurons for high-contrast stimuli and the m-FF was abnormally high in amblyopic neurons for low-contrast gratings. The spike count, CV 2 , and m-FF of spontaneous activity were also elevated in amblyopic neurons. These contrast-dependent spiking irregularities were correlated with the level of binocular suppression in these V2 neurons and with the severity of perceptual loss for individual monkeys. Our results suggest that the developmental alterations in normalization mechanisms resulting from early binocular suppression can explain much of these contrast-dependent spiking abnormalities in V2 neurons and the perceptual performance of our amblyopic monkeys. Amblyopia is a common developmental vision disorder in humans. Despite the extensive animal studies on how amblyopia emerges, we know surprisingly little about the neural basis of amblyopia in humans and nonhuman primates. Although the vision of amblyopic humans is often described as

Validation of a pretreatment delivery quality assurance method for the CyberKnife Synchrony system

Energy Technology Data Exchange (ETDEWEB)

Mastella, E., E-mail: edoardo.mastella@cnao.it [Medical Physics Unit, CNAO Foundation—National Centre for Oncological Hadron Therapy, Pavia I-27100, Italy and Medical Physics Unit, IEO—European Institute of Oncology, Milan I-20141 (Italy); Vigorito, S.; Rondi, E.; Cattani, F. [Medical Physics Unit, IEO—European Institute of Oncology, Milan I-20141 (Italy); Piperno, G.; Ferrari, A.; Strata, E.; Rozza, D. [Department of Radiation Oncology, IEO—European Institute of Oncology, Milan I-20141 (Italy); Jereczek-Fossa, B. A. [Department of Radiation Oncology, IEO—European Institute of Oncology, Milan I-20141, Italy and Department of Oncology and Hematology Oncology, University of Milan, Milan I-20122 (Italy)

2016-08-15

Purpose: To evaluate the geometric and dosimetric accuracies of the CyberKnife Synchrony respiratory tracking system (RTS) and to validate a method for pretreatment patient-specific delivery quality assurance (DQA). Methods: An EasyCube phantom was mounted on the ExacTrac gating phantom, which can move along the superior–inferior (SI) axis of a patient to simulate a moving target. The authors compared dynamic and static measurements. For each case, a Gafchromic EBT3 film was positioned between two slabs of the EasyCube, while a PinPoint ionization chamber was placed in the appropriate space. There were three steps to their evaluation: (1) the field size, the penumbra, and the symmetry of six secondary collimators were measured along the two main orthogonal axes. Dynamic measurements with deliberately simulated errors were also taken. (2) The delivered dose distributions (from step 1) were compared with the planned ones, using the gamma analysis method. The local gamma passing rates were evaluated using three acceptance criteria: 3% local dose difference (LDD)/3 mm, 2%LDD/2 mm, and 3%LDD/1 mm. (3) The DQA plans for six clinical patients were irradiated in different dynamic conditions, to give a total of 19 cases. The measured and planned dose distributions were evaluated with the same gamma-index criteria used in step 2 and the measured chamber doses were compared with the planned mean doses in the sensitive volume of the chamber. Results: (1) A very slight enlargement of the field size and of the penumbra was observed in the SI direction (on average <1 mm), in line with the overall average CyberKnife system error for tracking treatments. (2) Comparison between the planned and the correctly delivered dose distributions confirmed the dosimetric accuracy of the RTS for simple plans. The multicriteria gamma analysis was able to detect the simulated errors, proving the robustness of their method of analysis. (3) All of the DQA clinical plans passed the tests, both in

Voltage spikes in Nb3Sn and NbTi strands

International Nuclear Information System (INIS)

Bordini, B.; Ambrosio, G.; Barzi, E.; Carcagno, R.; Feher, S.; Kashikhin, V.V.; Lamm, M.J.; Orris, D.; Tartaglia, M.; Tompkins, J.C.; Turrioni, D.; Yamada, R.; Zlobin, A.V.; Fermilab

2005-01-01

As part of the High Field Magnet program at Fermilab several NbTi and Nb 3 Sn strands were tested with particular emphasis on the study of voltage spikes and their relationship to superconductor instabilities. The voltage spikes were detected under various experimental conditions using voltage-current (V-I) and voltage-field (V-H) methods. Two types of spikes, designated ''magnetization'' and ''transport current'' spikes, have been identified. Their origin is most likely related to magnetization flux jump and transport current redistribution, respectively. Many of the signals observed appear to be a combination of these two types of spikes; the combination of these two instability mechanisms should play a dominant role in determining the minimum quench current

A supervised learning rule for classification of spatiotemporal spike patterns.

Science.gov (United States)

Lilin Guo; Zhenzhong Wang; Adjouadi, Malek

2016-08-01

This study introduces a novel supervised algorithm for spiking neurons that take into consideration synapse delays and axonal delays associated with weights. It can be utilized for both classification and association and uses several biologically influenced properties, such as axonal and synaptic delays. This algorithm also takes into consideration spike-timing-dependent plasticity as in Remote Supervised Method (ReSuMe). This paper focuses on the classification aspect alone. Spiked neurons trained according to this proposed learning rule are capable of classifying different categories by the associated sequences of precisely timed spikes. Simulation results have shown that the proposed learning method greatly improves classification accuracy when compared to the Spike Pattern Association Neuron (SPAN) and the Tempotron learning rule.

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'.

Science.gov (United States)

Swindale, Nicholas V; Mitelut, Catalin; Murphy, Timothy H; Spacek, Martin A

2017-02-10

Few stand-alone software applications are available for sorting spikes from recordings made with multi-electrode arrays. Ideally, an application should be user friendly with a graphical user interface, able to read data files in a variety of formats, and provide users with a flexible set of tools giving them the ability to detect and sort extracellular voltage waveforms from different units with some degree of reliability. Previously published spike sorting methods are now available in a software program, SpikeSorter, intended to provide electrophysiologists with a complete set of tools for sorting, starting from raw recorded data file and ending with the export of sorted spikes times. Procedures are automated to the extent this is currently possible. The article explains and illustrates the use of the program. A representative data file is opened, extracellular traces are filtered, events are detected and then clustered. A number of problems that commonly occur during sorting are illustrated, including the artefactual over-splitting of units due to the tendency of some units to fire spikes in pairs where the second spike is significantly smaller than the first, and over-splitting caused by slow variation in spike height over time encountered in some units. The accuracy of SpikeSorter's performance has been tested with surrogate ground truth data and found to be comparable to that of other algorithms in current development.

A Markovian event-based framework for stochastic spiking neural networks.

Science.gov (United States)

Touboul, Jonathan D; Faugeras, Olivier D

2011-11-01

In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks.

Fast convergence of spike sequences to periodic patterns in recurrent networks

International Nuclear Information System (INIS)

Jin, Dezhe Z.

2002-01-01

The dynamical attractors are thought to underlie many biological functions of recurrent neural networks. Here we show that stable periodic spike sequences with precise timings are the attractors of the spiking dynamics of recurrent neural networks with global inhibition. Almost all spike sequences converge within a finite number of transient spikes to these attractors. The convergence is fast, especially when the global inhibition is strong. These results support the possibility that precise spatiotemporal sequences of spikes are useful for information encoding and processing in biological neural networks

Noise-enhanced coding in phasic neuron spike trains.

Science.gov (United States)

Ly, Cheng; Doiron, Brent

2017-01-01

The stochastic nature of neuronal response has lead to conjectures about the impact of input fluctuations on the neural coding. For the most part, low pass membrane integration and spike threshold dynamics have been the primary features assumed in the transfer from synaptic input to output spiking. Phasic neurons are a common, but understudied, neuron class that are characterized by a subthreshold negative feedback that suppresses spike train responses to low frequency signals. Past work has shown that when a low frequency signal is accompanied by moderate intensity broadband noise, phasic neurons spike trains are well locked to the signal. We extend these results with a simple, reduced model of phasic activity that demonstrates that a non-Markovian spike train structure caused by the negative feedback produces a noise-enhanced coding. Further, this enhancement is sensitive to the timescales, as opposed to the intensity, of a driving signal. Reduced hazard function models show that noise-enhanced phasic codes are both novel and separate from classical stochastic resonance reported in non-phasic neurons. The general features of our theory suggest that noise-enhanced codes in excitable systems with subthreshold negative feedback are a particularly rich framework to study.

Financial time series prediction using spiking neural networks.

Science.gov (United States)

Reid, David; Hussain, Abir Jaafar; Tawfik, Hissam

2014-01-01

In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.

Genes with a spike expression are clustered in chromosome (sub)bands and spike (sub)bands have a powerful prognostic value in patients with multiple myeloma

Science.gov (United States)

Kassambara, Alboukadel; Hose, Dirk; Moreaux, Jérôme; Walker, Brian A.; Protopopov, Alexei; Reme, Thierry; Pellestor, Franck; Pantesco, Véronique; Jauch, Anna; Morgan, Gareth; Goldschmidt, Hartmut; Klein, Bernard

2012-01-01

Background Genetic abnormalities are common in patients with multiple myeloma, and may deregulate gene products involved in tumor survival, proliferation, metabolism and drug resistance. In particular, translocations may result in a high expression of targeted genes (termed spike expression) in tumor cells. We identified spike genes in multiple myeloma cells of patients with newly-diagnosed myeloma and investigated their prognostic value. Design and Methods Genes with a spike expression in multiple myeloma cells were picked up using box plot probe set signal distribution and two selection filters. Results In a cohort of 206 newly diagnosed patients with multiple myeloma, 2587 genes/expressed sequence tags with a spike expression were identified. Some spike genes were associated with some transcription factors such as MAF or MMSET and with known recurrent translocations as expected. Spike genes were not associated with increased DNA copy number and for a majority of them, involved unknown mechanisms. Of spiked genes, 36.7% clustered significantly in 149 out of 862 documented chromosome (sub)bands, of which 53 had prognostic value (35 bad, 18 good). Their prognostic value was summarized with a spike band score that delineated 23.8% of patients with a poor median overall survival (27.4 months versus not reached, Pband score was independent of other gene expression profiling-based risk scores, t(4;14), or del17p in an independent validation cohort of 345 patients. Conclusions We present a new approach to identify spike genes and their relationship to patients’ survival. PMID:22102711

Predictive coding of dynamical variables in balanced spiking networks.

Science.gov (United States)

Boerlin, Martin; Machens, Christian K; Denève, Sophie

2013-01-01

Two observations about the cortex have puzzled neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks that represent information efficiently in their spikes. We illustrate this insight with spiking networks that represent dynamical variables. Our approach is based on two assumptions: We assume that information about dynamical variables can be read out linearly from neural spike trains, and we assume that neurons only fire a spike if that improves the representation of the dynamical variables. Based on these assumptions, we derive a network of leaky integrate-and-fire neurons that is able to implement arbitrary linear dynamical systems. We show that the membrane voltage of the neurons is equivalent to a prediction error about a common population-level signal. Among other things, our approach allows us to construct an integrator network of spiking neurons that is robust against many perturbations. Most importantly, neural variability in our networks cannot be equated to noise. Despite exhibiting the same single unit properties as widely used population code models (e.g. tuning curves, Poisson distributed spike trains), balanced networks are orders of magnitudes more reliable. Our approach suggests that spikes do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly underestimated.

A spiking neuron circuit based on a carbon nanotube transistor

International Nuclear Information System (INIS)

Chen, C-L; Kim, K; Truong, Q; Shen, A; Li, Z; Chen, Y

2012-01-01

A spiking neuron circuit based on a carbon nanotube (CNT) transistor is presented in this paper. The spiking neuron circuit has a crossbar architecture in which the transistor gates are connected to its row electrodes and the transistor sources are connected to its column electrodes. An electrochemical cell is incorporated in the gate of the transistor by sandwiching a hydrogen-doped poly(ethylene glycol)methyl ether (PEG) electrolyte between the CNT channel and the top gate electrode. An input spike applied to the gate triggers a dynamic drift of the hydrogen ions in the PEG electrolyte, resulting in a post-synaptic current (PSC) through the CNT channel. Spikes input into the rows trigger PSCs through multiple CNT transistors, and PSCs cumulate in the columns and integrate into a ‘soma’ circuit to trigger output spikes based on an integrate-and-fire mechanism. The spiking neuron circuit can potentially emulate biological neuron networks and their intelligent functions. (paper)

An online supervised learning method based on gradient descent for spiking neurons.

Science.gov (United States)

Xu, Yan; Yang, Jing; Zhong, Shuiming

2017-09-01

The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified in the current research. Although the existing GDB multi-spike learning (or spike sequence learning) methods have good performance, they work in an offline manner and still have some limitations. This paper proposes an online GDB spike sequence learning method for spiking neurons that is based on the online adjustment mechanism of real biological neuron synapses. The method constructs error function and calculates the adjustment of synaptic weights as soon as the neurons emit a spike during their running process. We analyze and synthesize desired and actual output spikes to select appropriate input spikes in the calculation of weight adjustment in this paper. The experimental results show that our method obviously improves learning performance compared with the offline learning manner and has certain advantage on learning accuracy compared with other learning methods. Stronger learning ability determines that the method has large pattern storage capacity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Bistability induces episodic spike communication by inhibitory neurons in neuronal networks.

Science.gov (United States)

Kazantsev, V B; Asatryan, S Yu

2011-09-01

Bistability is one of the important features of nonlinear dynamical systems. In neurodynamics, bistability has been found in basic Hodgkin-Huxley equations describing the cell membrane dynamics. When the neuron is clamped near its threshold, the stable rest potential may coexist with the stable limit cycle describing periodic spiking. However, this effect is often neglected in network computations where the neurons are typically reduced to threshold firing units (e.g., integrate-and-fire models). We found that the bistability may induce spike communication by inhibitory coupled neurons in the spiking network. The communication is realized in the form of episodic discharges with synchronous (correlated) spikes during the episodes. A spiking phase map is constructed to describe the synchronization and to estimate basic spike phase locking modes.

Voltage spikes in Nb3Sn and NbTi strands

Energy Technology Data Exchange (ETDEWEB)

Bordini, B.; Ambrosio, G.; Barzi, E.; Carcagno, R.; Feher, S.; Kashikhin, V.V.; Lamm, M.J.; Orris, D.; Tartaglia, M.; Tompkins, J.C.; Turrioni, D.; Yamada, R.; Zlobin,; /Fermilab

2005-09-01

As part of the High Field Magnet program at Fermilab several NbTi and Nb{sub 3}Sn strands were tested with particular emphasis on the study of voltage spikes and their relationship to superconductor instabilities. The voltage spikes were detected under various experimental conditions using voltage-current (V-I) and voltage-field (V-H) methods. Two types of spikes, designated ''magnetization'' and ''transport current'' spikes, have been identified. Their origin is most likely related to magnetization flux jump and transport current redistribution, respectively. Many of the signals observed appear to be a combination of these two types of spikes; the combination of these two instability mechanisms should play a dominant role in determining the minimum quench current.

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

Directory of Open Access Journals (Sweden)

Peter U. Diehl

2015-08-01

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

Contamination spike simulation and measurement in a clean metal vapor laser

International Nuclear Information System (INIS)

Lin, C.E.; Yang, C.Y.

1990-01-01

This paper describes a new method for the generation of contamination-induced voltage spikes in a clean metal vapor laser. The method facilitates the study of the characteristics of this troublesome phenomenon in laser systems. Analysis of these artificially generated dirt spikes shows that the breakdown time of the laser tube is increased when these spike appear. The concept of a Townsend discharge is used to identify the parameter which changes the breakdown time of the discharges. The residual ionization control method is proposed to generate dirt spikes in a clean laser. Experimental results show that a wide range of dirt spike magnitudes can be obtained by using the proposed method. The method provides easy and accurate control of the magnitude of the dirt spike, and the laser tube does not become polluted. Results based on the measurements can be used in actual laser systems to monitor the appearance of dirt spikes and thus avoid the danger of thyratron failure

Spike and burst coding in thalamocortical relay cells.

Directory of Open Access Journals (Sweden)

Fleur Zeldenrust

2018-02-01

Full Text Available Mammalian thalamocortical relay (TCR neurons switch their firing activity between a tonic spiking and a bursting regime. In a combined experimental and computational study, we investigated the features in the input signal that single spikes and bursts in the output spike train represent and how this code is influenced by the membrane voltage state of the neuron. Identical frozen Gaussian noise current traces were injected into TCR neurons in rat brain slices as well as in a validated three-compartment TCR model cell. The resulting membrane voltage traces and spike trains were analyzed by calculating the coherence and impedance. Reverse correlation techniques gave the Event-Triggered Average (ETA and the Event-Triggered Covariance (ETC. This demonstrated that the feature selectivity started relatively long before the events (up to 300 ms and showed a clear distinction between spikes (selective for fluctuations and bursts (selective for integration. The model cell was fine-tuned to mimic the frozen noise initiated spike and burst responses to within experimental accuracy, especially for the mixed mode regimes. The information content carried by the various types of events in the signal as well as by the whole signal was calculated. Bursts phase-lock to and transfer information at lower frequencies than single spikes. On depolarization the neuron transits smoothly from the predominantly bursting regime to a spiking regime, in which it is more sensitive to high-frequency fluctuations. The model was then used to elucidate properties that could not be assessed experimentally, in particular the role of two important subthreshold voltage-dependent currents: the low threshold activated calcium current (IT and the cyclic nucleotide modulated h current (Ih. The ETAs of those currents and their underlying activation/inactivation states not only explained the state dependence of the firing regime but also the long-lasting concerted dynamic action of the two

2D co-ordinate transformation based on a spike timing-dependent plasticity learning mechanism.

Science.gov (United States)

Wu, QingXiang; McGinnity, Thomas Martin; Maguire, Liam; Belatreche, Ammar; Glackin, Brendan

2008-11-01

In order to plan accurate motor actions, the brain needs to build an integrated spatial representation associated with visual stimuli and haptic stimuli. Since visual stimuli are represented in retina-centered co-ordinates and haptic stimuli are represented in body-centered co-ordinates, co-ordinate transformations must occur between the retina-centered co-ordinates and body-centered co-ordinates. A spiking neural network (SNN) model, which is trained with spike-timing-dependent-plasticity (STDP), is proposed to perform a 2D co-ordinate transformation of the polar representation of an arm position to a Cartesian representation, to create a virtual image map of a haptic input. Through the visual pathway, a position signal corresponding to the haptic input is used to train the SNN with STDP synapses such that after learning the SNN can perform the co-ordinate transformation to generate a representation of the haptic input with the same co-ordinates as a visual image. The model can be applied to explain co-ordinate transformation in spiking neuron based systems. The principle can be used in artificial intelligent systems to process complex co-ordinate transformations represented by biological stimuli.

Stochastic optimal control of single neuron spike trains

DEFF Research Database (Denmark)

Iolov, Alexandre; Ditlevsen, Susanne; Longtin, Andrë

2014-01-01

stimulation of a neuron to achieve a target spike train under the physiological constraint to not damage tissue. Approach. We pose a stochastic optimal control problem to precisely specify the spike times in a leaky integrate-and-fire (LIF) model of a neuron with noise assumed to be of intrinsic or synaptic...... origin. In particular, we allow for the noise to be of arbitrary intensity. The optimal control problem is solved using dynamic programming when the controller has access to the voltage (closed-loop control), and using a maximum principle for the transition density when the controller only has access...... to the spike times (open-loop control). Main results. We have developed a stochastic optimal control algorithm to obtain precise spike times. It is applicable in both the supra-threshold and sub-threshold regimes, under open-loop and closed-loop conditions and with an arbitrary noise intensity; the accuracy...

Anticipating Activity in Social Media Spikes

OpenAIRE

Higham, Desmond J.; Grindrod, Peter; Mantzaris, Alexander V.; Otley, Amanda; Laflin, Peter

2014-01-01

We propose a novel mathematical model for the activity of microbloggers during an external, event-driven spike. The model leads to a testable prediction of who would become most active if a spike were to take place. This type of information is of great interest to commercial organisations, governments and charities, as it identifies key players who can be targeted with information in real time when the network is most receptive. The model takes account of the fact that dynamic interactions ev...

Multimodal imaging of spike propagation: a technical case report.

Science.gov (United States)

Tanaka, N; Grant, P E; Suzuki, N; Madsen, J R; Bergin, A M; Hämäläinen, M S; Stufflebeam, S M

2012-06-01

We report an 11-year-old boy with intractable epilepsy, who had cortical dysplasia in the right superior frontal gyrus. Spatiotemporal source analysis of MEG and EEG spikes demonstrated a similar time course of spike propagation from the superior to inferior frontal gyri, as observed on intracranial EEG. The tractography reconstructed from DTI showed a fiber connection between these areas. Our multimodal approach demonstrates spike propagation and a white matter tract guiding the propagation.

The Analysis and Suppression of the spike noise in vibrator record

Science.gov (United States)

Jia, H.; Jiang, T.; Xu, X.; Ge, L.; Lin, J.; Yang, Z.

2013-12-01

During the seismic exploration with vibrator, seismic recording systems have often been affected by random spike noise in the background, which leads to strong data distortions as a result of the cross-correlation processing of the vibrator method. Partial or total loss of the desired seismic information is possible if no automatic spike reduction is available in the field prior to correlation of the field record. Generally speaking, original record of vibrator is uncorrelated data, in which the signal is non-wavelet form. In order to obtain the seismic record similar to explosive source, the signal of uncorrelated data needs to use the correlation algorithm to compress into wavelet form. The correlation process results in that the interference of spike in correlated data is not only being suppressed, but also being expanded. So the spike noise suppression of vibrator is indispensable. According to numerical simulation results, the effect of spike in the vibrator record is mainly affected by the amplitude and proportional points in the uncorrelated record. When the spike noise ratio in uncorrelated record reaches 1.5% and the average amplitude exceeds 200, it will make the SNR(signal-to-noise ratio) of the correlated record lower than 0dB, so that it is difficult to separate the signal. While the amplitude and ratio is determined by the intensity of background noise. Therefore, when the noise level is strong, in order to improve SNR of the seismic data, the uncorrelated record of vibrator need to take necessary steps to suppress spike noise. For the sake of reducing the influence of the spike noise, we need to make the detection and suppression of spike noise process for the uncorrelated record. Because vibrator works by inputting sweep signal into the underground long time, ideally, the peak and valley values of each trace have little change. On the basis of the peak and valley values, we can get a reference amplitude value. Then the spike can be detected and

Effect of volume-oriented versus flow-oriented incentive spirometry on chest wall volumes, inspiratory muscle activity, and thoracoabdominal synchrony in the elderly.

Science.gov (United States)

Lunardi, Adriana C; Porras, Desiderio C; Barbosa, Renata Cc; Paisani, Denise M; Marques da Silva, Cibele C B; Tanaka, Clarice; Carvalho, Celso R F

2014-03-01

Aging causes physiological and functional changes that impair pulmonary function. Incentive spirometry is widely used for lung expansion, but the effects of volume-oriented incentive spirometry (VIS) versus flow-oriented incentive spirometry (FIS) on chest wall volumes, inspiratory muscle activity, and thoracoabdominal synchrony in the elderly are poorly understood. We compared VIS and FIS in elderly subjects and healthy adult subjects. Sixteen elderly subjects (9 women, mean ± SD age 70.6 ± 3.9 y, mean ± SD body mass index 23.8 ± 2.5 kg/m(2)) and 16 healthy adults (8 women, mean ± age 25.9 ± 4.3 y, mean ± body mass index 23.6 ± 2.4 kg/m(2)) performed quiet breathing, VIS, and FIS in randomized sequence. Chest wall kinematics (via optoelectronic plethysmography) and inspiratory muscle activity (via surface electromyography) were assessed simultaneously. Synchrony between the superior thorax and abdominal motion was calculated (phase angle). In the elderly subjects both types of incentive spirometry increased chest wall volumes similarly, whereas in the healthy adult subjects VIS increased the chest wall volume more than did FIS. FIS and VIS triggered similar lower thoracoabdominal synchrony in the elderly subjects, whereas in the healthy adults FIS induced lower synchrony than did VIS. FIS required more muscle activity in the elderly subjects to create an increase in chest wall volume. Incentive spirometry performance is influenced by age, and the differences between elderly and healthy adults response should be considered in clinical practice.

Segmental Bayesian estimation of gap-junctional and inhibitory conductance of inferior olive neurons from spike trains with complicated dynamics

Directory of Open Access Journals (Sweden)

Huu eHoang

2015-05-01

Full Text Available The inverse problem for estimating model parameters from brain spike data is an ill-posed problem because of a huge mismatch in the system complexity between the model and the brain as well as its non-stationary dynamics, and needs a stochastic approach that finds the most likely solution among many possible solutions. In the present study, we developed a segmental Bayesian method to estimate the two parameters of interest, the gap-junctional (gc and inhibitory conductance (gi from inferior olive spike data. Feature vectors were estimated for the spike data in a segment-wise fashion to compensate for the non-stationary firing dynamics. Hierarchical Bayesian estimation was conducted to estimate the gc and gi for every spike segment using a forward model constructed in the principal component analysis (PCA space of the feature vectors, and to merge the segmental estimates into single estimates for every neuron. The segmental Bayesian estimation gave smaller fitting errors than the conventional Bayesian inference, which finds the estimates once across the entire spike data, or the minimum error method, which directly finds the closest match in the PCA space. The segmental Bayesian inference has the potential to overcome the problem of non-stationary dynamics and resolve the ill-posedness of the inverse problem because of the mismatch between the model and the brain under the constraints based, and it is a useful tool to evaluate parameters of interest for neuroscience from experimental spike train data.

Femtosecond laser fabricated spike structures for selective control of cellular behavior.

Science.gov (United States)

Schlie, Sabrina; Fadeeva, Elena; Koch, Jürgen; Ngezahayo, Anaclet; Chichkov, Boris N

2010-09-01

In this study we investigate the potential of femtosecond laser generated micrometer sized spike structures as functional surfaces for selective cell controlling. The spike dimensions as well as the average spike to spike distance can be easily tuned by varying the process parameters. Moreover, negative replications in soft materials such as silicone elastomer can be produced. This allows tailoring of wetting properties of the spike structures and their negative replicas representing a reduced surface contact area. Furthermore, we investigated material effects on cellular behavior. By comparing human fibroblasts and SH-SY5Y neuroblastoma cells we found that the influence of the material was cell specific. The cells not only changed their morphology, but also the cell growth was affected. Whereas, neuroblastoma cells proliferated at the same rate on the spike structures as on the control surfaces, the proliferation of fibroblasts was reduced by the spike structures. These effects can result from the cell specific adhesion patterns as shown in this work. These findings show a possibility to design defined surface microstructures, which could control cellular behavior in a cell specific manner.

Google Searches for "Cheap Cigarettes" Spike at Tax Increases: Evidence from an Algorithm to Detect Spikes in Time Series Data.

Science.gov (United States)

Caputi, Theodore L

2018-05-03

Online cigarette dealers have lower prices than brick-and-mortar retailers and advertise tax-free status.1-8 Previous studies show smokers search out these online alternatives at the time of a cigarette tax increase.9,10 However, these studies rely upon researchers' decision to consider a specific date and preclude the possibility that researchers focus on the wrong date. The purpose of this study is to introduce an unbiased methodology to the field of observing search patterns and to use this methodology to determine whether smokers search Google for "cheap cigarettes" at cigarette tax increases and, if so, whether the increased level of searches persists. Publicly available data from Google Trends is used to observe standardized search volumes for the term, "cheap cigarettes". Seasonal Hybrid Extreme Studentized Deviate and E-Divisive with Means tests were performed to observe spikes and mean level shifts in search volume. Of the twelve cigarette tax increases studied, ten showed spikes in searches for "cheap cigarettes" within two weeks of the tax increase. However, the mean level shifts did not occur for any cigarette tax increase. Searches for "cheap cigarettes" spike around the time of a cigarette tax increase, but the mean level of searches does not shift in response to a tax increase. The SHESD and EDM tests are unbiased methodologies that can be used to identify spikes and mean level shifts in time series data without an a priori date to be studied. SHESD and EDM affirm spikes in interest are related to tax increases. • Applies improved statistical techniques (SHESD and EDM) to Google search data related to cigarettes, reducing bias and increasing power • Contributes to the body of evidence that state and federal tax increases are associated with spikes in searches for cheap cigarettes and may be good dates for increased online health messaging related to tobacco.

Long-term meditation training induced changes in the operational synchrony of default mode network modules during a resting state.

Science.gov (United States)

Fingelkurts, Andrew A; Fingelkurts, Alexander A; Kallio-Tamminen, Tarja

2016-02-01

Using theoretical analysis of self-consciousness concept and experimental evidence on the brain default mode network (DMN) that constitutes the neural signature of self-referential processes, we hypothesized that the anterior and posterior subnets comprising the DMN should show differences in their integrity as a function of meditation training. Functional connectivity within DMN and its subnets (measured by operational synchrony) has been measured in ten novice meditators using an electroencephalogram (EEG) recording in a pre-/post-meditation intervention design. We have found that while the whole DMN was clearly suppressed, different subnets of DMN responded differently after 4 months of meditation training: The strength of EEG operational synchrony in the right and left posterior modules of the DMN decreased in resting post-meditation condition compared to a pre-meditation condition, whereas the frontal DMN module on the contrary exhibited an increase in the strength of EEG operational synchrony. These findings combined with published data on functional-anatomic heterogeneity within the DMN and on trait subjective experiences commonly found following meditation allow us to propose that the first-person perspective and the sense of agency (the witnessing observer) are presented by the frontal DMN module, while the posterior modules of the DMN are generally responsible for the experience of the continuity of 'I' as embodied and localized within bodily space. Significance of these findings is discussed.

Spike-timing theory of working memory.

Directory of Open Access Journals (Sweden)

Botond Szatmáry

Full Text Available Working memory (WM is the part of the brain's memory system that provides temporary storage and manipulation of information necessary for cognition. Although WM has limited capacity at any given time, it has vast memory content in the sense that it acts on the brain's nearly infinite repertoire of lifetime long-term memories. Using simulations, we show that large memory content and WM functionality emerge spontaneously if we take the spike-timing nature of neuronal processing into account. Here, memories are represented by extensively overlapping groups of neurons that exhibit stereotypical time-locked spatiotemporal spike-timing patterns, called polychronous patterns; and synapses forming such polychronous neuronal groups (PNGs are subject to associative synaptic plasticity in the form of both long-term and short-term spike-timing dependent plasticity. While long-term potentiation is essential in PNG formation, we show how short-term plasticity can temporarily strengthen the synapses of selected PNGs and lead to an increase in the spontaneous reactivation rate of these PNGs. This increased reactivation rate, consistent with in vivo recordings during WM tasks, results in high interspike interval variability and irregular, yet systematically changing, elevated firing rate profiles within the neurons of the selected PNGs. Additionally, our theory explains the relationship between such slowly changing firing rates and precisely timed spikes, and it reveals a novel relationship between WM and the perception of time on the order of seconds.

Dual roles for spike signaling in cortical neural populations

Directory of Open Access Journals (Sweden)

Dana eBallard

2011-06-01

Full Text Available A prominent feature of signaling in cortical neurons is that of randomness in the action potential. The output of a typical pyramidal cell can be well fit with a Poisson model, and variations in the Poisson rate repeatedly have been shown to be correlated with stimuli. However while the rate provides a very useful characterization of neural spike data, it may not be the most fundamental description of the signaling code. Recent data showing γ frequency range multi-cell action potential correlations, together with spike timing dependent plasticity, are spurring a re-examination of the classical model, since precise timing codes imply that the generation of spikes is essentially deterministic. Could the observed Poisson randomness and timing determinism reflect two separate modes of communication, or do they somehow derive from a single process? We investigate in a timing-based model whether the apparent incompatibility between these probabilistic and deterministic observations may be resolved by examining how spikes could be used in the underlying neural circuits. The crucial component of this model draws on dual roles for spike signaling. In learning receptive fields from ensembles of inputs, spikes need to behave probabilistically, whereas for fast signaling of individual stimuli, the spikes need to behave deterministically. Our simulations show that this combination is possible if deterministic signals using γ latency coding are probabilistically routed through different members of a cortical cell population at different times. This model exhibits standard features characteristic of Poisson models such as orientation tuning post-stimulus histograms and exponential interval histograms. In addition it makes testable predictions that follow from the γ latency coding.

Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

Directory of Open Access Journals (Sweden)

Nicolas Frémaux

2013-04-01

Full Text Available Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD learning of Doya (2000 to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

Mechanisms of zero-lag synchronization in cortical motifs.

Directory of Open Access Journals (Sweden)

Leonardo L Gollo

2014-04-01

Full Text Available Zero-lag synchronization between distant cortical areas has been observed in a diversity of experimental data sets and between many different regions of the brain. Several computational mechanisms have been proposed to account for such isochronous synchronization in the presence of long conduction delays: Of these, the phenomenon of "dynamical relaying"--a mechanism that relies on a specific network motif--has proven to be the most robust with respect to parameter mismatch and system noise. Surprisingly, despite a contrary belief in the community, the common driving motif is an unreliable means of establishing zero-lag synchrony. Although dynamical relaying has been validated in empirical and computational studies, the deeper dynamical mechanisms and comparison to dynamics on other motifs is lacking. By systematically comparing synchronization on a variety of small motifs, we establish that the presence of a single reciprocally connected pair--a "resonance pair"--plays a crucial role in disambiguating those motifs that foster zero-lag synchrony in the presence of conduction delays (such as dynamical relaying from those that do not (such as the common driving triad. Remarkably, minor structural changes to the common driving motif that incorporate a reciprocal pair recover robust zero-lag synchrony. The findings are observed in computational models of spiking neurons, populations of spiking neurons and neural mass models, and arise whether the oscillatory systems are periodic, chaotic, noise-free or driven by stochastic inputs. The influence of the resonance pair is also robust to parameter mismatch and asymmetrical time delays amongst the elements of the motif. We call this manner of facilitating zero-lag synchrony resonance-induced synchronization, outline the conditions for its occurrence, and propose that it may be a general mechanism to promote zero-lag synchrony in the brain.

A novel unsupervised spike sorting algorithm for intracranial EEG.

Science.gov (United States)

Yadav, R; Shah, A K; Loeb, J A; Swamy, M N S; Agarwal, R

2011-01-01

This paper presents a novel, unsupervised spike classification algorithm for intracranial EEG. The method combines template matching and principal component analysis (PCA) for building a dynamic patient-specific codebook without a priori knowledge of the spike waveforms. The problem of misclassification due to overlapping classes is resolved by identifying similar classes in the codebook using hierarchical clustering. Cluster quality is visually assessed by projecting inter- and intra-clusters onto a 3D plot. Intracranial EEG from 5 patients was utilized to optimize the algorithm. The resulting codebook retains 82.1% of the detected spikes in non-overlapping and disjoint clusters. Initial results suggest a definite role of this method for both rapid review and quantitation of interictal spikes that could enhance both clinical treatment and research studies on epileptic patients.

No WIMP mini-spikes in dwarf spheroidal galaxies

NARCIS (Netherlands)

Wanders, M.; Bertone, G.; Volonteri, M.; Weniger, C.

2015-01-01

The formation of black holes inevitably affects the distribution of dark and baryonic matter in their vicinity, leading to an enhancement of the dark matter density, called spike, and if dark matter is made of WIMPs, to a strong enhancement of the dark matter annihilation rate. Spikes at the center

Building functional networks of spiking model neurons.

Science.gov (United States)

Abbott, L F; DePasquale, Brian; Memmesheimer, Raoul-Martin

2016-03-01

Most of the networks used by computer scientists and many of those studied by modelers in neuroscience represent unit activities as continuous variables. Neurons, however, communicate primarily through discontinuous spiking. We review methods for transferring our ability to construct interesting networks that perform relevant tasks from the artificial continuous domain to more realistic spiking network models. These methods raise a number of issues that warrant further theoretical and experimental study.

Spike morphology in blast-wave-driven instability experiments

International Nuclear Information System (INIS)

Kuranz, C. C.; Drake, R. P.; Grosskopf, M. J.; Fryxell, B.; Budde, A.; Hansen, J. F.; Miles, A. R.; Plewa, T.; Hearn, N.; Knauer, J.

2010-01-01

The laboratory experiments described in the present paper observe the blast-wave-driven Rayleigh-Taylor instability with three-dimensional (3D) initial conditions. About 5 kJ of energy from the Omega laser creates conditions similar to those of the He-H interface during the explosion phase of a supernova. The experimental target is a 150 μm thick plastic disk followed by a low-density foam. The plastic piece has an embedded, 3D perturbation. The basic structure of the pattern is two orthogonal sine waves where each sine wave has an amplitude of 2.5 μm and a wavelength of 71 μm. In some experiments, an additional wavelength is added to explore the interaction of modes. In experiments with 3D initial conditions the spike morphology differs from what has been observed in other Rayleigh-Taylor experiments and simulations. Under certain conditions, experimental radiographs show some mass extending from the interface to the shock front. Current simulations show neither the spike morphology nor the spike penetration observed in the experiments. The amount of mass reaching the shock front is analyzed and potential causes for the spike morphology and the spikes reaching the shock are discussed. One such hypothesis is that these phenomena may be caused by magnetic pressure, generated by an azimuthal magnetic field produced by the plasma dynamics.

Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks

NARCIS (Netherlands)

D. Zambrano (Davide); S.M. Bohte (Sander)

2016-01-01

textabstractBiological neurons communicate with a sparing exchange of pulses - spikes. It is an open question how real spiking neurons produce the kind of powerful neural computation that is possible with deep artificial neural networks, using only so very few spikes to communicate. Building on

Characterizing neural activities evoked by manual acupuncture through spiking irregularity measures

International Nuclear Information System (INIS)

Xue Ming; Wang Jiang; Deng Bin; Wei Xi-Le; Yu Hai-Tao; Chen Ying-Yuan

2013-01-01

The neural system characterizes information in external stimulations by different spiking patterns. In order to examine how neural spiking patterns are related to acupuncture manipulations, experiments are designed in such a way that different types of manual acupuncture (MA) manipulations are taken at the ‘Zusanli’ point of experimental rats, and the induced electrical signals in the spinal dorsal root ganglion are detected and recorded. The interspike interval (ISI) statistical histogram is fitted by the gamma distribution, which has two parameters: one is the time-dependent firing rate and the other is a shape parameter characterizing the spiking irregularities. The shape parameter is the measure of spiking irregularities and can be used to identify the type of MA manipulations. The coefficient of variation is mostly used to measure the spike time irregularity, but it overestimates the irregularity in the case of pronounced firing rate changes. However, experiments show that each acupuncture manipulation will lead to changes in the firing rate. So we combine four relatively rate-independent measures to study the irregularity of spike trains evoked by different types of MA manipulations. Results suggest that the MA manipulations possess unique spiking statistics and characteristics and can be distinguished according to the spiking irregularity measures. These studies have offered new insights into the coding processes and information transfer of acupuncture. (interdisciplinary physics and related areas of science and technology)

Coincidence Detection Using Spiking Neurons with Application to Face Recognition

Directory of Open Access Journals (Sweden)

Fadhlan Kamaruzaman

2015-01-01

Full Text Available We elucidate the practical implementation of Spiking Neural Network (SNN as local ensembles of classifiers. Synaptic time constant τs is used as learning parameter in representing the variations learned from a set of training data at classifier level. This classifier uses coincidence detection (CD strategy trained in supervised manner using a novel supervised learning method called τs Prediction which adjusts the precise timing of output spikes towards the desired spike timing through iterative adaptation of τs. This paper also discusses the approximation of spike timing in Spike Response Model (SRM for the purpose of coincidence detection. This process significantly speeds up the whole process of learning and classification. Performance evaluations with face datasets such as AR, FERET, JAFFE, and CK+ datasets show that the proposed method delivers better face classification performance than the network trained with Supervised Synaptic-Time Dependent Plasticity (STDP. We also found that the proposed method delivers better classification accuracy than k nearest neighbor, ensembles of kNN, and Support Vector Machines. Evaluation on several types of spike codings also reveals that latency coding delivers the best result for face classification as well as for classification of other multivariate datasets.

Adaptive coupling optimized spiking coherence and synchronization in Newman-Watts neuronal networks.

Science.gov (United States)

Gong, Yubing; Xu, Bo; Wu, Ya'nan

2013-09-01

In this paper, we have numerically studied the effect of adaptive coupling on the temporal coherence and synchronization of spiking activity in Newman-Watts Hodgkin-Huxley neuronal networks. It is found that random shortcuts can enhance the spiking synchronization more rapidly when the increment speed of adaptive coupling is increased and can optimize the temporal coherence of spikes only when the increment speed of adaptive coupling is appropriate. It is also found that adaptive coupling strength can enhance the synchronization of spikes and can optimize the temporal coherence of spikes when random shortcuts are appropriate. These results show that adaptive coupling has a big influence on random shortcuts related spiking activity and can enhance and optimize the temporal coherence and synchronization of spiking activity of the network. These findings can help better understand the roles of adaptive coupling for improving the information processing and transmission in neural systems.

A matched-filter algorithm to detect amperometric spikes resulting from quantal secretion.

Science.gov (United States)

Balaji Ramachandran, Supriya; Gillis, Kevin D

2018-01-01

Electrochemical microelectrodes located immediately adjacent to the cell surface can detect spikes of amperometric current during exocytosis as the transmitter released from a single vesicle is oxidized on the electrode surface. Automated techniques to detect spikes are needed in order to quantify the spike rate as a measure of the rate of exocytosis. We have developed a Matched Filter (MF) detection algorithm that scans the data set with a library of prototype spike templates while performing a least-squares fit to determine the amplitude and standard error. The ratio of the fit amplitude to the standard error constitutes a criterion score that is assigned for each time point and for each template. A spike is detected when the criterion score exceeds a threshold and the highest-scoring template and the time of peak score is identified. The search for the next spike commences only after the score falls below a second, lower threshold to reduce false positives. The approach was extended to detect spikes with double-exponential decays with the sum of two templates. Receiver Operating Characteristic plots (ROCs) demonstrate that the algorithm detects >95% of manually identified spikes with a false-positive rate of ∼2%. ROCs demonstrate that the MF algorithm performs better than algorithms that detect spikes based on a derivative-threshold approach. The MF approach performs well and leads into approaches to identify spike parameters. Copyright © 2017 Elsevier B.V. All rights reserved.

Abnormal synchrony and effective connectivity in patients with schizophrenia and auditory hallucinations

Science.gov (United States)

de la Iglesia-Vaya, Maria; Escartí, Maria José; Molina-Mateo, Jose; Martí-Bonmatí, Luis; Gadea, Marien; Castellanos, Francisco Xavier; Aguilar García-Iturrospe, Eduardo J.; Robles, Montserrat; Biswal, Bharat B.; Sanjuan, Julio

2014-01-01

Auditory hallucinations (AH) are the most frequent positive symptoms in patients with schizophrenia. Hallucinations have been related to emotional processing disturbances, altered functional connectivity and effective connectivity deficits. Previously, we observed that, compared to healthy controls, the limbic network responses of patients with auditory hallucinations differed when the subjects were listening to emotionally charged words. We aimed to compare the synchrony patterns and effective connectivity of task-related networks between schizophrenia patients with and without AH and healthy controls. Schizophrenia patients with AH (n = 27) and without AH (n = 14) were compared with healthy participants (n = 31). We examined functional connectivity by analyzing correlations and cross-correlations among previously detected independent component analysis time courses. Granger causality was used to infer the information flow direction in the brain regions. The results demonstrate that the patterns of cortico-cortical functional synchrony differentiated the patients with AH from the patients without AH and from the healthy participants. Additionally, Granger-causal relationships between the networks clearly differentiated the groups. In the patients with AH, the principal causal source was an occipital–cerebellar component, versus a temporal component in the patients without AH and the healthy controls. These data indicate that an anomalous process of neural connectivity exists when patients with AH process emotional auditory stimuli. Additionally, a central role is suggested for the cerebellum in processing emotional stimuli in patients with persistent AH. PMID:25379429

Spike sorting based upon machine learning algorithms (SOMA).

Science.gov (United States)

Horton, P M; Nicol, A U; Kendrick, K M; Feng, J F

2007-02-15

We have developed a spike sorting method, using a combination of various machine learning algorithms, to analyse electrophysiological data and automatically determine the number of sampled neurons from an individual electrode, and discriminate their activities. We discuss extensions to a standard unsupervised learning algorithm (Kohonen), as using a simple application of this technique would only identify a known number of clusters. Our extra techniques automatically identify the number of clusters within the dataset, and their sizes, thereby reducing the chance of misclassification. We also discuss a new pre-processing technique, which transforms the data into a higher dimensional feature space revealing separable clusters. Using principal component analysis (PCA) alone may not achieve this. Our new approach appends the features acquired using PCA with features describing the geometric shapes that constitute a spike waveform. To validate our new spike sorting approach, we have applied it to multi-electrode array datasets acquired from the rat olfactory bulb, and from the sheep infero-temporal cortex, and using simulated data. The SOMA sofware is available at http://www.sussex.ac.uk/Users/pmh20/spikes.

Thermal spike analysis of highly charged ion tracks

International Nuclear Information System (INIS)

Karlušić, M.; Jakšić, M.

2012-01-01

The irradiation of material using swift heavy ion or highly charged ion causes excitation of the electron subsystem at nanometer scale along the ion trajectory. According to the thermal spike model, energy deposited into the electron subsystem leads to temperature increase due to electron–phonon coupling. If ion-induced excitation is sufficiently intensive, then melting of the material can occur, and permanent damage (i.e., ion track) can be formed upon rapid cooling. We present an extension of the analytical thermal spike model of Szenes for the analysis of surface ion track produced after the impact of highly charged ion. By applying the model to existing experimental data, more than 60% of the potential energy of the highly charged ion was shown to be retained in the material during the impact and transformed into the energy of the thermal spike. This value is much higher than 20–40% of the transferred energy into the thermal spike by swift heavy ion. Thresholds for formation of highly charged ion track in different materials show uniform behavior depending only on few material parameters.

A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings.

Science.gov (United States)

Pillow, Jonathan W; Shlens, Jonathon; Chichilnisky, E J; Simoncelli, Eero P

2013-01-01

We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call "binary pursuit". The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth.

Inherently stochastic spiking neurons for probabilistic neural computation

KAUST Repository

Al-Shedivat, Maruan

2015-04-01

Neuromorphic engineering aims to design hardware that efficiently mimics neural circuitry and provides the means for emulating and studying neural systems. In this paper, we propose a new memristor-based neuron circuit that uniquely complements the scope of neuron implementations and follows the stochastic spike response model (SRM), which plays a cornerstone role in spike-based probabilistic algorithms. We demonstrate that the switching of the memristor is akin to the stochastic firing of the SRM. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards memristive, scalable and efficient stochastic neuromorphic platforms. © 2015 IEEE.

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

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Pavel Sanda

2017-09-01

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

Risk of punishment influences discrete and coordinated encoding of reward-guided actions by prefrontal cortex and VTA neurons

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Park, Junchol

2017-01-01

Actions motivated by rewards are often associated with risk of punishment. Little is known about the neural representation of punishment risk during reward-seeking behavior. We modeled this circumstance in rats by designing a task where actions were consistently rewarded but probabilistically punished. Spike activity and local field potentials were recorded during task performance simultaneously from VTA and mPFC, two reciprocally connected regions implicated in reward-seeking and aversive behaviors. At the single unit level, we found that ensembles of putative dopamine and non-dopamine VTA neurons and mPFC neurons encode the relationship between action and punishment. At the network level, we found that coherent theta oscillations synchronize VTA and mPFC in a bottom-up direction, effectively phase-modulating the neuronal spike activity in the two regions during punishment-free actions. This synchrony declined as a function of punishment probability, suggesting that during reward-seeking actions, risk of punishment diminishes VTA-driven neural synchrony between the two regions. PMID:29058673

Perception of audio-visual speech synchrony in Spanish-speaking children with and without specific language impairment.

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Pons, Ferran; Andreu, Llorenç; Sanz-Torrent, Monica; Buil-Legaz, Lucía; Lewkowicz, David J

2013-06-01

Speech perception involves the integration of auditory and visual articulatory information, and thus requires the perception of temporal synchrony between this information. There is evidence that children with specific language impairment (SLI) have difficulty with auditory speech perception but it is not known if this is also true for the integration of auditory and visual speech. Twenty Spanish-speaking children with SLI, twenty typically developing age-matched Spanish-speaking children, and twenty Spanish-speaking children matched for MLU-w participated in an eye-tracking study to investigate the perception of audiovisual speech synchrony. Results revealed that children with typical language development perceived an audiovisual asynchrony of 666 ms regardless of whether the auditory or visual speech attribute led the other one. Children with SLI only detected the 666 ms asynchrony when the auditory component preceded [corrected] the visual component. None of the groups perceived an audiovisual asynchrony of 366 ms. These results suggest that the difficulty of speech processing by children with SLI would also involve difficulties in integrating auditory and visual aspects of speech perception.

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

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Christian Albers

Full Text Available Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP. Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious and strong (teacher spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

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Albers, Christian; Westkott, Maren; Pawelzik, Klaus

2016-01-01

Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

Correlations decrease with propagation of spiking activity in the mouse barrel cortex

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Gayathri Nattar Ranganathan

2011-05-01

Full Text Available Propagation of suprathreshold spiking activity through neuronal populations is important for the function of the central nervous system. Neural correlations have an impact on cortical function particularly on the signaling of information and propagation of spiking activity. Therefore we measured the change in correlations as suprathreshold spiking activity propagated between recurrent neuronal networks of the mammalian cerebral cortex. Using optical methods we recorded spiking activity from large samples of neurons from two neural populations simultaneously. The results indicate that correlations decreased as spiking activity propagated from layer 4 to layer 2/3 in the rodent barrel cortex.

Families that fire together smile together: Resting state connectome similarity and daily emotional synchrony in parent-child dyads.

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Lee, Tae-Ho; Miernicki, Michelle E; Telzer, Eva H

2017-05-15

Despite emerging evidence suggesting a biological basis to our social tiles, our understanding of the neural processes which link two minds is unknown. We implemented a novel approach, which included connectome similarity analysis using resting state intrinsic networks of parent-child dyads as well as daily diaries measured across 14 days. Intrinsic resting-state networks for both parents and their adolescent child were identified using independent component analysis (ICA). Results indicate that parents and children who had more similar RSN connectome also had more similar day-to-day emotional synchrony. Furthermore, dyadic RSN connectome similarity was associated with children's emotional competence, suggesting that being neurally in-tune with their parents confers emotional benefits. We provide the first evidence that dyadic RSN similarity is associated with emotional synchrony in what is often our first and most essential social bond, the parent-child relationship. Copyright © 2017 Elsevier Inc. All rights reserved.

Absolute Ca Isotopic Measurement Using an Improved Double Spike Technique

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Jason Jiun-San Shen

2009-01-01

Full Text Available A new vector analytical method has been developed in order to obtain the true isotopic composition of the 42Ca-48Ca double spike. This is achieved by using two different sample-spike mixtures combined with the double spike and natural Ca data. Be cause the natural sample (two mixtures and the spike should all lie on a single mixing line, we are able to con strain the true isotopic composition of our double spike using this new approach. Once the isotopic composition of the Ca double spike is established, we are able to obtain the true Ca isotopic composition of the NIST Ca standard SRM915a, 40Ca/44Ca = 46.537 ± 2 (2sm, n = 55, 42Ca/44Ca = 0.31031 ± 1, 43Ca/44Ca = 0.06474 ± 1, and 48Ca/44Ca = 0.08956 ± 1. De spite an off set of 1.3% in 40Ca/44Ca between our result and the previously re ported value (Russell et al. 1978, our data indicate an off set of 1.89__in 40Ca/44Ca between SRM915a and seawater, entirely consistent with the published results.

Spike-Timing of Orbitofrontal Neurons Is Synchronized With Breathing.

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Kőszeghy, Áron; Lasztóczi, Bálint; Forro, Thomas; Klausberger, Thomas

2018-01-01

The orbitofrontal cortex (OFC) has been implicated in a multiplicity of complex brain functions, including representations of expected outcome properties, post-decision confidence, momentary food-reward values, complex flavors and odors. As breathing rhythm has an influence on odor processing at primary olfactory areas, we tested the hypothesis that it may also influence neuronal activity in the OFC, a prefrontal area involved also in higher order processing of odors. We recorded spike timing of orbitofrontal neurons as well as local field potentials (LFPs) in awake, head-fixed mice, together with the breathing rhythm. We observed that a large majority of orbitofrontal neurons showed robust phase-coupling to breathing during immobility and running. The phase coupling of action potentials to breathing was significantly stronger in orbitofrontal neurons compared to cells in the medial prefrontal cortex. The characteristic synchronization of orbitofrontal neurons with breathing might provide a temporal framework for multi-variable processing of olfactory, gustatory and reward-value relationships.

Spike-Timing of Orbitofrontal Neurons Is Synchronized With Breathing

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Áron Kőszeghy

2018-04-01

Full Text Available The orbitofrontal cortex (OFC has been implicated in a multiplicity of complex brain functions, including representations of expected outcome properties, post-decision confidence, momentary food-reward values, complex flavors and odors. As breathing rhythm has an influence on odor processing at primary olfactory areas, we tested the hypothesis that it may also influence neuronal activity in the OFC, a prefrontal area involved also in higher order processing of odors. We recorded spike timing of orbitofrontal neurons as well as local field potentials (LFPs in awake, head-fixed mice, together with the breathing rhythm. We observed that a large majority of orbitofrontal neurons showed robust phase-coupling to breathing during immobility and running. The phase coupling of action potentials to breathing was significantly stronger in orbitofrontal neurons compared to cells in the medial prefrontal cortex. The characteristic synchronization of orbitofrontal neurons with breathing might provide a temporal framework for multi-variable processing of olfactory, gustatory and reward-value relationships.

The Nature of Power Spikes: a regime-switch approach

NARCIS (Netherlands)

C.M. de Jong (Cyriel)

2005-01-01

textabstractDue to its non-storable nature, electricity is a commodity with probably the most volatile spot prices, exemplified by occasional spikes. Appropriate pricing, portfolio, and risk management models have to incorporate these characteristics, and the spikes in particular. We investigate the

Transcriptome Analysis for Abnormal Spike Development of the Wheat Mutant dms.

Science.gov (United States)

Zhu, Xin-Xin; Li, Qiao-Yun; Shen, Chun-Cai; Duan, Zong-Biao; Yu, Dong-Yan; Niu, Ji-Shan; Ni, Yong-Jing; Jiang, Yu-Mei

2016-01-01

Wheat (Triticum aestivum L.) spike development is the foundation for grain yield. We obtained a novel wheat mutant, dms, characterized as dwarf, multi-pistil and sterility. Although the genetic changes are not clear, the heredity of traits suggests that a recessive gene locus controls the two traits of multi-pistil and sterility in self-pollinating populations of the medium plants (M), such that the dwarf genotype (D) and tall genotype (T) in the progeny of the mutant are ideal lines for studies regarding wheat spike development. The objective of this study was to explore the molecular basis for spike abnormalities of dwarf genotype. Four unigene libraries were assembled by sequencing the mRNAs of the super-bulked differentiating spikes and stem tips of the D and T plants. Using integrative analysis, we identified 419 genes highly expressed in spikes, including nine typical homeotic genes of the MADS-box family and the genes TaAP2, TaFL and TaDL. We also identified 143 genes that were significantly different between young spikes of T and D, and 26 genes that were putatively involved in spike differentiation. The result showed that the expression levels of TaAP1-2, TaAP2, and other genes involved in the majority of biological processes such as transcription, translation, cell division, photosynthesis, carbohydrate transport and metabolism, and energy production and conversion were significantly lower in D than in T. We identified a set of genes related to wheat floral organ differentiation, including typical homeotic genes. Our results showed that the major causal factors resulting in the spike abnormalities of dms were the lower expression homeotic genes, hormonal imbalance, repressed biological processes, and deficiency of construction materials and energy. We performed a series of studies on the homeotic genes, however the other three causal factors for spike abnormal phenotype of dms need further study.

Fluid-thermal analysis of aerodynamic heating over spiked blunt body configurations

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Qin, Qihao; Xu, Jinglei; Guo, Shuai

2017-03-01

When flying at hypersonic speeds, the spiked blunt body is constantly subjected to severe aerodynamic heating. To illustrate the thermal response of different configurations and the relevant flow field variation, a loosely-coupled fluid-thermal analysis is performed in this paper. The Mesh-based parallel Code Coupling Interface (MpCCI) is adopted to implement the data exchange between the fluid solver and the thermal solver. The results indicate that increases in spike diameter and length will result in a sharp decline of the wall temperature along the spike, and the overall heat flux is remarkably reduced to less than 300 W/cm2 with the aerodome mounted at the spike tip. Moreover, the presence and evolution of small vortices within the recirculation zone are observed and proved to be induced by the stagnation effect of reattachment points on the spike. In addition, the drag coefficient of the configuration with a doubled spike length presents a maximum drop of 4.59% due to the elevated wall temperature. And the growing difference of the drag coefficient is further increased during the accelerating process.

The chronotron: a neuron that learns to fire temporally precise spike patterns.

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Răzvan V Florian

Full Text Available In many cases, neurons process information carried by the precise timings of spikes. Here we show how neurons can learn to generate specific temporally precise output spikes in response to input patterns of spikes having precise timings, thus processing and memorizing information that is entirely temporally coded, both as input and as output. We introduce two new supervised learning rules for spiking neurons with temporal coding of information (chronotrons, one that provides high memory capacity (E-learning, and one that has a higher biological plausibility (I-learning. With I-learning, the neuron learns to fire the target spike trains through synaptic changes that are proportional to the synaptic currents at the timings of real and target output spikes. We study these learning rules in computer simulations where we train integrate-and-fire neurons. Both learning rules allow neurons to fire at the desired timings, with sub-millisecond precision. We show how chronotrons can learn to classify their inputs, by firing identical, temporally precise spike trains for different inputs belonging to the same class. When the input is noisy, the classification also leads to noise reduction. We compute lower bounds for the memory capacity of chronotrons and explore the influence of various parameters on chronotrons' performance. The chronotrons can model neurons that encode information in the time of the first spike relative to the onset of salient stimuli or neurons in oscillatory networks that encode information in the phases of spikes relative to the background oscillation. Our results show that firing one spike per cycle optimizes memory capacity in neurons encoding information in the phase of firing relative to a background rhythm.

Perceptron learning rule derived from spike-frequency adaptation and spike-time-dependent plasticity.

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D'Souza, Prashanth; Liu, Shih-Chii; Hahnloser, Richard H R

2010-03-09

It is widely believed that sensory and motor processing in the brain is based on simple computational primitives rooted in cellular and synaptic physiology. However, many gaps remain in our understanding of the connections between neural computations and biophysical properties of neurons. Here, we show that synaptic spike-time-dependent plasticity (STDP) combined with spike-frequency adaptation (SFA) in a single neuron together approximate the well-known perceptron learning rule. Our calculations and integrate-and-fire simulations reveal that delayed inputs to a neuron endowed with STDP and SFA precisely instruct neural responses to earlier arriving inputs. We demonstrate this mechanism on a developmental example of auditory map formation guided by visual inputs, as observed in the external nucleus of the inferior colliculus (ICX) of barn owls. The interplay of SFA and STDP in model ICX neurons precisely transfers the tuning curve from the visual modality onto the auditory modality, demonstrating a useful computation for multimodal and sensory-guided processing.

Real-time computing platform for spiking neurons (RT-spike).

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Ros, Eduardo; Ortigosa, Eva M; Agís, Rodrigo; Carrillo, Richard; Arnold, Michael

2006-07-01

A computing platform is described for simulating arbitrary networks of spiking neurons in real time. A hybrid computing scheme is adopted that uses both software and hardware components to manage the tradeoff between flexibility and computational power; the neuron model is implemented in hardware and the network model and the learning are implemented in software. The incremental transition of the software components into hardware is supported. We focus on a spike response model (SRM) for a neuron where the synapses are modeled as input-driven conductances. The temporal dynamics of the synaptic integration process are modeled with a synaptic time constant that results in a gradual injection of charge. This type of model is computationally expensive and is not easily amenable to existing software-based event-driven approaches. As an alternative we have designed an efficient time-based computing architecture in hardware, where the different stages of the neuron model are processed in parallel. Further improvements occur by computing multiple neurons in parallel using multiple processing units. This design is tested using reconfigurable hardware and its scalability and performance evaluated. Our overall goal is to investigate biologically realistic models for the real-time control of robots operating within closed action-perception loops, and so we evaluate the performance of the system on simulating a model of the cerebellum where the emulation of the temporal dynamics of the synaptic integration process is important.

Extraction and characterization of essential discharge patterns from multisite recordings of spiking ongoing activity.

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Riccardo Storchi

Full Text Available Neural activation patterns proceed often by schemes or motifs distributed across the involved cortical networks. As neurons are correlated, the estimate of all possible dependencies quickly goes out of control. The complex nesting of different oscillation frequencies and their high non-stationariety further hamper any quantitative evaluation of spiking network activities. The problem is exacerbated by the intrinsic variability of neural patterns.Our technique introduces two important novelties and enables to insulate essential patterns on larger sets of spiking neurons and brain activity regimes. First, the sampling procedure over N units is based on a fixed spike number k in order to detect N-dimensional arrays (k-sequences, whose sum over all dimension is k. Then k-sequences variability is greatly reduced by a hierarchical separative clustering, that assigns large amounts of distinct k-sequences to few classes. Iterative separations are stopped when the dimension of each cluster comes to be smaller than a certain threshold. As threshold tuning critically impacts on the number of classes extracted, we developed an effective cost criterion to select the shortest possible description of our dataset. Finally we described three indexes (C,S,R to evaluate the average pattern complexity, the structure of essential classes and their stability in time.We validated this algorithm with four kinds of surrogated activity, ranging from random to very regular patterned. Then we characterized a selection of ongoing activity recordings. By the S index we identified unstable, moderatly and strongly stable patterns while by the C and the R indices we evidenced their non-random structure. Our algorithm seems able to extract interesting and non-trivial spatial dynamics from multisource neuronal recordings of ongoing and potentially stimulated activity. Combined with time-frequency analysis of LFPs could provide a powerful multiscale approach linking population

Impact of substance P on the correlation of spike train evoked by electro acupuncture

International Nuclear Information System (INIS)

Jin, Chen; Zhang, Xuan; Wang, Jiang; Guo, Yi; Zhao, Xue; Guo, Yong-Ming

2016-01-01

Highlights: • We analyze spike trains induced by EA before and after inhibiting SP in PC6 area. • Inhibiting SP leads to an increase of spiking rate of median nerve. • SP may modulate membrane potential to affect the spiking rate. • SP has an influence on long-range correlation of spike train evoked by EA. • SP play an important role in EA-induced neural spiking and encoding. - Abstract: Substance P (SP) participates in the neural signal transmission evoked by electro-acupuncture (EA). This paper investigates the impact of SP on the correlation of spike train in the median nerve evoked by EA at 'Neiguan' acupoint (PC6). It shows that the spiking rate and interspike interval (ISI) distribution change obviously after inhibiting SP. This variation of spiking activity indicates that SP affects the temporal structure of spike train through modulating the action potential on median nerve filaments. Furtherly, the correlation coefficient and scaling exponent are considered to measure the correlation of spike train. Scaled Windowed Variance (SWV) method is applied to calculate scaling exponent which quantifies the long-range correlation of the neural electrical signals. It is found that the correlation coefficients of ISI increase after inhibiting SP released. In addition, the scaling exponents of neuronal spike train have significant differences between before and after inhibiting SP. These findings demonstrate that SP has an influence on the long-range correlation of spike train. Our results indicate that SP may play an important role in EA-induced neural spiking and encoding.

Robust spike sorting of retinal ganglion cells tuned to spot stimuli.

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Ghahari, Alireza; Badea, Tudor C

2016-08-01

We propose an automatic spike sorting approach for the data recorded from a microelectrode array during visual stimulation of wild type retinas with tiled spot stimuli. The approach first detects individual spikes per electrode by their signature local minima. With the mixture probability distribution of the local minima estimated afterwards, it applies a minimum-squared-error clustering algorithm to sort the spikes into different clusters. A template waveform for each cluster per electrode is defined, and a number of reliability tests are performed on it and its corresponding spikes. Finally, a divisive hierarchical clustering algorithm is used to deal with the correlated templates per cluster type across all the electrodes. According to the measures of performance of the spike sorting approach, it is robust even in the cases of recordings with low signal-to-noise ratio.

Cochlear spike synchronization and neuron coincidence detection model

Science.gov (United States)

Bader, Rolf

2018-02-01

Coincidence detection of a spike pattern fed from the cochlea into a single neuron is investigated using a physical Finite-Difference model of the cochlea and a physiologically motivated neuron model. Previous studies have shown experimental evidence of increased spike synchronization in the nucleus cochlearis and the trapezoid body [Joris et al., J. Neurophysiol. 71(3), 1022-1036 and 1037-1051 (1994)] and models show tone partial phase synchronization at the transition from mechanical waves on the basilar membrane into spike patterns [Ch. F. Babbs, J. Biophys. 2011, 435135]. Still the traveling speed of waves on the basilar membrane cause a frequency-dependent time delay of simultaneously incoming sound wavefronts up to 10 ms. The present model shows nearly perfect synchronization of multiple spike inputs as neuron outputs with interspike intervals (ISI) at the periodicity of the incoming sound for frequencies from about 30 to 300 Hz for two different amounts of afferent nerve fiber neuron inputs. Coincidence detection serves here as a fusion of multiple inputs into one single event enhancing pitch periodicity detection for low frequencies, impulse detection, or increased sound or speech intelligibility due to dereverberation.

The variational spiked oscillator

International Nuclear Information System (INIS)

Aguilera-Navarro, V.C.; Ullah, N.

1992-08-01

A variational analysis of the spiked harmonic oscillator Hamiltonian -d 2 / d x 2 + x 2 + δ/ x 5/2 , δ > 0, is reported in this work. A trial function satisfying Dirichlet boundary conditions is suggested. The results are excellent for a large range of values of the coupling parameter. (author)

Trace element ink spiking for signature authentication

International Nuclear Information System (INIS)

Hatzistavros, V.S.; Kallithrakas-Kontos, N.G.

2008-01-01

Signature authentication is a critical question in forensic document examination. Last years the evolution of personal computers made signature copying a quite easy task, so the development of new ways for signature authentication is crucial. In the present work a commercial ink was spiked with many trace elements in various concentrations. Inorganic and organometallic ink soluble compounds were used as spiking agents, whilst ink retained its initial properties. The spiked inks were used for paper writing and the documents were analyzed by a non destructive method, the energy dispersive X-ray fluorescence. The thin target model was proved right for quantitative analysis and a very good linear relationship of the intensity (X-ray signal) against concentration was estimated for all used elements. Intensity ratios between different elements in the same ink gave very stable results, independent on the writing alterations. The impact of time both to written document and prepared inks was also investigated. (author)

A Novel and Simple Spike Sorting Implementation.

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Petrantonakis, Panagiotis C; Poirazi, Panayiota

2017-04-01

Monitoring the activity of multiple, individual neurons that fire spikes in the vicinity of an electrode, namely perform a Spike Sorting (SS) procedure, comprises one of the most important tools for contemporary neuroscience in order to reverse-engineer the brain. As recording electrodes' technology rabidly evolves by integrating thousands of electrodes in a confined spatial setting, the algorithms that are used to monitor individual neurons from recorded signals have to become even more reliable and computationally efficient. In this work, we propose a novel framework of the SS approach in which a single-step processing of the raw (unfiltered) extracellular signal is sufficient for both the detection and sorting of the activity of individual neurons. Despite its simplicity, the proposed approach exhibits comparable performance with state-of-the-art approaches, especially for spike detection in noisy signals, and paves the way for a new family of SS algorithms with the potential for multi-recording, fast, on-chip implementations.

A Fully Automated Approach to Spike Sorting.

Science.gov (United States)

Chung, Jason E; Magland, Jeremy F; Barnett, Alex H; Tolosa, Vanessa M; Tooker, Angela C; Lee, Kye Y; Shah, Kedar G; Felix, Sarah H; Frank, Loren M; Greengard, Leslie F

2017-09-13

Understanding the detailed dynamics of neuronal networks will require the simultaneous measurement of spike trains from hundreds of neurons (or more). Currently, approaches to extracting spike times and labels from raw data are time consuming, lack standardization, and involve manual intervention, making it difficult to maintain data provenance and assess the quality of scientific results. Here, we describe an automated clustering approach and associated software package that addresses these problems and provides novel cluster quality metrics. We show that our approach has accuracy comparable to or exceeding that achieved using manual or semi-manual techniques with desktop central processing unit (CPU) runtimes faster than acquisition time for up to hundreds of electrodes. Moreover, a single choice of parameters in the algorithm is effective for a variety of electrode geometries and across multiple brain regions. This algorithm has the potential to enable reproducible and automated spike sorting of larger scale recordings than is currently possible. Copyright © 2017 Elsevier Inc. All rights reserved.

Analysis and modeling of ensemble recordings from respiratory pre-motor neurons indicate changes in functional network architecture after acute hypoxia

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Roberto F Galán

2010-09-01

Full Text Available We have combined neurophysiologic recording, statistical analysis, and computational modeling to investigate the dynamics of the respiratory network in the brainstem. Using a multielectrode array, we recorded ensembles of respiratory neurons in perfused in situ rat preparations that produce spontaneous breathing patterns, focusing on inspiratory pre-motor neurons. We compared firing rates and neuronal synchronization among these neurons before and after a brief hypoxic stimulus. We observed a significant decrease in the number of spikes after stimulation, in part due to a transient slowing of the respiratory pattern. However, the median interspike interval did not change, suggesting that the firing threshold of the neurons was not affected but rather the synaptic input was. A bootstrap analysis of synchrony between spike trains revealed that, both before and after brief hypoxia, up to 45 % (but typically less than 5 % of coincident spikes across neuronal pairs was not explained by chance. Most likely, this synchrony resulted from common synaptic input to the pre-motor population, an example of stochastic synchronization. After brief hypoxia most pairs were less synchronized, although some were more, suggesting that the respiratory network was “rewired” transiently after the stimulus. To investigate this hypothesis, we created a simple computational model with feed-forward divergent connections along the inspiratory pathway. Assuming that 1 the number of divergent projections was not the same for all presynaptic cells, but rather spanned a wide range and 2 that the stimulus increased inhibition at the top of the network; this model reproduced the reduction in firing rate and bootstrap-corrected synchrony subsequent to hypoxic stimulation observed in our experimental data.

A case-control study of wicket spikes using video-EEG monitoring.

Science.gov (United States)

Vallabhaneni, Maya; Baldassari, Laura E; Scribner, James T; Cho, Yong Won; Motamedi, Gholam K

2013-01-01

To investigate clinical characteristics associated with wicket spikes in patients undergoing long-term video-EEG monitoring. A case-control study was performed in 479 patients undergoing video-EEG monitoring, with 3 age- (±3 years) and gender-matched controls per patient with wicket spikes. Logistic regression was utilized to investigate the association between wicket spikes and other factors, including conditions that have been previously associated with wicket spikes. Wicket spikes were recorded in 48 patients. There was a significantly higher prevalence of dizziness/vertigo (p=0.002), headaches (p=0.005), migraine (p=0.015), and seizures (p=0.016) in patients with wickets. The majority of patients with wicket spikes did not exhibit epileptiform activity on EEG; however, patients with history of seizures were more likely to have wickets (p=0.017). There was no significant difference in the prevalence of psychogenic non-epileptic seizures between the groups. Wickets were more common on the left, during sleep, and more likely to be first recorded on day 1-2 of monitoring. Patients with wicket spikes are more likely to have dizziness/vertigo, headaches, migraine, and seizures. Patients with history of seizures are more likely to have wickets. The prevalence of psychogenic non-epileptic seizures is not significantly higher in patients with wickets. Copyright © 2012 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Distributed Cerebellar Motor Learning; a Spike-Timing-Dependent Plasticity Model

Directory of Open Access Journals (Sweden)

Niceto Rafael Luque

2016-03-01

Full Text Available Deep cerebellar nuclei neurons receive both inhibitory (GABAergic synaptic currents from Purkinje cells (within the cerebellar cortex and excitatory (glutamatergic synaptic currents from mossy fibres. Those two deep cerebellar nucleus inputs are thought to be also adaptive, embedding interesting properties in the framework of accurate movements. We show that distributed spike-timing-dependent plasticity mechanisms (STDP located at different cerebellar sites (parallel fibres to Purkinje cells, mossy fibres to deep cerebellar nucleus cells, and Purkinje cells to deep cerebellar nucleus cells in close-loop simulations provide an explanation for the complex learning properties of the cerebellum in motor learning. Concretely, we propose a new mechanistic cerebellar spiking model. In this new model, deep cerebellar nuclei embed a dual functionality: deep cerebellar nuclei acting as a gain adaptation mechanism and as a facilitator for the slow memory consolidation at mossy fibres to deep cerebellar nucleus synapses. Equipping the cerebellum with excitatory (e-STDP and inhibitory (i-STDP mechanisms at deep cerebellar nuclei afferents allows the accommodation of synaptic memories that were formed at parallel fibres to Purkinje cells synapses and then transferred to mossy fibres to deep cerebellar nucleus synapses. These adaptive mechanisms also contribute to modulate the deep-cerebellar-nucleus-output firing rate (output gain modulation towards optimising its working range.

On the Spike Train Variability Characterized by Variance-to-Mean Power Relationship.

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