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.

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.

Inferring oscillatory modulation in neural spike trains.

Science.gov (United States)

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.

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 ...

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.

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.

On the Spike Train Variability Characterized by Variance-to-Mean Power Relationship.

Science.gov (United States)

Koyama, Shinsuke

2015-07-01

We propose a statistical method for modeling the non-Poisson variability of spike trains observed in a wide range of brain regions. Central to our approach is the assumption that the variance and the mean of interspike intervals are related by a power function characterized by two parameters: the scale factor and exponent. It is shown that this single assumption allows the variability of spike trains to have an arbitrary scale and various dependencies on the firing rate in the spike count statistics, as well as in the interval statistics, depending on the two parameters of the power function. We also propose a statistical model for spike trains that exhibits the variance-to-mean power relationship. Based on this, a maximum likelihood method is developed for inferring the parameters from rate-modulated spike trains. The proposed method is illustrated on simulated and experimental spike trains.

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.

Fitting neuron models to spike trains

Directory of Open Access Journals (Sweden)

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.

An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks.

Science.gov (United States)

Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen

2016-01-01

The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper.

Neural Spike Train Synchronisation Indices: Definitions, Interpretations and Applications.

Science.gov (United States)

Halliday, D M; Rosenberg, J R

2017-04-24

A comparison of previously defined spike train syncrhonization indices is undertaken within a stochastic point process framework. The second order cumulant density (covariance density) is shown to be common to all the indices. Simulation studies were used to investigate the sampling variability of a single index based on the second order cumulant. The simulations used a paired motoneurone model and a paired regular spiking cortical neurone model. The sampling variability of spike trains generated under identical conditions from the paired motoneurone model varied from 50% { 160% of the estimated value. On theoretical grounds, and on the basis of simulated data a rate dependence is present in all synchronization indices. The application of coherence and pooled coherence estimates to the issue of synchronization indices is considered. This alternative frequency domain approach allows an arbitrary number of spike train pairs to be evaluated for statistically significant differences, and combined into a single population measure. The pooled coherence framework allows pooled time domain measures to be derived, application of this to the simulated data is illustrated. Data from the cortical neurone model is generated over a wide range of firing rates (1 - 250 spikes/sec). The pooled coherence framework correctly characterizes the sampling variability as not significant over this wide operating range. The broader applicability of this approach to multi electrode array data is briefly discussed.

Stochastic synchronization in finite size spiking networks

Science.gov (United States)

Doiron, Brent; Rinzel, John; Reyes, Alex

2006-09-01

We study a stochastic synchronization of spiking activity in feedforward networks of integrate-and-fire model neurons. A stochastic mean field analysis shows that synchronization occurs only when the network size is sufficiently small. This gives evidence that the dynamics, and hence processing, of finite size populations can be drastically different from that observed in the infinite size limit. Our results agree with experimentally observed synchrony in cortical networks, and further strengthen the link between synchrony and propagation in cortical systems.

Advanced correlation grid: Analysis and visualisation of functional connectivity among multiple spike trains.

Science.gov (United States)

Masud, Mohammad Shahed; Borisyuk, Roman; Stuart, Liz

2017-07-15

This study analyses multiple spike trains (MST) data, defines its functional connectivity and subsequently visualises an accurate diagram of connections. This is a challenging problem. For example, it is difficult to distinguish the common input and the direct functional connection of two spike trains. The new method presented in this paper is based on the traditional pairwise cross-correlation function (CCF) and a new combination of statistical techniques. First, the CCF is used to create the Advanced Correlation Grid (ACG) correlation where both the significant peak of the CCF and the corresponding time delay are used for detailed analysis of connectivity. Second, these two features of functional connectivity are used to classify connections. Finally, the visualization technique is used to represent the topology of functional connections. Examples are presented in the paper to demonstrate the new Advanced Correlation Grid method and to show how it enables discrimination between (i) influence from one spike train to another through an intermediate spike train and (ii) influence from one common spike train to another pair of analysed spike trains. The ACG method enables scientists to automatically distinguish between direct connections from spurious connections such as common source connection and indirect connection whereas existing methods require in-depth analysis to identify such connections. The ACG is a new and effective method for studying functional connectivity of multiple spike trains. This method can identify accurately all the direct connections and can distinguish common source and indirect connections automatically. Copyright © 2017 Elsevier B.V. All rights reserved.

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

Stochastic models for spike trains of single neurons

CERN Document Server

Sampath, G

1977-01-01

1 Some basic neurophysiology 4 The neuron 1. 1 4 1. 1. 1 The axon 7 1. 1. 2 The synapse 9 12 1. 1. 3 The soma 1. 1. 4 The dendrites 13 13 1. 2 Types of neurons 2 Signals in the nervous system 14 2. 1 Action potentials as point events - point processes in the nervous system 15 18 2. 2 Spontaneous activi~ in neurons 3 Stochastic modelling of single neuron spike trains 19 3. 1 Characteristics of a neuron spike train 19 3. 2 The mathematical neuron 23 4 Superposition models 26 4. 1 superposition of renewal processes 26 4. 2 Superposition of stationary point processe- limiting behaviour 34 4. 2. 1 Palm functions 35 4. 2. 2 Asymptotic behaviour of n stationary point processes superposed 36 4. 3 Superposition models of neuron spike trains 37 4. 3. 1 Model 4. 1 39 4. 3. 2 Model 4. 2 - A superposition model with 40 two input channels 40 4. 3. 3 Model 4. 3 4. 4 Discussion 41 43 5 Deletion models 5. 1 Deletion models with 1nd~endent interaction of excitatory and inhibitory sequences 44 VI 5. 1. 1 Model 5. 1 The basic de...

Interspike Interval Based Filtering of Directional Selective Retinal Ganglion Cells Spike Trains

Directory of Open Access Journals (Sweden)

Aurel Vasile Martiniuc

2012-01-01

Full Text Available The information regarding visual stimulus is encoded in spike trains at the output of retina by retinal ganglion cells (RGCs. Among these, the directional selective cells (DSRGC are signaling the direction of stimulus motion. DSRGCs' spike trains show accentuated periods of short interspike intervals (ISIs framed by periods of isolated spikes. Here we use two types of visual stimulus, white noise and drifting bars, and show that short ISI spikes of DSRGCs spike trains are more often correlated to their preferred stimulus feature (that is, the direction of stimulus motion and carry more information than longer ISI spikes. Firstly, our results show that correlation between stimulus and recorded neuronal response is best at short ISI spiking activity and decrease as ISI becomes larger. We then used grating bars stimulus and found that as ISI becomes shorter the directional selectivity is better and information rates are higher. Interestingly, for the less encountered type of DSRGC, known as ON-DSRGC, short ISI distribution and information rates revealed consistent differences when compared with the other directional selective cell type, the ON-OFF DSRGC. However, these findings suggest that ISI-based temporal filtering integrates a mechanism for visual information processing at the output of retina toward higher stages within early visual system.

iRaster: a novel information visualization tool to explore spatiotemporal patterns in multiple spike trains.

Science.gov (United States)

Somerville, J; Stuart, L; Sernagor, E; Borisyuk, R

2010-12-15

Over the last few years, simultaneous recordings of multiple spike trains have become widely used by neuroscientists. Therefore, it is important to develop new tools for analysing multiple spike trains in order to gain new insight into the function of neural systems. This paper describes how techniques from the field of visual analytics can be used to reveal specific patterns of neural activity. An interactive raster plot called iRaster has been developed. This software incorporates a selection of statistical procedures for visualization and flexible manipulations with multiple spike trains. For example, there are several procedures for the re-ordering of spike trains which can be used to unmask activity propagation, spiking synchronization, and many other important features of multiple spike train activity. Additionally, iRaster includes a rate representation of neural activity, a combined representation of rate and spikes, spike train removal and time interval removal. Furthermore, it provides multiple coordinated views, time and spike train zooming windows, a fisheye lens distortion, and dissemination facilities. iRaster is a user friendly, interactive, flexible tool which supports a broad range of visual representations. This tool has been successfully used to analyse both synthetic and experimentally recorded datasets. In this paper, the main features of iRaster are described and its performance and effectiveness are demonstrated using various types of data including experimental multi-electrode array recordings from the ganglion cell layer in mouse retina. iRaster is part of an ongoing research project called VISA (Visualization of Inter-Spike Associations) at the Visualization Lab in the University of Plymouth. The overall aim of the VISA project is to provide neuroscientists with the ability to freely explore and analyse their data. The software is freely available from the Visualization Lab website (see www.plymouth.ac.uk/infovis). Copyright © 2010

Supervised learning in spiking neural networks with FORCE training.

Science.gov (United States)

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.

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...

Spike-timing-based computation in sound localization.

Directory of Open Access Journals (Sweden)

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.

Local Variation of Hashtag Spike Trains and Popularity in Twitter

Science.gov (United States)

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

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

Directory of Open Access Journals (Sweden)

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.

Which spike train distance is most suitable for distinguishing rate and temporal coding?

Science.gov (United States)

Satuvuori, Eero; Kreuz, Thomas

2018-04-01

It is commonly assumed in neuronal coding that repeated presentations of a stimulus to a coding neuron elicit similar responses. One common way to assess similarity are spike train distances. These can be divided into spike-resolved, such as the Victor-Purpura and the van Rossum distance, and time-resolved, e.g. the ISI-, the SPIKE- and the RI-SPIKE-distance. We use independent steady-rate Poisson processes as surrogates for spike trains with fixed rate and no timing information to address two basic questions: How does the sensitivity of the different spike train distances to temporal coding depend on the rates of the two processes and how do the distances deal with very low rates? Spike-resolved distances always contain rate information even for parameters indicating time coding. This is an issue for reasonably high rates but beneficial for very low rates. In contrast, the operational range for detecting time coding of time-resolved distances is superior at normal rates, but these measures produce artefacts at very low rates. The RI-SPIKE-distance is the only measure that is sensitive to timing information only. While our results on rate-dependent expectation values for the spike-resolved distances agree with Chicharro et al. (2011), we here go one step further and specifically investigate applicability for very low rates. The most appropriate measure depends on the rates of the data being analysed. Accordingly, we summarize our results in one table that allows an easy selection of the preferred measure for any kind of data. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains.

Directory of Open Access Journals (Sweden)

Arno Onken

2016-11-01

Full Text Available Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations, in their temporal dimension (temporal neural response variations, or in their combination (temporally coordinated neural population firing. Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together, temporal firing patterns (temporal activation of these groups of neurons and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial. We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine

Emergent dynamics of spiking neurons with fluctuating threshold

Science.gov (United States)

Bhattacharjee, Anindita; Das, M. K.

2017-05-01

Role of fluctuating threshold on neuronal dynamics is investigated. The threshold function is assumed to follow a normal probability distribution. Standard deviation of inter-spike interval of the response is computed as an indicator of irregularity in spike emission. It has been observed that, the irregularity in spiking is more if the threshold variation is more. A significant change in modal characteristics of Inter Spike Intervals (ISI) is seen to occur as a function of fluctuation parameter. Investigation is further carried out for coupled system of neurons. Cooperative dynamics of coupled neurons are discussed in view of synchronization. Total and partial synchronization regimes are depicted with the help of contour plots of synchrony measure under various conditions. Results of this investigation may provide a basis for exploring the complexities of neural communication and brain functioning.

A unified approach to linking experimental, statistical and computational analysis of spike train data.

Directory of Open Access Journals (Sweden)

Liang Meng

Full Text Available A fundamental issue in neuroscience is how to identify the multiple biophysical mechanisms through which neurons generate observed patterns of spiking activity. In previous work, we proposed a method for linking observed patterns of spiking activity to specific biophysical mechanisms based on a state space modeling framework and a sequential Monte Carlo, or particle filter, estimation algorithm. We have shown, in simulation, that this approach is able to identify a space of simple biophysical models that were consistent with observed spiking data (and included the model that generated the data, but have yet to demonstrate the application of the method to identify realistic currents from real spike train data. Here, we apply the particle filter to spiking data recorded from rat layer V cortical neurons, and correctly identify the dynamics of an slow, intrinsic current. The underlying intrinsic current is successfully identified in four distinct neurons, even though the cells exhibit two distinct classes of spiking activity: regular spiking and bursting. This approach--linking statistical, computational, and experimental neuroscience--provides an effective technique to constrain detailed biophysical models to specific mechanisms consistent with observed spike train data.

Detecting dependencies between spike trains of pairs of neurons through copulas

DEFF Research Database (Denmark)

Sacerdote, Laura; Tamborrino, Massimiliano; Zucca, Cristina

2011-01-01

The dynamics of a neuron are influenced by the connections with the network where it lies. Recorded spike trains exhibit patterns due to the interactions between neurons. However, the structure of the network is not known. A challenging task is to investigate it from the analysis of simultaneously...... the two neurons. Furthermore, the method recognizes the presence of delays in the spike propagation....

Visual Orientation and Directional Selectivity through Thalamic Synchrony

Science.gov (United States)

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

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.

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.

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

Directory of Open Access Journals (Sweden)

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.

Spike train generation and current-to-frequency conversion in silicon diodes

Science.gov (United States)

Coon, D. D.; Perera, A. G. U.

1989-01-01

A device physics model is developed to analyze spontaneous neuron-like spike train generation in current driven silicon p(+)-n-n(+) devices in cryogenic environments. The model is shown to explain the very high dynamic range (0 to the 7th) current-to-frequency conversion and experimental features of the spike train frequency as a function of input current. The devices are interesting components for implementation of parallel asynchronous processing adjacent to cryogenically cooled focal planes because of their extremely low current and power requirements, their electronic simplicity, and their pulse coding capability, and could be used to form the hardware basis for neural networks which employ biologically plausible means of information coding.

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.

Model-based decoding, information estimation, and change-point detection techniques for multineuron spike trains.

Science.gov (United States)

Pillow, Jonathan W; Ahmadian, Yashar; Paninski, Liam

2011-01-01

One of the central problems in systems neuroscience is to understand how neural spike trains convey sensory information. Decoding methods, which provide an explicit means for reading out the information contained in neural spike responses, offer a powerful set of tools for studying the neural coding problem. Here we develop several decoding methods based on point-process neural encoding models, or forward models that predict spike responses to stimuli. These models have concave log-likelihood functions, which allow efficient maximum-likelihood model fitting and stimulus decoding. We present several applications of the encoding model framework to the problem of decoding stimulus information from population spike responses: (1) a tractable algorithm for computing the maximum a posteriori (MAP) estimate of the stimulus, the most probable stimulus to have generated an observed single- or multiple-neuron spike train response, given some prior distribution over the stimulus; (2) a gaussian approximation to the posterior stimulus distribution that can be used to quantify the fidelity with which various stimulus features are encoded; (3) an efficient method for estimating the mutual information between the stimulus and the spike trains emitted by a neural population; and (4) a framework for the detection of change-point times (the time at which the stimulus undergoes a change in mean or variance) by marginalizing over the posterior stimulus distribution. We provide several examples illustrating the performance of these estimators with simulated and real neural data.

Training Spiking Neural Models Using Artificial Bee Colony

Science.gov (United States)

Vazquez, Roberto A.; Garro, Beatriz A.

2015-01-01

Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy. PMID:25709644

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

Science.gov (United States)

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.

Information Entropy Production of Maximum Entropy Markov Chains from Spike Trains

Directory of Open Access Journals (Sweden)

Rodrigo Cofré

2018-01-01

Full Text Available The spiking activity of neuronal networks follows laws that are not time-reversal symmetric; the notion of pre-synaptic and post-synaptic neurons, stimulus correlations and noise correlations have a clear time order. Therefore, a biologically realistic statistical model for the spiking activity should be able to capture some degree of time irreversibility. We use the thermodynamic formalism to build a framework in the context maximum entropy models to quantify the degree of time irreversibility, providing an explicit formula for the information entropy production of the inferred maximum entropy Markov chain. We provide examples to illustrate our results and discuss the importance of time irreversibility for modeling the spike train statistics.

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

Self-consistent determination of the spike-train power spectrum in a neural network with sparse connectivity

Directory of Open Access Journals (Sweden)

Benjamin eDummer

2014-09-01

Full Text Available A major source of random variability in cortical networks is the quasi-random arrival of presynaptic action potentials from many other cells. In network studies as well as in the study of the response properties of single cells embedded in a network, synaptic background input is often approximated by Poissonian spike trains. However, the output statistics of the cells is in most cases far from being Poisson. This is inconsistent with the assumption of similar spike-train statistics for pre- and postsynaptic cells in a recurrent network. Here we tackle this problem for the popular class of integrate-and-fire neurons and study a self-consistent statistics of input and output spectra of neural spike trains. Instead of actually using a large network, we use an iterative scheme, in which we simulate a single neuron over several generations. In each of these generations, the neuron is stimulated with surrogate stochastic input that has a similar statistics as the output of the previous generation. For the surrogate input, we employ two distinct approximations: (i a superposition of renewal spike trains with the same interspike interval density as observed in the previous generation and (ii a Gaussian current with a power spectrum proportional to that observed in the previous generation. For input parameters that correspond to balanced input in the network, both the renewal and the Gaussian iteration procedure converge quickly and yield comparable results for the self-consistent spike-train power spectrum. We compare our results to large-scale simulations of a random sparsely connected network of leaky integrate-and-fire neurons (Brunel, J. Comp. Neurosci. 2000 and show that in the asynchronous regime close to a state of balanced synaptic input from the network, our iterative schemes provide excellent approximations to the autocorrelation of spike trains in the recurrent network.

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

Science.gov (United States)

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.

The geography of spatial synchrony.

Science.gov (United States)

Walter, Jonathan A; Sheppard, Lawrence W; Anderson, Thomas L; Kastens, Jude H; Bjørnstad, Ottar N; Liebhold, Andrew M; Reuman, Daniel C

2017-07-01

Spatial synchrony, defined as correlated temporal fluctuations among populations, is a fundamental feature of population dynamics, but many aspects of synchrony remain poorly understood. Few studies have examined detailed geographical patterns of synchrony; instead most focus on how synchrony declines with increasing linear distance between locations, making the simplifying assumption that distance decay is isotropic. By synthesising and extending prior work, we show how geography of synchrony, a term which we use to refer to detailed spatial variation in patterns of synchrony, can be leveraged to understand ecological processes including identification of drivers of synchrony, a long-standing challenge. We focus on three main objectives: (1) showing conceptually and theoretically four mechanisms that can generate geographies of synchrony; (2) documenting complex and pronounced geographies of synchrony in two important study systems; and (3) demonstrating a variety of methods capable of revealing the geography of synchrony and, through it, underlying organism ecology. For example, we introduce a new type of network, the synchrony network, the structure of which provides ecological insight. By documenting the importance of geographies of synchrony, advancing conceptual frameworks, and demonstrating powerful methods, we aim to help elevate the geography of synchrony into a mainstream area of study and application. © 2017 John Wiley & Sons Ltd/CNRS.

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

Science.gov (United States)

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.

Learning to Recognize Actions From Limited Training Examples Using a Recurrent Spiking Neural Model

Science.gov (United States)

Panda, Priyadarshini; Srinivasa, Narayan

2018-01-01

A fundamental challenge in machine learning today is to build a model that can learn from few examples. Here, we describe a reservoir based spiking neural model for learning to recognize actions with a limited number of labeled videos. First, we propose a novel encoding, inspired by how microsaccades influence visual perception, to extract spike information from raw video data while preserving the temporal correlation across different frames. Using this encoding, we show that the reservoir generalizes its rich dynamical activity toward signature action/movements enabling it to learn from few training examples. We evaluate our approach on the UCF-101 dataset. Our experiments demonstrate that our proposed reservoir achieves 81.3/87% Top-1/Top-5 accuracy, respectively, on the 101-class data while requiring just 8 video examples per class for training. Our results establish a new benchmark for action recognition from limited video examples for spiking neural models while yielding competitive accuracy with respect to state-of-the-art non-spiking neural models. PMID:29551962

Synchrony in Dyadic Psychotherapy Sessions

Science.gov (United States)

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.

Improved SpikeProp for Using Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

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.

Dynamics and spike trains statistics in conductance-based integrate-and-fire neural networks with chemical and electric synapses

International Nuclear Information System (INIS)

Cofré, Rodrigo; Cessac, Bruno

2013-01-01

We investigate the effect of electric synapses (gap junctions) on collective neuronal dynamics and spike statistics in a conductance-based integrate-and-fire neural network, driven by Brownian noise, where conductances depend upon spike history. We compute explicitly the time evolution operator and show that, given the spike-history of the network and the membrane potentials at a given time, the further dynamical evolution can be written in a closed form. We show that spike train statistics is described by a Gibbs distribution whose potential can be approximated with an explicit formula, when the noise is weak. This potential form encompasses existing models for spike trains statistics analysis such as maximum entropy models or generalized linear models (GLM). We also discuss the different types of correlations: those induced by a shared stimulus and those induced by neurons interactions

Estimation of parameters in Shot-Noise-Driven Doubly Stochastic Poisson processes using the EM algorithm--modeling of pre- and postsynaptic spike trains.

Science.gov (United States)

Mino, H

2007-01-01

To estimate the parameters, the impulse response (IR) functions of some linear time-invariant systems generating intensity processes, in Shot-Noise-Driven Doubly Stochastic Poisson Process (SND-DSPP) in which multivariate presynaptic spike trains and postsynaptic spike trains can be assumed to be modeled by the SND-DSPPs. An explicit formula for estimating the IR functions from observations of multivariate input processes of the linear systems and the corresponding counting process (output process) is derived utilizing the expectation maximization (EM) algorithm. The validity of the estimation formula was verified through Monte Carlo simulations in which two presynaptic spike trains and one postsynaptic spike train were assumed to be observable. The IR functions estimated on the basis of the proposed identification method were close to the true IR functions. The proposed method will play an important role in identifying the input-output relationship of pre- and postsynaptic neural spike trains in practical situations.

When the Ostrich-Algorithm Fails: Blanking Method Affects Spike Train Statistics.

Science.gov (United States)

Joseph, Kevin; Mottaghi, Soheil; Christ, Olaf; Feuerstein, Thomas J; Hofmann, Ulrich G

2018-01-01

Modern electroceuticals are bound to employ the usage of electrical high frequency (130-180 Hz) stimulation carried out under closed loop control, most prominent in the case of movement disorders. However, particular challenges are faced when electrical recordings of neuronal tissue are carried out during high frequency electrical stimulation, both in-vivo and in-vitro . This stimulation produces undesired artifacts and can render the recorded signal only partially useful. The extent of these artifacts is often reduced by temporarily grounding the recording input during stimulation pulses. In the following study, we quantify the effects of this method, "blanking," on the spike count and spike train statistics. Starting from a theoretical standpoint, we calculate a loss in the absolute number of action potentials, depending on: width of the blanking window, frequency of stimulation, and intrinsic neuronal activity. These calculations were then corroborated by actual high signal to noise ratio (SNR) single cell recordings. We state that, for clinically relevant frequencies of 130 Hz (used for movement disorders) and realistic blanking windows of 2 ms, up to 27% of actual existing spikes are lost. We strongly advice cautioned use of the blanking method when spike rate quantification is attempted. Blanking (artifact removal by temporarily grounding input), depending on recording parameters, can lead to significant spike loss. Very careful use of blanking circuits is advised.

When the Ostrich-Algorithm Fails: Blanking Method Affects Spike Train Statistics

Directory of Open Access Journals (Sweden)

Kevin Joseph

2018-04-01

Full Text Available Modern electroceuticals are bound to employ the usage of electrical high frequency (130–180 Hz stimulation carried out under closed loop control, most prominent in the case of movement disorders. However, particular challenges are faced when electrical recordings of neuronal tissue are carried out during high frequency electrical stimulation, both in-vivo and in-vitro. This stimulation produces undesired artifacts and can render the recorded signal only partially useful. The extent of these artifacts is often reduced by temporarily grounding the recording input during stimulation pulses. In the following study, we quantify the effects of this method, “blanking,” on the spike count and spike train statistics. Starting from a theoretical standpoint, we calculate a loss in the absolute number of action potentials, depending on: width of the blanking window, frequency of stimulation, and intrinsic neuronal activity. These calculations were then corroborated by actual high signal to noise ratio (SNR single cell recordings. We state that, for clinically relevant frequencies of 130 Hz (used for movement disorders and realistic blanking windows of 2 ms, up to 27% of actual existing spikes are lost. We strongly advice cautioned use of the blanking method when spike rate quantification is attempted.Impact statementBlanking (artifact removal by temporarily grounding input, depending on recording parameters, can lead to significant spike loss. Very careful use of blanking circuits is advised.

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

Robustness and versatility of a nonlinear interdependence method for directional coupling detection from spike trains

Science.gov (United States)

Malvestio, Irene; Kreuz, Thomas; Andrzejak, Ralph G.

2017-08-01

The detection of directional couplings between dynamics based on measured spike trains is a crucial problem in the understanding of many different systems. In particular, in neuroscience it is important to assess the connectivity between neurons. One of the approaches that can estimate directional coupling from the analysis of point processes is the nonlinear interdependence measure L . Although its efficacy has already been demonstrated, it still needs to be tested under more challenging and realistic conditions prior to an application to real data. Thus, in this paper we use the Hindmarsh-Rose model system to test the method in the presence of noise and for different spiking regimes. We also examine the influence of different parameters and spike train distances. Our results show that the measure L is versatile and robust to various types of noise, and thus suitable for application to experimental data.

Detection of bursts in extracellular spike trains using hidden semi-Markov point process models.

Science.gov (United States)

Tokdar, Surya; Xi, Peiyi; Kelly, Ryan C; Kass, Robert E

2010-08-01

Neurons in vitro and in vivo have epochs of bursting or "up state" activity during which firing rates are dramatically elevated. Various methods of detecting bursts in extracellular spike trains have appeared in the literature, the most widely used apparently being Poisson Surprise (PS). A natural description of the phenomenon assumes (1) there are two hidden states, which we label "burst" and "non-burst," (2) the neuron evolves stochastically, switching at random between these two states, and (3) within each state the spike train follows a time-homogeneous point process. If in (2) the transitions from non-burst to burst and burst to non-burst states are memoryless, this becomes a hidden Markov model (HMM). For HMMs, the state transitions follow exponential distributions, and are highly irregular. Because observed bursting may in some cases be fairly regular-exhibiting inter-burst intervals with small variation-we relaxed this assumption. When more general probability distributions are used to describe the state transitions the two-state point process model becomes a hidden semi-Markov model (HSMM). We developed an efficient Bayesian computational scheme to fit HSMMs to spike train data. Numerical simulations indicate the method can perform well, sometimes yielding very different results than those based on PS.

Spatial synchrony in cisco recruitment

Science.gov (United States)

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.

Fractal characterization of acupuncture-induced spike trains of rat WDR neurons

International Nuclear Information System (INIS)

Chen, Yingyuan; Guo, Yi; Wang, Jiang; Hong, Shouhai; Wei, Xile; Yu, Haitao; Deng, Bin

2015-01-01

Highlights: •Fractal analysis is a valuable tool for measuring MA-induced neural activities. •In course of the experiments, the spike trains display different fractal properties. •The fractal properties reflect the long-term modulation of MA on WDR neurons. •The results may explain the long-lasting effects induced by acupuncture. -- Abstract: The experimental and the clinical studies have showed manual acupuncture (MA) could evoke multiple responses in various neural regions. Characterising the neuronal activities in these regions may provide more deep insights into acupuncture mechanisms. This paper used fractal analysis to investigate MA-induced spike trains of Wide Dynamic Range (WDR) neurons in rat spinal dorsal horn, an important relay station and integral component in processing acupuncture information. Allan factor and Fano factor were utilized to test whether the spike trains were fractal, and Allan factor were used to evaluate the scaling exponents and Hurst exponents. It was found that these two fractal exponents before and during MA were different significantly. During MA, the scaling exponents of WDR neurons were regulated in a small range, indicating a special fractal pattern. The neuronal activities were long-range correlated over multiple time scales. The scaling exponents during and after MA were similar, suggesting that the long-range correlations not only displayed during MA, but also extended to after withdrawing the needle. Our results showed that fractal analysis is a useful tool for measuring acupuncture effects. MA could modulate neuronal activities of which the fractal properties change as time proceeding. This evolution of fractal dynamics in course of MA experiments may explain at the level of neuron why the effect of MA observed in experiment and in clinic are complex, time-evolutionary, long-range even lasting for some time after stimulation

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

Science.gov (United States)

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

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.

Cooperative Learning and Interpersonal Synchrony.

Science.gov (United States)

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.

Stochastic resonance of ensemble neurons for transient spike trains: Wavelet analysis

International Nuclear Information System (INIS)

Hasegawa, Hideo

2002-01-01

By using the wavelet transformation (WT), I have analyzed the response of an ensemble of N (=1, 10, 100, and 500) Hodgkin-Huxley neurons to transient M-pulse spike trains (M=1 to 3) with independent Gaussian noises. The cross correlation between the input and output signals is expressed in terms of the WT expansion coefficients. The signal-to-noise ratio (SNR) is evaluated by using the denoising method within the WT, by which the noise contribution is extracted from the output signals. Although the response of a single (N=1) neuron to subthreshold transient signals with noises is quite unreliable, the transmission fidelity assessed by the cross correlation and SNR is shown to be much improved by increasing the value of N: a population of neurons plays an indispensable role in the stochastic resonance (SR) for transient spike inputs. It is also shown that in a large-scale ensemble, the transmission fidelity for suprathreshold transient spikes is not significantly degraded by a weak noise which is responsible to SR for subthreshold inputs

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

Breaking Bad News Training Program Based on Video Reviews and SPIKES Strategy: What do Perinatology Residents Think about It?

Science.gov (United States)

Setubal, Maria Silvia Vellutini; Gonçalves, Andrea Vasconcelos; Rocha, Sheyla Ribeiro; Amaral, Eliana Martorano

2017-10-01

Objective  Resident doctors usually face the task to communicate bad news in perinatology without any formal training. The impact on parents can be disastrous. The objective of this paper is to analyze the perception of residents regarding a training program in communicating bad news in perinatology based on video reviews and setting, perception, invitation, knowledge, emotion, and summary (SPIKES) strategy. Methods  We performed the analysis of complementary data collected from participants in a randomized controlled intervention study to evaluate the efficacy of a training program on improving residents' skills to communicate bad news. Data were collected using a Likert scale. Through a thematic content analysis we tried to to apprehend the meanings, feelings and experiences expressed by resident doctors in their comments as a response to an open-ended question. Half of the group received training, consisting of discussions of video reviews of participants' simulated encounters communicating a perinatal loss to a "mother" based on the SPIKES strategy. We also offered training sessions to the control group after they completed participation. Twenty-eight residents who were randomized to intervention and 16 from the control group received training. Twenty written comments were analyzed. Results  The majority of the residents evaluated training highly as an education activity to help increase knowledge, ability and understanding about breaking bad news in perinatology. Three big categories emerged from residents' comments: SPIKES training effects; bad news communication in medical training; and doctors' feelings and relationship with patients. Conclusions  Residents took SPIKES training as a guide to systematize the communication of bad news and to amplify perceptions of the emotional needs of the patients. They suggested the insertion of a similar training in their residency programs curricula. Thieme Revinter Publicações Ltda Rio de Janeiro, Brazil.

Prosocial Consequences of Interpersonal Synchrony

Science.gov (United States)

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

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

Science.gov (United States)

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.

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

Science.gov (United States)

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

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

Exit from Synchrony in Joint Improvised Motion.

Directory of Open Access Journals (Sweden)

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.

Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks.

Science.gov (United States)

Pena, Rodrigo F O; Vellmer, Sebastian; Bernardi, Davide; Roque, Antonio C; Lindner, Benjamin

2018-01-01

Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations) and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input) can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners) but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i) different neural subpopulations (e.g., excitatory and inhibitory neurons) have different cellular or connectivity parameters; (ii) the number and strength of the input connections are random (Erdős-Rényi topology) and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of parameters as

Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks

Directory of Open Access Journals (Sweden)

Rodrigo F. O. Pena

2018-03-01

Full Text Available Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i different neural subpopulations (e.g., excitatory and inhibitory neurons have different cellular or connectivity parameters; (ii the number and strength of the input connections are random (Erdős-Rényi topology and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of

Spike-based population coding and working memory.

Directory of Open Access Journals (Sweden)

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.

Nonverbal synchrony and affect in dyadic interactions

Directory of Open Access Journals (Sweden)

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.

Continuous detection of weak sensory signals in afferent spike trains: the role of anti-correlated interspike intervals in detection performance.

Science.gov (United States)

Goense, J B M; Ratnam, R

2003-10-01

An important problem in sensory processing is deciding whether fluctuating neural activity encodes a stimulus or is due to variability in baseline activity. Neurons that subserve detection must examine incoming spike trains continuously, and quickly and reliably differentiate signals from baseline activity. Here we demonstrate that a neural integrator can perform continuous signal detection, with performance exceeding that of trial-based procedures, where spike counts in signal- and baseline windows are compared. The procedure was applied to data from electrosensory afferents of weakly electric fish (Apteronotus leptorhynchus), where weak perturbations generated by small prey add approximately 1 spike to a baseline of approximately 300 spikes s(-1). The hypothetical postsynaptic neuron, modeling an electrosensory lateral line lobe cell, could detect an added spike within 10-15 ms, achieving near ideal detection performance (80-95%) at false alarm rates of 1-2 Hz, while trial-based testing resulted in only 30-35% correct detections at that false alarm rate. The performance improvement was due to anti-correlations in the afferent spike train, which reduced both the amplitude and duration of fluctuations in postsynaptic membrane activity, and so decreased the number of false alarms. Anti-correlations can be exploited to improve detection performance only if there is memory of prior decisions.

Parametric models to relate spike train and LFP dynamics with neural information processing.

Science.gov (United States)

Banerjee, Arpan; Dean, Heather L; Pesaran, Bijan

2012-01-01

Spike trains and local field potentials (LFPs) resulting from extracellular current flows provide a substrate for neural information processing. Understanding the neural code from simultaneous spike-field recordings and subsequent decoding of information processing events will have widespread applications. One way to demonstrate an understanding of the neural code, with particular advantages for the development of applications, is to formulate a parametric statistical model of neural activity and its covariates. Here, we propose a set of parametric spike-field models (unified models) that can be used with existing decoding algorithms to reveal the timing of task or stimulus specific processing. Our proposed unified modeling framework captures the effects of two important features of information processing: time-varying stimulus-driven inputs and ongoing background activity that occurs even in the absence of environmental inputs. We have applied this framework for decoding neural latencies in simulated and experimentally recorded spike-field sessions obtained from the lateral intraparietal area (LIP) of awake, behaving monkeys performing cued look-and-reach movements to spatial targets. Using both simulated and experimental data, we find that estimates of trial-by-trial parameters are not significantly affected by the presence of ongoing background activity. However, including background activity in the unified model improves goodness of fit for predicting individual spiking events. Uncovering the relationship between the model parameters and the timing of movements offers new ways to test hypotheses about the relationship between neural activity and behavior. We obtained significant spike-field onset time correlations from single trials using a previously published data set where significantly strong correlation was only obtained through trial averaging. We also found that unified models extracted a stronger relationship between neural response latency and trial

A prolongation of the postspike afterhyperpolarization following spike trains can partly explain the lower firing rates at derecruitment than those at recruitment

DEFF Research Database (Denmark)

Wienecke, Jacob; Zhang, Mengliang; Hultborn, Hans

2009-01-01

rates at derecruitment correlated with a change in the postspike afterhyperpolarization (AHP) after preceding spike trains? This question was investigated by intracellular recordings from cat motor neurons in both unanesthetized and anesthetized preparations. The firing frequencies at recruitment...... for the lower frequencies at derecruitment. This was independent of whether the current injection had activated persistent inward current (PIC; plateau potentials, secondary range firing). It was found that a preceding spike train could prolong the AHP duration following a subsequent spike. The lower rate...... from AHP duration in fast motoneurons and higher than expected in slow motoneurons. It is suggested that these deviations are explained by the presence of synaptic noise as well as recruitment of PICs below firing threshold. Thus synaptic noise may allow spike discharge even after the end of the AHP...

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

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.

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

Information Entropy Production of Maximum Entropy Markov Chains from Spike Trains

Science.gov (United States)

Cofré, Rodrigo; Maldonado, Cesar

2018-01-01

We consider the maximum entropy Markov chain inference approach to characterize the collective statistics of neuronal spike trains, focusing on the statistical properties of the inferred model. We review large deviations techniques useful in this context to describe properties of accuracy and convergence in terms of sampling size. We use these results to study the statistical fluctuation of correlations, distinguishability and irreversibility of maximum entropy Markov chains. We illustrate these applications using simple examples where the large deviation rate function is explicitly obtained for maximum entropy models of relevance in this field.

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.

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

Reconstruction of sparse connectivity in neural networks from spike train covariances

International Nuclear Information System (INIS)

Pernice, Volker; Rotter, Stefan

2013-01-01

The inference of causation from correlation is in general highly problematic. Correspondingly, it is difficult to infer the existence of physical synaptic connections between neurons from correlations in their activity. Covariances in neural spike trains and their relation to network structure have been the subject of intense research, both experimentally and theoretically. The influence of recurrent connections on covariances can be characterized directly in linear models, where connectivity in the network is described by a matrix of linear coupling kernels. However, as indirect connections also give rise to covariances, the inverse problem of inferring network structure from covariances can generally not be solved unambiguously. Here we study to what degree this ambiguity can be resolved if the sparseness of neural networks is taken into account. To reconstruct a sparse network, we determine the minimal set of linear couplings consistent with the measured covariances by minimizing the L 1 norm of the coupling matrix under appropriate constraints. Contrary to intuition, after stochastic optimization of the coupling matrix, the resulting estimate of the underlying network is directed, despite the fact that a symmetric matrix of count covariances is used for inference. The performance of the new method is best if connections are neither exceedingly sparse, nor too dense, and it is easily applicable for networks of a few hundred nodes. Full coupling kernels can be obtained from the matrix of full covariance functions. We apply our method to networks of leaky integrate-and-fire neurons in an asynchronous–irregular state, where spike train covariances are well described by a linear model. (paper)

Spike Code Flow in Cultured Neuronal Networks

Directory of Open Access Journals (Sweden)

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.

Audiovisual synchrony enhances BOLD responses in a brain network including multisensory STS while also enhancing target-detection performance for both modalities

Science.gov (United States)

Marchant, Jennifer L; Ruff, Christian C; Driver, Jon

2012-01-01

The brain seeks to combine related inputs from different senses (e.g., hearing and vision), via multisensory integration. Temporal information can indicate whether stimuli in different senses are related or not. A recent human fMRI study (Noesselt et al. [2007]: J Neurosci 27:11431–11441) used auditory and visual trains of beeps and flashes with erratic timing, manipulating whether auditory and visual trains were synchronous or unrelated in temporal pattern. A region of superior temporal sulcus (STS) showed higher BOLD signal for the synchronous condition. But this could not be related to performance, and it remained unclear if the erratic, unpredictable nature of the stimulus trains was important. Here we compared synchronous audiovisual trains to asynchronous trains, while using a behavioral task requiring detection of higher-intensity target events in either modality. We further varied whether the stimulus trains had predictable temporal pattern or not. Synchrony (versus lag) between auditory and visual trains enhanced behavioral sensitivity (d') to intensity targets in either modality, regardless of predictable versus unpredictable patterning. The analogous contrast in fMRI revealed BOLD increases in several brain areas, including the left STS region reported by Noesselt et al. [2007: J Neurosci 27:11431–11441]. The synchrony effect on BOLD here correlated with the subject-by-subject impact on performance. Predictability of temporal pattern did not affect target detection performance or STS activity, but did lead to an interaction with audiovisual synchrony for BOLD in inferior parietal cortex. PMID:21953980

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

Eyeblink Synchrony in Multimodal Human-Android Interaction.

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

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.

Method for stationarity-segmentation of spike train data with application to the Pearson cross-correlation.

Science.gov (United States)

Quiroga-Lombard, Claudio S; Hass, Joachim; Durstewitz, Daniel

2013-07-01

Correlations among neurons are supposed to play an important role in computation and information coding in the nervous system. Empirically, functional interactions between neurons are most commonly assessed by cross-correlation functions. Recent studies have suggested that pairwise correlations may indeed be sufficient to capture most of the information present in neural interactions. Many applications of correlation functions, however, implicitly tend to assume that the underlying processes are stationary. This assumption will usually fail for real neurons recorded in vivo since their activity during behavioral tasks is heavily influenced by stimulus-, movement-, or cognition-related processes as well as by more general processes like slow oscillations or changes in state of alertness. To address the problem of nonstationarity, we introduce a method for assessing stationarity empirically and then "slicing" spike trains into stationary segments according to the statistical definition of weak-sense stationarity. We examine pairwise Pearson cross-correlations (PCCs) under both stationary and nonstationary conditions and identify another source of covariance that can be differentiated from the covariance of the spike times and emerges as a consequence of residual nonstationarities after the slicing process: the covariance of the firing rates defined on each segment. Based on this, a correction of the PCC is introduced that accounts for the effect of segmentation. We probe these methods both on simulated data sets and on in vivo recordings from the prefrontal cortex of behaving rats. Rather than for removing nonstationarities, the present method may also be used for detecting significant events in spike trains.

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

Science.gov (United States)

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

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.

Capturing spike variability in noisy Izhikevich neurons using point process generalized linear models

DEFF Research Database (Denmark)

Østergaard, Jacob; Kramer, Mark A.; Eden, Uri T.

2018-01-01

current. We then fit these spike train datawith a statistical model (a generalized linear model, GLM, with multiplicative influences of past spiking). For different levels of noise, we show how the GLM captures both the deterministic features of the Izhikevich neuron and the variability driven...... by the noise. We conclude that the GLM captures essential features of the simulated spike trains, but for near-deterministic spike trains, goodness-of-fit analyses reveal that the model does not fit very well in a statistical sense; the essential random part of the GLM is not captured....... are separately applied; understanding the relationships between these modeling approaches remains an area of active research. In this letter, we examine this relationship using simulation. To do so, we first generate spike train data from a well-known dynamical model, the Izhikevich neuron, with a noisy input...

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.

Automatic fitting of spiking neuron models to electrophysiological recordings

Directory of Open Access Journals (Sweden)

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.

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.

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.

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.

Surfing a spike wave down the ventral stream.

Science.gov (United States)

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.

Neural Spike-Train Analyses of the Speech-Based Envelope Power Spectrum Model

Science.gov (United States)

Rallapalli, Varsha H.

2016-01-01

Diagnosing and treating hearing impairment is challenging because people with similar degrees of sensorineural hearing loss (SNHL) often have different speech-recognition abilities. The speech-based envelope power spectrum model (sEPSM) has demonstrated that the signal-to-noise ratio (SNRENV) from a modulation filter bank provides a robust speech-intelligibility measure across a wider range of degraded conditions than many long-standing models. In the sEPSM, noise (N) is assumed to: (a) reduce S + N envelope power by filling in dips within clean speech (S) and (b) introduce an envelope noise floor from intrinsic fluctuations in the noise itself. While the promise of SNRENV has been demonstrated for normal-hearing listeners, it has not been thoroughly extended to hearing-impaired listeners because of limited physiological knowledge of how SNHL affects speech-in-noise envelope coding relative to noise alone. Here, envelope coding to speech-in-noise stimuli was quantified from auditory-nerve model spike trains using shuffled correlograms, which were analyzed in the modulation-frequency domain to compute modulation-band estimates of neural SNRENV. Preliminary spike-train analyses show strong similarities to the sEPSM, demonstrating feasibility of neural SNRENV computations. Results suggest that individual differences can occur based on differential degrees of outer- and inner-hair-cell dysfunction in listeners currently diagnosed into the single audiological SNHL category. The predicted acoustic-SNR dependence in individual differences suggests that the SNR-dependent rate of susceptibility could be an important metric in diagnosing individual differences. Future measurements of the neural SNRENV in animal studies with various forms of SNHL will provide valuable insight for understanding individual differences in speech-in-noise intelligibility.

Influences on and measures of unintentional group synchrony

Directory of Open Access Journals (Sweden)

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.

Evoking prescribed spike times in stochastic neurons

Science.gov (United States)

Doose, Jens; Lindner, Benjamin

2017-09-01

Single cell stimulation in vivo is a powerful tool to investigate the properties of single neurons and their functionality in neural networks. We present a method to determine a cell-specific stimulus that reliably evokes a prescribed spike train with high temporal precision of action potentials. We test the performance of this stimulus in simulations for two different stochastic neuron models. For a broad range of parameters and a neuron firing with intermediate firing rates (20-40 Hz) the reliability in evoking the prescribed spike train is close to its theoretical maximum that is mainly determined by the level of intrinsic noise.

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

Science.gov (United States)

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

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.

Hysteresis in audiovisual synchrony perception.

Directory of Open Access Journals (Sweden)

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.

Rising climate variability and synchrony in North Pacific ecosystems

Science.gov (United States)

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

Directory of Open Access Journals (Sweden)

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.

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.

Bursts generate a non-reducible spike-pattern code

Directory of Open Access Journals (Sweden)

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.

The Effects of Rhythm and Robotic Interventions on the Imitation/Praxis, Interpersonal Synchrony, and Motor Performance of Children with Autism Spectrum Disorder (ASD): A Pilot Randomized Controlled Trial

Science.gov (United States)

Srinivasan, Sudha M.; Kaur, Maninderjit; Park, Isabel K.; Gifford, Timothy D.; Marsh, Kerry L.; Bhat, Anjana N.

2015-01-01

We assessed the effects of three interventions, rhythm, robotic, and standard-of-care, on the imitation/praxis, interpersonal synchrony, and overall motor performance of 36 children with Autism Spectrum Disorder (ASD) between 5 and 12 years of age. Children were matched on age, level of functioning, and services received, prior to random assignment to one of the three groups. Training was provided for 8 weeks with 4 sessions provided each week. We assessed generalized changes in motor skills from the pretest to the posttest using a standardized test of motor performance, the Bruininks-Oseretsky Test of Motor Proficiency, 2nd edition (BOT-2). We also assessed training-specific changes in imitation/praxis and interpersonal synchrony during an early and a late session. Consistent with the training activities practiced, the rhythm and robot groups improved on the body coordination composite of the BOT-2, whereas the comparison group improved on the fine manual control composite of the BOT-2. All three groups demonstrated improvements in imitation/praxis. The rhythm and robot groups also showed improved interpersonal synchrony performance from the early to the late session. Overall, socially embedded movement-based contexts are valuable in promoting imitation/praxis, interpersonal synchrony, and motor performance and should be included within the standard-of-care treatment for children with ASD. PMID:26793394

The Effects of Rhythm and Robotic Interventions on the Imitation/Praxis, Interpersonal Synchrony, and Motor Performance of Children with Autism Spectrum Disorder (ASD: A Pilot Randomized Controlled Trial

Directory of Open Access Journals (Sweden)

Sudha M. Srinivasan

2015-01-01

Full Text Available We assessed the effects of three interventions, rhythm, robotic, and standard-of-care, on the imitation/praxis, interpersonal synchrony, and overall motor performance of 36 children with Autism Spectrum Disorder (ASD between 5 and 12 years of age. Children were matched on age, level of functioning, and services received, prior to random assignment to one of the three groups. Training was provided for 8 weeks with 4 sessions provided each week. We assessed generalized changes in motor skills from the pretest to the posttest using a standardized test of motor performance, the Bruininks-Oseretsky Test of Motor Proficiency, 2nd edition (BOT-2. We also assessed training-specific changes in imitation/praxis and interpersonal synchrony during an early and a late session. Consistent with the training activities practiced, the rhythm and robot groups improved on the body coordination composite of the BOT-2, whereas the comparison group improved on the fine manual control composite of the BOT-2. All three groups demonstrated improvements in imitation/praxis. The rhythm and robot groups also showed improved interpersonal synchrony performance from the early to the late session. Overall, socially embedded movement-based contexts are valuable in promoting imitation/praxis, interpersonal synchrony, and motor performance and should be included within the standard-of-care treatment for children with ASD.

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

Directory of Open Access Journals (Sweden)

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.

The chronotron: a neuron that learns to fire temporally precise spike patterns.

Directory of Open Access Journals (Sweden)

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.

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

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.

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.

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.

Adrenalectomy eliminates the extinction spike in autoshaping with rats.

Science.gov (United States)

Thomas, B L; Papini, M R

2001-03-01

Experiment 1, using rats, investigated the effect of adrenalectomy (ADX) on the invigoration of lever-contact performance that occurs in the autoshaping situation after a shift from acquisition to extinction (called the extinction spike). Groups of rats with ADX or sham operations were trained under spaced and massed conditions [average intertrial intervals (ITI) of either 15 or 90 s] for 10 sessions and then shifted to extinction. ADX did not affect acquisition training but it eliminated the extinction spike. Plasma corticosterone levels during acquisition were shown in Experiment 2 to be similar in rats trained under spaced or massed conditions. Adrenal participation in the emotional arousal induced by conditions of surprising nonreward (e.g., extinction) is discussed.

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.

A Simple Deep Learning Method for Neuronal Spike Sorting

Science.gov (United States)

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.

Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems

Directory of Open Access Journals (Sweden)

Emre eNeftci

2014-01-01

Full Text Available Restricted Boltzmann Machines (RBMs and Deep Belief Networks have been demonstrated to perform efficiently in variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The reverberating activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP carries out the weight updates in an online, asynchronous fashion.We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.

Event-driven contrastive divergence for spiking neuromorphic systems.

Science.gov (United States)

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2013-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.

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

Science.gov (United States)

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

Optimal decision making on the basis of evidence represented in spike trains.

Science.gov (United States)

Zhang, Jiaxiang; Bogacz, Rafal

2010-05-01

Experimental data indicate that perceptual decision making involves integration of sensory evidence in certain cortical areas. Theoretical studies have proposed that the computation in neural decision circuits approximates statistically optimal decision procedures (e.g., sequential probability ratio test) that maximize the reward rate in sequential choice tasks. However, these previous studies assumed that the sensory evidence was represented by continuous values from gaussian distributions with the same variance across alternatives. In this article, we make a more realistic assumption that sensory evidence is represented in spike trains described by the Poisson processes, which naturally satisfy the mean-variance relationship observed in sensory neurons. We show that for such a representation, the neural circuits involving cortical integrators and basal ganglia can approximate the optimal decision procedures for two and multiple alternative choice tasks.

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

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.

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

Directory of Open Access Journals (Sweden)

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.

Science.gov (United States)

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.

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space

Directory of Open Access Journals (Sweden)

Kan Li

2018-04-01

Full Text Available This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM speech processing as well as neuromorphic implementations based on spiking neural network (SNN, yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR regime.

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space.

Science.gov (United States)

Li, Kan; Príncipe, José C

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime.

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.

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...

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

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.

Unsupervised spike sorting based on discriminative subspace learning.

Science.gov (United States)

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.

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).

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

Reconstructing stimuli from the spike-times of leaky integrate and fire neurons

Directory of Open Access Journals (Sweden)

Sebastian eGerwinn

2011-02-01

Full Text Available Reconstructing stimuli from the spike-trains of neurons is an important approach for understanding the neural code. One of the difficulties associated with this task is that signals which are varying continuously in time are encoded into sequences of discrete events or spikes. An important problem is to determine how much information about the continuously varying stimulus can be extracted from the time-points at which spikes were observed, especially if these time-points are subject to some sort of randomness. For the special case of spike trains generated by leaky integrate and fire neurons, noise can be introduced by allowing variations in the threshold every time a spike is released. A simple decoding algorithm previously derived for the noiseless case can be extended to the stochastic case, but turns out to be biased. Here, we review a solution to this problem, by presenting a simple yet efficient algorithm which greatly reduces the bias, and therefore leads to better decoding performance in the stochastic case.

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.

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.

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

Directory of Open Access Journals (Sweden)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

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.

Spiking neural networks for handwritten digit recognition-Supervised learning and network optimization.

Science.gov (United States)

Kulkarni, Shruti R; Rajendran, Bipin

2018-07-01

We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse biological spike rates below 300Hz achieves a classification accuracy of 98.17% on the MNIST test database with four times fewer parameters compared to the state-of-the-art. We present several insights from extensive numerical experiments regarding optimization of learning parameters and network configuration to improve its accuracy. We also describe a number of strategies to optimize the SNN for implementation in memory and energy constrained hardware, including approximations in computing the neuronal dynamics and reduced precision in storing the synaptic weights. Experiments reveal that even with 3-bit synaptic weights, the classification accuracy of the designed SNN does not degrade beyond 1% as compared to the floating-point baseline. Further, the proposed SNN, which is trained based on the precise spike timing information outperforms an equivalent non-spiking artificial neural network (ANN) trained using back propagation, especially at low bit precision. Thus, our study shows the potential for realizing efficient neuromorphic systems that use spike based information encoding and learning for real-world applications. Copyright © 2018 Elsevier Ltd. All rights reserved.

Inference of neuronal network spike dynamics and topology from calcium imaging data

Directory of Open Access Journals (Sweden)

Henry eLütcke

2013-12-01

Full Text Available Two-photon calcium imaging enables functional analysis of neuronal circuits by inferring action potential (AP occurrence ('spike trains' from cellular fluorescence signals. It remains unclear how experimental parameters such as signal-to-noise ratio (SNR and acquisition rate affect spike inference and whether additional information about network structure can be extracted. Here we present a simulation framework for quantitatively assessing how well spike dynamics and network topology can be inferred from noisy calcium imaging data. For simulated AP-evoked calcium transients in neocortical pyramidal cells, we analyzed the quality of spike inference as a function of SNR and data acquisition rate using a recently introduced peeling algorithm. Given experimentally attainable values of SNR and acquisition rate, neural spike trains could be reconstructed accurately and with up to millisecond precision. We then applied statistical neuronal network models to explore how remaining uncertainties in spike inference affect estimates of network connectivity and topological features of network organization. We define the experimental conditions suitable for inferring whether the network has a scale-free structure and determine how well hub neurons can be identified. Our findings provide a benchmark for future calcium imaging studies that aim to reliably infer neuronal network properties.

Spatio-temporal spike train analysis for large scale networks using the maximum entropy principle and Monte Carlo method

International Nuclear Information System (INIS)

Nasser, Hassan; Cessac, Bruno; Marre, Olivier

2013-01-01

Understanding the dynamics of neural networks is a major challenge in experimental neuroscience. For that purpose, a modelling of the recorded activity that reproduces the main statistics of the data is required. In the first part, we present a review on recent results dealing with spike train statistics analysis using maximum entropy models (MaxEnt). Most of these studies have focused on modelling synchronous spike patterns, leaving aside the temporal dynamics of the neural activity. However, the maximum entropy principle can be generalized to the temporal case, leading to Markovian models where memory effects and time correlations in the dynamics are properly taken into account. In the second part, we present a new method based on Monte Carlo sampling which is suited for the fitting of large-scale spatio-temporal MaxEnt models. The formalism and the tools presented here will be essential to fit MaxEnt spatio-temporal models to large neural ensembles. (paper)

Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks

Science.gov (United States)

Pyle, Ryan; Rosenbaum, Robert

2017-01-01

Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks.

Science.gov (United States)

Pyle, Ryan; Rosenbaum, Robert

2017-01-06

Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

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.

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.

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.

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

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

Science.gov (United States)

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…

Neuronal spike sorting based on radial basis function neural networks

Directory of Open Access Journals (Sweden)

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.

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.

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

Science.gov (United States)

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.

Neuronal coding and spiking randomness

Czech Academy of Sciences Publication Activity Database

Košťál, Lubomír; Lánský, Petr; Rospars, J. P.

2007-01-01

Roč. 26, č. 10 (2007), s. 2693-2988 ISSN 0953-816X R&D Projects: GA MŠk(CZ) LC554; GA AV ČR(CZ) 1ET400110401; GA AV ČR(CZ) KJB100110701 Grant - others:ECO-NET(FR) 112644PF Institutional research plan: CEZ:AV0Z50110509 Keywords : spike train * variability * neurovědy Subject RIV: FH - Neurology Impact factor: 3.673, year: 2007

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

Directory of Open Access Journals (Sweden)

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

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.

Directory of Open Access Journals (Sweden)

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.

Noise-robust speech recognition through auditory feature detection and spike sequence decoding.

Science.gov (United States)

Schafer, Phillip B; Jin, Dezhe Z

2014-03-01

Speech recognition in noisy conditions is a major challenge for computer systems, but the human brain performs it routinely and accurately. Automatic speech recognition (ASR) systems that are inspired by neuroscience can potentially bridge the performance gap between humans and machines. We present a system for noise-robust isolated word recognition that works by decoding sequences of spikes from a population of simulated auditory feature-detecting neurons. Each neuron is trained to respond selectively to a brief spectrotemporal pattern, or feature, drawn from the simulated auditory nerve response to speech. The neural population conveys the time-dependent structure of a sound by its sequence of spikes. We compare two methods for decoding the spike sequences--one using a hidden Markov model-based recognizer, the other using a novel template-based recognition scheme. In the latter case, words are recognized by comparing their spike sequences to template sequences obtained from clean training data, using a similarity measure based on the length of the longest common sub-sequence. Using isolated spoken digits from the AURORA-2 database, we show that our combined system outperforms a state-of-the-art robust speech recognizer at low signal-to-noise ratios. Both the spike-based encoding scheme and the template-based decoding offer gains in noise robustness over traditional speech recognition methods. Our system highlights potential advantages of spike-based acoustic coding and provides a biologically motivated framework for robust ASR development.

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

Directory of Open Access Journals (Sweden)

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.

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.

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.

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)

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

Science.gov (United States)

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

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.

Science.gov (United States)

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.

Science.gov (United States)

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Directory of Open Access Journals (Sweden)

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.

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

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

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

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)

An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data

Directory of Open Access Journals (Sweden)

Evangelos Stromatias

2017-06-01

Full Text Available This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77% and Poker-DVS (100% real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data.

Science.gov (United States)

Stromatias, Evangelos; Soto, Miguel; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

2017-01-01

This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN) System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS) chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77%) and Poker-DVS (100%) real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

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.

Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms.

Science.gov (United States)

Stromatias, Evangelos; Neil, Daniel; Pfeiffer, Michael; Galluppi, Francesco; Furber, Steve B; Liu, Shih-Chii

2015-01-01

Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time.

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.

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

Science.gov (United States)

Evans, Benjamin D; Stringer, Simon M

2012-01-01

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

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

Directory of Open Access Journals (Sweden)

Benjamin eEvans

2012-07-01

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

Design of Spiking Central Pattern Generators for Multiple Locomotion Gaits in Hexapod Robots by Christiansen Grammar Evolution.

Science.gov (United States)

Espinal, Andres; Rostro-Gonzalez, Horacio; Carpio, Martin; Guerra-Hernandez, Erick I; Ornelas-Rodriguez, Manuel; Sotelo-Figueroa, Marco

2016-01-01

This paper presents a method to design Spiking Central Pattern Generators (SCPGs) to achieve locomotion at different frequencies on legged robots. It is validated through embedding its designs into a Field-Programmable Gate Array (FPGA) and implemented on a real hexapod robot. The SCPGs are automatically designed by means of a Christiansen Grammar Evolution (CGE)-based methodology. The CGE performs a solution for the configuration (synaptic weights and connections) for each neuron in the SCPG. This is carried out through the indirect representation of candidate solutions that evolve to replicate a specific spike train according to a locomotion pattern (gait) by measuring the similarity between the spike trains and the SPIKE distance to lead the search to a correct configuration. By using this evolutionary approach, several SCPG design specifications can be explicitly added into the SPIKE distance-based fitness function, such as looking for Spiking Neural Networks (SNNs) with minimal connectivity or a Central Pattern Generator (CPG) able to generate different locomotion gaits only by changing the initial input stimuli. The SCPG designs have been successfully implemented on a Spartan 6 FPGA board and a real time validation on a 12 Degrees Of Freedom (DOFs) hexapod robot is presented.

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

Directory of Open Access Journals (Sweden)

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...

Simulations of drastically reduced SBS with laser pulses composed of a Spike Train of Uneven Duration and Delay (STUD pulses)

International Nuclear Information System (INIS)

Hueller, S.; Afeyan, B.

2013-01-01

By comparing the impact of established laser smoothing techniques like Random Phase Plates (RPP) and Smoothing by Spectral Dispersion (SSD) to the concept of 'Spike Trains of Uneven Duration and Delay' (STUD pulses) on the amplification of parametric instabilities in laser-produced plasmas, we show with the help of numerical simulations, that STUD pulses can drastically reduce instability growth by orders of magnitude. The simulation results, obtained with the code Harmony in a nonuniformly flowing mm-size plasma for the Stimulated Brillouin Scattering (SBS) instability, show that the efficiency of the STUD pulse technique is due to the fact that successive re-amplification in space and time of parametrically excited plasma waves inside laser hot spots is minimized. An overall mean fluctuation level of ion acoustic waves at low amplitude is established because of the frequent change of the speckle pattern in successive spikes. This level stays orders of magnitude below the levels of ion acoustic waves excited in hot spots of RPP and SSD laser beams. (authors)

A memristive spiking neuron with firing rate coding

Directory of Open Access Journals (Sweden)

Marina eIgnatov

2015-10-01

Full Text Available Perception, decisions, and sensations are all encoded into trains of action potentials in the brain. The relation between stimulus strength and all-or-nothing spiking of neurons is widely believed to be the basis of this coding. This initiated the development of spiking neuron models; one of today's most powerful conceptual tool for the analysis and emulation of neural dynamics. The success of electronic circuit models and their physical realization within silicon field-effect transistor circuits lead to elegant technical approaches. Recently, the spectrum of electronic devices for neural computing has been extended by memristive devices, mainly used to emulate static synaptic functionality. Their capabilities for emulations of neural activity were recently demonstrated using a memristive neuristor circuit, while a memristive neuron circuit has so far been elusive. Here, a spiking neuron model is experimentally realized in a compact circuit comprising memristive and memcapacitive devices based on the strongly correlated electron material vanadium dioxide (VO2 and on the chemical electromigration cell Ag/TiO2-x/Al. The circuit can emulate dynamical spiking patterns in response to an external stimulus including adaptation, which is at the heart of firing rate coding as first observed by E.D. Adrian in 1926.

Spike Neural Models Part II: Abstract Neural Models

Directory of Open Access Journals (Sweden)

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.

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

Structured chaos shapes spike-response noise entropy in balanced neural networks

Directory of Open Access Journals (Sweden)

Guillaume eLajoie

2014-10-01

Full Text Available Large networks of sparsely coupled, excitatory and inhibitory cells occur throughout the brain. For many models of these networks, a striking feature is that their dynamics are chaotic and thus, are sensitive to small perturbations. How does this chaos manifest in the neural code? Specifically, how variable are the spike patterns that such a network produces in response to an input signal? To answer this, we derive a bound for a general measure of variability -- spike-train entropy. This leads to important insights on the variability of multi-cell spike pattern distributions in large recurrent networks of spiking neurons responding to fluctuating inputs. The analysis is based on results from random dynamical systems theory and is complemented by detailed numerical simulations. We find that the spike pattern entropy is an order of magnitude lower than what would be extrapolated from single cells. This holds despite the fact that network coupling becomes vanishingly sparse as network size grows -- a phenomenon that depends on ``extensive chaos, as previously discovered for balanced networks without stimulus drive. Moreover, we show how spike pattern entropy is controlled by temporal features of the inputs. Our findings provide insight into how neural networks may encode stimuli in the presence of inherently chaotic dynamics.

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

Science.gov (United States)

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.

Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms

Directory of Open Access Journals (Sweden)

Evangelos eStromatias

2015-07-01

Full Text Available Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks requires vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost 2 bits, and shows that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time.

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

Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks.

Science.gov (United States)

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.

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.

Gradient Learning in Spiking Neural Networks by Dynamic Perturbation of Conductances

International Nuclear Information System (INIS)

Fiete, Ila R.; Seung, H. Sebastian

2006-01-01

We present a method of estimating the gradient of an objective function with respect to the synaptic weights of a spiking neural network. The method works by measuring the fluctuations in the objective function in response to dynamic perturbation of the membrane conductances of the neurons. It is compatible with recurrent networks of conductance-based model neurons with dynamic synapses. The method can be interpreted as a biologically plausible synaptic learning rule, if the dynamic perturbations are generated by a special class of 'empiric' synapses driven by random spike trains from an external source

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

On a Possible Relationship between Linguistic Expertise and EEG Gamma Band Phase Synchrony

Science.gov (United States)

Reiterer, Susanne; Pereda, Ernesto; Bhattacharya, Joydeep

2011-01-01

Recent research has shown that extensive training in and exposure to a second language can modify the language organization in the brain by causing both structural and functional changes. However it is not yet known how these changes are manifested by the dynamic brain oscillations and synchronization patterns subserving the language networks. In search for synchronization correlates of proficiency and expertise in second language acquisition, multivariate EEG signals were recorded from 44 high and low proficiency bilinguals during processing of natural language in their first and second languages. Gamma band (30–45 Hz) phase synchronization (PS) was calculated mainly by two recently developed methods: coarse-graining of Markov chains (estimating global phase synchrony, measuring the degree of PS between one electrode and all other electrodes), and phase lag index (PLI; estimating bivariate phase synchrony, measuring the degree of PS between a pair of electrodes). On comparing second versus first language processing, global PS by coarse-graining Markov chains indicated that processing of the second language needs significantly higher synchronization strength than first language. On comparing the proficiency groups, bivariate PS measure (i.e., PLI) revealed that during second language processing the low proficiency group showed stronger and broader network patterns than the high proficiency group, with interconnectivities between a left fronto-parietal network. Mean phase coherence analysis also indicated that the network activity was globally stronger in the low proficiency group during second language processing. PMID:22125542

Spike-train acquisition, analysis and real-time experimental control using a graphical programming language (LabView).

Science.gov (United States)

Nordstrom, M A; Mapletoft, E A; Miles, T S

1995-11-01

A solution is described for the acquisition on a personal computer of standard pulses derived from neuronal discharge, measurement of neuronal discharge times, real-time control of stimulus delivery based on specified inter-pulse interval conditions in the neuronal spike train, and on-line display and analysis of the experimental data. The hardware consisted of an Apple Macintosh IIci computer and a plug-in card (National Instruments NB-MIO16) that supports A/D, D/A, digital I/O and timer functions. The software was written in the object-oriented graphical programming language LabView. Essential elements of the source code of the LabView program are presented and explained. The use of the system is demonstrated in an experiment in which the reflex responses to muscle stretch are assessed for a single motor unit in the human masseter muscle.

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

Directory of Open Access Journals (Sweden)

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.

Science.gov (United States)

Albers, Christian; Westkott, Maren; Pawelzik, Klaus

2016-01-01

Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

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.

The dynamic relationship between cerebellar Purkinje cell simple spikes and the spikelet number of complex spikes.

Science.gov (United States)

Burroughs, Amelia; Wise, Andrew K; Xiao, Jianqiang; Houghton, Conor; Tang, Tianyu; Suh, Colleen Y; Lang, Eric J; Apps, Richard; Cerminara, Nadia L

2017-01-01

Purkinje cells are the sole output of the cerebellar cortex and fire two distinct types of action potential: simple spikes and complex spikes. Previous studies have mainly considered complex spikes as unitary events, even though the waveform is composed of varying numbers of spikelets. The extent to which differences in spikelet number affect simple spike activity (and vice versa) remains unclear. We found that complex spikes with greater numbers of spikelets are preceded by higher simple spike firing rates but, following the complex spike, simple spikes are reduced in a manner that is graded with spikelet number. This dynamic interaction has important implications for cerebellar information processing, and suggests that complex spike spikelet number may maintain Purkinje cells within their operational range. Purkinje cells are central to cerebellar function because they form the sole output of the cerebellar cortex. They exhibit two distinct types of action potential: simple spikes and complex spikes. It is widely accepted that interaction between these two types of impulse is central to cerebellar cortical information processing. Previous investigations of the interactions between simple spikes and complex spikes have mainly considered complex spikes as unitary events. However, complex spikes are composed of an initial large spike followed by a number of secondary components, termed spikelets. The number of spikelets within individual complex spikes is highly variable and the extent to which differences in complex spike spikelet number affects simple spike activity (and vice versa) remains poorly understood. In anaesthetized adult rats, we have found that Purkinje cells recorded from the posterior lobe vermis and hemisphere have high simple spike firing frequencies that precede complex spikes with greater numbers of spikelets. This finding was also evident in a small sample of Purkinje cells recorded from the posterior lobe hemisphere in awake cats. In addition

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

Science.gov (United States)

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

Analysis and modeling of ensemble recordings from respiratory pre-motor neurons indicate changes in functional network architecture after acute hypoxia

Directory of Open Access Journals (Sweden)

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.

Scaling up spike-and-slab models for unsupervised feature learning.

Science.gov (United States)

Goodfellow, Ian J; Courville, Aaron; Bengio, Yoshua

2013-08-01

We describe the use of two spike-and-slab models for modeling real-valued data, with an emphasis on their applications to object recognition. The first model, which we call spike-and-slab sparse coding (S3C), is a preexisting model for which we introduce a faster approximate inference algorithm. We introduce a deep variant of S3C, which we call the partially directed deep Boltzmann machine (PD-DBM) and extend our S3C inference algorithm for use on this model. We describe learning procedures for each. We demonstrate that our inference procedure for S3C enables scaling the model to unprecedented large problem sizes, and demonstrate that using S3C as a feature extractor results in very good object recognition performance, particularly when the number of labeled examples is low. We show that the PD-DBM generates better samples than its shallow counterpart, and that unlike DBMs or DBNs, the PD-DBM may be trained successfully without greedy layerwise training.

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

Science.gov (United States)

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.

A network of spiking neurons that can represent interval timing: mean field analysis.

Science.gov (United States)

Gavornik, Jeffrey P; Shouval, Harel Z

2011-04-01

Despite the vital importance of our ability to accurately process and encode temporal information, the underlying neural mechanisms are largely unknown. We have previously described a theoretical framework that explains how temporal representations, similar to those reported in the visual cortex, can form in locally recurrent cortical networks as a function of reward modulated synaptic plasticity. This framework allows networks of both linear and spiking neurons to learn the temporal interval between a stimulus and paired reward signal presented during training. Here we use a mean field approach to analyze the dynamics of non-linear stochastic spiking neurons in a network trained to encode specific time intervals. This analysis explains how recurrent excitatory feedback allows a network structure to encode temporal representations.

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

Science.gov (United States)

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.

Science.gov (United States)

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.

Science.gov (United States)

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.

Directory of Open Access Journals (Sweden)

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

Energy Technology Data Exchange (ETDEWEB)

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.

Directory of Open Access Journals (Sweden)

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

A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings.

Science.gov (United States)

Magri, Cesare; Whittingstall, Kevin; Singh, Vanessa; Logothetis, Nikos K; Panzeri, Stefano

2009-07-16

Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses. Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies. The new toolbox presented here implements fast and data-robust computations of the most relevant

A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings

Directory of Open Access Journals (Sweden)

Logothetis Nikos K

2009-07-01

Full Text Available Abstract Background Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses. Results Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies. Conclusion The new toolbox presented here implements fast

A Three-Dimensional Movement Analysis of the Spike in Fistball

Directory of Open Access Journals (Sweden)

Andreas Bund

2016-12-01

Full Text Available Due to its relevancy to point scoring, the spike is considered as one of the most important skills in fistball. Biomechanical analyses of this sport are very rare. In the present study, we performed a three-dimensional kinematic analysis of the fistball spike, which helps to specify performance parameters on a descriptive level. Recorded by four synchronized cameras (120 Hz and linked to the motion capture software Simi Motion® 5.0, three female fistball players of the second German league (24–26 years, 1.63–1.69 m performed several spikes under standardized conditions. Results show that the segment velocities of the arm reached their maximum successively from proximal to distal, following the principle of temporal coordination of single impulses. The wrist shows maximum speed when the fist hits the ball. The elbow joint angle performs a rapid transition from a strong flexion to a (almost full extension; however, the extension is completed after the moment of ball impact. In contrast, the shoulder joint angle increases almost linearly until the fistball contact and decreases afterward. The findings can be used to optimize the training of the spike.

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

Science.gov (United States)

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.

Spike Timing Matters in Novel Neuronal Code Involved in Vibrotactile Frequency Perception.

Science.gov (United States)

Birznieks, Ingvars; Vickery, Richard M

2017-05-22

Skin vibrations sensed by tactile receptors contribute significantly to the perception of object properties during tactile exploration [1-4] and to sensorimotor control during object manipulation [5]. Sustained low-frequency skin vibration (perception of frequency is still unknown. Measures based on mean spike rates of neurons in the primary somatosensory cortex are sufficient to explain performance in some frequency discrimination tasks [7-11]; however, there is emerging evidence that stimuli can be distinguished based also on temporal features of neural activity [12, 13]. Our study's advance is to demonstrate that temporal features are fundamental for vibrotactile frequency perception. Pulsatile mechanical stimuli were used to elicit specified temporal spike train patterns in tactile afferents, and subsequently psychophysical methods were employed to characterize human frequency perception. Remarkably, the most salient temporal feature determining vibrotactile frequency was not the underlying periodicity but, rather, the duration of the silent gap between successive bursts of neural activity. This burst gap code for frequency represents a previously unknown form of neural coding in the tactile sensory system, which parallels auditory pitch perception mechanisms based on purely temporal information where longer inter-pulse intervals receive higher perceptual weights than short intervals [14]. Our study also demonstrates that human perception of stimuli can be determined exclusively by temporal features of spike trains independent of the mean spike rate and without contribution from population response factors. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

Science.gov (United States)

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…

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

Science.gov (United States)

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 Use Of Spikes Protocol In Cancer: An Integrative Review

Directory of Open Access Journals (Sweden)

Fernando Henrique de Sousa

2017-03-01

Full Text Available This is an integrative review which aimed to evaluate the use of the SPIKES protocol in Oncology. We selected articles published in Medline and CINAHL databases between 2005-2015, in English, with the descriptors defined by the Medical Subject Headings (MeSH:cancer, neoplasms, plus the uncontrolled descriptor: protocol spikes.  Six articles met the inclusion criteria and were analyzed in full, three thematic categories were established: aspects inherent to the health care professional; Aspects related to the patient and aspects related to the protocol. The main effects of the steps of SPIKES protocol can provide the strengthening of ties between health professionals and patients, and ensure the maintenance and quality of this relationship.  The results indicate an important limiting factor for effective doctor-patient relationship, the little training provided to medical professionals communication of bad news, verified by the difficulty reported in this moment through interviews in the analyzed studies.

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

Science.gov (United States)

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

Directory of Open Access Journals (Sweden)

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

International Nuclear Information System (INIS)

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

Science.gov (United States)

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.

Science.gov (United States)

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.

Science.gov (United States)

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.

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

Bio-inspired spiking neural network for nonlinear systems control.

Science.gov (United States)

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.

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.)

Spike Pattern Recognition for Automatic Collimation Alignment

CERN Document Server

Azzopardi, Gabriella; Salvachua Ferrando, Belen Maria; Mereghetti, Alessio; Redaelli, Stefano; CERN. Geneva. ATS Department

2017-01-01

The LHC makes use of a collimation system to protect its sensitive equipment by intercepting potentially dangerous beam halo particles. The appropriate collimator settings to protect the machine against beam losses relies on a very precise alignment of all the collimators with respect to the beam. The beam center at each collimator is then found by touching the beam halo using an alignment procedure. Until now, in order to determine whether a collimator is aligned with the beam or not, a user is required to follow the collimator’s BLM loss data and detect spikes. A machine learning (ML) model was trained in order to automatically recognize spikes when a collimator is aligned. The model was loosely integrated with the alignment implementation to determine the classification performance and reliability, without effecting the alignment process itself. The model was tested on a number of collimators during this MD and the machine learning was able to output the classifications in real-time.

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

Science.gov (United States)

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

Directory of Open Access Journals (Sweden)

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.

Science.gov (United States)

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

Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights.

Science.gov (United States)

Samadi, Arash; Lillicrap, Timothy P; Tweed, Douglas B

2017-03-01

Recent work in computer science has shown the power of deep learning driven by the backpropagation algorithm in networks of artificial neurons. But real neurons in the brain are different from most of these artificial ones in at least three crucial ways: they emit spikes rather than graded outputs, their inputs and outputs are related dynamically rather than by piecewise-smooth functions, and they have no known way to coordinate arrays of synapses in separate forward and feedback pathways so that they change simultaneously and identically, as they do in backpropagation. Given these differences, it is unlikely that current deep learning algorithms can operate in the brain, but we that show these problems can be solved by two simple devices: learning rules can approximate dynamic input-output relations with piecewise-smooth functions, and a variation on the feedback alignment algorithm can train deep networks without having to coordinate forward and feedback synapses. Our results also show that deep spiking networks learn much better if each neuron computes an intracellular teaching signal that reflects that cell's nonlinearity. With this mechanism, networks of spiking neurons show useful learning in synapses at least nine layers upstream from the output cells and perform well compared to other spiking networks in the literature on the MNIST digit recognition task.

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

Statistical detection of EEG synchrony using empirical bayesian inference.

Directory of Open Access Journals (Sweden)

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.

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Directory of Open Access Journals (Sweden)

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

Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering

Directory of Open Access Journals (Sweden)

Oliynyk Andriy

2012-08-01

Full Text Available Abstract Background Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue. Results Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting, which is designed to optimize: (i fast and accurate detection, (ii offline sorting and (iii online classification of neuronal spikes with very limited or null human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com using LabVIEW (National Instruments, USA. We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is

Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering.

Science.gov (United States)

Oliynyk, Andriy; Bonifazzi, Claudio; Montani, Fernando; Fadiga, Luciano

2012-08-08

Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue. Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting), which is designed to optimize: (i) fast and accurate detection, (ii) offline sorting and (iii) online classification of neuronal spikes with very limited or null human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com) using LabVIEW (National Instruments, USA). We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is competitive with respect to other robust spike

An Investigation on the Role of Spike Latency in an Artificial Olfactory System

Directory of Open Access Journals (Sweden)

Corrado eDi Natale

2011-12-01

Full Text Available Experimental studies have shown that the reactions to external stimuli may appear only few hundreds of milliseconds after the physical interaction of the stimulus with the proper receptor. This behavior suggests that neurons transmit the largest meaningful part of their signal in the first spikes, and than that the spike latency is a good descriptor of the information content in biological neural networks. In this paper this property has been investigated in an artificial sensorial system where a single layer of spiking neurons is trained with the data generated by an artificial olfactory platform based on a large array of chemical sensors. The capability to discriminate between distinct chemicals and mixtures of them was studied with spiking neural networks endowed with and without lateral inhibitions and considering as output feature of the network both the spikes latency and the average firing rate. Results show that the average firing rate of the output spikes sequences shows the best separation among the experienced vapors, however the latency code is able in a shorter time to correctly discriminate all the tested volatile compounds. This behavior is qualitatively similar to those recently found in natural olfaction, and noteworthy it provides practical suggestions to tail the measurement conditions of artificial olfactory systems defining for each specific case a proper measurement time.

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

Science.gov (United States)

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

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.

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

Science.gov (United States)

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.

Science.gov (United States)

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.

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

Science.gov (United States)

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

On the robustness of EC-PC spike detection method for online neural recording.

Science.gov (United States)

Zhou, Yin; Wu, Tong; Rastegarnia, Amir; Guan, Cuntai; Keefer, Edward; Yang, Zhi

2014-09-30

Online spike detection is an important step to compress neural data and perform real-time neural information decoding. An unsupervised, automatic, yet robust signal processing is strongly desired, thus it can support a wide range of applications. We have developed a novel spike detection algorithm called "exponential component-polynomial component" (EC-PC) spike detection. We firstly evaluate the robustness of the EC-PC spike detector under different firing rates and SNRs. Secondly, we show that the detection Precision can be quantitatively derived without requiring additional user input parameters. We have realized the algorithm (including training) into a 0.13 μm CMOS chip, where an unsupervised, nonparametric operation has been demonstrated. Both simulated data and real data are used to evaluate the method under different firing rates (FRs), SNRs. The results show that the EC-PC spike detector is the most robust in comparison with some popular detectors. Moreover, the EC-PC detector can track changes in the background noise due to the ability to re-estimate the neural data distribution. Both real and synthesized data have been used for testing the proposed algorithm in comparison with other methods, including the absolute thresholding detector (AT), median absolute deviation detector (MAD), nonlinear energy operator detector (NEO), and continuous wavelet detector (CWD). Comparative testing results reveals that the EP-PC detection algorithm performs better than the other algorithms regardless of recording conditions. The EC-PC spike detector can be considered as an unsupervised and robust online spike detection. It is also suitable for hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

Evolving spiking neural networks: a novel growth algorithm exhibits unintelligent design

Science.gov (United States)

Schaffer, J. David

2015-06-01

Spiking neural networks (SNNs) have drawn considerable excitement because of their computational properties, believed to be superior to conventional von Neumann machines, and sharing properties with living brains. Yet progress building these systems has been limited because we lack a design methodology. We present a gene-driven network growth algorithm that enables a genetic algorithm (evolutionary computation) to generate and test SNNs. The genome for this algorithm grows O(n) where n is the number of neurons; n is also evolved. The genome not only specifies the network topology, but all its parameters as well. Experiments show the algorithm producing SNNs that effectively produce a robust spike bursting behavior given tonic inputs, an application suitable for central pattern generators. Even though evolution did not include perturbations of the input spike trains, the evolved networks showed remarkable robustness to such perturbations. In addition, the output spike patterns retain evidence of the specific perturbation of the inputs, a feature that could be exploited by network additions that could use this information for refined decision making if required. On a second task, a sequence detector, a discriminating design was found that might be considered an example of "unintelligent design"; extra non-functional neurons were included that, while inefficient, did not hamper its proper functioning.

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

Science.gov (United States)

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.

Directory of Open Access Journals (Sweden)

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

Directory of Open Access Journals (Sweden)

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.

Supervised learning with decision margins in pools of spiking neurons.

Science.gov (United States)

Le Mouel, Charlotte; Harris, Kenneth D; Yger, Pierre

2014-10-01

Learning to categorise sensory inputs by generalising from a few examples whose category is precisely known is a crucial step for the brain to produce appropriate behavioural responses. At the neuronal level, this may be performed by adaptation of synaptic weights under the influence of a training signal, in order to group spiking patterns impinging on the neuron. Here we describe a framework that allows spiking neurons to perform such "supervised learning", using principles similar to the Support Vector Machine, a well-established and robust classifier. Using a hinge-loss error function, we show that requesting a margin similar to that of the SVM improves performance on linearly non-separable problems. Moreover, we show that using pools of neurons to discriminate categories can also increase the performance by sharing the load among neurons.

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…

An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks

Directory of Open Access Journals (Sweden)

Huan-Yuan Chen

2017-09-01

Full Text Available This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting.

An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks

Science.gov (United States)

Chen, Huan-Yuan; Chen, Chih-Chang

2017-01-01

This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL) neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC) implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting. PMID:28956859

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

Science.gov (United States)

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.

Emergent properties of interacting populations of spiking neurons

Directory of Open Access Journals (Sweden)

Stefano eCardanobile

2011-12-01

Full Text Available Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system.Here, we discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks on the population level is faithfully reflected by a set of non-linear rate equations, describing all interactions on this level. These equations, in turn, are similar in structure to the Lotka-Volterra equations, well known by their use in modeling predator-prey relationships in population biology, but abundant applications to economic theory have also been described.We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of neural populations.

Emergent properties of interacting populations of spiking neurons.

Science.gov (United States)

Cardanobile, Stefano; Rotter, Stefan

2011-01-01

Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system. Here, we present and discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks is faithfully reflected by a set of non-linear rate equations, describing all interactions on the population level. These equations are similar in structure to Lotka-Volterra equations, well known by their use in modeling predator-prey relations in population biology, but abundant applications to economic theory have also been described. We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of interacting neuronal populations.

Heterogeneity of Purkinje cell simple spike-complex spike interactions: zebrin- and non-zebrin-related variations.

Science.gov (United States)

Tang, Tianyu; Xiao, Jianqiang; Suh, Colleen Y; Burroughs, Amelia; Cerminara, Nadia L; Jia, Linjia; Marshall, Sarah P; Wise, Andrew K; Apps, Richard; Sugihara, Izumi; Lang, Eric J

2017-08-01

Cerebellar Purkinje cells (PCs) generate two types of action potentials, simple and complex spikes. Although they are generated by distinct mechanisms, interactions between the two spike types exist. Zebrin staining produces alternating positive and negative stripes of PCs across most of the cerebellar cortex. Thus, here we compared simple spike-complex spike interactions both within and across zebrin populations. Simple spike activity undergoes a complex modulation preceding and following a complex spike. The amplitudes of the pre- and post-complex spike modulation phases were correlated across PCs. On average, the modulation was larger for PCs in zebrin positive regions. Correlations between aspects of the complex spike waveform and simple spike activity were found, some of which varied between zebrin positive and negative PCs. The implications of the results are discussed with regard to hypotheses that complex spikes are triggered by rises in simple spike activity for either motor learning or homeostatic functions. Purkinje cells (PCs) generate two types of action potentials, called simple and complex spikes (SSs and CSs). We first investigated the CS-associated modulation of SS activity and its relationship to the zebrin status of the PC. The modulation pattern consisted of a pre-CS rise in SS activity, and then, following the CS, a pause, a rebound, and finally a late inhibition of SS activity for both zebrin positive (Z+) and negative (Z-) cells, though the amplitudes of the phases were larger in Z+ cells. Moreover, the amplitudes of the pre-CS rise with the late inhibitory phase of the modulation were correlated across PCs. In contrast, correlations between modulation phases across CSs of individual PCs were generally weak. Next, the relationship between CS spikelets and SS activity was investigated. The number of spikelets/CS correlated with the average SS firing rate only for Z+ cells. In contrast, correlations across CSs between spikelet numbers and the

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

Science.gov (United States)

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

Directory of Open Access Journals (Sweden)

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.

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.

Unsupervised neural spike sorting for high-density microelectrode arrays with convolutive independent component analysis.

Science.gov (United States)

Leibig, Christian; Wachtler, Thomas; Zeck, Günther

2016-09-15

Unsupervised identification of action potentials in multi-channel extracellular recordings, in particular from high-density microelectrode arrays with thousands of sensors, is an unresolved problem. While independent component analysis (ICA) achieves rapid unsupervised sorting, it ignores the convolutive structure of extracellular data, thus limiting the unmixing to a subset of neurons. Here we present a spike sorting algorithm based on convolutive ICA (cICA) to retrieve a larger number of accurately sorted neurons than with instantaneous ICA while accounting for signal overlaps. Spike sorting was applied to datasets with varying signal-to-noise ratios (SNR: 3-12) and 27% spike overlaps, sampled at either 11.5 or 23kHz on 4365 electrodes. We demonstrate how the instantaneity assumption in ICA-based algorithms has to be relaxed in order to improve the spike sorting performance for high-density microelectrode array recordings. Reformulating the convolutive mixture as an instantaneous mixture by modeling several delayed samples jointly is necessary to increase signal-to-noise ratio. Our results emphasize that different cICA algorithms are not equivalent. Spike sorting performance was assessed with ground-truth data generated from experimentally derived templates. The presented spike sorter was able to extract ≈90% of the true spike trains with an error rate below 2%. It was superior to two alternative (c)ICA methods (≈80% accurately sorted neurons) and comparable to a supervised sorting. Our new algorithm represents a fast solution to overcome the current bottleneck in spike sorting of large datasets generated by simultaneous recording with thousands of electrodes. Copyright © 2016 Elsevier B.V. All rights reserved.

Temporally coordinated spiking activity of human induced pluripotent stem cell-derived neurons co-cultured with astrocytes.

Science.gov (United States)

Kayama, Tasuku; Suzuki, Ikuro; Odawara, Aoi; Sasaki, Takuya; Ikegaya, Yuji

2018-01-01

In culture conditions, human induced-pluripotent stem cells (hiPSC)-derived neurons form synaptic connections with other cells and establish neuronal networks, which are expected to be an in vitro model system for drug discovery screening and toxicity testing. While early studies demonstrated effects of co-culture of hiPSC-derived neurons with astroglial cells on survival and maturation of hiPSC-derived neurons, the population spiking patterns of such hiPSC-derived neurons have not been fully characterized. In this study, we analyzed temporal spiking patterns of hiPSC-derived neurons recorded by a multi-electrode array system. We discovered that specific sets of hiPSC-derived neurons co-cultured with astrocytes showed more frequent and highly coherent non-random synchronized spike trains and more dynamic changes in overall spike patterns over time. These temporally coordinated spiking patterns are physiological signs of organized circuits of hiPSC-derived neurons and suggest benefits of co-culture of hiPSC-derived neurons with astrocytes. Copyright © 2017 Elsevier Inc. All rights reserved.

A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses

Directory of Open Access Journals (Sweden)

Karim El-Laithy

2011-01-01

Full Text Available An integration of both the Hebbian-based and reinforcement learning (RL rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling.

Science.gov (United States)

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling

Science.gov (United States)

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

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

Directory of Open Access Journals (Sweden)

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

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.

Deconstruction of spatial integrity in visual stimulus detected by modulation of synchronized activity in cat visual cortex.

Science.gov (United States)

Zhou, Zhiyi; Bernard, Melanie R; Bonds, A B

2008-04-02

Spatiotemporal relationships among contour segments can influence synchronization of neural responses in the primary visual cortex. We performed a systematic study to dissociate the impact of spatial and temporal factors in the signaling of contour integration via synchrony. In addition, we characterized the temporal evolution of this process to clarify potential underlying mechanisms. With a 10 x 10 microelectrode array, we recorded the simultaneous activity of multiple cells in the cat primary visual cortex while stimulating with drifting sine-wave gratings. We preserved temporal integrity and systematically degraded spatial integrity of the sine-wave gratings by adding spatial noise. Neural synchronization was analyzed in the time and frequency domains by conducting cross-correlation and coherence analyses. The general association between neural spike trains depends strongly on spatial integrity, with coherence in the gamma band (35-70 Hz) showing greater sensitivity to the change of spatial structure than other frequency bands. Analysis of the temporal dynamics of synchronization in both time and frequency domains suggests that spike timing synchronization is triggered nearly instantaneously by coherent structure in the stimuli, whereas frequency-specific oscillatory components develop more slowly, presumably through network interactions. Our results suggest that, whereas temporal integrity is required for the generation of synchrony, spatial integrity is critical in triggering subsequent gamma band synchronization.

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...

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

Directory of Open Access Journals (Sweden)

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

Directory of Open Access Journals (Sweden)

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.

Anti-correlations in the degree distribution increase stimulus detection performance in noisy spiking neural networks

NARCIS (Netherlands)

Martens, M.B. (Marijn B.); A.R. Houweling (Arthur); E. Tiesinga, P.H. (Paul H.)

2017-01-01

textabstractNeuronal circuits in the rodent barrel cortex are characterized by stable low firing rates. However, recent experiments show that short spike trains elicited by electrical stimulation in single neurons can induce behavioral responses. Hence, the underlying neural networks provide

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 »

Rotational Angles and Velocities During Down the Line and Diagonal Across Court Volleyball Spikes

Directory of Open Access Journals (Sweden)

Justin R. Brown

2014-05-01

Full Text Available The volleyball spike is an explosive movement that is frequently used to end a rally and earn a point. High velocity spikes are an important skill for a successful volleyball offense. Although the influence of vertical jump height and arm velocity on spiked ball velocity (SBV have been investigated, little is known about the relationship of shoulder and hip angular kinematics with SBV. Other sport skills, like the baseball pitch share similar movement patterns and suggest trunk rotation is important for such movements. The purpose of this study was to examine the relationship of both shoulder and hip angular kinematics with ball velocity during the volleyball spike. Methods: Fourteen Division I collegiate female volleyball players executed down the line (DL and diagonally across-court (DAC spikes in a laboratory setting to measure shoulder and hip angular kinematics and velocities. Each spike was analyzed using a 10 Camera Raptor-E Digital Real Time Camera System.  Results: DL SBV was significantly greater than for DAC, respectively (17.54±2.35 vs. 15.97±2.36 m/s, p<0.05.  The Shoulder Hip Separation Angle (S-HSA, Shoulder Angular Velocity (SAV, and Hip Angular Velocity (HAV were all significantly correlated with DAC SBV. S-HSA was the most significant predictor of DAC SBV as determined by regression analysis.  Conclusions: This study provides support for a relationship between a greater S-HSA and SBV. Future research should continue to 1 examine the influence of core training exercise and rotational skill drills on SBV and 2 examine trunk angular velocities during various types of spikes during play.

Spike voltage topography in temporal lobe epilepsy.

Science.gov (United States)

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.

Science.gov (United States)

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.

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

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

Science.gov (United States)

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.

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.

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

Directory of Open Access Journals (Sweden)

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

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.

Science.gov (United States)

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.

Science.gov (United States)

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

Monte Carlo point process estimation of electromyographic envelopes from motor cortical spikes for brain-machine interfaces

Science.gov (United States)

Liao, Yuxi; She, Xiwei; Wang, Yiwen; Zhang, Shaomin; Zhang, Qiaosheng; Zheng, Xiaoxiang; Principe, Jose C.

2015-12-01

Objective. Representation of movement in the motor cortex (M1) has been widely studied in brain-machine interfaces (BMIs). The electromyogram (EMG) has greater bandwidth than the conventional kinematic variables (such as position, velocity), and is functionally related to the discharge of cortical neurons. As the stochastic information of EMG is derived from the explicit spike time structure, point process (PP) methods will be a good solution for decoding EMG directly from neural spike trains. Previous studies usually assume linear or exponential tuning curves between neural firing and EMG, which may not be true. Approach. In our analysis, we estimate the tuning curves in a data-driven way and find both the traditional functional-excitatory and functional-inhibitory neurons, which are widely found across a rat’s motor cortex. To accurately decode EMG envelopes from M1 neural spike trains, the Monte Carlo point process (MCPP) method is implemented based on such nonlinear tuning properties. Main results. Better reconstruction of EMG signals is shown on baseline and extreme high peaks, as our method can better preserve the nonlinearity of the neural tuning during decoding. The MCPP improves the prediction accuracy (the normalized mean squared error) 57% and 66% on average compared with the adaptive point process filter using linear and exponential tuning curves respectively, for all 112 data segments across six rats. Compared to a Wiener filter using spike rates with an optimal window size of 50 ms, MCPP decoding EMG from a point process improves the normalized mean square error (NMSE) by 59% on average. Significance. These results suggest that neural tuning is constantly changing during task execution and therefore, the use of spike timing methodologies and estimation of appropriate tuning curves needs to be undertaken for better EMG decoding in motor BMIs.

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

Science.gov (United States)

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.

Science.gov (United States)

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

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.

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...

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...

Spike Train Auto-Structure Impacts Post-Synaptic Firing and Timing-Based Plasticity

Science.gov (United States)

Scheller, Bertram; Castellano, Marta; Vicente, Raul; Pipa, Gordon

2011-01-01

Cortical neurons are typically driven by several thousand synapses. The precise spatiotemporal pattern formed by these inputs can modulate the response of a post-synaptic cell. In this work, we explore how the temporal structure of pre-synaptic inhibitory and excitatory inputs impact the post-synaptic firing of a conductance-based integrate and fire neuron. Both the excitatory and inhibitory input was modeled by renewal gamma processes with varying shape factors for modeling regular and temporally random Poisson activity. We demonstrate that the temporal structure of mutually independent inputs affects the post-synaptic firing, while the strength of the effect depends on the firing rates of both the excitatory and inhibitory inputs. In a second step, we explore the effect of temporal structure of mutually independent inputs on a simple version of Hebbian learning, i.e., hard bound spike-timing-dependent plasticity. We explore both the equilibrium weight distribution and the speed of the transient weight dynamics for different mutually independent gamma processes. We find that both the equilibrium distribution of the synaptic weights and the speed of synaptic changes are modulated by the temporal structure of the input. Finally, we highlight that the sensitivity of both the post-synaptic firing as well as the spike-timing-dependent plasticity on the auto-structure of the input of a neuron could be used to modulate the learning rate of synaptic modification. PMID:22203800

Different propagation speeds of recalled sequences in plastic spiking neural networks

Science.gov (United States)

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

2015-03-01

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

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...

SPICODYN: A Toolbox for the Analysis of Neuronal Network Dynamics and Connectivity from Multi-Site Spike Signal Recordings.

Science.gov (United States)

Pastore, Vito Paolo; Godjoski, Aleksandar; Martinoia, Sergio; Massobrio, Paolo

2018-01-01

We implemented an automated and efficient open-source software for the analysis of multi-site neuronal spike signals. The software package, named SPICODYN, has been developed as a standalone windows GUI application, using C# programming language with Microsoft Visual Studio based on .NET framework 4.5 development environment. Accepted input data formats are HDF5, level 5 MAT and text files, containing recorded or generated time series spike signals data. SPICODYN processes such electrophysiological signals focusing on: spiking and bursting dynamics and functional-effective connectivity analysis. In particular, for inferring network connectivity, a new implementation of the transfer entropy method is presented dealing with multiple time delays (temporal extension) and with multiple binary patterns (high order extension). SPICODYN is specifically tailored to process data coming from different Multi-Electrode Arrays setups, guarantying, in those specific cases, automated processing. The optimized implementation of the Delayed Transfer Entropy and the High-Order Transfer Entropy algorithms, allows performing accurate and rapid analysis on multiple spike trains from thousands of electrodes.

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.

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

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.

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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

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

Science.gov (United States)

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.

Directory of Open Access Journals (Sweden)

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

Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone.

Directory of Open Access Journals (Sweden)

Felipe Gerhard

Full Text Available Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology. More recently, causal network inference algorithms have been proposed to deduce connectivity directly from electrophysiological signals, such as extracellularly recorded spiking activity. Usually, these algorithms have not been validated on a neurophysiological data set for which the actual circuitry is known. Recent work has shown that traditional network inference algorithms based on linear models typically fail to identify the correct coupling of a small central pattern generating circuit in the stomatogastric ganglion of the crab Cancer borealis. In this work, we show that point process models of observed spike trains can guide inference of relative connectivity estimates that match the known physiological connectivity of the central pattern generator up to a choice of threshold. We elucidate the necessary steps to derive faithful connectivity estimates from a model that incorporates the spike train nature of the data. We then apply the model to measure changes in the effective connectivity pattern in response to two pharmacological interventions, which affect both intrinsic neural dynamics and synaptic transmission. Our results provide the first successful application of a network inference algorithm to a circuit for which the actual physiological synapses between neurons are known. The point process methodology presented here generalizes well to larger networks and can describe the statistics of neural populations. In general we show that advanced statistical models allow for the characterization of effective network structure, deciphering underlying network dynamics and estimating information-processing capabilities.

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

Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition.

Science.gov (United States)

Kasabov, Nikola; Dhoble, Kshitij; Nuntalid, Nuttapod; Indiveri, Giacomo

2013-05-01

On-line learning and recognition of spatio- and spectro-temporal data (SSTD) is a very challenging task and an important one for the future development of autonomous machine learning systems with broad applications. Models based on spiking neural networks (SNN) have already proved their potential in capturing spatial and temporal data. One class of them, the evolving SNN (eSNN), uses a one-pass rank-order learning mechanism and a strategy to evolve a new spiking neuron and new connections to learn new patterns from incoming data. So far these networks have been mainly used for fast image and speech frame-based recognition. Alternative spike-time learning methods, such as Spike-Timing Dependent Plasticity (STDP) and its variant Spike Driven Synaptic Plasticity (SDSP), can also be used to learn spatio-temporal representations, but they usually require many iterations in an unsupervised or semi-supervised mode of learning. This paper introduces a new class of eSNN, dynamic eSNN, that utilise both rank-order learning and dynamic synapses to learn SSTD in a fast, on-line mode. The paper also introduces a new model called deSNN, that utilises rank-order learning and SDSP spike-time learning in unsupervised, supervised, or semi-supervised modes. The SDSP learning is used to evolve dynamically the network changing connection weights that capture spatio-temporal spike data clusters both during training and during recall. The new deSNN model is first illustrated on simple examples and then applied on two case study applications: (1) moving object recognition using address-event representation (AER) with data collected using a silicon retina device; (2) EEG SSTD recognition for brain-computer interfaces. The deSNN models resulted in a superior performance in terms of accuracy and speed when compared with other SNN models that use either rank-order or STDP learning. The reason is that the deSNN makes use of both the information contained in the order of the first input spikes

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

Science.gov (United States)

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.

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.

Evolving Spiking Neural Networks for Recognition of Aged Voices.

Science.gov (United States)

Silva, Marco; Vellasco, Marley M B R; Cataldo, Edson

2017-01-01

The aging of the voice, known as presbyphonia, is a natural process that can cause great change in vocal quality of the individual. This is a relevant problem to those people who use their voices professionally, and its early identification can help determine a suitable treatment to avoid its progress or even to eliminate the problem. This work focuses on the development of a new model for the identification of aging voices (independently of their chronological age), using as input attributes parameters extracted from the voice and glottal signals. The proposed model, named Quantum binary-real evolving Spiking Neural Network (QbrSNN), is based on spiking neural networks (SNNs), with an unsupervised training algorithm, and a Quantum-Inspired Evolutionary Algorithm that automatically determines the most relevant attributes and the optimal parameters that configure the SNN. The QbrSNN model was evaluated in a database composed of 120 records, containing samples from three groups of speakers. The results obtained indicate that the proposed model provides better accuracy than other approaches, with fewer input attributes. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

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

Supervised spike-timing-dependent plasticity: a spatiotemporal neuronal learning rule for function approximation and decisions.

Science.gov (United States)

Franosch, Jan-Moritz P; Urban, Sebastian; van Hemmen, J Leo

2013-12-01

How can an animal learn from experience? How can it train sensors, such as the auditory or tactile system, based on other sensory input such as the visual system? Supervised spike-timing-dependent plasticity (supervised STDP) is a possible answer. Supervised STDP trains one modality using input from another one as "supervisor." Quite complex time-dependent relationships between the senses can be learned. Here we prove that under very general conditions, supervised STDP converges to a stable configuration of synaptic weights leading to a reconstruction of primary sensory input.

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

Directory of Open Access Journals (Sweden)

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.

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...

Adaptive training of neural networks for control of autonomous mobile robots

NARCIS (Netherlands)

Steur, E.; Vromen, T.; Nijmeijer, H.; Fossen, T.I.; Nijmeijer, H.; Pettersen, K.Y.

2017-01-01

We present an adaptive training procedure for a spiking neural network, which is used for control of a mobile robot. Because of manufacturing tolerances, any hardware implementation of a spiking neural network has non-identical nodes, which limit the performance of the controller. The adaptive

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

Science.gov (United States)

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

Directory of Open Access Journals (Sweden)

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

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

Directory of Open Access Journals (Sweden)

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.

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

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

Reinforcement learning of targeted movement in a spiking neuronal model of motor cortex.

Directory of Open Access Journals (Sweden)

George L Chadderdon

Full Text Available Sensorimotor control has traditionally been considered from a control theory perspective, without relation to neurobiology. In contrast, here we utilized a spiking-neuron model of motor cortex and trained it to perform a simple movement task, which consisted of rotating a single-joint "forearm" to a target. Learning was based on a reinforcement mechanism analogous to that of the dopamine system. This provided a global reward or punishment signal in response to decreasing or increasing distance from hand to target, respectively. Output was partially driven by Poisson motor babbling, creating stochastic movements that could then be shaped by learning. The virtual forearm consisted of a single segment rotated around an elbow joint, controlled by flexor and extensor muscles. The model consisted of 144 excitatory and 64 inhibitory event-based neurons, each with AMPA, NMDA, and GABA synapses. Proprioceptive cell input to this model encoded the 2 muscle lengths. Plasticity was only enabled in feedforward connections between input and output excitatory units, using spike-timing-dependent eligibility traces for synaptic credit or blame assignment. Learning resulted from a global 3-valued signal: reward (+1, no learning (0, or punishment (-1, corresponding to phasic increases, lack of change, or phasic decreases of dopaminergic cell firing, respectively. Successful learning only occurred when both reward and punishment were enabled. In this case, 5 target angles were learned successfully within 180 s of simulation time, with a median error of 8 degrees. Motor babbling allowed exploratory learning, but decreased the stability of the learned behavior, since the hand continued moving after reaching the target. Our model demonstrated that a global reinforcement signal, coupled with eligibility traces for synaptic plasticity, can train a spiking sensorimotor network to perform goal-directed motor behavior.

Reinforcement learning of targeted movement in a spiking neuronal model of motor cortex.

Science.gov (United States)

Chadderdon, George L; Neymotin, Samuel A; Kerr, Cliff C; Lytton, William W

2012-01-01

Sensorimotor control has traditionally been considered from a control theory perspective, without relation to neurobiology. In contrast, here we utilized a spiking-neuron model of motor cortex and trained it to perform a simple movement task, which consisted of rotating a single-joint "forearm" to a target. Learning was based on a reinforcement mechanism analogous to that of the dopamine system. This provided a global reward or punishment signal in response to decreasing or increasing distance from hand to target, respectively. Output was partially driven by Poisson motor babbling, creating stochastic movements that could then be shaped by learning. The virtual forearm consisted of a single segment rotated around an elbow joint, controlled by flexor and extensor muscles. The model consisted of 144 excitatory and 64 inhibitory event-based neurons, each with AMPA, NMDA, and GABA synapses. Proprioceptive cell input to this model encoded the 2 muscle lengths. Plasticity was only enabled in feedforward connections between input and output excitatory units, using spike-timing-dependent eligibility traces for synaptic credit or blame assignment. Learning resulted from a global 3-valued signal: reward (+1), no learning (0), or punishment (-1), corresponding to phasic increases, lack of change, or phasic decreases of dopaminergic cell firing, respectively. Successful learning only occurred when both reward and punishment were enabled. In this case, 5 target angles were learned successfully within 180 s of simulation time, with a median error of 8 degrees. Motor babbling allowed exploratory learning, but decreased the stability of the learned behavior, since the hand continued moving after reaching the target. Our model demonstrated that a global reinforcement signal, coupled with eligibility traces for synaptic plasticity, can train a spiking sensorimotor network to perform goal-directed motor behavior.

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.

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.

Music training for the development of reading skills.

Science.gov (United States)

Tierney, Adam; Kraus, Nina

2013-01-01

The beneficial effects of musical training are not limited to enhancement of musical skills, but extend to language skills. Here, we review evidence that musical training can enhance reading ability. First, we discuss five subskills underlying reading acquisition-phonological awareness, speech-in-noise perception, rhythm perception, auditory working memory, and the ability to learn sound patterns-and show that each is linked to music experience. We link these five subskills through a unifying biological framework, positing that they share a reliance on auditory neural synchrony. After laying this theoretical groundwork for why musical training might be expected to enhance reading skills, we review the results of longitudinal studies providing evidence for a role for musical training in enhancing language abilities. Taken as a whole, these findings suggest that musical training can provide an effective developmental educational strategy for all children, including those with language learning impairments. © 2013 Elsevier B.V. All rights reserved.

Breaking HIV News to Clients: SPIKES Strategy in Post-Test Counseling Session

Directory of Open Access Journals (Sweden)

Hamid Emadi-Koochak

2016-05-01

Full Text Available Breaking bad news is one of the most burdensome tasks physicians face in their everyday practice. It becomes even more challenging in the context of HIV+ patients because of stigma and discrimination. The aim of the current study is to evaluate the quality of giving HIV seroconversion news according to SPIKES protocol. Numbers of 154 consecutive HIV+ patients from Imam Khomeini Hospital testing and counseling center were enrolled in this study. Patients were inquired about how they were given the HIV news and whether or not they received pre- and post-test counseling sessions. Around 51% of them were men, 80% had high school education, and 56% were employed. Regarding marital status, 32% were single, and 52% were married at the time of the interview. Among them, 31% had received the HIV news in a counseling center, and only 29% had pre-test counseling. SPIKES criteria were significantly met when the HIV news was given in an HIV counseling and testing center (P.value<0.05. Low coverage of HIV counseling services was observed in the study. SPIKES criteria were significantly met when the HIV seroconversion news was given in a counseling center. The need to further train staff to deliver HIV news seems a priority in the field of HIV care and treatment.

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/.

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

Science.gov (United States)

Panda, Priyadarshini; Roy, Kaushik

2017-01-01

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

α-Oscillations in the monkey sensorimotor network influence discrimination performance by rhythmical inhibition of neuronal spiking.

Science.gov (United States)

Haegens, Saskia; Nácher, Verónica; Luna, Rogelio; Romo, Ranulfo; Jensen, Ole

2011-11-29

Extensive work in humans using magneto- and electroencephalography strongly suggests that decreased oscillatory α-activity (8-14 Hz) facilitates processing in a given region, whereas increased α-activity serves to actively suppress irrelevant or interfering processing. However, little work has been done to understand how α-activity is linked to neuronal firing. Here, we simultaneously recorded local field potentials and spikes from somatosensory, premotor, and motor regions while a trained monkey performed a vibrotactile discrimination task. In the local field potentials we observed strong activity in the α-band, which decreased in the sensorimotor regions during the discrimination task. This α-power decrease predicted better discrimination performance. Furthermore, the α-oscillations demonstrated a rhythmic relation with the spiking, such that firing was highest at the trough of the α-cycle. Firing rates increased with a decrease in α-power. These findings suggest that α-oscillations exercise a strong inhibitory influence on both spike timing and firing rate. Thus, the pulsed inhibition by α-oscillations plays an important functional role in the extended sensorimotor system.

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.

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...

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.

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...

Spike sorting for polytrodes: a divide and conquer approach

Directory of Open Access Journals (Sweden)

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.

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,

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.

Science.gov (United States)

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.

Neuronal spike-train responses in the presence of threshold noise.

Science.gov (United States)

Coombes, S; Thul, R; Laudanski, J; Palmer, A R; Sumner, C J

2011-03-01

The variability of neuronal firing has been an intense topic of study for many years. From a modelling perspective it has often been studied in conductance based spiking models with the use of additive or multiplicative noise terms to represent channel fluctuations or the stochastic nature of neurotransmitter release. Here we propose an alternative approach using a simple leaky integrate-and-fire model with a noisy threshold. Initially, we develop a mathematical treatment of the neuronal response to periodic forcing using tools from linear response theory and use this to highlight how a noisy threshold can enhance downstream signal reconstruction. We further develop a more general framework for understanding the responses to large amplitude forcing based on a calculation of first passage times. This is ideally suited to understanding stochastic mode-locking, for which we numerically determine the Arnol'd tongue structure. An examination of data from regularly firing stellate neurons within the ventral cochlear nucleus, responding to sinusoidally amplitude modulated pure tones, shows tongue structures consistent with these predictions and highlights that stochastic, as opposed to deterministic, mode-locking is utilised at the level of the single stellate cell to faithfully encode periodic stimuli.

Multiplexed Spike Coding and Adaptation in the Thalamus

Directory of Open Access Journals (Sweden)

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.

Implementing Signature Neural Networks with Spiking Neurons.

Science.gov (United States)

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

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

Science.gov (United States)

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...

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

Directory of Open Access Journals (Sweden)

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.

Science.gov (United States)

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.

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.

Spike Bursts from an Excitable Optical System