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Redundancy and synergy arising from pairwise correlations in neuronal ensembles

JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2002年第3期12卷 165-174页

作者： Bezzi, M Diamond, ME Treves, A SISSA Program Neurosci I-34014 Trieste Italy

Multielectrode arrays allow recording of the activity of many single neurons, from which correlations can be calculated. The functional roles of correlations can be revealed by measures of the information conveyed by neuronal activity;a simple formula has been shown to discriminate the information transmitted by individual spikes from the positive or negative contributions due to correlations (Panzeri et al., 1999). Here, this analysis, previously applied to recordings from small ensembles, is developed further by considering a model of a large ensemble, in which correlations among the signal and noise components of neuronal firing are small in absolute value and entirely random in origin. Even such small random correlations are shown to lead to large possible synergy or redundancy, whenever the time window for extracting information from neuronal firing extends to the order of the mean interspike interval. In addition, a sample of recordings from rat barrel cortex illustrates the mean time window at which such "corrections" dominate when correlations are, as often in the real brain, neither random nor small. The presence of this kind of correlations for a large ensemble of cells restricts further the time of validity of the expansion.

关键词： mutual information population coding short-time expansion temporal coding somatosensory system barrel cortex

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A Multi-Spiking Neural Network Learning Model for Data Classification

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IEEE ACCESS 2020年 8卷 72360-72371页

作者： Yellakuor, Baagyere Edward Moses, Agebure Apambila Zhen, Qin Olaosebikan, Oyetunji Elkanah Qin, Zhiguang Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 610054 Peoples R China Univ Dev Studies Dept Comp Sci Tamale Tl 1350 Ghana Lagos State Univ Dept Mech Engn Lagos 102101 Nigeria

Classical Artificial Neural Networks (ANNs) though well exploited in solving classification problems, do not model perfectly the information encoding process in the human brain because ANNs encode information using rate-based coding. However, biological neurons in the brain are known to encode information using temporal coding. In order to mimic the biological method of encoding information, various Spiking Neural Network (SNN) models have been developed. However, some of these models are limited in the number of spikes and do not leverage well on some classification problems. In order to address some of the inherent challenges associated with SNN, a multi-layer learning model for a multi-spiking network is proposed in this paper. The model exploits the temporal coding of spikes and the least-squares method to derive a weight update scheme. It also employs a spike locality concept in order to determine how the synaptic weights are to be adjusted at a particular spike time so as to minimize the learning interference, and thereby, increasing the number of spikes for learning. The performance of the model is evaluated on benchmarked classification datasets. A correlation-based metric is combined with a threshold concept to measure the classification accuracy of the model. The experimental results showed that the proposed model achieved better classification accuracy than some state-of-the-art multi-layer SNN learning models.

关键词： Neurons Biological neural networks Biological system modeling Data models Encoding Supervised learning Task analysis Multi-spiking neural network supervised learning temporal coding

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Spike-based encoding and learning of spectrum features for robust sound recognition

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NEUROCOMPUTING 2018年 313卷 65-73页

作者： Xiao, Rong Tang, Huajin Gu, Pengjie Xu, Xiaoliang Sichuan Univ Coll Comp Sci Neuromorph Comp Res Ctr Chengdu Sichuan Peoples R China Hangzhou Dianzi Univ Coll Comp Sci Hangzhou Zhejiang Peoples R China

Biological evidence suggests that local time-frequency (LTF) information can be utilized to improve the recognition rate of sounds in the presence of noise. However, most of conventional methods use stationary (frequency-based) features which are not robust to noise, as each stationary feature contains a mixture of spectral information from both noise and signal. This paper proposes a spike-timing based model to encode and learn the LTF features extracted from sound spectrogram using spiking neural networks (SNNs), named LTF-SNN. In this model, we encode the reliable LTF features into spike train patterns and train with different spike-based learning rules. We analyze the efficacy of the spike-based feature encoding method and the recognition performance of the model by using two classes of SNN learning algorithms: ReSuMe and Tempotron. Utilizing the temporal coding and learning, networks of spiking neurons can effectively perform robust sound recognition tasks. Experimental results demonstrate that the model achieves superior performance in mismatched conditions compared with benchmark approaches. (C) 2018 Elsevier B.V. All rights reserved.

关键词： temporal coding temporal learning Time-frequency information Spiking neural network Sound recognition

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Possible role of synchronous input spike trains in controlling the function of neural networks

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NEUROCOMPUTING 2004年 58卷 259-264页

作者： Aoyagi, T Aoki, T Kyoto Univ Grad Sch Informat Dept Appl Anal & Complex Dynam Syst Kyoto 6068501 Japan Kyoto Univ Grad Sch Sci Dept Phys Kyoto 6068501 Japan

To investigate how the temporal structure of neuronal activity affects the function of neural networks, we consider the effects of synchronous firing inputs on two typical functions of neural networks: winner-take-all competition and associative memory. Using a network of integrate-and-fire neurons, we study their effect. The results show that uncorrelated discharge among neuronal inputs allows the standard winner-take-all competition, whereas synchronization of the neuronal inputs prevents it. In the case of the associative memory model, we find that the timing of the next retrieval can be controlled by such a synchrony-asynchrony transition of external inputs. (C) 2004 Elsevier B.V. All rights reserved.

关键词： temporal coding winner-take-all associative memory synchronization

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Neural timing nets

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NEURAL NETWORKS 2001年第6-7期14卷 737-753页

作者： Cariani, PA Massachusetts Eye & Ear Infirm Eaton Peabody Lab Auditory Physiol Boston MA 02114 USA

Formulations of artificial neural networks are directly related to assumptions about neural coding in the brain. Traditional connectionist networks assume channel-based rate coding, while time-delay networks convert temporally-coded inputs into rate-coded outputs. Neural timing nets that operate on time structured input spike trains to produce meaningful time-structured outputs are proposed. Basic computational properties of simple feedforward and recurrent timing nets are outlined and applied to auditory computations. Feed-forward timing nets consist of arrays of coincidence detectors connected via tapped delay lines. These temporal sieves extract common spike patterns in their inputs that can subserve extraction of common fundamental frequencies (periodicity pitch) and common spectrum (timbre). Feedforward timing nets can also be used to separate time-shifted patterns, fusing patterns with similar internal temporal structure and spatially segregating different ones. Simple recurrent timing nets consisting of arrays of delay loops amplify and separate recurring time patterns. Single- and multichannel recurrent timing nets are presented that demonstrate the separation of concurrent, double vowels. Timing nets constitute a new and general neural network strategy for performing temporal computations on neural spike trains: extraction of common periodicities, detection of recurring temporal patterns, and formation and separation of invariant spike patterns that subserve auditory objects. (C) 2001 Elsevier Science Ltd. All rights reserved.

关键词： neural timing networks time-delay neural networks temporal coding spiking neurons scene analysis temporal correlation auditory neurocomputation

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A biophysical model of synaptic delay learning and temporal pattern recognition in a cerebellar Purkinje cell

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2004年第2期17卷 149-164页

作者： Steuber, V Willshaw, D Univ Edinburgh Inst Adapt & Neural Computat Edinburgh EH1 2QL Midlothian Scotland

It has been suggested that information in the brain is encoded in temporal spike patterns which are decoded by a combination of time delays and coincidence detection. Here, we show how a multi-compartmental model of a cerebellar Purkinje cell can learn to recognise temporal parallel fibre activity patterns by adapting latencies of calcium responses after activation of metabotropic glutamate receptors (mGluRs). In each compartment of our model, the mGluR signalling cascade is represented by a set of differential equations that reflect the underlying biochemistry. Phosphorylation of the mGluRs changes the concentration of receptors which are available for activation by glutamate and thereby adjusts the time delay between mGluR stimulation and voltage response. The adaptation of a synaptic delay as opposed to a weight represents a novel non-Hebbian learning mechanism that can also implement the adaptive timing of the classically conditioned eye-blink response.

关键词： cerebellum calcium mGluR temporal coding

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A network of coincidence detector neurons with periodic and chaotic dynamics

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2004年第5期15卷 980-986页

作者： Watanabe, M Aihara, K Univ Tokyo Grad Sch Engn Dept Quantum Engn & Syst Sci Tokyo 113 Japan Univ Tokyo Dept Elect & Informat Inst Ind Sci Tokyo 113 Japan

We propose a simple neural network model to understand the dynamics of temporal pulse coding. The model is composed of coincidence detector neurons with uniform synaptic efficacies and random pulse propagation delays. We also assume a global negative feedback mechanism which controls the network activity, leading to a fixed number of neurons firing within a certain time window. Due to this constraint, the network state becomes well defined and the dynamics equivalent to a piecewise nonlinear map. Numerical simulations of the model indicate that the latency of neuronal firing is crucial to the global network dynamics;when the timing of postsynaptic firing is less sensitive to perturbations in timing of presynaptic spikes, the network dynamics become stable and periodic, whereas increased sensitivity leads to instability and chaotic dynamics. Furthermore, we introduce a learning rule which decreases the Lyapunov exponent of an attractor and enlarges the basin of attraction.

关键词： chaos coincidence detector firing latency learning rule lyapunov exponent temporal coding

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STDP-based spiking deep convolutional neural networks for object recognition

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NEURAL NETWORKS 2018年 99卷 56-67页

作者： Kheradpisheh, Saeed Reza Ganjtabesh, Mohammad Thorpe, Simon J. Masquelier, Timothee Univ Tehran Sch Math Stat & Comp Sci Dept Comp Sci Tehran Iran Univ Toulouse 3 CNRS CERCO UMR 5549 Toulouse France

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

关键词： Spiking neural network STDP Deep learning Object recognition temporal coding

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Computer assisted neurophysiological analysis of cell assemblies activity

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NEUROCOMPUTING 2001年 38卷 1025-1030页

作者： Villa, AEP Tetko, I Iglesias, J Univ Lausanne Inst Physiol Lab Nueroheurist CH-1005 Lausanne Switzerland Ukrainian Acad Sci IBPC UA-253660 Kyiv Ukraine

A critical feature of brain theories is whether neurons convey a noisy rate code or a precise temporal code. One of most valuable ways to test these theories consists in collecting the electrophysiological activity of cell assembles under several experimental conditions. The sequences of cell discharges-the spike trains-form time series whose dynamics is strongly related to the information processing carried out in the brain areas under study. Our purpose is to provide a user-friendly framework of a 'Virtual Laboratory' where computational neuroscience analyses and display of results can be distributed over a computer network, like Internet. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： multivariate time series multiple spike trains temporal coding virtual laboratory Internet distributed software

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The transient precision of integrate and fire neurons: Effect of background activity and noise

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2001年第3期10卷 303-311页

作者： Van Rossum, MCW Brandeis Univ Dept Biol Waltham MA 02454 USA

We study the response of an integrate and fire neuron to a randomly timed step stimulus. We calculate the latency to the first spike after stimulus onset and its jitter. Background activity, seen in most neurons, reduces latency but causes substantial jitter in the response, indicating a tradeoff between timing precision and latency. The effect of intrinsic noise and synaptic noise on this tradeoff is studied. For synaptic noise we find that, unexpectedly, jitter does not increase for larger synaptic amplitudes, instead, jitter is practically independent of synaptic amplitude. Constant intrinsic noise interacts counterintuitively with latency and jitter, and depending on the stimulus strength, noise shifts the tradeoff in either direction.

关键词： integrate and fire neurons temporal coding noise precision

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