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White noise analysis for the correlation-type elementary motion detectors with half-wave rectifiers

neural NETWORKS 2018年 102卷 96-106页

作者： Ikeda, Hideaki Aonishi, Toru Tokyo Inst Technol Grad Sch Interdisciplinary Sci & Engn Midori Ku Nagatsuda Cho 4259-G5-17 Yokohama Kanagawa 2268502 Japan Tokyo Inst Technol Sch Comp Midori Ku Nagatsuda Cho 4259-G5-17 Yokohama Kanagawa 2268502 Japan

The motion detection mechanism of insects has been attracted attention of many researchers. Several motion-detection models have been proposed on the basis of insect visual system studies. Here, we examine two models, the Hassenstein-Reichardt (HR) model and the two-detector (2D) model. We analytically obtain the mean and variance of the stationary responses of the HR and the 2D models to white noise, and we derive the signal-to-fluctuation-noise ratio (SFNR) to evaluate encoding abilities of the two models. Especially when analyzing the 2D model, we calculate higher-order cumulants of a rectified Gaussian. The results show that the 2D model robustly works almost as well as the HR model in several sets of parameters estimated on the basis of experimental data. (c) 2018 Elsevier Ltd. All rights reserved.

关键词： Motion detection neural coding White-noise analysis Hassenstein-Reichardt model Two-detector model

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Stimulation location encoding on the spike train of neuron models with passive dendrite

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APPLIED MATHEMATICAL MODELLING 2023年 115卷 414-430页

作者： Wang, Ruyue Liang, Jinling Southeast Univ Sch Math Nanjing 210096 Peoples R China

Spike is the basic unit in the neuron communication, and different selections of the stim-ulation locations on the neuron might cause different spike trains, which infers that the spike trains may determine the information of the stimulation locations. The research on this subject deserves intensive attention, whether by numerical experiments or by elec-trophysiological ones. In this article, to answer the question of how does the spike train encode the stimulus location, by combining the cable model with the leaky integral fir-ing model, a new neuron model called leaky integral firing model with passive dendrite is reconstructed from two levels (i.e., space and time) and in three forms (i.e., the con-ceptual model, the circuit model, and the mathematical model). Two types of stimulation are performed on this new model, which contain the constant electrode current and the synaptic one, where the latter is also divided into the excitatory current and the inhibitory one. Four coding ways are employed to encode the spike train, among them, by numeri-cal experiments and some theoretical verification, it is shown that the first-to-spike-time coding method is the best one, which could clearly reflect the information of the stimu-lus position. To be more specific, the closer the stimulation location is to the axon hillock, the shorter the first-to-spike-time is. The neuron model proposed in this paper and the relating encoding methods for the stimulus location could also be applied to the brain -computer interface or constructing new types of neural networks.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Cable equation Leaky integral firing model Axon hillock Stimulation location neural coding Hodgkin-Huxley model

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Model architecture for associative memory in a neural network of spiking neurons

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2012年第3期391卷 843-848页

作者： Agnes, Everton J. Erichsen, Rubem, Jr. Brunnet, Leonardo G. Univ Fed Rio Grande do Sul Inst Fis BR-91501970 Porto Alegre RS Brazil

A synaptic connectivity model is assembled on a spiking neuron network aiming to build up a dynamic pattern recognition system. The connection architecture includes gap junctions and both inhibitory and excitatory chemical synapses based on Hebb's hypothesis. The network evolution resulting from external stimulus is sampled in a properly defined frequency space. Neurons' responses to different current injections are mapped onto a subspace using Principal Component Analysis. Departing from the base attractor, related to a quiescent state, different external stimuli drive the network to different fixed points through specific trajectories in this subspace. (C) 2011 Elsevier B.V. All rights reserved.

关键词： neural networks Chemical synapses Gap junctions Map-based neuron neural coding Principal Component Analysis

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Design Space Exploration of Hardware Spiking Neurons for Embedded Artificial Intelligence

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neural NETWORKS 2020年 121卷 366-386页

作者： Abderrahmane, Nassim Lemaire, Edgar Miramond, Benoit Univ Cote Azur CNRS LEAT Nice France Thales Res Technol STI Grp LCHP Palaiseau France

Machine learning is yielding unprecedented interest in research and industry, due to recent success in many applied contexts such as image classification and object recognition. However, the deployment of these systems requires huge computing capabilities, thus making them unsuitable for embedded systems. To deal with this limitation, many researchers are investigating brain-inspired computing, which would be a perfect alternative to the conventional Von Neumann architecture based computers (CPU/GPU) that meet the requirements for computing performance, but not for energy-efficiency. Therefore, neuromorphic hardware circuits that are adaptable for both parallel and distributed computations need to be designed. In this paper, we focus on Spiking neural Networks (SNNs) with a comprehensive study of neural coding methods and hardware exploration. In this context, we propose a framework for neuromorphic hardware design space exploration, which allows to define a suitable architecture based on application-specific constraints and starting from a wide variety of possible architectural choices. For this framework, we have developed a behavioral level simulator for neuromorphic hardware architectural exploration named NAXT. Moreover, we propose modified versions of the standard Rate coding technique to make trade-offs with the Time coding paradigm, which is characterized by the low number of spikes propagating in the network. Thus, we are able to reduce the number of spikes while keeping the same neuron's model, which results in an SNN with fewer events to process. By doing so, we seek to reduce the amount of power consumed by the hardware. Furthermore, we present three neuromorphic hardware architectures in order to quantitatively study the implementation of SNNs. One of these architectures integrates a novel hybrid structure: a highly-parallel computation core for most solicited layers, and time-multiplexed computation units for deeper layers. These architectures are deri

关键词： Artificial intelligence Neuromorphic computing Spiking neural networks neural coding Embedded systems Power consumption

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Neurophysiological study on sensorimotor control mechanism in superior colliculus of echolocating bat

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ISA TRANSACTIONS 2007年第2期46卷 157-165页

作者： Li, Yao Song, Y. D. N Carolina Agr & Tech State Univ Dept Elect Engn Greensboro NC 27411 USA Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA

This paper investigates the neural processes associated with bat sonar vocal production and their relationship with spatial orientation. The bat's heavy reliance on sound processing is reflected in specializations of auditory and motor neural structures. These specializations were utilized by investigating the mammalian superior colliculus (SC);a midbrain sensory motor nucleus mediating orientating behaviours in mammals, including vocal motor orientating. Behavioural and neurophysiological experiments were conducted in the insectivorous echolocating bat, Eptesicus fuscus. Chronic neural recording techniques were specifically developed to study neuronal activity. Potential application of the results on control systems is also addressed. (c) 2007, ISA. Published by Elsevier Ltd. All rights reserved.

关键词： echolocating bat Eptesicus fuscus sensorimotor control superior colliculus neural coding neurophysiology

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Membrane potential fluctuations determine the precision of spike timing and synchronous activity: A model study

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2001年第1期10卷 79-97页

作者： Kretzberg, J Egelhaaf, M Warzecha, AK Univ Bielefeld Fak Biol Lehrstuhl Neurobiol D-33501 Bielefeld Germany

It is much debated on what time scale information is encoded by neuronal spike activity. With a phenomenological model that transforms time-dependent membrane potential fluctuations into spike trains, we investigate constraints for the timing of spikes and for synchronous activity of neurons with common input. The model of spike generation has a variable threshold that depends on the time elapsed since the previous action potential and on the preceding membrane potential changes. To ensure that the model operates in a biologically meaningful range, the model was adjusted to fit the responses of a fly visual interneuron to motion stimuli. The dependence of spike timing on the membrane potential dynamics was analyzed. Fast membrane potential fluctuations are needed to trigger spikes with a high temporal precision. Slow fluctuations lead to spike activity with a rate about proportional to the membrane potential. Thus, for a given level of stochastic input, the frequency range of membrane potential fluctuations induced by a stimulus determines whether a neuron can use a rate code or a temporal code. The relationship between the steepness of membrane potential fluctuations and the timing of spikes has also implications for synchronous activity in neurons with common input. Fast membrane potential changes must be shared by the neurons to produce synchronous activity.

关键词： spike mechanism model spike timing synchronization reliability neural coding

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Altered sensory filtering and coding properties by synaptic dynamics in the electric sense

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NEUROCOMPUTING 2006年第10-12期69卷 1070-1075页

作者： Szlisznyo, Krisztina Longtin, Andre Maler, Leonard Hungarian Acad Sci Dept Biophys KFKI Res Inst H-1525 Budapest Hungary Univ Ottawa Dept Phys Ottawa ON K1N 6N5 Canada Univ Ottawa Dept Cellular & Mol Med Ottawa ON K1H 8M5 Canada

This modeling study examines the short-term synaptic plasticity properties of the electrosensory lateral lobe (ELL) afferent pathway in the weakly electric fish, Apteronotus leptorhynchus. We studied the possible functional consequences of a simple phenomenological model of synaptic depression by taking into consideration the available in vivo and in vitro results [N. Berman, L. Maler, Inhibition evoked from primary afferents in the electrosensory lateral line lobe of the weakly electric fish (Apteronotus leptorhynchus), J. Neurophysiol. 80(6) (1998) 3173-3196;M.J. Chacron, B. Doiron, L. Maler, A. Longtin, J. Bastian, Non-classical receptive field mediates switch in a sensory neuron's frequency tuning, Nature 26(424) (2003) 1018-1022]. Filtering and coding properties were examined. We find that simple short-term phenomenological synaptic depression can change steady-state filtering properties and explain how the known physiological constraints influence the coding capabilities of the ELL pyramidal cells via dynamic synaptic transmission. (c) 2006 Elsevier B.V. All rights reserved.

关键词： short-term synaptic dynamics neural coding neural filtering synaptic depression electric fish electroreception ELL

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A normative model of attention: receptive field modulation

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NEUROCOMPUTING 2004年 58卷 613-618页

作者： Jaramillo, S Pearlmutter, BA Natl Univ Ireland Hamilton Inst Maynooth Kildare Ireland

When sensory stimuli are encoded in a lossy fashion for efficient transmission, there are necessarily tradeoffs between the represented fidelity of various aspects of the stimuli. In the model of attention presented here, a top-down signal informs the encoder of these tradeoffs. Given the stimulus ensemble and tradeoff requirements, our system learns an optimal encoder. This general model is instantiated in a simple network: an autoencoder with a bottleneck, innervated by a top-down attentional signal, and trained using backpropagation. The modulation of neural activity learned by this model qualitatively matches that measured in animals during visual attention tasks. (C) 2003 Published by Elsevier B.V.

关键词： attention neural coding learning receptive field

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Processing and Transmission of Confidence in Recurrent neural Hierarchies

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neural PROCESSING LETTERS 2014年第1期40卷 75-91页

作者： Gepperth, Alexander ENSTA ParisTech F-91762 Palaiseau France INRIA FLOWERS INRIA Bordeaux Sud Ouest FLOWERS Res Team F-33405 Talence France

This article addresses the construction of hierarchies from dynamic attractor networks. We claim that such networks, e.g., dynamic neural fields (DNFs), contain a data model which is encoded in their lateral connections, and which describes typical properties of afferent inputs. This allows to infer the most likely interpretation of inputs, robustly expressed through the position of the attractor state. The principal problem resides in the fact that positions of attractor states alone do not reflect the quality of match between input and data model, termed decision confidence. In hierarchies, this inevitably leads to final decisions which are not Bayes-optimal when inputs exhibit different degrees of ambiguity or conflict, since the resulting differences in confidence will be ignored by downstream layers. We demonstrate a solution to this problem by showing that a correctly parametrized DNF layer can encode decision confidence into the latency of the attractor state in a well-defined way. Conversely, we show that input stimuli gain competitive advantages w.r.t. each other as a function of their relative latency, thus allowing downstream layers to decode attractor latency in an equally well-defined way. Putting these encoding and decoding mechanisms together, we construct a three-stage hierarchy of DNF layers and show that the top-level layer can take Bayes-optimal decisions when the decisions in the lowest hierarchy levels have variable degrees of confidence. In the discussion, we generalize these findings, suggesting a novel possibility to represent and manipulate probabilistic information in recurrent networks without any need for log-encoding, just using the biologically well-founded effect of response latency as an additional coding dimension.

关键词： Recurrent neural networks neural coding Bayesian inference Response latency

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Mutual information calculation using empirical classification

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NEUROCOMPUTING 2002年 44卷 1083-1088页

作者： Gruner, CM Pronghorn Engn Casper WY 82604 USA

In the past decade. researchers working on understanding the neural code have turned to mutual information as a measure of how well a given stimulus, response set codes information despite numerous difficulties, including convoluted calculation methods and difficulty interpreting the results. In this work, we use a new method for calculating mutual information based on empirical classification to show how mutual information and the Kullback-Leibler distance summarize coding efficacy. Our results suggest that knowledge gained through mutual information methods could be more easily obtained and interpreted using the Kullback-Leibler distance. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： neural coding mutual information Kullback-Leibler distance

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