Maximum Likelihood Decoding of Neuronal Inputs from an Interspike Interval Distribution-Reference-Cited by-同舟云学术

Maximum Likelihood Decoding of Neuronal Inputs from an Interspike Interval Distribution

Published:2009-11 Issue:11 Volume:21 Page:3079-3105
ISSN:0899-7667
Container-title:Neural Computation
language:en
Short-container-title:Neural Computation

Author:

Zhang Xuejuan¹,You Gongqiang²,Chen Tianping³,Feng Jianfeng⁴

Affiliation:

1. Mathematical Department, Zhejiang Normal University, Jinhua, 321004, and Mathematical Department, Shaoxing University, Shaoxing, 312000, P.R.C.

2. Mathematical Department, Shaoxing University, Shaoxing, 312004, P.R.C.

3. Centre for Computational Systems Biology, Fudan University, Shanghai, 312000, P.R.C.

4. Centre for Computational Systems Biology, Fudan University, Shanghai, 312000, P.R.C., and Department of Computer Science, Warwick University, Coventry CV4 7AL, U.K.

Abstract

An expression for the probability distribution of the interspike interval of a leaky integrate-and-fire (LIF) model neuron is rigorously derived, based on recent theoretical developments in the theory of stochastic processes. This enables us to find for the first time a way of developing maximum likelihood estimates (MLE) of the input information (e.g., afferent rate and variance) for an LIF neuron from a set of recorded spike trains. Dynamic inputs to pools of LIF neurons both with and without interactions are efficiently and reliably decoded by applying the MLE, even within time windows as short as 25 msec.

Publisher

MIT Press - Journals

Subject

Cognitive Neuroscience,Arts and Humanities (miscellaneous)

Link

https://www.mitpressjournals.org/doi/pdf/10.1162/neco.2009.06-08-807

Reference32 articles.

1. Representations of the First Hitting Time Density of an Ornstein-Uhlenbeck Process1

2. DynamicI-VCurves Are Reliable Predictors of Naturalistic Pyramidal-Neuron Voltage Traces

3. Sparsely synchronized neuronal oscillations

4. Firing Rate of the Noisy Quadratic Integrate-and-Fire Neuron

5. Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity

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