Literature DB >> 17358202

State space method for predicting the spike times of a neuron.

Ryota Kobayashi1, Shigeru Shinomoto.   

Abstract

It has been established that a biological neuron reproduces the precise spike response to identical fluctuating input currents. We wish to predict the firing times of a given neuron for any input current. For this purpose, a mathematical model is introduced for mimicking the voltage response of the neuron to an input current. In predicting the firing times of a target neuron for a novel input current, we propose here the method of estimating the probability of spike occurrence, instead of naively thresholding an instantaneous value of the model voltage. The assessment is carried out maximally utilizing the information about the state space of the voltage and its time derivative(s) of the model, in advance of a possible spike, with a time lag that is determined by maximizing the mutual information. The prediction is significantly improved by the present method in comparison to the naive thresholding method.

Mesh:

Year:  2007        PMID: 17358202     DOI: 10.1103/PhysRevE.75.011925

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  6 in total

1.  Impact of network topology on inference of synaptic connectivity from multi-neuronal spike data simulated by a large-scale cortical network model.

Authors:  Ryota Kobayashi; Katsunori Kitano
Journal:  J Comput Neurosci       Date:  2013-02-07       Impact factor: 1.621

2.  Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold.

Authors:  Ryota Kobayashi; Yasuhiro Tsubo; Shigeru Shinomoto
Journal:  Front Comput Neurosci       Date:  2009-07-30       Impact factor: 2.380

3.  A point-process response model for spike trains from single neurons in neural circuits under optogenetic stimulation.

Authors:  X Luo; S Gee; V Sohal; D Small
Journal:  Stat Med       Date:  2015-09-27       Impact factor: 2.373

4.  Elemental spiking neuron model for reproducing diverse firing patterns and predicting precise firing times.

Authors:  Satoshi Yamauchi; Hideaki Kim; Shigeru Shinomoto
Journal:  Front Comput Neurosci       Date:  2011-10-04       Impact factor: 2.380

5.  Estimation of the synaptic input firing rates and characterization of the stimulation effects in an auditory neuron.

Authors:  Ryota Kobayashi; Jufang He; Petr Lansky
Journal:  Front Comput Neurosci       Date:  2015-05-18       Impact factor: 2.380

6.  Impact of slow K(+) currents on spike generation can be described by an adaptive threshold model.

Authors:  Ryota Kobayashi; Katsunori Kitano
Journal:  J Comput Neurosci       Date:  2016-04-16       Impact factor: 1.621

  6 in total

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