2010 Special Issue: Fitting a stochastic spiking model to neuronal current injection data

Authors:
Shigeru Shinomoto
Affiliations:
Department of Physics, Graduate School of Sciences, Kyoto University, Sakyo-ku, Kyoto 606-8502, Japan
Venue:
Neural Networks
Year:
2010

Citing 10
Cited 1

Introduction to the theory of neural computation

Introduction to the theory of neural computation
Learning a decision boundary from stochastic examples: incremental algorithms with and without queries

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Physiological gain leads to high ISI variability in a simple model of a cortical regular spiking cell

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Role of temporal integration and fluctuation detection in the highly irregular firing of a leaky integrator neuron model with partial reset

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A simple model of long-term spike train regularization

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Interspike interval correlations, memory, adaptation, and refractoriness in a leaky integrate-and-fire model with threshold fatigue

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Differences in spiking patterns among cortical neurons

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Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model

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The quantitative single-neuron modeling competition

Biological Cybernetics - Special Issue: Quantitative Neuron Modeling
A generalized linear integrate-and-fire neural model produces diverse spiking behaviors

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Improved similarity measures for small sets of spike trains

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Abstract

Spiking neuron models have advanced to the stage of accurately predicting the spike times of individual biological neurons for given fluctuating current. Most of the successful models are based on deterministic mechanistic modeling. In order to describe the stochastic aspect of neuronal firing, I propose setting a deterministic model in a stochastic framework, namely, incorporating the multi-timescale adaptive threshold (MAT) model of neuronal spiking into the stochastic framework of the linear-nonlinear Poisson (LNP) model in the form of a generalized linear model (GLM). In this setting, the probability of spike occurrence is updated each time a spike is derived from the past probability. Accordingly, the model may account for nontrivial firing patterns of various neurons that cannot be realized with the inhomogeneous Poisson process. The stochastic MAT model is not only capable of characterizing firing mechanisms specific to individual neurons, but may render the statistical inference feasible for the underlying mechanisms from the data. I also examine here two plausible principles for adjusting the model parameters: maximizing the spike time coincidence between the model and data, and maximizing the likelihood. It is found that these principles bring about greatly different characteristics for an identical set of data.