Mean-field approximations for coupled populations of generalized linear model spiking neurons with markov refractoriness

Authors:
Taro Toyoizumi;Kamiar Rahnama Rad;Liam Paninski
Affiliations:
-;-;-
Venue:
Neural Computation
Year:
2009

Citing 12
Cited 2

Introduction to the theory of neural computation

Introduction to the theory of neural computation
Stochastic differential equations (3rd ed.): an introduction with applications

Stochastic differential equations (3rd ed.): an introduction with applications
Chaotic balanced state in a model of cortical circuits

Neural Computation
Spikes: exploring the neural code

Spikes: exploring the neural code
Fast global oscillations in networks of integrate-and-fire neurons with low firing rates

Neural Computation
Spiking Neuron Models: An Introduction

Spiking Neuron Models: An Introduction
Temporal correlations in stochastic networks of spiking neurons

Neural Computation
Rate models for conductance-based cortical neuronal networks

Neural Computation
Analyzing Functional Connectivity Using a Network Likelihood Model of Ensemble Neural Spiking Activity

Neural Computation
A Spike-Train Probability Model

Neural Computation
Neural Coding: Higher-Order Temporal Patterns in the Neurostatistics of Cell Assemblies

Neural Computation
The Approach of a Neuron Population Firing Rate to a New Equilibrium: An Exact Theoretical Result

Neural Computation

Applying the multivariate time-rescaling theorem to neural population models

Neural Computation
Optimal sequential detection of stimuli from multiunit recordings taken in densely populated brain regions

Neural Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Resampling methods are popular tools for exploring the statistical structure of neural spike trains. In many applications, it is desirable to have resamples that preserve certain non-Poisson properties, like refractory periods and bursting, and that ...