A framework for preparing a stochastic nonlinear integrate-and-fire model for integrated information theory. (3rd April 2022)

• Record Type: Journal Article
• Authors: Mojarrad, Hossein; Azimirad, Vahid; Koohestani, Behrooz
• Is Part Of: Network. Volume 33:Number 1/2(2022)
• Page Start: 17
• Page End: 61
• Publication Date: 2022-04-03
• Subjects: Integrated information theory (IIT) -- causality -- stochastic spiking neural networks -- stochastic differential equations -- transition probability distributions; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; 006.32
• DOI: 10.1080/0954898X.2022.2049644
• Languages: English
• ISSNs: 0954-898X

Abstract

ABSTRACT: This paper presents a framework for spiking neural networks to be prepared for the Integrated Information Theory (IIT) analysis, using a stochastic nonlinear integrate-and-fire model. The model includes the crucial dynamics of the all-or-none law and after-spike refractoriness. The noise is modelled as an additive term in the system's equations. By preparing the model for the IIT analysis, it is meant to determine the length of the analysis time-window and the transition probability distributions required for the IIT 3.0. To this end, a system of differential equations is proposed to estimate the time evolution of the system's mean and covariance. Assuming the binary Fired/Silent activity as the possible states of each neuron, an algorithm is proposed to calculate the required probability distributions. As long as the Fired/Silent probabilities are only concerned, the Gaussian density assumption with the estimated moments is a reasonable estimate. The synaptic inputs are treated as random variables with low variances to avoid the costs of conditioning on the system's past activities. The Monte-Carlo simulation is used to validate the estimation methods. To increase the reliability of the inductive inference behind the Monte-Carlo method, various stimulation protocols are applied to evoke the dynamics of the equations.