Effects of random inputs and short-term synaptic plasticity in a LIF conductance model for working memory applications

TL;DR

This paper investigates how random inputs and short-term synaptic plasticity influence the behavior of a leaky integrate-and-fire neuron model, revealing that increased randomness enhances spike train irregularity, with implications for working memory mechanisms.

Contribution

It provides a detailed analysis of the effects of stochastic inputs and synaptic plasticity on LIF neuron dynamics, highlighting their role in neural variability and working memory models.

Findings

Increased input randomness raises spike train irregularity.

Short-term synaptic plasticity affects neuron response variability.

Irregular spike trains encode information about previous stimuli.

Abstract

Working memory (WM) has been intensively used to enable the temporary storing of information for processing purposes, playing an important role in the execution of various cognitive tasks. Recent studies have shown that information in WM is not only maintained through persistent recurrent activity but also can be stored in activity-silent states such as in short-term synaptic plasticity (STSP). Motivated by important applications of the STSP mechanisms in WM, the main focus of the present work is on the analysis of the effects of random inputs on a leaky integrate-and-fire (LIF) synaptic conductance neuron under STSP. Furthermore, the irregularity of spike trains can carry the information about previous stimulation in a neuron. A LIF conductance neuron with multiple inputs and coefficient of variation (CV) of the inter-spike-interval (ISI) can bring an output decoded neuron. Our numerical results show that an increase in the standard deviations in the random input current and the random refractory period can lead to an increased irregularity of spike trains of the output neuron.