Irregular dynamics in up and down cortical states

TL;DR

This paper models the complex up and down neural state transitions in the cortex using a stochastic bistable rate model with synaptic noise, explaining experimental fluctuations in state durations.

Contribution

It introduces a biologically motivated stochastic model incorporating synaptic processes that captures the irregular dynamics of cortical up and down states, including power-law distributed durations.

Findings

Power-law distribution of up state durations.

Synaptic noise with large recovery times explains fluctuations.

Static synapses and noise-free dynamical synapses are insufficient.

Abstract

Complex coherent dynamics is present in a wide variety of neural systems. A typical example is the voltage transitions between up and down states observed in cortical areas in the brain. In this work, we study this phenomenon via a biologically motivated stochastic model of up and down transitions. The model is constituted by a simple bistable rate model, where the synaptic current is modulated by short-term synaptic processes which introduce stochasticity and temporal correlations. A complete analysis of our model, both with mean-field approaches and numerical simulations, shows the appearance of complex transitions between high (up) and low (down) neural activity states, driven by the synaptic noise, with permanence times in the up state distributed according to a power-law. We show that the experimentally observed large fluctuation in up and down permanence times can be explained as the result of sufficiently noisy dynamical synapses with sufficiently large recovery times. Static synapses cannot account for this behavior, nor can dynamical synapses in the absence of noise.