# Jensen's force and the statistical mechanics of cortical asynchronous states

## TL;DR

This paper introduces Jensen's force as a noise-induced mechanism in a neural network model that explains the emergence of asynchronous states in the cortex, reconciling different hypotheses about their origin.

## Contribution

It presents a simple neural-network model demonstrating Jensen's force as a key factor in cortical asynchronous activity, bridging asynchronous and critical-state theories.

## Key findings

- Jensen's force induces a novel self-sustained activity phase.
- The model reproduces experimental features of asynchronous cortical states.
- Phase transitions between different neural activity regimes are characterized.

## Abstract

The cortex exhibits self-sustained highly-irregular activity even under resting conditions, whose origin and function need to be fully understood. It is believed that this can be described as an "asynchronous state" stemming from the balance between excitation and inhibition, with important consequences for information-processing, though a competing hypothesis claims it stems from critical dynamics. By analyzing a parsimonious neural-network model with excitatory and inhibitory interactions, we elucidate a noise-induced mechanism called "Jensen's force" responsible for the emergence of a novel phase of arbitrarily-low but self-sustained activity, which reproduces all the experimental features of asynchronous states. The simplicity of our framework allows for a deep understanding of asynchronous states from a broad statistical-mechanics perspective and of the phase transitions to other standard phases it exhibits, opening the door to reconcile, asynchronous-state and critical-state hypotheses. We argue that Jensen's forces are measurable experimentally and might be relevant in contexts beyond neuroscience.