Supercritical dynamics at the edge-of-chaos underlies optimal decision-making

Adrián F Amil,Paul F M J Verschure

doi:10.1088/2632-072x/ac3ad2

Abstract

Critical dynamics, characterized by scale-free neuronal avalanches, is thought to underlie optimal function in the sensory cortices by maximizing information transmission, capacity, and dynamic range. In contrast, deviations from criticality have not yet been considered to support any cognitive processes. Nonetheless, neocortical areas related to working memory and decision-making seem to rely on long-lasting periods of ignition-like persistent firing. Such firing patterns are reminiscent of supercritical states where runaway excitation dominates the circuit dynamics. In addition, a macroscopic gradient of the relative density of Somatostatin (SST+) and Parvalbumin (PV+) inhibitory interneurons throughout the cortical hierarchy has been suggested to determine the functional specialization of low- versus high-order cortex. These observations thus raise the question of whether persistent activity in high-order areas results from the intrinsic features of the neocortical circuitry. We used an attractor model of the canonical cortical circuit performing a perceptual decision-making task to address this question. Our model reproduces the known saddle-node bifurcation where persistent activity emerges, merely by increasing the SST+/PV+ ratio while keeping the input and recurrent excitation constant. The regime beyond such a phase transition renders the circuit increasingly sensitive to random fluctuations of the inputs—i.e., chaotic—, defining an optimal SST+/PV+ ratio around the edge-of-chaos. Further, we show that both the optimal SST+/PV+ ratio and the region of the phase transition decrease monotonically with increasing input noise. This suggests that cortical circuits regulate their intrinsic dynamics via inhibitory interneurons to attain optimal sensitivity in the face of varying uncertainty. Hence, on the one hand, we link the emergence of supercritical dynamics at the edge-of-chaos to the gradient of the SST+/PV+ ratio along the cortical hierarchy, and, on the other hand, explain the behavioral effects of the differential regulation of SST+ and PV+ interneurons by acetylcholine in the presence of input uncertainty.

Highlights

Self-organized criticality, as characterized by power-law scaling of neuronal avalanches, occurs as a continuous second-order phase transition [1], and has been proposed as a hallmark of optimal function in cortical networks [2]
We addressed the question of how persistent activity can arise from attractor dynamics with inherent features of supercriticality—i.e., the tendency to generate long-lasting avalanches
Following previous distinctions between avalanche-based and edge-of-chaos criticality [23], we link the concept of edge-of-chaos with ignition-like supercritical states, to argue that optimal decision-making might occur at such a phase transition, where persistent activity emerges

Summary

Introduction

Self-organized criticality, as characterized by power-law scaling of neuronal avalanches, occurs as a continuous second-order phase transition [1], and has been proposed as a hallmark of optimal function in cortical networks [2]. Increased recurrent excitation—through the higher density of N-methyl-d-aspartate, or NMDA, receptors in pyramidal dendrites [11, 12]—has been hypothesized to be the key feature giving rise to distinct dynamics and function in low- and high-order cortical areas, a recently reported macroscopic gradient of increasing Somatostatin-to-Parvalbumin (SST+/PV+) ratio along the sensory hierarchy has been suggested to play a key role as well [13] This suggests that the SST+/PV+ gradient might be a key feature determining whether the local circuits throughout the cortical hierarchy have critical or supercritical dynamics, which in turn would dictate their respective functions during information processing

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Conclusion