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

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.

From mechanisms to markers: novel noninvasive EEG proxy markers of the neural excitation and inhibition system in humans

“…The evidence so far suggests that recurrent networks that are shaped by E/I plasticity mechanisms (i.e., the E and I firing patterns that govern the extent to which specific combinations of neurons fire together) tend to develop self-organized criticality [ 82 , 83 ]. Further computational studies suggest that self-regulatory mechanisms modulating the activity of parvalbumin and somatostatin inhibitory interneurons should operate to keep the recurrent excitatory networks near the critical point of edge-of-chaos phase transition to dampen the effects of stimulus variability and ‘noise’ in the network dynamics [ 84 , 85 ]. Moreover, critical states related to alpha oscillatory activity have also been suggested to optimize information transmission across cortical areas [ 86 ].…”

Drive competition underlies effective allostatic orchestration

“…In both the first and the second studies, the agent must constantly decide what internal drives should base its navigation on to maximize stability. This continuous decision-making condition represents a major difference from the original work in which the model's design is based on (Amil and Verschure, 2021) and is a novel challenge to overcome. Therefore, a third study was conducted to evaluate the advantages of inducting subcritical dynamics after need resolution.…”

“…Wilson-Cowan equations modified as in (Amil and Verschure, 2021) were used to model two need-sensitive neural populations (Figure 1). This modification allowed to account for mutual and shared feedback inhibition held between the excitatory populations, as follows:…”

Brain criticality predicts individual levels of inter-areal synchronization in human electrophysiological data

“…1a ). Such an operating point at the edge of chaos 21 , 22 would yield as observables emergent synchronization dynamics with large variance and scale-free and long-range spatiotemporal correlations 23 , 24 (see Fig. 1a ).…”

“…Excessive inhibition leads to operation in the subcritical regime where neuronal signaling is attenuated, and spatiotemporal correlations are exclusively short-ranged. Excessive excitation leads to supercritical dynamics with escalating, self-amplifying neuronal activity that propagates across the system 21 . As a hallmark of brain criticality, in electrophysiological data, local oscillations also demonstrate scale-free long-range temporal correlations (LRTCs), i.e., power-law autocorrelations in amplitude fluctuations across lags of hundreds of seconds 30 – 33 and neuronal avalanches that are power-law scaled cascades of neuronal activity propagating across the neocortex in both microscopic 34 and macroscopic 32 , 33 , 35 scales of brain networks.…”