Synaptic Plasticity Enables Adaptive Self-Tuning Critical Networks

Abstract

During rest, the mammalian cortex displays spontaneous neural activity. Spiking of single neurons during rest has been described as irregular and asynchronous. In contrast, recent in vivo and in vitro population measures of spontaneous activity, using the LFP, EEG, MEG or fMRI suggest that the default state of the cortex is critical, manifested by spontaneous, scale-invariant, cascades of activity known as neuronal avalanches. Criticality keeps a network poised for optimal information processing, but this view seems to be difficult to reconcile with apparently irregular single neuron spiking. Here, we simulate a 10,000 neuron, deterministic, plastic network of spiking neurons. We show that a combination of short- and long-term synaptic plasticity enables these networks to exhibit criticality in the face of intrinsic, i.e. self-sustained, asynchronous spiking. Brief external perturbations lead to adaptive, long-term modification of intrinsic network connectivity through long-term excitatory plasticity, whereas long-term inhibitory plasticity enables rapid self-tuning of the network back to a critical state. The critical state is characterized by a branching parameter oscillating around unity, a critical exponent close to -3/2 and a long tail distribution of a self-similarity parameter between 0.5 and 1.

Psychedelics as Medicines: An Emerging New Paradigm

“…The empirical demonstration of brain resting-state activity simultaneously exhibiting functional segregation and integration has led to the suggestion that the cortex normally resides at a critical state, manifested by spontaneous, scale-invariant, cascades of activity known as neuronal avalanches, and that the human brain as a whole behaves as a system at criticality. 71,72 Indeed, investigators have thought that the brain spends most time at an activity level that corresponds to the critical point, that is, fluctuating around a phase transition. 73 This conjecture has, however, been challenged by Priesemann et al, 74 who analyzed in vivo spiking activity from three mammalian species and local field potential recordings from human brain.…”

Criticality meets learning: Criticality signatures in a self-organizing recurrent neural network

“…For example, deterministic networks combining short-term and long-term plasticity have been shown to have power-law distributed avalanches [9]. Typically, previous studies either described how models are tuned to criticality by plasticity rules or they investigated information processing properties of critical networks.…”

Cooperation, competition and the emergence of criticality in communities of adaptive systems

“…In particular, in the brain -one of the flagships of the criticality hypothesis-it could lead to maximal dynamic ranges, high sensitivity to stimuli, optimal transmission and storage of information, and very diverse dynamical repertoires [16,17,18,19,20,21]. Different mechanisms and scenarios have been described in the recent literature to explain how such a critical behavior comes about [22,23,24,25,26]. On the other hand, some authors have argued that apparent criticality could be just an artifact of the attempt to fit overly simplified models to complex and highly heterogeneous systems [27,28].…”