Abstract
The critical brain hypothesis states that there are information processing advantages for neuronal networks working close to the critical region of a phase transition. If this is true, we must ask how the networks achieve and maintain this critical state. Here, we review several proposed biological mechanisms that turn the critical region into an attractor of a dynamics in network parameters like synapses, neuronal gains, and firing thresholds. Since neuronal networks (biological and models) are not conservative but dissipative, we expect not exact criticality but self-organized quasicriticality, where the system hovers around the critical point.
Highlights
Thirty-three years after the initial formulation of the self-organized criticality (SOC) concept [1], one of the most active areas that employ these ideas is theoretical neuroscience
Α(t) is a continuous estimator of the network firing rate, τη is the recovery time of the resources availability, and the term η0(α(t)) (1 + α/k)− 1 in the denominator ensures that depletion is fast and recovery is slow (k 20 Hz). They called this mechanism as network spiking–dependent plasticity and showed that, in contrast to pure STDP, it leads to power-law avalanches with branching ratio around one
We described several examples of self-organization mechanisms that drive neuronal networks to the border of a phase transition
Summary
Thirty-three years after the initial formulation of the self-organized criticality (SOC) concept [1] (and 37 years after the self-organizing extremal invasion percolation model [2]), one of the most active areas that employ these ideas is theoretical neuroscience. The question arises because, like earthquake and forest-fire models, neuronal networks are not conservative systems, which means that in principle they cannot be exactly critical [5, 6, 45, 46] In these networks, we can vary control parameters like the strength of synapses and obtain subcritical, critical, and supercritical behavior. The control parameter W (the dial) is an external quantity that observes and governs the global (i.e., the collective), whereas [homeostasis] provides the system with local mechanisms that have an effect over the collective This is the main feature of a complex system
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