A unified theory of E/I synaptic balance, quasicritical neuronal avalanches and asynchronous irregular spiking

Abstract

AbstractNeuronal avalanches and asynchronous irregular (AI) firing patterns have been thought to represent distinct frameworks to understand the brain spontaneous activity. The former is typically present in systems where there is a balance between the slow accumulation of tension and its fast dissipation, whereas the latter is accompanied by the balance between synaptic excitation and inhibition (E/I). Here, we develop a new theory of E/I balance that relies on two homeostatic adaptation mechanisms: the short-term depression of inhibition and the spike-dependent threshold increase. First, we turn off the adaptation and show that the so-called static system has a typical critical point commonly attributed to self-organized critical models. Then, we turn on the adaptation and show that the network evolves to a dynamic regime in which: (I) E/I synapses balance regardless of any parameter choice; (II) an AI firing pattern emerges; and (III) neuronal avalanches display power laws. This is the first time that these three phenomena appear simultaneously in the same network activity. Thus, we show that the once thought opposing frameworks may be unified into a single dynamics, provided that adaptation mechanisms are in place. In our model, the AI firing pattern is a direct consequence of the hovering close to the critical line where external inputs are compensated by threshold growth, creating synaptic balance for any E/I weight ratio.HighlightsAsynchronous irregular (AI) firing happens together with power-law neuronal avalanches under self-organized synaptic balance.Self-organization towards the critical and balanced state (with AI and power-law avalanches) occur via short-term inhibition depression and firing threshold adaptation.The avalanche exponents match experimental findings.The adaptation time scales drive the self-organized dynamics towards different firing regimes.Author summaryTwo competing frameworks are employed to understand the brain spontaneous activity, both of which are backed by computational and experimental evidence: globally asynchronous and locally irregular (AI) activity arises in excitatory/inhibitory balanced networks subjected to external stimuli, whereas avalanche activity emerge in excitable systems on the critical point between active and inactive states. Here, we develop a new theory for E/I networks and show that there is a state where synaptic balance coexists with AI firing and power-law distributed neuronal avalanches. This regime is achieved through the introducing of short-term depression of inhibitory synapses and spike-dependent threshold adaptation. Thus, the system self-organizes towards the balance point, such that its AI activity arises from quasicritical fluctuations. The need for two independent adaptive mechanisms explains why different dynamical states are observed in the brain.