A unified theory of E/I synaptic balance, quasicritical neuronal avalanches and asynchronous irregular spiking
2020
Neuronal 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.
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