Scale Free Avalanches in Excitatory-Inhibitory Populations of Spiking Neurons with Conductance Based Synaptic Currents
Masud Ehsani, J\"urgen Jost

TL;DR
This paper models excitatory-inhibitory neural networks with conductance-based synapses, revealing critical avalanche dynamics at bifurcation points that resemble cortical activity patterns.
Contribution
It introduces a mean-field framework for EI spiking neurons with conductance-based synapses, identifying bifurcations associated with critical avalanche behavior.
Findings
Avalanche size and duration follow power-law distributions.
The network operates near bifurcation points with balanced excitation and inhibition.
Neuron firing is fluctuation-driven and Poisson-like near the Bogdanov-Takens point.
Abstract
We investigate spontaneous critical dynamics of excitatory and inhibitory (EI) sparsely connected populations of spiking leaky integrate-and-fire neurons with conductance-based synapses. We use a bottom-up approach to derive a single neuron gain function and a linear Poisson neuron approximation which we use to study mean-field dynamics of the EI population and its bifurcations. In the low firing rate regime, the quiescent state loses stability due to saddle-node or Hopf bifurcations. In particular, at the Bogdanov-Takens (BT) bifurcation point which is the intersection of the Hopf bifurcation and the saddle-node bifurcation lines of the 2D dynamical system, the network shows avalanche dynamics with power-law avalanche size and duration distributions. This matches the characteristics of low firing spontaneous activity in the cortex. By linearizing gain functions and excitatory and…
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