Neural Network Wiring and Topological Stochastic Resonance

The aim of this work is to determine how variability in synaptic connectivityshapes activity in balanced spiking networks and whether “topological stochasticresonance” (amplified responses due to structural disorder) can emerge withoutchanging the connection count. For that purpose we have simulated balancednetworks of excitatory and inhibitory leaky integrate-and-fire neurons driven byexternal Poisson input. Four connectivity schemes were constructed each witha progressively more unequal distribution of the number of inputs received byindividual neurons but with the same total number of conncetions. Topologicalvariability was quantified by the standard deviation (σ) of a normal distribu-tion describing the number of inputs received from each presynaptic populationto each neuron. Our results indicate that by increasing variability in connectiv-ity produces a strong effect of increased mean firing rates and rate heterogeneityacross neuron classes, despite the total number of synapses per population beingconserved. Network balance remained globally preserved, but local deviationsgrew with σ, revealing regimes of enhanced responsiveness. We demonstratehow the structural “noise” in the form of variable synaptic connection countcan strongly modulate activity in balanced networks without altering overallconnection count. These results support the novel concept of Topological Stochas-tic Resonance and suggest that controlled connectivity disorder may help tunesensitivity and stability in cortical-like circuits.