Highly connected neurons spike less frequently in balanced networks

Many biological neuronal networks exhibit highly variable spiking activity. Balanced networks offer a parsimonious model of this variability. In balanced networks, strong excitatory synaptic inputs are canceled by strong inhibitory inputs on average and spiking activity is driven by transient breaks in this balance. Most previous studies of balanced networks assume a homogeneous or distance-dependent connectivity structure, but connectivity in biological cortical networks is more intricate. We use a heterogeneous mean-field theory of balanced networks to show that heterogeneous in-degrees can break balance, but balance can be restored by heterogeneous out-degrees that are correlated with in-degrees. In all examples considered, we find that highly connected neurons spike less frequently, consistent with recent experimental observations.

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