Highly connected neurons spike less frequently in balanced networks

Abstract

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.

References in corpus (3)

• Dynamics of a FitzHugh-Nagumo system subjected to autocorrelated noise
• Balanced networks of spiking neurons with spatially dependent recurrent connections
• Chaos and reliability in balanced spiking networks with temporal drive

Cited by in corpus (6)

• Transition from asynchronous to oscillatory dynamics in balanced spiking networks with instantaneous synapses
• Spatiotemporal dynamics and reliable computations in recurrent spiking neural networks
• Coexistence of fast and slow gamma oscillations in one population of inhibitory spiking neurons
• Coherent oscillations in balanced neural networks driven by endogenous fluctuations
• Macroscopic Fluctuations Emerge in Balanced Networks with Incomplete Recurrent Alignment
• A robust balancing mechanism for spiking neural networks