How single neuron properties shape chaotic dynamics and signal transmission in random neural networks

Abstract

While most models of randomly connected networks assume nodes with simple dynamics, nodes in realistic highly connected networks, such as neurons in the brain, exhibit intrinsic dynamics over multiple timescales. We analyze how the dynamical properties of nodes (such as single neurons) and recurrent connections interact to shape the effective dynamics in large randomly connected networks. A novel dynamical mean-field theory for strongly connected networks of multi-dimensional rate units shows that the power spectrum of the network activity in the chaotic phase emerges from a nonlinear sharpening of the frequency response function of single units. For the case of two-dimensional rate units with strong adaptation, we find that the network exhibits a state of "resonant chaos", characterized by robust, narrow-band stochastic oscillations. The coherence of stochastic oscillations is maximal at the onset of chaos and their correlation time scales with the adaptation timescale of single units. Surprisingly, the resonance frequency can be predicted from the properties of isolated units, even in the presence of heterogeneity in the adaptation parameters. In the presence of these internally-generated chaotic fluctuations, the transmission of weak, low-frequency signals is strongly enhanced by adaptation, whereas signal transmission is not influenced by adaptation in the non-chaotic regime. Our theoretical framework can be applied to other mechanisms at the level of single nodes, such as synaptic filtering, refractoriness or spike synchronization. These results advance our understanding of the interaction between the dynamics of single units and recurrent connectivity, which is a fundamental step toward the description of biologically realistic network models in the brain, or, more generally, networks of other physical or man-made complex dynamical units.

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Cited by in corpus (10)

• How single neuron properties shape chaotic dynamics and signal transmission in random neural networks
• Dimension of activity in random neural networks
• Contrasting the effects of adaptation and synaptic filtering on the timescales of dynamics in recurrent networks
• Low-dimensional firing-rate dynamics for populations of renewal-type spiking neurons
• Input correlations impede suppression of chaos and learning in balanced rate networks
• A Microscopic Theory of Intrinsic Timescales in Spiking Neural Networks
• Dynamical Theory for Adaptive Systems
• Effect of Synaptic Heterogeneity on Neuronal Coordination
• Irreversibility in Non-reciprocal Chaotic Systems
• How random connectivity shapes the fluctuating dynamics of finite-size neural populations