Bifurcation analysis of the dynamics of interacting populations of spiking networks

TL;DR

This paper investigates how the collective dynamics of hierarchical spiking neuron networks depend on synaptic coupling, revealing bifurcation structures similar to predator-prey models and providing insights into neuronal assembly behavior.

Contribution

It demonstrates that generalized Lotka-Volterra equations effectively describe the bifurcation dynamics of complex spiking neuronal networks, linking population models to neural activity patterns.

Findings

Bifurcation diagrams of neuronal networks match those of GLV equations.

Hierarchical network interactions can be understood through predator-prey dynamics.

Changing synaptic strength alters network dynamical regimes.

Abstract

We analyze the collective dynamics of hierarchically structured networks of densely connected spiking neurons. These networks of sub-networks may represent interactions between cell assemblies or different nuclei in the brain. The dynamical activity pattern that results from these interactions depends on the strength of synaptic coupling between them. Importantly, the overall dynamics of a brain region in the absence of external input, so called ongoing brain activity, has been attributed to the dynamics of such interactions. In our study, two different network scenarios are considered: a system with one inhibitory and two excitatory sub-networks, and a network representation with three inhibitory sub-networks. To study the effect of synaptic strength on the global dynamics of the network, two parameters for relative couplings between these sub-networks are considered. For each case, a co-dimension two bifurcation analysis is performed and the results have been compared to large-scale network simulations. Our analysis shows that Generalized Lotka-Volterra (GLV) equations, well-known in predator-prey studies, yield a meaningful population-level description for the collective behavior of spiking neuronal interaction, which have a hierarchical structure. In particular, we observed a striking equivalence between the bifurcation diagrams of spiking neuronal networks and their corresponding GLV equations. This study gives new insight on the behavior of neuronal assemblies, and can potentially suggest new mechanisms for altering the dynamical patterns of spiking networks based on changing the synaptic strength between some groups of neurons.