Emergence of slow-switching assemblies in structured neuronal networks

TL;DR

This paper investigates how specific connectivity features in neuronal networks lead to slow-switching assembly dynamics, revealing the spectral properties and network topologies that underpin these sustained, transitioning activity patterns.

Contribution

It links spectral properties of the synaptic weight matrix to SSA emergence and introduces new connectivity paradigms that produce SSA activity, including in small-world networks.

Findings

SSA activity is associated with spectral gaps in the eigenvalues.

Block-localized Schur vectors correspond to coherent neural groups.

Modified connectivity patterns can induce SSA in excitatory-inhibitory networks.

Abstract

Unraveling the interplay between connectivity and spatio-temporal dynamics in neuronal networks is a key step to advance our understanding of neuronal information processing. Here we investigate how particular features of network connectivity underpin the propensity of neural networks to generate slow-switching assembly (SSA) dynamics, i.e., sustained epochs of increased firing within assemblies of neurons which transition slowly between different assemblies throughout the network. We show that the emergence of SSA activity is linked to spectral properties of the asymmetric synaptic weight matrix. In particular, the leading eigenvalues that dictate the slow dynamics exhibit a gap with respect to the bulk of the spectrum, and the associated Schur vectors exhibit a measure of block-localization on groups of neurons, thus resulting in coherent dynamical activity on those groups. Through simple rate models, we gain analytical understanding of the origin and importance of the spectral gap, and use these insights to develop new network topologies with alternative connectivity paradigms which also display SSA activity. Specifically, SSA dynamics involving excitatory and inhibitory neurons can be achieved by modifying the connectivity patterns between both types of neurons. We also show that SSA activity can occur at multiple timescales reflecting a hierarchy in the connectivity, and demonstrate the emergence of SSA in small-world like networks. Our work provides a step towards understanding how network structure (uncovered through advancements in neuroanatomy and connectomics) can impact on spatio-temporal neural activity and constrain the resulting dynamics.