Title: Network topology of neural systems supporting avalanche dynamics predicts stimulus propagation and recovery,
Abstract: Many neural systems display avalanche behavior characterized by uninterrupted
sequences of neuronal firing whose distributions of size and durations are
heavy-tailed. Theoretical models of such systems suggest that these dynamics
support optimal information transmission and storage. However, the unknown role
of network structure precludes an understanding of how variations in network
topology manifest in neural dynamics and either support or impinge upon
information processing. Here, using a generalized spiking model, we develop a
mechanistic understanding of how network topology supports information
processing through network dynamics. First, we show how network topology
determines network dynamics by analytically and numerically demonstrating that
network topology can be designed to propagate stimulus patterns for long
durations. We then identify strongly connected cycles as empirically observable
network motifs that are prevalent in such networks. Next, we show that within a
network, mathematical intuitions from network control theory are tightly linked
with dynamics initiated by node-specific stimulation and can identify stimuli
that promote long-lasting cascades. Finally, we use these network-based metrics
and control-based stimuli to demonstrate that long-lasting cascade dynamics
facilitate delayed recovery of stimulus patterns from network activity, as
measured by mutual information. Collectively, our results provide evidence that
cortical networks are structured with architectural motifs that support
long-lasting propagation and recovery of a few crucial patterns of stimulation,
especially those consisting of activity in highly controllable neurons.
Broadly, our results imply that avalanching neural networks could contribute to
cognitive faculties that require persistent activation of neuronal patterns,
such as working memory or attention.