Abstract
Linking synaptic connectivity to dynamics is key to understanding information processing in neocortex. Circuit dynamics emerge from complex interactions of interconnected neurons, necessitating that links between connectivity and dynamics be evaluated at the network level. Here we map propagating activity in large neuronal ensembles from mouse neocortex and compare it to a recurrent network model, where connectivity can be precisely measured and manipulated. We find that a dynamical feature dominates statistical descriptions of propagating activity for both neocortex and the model: convergent clusters comprised of fan-in triangle motifs, where two input neurons are themselves connected. Fan-in triangles coordinate the timing of presynaptic inputs during ongoing activity to effectively generate postsynaptic spiking. As a result, paradoxically, fan-in triangles dominate the statistics of spike propagation even in randomly connected recurrent networks. Interplay between higher-order synaptic connectivity and the integrative properties of neurons constrains the structure of network dynamics and shapes the routing of information in neocortex.
Highlights
Understanding any complex system requires a mechanistic account of how dynamics arise from underlying architecture
We identify a canonical higher-order correlation in network dynamics and trace its emergence to synaptic integration
The influence of fan-in clustering leads to the surprising emergence of non-random routing of spiking in random synaptic networks
Summary
Understanding any complex system requires a mechanistic account of how dynamics arise from underlying architecture. Patterns of connections shape dynamics in diverse settings ranging from electric power grids to gene transcription networks[1,2,3,4,5]. It is important to understand the transformation from connectivity to activity within local populations of neurons since this is the scale at which the majority of connections arise. Neocortical neurons are highly interconnected, and their connectivity schemes are characterized by the prevalence of specific motifs[12]. At the level of local populations, functional coordination has been demonstrated in diverse ways, e.g. on the basis of active neurons[13,14] and their correlation patterns[15]. Predicting population responses on the basis of pairwise connections alone has proven to be difficult
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