Inhibitory control of shared variability in cortical networks

BioRxiv : the Preprint Server for Biology
Marius PachitariuNicholas A Lesica


Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different species, sensory modalities, and behavioral states. The model accurately reproduced the wide variety of activity patterns in our recordings, and analysis of its parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory interneurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate the power of fitting spiking network models directly to multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs by controlling network stability.

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