Strong and localized recurrence controls dimensionality of neural activity across brain areas

Abstract

The brain contains an astronomical number of neurons, but it is their collective activity that underlies brain function. The number of degrees of freedom that this collective activity explores - its dimensionality - is therefore a fundamental signature of neural dynamics and computation (1-7). However, it is not known what controls this dimensionality in the biological brain - and in particular whether and how recurrent synaptic networks play a role (8-10). Through analysis of high-density Neuropixels recordings (11), we argue that areas across the mouse cortex operate in a sensitive regime that gives these synaptic networks a very strong role in controlling dimensionality. We show that this control is expressed across time, as cortical activity transitions among states with different dimensionalities. Moreover, we show that the control is mediated through highly tractable features of synaptic networks. We then analyze these key features via a massive synaptic physiology dataset (12). Quantifying these features in terms of cell-type specific network motifs, we find that the synaptic patterns that impact dimensionality are prevalent in both mouse and human brains. Thus local circuitry scales up systematically to help control the degrees of freedom that brain networks may explore and exploit.