The Structure and Dimension of Variability Across Multi-Area Cortical Circuits

<div>Sensory and motor computations require tens of thousands of highly stochastic neurons in a cortical circuit to meaningfully coordinate their firing activity for a common goal. The trialto- trial variability structure of neuronal population activity characterizes the coordinated neural</div><div>dynamics underlying computation. Unsurprisingly, the dimension of the variability shared across neurons in one cortical population is generally orders of magnitude smaller than the number of neurons involved in a task. But how does this shared neuronal variability map across multiple cortical areas involved in the same computation? In this thesis, I study the propagation of low dimensional shared variance across cortical regions as a means to understand the dynamics of multi-area brain computation. I first present</div><div>a statistical model of movement encoding in human primary motor cortex that uncovers a one dimensional trajectory of latent activity differentially modulated during movements in which the subject received somatosensory feedback. I then present new evidence that the dimension of shared variability increases from V4 to PFC during distributed processing of visual stimuli. I develop a multi-layer spiking network model with tuning-structured connectivity that, through non-linear recurrent dynamics, replicates the dimensionality expansion observed in vivo. Finally, I show evidence that my model’s non-linear recurrent dynamics can be interpreted as timesharing between multiple states of low-dimension, linear dynamics inherited from the upstream</div><div>brain area. Together, these results aid our understanding of the subspaces of neuronal activity that are relevant across multiple brain areas during sensory and motor behaviors.</div>