Tatiana Engel

Cold Spring Harbor Lab

May,12, 2021

Computational frameworks for integrating large-scale

neural dynamics, connectivity, and behavior

Modern neurotechnologies generate high-resolution maps of the brain-wide neural activity and anatomical connectivity. However, theoretical frameworks are missing to explain how global activity arises from connectivity to drive animal behaviors. I will present our recent work developing computational frameworks for modeling global neural dynamics, which utilize anatomical connectivity and predict rich behavioral outputs. First, we took advantage of recently available large-scale datasets of neural activity and connectivity to construct a model of mesoscopic functional dynamics across the mouse cortex. We found that global activity is restricted to a low-dimensional subspace spanned by a few cortical areas and explores different parts of this subspace in different behavioral contexts. Our framework provides an interpretable dimensionality reduction of cortex-wide neural activity grounded on the connectome, which generalizes across animals and behaviors. Second, we developed a circuit reduction method for inferring interpretable low-dimensional circuit mechanisms of cognitive computations from high-dimensional neural activity data. Our method infers the structural connectivity of an equivalent low-dimensional circuit that fits projections of high-dimensional neural activity data and implements the behavioral task. Our computational frameworks make quantitative predictions for perturbation experiments.

Chengcheng Huang

University of Pittsburgh

May 19, 2021

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Neuronal variability and spatiotemporal dynamics

in cortical network models 

Neuronal variability is a reflection of recurrent circuitry and cellular physiology. The modulation of neuronal variability is a reliable signature of cognitive and processing state. A pervasive yet puzzling feature of cortical circuits is that despite their complex wiring, population-wide shared spiking variability is low dimensional with all neurons fluctuating en masse. We show that the spatiotemporal dynamics in a spatially structured network produce large population-wide shared variability.  When the spatial and temporal scales of inhibitory coupling match known physiology, model spiking neurons naturally generate low dimensional shared variability that captures in vivo population recordings along the visual pathway. Further, we show that firing rate models with spatial coupling can also generate chaotic and low-dimensional rate dynamics. The chaotic parameter region expands when the network is driven by correlated noisy inputs, while being insensitive to the intensity of independent noise.