Stringer, Carsen;
(2018)
Discovering structure in multi-neuron recordings through network modelling.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Our brains contain billions of neurons, which are continually producing electrical signals to relay information around the brain. Yet most of our knowledge of how the brain works comes from studying the activity of one neuron at a time. Recently, studies of multiple neurons have shown that they tend to be active together. These coordinated dynamics vary across brain states and impact the way that external sensory information is processed. 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 rodent species, sensory modalities, and behavioral states. We found that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the reliability of sensory representations. We next recorded from awake mice using calcium imaging techniques, and acquired activity from 10,000 neurons simultaneously in visual cortex while presenting 2,800 different natural images. In awake mice, these intrinsic population-wide fluctuations were suppressed and responses to visual stimuli were reliable. The stimulus-related information was stored in a high-dimensional neural space: 1,000 dimensions of neural activity accounted for 90\% of the variance. Although awake mice lacked large population-wide fluctuations in activity, we observed several dozen dimensions of spontaneous activity. These dimensions of spontaneous activity were not spatially organized in cortex. Instead they were related to the orofacial behaviors of the mouse: over 50\% of the shared variability of the network could be predicted from the facial movements of the mouse. In simulations of high-dimensional network activity, flexible patterns of activity were reproduced only if the network contained multiple dimensions of inhibitory activity. We tested this hypothesis in our recordings and found that inhibitory neuron activity did track excitatory neuron activity across multiple dimensions.
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