Coordinated neural activity: Mechanistic origins and impact on stimulus coding

Thesis (Ph.D.)--University of Washington, 2015How does the activity of populations of neurons encode the signals they receive? Since neurons in vivo are inherently variable, each fixed input to a population will elicit not a deterministic response, but rather a probability distribution of states of the individual neurons. Traditional theories of neural coding rely on single-cell tuning curves that describe the average response of each neuron to stimulus features. Adding complexity to this neuron- by-neuron view is the fact that neural activity is not independent: it is often correlated, reflecting shared input and connectivity. Such "coordinated" activity can have diverse and potentially strong impacts on how neural circuits encode stimuli. In this dissertation, we combine dynamical and statistical tools to examine how single-cell and network properties dynamically generate coordinated neural spiking, and how this affects stimulus coding in populations of cells. First, we show how feedforward connectivity leads to the emergence of a neutrally stable subspace that allows information about input rates to be transmitted through layers. At this critical parameter regime, neural activity is characterized by higher-order interactions, meaning that the activity cannot be described by minimal models including only the lower-order moments (mean and pairwise interactions). Interestingly, recent experiments have also demonstrated the existence of higher-order correlations in the neural activity patterns in retina and cortex. Using maximum entropy techniques, we show that in general populations, higher-order correlations can facilitate the encoding of stimulus information in neural activity patterns. We propose a statistical model for fitting neurophysiological data that incorporates only the most significant higher-order interactions. We apply this model to analyze the statistics of population firing patterns in the lateral geniculate nucleus of awake mice. Finally, we analyze dendritic nonlinearities as a novel mechanism by which intrinsic cell properties can generate higher-order correlations. Together, these results work towards determining the origins of coordinated spiking, understanding its impact on neural coding, and building better tools for quantification in electrophysiological data

Triplet correlations among similarly tuned cells impact population coding

Which statistical features of spiking activity matter for how stimuli are encoded in neural populations? A vast body of work has explored how firing rates in individual cells and correlations in the spikes of cell pairs impact coding. Recent experiments have shown evidence for the existence of higher-order spiking correlations, which describe simultaneous firing in triplets and larger ensembles of cells; however, little is known about their impact on encoded stimulus information. Here, we take a first step toward closing this gap. We vary triplet correlations in small (approximately 10 cell) neural populations while keeping single cell and pairwise statistics fixed at typically reported values. This connection with empirically observed lower-order statistics important, as it places strong constraints on the level of triplet correlations that can occur. For each value of triplet correlations, we estimate the performance of the neural population on a two-stimulus discrimination task. We find that the allowed changes in the level of triplet correlations can significantly enhance coding, in particular if triplet correlations differ for the two stimuli. In this scenario, triplet correlations must be included in order to accurately quantify the functionality of neural populations. When both stimuli elicit similar triplet correlations, however, pairwise models provide relatively accurate descriptions of coding accuracy. We explain our findings geometrically via the skew that triplet correlations induce in population-wide distributions of neural responses. Finally, we calculate how many samples are necessary to accurately measure spiking correlations of this type, providing an estimate of the necessary recording times in future experiments