Discovering dynamic computations from large-scale neural activity recordings

点击翻译按钮获取中文摘要

Project Summary/Abstract How neural activity is coordinated within local microcircuits and across brain regions to drive behavior is a central open question in neuroscience. Recent advances in massively-parallel neural recording tech- nologies are producing dynamic activity maps during complex behaviors, with single-neuron granularity and single-spike resolution. To reveal fundamental dynamic features in these large-scale datasets, new principled and scalable computational methods are urgently needed. To address this need, we will de- velop a broadly applicable, non-parametric inference framework for discovering dynamic computations from large-scale neural activity recordings. Our framework seeks a dynamical model of the data, but unlike existing techniques, does not require a priori model assumptions. Existing techniques commonly fit data with simple ad hoc models, which often miss or distort defining dynamic features. Instead, our non-parametric approach explores the entire space of all possible dynamics in search for the model consistent with the data, and thereby eliminates a priori guess work, ambiguous model comparisons and model-induced biases. We aim to develop optimization algorithms to effectively search through the space of all dynamical models, implement these algorithms on GPUs to achieve maximal computational speed, and derive information-theoretic bounds to quantify reliability of our computational methods. To demonstrate how our novel methods aid scientific discovery, we will employ them to examine decision- related activity in parietal and premotor cortices. While different theoretical models of decision-making have been proposed, it still remains unknown how decision computations are implemented on the level of individual neurons and neural populations. Our analyses will offer the first computational models of decision-making rooted directly in neural data, reconcile stability of population dynamics with hetero- geneity of single-neuron responses, reveal differences in decision-computations across cortical layers, and identify differences in decision-related dynamics of excitatory vs. inhibitory neurons.