Sara A. Solla, “Population Dynamics in Neural Systems”
Sara A. Solla, PhD
Professor, Northwestern University
Title: Population Dynamics in Neural Systems
Abstract: The ability to simultaneously record the activity from tens to thousands and maybe even tens of thousands of neurons has allowed us to analyze the computational role of neural population activity as opposed to single neuron activity. Recent work on a variety of cortical areas suggests that neural function may be built on the activation of population-wide activity patterns, the neural modes, rather than on the independent modulation of individual neural activity. These neural modes, the dominant covariation patterns within the neural population, define a low dimensional neural manifold that captures most of the variance in the recorded neural activity. We refer to the time-dependent activation of the neural modes as their latent dynamics, and argue that latent cortical dynamics within the manifold are the fundamental and stable building blocks of neural population activity.
Biography: Sara Solla’s interest is in the brain as a device for the acquisition, storage, transmission, and processing of information. Her work is theoretical; it combines numerical modeling with analytic and conceptual tools from statistical physics, information theory, and nonlinear dynamics. At the systems level, they work with neural network models consisting of arrays of highly interconnected nonlinear units that incorporate salient features of biological neurons. One of their projects has led to the development of a modular neural network that is capable of executing an oculomotor delayed response task. Damage experiments in this simulated network attempt to reproduce the deficient performance of schizophrenic patients in this task, as measured by their colleague, Sohee Park, at the Department of Psychology.
In addition to their ability to model neuronal processing at the systems level, neural network models provide powerful computational tools for pattern recognition and nonlinear control. Current applications include linkage analysis of genetic data (in collaboration with the Laboratory of Statistical Genetics, headed by Jurg Ott, at Rockefeller University) and the control of springback angle and maximum strain in the manufacture of sheet metal parts (in collaboration with Jian Cao from the Department of Mechanical Engineering).
Neural network models provide a prototype for the investigation of systems that interact with the environment through the execution of an action in response to a stimulus. If the action generates an error signal that is used by the system to modify its internal state, the system exhibits learning and adaptation capabilities. Much of Dr. Solla’s work in recent years has focused on learning and adaptation; she is currently involved in studies of the dynamical properties of online algorithms for supervised and unsupervised learning.
Topic: MINDS & CIS Fall Seminar Series
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