Solla, Sara, PhD

Information

Name

Solla, Sara, PhD

Title

Professor

Email

solla@northwestern.edu

Office Phone

312-503-1408

Office Fax

312-503-5101

Department

Physiology

Office

Tarry 5-711 Evanston

NU Scholar Profile

http://www.scholars.northwestern.edu/expert.asp?u_id=2279

Recent Publications on PubMed

http://www.ncbi.nlm.nih.gov/pubmed?term=Solla%2C%20Sara%5BFull%20Author%20Name%5D&cmd=DetailsSearch

Current Research

Current Research

<strong>Neural Computation</strong>

My interest is in the brain as a device for the acquisition, storage, transmission, and processing of information. My work is theoretical; it combines numerical modeling with analytic and conceptual tools from statistical physics, information theory, and nonlinear dynamics.

At the systems level, we work with neural network models consisting of arrays of highly interconnected nonlinear units that incorporate salient features of biological neurons. One of our 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 our 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 my work in recent years has focused on learning and adaptation; we are currently involved in studies of the dynamical properties of online algorithms for supervised and unsupervised learning.

To address the biophysics of computation we have formed a study group in collaboration with Hermann Riecke and Mary Silber, from the Department of Engineering Sciences and Applied Mathematics. Our current reading focuses on the nonlinear dynamics of synaptically coupled neural oscillators.