DEPARTMENT OF PHYSICS AND ASTRONOMY

Sara Solla's research interests lie in the application of statistical mechanics to the analysis of complex systems. Her research has led her to the study of neural networks, which are theoretical models that incorporate "fuzzy logic" and are thought to be in some aspects analogous to the way the human brain stores and processes information. She has used spin-glass models (originally developed to explain magnetism in amorphous materials) to describe associative memory, worked on a statistical description of supervised learning, investigated the emergence of generalization abilities in adaptive systems, and studied the dynamics of incremental learning algorithms. Solla has also helped develop constrained neural networks for pattern-recognition tasks, along with descriptions of the computational capabilities of neural networks and learning algorithms for the design of neural network controllers.

Selected Publications

• D. T. Westwick, E. A. Pohlmeyer, S. A. Solla, et al.
  Identification Of Multiple-Input Systems With Highly Coupled Inputs: Application To EMG Prediction From Multiple Intracortical Electrodes
  Neural Computation 18, 329 (2006)
• A. J. Gruber, P. Dayan, B. S. Gutkin, et al.
  Dopamine Modulation In The Basal Ganglia Locks The Gate To Working Memory
  Joournal Of Computational Neuroscience 20, 153 (2006)
• S. A. Solla
  How Do Neurons Look At The World?
  PLOS Biology 4, 491 (2006)
• F. A. Mussa-Ivaldi and S. A. Solla
  Neural Primitives for Motion Control
  IEEE Journal of Oceanic Engineering 29, 640 (2004)
• A. Roxin, H. Riecke, and S. A. Solla
  Self-Sustained Activity in a Small-World Network of Excitable Neurons
  Physical Review Letters 92, 198101 (2004)