Sara Solla is a physicist and neuroscientist. Her research interests lie in the application of statistical mechanics and nonlinear dynamics 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 ability 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. Solla currently investigates the neural process associated with sensory processing, decision making, and the control of movement.
Solla is a fellow of the American Physical Society and an elected member of the American Academy of Arts and Sciences. She is also a member of the Society for Neuroscience, the New York Academy of Sciences, and the Society for the Neural Control of Movement.