Stefano Fusi, PhD
Research Interest
The complexity of real world behavior cannot be reduced to a simple mapping between a sensory stimulus and a motor response. Indeed the same stimulus should sometimes lead to different behaviors depending on the situation, the intention of the subject, and the rules imposed by the task to be performed. In many complex tasks we need to go through a series of different inner mental states each representing a particular disposition to behavior. Our group investigates the neural mechanisms which underlie the formation of rule representations (learning) and their expression. In particular we are developing a model of a neural network which encodes the inner mental states as attractor of the neural dynamics. Moreover we study the theory of synaptic mechanisms leading to the abstraction of the rules (learning and memory). In collaboration with experimentalists from different fields we test our ideas on real biological brains.
- M. Rigotti, O. Barak, M.R. Warden, X.-J. Wang, N.D. Daw, E.K. Miller, S. Fusi, The importance of mixed selectivity in complex cognitive tasks, Nature 497, 585-590 (2013)
- A. Roxin, S. Fusi, Efficient Partitioning of Memory Systems and Its Importance for Memory Consolidation, PLoS Comput Biol 9(7): e1003146. doi:10.1371/journal.pcbi.1003146 (2013)
- O. Barak, M. Rigotti, S. Fusi, The sparseness of mixed selectivity neurons controls the generalization-discrimination trade-off, Journal of Neuroscience 33, 3844-56 (2013)
- M. Rigotti, D. D. Ben Dayan Rubin, X.-J. Wang and S. Fusi, Internal representation of task rules by recurrent dynamics: the importance of the diversity of neural responses, Frontiers in Computational Neuroscience, 4, doi: 10.3389/fncom.2010.00024 (2010)
- S. Fusi, P.J. Drew, L.F. Abbott, Cascade models of synaptically stored memories, Neuron, 45, 599-611, (2005)
For a complete list of publications, please visit:
http://www.neurotheory.columbia.edu/Fusi/pubs.html
- Computation and Theory