Origins of short-term memory traces in neuronal networks
Wednesday 13th of May 2009 at 12:00pm
Critical cognitive phenomena such as planning and decision making rely on the ability of the brain to hold information in working memory. Many proposals exist for the maintenance of such memories in persistent activity that arises from stable fixed point attractors in the dynamics of recurrent neural networks. However such fixed points are incapable of storing temporal sequences of recent events. An alternate, and relatively less explored paradigm, is the storage of arbitrary temporal input sequences in the transient responses of a recurrent neural network. Such a paradigm raises a host of important questions. Are there any fundamental limits on the duration of such transient memory traces? How do these limits depend on the size of the network? What patterns of neural connectivity yield good performance on generic working memory tasks? To what extent do these traces degrade in the presence of noise? We combine Fisher information theory with dynamical systems theory to give precise answers to these questions for the class of all linear, and some nonlinear, neuronal networks. We uncover an important role for a special class of networks, known as nonnormal networks. Such networks are characterized by a (possibly hidden) feedforward structure, which is crucial for the maintenance of robust memory traces.
508-20 Evans Hall
Join Email List
You can subscribe to our weekly seminar email list by sending an email to
email@example.com that contains the words
subscribe redwood in the body of the message.
(Note: The subject line can be arbitrary and will be ignored)