The purpose of the workshop is to build ties between the Computational Neuroscience groups at the University of Arizona and the Courant Institute for Mathematical Sciences, NYU. Recently there has been great progress in the study and modeling of neural activity in biological systems. This is an area with tremendous challenges, both because of the scale of the problems (billions of neurons) and the lack of specific knowledge of the details of the processes that underlie the biology.
All are invited to attend the sessions and discussions. The detailed schedule is below.
Jean-Marc Fellous, Dept of Psychology, Applied Mathematics, The University of Arizona, will speak on “Making Models of the Brain” at 12:00 PM in Math 402. (Refreshments will be served.)
I will review the basic levels of neural modeling, from the McCulloch-Pitts neurons to detailed biophysical models of single neurons. I will discuss the three major features of successful models: Biological soundness, explanatory and predictive powers. Examples of computational models will be given to illustrate these points. This lecture is meant to introduce newcomers to computational neuroscience, in preparation for the lectures that will be given during the workshop.
Adi Rangan, Courant Institute for Mathematical Sciences, NYU, will speak on “Causality and Coding in the Cortex” at 1:30 PM in Math 402.
Many cortical areas respond to different stimuli in different ways. For example, within the locust olfactory cortex, distinct odors generate reproducibly distinct activity profiles. I will present a new way to examine the coding properties of such a system, and illustrate several features of this new method.
Michael Frank, Dept of Psychology, The University of Arizona, will speak on “Systems-level neural modeling of learning and decision making” at 2:45 PM in Math East 241.
The basal ganglia and frontal cortex interact intimately to facilitate adaptive action plans while suppressing those that are less adaptive. The dynamics of this circuitry in reinforcement learning and decision making have been explored via a series of inter-related computational models. The models suggest distinct neurobiological mechanisms associated with (a) action selection; (b) learning the probability of an action leading to reward; (c) holding in mind graded values of reinforcement magnitude in working memory; and (d) dynamic modulation of decision thresholds. I will present novel predictions arising from these models that have been confirmed in experiments with multiple patient populations, pharmacological manipulation, neuroimaging and genetics
David Cai, Courant Institute of Mathematical Sciences, New York University, will speak on “One Model, One Regime, and Many Phenomena” at 4:00 PM in Math 501. (Refreshments at 3:30 PM in Math 401N.)
We will present our large-scale computational modeling of the primary visual cortex (V1). In particular, we will discuss network mechanisms underlying spatiotemporal dynamics associated with spontaneous on-going activity of the V1 and the line-motion illusion --- which is the illusory motion sensation from a static cue of a flashed stationary square quickly followed by a stationary bar. Related issues, such as kinetic theory of neuronal network dynamics, will also be addressed.
“Discussion sessions and working groups (9:00-12:00)” to be held at 9:00 AM in Math East 241.