Plenary Speaker

Each month, another large pile of electrophysiological papers is published, adding to the mountain of information that each of us in the community is expected to absorb and apply. Often, it is difficult to ascribe a causal role to these electrophysiological phenomena. Computational models are a common method for attempting to establish causality, but the immense complexity of neurons and neural circuits make it essentially impossible to develop models that are wholly satisfactory. In this talk, I will discuss the use of real-time computing in performing experiments that, with caveats, combine the strengths of computational and experimental approaches. Examples will come from our work on the study of neuronal synchronization and human perception. We will examine how the properties of individual neurons, complex neuronal networks, and behaviorally relevant stimuli can be altered with quantitative rigor to establish some “rules” of neuronal computing.