Neuroscience

Although advances have been made recently in machine learning and artificial systems, major issues remain unresolved. These regard abilities that are difficult to mimic by machines but that humans and animals display ubiquitously, such as adaptation, generalization, continuous learning with experience, and conceptualization. Do neural brain cells provide a computational platform with characteristics and representations that could permit such abilities to be expressed in machines and be applied in practice? Do neural brain processes use unique methods for performing 'computation' in general, in contrast to those used in current computer and integrated circuit technologies?

The Neuroscience group attempts to answer these questions by investigating the processing used by the brain and its neural substrate. One of our research goals is to understand and make use of the advantages that a spiking neural representation provides for rapid computation and learning. With these representations, we are investigating the adaptive mechanisms to learn continuously and in real-time. Such abilities are required for constructing truly reactive and adaptive systems. We have chosen the cerebellum, with its participation in brain function that ranges from regulation of motor reflexes to participation in higher cognitive functions, as a focus to investigating these issues.

We build mathematical and computational models of the cerebellum and investigate plasticity, information processing and representation in the cerebellum during complete sensorimotor action-perception tasks. The models are informed and constrained by neurophysiological and psychological data.

Other issues under investigation in the Neuroscience group include analysis of sensorimotor laws, applications of information theory to neural coding, and large scale, and whenever possible, real-time, simulations of spiking networks for model validation in real and simulated environments.