Postdoc: Subhaneil Lahiri (Applied Physics)
Faculty Advisor: Surya Ganguli (Applied Physics)

A plethora of molecular biology techniques are yielding an unprecedented experimental view into the immense complexity of synaptic signalling pathways.  However, it is completely unclear how to theoretically understand the functional contribution of such synaptic complexity to higher order phenomena like learning and memory.  A key step to making progress in obtaining this understanding is to bridge the gap between theory and experiment, especially as current theoretical models of neuronal systems describe synapses by a single scalar value, the size of the postsynaptic potential induced by a presynaptic spike.  We will study the relationship between synaptic complexity and learning and memory by systematically analyzing neuronal network models in which each synapse itself is a complex dynamical system.  We will study what kinds of synaptic dynamical systems optimize network memory performance, thereby making predictions about how intracellular synaptic signalling pathways should be functionally organized, and how their disruption in disease may lead to different kinds of memory deficits.