Functional Roles for Short-term Synaptic Plasticity in Neuronal Networks

Bose, Booth The primary goal of this project is to derive general principles that underlie how short-term synaptic plasticity (STSP) is utilized by neuronal networks in three different computational and architectural settings. These settings are motivated by concrete biological examples arising in crustacean stomatogastric ganglion (STG), rat hippocampus, and cortex. Specific aims include determining the effect on phase maintenance of multiple depressing synapses and intrinsic cell properties in pacemaker-driven networks, determining how multiple depressing synapses can introduce multiple, co-existent stable firing patterns in reciprocally-connected networks, and determining how depressing excitatory and inhibitory synaptic inputs in an afferent-driven network can generate frequency-selective, steady state, and transient network responses. The investigators develop and use techniques of geometric singular perturbation theory to project and analyze the dynamics of these complicated, high-dimensional neuronal networks onto lower-dimensional slow manifolds. These techniques allow them to understand how different synaptic and intrinsic parameters contribute to and modulate network behavior. They work closely with experimentalists who, in a parallel research program, are investigating the effects of synaptic depression in the crustacean STG. Synaptic plasticity refers to the ability of a synapse to change its strength as a function of its usage. It is widely found in neuronal circuits across the brain. While experimental studies of short=term synaptic plasticity (STSP) are necessarily focused on the particulars of the neural system under investigation, modeling of the type proposed here can provide insights into the more general properties of STSP. The investigators study the possibility that seemingly independent roles for STSP can be grouped together based on how the network architecture constrains STSP to operate. Elucidating the general principles behind these operations provides a framework for understanding how STSP participates in very diverse neuronal computations across brain regions. Due to its interdisciplinary nature, this project is expected to be of interest to members of the experimental, computational and analytic neuroscience communities. Additionally, the investigators continue to teach computational neuroscience and mathematical biology courses that they recently developed. Graduate and undergraduate students have the opportunity to work directly with their experimental collaborators. Thus students become well positioned to continue pursuits in either experimental or theoretical fields, or in an area that combines the two. This enhances the development of a trained workforce at the critical intersection of mathematics and biology.