Larry Abbott

My research involves the computational modeling and mathematical analysis of neurons and neural networks. Analytic techniques and computer simulation are used to study how single neurons respond to their many synaptic inputs, how neurons interact to produce functioning neural circuits, and how large populations of neurons represent, store, and process information. Areas of particular interest include spike-timing dependent forms of synaptic plasticity, the roles of neuronal adaptation and synaptic modification taking place over multiple time scales in sensory processing and memory, and the dynamics of internally generated activity and signal propagation in large neural networks.

Long-term changes in synaptic strength can depend on the relative timing of pre- and postsynaptic action potentials, with important functional implications. Spike-timing-dependent synaptic plasticity (STDP) can generate a balance of excitation and inhibition, support the learning of temporal sequences, enhance responses to temporally correlated inputs, and equalize synaptic efficacies over complex dendrites. STDP is just one of many forms of synaptic plasticity that act over a wide range of timescales. We have shown that having multiple timescales of plasticity is critical for enhancing memory capacity and protecting memories from "over-writing". We continue to explore the implications of multi-timescale adaptation and plasticity for sensory processing and learning.