My research focuses on how the properties of single neurons contribute to the performance of networks of neurons. Neural systems possess a remarkable stability that enables them to function robustly, adapting to and learning from a constantly changing environment. This observation leads naturally to two sets of questions: First, how do the adaptive properties of individual neurons enable them to maintain a useful functional role within a network? Second, how do neurons act collectively to allow the network to assume a robustness and computational ability that goes beyond the stability and capability of the individual neurons themselves?
Robustness of the Oculomotor Neural Integrator
My present work focuses on the network of neurons known as the oculomotor neural integrator. This network converts (integrates) eye movement commands that code for eye velocity into eye position signals that control the tension of the eye muscles. It is important in the study of short-term memory because integrator neurons continue to fire at a rate proportional to eye position long after the eye movement command has terminated. Thus, the integrator neurons are said to maintain a "memory of eye position".
Previous modeling of this system has shown how a network of relatively simple model neurons can convert an eye velocity input signal into an eye position output signal. However, these models require extreme fine tuning to perform correctly. I am modeling single neuron properties that lend robustness to the neural integrator network. Synaptic dynamics, dendritic properties, and plasticity mechanisms are being explored as means of reducing the need for precise tuning of network parameters.
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