NONLINEAR MODEL OF HIPPOCAMPUS

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During this past year we have made major progress on developing and applying several of the essential modeling methodologies for input/output, or nonparametric, models of point-process input neural systems such as the hippocampus. First, we have pursued development and application of adaptive estimation procedures for decomposition studies of the hippocampus. Using this approach, we have been able to successfully estimate linear and nonlinear properties of negative feedback elements (analogous to inhibitory interneurons) given input/output data for only the feedthrough element in the presence and absence of inhibition, i.e., without direct observations from the feedback pathway. Second, we have developed a new means for estimating input/output properties of neural elements using artificial (feedforward) neural networks, which we have demonstrated is particularly powerful for estimating higher order nonlinearities from electrophysiological data. We are particularly excited about the potential for expanding this approach for estimating higher order cross-kernels for multiple-input conditions. Third, for the first time we have applied our nonlinear systems approach at the level of intracellularly recorded synaptic potentials. We have recently modeled the closed- and open-loop AMPA and NMDA components of glutamatergic synaptic input to hippocampal neurons, have derived a representation of the dynamics of the positive feedback to the NMDA receptor-channel, and can accurately predict control EPSPs to a wide range of input patterns. In addition, the model can account for differences between closed- and open-loop NMDA receptor-mediated synaptic responses. These are two of the most widely studied receptor subtypes, as the dynamics of their interaction determine the direction and magnitude of activity-dependent synaptic plasticity in hippocampus, neocortex, and other brain regions. Finally, we have made considerable progress in developing highly efficient circuit designs for analog VLSI implementation of kernel models of hippocampal neurons, a direction that should provide the means for developing massively parallel architectures for simulating global hippocampal system properties.