Real-Time Decoding of an Integrate and Fire Encoder
Author(s)Saxena, Shreya; Dahleh, Munther A.
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Neuronal encoding models range from the detailed biophysically-based Hodgkin Huxley model, to the statistical linear time invariant model specifying firing rates in terms of the extrinsic signal. Decoding the former becomes intractable, while the latter does not adequately capture the nonlinearities present in the neuronal encoding system. For use in practical applications, we wish to record the output of neurons, namely spikes, and decode this signal fast in order to act on this signal, for example to drive a prosthetic device. Here, we introduce a causal, real-time decoder of the biophysically-based Integrate and Fire encoding neuron model. We show that the upper bound of the real-time reconstruction error decreases polynomially in time, and that the L[subscript 2] norm of the error is bounded by a constant that depends on the density of the spikes, as well as the bandwidth and the decay of the input signal. We numerically validate the effect of these parameters on the reconstruction error.
DepartmentMIT Institute for Data, Systems, and Society; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the Twenty-eighth Annual Conference on Neural Information Processing Systems (NIPS)
Neural Information Processing Systems Foundation
Saxena, Shreya, and Munther Dahleh. "Real-Time Decoding of an Integrate and Fire Encoder." Twenty-eighth Annual Conference on Neural Information Processing Systems (NIPS) (December 2014).
Final published version