Real-Time Decoding of an Integrate and Fire Encoder
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 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.
The paper introduces a model and algorithms for decoding spikes from an integrate and fire decoder. Theorems that are providing upper bounds on the reconstruction error are given and the parameters and and error of the model are studied. The difference to prior work by the Lazar group does not become clear from the ms to the reviewer. Potentially the contribution comes from this lab. The simulations and numerical studies are on the weaker side. What I do not like is that the authors build up an expectation that their methods may be used in BMI decoding and then do not show any data. As a theoretical contribution, I consider it solid, but as said above, the margin to prior work does not become clear.