Instantaneous rate estimation of neuronal point processes from a compressed representation of their nonbinary spike trains

Estimating rate functions underlying neural point processes is essential for characterizing the firing patterns of cortical neurons involved in sensory and motor processing. This paper introduces a new method for directly estimating neuronal firing rates from a compressed representation of their extracellular recordings. The approach is based on extending a near-optimal sparse representation of the extracellular recordings to time scales matching those of the underlying rate functions, thereby performing the same role as a kernel-density estimator but at a much lower computational cost. Experimental results demonstrate that this method achieves comparable performance to the conventional Gaussian kernel estimation methods at a fraction of the computational cost. This makes it suitable to brain machine interface applications where real time decoding of neural activity for neuro-motor prosthetic control using firing rate estimates is strongly desired.