Compressed and Distributed Sensing of Multivariate Neural Point Processes

This paper proposes a new approach to simultaneously estimate time-varying intensity functions of multiple point processes from their continuous-time signal representation. We use models of neural response properties in the cortex to illustrate the theory of the proposed approach. Based on sparse representation of the continuous-time signals in the context of compression, it is shown that intensity functions can be approximated reasonably well without the need to decompress and classify the source signals. The approach is best suited for the case when multiple point processes are characterized by non-binary spike waveforms observed with an array of sensors. When spike waveforms from different sources are correlated, the estimated intensities can be inaccurate due to spike classification errors. We therefore build on our previous work for separating correlated spike waveforms to enable enhanced separation of those intensity functions. We finally show that this framework leads to substantial savings in computational complexity for real time operation in resource constrained signal processing systems.