Topic: cumulative spike response model (CSRM)

Imagine predicting the precise responses to arbitrary sensory stimuli for a neuron probed deeply within a brain. We developed a robust characterization technique called spike-triggered descent (STD) which optimally recovers n-order kernels for the very general cumulative spike response model (CSRM). This works by simulating a model neuron that learns to reproduce the observed spike train. Learning is achieved via parameter optimization that relies on a metric induced on the space of spike trains modeled as a novel inner product space. This technique can precisely learn kernels on extremely