  The characterization of neural responses to sensory stimuli is a central
problem in neuroscience. Spike-triggered average (STA), an influential
technique, has been used to extract optimal linear kernels in a variety of
animal subjects. However, when the model assumptions are not met, it can lead
to misleading and imprecise results. We introduce a technique, called
spike-triggered descent (STD), which can be used alone or in conjunction with
STA to increase precision and yield success in scenarios where STA fails. STD
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 higher order kernels using limited data.
Kernels extracted from a Locusta migratoria tympanal nerve dataset demonstrate
the strength of this approach.
