Spike-Triggered Descent
This addresses a central problem in neuroscience for researchers needing accurate neural response models, though it appears incremental as an enhancement to STA.
The authors tackled the problem of imprecise neural response characterization when spike-triggered average (STA) fails due to unmet model assumptions, introducing spike-triggered descent (STD) to increase precision and successfully learn higher-order kernels from limited data, as demonstrated on a Locusta migratoria dataset.
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.