Learning about GLMs and GAMs in neuroscience

During my lecture on Wednesday, a few students asked me where they could learn more about generalized linear and additive models (GLMs and GAMs) and their applications to systems identification in neuroscience. Unfortunately, there are few textbooks in computational neuroscience, and most cover systems identification to some degree, most notably Marmarelis’ latest. To the best … More Learning about GLMs and GAMs in neuroscience

Hamiltonian Monte Carlo

I’ve been getting more into MCMC methodology recently. There’s a paper published this year by Ahmadian, Pillow & Paninski on different efficient MCMC samplers in the context of decoding spike trains with GLMs. The same methods could potentially be used, of course, for other purposes, like tracking receptive fields. Of particular interest is a remarkably … More Hamiltonian Monte Carlo

Estimating a PSTH with Bayesian splines (BARS)

The PSTH (post-stimulus time histogram) summarizes the timing of neuronal spikes following a stimulus. When few trials are available, or the neuron being recorded seldom fires, the PSTH can be quite noisy. Thus, the PSTH is frequently smoothed with a Gaussian kernel — for example, to reliably estimate the latency of the response. It is … More Estimating a PSTH with Bayesian splines (BARS)