CSHL projects: variance estimation and N-AFC psychophysics

CSHL computational neuroscience: vision is over, and we presented projects using some of the new methods and ideas we explored in class. Earlier in the class, Geoff Boynton did a presentation on psychophysics, and illustrated how to estimate a threshold through Maximum Likelihood in a N-AFC task, where N >= 2. I was curious to … More CSHL projects: variance estimation and N-AFC psychophysics

Spike identification through Gibbs sampling #2

Last time, I demonstrated how to use Gibbs sampling to obtain an estimate of the probability of spikes at different time points given a wideband signal. Unfortunately the method I proposed suffered from long correlation times. In this post I expand upon the previous method to obtain a practical method for identifying spikes. Making it … More Spike identification through Gibbs sampling #2

Spike identification through Gibbs sampling #1

Multi-unit activity (MUA) is usually derived by high-pass filtering a raw wideband signal, thresholding with a low threshold or rectifying, and subsequently using a low-pass filter. The intuition I think is correct, in that spikes from far away neurons will cause transient blips in the wideband signal which can be amplified by thresholding. The usual … More Spike identification through Gibbs sampling #1

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

Adaptive Metropolis-Hastings – a plug-and-play MCMC sampler

Gibbs sampling is great but convergence is slow when parameters are correlated. If the covariance structure is known, you can reparametrize to get better mixing. Alternatively you can keep the same parametrization but switch to Metropolis-Hastings with a Gaussian proposal distribution whose covariance is similar to the model parameters. But what if you don’t know … More Adaptive Metropolis-Hastings – a plug-and-play MCMC sampler