Category: HMM

• 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…

  October 28, 2011

• GLMs and Hidden Markov models for single neurons

  I posted recently about modeling neurons with continuous state-space dynamics. It’s also possible to model neurons with Hidden Markov models (HMMs), which are state-space models with discrete rather than continuous states. In this post I’m going to focus on the application of HMMs to single neuron data. Single neurons with simple states Suppose that a…

  October 21, 2011

• State-space GLMs for neural data: a simple example

  State space methods are a very broad class of methods to analyze temporal data. At its most basic, you assume that your temporal data is generated by a model with underlying parameters. The parameters are time-varying, and usually it’s assumed that the temporal variation is generated through Markovian dynamics. That is, at every time-step, the…

  October 10, 2011

• Waiting times in cyclical HMMs: modeling neuronal refractoriness

  Cyclical Hidden Markov models (HMMs) can be used to sort spikes. For example, in Herbst et al. (2008), wideband data is assumed to be generated by an HMM, where for most of the time the HMM is in the rest state, and with small probability jumps to the spike initiation state. Once it’s in the…

  September 22, 2011