Tag: Hidden Markov Model

Spike identification through Gibbs sampling #1
Multiunit activity (MUA) is usually derived by highpass filtering a raw wideband signal, thresholding with a low threshold or rectifying, and subsequently using a lowpass 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 […]

GLMs and Hidden Markov models for single neurons
I posted recently about modeling neurons with continuous statespace dynamics. It’s also possible to model neurons with Hidden Markov models (HMMs), which are statespace 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 […]