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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
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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
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Obscure Matlab features #4 – Writing help
Keeping code undocumented now might save you a few minutes, but it will undoubtedly cause you pain and misery when you do revisions 6 months from now. As you surely know, Matlab’s built-in help function parses comments immediately following the function byline and outputs these comments in the command window. It has a couple of
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Plexon Matlab API now available for Linux
Regular readers of the blog will remember my previous struggles reading .plx files on platforms other than Windows. I got contacted by none other than Plexon boss Harvey Wiggins about this. I’m happy to report that they’ve posted an updated Matlab offline SDK which compiles on 32- and 64-bit Linux. It may be possible to
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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
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Fat spikes, thin spikes
A lot of studies have popped recently that look at duration of spikes in an attempt to determine whether the measured neurons are inhibitory or excitatory. Thin (fast) spikes are identified with putative inhibitory interneurons, while thick (slow) spikes are identified with excitatory pyramidal cells. This is grounded in some physiological evidence, yet it still
xcorr: AI & neuro 