Filtering by the cell != filtering by the network

Back in 2014 I read this paper, from Judith Hirsch’s lab. To my simple mind, it was a pretty complex model, but it had a cute little video of the thalamus reducing position noise from the retina. I’m not going to lie, I still don’t fully understand the original paper, but probably due to an urge to avoid doing experiments, I felt drawn to make simple integrate and fire model of the retina -> thalamus circuit to see if it filtered noise. The result were somewhat predictable, and I tucked them away. But then an opportunity came to fish them out of the proverbial desk draw, and then I noted something quite interesting: The way the individual cells filtered their input was the opposite of how the network filtered its input. It led me to publish this article. Below you can play with some of the simulations I used to produce the paper*.

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Visualizing how FFTs work.

A lot of scientists have performed Fast Fourier Transforms at some point, and those that haven’t, probably are going to in future, or at the very least, have read a paper using it. I’d used them for years before I ever began to think about how they algorithm actually worked. However, if you’ve ever looked it up, unless math is your first language, the explanation probably didn’t help you a lot. Normally you either get an explanation along the lines of “FFTs convert the signal from the time domain to the frequency domain” or you just get this:

X_{(k)}\ = \sum_{n=0}^{N-1} x_{(n)} \cdot e^{-2 \pi i k n / N}

However, the other day I came across an amazing explanation of the algorithm, and I really wanted to share it. While I might not be able to get you to the point that you completely understand the FFT, I think think it might seriously enhance your understanding.
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Extracting data from a scatter graph

I’ve already made a rudimentary script for extracting data from published waveforms and other line graphs. But what I’ve needed recently is to be able to extract data from XY scatter graphs. This is a slightly more complex problem because it requires feature detection of an unknown number of points. You can access the script here, and I’ll go over its use and some of the code in the rest of this post. Continue reading

Extracting raw data from figures

Because I’m a cynical bastard, I regularly try to figure out what the real content of a published waveform is. For me, it’s usually someones EEG data that supposedly has some FFT peak that I can’t really believe. So instead of pouring over waveforms with Photoshop (read: Microsoft Paint) to figure out the data that’s in the an image, some time agoe ago I wrote a program in python to allow you to automatically get the numbers.

So I finally translated it to JavaScript, so all of you can benefit from it (and also I can use it at SfN).
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