OpenMV is an open source machine vision system. It’s designed to be easy-to-use, with a gentle learning curve. They want this to be the “Arduino of Machine Vision”. The software IDE is free and open. The hardware is available from Sparkfun and other places. Color blob detection, eye and pupil tracking, motion detection, etc. You
Moiré lenses are cool: two patterned diffractive optical elements are rotated relative to each other to vary the power of the “lens”. Just watch the video (below). Demonstration of focus-tunable diffractive Moiré-lenses Stefan Bernet, Walter Harm, and Monika Ritsch-Marte Vol. 21, Issue 6, pp. 6955-6966 (2013) •https://doi.org/10.1364/OE.21.006955 And their 2008 theoretical work.
Andre and colleagues have a preprint up on their project generating inexpensive instrumentation for imaging and optogenetics experiments. The 100 Euro Lab: A 3-D Printable Open Source Platform For Fluorescence Microscopy, Optogenetics And Accurate Temperature Control During Behaviour Of Zebrafish, Drosophila And C. elegans. The authors use this instrumentation in courses at African universities. Open
I came across Rachel Thomas’ comments on blogging in the context of machine learning, and they’re good. I’ve been telling people things like this for awhile now and Rachel distills them better than I do. I’ll quote some of my favorites, but check out her post to read the full story. This single point was
I was cleaning house a bit, and among my old files I found this, which might be worth sharing. Years ago I made a centralized power supply for a custom 2-photon imaging system I built. There were two epi detectors (for red & green fluorescence) and three trans detectors (red, green, and IR-based “DIC-like Dodt”
Reminder: green laser pointers have a ton of IR in them. It needs to be filtered out if you don’t want it. See this old (2011!) Labrigger post. I.e., Paul and Kurt are right (see above). The absurd amount of power you measure out of a “green” laser pointer is not because it is recklessly
Pixy is an open source computer vision system. Mostafa Nashaat, Robert Sachdev, and colleagues including Matthew Larkum have developed software for use with the Pixy, that can be used to track mouse behavior, including free movement around an enclosure (top image), or track the movement of individual whiskers (bottom image), all at 50 Hz. Here’s
We recently tweeted about a preprint from Eftychios A Pnevmatikakis and Andrea Giovannucci (code). The preprint is on motion correction for calcium imaging data. It is a nice quick read and discusses earlier work in the area. (That’s Eftychios of constrained-non-negative-matrix-factorization-for-calcium-imaging-analysis fame). Marius Pachitariu recognized the algorithm as very similar to one that he uses
Multiple people have asked for a discussion of Intan CLAMP patch clamp amplifiers. For those who are unfamiliar with them, they’re miniaturized patch clamp amplifiers. What looks like a headstage, is actually an entire amplifier. So that cable out the back of the “headstage” is actually a digital signal, and thus less susceptible to noise.
It’s a microcontroller built into a breadboard. Actually, TWO microcontrollers. Both are Arduino-compatible. ATmega16U2 and ATmega328P. It comes in both black, white, and pink because style matters. P.S. The bottom side is filled with Lego connectors. We love Legos, but we bet we’ll never use that feature. Still, it can’t hurt.
For teaching electrophysiology, there’s still a lack of comprehensive references. In particular, it can be difficult to impart to students an intuitive feel for the quality or fidelity of electrophysiological recordings. How close to the truth are those traces you just recorded? This sort of practical discussion is often touched upon in electrophysiological texts, but
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