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Fiber photometry is the process of recording bulk neural activity by measuring fluorescence changes in activity sensitive indicators such as GCaMP through an optical fiber. We present a system of open source hardware and software for fiber photometry data acquisition consisting of a compact, low cost, data acquisition board built around the Micropython microcontroller, and a cross platform graphical user interface (GUI) for controlling acquisition and visualising signals. The system can acquire two analog and two digital signals, and control two external LEDs via built in LED drivers. Time-division multiplexed illumination allows independent readout of fluorescence evoked by different excitation wavelengths from a single photoreceiver signal. Validation experiments indicate this approach offers better signal to noise for a given average excitation light intensity than sinusoidally-modulated illumination. pyPhotometry is substantially cheaper than commercial hardware filling the same role, and we anticipate, as an open source and comparatively simple tool, it will be easily adaptable and therefore of broad interest to a wide range of users.


Journal article


Scientific Reports


Nature Research (part of Springer Nature)

Publication Date