Abstract
Electrophysiology is increasingly moving towards highly parallel recording techniques which generate large data sets. We record extracellularly in vivo in cat and rat visual cortex with 54-channel silicon polytrodes, under time-locked visual stimulation, from localized neuronal populations within a cortical column. To help deal with the complexity of generating and analysing these data, we used the Python programming language to develop three software projects: one for temporally precise visual stimulus generation (“dimstim”); one for electrophysiological waveform visualization and spike sorting (“spyke”); and one for spike train and stimulus analysis (“neuropy”). All three are open source and available for download (http://swindale.ecc.ubc.ca/code). The requirements and solutions for these projects differed greatly, yet we found Python to be well suited for all three. Here we present our software as a showcase of the extensive capabilities of Python in neuroscience.
Highlights
As systems neuroscience moves increasingly towards highly parallel physiological recording techniques, generation, management, and analysis of large complex data sets is becoming the norm
We are interested in the function of localized neuronal populations in visual cortex
Spyke has a graphical user interface (GUI) and looks like a native application, while neuropy is typically accessed from the Python command line as a library
Summary
As systems neuroscience moves increasingly towards highly parallel physiological recording techniques, generation, management, and analysis of large complex data sets is becoming the norm. Spyke has a graphical user interface (GUI) and looks like a native application, while neuropy is typically accessed from the Python command line as a library. DIMSTIM: VISUAL STIMULUS GENERATION In our experiments, we needed a way to display and control a wide variety of stimuli with many different parameters, often shuffled with respect to each other in various ways.
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