PyNeuroTrace - Python code for neural activity timeseries

Modern techniques for measuring neuronal activity using fluorescent biosensors and ultra-fast microscopy have allowed neuroscientists unprecedented access to neural information processing in vivo.The time series datasets generated from experiments sampling somatic action potentials from populations of neurons, or full-dendritic arbor sampling of populations of synapses, are becoming increasingly larger as new technologies allow for faster acquisition rates and higher temporal resolution of neural signals.Neuronal activities are sourced from an ever-expanding library of fluorescent indicators of distinct measures, including detectors of calcium, membrane voltage, and a range of neurotransmitters and neuromodulators.These biosensors are impacted by their unique molecular kinetics and inherent signal-to-noise properties.The quality of neural signal data sets are also impacted by acquisition instruments, which differ in sensitivity and sampling rate.All of these features, including underlying neural signals, biosensor properties, and microscope capabilities, must be considered during post-imaging signal processing with techniques that can scale to the size of modern neural datasets.To address this problem, here, we describe pyNeuroTrace, an open-source Python library developed to aid in processing neuronal signals from large fluorescent biosensor data sets, which allows dynamic control of filtering and signal processing with these unique aspects in mind before analyses of the underlying neuronal activity can be conducted.