Rastermap: A Discovery Method for Neural Population Recordings
Analysis tool for large-scale neural data which allows users to explore dynamical and spatial relationships among neurons.
Abstract
Neurophysiology has long progressed through exploratory experiments and chance discoveries. Anecdotes abound of researchers setting up experiments while listening to spikes in real time and observing a pattern of consistent firing when certain stimuli or behaviors happened. With the advent of large-scale recordings, such close observation of data has become harder because high-dimensional spaces are impenetrable to our pattern-finding intuitions. To help ourselves find patterns in neural data, our lab has been openly developing a visualization framework known as “Rastermap” over the past five years. Rastermap takes advantage of a new global optimization algorithm for sorting neural responses along a one-dimensional manifold. Displayed as a raster plot, the sorted neurons show a variety of activity patterns, which can be more easily identified and interpreted. We first benchmark Rastermap on realistic simulations with multiplexed cognitive variables. Then we demonstrate it on recordings of tens of thousands of neurons from mouse visual and sensorimotor cortex during spontaneous, stimulus-evoked and task-evoked epochs, as well as on whole-brain zebrafish recordings, widefield calcium imaging data, population recordings from rat hippocampus and artificial neural networks. Finally, we illustrate high-dimensional scenarios where Rastermap and similar algorithms cannot be used effectively.
Make your next discovery using #Rastermap, a visualization method for large-scale neural data.
You can explore your data in the #Rastermap graphical user interface:
Rastermap finds single-trial sequences of neural activity in a virtual reality experiment:
Rastermap finds movement-related structure in spontaneous activity in complete darkness:
Rastermap sorting of hippocampus data from Grosmark & Buzsaki, 2016:
Rastermap sorting of wholebrain zebrafish activity from Chen et al, 2018:
Rastermap sorting of neurons from Reinforcement Learning agents playing Atari games:
Learn more on our github: https://github.com/MouseLand/rastermap. Rastermap is fast thanks to numpy, scipy, numba, and scikit-learn. The GUI is powered by pyqt and pyqtgraph, and supports npz, npy, mat and nwb ophys files.
Excited to see new datasets explored with this! If you have issues, please post an issue on the Rastermap github: https://github.com/MouseLand/rastermap/issues.
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