Building a Random Forest Classifier to Predict Neural Spikes


A step-by-step guide to building a Random Forest classifier in Python to predict subtypes of neural extracellular spikes using a real data-set recorded from Human brain organoids. Given the heterogeneity of neurons within the human brain itself, classification tools are commonly utilised to correlate electrical activity with different cell types and/or morphologies. This is a long-standing question in Neuroscience circles, and can be considerably variable between different species, pathologies, brain regions and layers. Fortunately, with the readily increasing computational power allowing improvements in machine-learning and deep-learning algorithms, Neuroscientists are provided with the tools to dive further into asking these important questions. However, as stated by Juavinett et al., for the most part programming skills are underrepresented in the community and new resources to teach them are crucial to solving the complexity of the human brain.

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