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Multi-scale relevance is a method for identifying neurons relevant to the animal's behaviour being probed in an experiment without an a priori knowledge of any external features. It is described in the paper:
RJ Cubero, M Marsili, Y Roudi
Finding informative neurons in the brain using Multi-Scale Relevance
This repository contains Python codes for implementing multi-scale relevance and for reproducing the figures in the paper:
-relevance.py is the main Python code which is used to calculate for the multiscale relevance. In particular, the function parallelized_relevance takes as an input the total number of time points (must be the same number as the size of the spike train) and the spike train of a single neuron and returns as an output a scalar value which is the multiscale relevance.
-src/ contains personalised Python codes (including a duplicate of relevance.py) which is used to in the paper.
-test/ contains the personalised Python codes to reproduce the figures in the paper.
-Flekken_Data/ contains one recording session of Stensola et. al. (2012) which is used in the paper.