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We present a flexible modulated point process model for characterising variability in non-stationary neural spike train data.

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Bayesian nonparametric (non-)renewal processes

Overview

This is the code repository for this paper. Models are implemented in Python and JAX with dependencies on libraries listed below at the end. See data/ for data preprocessing, synthetic data generation, and visualization. See notebooks in notebooks/ for a examples on loading and analyzing fitted models. See scripts/ for model fitting, analysis and plotting code.

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Formatting done with ufmt, run ufmt format .

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We present a flexible modulated point process model for characterising variability in non-stationary neural spike train data.

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