This project is a part of a technical internship at Ultser University (Northern Ireland) within the context of an engineering degree. It focuses on making existing Bayesian statistical models accessible and practical for experimental neuroscientists.
Adapt and demonstrate existing Bayesian models so that non-specialist users can apply them to their own neuroscience datasets.
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Phase 1 : Apply the existing Poisson and Zero-Inflated Poisson models to a subset of neuroscience data and document every step from a "naïve user" perspective (using PyMC in Python).
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Phase 2 : Design a simple analysis pipeline and produce an example of Python script/Jupyter Notebook implementing it on example datasets.
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Phase 3 : Develop step-by-step tutorial notebook for novice users, showing how to load, transform, analyse and visualize their data.
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Team : Mathis DA SILVA (intern) under the supervision of Cian O'Donnell (project tutor) and Conor Houghton (Cian's collaborator).
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Approach : This project is part of a technical internship at Ulster University (Northern Ireland) within the context of an engineering degree. It focuses on making existing Bayesian statistical models (developed by Dr O’Donnell) accessible and practical for experimental neuroscientists.