AD_pfc_modeling
View on GitHubA Python framework, built from scratch, that models the firing dynamics of prefrontal cortex populations with a system of ODEs: a 4-population rate model of the PFC microcircuit, extended with a ring attractor network for spatial working memory.
The heart of the project is the fitting side. I wrote a custom optimization pipeline (built on Nevergrad) to fit the model to real biological recordings. The core difficulty is that a plausible fit is not enough: the parameters have to produce a genuinely bistable network that matches the data in both the high and the low firing-rate states at once. Most of the engineering went into shaping the loss and search so the optimizer converges on that bistable regime rather than on a single-state solution that scores well on the average.
The model is then used to study how disruption of nicotinic acetylcholine receptors (α7, β2 and α5 subtypes) alters PFC dynamics in the context of Alzheimer’s disease.
The codebase ships a full CLI, a test suite, and extensive mathematical documentation covering Wong-Wang transfer functions, NMDA gating variables, and spike-frequency adaptation. Built during my master’s thesis at the Group of Neural Theory, ENS Paris.
Stack: Python, Jupyter Notebook, Nevergrad, NumPy, Matplotlib