Easy whole-brain modeling for computational neuroscientists 🧠💻👩🏿🔬
Physical units and unit-aware mathematical system for general-purpose brain dynamics modeling.
Inference-based time-resolved whole-brain imaging
A differentiable approach to multi-scale brain modeling.
Inference-based time-resolved cortical stability and chaos analysis
Neurobridge integrates functional and anatomical neural connectivity to design dynamic models of the cerebral cortex. Using graph theory and machine learning, it models brain region interactions and validates these models with real biological data to explore brain connectivity.
A unified benchmarking framework for clinical EEG dimensionality reduction, comparing biophysically grounded computational brain models against data-driven statistical baselines.
Analysis of the Izhikevich neuron model
Anatomy-to-electric field modeling pipeline for non-invasive brain stimulation (tDCS, N=590). Modular, JSON-configured R/Python statistical tools -- mixed-effects GLMs, parcellation-based network analysis, PCA with label clustering -- adaptable to any spatially distributed multivariate data.
Publicly available code for "Distributed network processes account for the majority of variance in localized visual category selectivity", Cocuzza et al., 2022.
Publicly available code for "A flexible hub connectivity mechanism for cognitive control". Manuscript in-preparation. Part of C. Cocuzza's dissertation (Aim 3).
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