MohamedRabieh

MohamedRabieh

@MohamedRabieh
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Language Breakdown

Lines of code distribution across 7 owned repositories

1.0M Total LOC
Python
500,447 lines
50.0%
N/A
Jupyter Notebook
500,119 lines
50.0%
N/A
π

Pi-Shaped Developer

π-shaped

Two strong pillars: Python & Jupyter Notebook

Python
Jupyter Notebook

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mohamedrabieh
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Repos

18

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Coding Streak

Contribution activity over the past year

1 day
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Top Repositories

nemos

NEural MOdelS, a statistical modeling framework for neuroscience.

1 0
Python
mea-nlx-spike-sorting-gui

Jupyter-based GUI for MEA .h5 and Neuralynx .ncs tetrode spike sorting, channel QC, sorter comparison, quality metrics, unit reports, and two-pass unit curation using SpikeInterface.

1 1
Python
MEA-Spike-Sorting-Connectivity-GUI

End-to-end Python GUI for MEA spike sorting, unit quality analysis, and functional connectivity with null models. Built with SpikeInterface + MountainSort4 + tkinter.

1 0
Jupyter Notebook
Baseline-Behavior-Models

A GitHub repository for evaluating freezing behavior using baseline statistical models from neuron-level features. This includes LOOCV Linear Regression, Standard Linear Regression, and Generalized Estimating Equations (GEE) at the neuron level.

1 0
Jupyter Notebook
Functional-Connectivity-Builder

This repository contains modular Python code for building functional connectivity networks from spike train data using dot product-based similarity and null-hypothesis testing. It was developed for use in electrophysiology analysis focused on brain network dysfunction after early-life seizures.

1 0
Jupyter Notebook
Graph-Metrics-Quantifications

This code was developed for analyzing network-level changes in neural dynamics during cognitive tasks in mouse models of early life seizures (ELS). Metrics were selected to capture node-level influence, clustering, modularity, and integration across functional connectivity graphs.

1 0
Jupyter Notebook
GAT-LOOCV-Freezing_Prediction

This repository contains a full implementation of a GAT model for predicting freezing behavior during fear extinction, using graph representations of single-unit electrophysiological data. The model uses per-neuron features and functional connectivity edge weights to build per-animal graphs and evaluate performance using LOOCV

1 0
Jupyter Notebook
pyhawkes

Python framework for inference in Hawkes processes.

0 0
Python
pyglmnet

Python implementation of elastic-net regularized generalized linear models

0 0
Python
granger

Granger causality for multielectrode array Plexon neuronal recordings.

0 0
MATLAB

Open Source Impact

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