Implementation of Mejias et al. 2016: Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex
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NeuroML2
Python
tests
.gitignore
README.md
requirements.txt

README.md

Mejias-2016

Implementation in Python and of NeuroML2/LEMS and Mejias, Jorge F., John D. Murray, Henry Kennedy, and Xiao-Jing Wang. 2016a. “Feedforward and Feedback Frequency-Dependent Interactions in a Large-Scale Laminar Network of the Primate Cortex.” https://doi.org/10.1101/065854.

The model

The model simulates the dynamics of a cortical laminar structure across multiple scales: (I) intralaminar, (II) interlaminar, (III) interareal, (IV) whole cortex. Interestingly, the authors show that while feedforward pathways are associated with gamma oscillations (30 - 70 Hz), feedback pathways are modulated by alpha/low beta oscillations (8 - 15 Hz).

Note: This repo is a work in progress. So far this repository contains the implementation for the model dynamics at the intralaminar and the interlaminar level. At the moment I am working on the implementation of the interareal level.

The Simulation

Python

So far, we have reproduced the main findings described by Mejias et al., 2016 at the intralaminar and interlaminar level. The main results are described here.

NeuroML2

A basic implementation and simulation of the intralaminar model have also been implemented in NeuroML2/LEMS. GenerateNetwork.py generates the LEMS file with the description of the network.

The simulation can be run by calling inside the NeuroML2 folder: python GenerateNetwork.py -jnml

Requirements

The necessary Python packages are listed on the requirements.txt files.