in_silico_bioANN

In silico recurrent neural network model of biological neural networks used in:

Trujillo CA, Adams JW, Negraes PD, Carromeu C, Tejwani L, Acab A, Tsuda B, Thomas CA, Sodhi N, Fichter KM, Romero S, Zanella F, Sejnowski TJ, Ulrich H, Muotri AR. Pharmacological reversal of synaptic and network pathology in human MECP2-KO neurons and cortical organoids. EMBO Molecular Medicine, e12523, 2020.

Organization of bioANN.py is

• definition of network class
• definition of simulation class
  • test fxn
• main
  • definition of parameters and output directory
  • creation of simulation of network
  • script to run simulation and gather data

Command to run bioANN:

python3 bioANN.py [NETSZ] [P_CON] [P_INH] [W_MASK] [KD]

where [NETSZ] is number of neurons in network, [P_CON] is connectivity, [P_INH] is fraction of network that are inhibitory neurons, [W_MASK] is the catergory of synapses to perturb, and [KD] is the synaptic perturbation factor.

Citation

If you use this repo in your research, please cite:

@article{Trujillo_2020,
Author = {Cleber A Trujillo and Jason W Adams and Priscilla D Negraes and Cassiano Carromeu and Leon Tejwani and Allan Acab and Ben Tsuda and Charles A Thomas and Neha Sodhi and Katherine M Fichter and Sarah Romero and Fabian Zanella and Terrence J Sejnowski and Henning Ulrich and Alysson R Muotri},  
Title = {Pharmacological reversal of synaptic and network pathology in human MECP2-KO neurons and cortical organoids},  
Journal = {EMBO Molecular Medicine},  
Pages = {e12523},  
DOI = {https://doi.org/10.15252/emmm.202012523},  
Year = {2020}}