NeuroFlame

Copyright © 2024 by Melanie Tschiersch. All rights reserved.

Introduction

This package provides an implementation of a recurrent neural network trainer and simulator with pytorch. Networks can have multiple neural populations with different connectivity types (all to all, sparse) that can be structured (tuned, low rank).

Network weights can be trained in a unsupervised or supervised manner just as vanilla RNNs in torch.

For more info look at the notebooks in ./notebooks and the configuration files in ./conf.

News

• Following the release of Pytorch 2.3, NeuroFlame now includes support for SemiSparseTensor operations!

Installation

pip install -r requirements.txt

or alternatively using conda (I recommend using mamba’s miniforge, a c++ conda implementation)

mamba install --file conda_requirements.txt

Documentation

The full documentation can be found here.

Basic Usage

Here is how to run a simulation

# import the network class
from src.network import Network

# Define repository root
repo_root = '/'

# Choose a config file
conf_file = './conf/conf_EI.yml'

# Other parameters can be overwriten with kwargs
# kwargs can be any of the args in the config file

# initialize model
model = Network(conf_file, repo_root, **kwargs)

# run a forward pass
rates = model()  

Advanced Usage

Project Structure

.
├── conf  # contains configuration files in yaml format.
│   ├── *.yml
├── notebooks  # contains ipython notebooks.
│   ├── setup.py
│   └── *.ipynb
├── org  # contains org notebooks.
│   ├── /doc/*.org
│   └── *.org
├── src  # contains source code.
│   ├── activation.py  # contains custom activation functions.
│   ├── connectivity.py  # contains custom connectivity profiles.
│   ├── decode.py
│   ├── lif_network.py  # implementation of a LIF network.
│   ├── lif_neuron.py
│   ├── lr_utils.py  # utils for low rank networks.
│   ├── network.py  # core of the project.
│   ├── plasticity.py  # contains STP.
│   ├── plot_utils.py
│   ├── sparse.py  # utils for large sparse matrices.
│   ├── stimuli.py  # contains custom stimuli for behavioral tasks.
│   ├── train.py  # utils to train networks.
└── └── utils.py 

Networks Dynamics

Network dynamics is described here.

Connectivity

The connectivities available in NeuroFlame are described here.

Single Network Simulation and Batched Simulations

I describe in detail how to run a network simulation and use NeuroFlame to effectively run parallel simulations for different parameters here.

Networks Training

Here, I show how to train networks.

Balanced Networks

Here, a tutorial on balanced networks.

Multistability

Fully connected

Here, a notebook that shows how to use NeuroFlame to locate ring attractors in parameter space.

Sparse

Here, a notebook that shows how to use NeuroFlame to locate multistable balance states in parameter space.

Short Term Plasticity

Here, a tutorial on STP with NeuroFlame.

Behavioral Tasks

Here, a tutorial on how to use different stimuli to get the model to perform different behavioral tasks.

Serial Bias

Here, a tutorial on how to get serial bias in a balanced network model.

Contributing

Feel free to contribute.

MIT License
Copyright (c) [2023] [A. Mahrach]