livn

A testbed for learning to interact with in vitro neural networks

livn is an open-source interactive simulation environment for learning to control in vitro neural networks. It generates synthetic neural data with ground truth at scale, enabling the development and testing of ML models in interactive settings that mimic experimental platforms such as multi-electrode arrays. By providing an extensible platform for developing and benchmarking machine learning models, livn aims to accelerate progress in both ML-driven understanding and engineering of in vitro neural systems and fundamental understanding of computation in biological neural networks.

pip install livn

Read the full documentation →

Features

Multiple backends — Run simulations using Brian2, NEURON, or JAX (diffrax) depending on your needs
Multi-electrode arrays — Observe neural activity through realistic MEA configurations with stimulation and recording
Differentiable — Differentiate through the full simulation environment (including IO) when using the JAX backend
RL integration — Standard Gymnasium interface for reinforcement learning experiments
Curated datasets — Access pre-generated datasets on Hugging Face for training and evaluation
Parallel execution — Scale up with MPI-based parallel simulations for large-scale experiments on CPU and GPU

Quickstart

Running a simulation

from livn import make

env = make("EI")

env.apply_model_defaults()
env.record_spikes()
env.record_voltage()

it, t, iv, v, *_ = env.run(100)

print("Initial voltages: ", v[:, 0])

Backends

livn supports four simulation backends:

export LIVN_BACKEND=native     # default; ships with livn, no further dependencies
export LIVN_BACKEND=brian2     # point-neuron models
export LIVN_BACKEND=diffrax    # JAX-based, differentiable
export LIVN_BACKEND=neuron     # MPI-parallel, multi-compartment

See the documentation for details.

Documentation

Full documentation at livn-org.github.io/livn covering:

Getting started — installation, first simulation, using datasets
Concepts — environments, systems, models, IO, encoding, decoding, stimulus
Systems — generating, tuning, and sampling custom systems
Examples — dataset usage, differentiable simulation, reinforcement learning

Full installation

Clone this repo and install with uv:

uv sync --all-packages --all-groups --all-extras

Citation

If you use livn in your research, please cite:

@article{GressmannLIVN2025,
	author = {Gressmann, Frithjof and Raikov, Ivan Georgiev and Pham, Hau Ngoc and Coats, Evan and Soltesz, Ivan and Rauchwerger, Lawrence},
	title = {livn: A testbed for learning to interact with in vitro neural networks},
	elocation-id = {2025.12.16.694706},
	year = {2025},
	doi = {10.64898/2025.12.16.694706},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2025/12/19/2025.12.16.694706},
	eprint = {https://www.biorxiv.org/content/early/2025/12/19/2025.12.16.694706.full.pdf},
	journal = {bioRxiv}
}

License

MIT, see LICENSE.txt.

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livn 2.0.0

livn

Features

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