Mind In Vitro - Computing with living neurons

Mind in Vitro (MiV) reimagines computation by engineering living, bio-hybrid neural systems that can learn, heal, and adapt. The expedition brings together advances in wetware platforms, computational architectures, programming models, and robotic embodiment to create emergent machines built from cellular substrates, all while embedding an in-design ethics program.

MiV pairs its research agenda with broad community building: recruiting and mentoring students across levels, developing shared curricula, practicing open science, and inspiring K-12 learners through hands-on experiences and art-science collaborations. Dive deeper into the project at mindinvitro.illinois.edu/about.

Please also check out our ICML25 position paper that provides an overview of key technologies, challenges, and principles behind this development and describe strategies and opportunities for novel machine learning research in this emerging field.

LIVN is a research platform for working with in vitro neural systems and large-scale neural simulations. It provides tools to generate, run, and analyze neural activity data across configurable environments and different simulation backends, making it useful for neuroscience research, modeling, and data-driven experiments. The system emphasizes reproducibility and flexibility, supporting scalable workflows for both small experiments and large simulations. Install LIVN here: LIVN

LIVN browser is designed to make complex electrophysiology data more intuitive, explorable, and accessible in real time. Combining browser-based simulation, linked visualizations, and modular analysis tools. Try it here: livn web.

    LIVN — interactive demo  ·  Ctrl+Enter to run
    Not started
  
from livn.system import make
import matplotlib.pyplot as plt

# Downloads EI1 on first run (~30 s); cached afterwards
if 'system' not in dir():
    system = make('EI1')

coords = system.neuron_coordinates  # columns: [id, x, y, z]
print(f"Loaded {len(coords)} neurons")

fig, ax = plt.subplots(figsize=(6, 5))
ax.scatter(coords[:, 1], coords[:, 2], s=4, alpha=0.4, color='steelblue')
ax.set_title('EI1 — Neuron Positions')
ax.set_xlabel('x (μm)')
ax.set_ylabel('y (μm)')
    
  

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