Embodied Neurocomputation: A Framework for Interfacing Biological Neural Cultures with Scaled Task-Driven Validation
Abstract
Biological neural networks (BNNs) have been established as a powerful and adaptive substrate that offer the potential for incredibly energy and data efficient information processing with distinct learning mechanisms. Yet a core challenge to utilizing BNN for neurocomputation is determining the optimal encoding and decoding mechanisms between the traditional silicon computing interface and the living biology. Here, we propose an Embodied Neurocomputation framework as a systems-level approach to this multi-variable optimization encoding/decoding problem. We operationalize this approach through the first large-scale parameter optimization of encoding configurations for a BNN agent performing closed-loop navigation along an odor-style gradient in a simulated grid-world. Despite the relative simplicity of the task, the biological interactions gave rise to a massive multi-combinatorial search space for optimal parameters. By considering how the components of the system are interconnected and parameterized, we evaluated approximately 1,300 parameter combinations, over 4,000 hours of real-time agent-environment interactions, to identify 12 configurations that consistently demonstrated learning across multiple episodes. These configurations achieved significantly higher task performances than optimized silicon-based DQN agents under the same interaction budget. These findings represent an initial step toward robust and scalable goal-oriented learning using BNNs. Our framework establishes a foundation for applying task-driven neurocomputing and supports the development of field-wide benchmarks. In the long term, this work supports the development of hybrid bio-silicon architectures capable of efficient, adaptive and real-time computation, including the potential for robotic control applications.
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BibTeX
@misc{zhou2026embodied,
title = {{Embodied Neurocomputation: A Framework for Interfacing Biological Neural Cultures with Scaled Task-Driven Validation}},
author = {Johnson Zhou and Daniel Tanneberg and Forough Habibollahi and Alon Loeffler and Kiaran Lawson and Valentina Baccetti and Kwaku Dad Abu-Bonsrah and Candice Desouza and Finn Doensen and Bradley Watmuff and Daria Kornienko and Azin Azadi and Justin Leigh Bourke and Bernhard Sendhoff and Brett J. Kagan},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2605.13315},
eprint = {2605.13315},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.13315},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Embodied Neurocomputation: A Framework for Interfacing Biological Neural Cultures with Scaled Task-Driven Validation
AU - Johnson Zhou
AU - Daniel Tanneberg
AU - Forough Habibollahi
AU - Alon Loeffler
AU - Kiaran Lawson
AU - Valentina Baccetti
AU - Kwaku Dad Abu-Bonsrah
AU - Candice Desouza
AU - Finn Doensen
AU - Bradley Watmuff
AU - Daria Kornienko
AU - Azin Azadi
AU - Justin Leigh Bourke
AU - Bernhard Sendhoff
AU - Brett J. Kagan
PY - 2026
DA - 2026/10/02
PB - arXiv
DO - 10.48550/arxiv.2605.13315
UR - https://arxiv.org/abs/2605.13315
AB - Biological neural networks (BNNs) have been established as a powerful and adaptive substrate that offer the potential for incredibly energy and data efficient information processing with distinct learning mechanisms. Yet a core challenge to utilizing BNN for neurocomputation is determining the optimal encoding and decoding mechanisms between the traditional silicon computing interface and the living biology. Here, we propose an Embodied Neurocomputation framework as a systems-level approach to this multi-variable optimization encoding/decoding problem. We operationalize this approach through the first large-scale parameter optimization of encoding configurations for a BNN agent performing closed-loop navigation along an odor-style gradient in a simulated grid-world. Despite the relative simplicity of the task, the biological interactions gave rise to a massive multi-combinatorial search space for optimal parameters. By considering how the components of the system are interconnected and parameterized, we evaluated approximately 1,300 parameter combinations, over 4,000 hours of real-time agent-environment interactions, to identify 12 configurations that consistently demonstrated learning across multiple episodes. These configurations achieved significantly higher task performances than optimized silicon-based DQN agents under the same interaction budget. These findings represent an initial step toward robust and scalable goal-oriented learning using BNNs. Our framework establishes a foundation for applying task-driven neurocomputing and supports the development of field-wide benchmarks. In the long term, this work supports the development of hybrid bio-silicon architectures capable of efficient, adaptive and real-time computation, including the potential for robotic control applications.
ER -