Embodied Neurocomputation: A Framework for Interfacing Biological Neural Cultures with Scaled Task-Driven Validation

Engineering notes

This research has practical implications for developing energy-efficient and adaptive computation systems in robotics, particularly for applications requiring real-time decision-making. The established framework may help in designing future hybrid bio-silicon control systems.

Chinese explanation / 中文解读

生物神经网络（BNN）被证明是一种强大且适应性极强的信息处理基质，能够高效地处理信息。本文提出了一种“具身神经计算”的框架，用于优化生物和硅基计算之间的编码和解码机制，并通过参数优化，成功提升了BNN在模拟环境中的任务表现。这为未来的生物-硅混合架构和机器人控制应用奠定了基础。

Original 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.

Links and sources

• Official / arXiv page
• PDF from original source

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