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.

翻译：生物神经网络（BNNs）已被证实是一种强大且具适应性的基质，其通过独特的学习机制，展现出极具能效与数据效率的信息处理潜力。然而，将BNN应用于神经计算的核心挑战在于：如何确定传统硅基计算接口与活体生物系统之间的最优编码与解码机制。本文提出具身神经计算框架，将其作为解决这一多变量编码/解码优化问题的系统级方法。我们通过首次大规模参数优化实验来实践该框架——以BNN智能体在模拟网格世界中沿类气味梯度执行闭环导航任务为场景。尽管任务相对简单，生物交互作用却产生了庞大的多组合参数搜索空间。通过系统组件的互联与参数化分析，我们评估了约1300组参数组合，累计超过4000小时的实时智能体-环境交互，最终识别出12种在多回合实验中持续展现学习能力的配置。在相同交互预算条件下，这些配置的任务表现显著优于优化的硅基DQN智能体。该发现标志着利用BNN实现鲁棒且可扩展的目标导向学习迈出关键一步。本框架为任务驱动型神经计算应用奠定基础，并支持跨领域基准的建立。长期而言，本研究为开发具备高效、自适应与实时计算能力的混合生物-硅基架构提供支撑，包括在机器人控制领域的应用潜力。