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

2026-05-13

AI Summary

This work addresses the challenge of establishing efficient encoding–decoding mechanisms between silicon-based systems and biological neural networks to enable task-driven biocomputation. The authors propose an embodied neurocomputational framework in which biological neural network agents learn closed-loop navigation along odor gradients within a simulated grid world through large-scale parameter optimization. For the first time, this study systematically explores the optimization of encoding configurations in biological networks under task-driven conditions, establishing a scalable, goal-directed learning paradigm. By integrating closed-loop simulation, real-time bio-environment interaction, and comparative evaluation against deep Q-networks (DQNs), the research identifies 12 stable learning configurations from approximately 1,300 parameter combinations. These configurations significantly outperform optimized DQN agents under equivalent interaction budgets, thereby laying a foundational framework for hybrid bio-silicon architectures.

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.

Problem

• Embodied Neurocomputation
• Biological Neural Networks
• Encoding/Decoding
• Neurocomputation
• Bio-silicon Interface

Innovation

• Embodied Neurocomputation
• Biological Neural Networks
• Encoding-Decoding Optimization
• Task-Driven Validation
• Hybrid Bio-Silicon Architecture