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Embodied Neurocomputation Framework

Paper: Embodied Neurocomputation: A Framework for Interfacing Biological Neural Cultures with Scaled Task-Driven Validation Authors: Johnson Zhou, Daniel Tanneberg, Forough Habibollahi, Alon Loeffler, Kiaran Lawson, Valentina Baccetti, Kwaku Dad Abu-Bonsrah, Candice Desouza, Finn Doensen, Bradley Watmuff, Daria Kornienko, Azin Azadi, Justin L. Bourke, Bernhard Sendhoff, Brett J. Kagan Institutions: Cortical Labs (Australia), Honda Research Institute Europe (Germany) arXiv: 2605.13315 (May 13, 2026) Category: cs.ET, cs.LG, cs.NE

Overview

This paper introduces a formal Embodied Neurocomputation Framework — a systems-level approach to interfacing biological neural networks (BNNs) with conventional computers via Micro-Electrode Arrays (MEAs). The framework conceptualizes the digital-biological interface as a multi-variable optimization problem across four interdependent modules: encoding, biological transformation, decoding, and feedback. It validates this through the first large-scale parameter optimization of encoding configurations for BNN agents performing closed-loop navigation.

Core Framework

Mathematical Formulation

Neurocomputation f at time t, with parameter set theta_t, as sequential feed-forward mappings:

y_t = f(x_t; theta_t) = d(b(e(x_t; theta_e); theta_b,t); theta_d)

Where:

• e(x_t; theta_e): Encoding — transforms task information into electrical stimuli
• b(u_t; theta_b,t): Biological transformation — BNN's intrinsic dynamics
• d(v_t; theta_d): Decoding — transforms neural responses into task-relevant outputs
• r(Score; theta_r): Feedback — drives BNN adaptation toward objectives

Biological adaptation:

theta_b,t+1 = g(r(Score; theta_r); theta_b,t)

Four Key Modules

1. Encoding (theta_e = theta_task union theta_stim)

• Transforms task-specific information into stimulation matrix u_t in {0,1}^(C x tau_in)
• C: stimulation channels, tau_in: pulse delivery time steps
• Parameters: frequency, amplitude, pulse width, waveform morphology, spatiotemporal distribution
• Rate encoding: sensor value -> sequence of stimulations at interpolated frequencies

2. Biological Transformation (b)

• Non-stationary mapping depending on temporal structure of input
• Parameters theta_b evolve according to stimulation/response history, feedback, and spontaneous processes
• Produces qualitative change: reorganizes informational structure, not just scaling/filtering
• "Third-order" information-processing system: response depends on how previous outputs shaped transformation
• Non-invertible: reflects reorganization and compression of information

3. Decoding (theta_d)

• Inverse of encoding: transforms BNN responses into task-relevant formats
• Response matrix v_t in R^(C x tau_out) -> output
• Count decoding: spike counts aggregated in spatial regions, normalized against baseline spontaneous activity
• Action with highest relative spike density is executed

4. Feedback (theta_r)

• Special form of encoding designed to drive BNN adaptation
• Reinforcing (r+): structured bursts for favorable outcomes
• Plasticity-inducing (r-): random stimulation to encourage alternative mappings

Empirical Evaluation

Task: Goal-Driven Navigation

• Simulated 6x6 gridworld with barrier, food source, and odor gradient
• Agent actions: move forward, turn left, turn right
• Scalar sensor: odor strongest to left (-1), front (0), right/behind (1)
• Three evaluation modes: 30 steps/1 episode, 150 steps/1 episode, 30 steps/5 episodes

Experimental Setup

• 26 BNN cultures via Cortical Labs CL1 platform
• Distributed optimization: Optuna HPO server + multiple CL1 clients
• 1,296 parameter combinations screened
• 4,000+ hours of real-time agent-environment interactions
• Two-stage screening: Stage 1 (n=1,296 -> n=64), Stage 2 (n=64 -> n=12 top configurations)

Encoding Parameters Screened

Key Findings

1. Maximum frequency is strongest driver: favors moderate values (40-60 Hz)
2. Higher amplitude, shorter pulse width, faster interaction rates support improved performance
3. BNN agents significantly outperform DQN benchmarks under equivalent training steps
4. 12 configurations consistently demonstrated learning across multiple episodes

SHAP Analysis Results

• Max Frequency: strongest positive impact on top 1% performance
• Amplitude: higher values (2.5 uA) favored
• Pulse Width: shorter (40 us) preferred
• Ticks/Step: moderate (4) optimal
• Tick Rate: lower (1-2 Hz) better
• Min Frequency: moderate (4 Hz) optimal

Biological Setups Tested

• Group 1: 7 cortical/hippocampal cultures in PDMS ring
• Group 2: 9 cortical/hippocampal cultures on astrocytes, monolayer
• Group 3: 5 cortical-only cultures, monolayer (Stage 2)
• Group 4: 5 cortical-only cultures, monolayer (Stage 2)

Applications

1. Hybrid bio-silicon computing: Bridging biological efficiency with silicon programmability
2. Robotic control: BNN-driven adaptive decision-making for embodied agents
3. Neuroscience research: Understanding biological learning mechanisms through task-driven validation
4. Energy-efficient computing: Alternative to von Neumann bottleneck limitations
5. Benchmarking framework: Establishing field-wide standards for neurocomputation

Framework Principles

Systems Thinking

• Each component is interconnected and highly parameterized
• Adjusting any single part changes the entire system response
• Configuration is a multi-variable optimization problem

Learning vs Training Distinction

• Learning: biological adaptation emerging from intrinsic biophysical plasticity
• Training: algorithmic optimization in artificial neural networks
• This distinction emphasizes that biological adaptation is fundamentally different from gradient-based updates

Hardware Agnosticism

• Framework encapsulates physical interactions within mapping functions
• Applicable to MEA, optogenetic, chemical, or other interfacing modalities

Key Insights for Practice

1. Parameter optimization is essential: Heuristic/ad-hoc stimulation protocols are insufficient for robust BNN computing
2. Biological stochasticity requires replication: Identical parameters must be evaluated across multiple cultures simultaneously
3. Rate encoding with moderate frequencies (40-60 Hz max) provides the best coupling with BNN biophysics
4. Feedback design matters: Structured bursts for reinforcement, random stimulation for plasticity induction
5. Calibration time: 4,000+ hours of real-time interaction needed — not trivial

Comparison to Silicon-Based AI

Pitfalls

1. Biological variability: Each culture has unique dynamics; parameters optimized for one may not transfer
2. Parameter space is vast: 6 encoding parameters x 4-5 values each = 1,296 combinations minimum
3. Real-time constraint: Experiments run at biological speed (not accelerated)
4. Hardware limitations: MEA platforms are expensive and require specialized expertise
5. Decoding simplicity: Current count decoding is rudimentary; more sophisticated methods needed
6. Feedback design: Fixed feedback regimen may not be optimal for all parameter configurations

References

• Cortical Labs CL1 platform: https://corticallabs.com/cloud
• Optuna HPO framework
• Count decoding from Cortical Labs prior work
• Related: Cortical Labs "DishBrain" (Pong-playing neural culture)