A Distributed Connectome-Based Neural Controller for Biomimetic Robotic Locomotion Using a C. elegans Neural Processing Service

 A Distributed Connectome-Based Neural Controller for Biomimetic Robotic Locomotion Using a C. elegans Neural Processing Service

Abstract

This paper presents a distributed biomimetic robotic architecture composed of a Python-based robotic controller (Celegansv2.py) and an ASP.NET Core neural processing service (Program.cs). The robot collects sensory information, transmits weighted sensory neuron activations through a REST interface, and receives computed muscle activations after propagation through a digital C. elegans connectome. The remote neural processor maintains independent nervous systems for each robot session, accumulates neural activity, applies threshold-based firing dynamics, propagates activity through weighted synapses, and generates motor neuron outputs. The robot converts returned muscle activations into differential wheel commands, forming a closed sensorimotor feedback loop. This paper describes the software architecture, mathematical formulation, experimental methodology, expected performance metrics, and future research directions.

Introduction

The implementation separates sensing, neural computation, and actuation into independent layers. The robot executes only hardware interaction while the web service performs neural computation using a connectome loaded from a spreadsheet that contains the C elegans Synaptome/Connectome. This separation permits multiple robots to share the same cortical processor while maintaining isolated neural state.

Related Work

The design combines concepts from connectomics, spiking neural networks, embodied robotics, and cloud robotics. Unlike conventional controllers where neural computation resides onboard, this architecture executes neural dynamics remotely through REST services, allowing centralized connectomic processing.

System Architecture

+-------------------+      REST      +----------------------+
| Celegansv2.py     |--------------->| Program.cs Service   |
| Distance Sensor   | POST /sense    | Session Manager      |
| GoPiGo3           |                | Connectome Engine    |
| Motor Controller  |<---------------| GET /motor           |
+---------+---------+                +----------+-----------+
          |                                      |
          v                                      v
   Wheel Motors                         Connectome.xlsx

REST API Sequence

Robot            Server
 | POST /session     |
 |------------------>|
 | sessionId         |
 |<------------------|
 | POST /sense       |
 |------------------>|
 | Neural propagation|
 |                   |
 | GET /motor        |
 |------------------>|
 | Muscle outputs    |
 |<------------------|
 | Drive motors      |

Neural Processing Algorithm

Sensory input: s_i=(n_i,w_i)

Accumulator:
A_j(t)=A_j(t-1)+Σw_i

Fire if:
|A_j| ≥ θ,   θ=12

Postsynaptic propagation:
x_k=A_j·W_jk

Motor output (C elegans body muscles):
m={MDL,MDR,MVL,MVR}

Connectome Propagation Mathematics

Let G=(V,E) represent the connectome graph.

Neuron state:
x(t+1)=f(Wx(t)+u(t))

where W is the weighted adjacency matrix, u(t) is sensory input,
and f() is the threshold activation function.

For each outgoing synapse:
Output = FiredValue × SynapseWeight

Motor neurons terminate propagation and are placed on the motor stack for retrieval by the robot.

Experimental Methodology

Experiments should evaluate navigation through obstacle courses while logging latency, neural throughput, motor response time, obstacle avoidance success, path efficiency, and REST communication overhead.

Results

Suggested metrics include REST latency, neural events processed per second, motor command latency, obstacle avoidance rate, navigation efficiency, and CPU utilization. Comparative experiments can compare onboard control versus remote connectome processing.

Discussion

The architecture demonstrates that biological computation can be decoupled from robotic embodiment. Session-based neural processing enables scalable cloud-hosted nervous systems while preserving independent neural dynamics.

Future Work

Future work includes learning through synaptic plasticity, reinforcement learning, larger connectomes, multimodal sensing, visual processing, distributed cortical modules, and neuromorphic hardware acceleration.

References

1. White et al. (1986). The structure of the nervous system of C. elegans.
2. OpenWorm Project.
3. Brooks, R. A. Intelligence without representation.
4. ASP.NET Core Documentation.
5. GoPiGo3 Documentation.