Connectomic AI

  The seven things that matter—and the traps that wreck most simulations The big idea: Connectomics tells you which neurons are connected . Synaptomics tells you what each connection actually does . A nervous-system emulator needs both, but synapses should be treated as active computational components—not lines drawn between neurons. 1. The synapse is the real computational unit A neuron is not simply “on” or “off,” and a connection is not merely a weight. Each synapse should be modeled as a stateful object : Synapse = source + target + type + strength + delay + kinetics + plasticity + current state Synapses differ enormously in molecular composition and functional behavior—even synapses belonging to the same neuron. Rule: Don’t build a brain-shaped spreadsheet of neurons and weights. 2. Wiring is necessary—but nowhere near sufficient A connectome can show hundreds of thousands of synapses, yet it cannot automatically tell you how the circuit will behave. The complete la...

  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, experimen...