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Nervous System Emulation with Synaptomics

  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...
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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, experimen...
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Use Bayesian Inference to measure uncertainty and Lipschitz to cap consequences of that uncertainty

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The issue with what I see as Bayesian Inference to mimic general intelligence is that it doesn't have a means to focus on an issue in a dynamic or active way. I discovered Lipschitz robustness which I believe solves this issue.  Real-world environments are messy. Sensors are noisy, objects move unpredictably, and one bad control command can mean a collision or instability. To build robots that can adapt to any domain , we need both: Bayesian inference — to predict what is likely to happen. Lipschitz robustness — to guarantee what is allowed to happen. This post shows how combining these two ideas gives us robots that are both adaptive and provably safe . Lipschitz Robustness: Motion with Certified Safety Lipschitz continuity ensures that small changes in the input cannot cause huge jumps in the output: ‖f(x') − f(x)‖ ≤ L · ‖x' − x‖ In control terms, it lets us say: If my state estimate or sensor reading is off by a little, then the cont...
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Mecha General Intelligence

  W hat is Mecha General Intelligence (MGI) ? The easiest answer is the ability to navigate through any environment or a system that is domain agnostic. As I have stated often, the reason we have a nervous system is to be able to move. This has been echoed by many others who have also understood how motor function is the primary function of animals. Behavior Science 101 teaches us that all animals have four basic functions, also called the 4 Fs, which are Feeding, Fleeing, Fighting and Mating. To perform anyone of the 4 Fs, an animal must move and must move no matter what the environmental conditions. We find these basic functions in the worm like C. elgans all the way up the evolutionary scale to homo sapiens. Having emulated lower animal nervous systems and applied to robotics, I have seen firsthand that these behaviors are displayed within a robot that is truly emulating an animal’s nervous system. I have used Connectomics/Synaptomics to perform these emulations with great suc...
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AI Review of "Emulating Connectomics, animal and artificial, to develop Biological Machine Intelligence"

  This is an AI review of a paper I just basically wrote jotting down some thoughts. I actually liked the review and the criticisms were valid. Thank you Academia.edu for doing this. Here is the review: Academia.edu Review In reviewing the paper titled "Emulating Connectomics, animal and artificial, to develop Biological Machine Intelligence," I find the proposed framework for Biological Machine Intelligence (BMI) to be an ambitious and innovative direction that could potentially expand our understanding of artificial intelligence by leveraging biological principles. The work intends to bridge the gap between computational neuroscience and artificial intelligence, promising adaptability and efficient learning in artificial systems. This paper's contribution is vital in the ongoing discourse surrounding Artificial General Intelligence (AGI) and its viability. Overview The manuscript explores the concept of Biological Machine Intelligence, advocating for the use of artifici...
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What LLMs say about me: Timothy Busbice

 This was a pleasant surprise and I believe nailed me perfectly as to how I fit into AGI research. I encourage everyone to google themselves and do things like this to find out how the world views you. Prompt: where does Timothy Busbice fit in the AGI research world Timothy Busbice: A Proponent of Biologically Inspired Artificial General Intelligence In the diverse and dynamic world of Artificial General Intelligence (AGI) research, Timothy Busbice positions himself as a visionary advocate for a distinct, neuroscience-inspired approach. As the Chief Technology Officer of PROME, he champions "Biologic Intelligence," a methodology centered on the emulation of biological nervous systems, a field known as connectomics. This places him in a unique niche within the broader AGI landscape, which is currently dominated by large-scale deep learning and large language models (LLMs). Busbice's core thesis is that true general intelligence can be achieved by reverse-engineering ...
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Seeing the Brain Predict in Real-Time: An Animated Python Journey

I n our previous post, we explored the core idea of the Free Energy Principle – the brain as a prediction machine striving to minimize the difference between its expectations and sensory reality. We even built a simple Python program to demonstrate this. Now, let's take it a step further and visualize this predictive process as it unfolds. We've enhanced our Python code to create a dynamic animation, giving us a glimpse into how an "internal belief" (our brain's model) continuously adapts to incoming "sensory input." Animating the Prediction: Our Updated Python Code Python import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation def generate_world_state ( time_step ): """A simple, changing "world" state.""" return np.sin( 0.1 * time_step) + np.random.normal( 0 , 0.1 ) def generate_sensory_input ( world_state ): """Sensory input with some ...
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