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I used an LLM model to emulate an earthworm! I will provide the full code in a Colab Notebook at the end of this article. I began with a simple premise, old school video game emulation. This process is relatively straightforward from a technical resources perspective, I take a much larger and powerful computer, and I brute force it into being a different, but far less powerful computer.
The emulation is never going to be 100% perfect Perhaps there are some games that do not run on the emulator. Perhaps the emulator runs just a single frame rate slower or faster than the original game. There could be a plethora of issues that get incorporated into this equation, but all of this involves what are known processes at this point.
Generally speaking, any limitations you run into can be solved by brute forcing the problem with massively superior hardware. Emulating the NES or the Playstation for example are trivial tasks by today’s standards. Any modern computer could easily brute force emulate these systems.
My next train of thought with this experiment was generated by a scientific breakthrough involving neural networks, scientists were able to utilize them to simulate the brain and nervous system of a fruit fly. Notice the difference in word choice here between simulate and emulate, it is important. Simulating is more like rebuilding a simpler model from scratch. Emulating is more like getting your computer to think it is a Playstation and to function like a Playstation.
Fruit flies have a lot of neurons to emulate though. All brains are made up of a massive amount of neurons. Even a worker ant has two problems, large amounts of neurons, and very complex central nervous systems. If you were to simulate an ant, you would also have to simulate that pheromone system. This is hard to do. So, what is one of the simpler biological models that we could take a neural network, or in our case, an LLM model, and conceivably get it to simulate? An earthworm, of course!
Earthworms hover around 2,000-10,000 neurons total in their brains, and are essentially made up of a series of self repeating structures called ganglia. This biological simplicity is the perfect candidate for our LLM model to emulate! If the LLM model is like the most advanced computer currently in existence, an earthworm is like an Atari. We can certainly get our LLM model to emulate an Atari.
An earthworm presents a fascinating model for emulation with its relatively simple structure. Let's see how we can use this model along with LLM techniques to achieve this:
The LLM's Role
While the provided code simulates individual neurons and the biomechanics of the worm, it lacks the high-level decision-making that an LLM might provide. Here's where the LLM could come in:
Sensory Input: The LLM could process sensory information about the simulated environment (e.g., light, touch). This input could be used to modulate the external current (I_ext) that stimulates the neurons.
Decision Making: The LLM could generate motor control patterns influencing the output of the neurons, leading to different muscle contractions and causing the worm to move and react to its environment.
This is, of course, a very rudimentary emulation of an earthworm. However, this project serves as an example of how neural networks can be used to simulate biological systems, and hints at larger potential. It allows us to study locomotion and neural control in a simplified model. It's conceivable that similar techniques could pave the way towards simulating more complex organisms or even aid the design of bio-inspired robots.
Let me provide the Colab Notebook with the code and instructions on how to run it. Experiment and see what happens when you change parameters or add new capabilities!
https://colab.research.google.com/drive/1REnBlhOuldbkA6bk6nnIx8xs3Uuio-WZ?usp=sharing
Explanation of the Code
The code simulates a simple earthworm using two main approaches:
Hodgkin-Huxley Model (Neurons):
Segment-based Biomechanics:
Worm Class (Putting it Together):
The Worm class combines these elements to represent the entire earthworm.
It contains a list of Segment objects for the body segments and a list of HodgkinHuxleyNeuron objects, one for each segment.
The update method in the Worm class is the core of the simulation. Here's what happens:
Update Neurons: It iterates through each neuron, simulating its state based on an external current (I_ext). This current could be adjusted to represent external stimuli (not implemented in the provided code).
Neurons to Segments: The membrane potential of each neuron is used as a force applied to the corresponding body segment. The idea is that higher neuron activity translates to a stronger force on that segment, influencing its movement.
Segment Interactions: The code simulates physical interactions between segments. Segments are connected like springs, pulling on each other when stretched and pushing when compressed. Additionally, a damping force is applied to slow down the movement due to friction with the environment.
Update Positions: Finally, based on the applied forces, the position of each segment is updated, effectively creating a wave-like movement for the worm.
What it Doesn't Do (Yet)
The current code focuses on the core biomechanics and basic neural representation. It lacks a few aspects for a more complete simulation:
Sensory Input: The worm doesn't have a way to sense its environment (light, touch). In a more complete model, sensory information could be incorporated to influence the I_ext applied to the neurons.
Decision Making: The LLM aspect mentioned in the article is not implemented here. The current version lacks a high-level decision-making process for the worm's behavior.
Experimenting in Colab
Feel free to play around with the code in Colab! Here are some ideas:
This code provides a starting point for simulating an earthworm with a combination of neural networks and biomechanics. You can extend this further to incorporate sensory input and LLM-based decision making to create a more comprehensive model.
The LLM as the Brain
Think of the Colab environment as providing the body of our simulated earthworm. The provided code meticulously simulates the neurons and the biomechanics of its movement. However, for true emulation, we need our worm to exhibit behavior and decision-making abilities – this is where the LLM comes in as the brain.
Sensory Input: Just like a real earthworm senses its environment, our LLM brain can take in sensory data provided by the Colab simulation (e.g., light levels, obstacles, etc.).
Decision Making: The LLM processes this sensory information and, much like a biological brain, decides on an action for the worm to take. These decisions can involve sending signals to the simulated neurons, influencing their activity and, ultimately, the worm's movement.
Learning and Adaptation: The true power of the LLM brain lies in its ability to learn. As the simulated earthworm interacts with its environment, the LLM brain can continuously adapt its decision-making process. This emulates how a real earthworm learns to navigate its surroundings, avoiding dangers and seeking out desirable conditions.
The LLM brain enables complex, emergent behaviors within the emulation. The intricate interplay between the LLM's decision-making, the neuron simulations, and the biomechanics provided by the code leads to a far more lifelike and intriguing earthworm simulation.