Is it a computer program or a living being? At TU Wien (Vienna), the boundaries become blurred. The neural system of a nematode was translated into computer code – and then the virtual worm was taught amazing tricks.
It is not much to look at: the nematode C. elegans is about one millimetre in length and is a very simple organism. But for science, it is extremely interesting. C. elegans is the only living being whose neural system has been analysed completely. It can be drawn as a circuit diagram or reproduced by computer software, so that the neural activity of the worm is simulated by a computer program.
Such an artificial C. elegans has now been trained at TU Wien (Vienna) to perform a remarkable trick: The computer worm has learned to balance a pole at the tip of its tail.
The Worm’s Reflexive behaviour as Computer Code
C. elegans has to get by with only 300 neurons. But they are enough to make sure that the worm can find its way, eat bacteria and react to certain external stimuli. It can, for example, react to a touch on its body. A reflexive response is triggered and the worm squirms away.
This behaviour can be perfectly explained: it is determined by the worm’s nerve cells and the strength of the connections between them. When this simple reflex-network is recreated on a computer, then the simulated worm reacts in exactly the same way to a virtual stimulation – not because anybody programmed it to do so, but because this kind of behaviour is hard-wired in its neural network.
“This reflexive response of such a neural circuit, is very similar to the reaction of a control agent balancing a pole”, says Ramin Hasani (Institute of Computer Engineering, TU Wien). This is a typical control problem which can be solved quite well by standard controllers: a pole is fixed on its lower end on a moving object, and it is supposed to stay in a vertical position. Whenever it starts tilting, the lower end has to move slightly to keep the pole from tipping over. Much like the worm has to change its direction whenever it is stimulated by a touch, the pole must be moved whenever it tilts.
Mathias Lechner, Radu Grosu and Ramin Hasani wanted to find out, whether the neural system of C. elegans, uploaded to a computer, could solve this problem – without adding any nerve cells, just by tuning the strength of the synaptic connections. This basic idea (tuning the connections between nerve cells) is also the characteristic feature of any natural learning process.
A Program without a Programmer
“With the help of reinforcement learning, a method also known as ‘learning based on experiment and reward’, the artificial reflex network was trained and optimized on the computer”, Mathias Lechner explains. And indeed, the team succeeded in teaching the virtual nerve system to balance a pole. “The result is a controller, which can solve a standard technology problem – stabilizing a pole, balanced on its tip. But no human being has written even one line of code for this controller, it just emerged by training a biological nerve system”, says Radu Grosu.
The team is going to explore the capabilities of such control-circuits further. The project raises the question, whether there is a fundamental difference between living nerve systems and computer code. Is machine learning and the activity of our brain the same on a fundamental level? At least we can be pretty sure that the simple nematode C. elegans does not care whether it lives as a worm in the ground or as a virtual worm on a computer hard drive.
Learn more: Worm Uploaded to a Computer and Trained to Balance a Pole
The Latest on: Reinforcement learning
[google_news title=”” keyword=”reinforcement learning” num_posts=”10″ blurb_length=”0″ show_thumb=”left”]
via Google News
The Latest on: Reinforcement learning
- Neuroscience Says You Can Improve Your Memory With Emotion (but There's 1 Important Catch)on April 27, 2024 at 1:25 am
Neuroscience research reveals that integrating emotion and meaning into information can significantly enhance memory and performance. This insight can transform training, communication, and leadership ...
- AI Model Shows Promise in Fighting Drug Resistanceon April 26, 2024 at 8:31 pm
As antibiotic resistance continues to pose a formidable challenge, AI-driven solutions offer a beacon of hope in the fight against drug-resistant pathogens.
- AI content creation: Ushering in the unimaginableon April 26, 2024 at 6:57 am
Embrace cutting-edge AI content creation tools to transform your marketing. Balance human input while harnessing machine efficiency. The post AI content creation: Ushering in the unimaginable appeared ...
- Inside Disney Imagineering and the Tech That Could Power the Company’s $60B Parks Beton April 26, 2024 at 12:45 am
New robots and an ominidirectional treadmill-like floor, which could help engineers walk through potential designs for new parks, are among the newer innovations.
- Rottweiler training: Positive reinforcement techniques you should followon April 26, 2024 at 12:43 am
Training a Rottweiler begins with establishing trust. Consistency is key; this means setting clear rules and sticking to them. Use the same commands each time you ask your dog to perform an action, ...
- Mastering AI Powerhouse: Unleashing C++ for Machine Learning and AI Programmingon April 25, 2024 at 10:10 pm
Why is C++ the preferred language for AI development? Explore emerging trends, essential tools, and prospects within this dynamic landscape.
- 8 AI Business Trends in 2024, According to Stanford Researcherson April 25, 2024 at 10:59 am
AI makes workers more productive, but we are still lacking in regulations, according to new research from Stanford University.
- AI Chatbots Need Large Language Models. Here's What to Know About LLMson April 24, 2024 at 4:01 pm
Well, LLMs use neural networks, which are machine learning models that take an input and perform mathematical calculations to produce an output. The number of variables in these computations are ...
- ETH Zurich’s wheeled-legged robot masters urban terrain autonomouslyon April 24, 2024 at 12:00 pm
ETH Zurich researchers use advanced learning methods to create an autonomous wheeled-legged robot's control system.
via Bing News