AI is Teaching us New, Surprising Things About the Human Mind

 ACM TECHNEWS

AI is Teaching Us New, Surprising Things About the Human Mind

By The Wall Street Journal, April 5, 2023

A researcher demonstrates the brain-scanning magnetoencephalography device at New York University.

Artificial intelligence is helping scientists decode how neurons in our brains communicate, and to explore the nature of cognition.

Scientists are gaining new insights into the human mind though artificial intelligence (AI), including the mechanism of communication between neurons, and the roots of cognition.

The University of California, Berkeley's Celeste Kidd and colleagues used a clustering model to find people's opinions tend to diverge about even the most fundamental properties of things. Researchers led by Princeton University's Tatiana Engel used artificial neurons to interpret hundreds of neurons' electrical impulses in animals' brains simultaneously, then trained them to perform identical tasks.

These networks self-organize into reasonable approximations of those in animals, indicating dynamic electrical activity forms the substance of thought, according to Engel.

From The Wall Street Journal

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Can Neural Networks Learn Better than Human Neurons

In the Opinion section of CACM:  

Can Neural Networks Learn Better than Human Neurons?

By Mind Matters News,  August 16, 2022

The modern neural network is a better learning algorithm than the brain. The brain neuron is limited by an all-or-nothing principle that makes rapid learning impossible.

By contrast, the differentiable "hotter" or "colder" function used by neural networks enables programmers to algorithmically train networks with trillions of parameters. Which raises the question: What if the human mind can learn better than a neural network?

From Mind Matters News

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Artificial Neuron Swaps Dopamine with Rat Brain Cells Like a Real One

Like that we are seeing ways to use biological neurons compared to artificial ones. 

Artificial Neuron Swaps Dopamine with Rat Brain Cells Like a Real One

By New Scientist,August 16, 2022

An artificial neuron that can release and receive dopamine in communication with real rat cells could be incorporated into future human-machine interfaces. Researchers in China and Singapore formed the device from a graphene and carbon nanotube electrode that can detect the release of dopamine, while a memristor dispatches more dopamine via a heat-activated hydrogel. This process mimics how brain cells change the amount of neurotransmitter sent between connections in response to external stimuli.

The artificial neuron also can trigger a mouse muscle through the sciatic nerve and move a robotic hand. "This actually has quite a lot of potential for expanding into more sophisticated learning systems," said Yoeri van de Burgt at the Netherlands' Eindhoven University of Technology.

From New Scientist

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A Brain With a Single Neuron?

 Novel idea here, workable? 

Artificial Brain with a Single Neuron

A striking mathematical idea could solve two hardware challenges in artificial intelligence (AI)

Researchers at Technische Universität Berlin (TU Berlin) have succeeded in simulating a neural network of thousands of nerve cells on a computer with only a single neuron programmed into the software code. The neuron is activated and read out with a time delay and can thus take on the roles of all virtual neurons within fractions of a second. This creates a completely new class of neural networks distributed through time rather than space. Such an approach would allow entirely new solutions in the future to integrate artificial neurons directly into hardware components, such as through optoelectronic building components. AI hardware using this method could also save energy and thus be more climate-friendly as it requires less power. These results were recently published as an "Editors' Highlight" in Nature Communications.... '

Lego Robot with an Organic 'Brain' Learns to Navigate a Maze

Mention of Carver Mead, who I followed for some time.

Lego Robot with an Organic 'Brain' Learns to Navigate a Maze  By Scientific American, January 28, 2022

In the winter of 1997 Carver Mead lectured on an unusual topic for a computer scientist: the nervous systems of animals, such as the humble fly. Mead, a researcher at the California Institute of Technology, described his earlier idea for an electronic problem-solving system inspired by nerve cells, a technique he had dubbed "neuromorphic" computing. A quarter-century later, researchers have designed a carbon-based neuromorphic computing device—essentially an organic robot brain—that can learn to navigate a maze.

A neuromorphic chip memorizes information similarly to the way an animal does. When a brain learns something new, a group of its neurons rearrange their connections so they can communicate more quickly and easily. As a common saying in neuroscience goes, "Neurons that fire together wire together." When a neuromorphic chip learns, it rewires its electric circuits to save the new behavior like a brain does to save a memory.

The idea of brainlike computation has been around for a while. But Paschalis Gkoupidenis of the Max Planck Institute for Polymer Research in Mainz, Germany, and his neuromorphic research team are pioneers in crafting this technology from organic materials. To build their chip, the researchers used long chains of carbon-based molecules called polymers, which are soft and, in some ways, behave similarly to living tissues. In order to let their material carry an electric charge like real neurons, which are energy-efficient and operate in a watery medium, the scientists coated the organic material with an ion-rich gel. This provided "more degrees of freedom to mimic biological processes," Gkoupidenis says.

From Scientific American

Brain Cells Learn Pong

Seems quite remarkable, if it is what I think it is, how far could be be extended, repeated?    Is it ethical to use human brain cells for this?

ACM TECHNEWS

Human Induced Cells Grown in Petri Dish Learn to Play Pong Faster Than AI

By DailyMail.com, December 30, 2021

Researchers at Cortical Labs in Australia demonstrated that human-induced neurons grown in a petri dish can be taught to play the retro videogame Pong in only five minutes.

The DishBrain system is made of brain cells grown on microelectrode arrays that can stimulate the cells. The researchers sent electrical signals either to the right or left of the array to indicate the video game ball's location, and the brain cells would fire neurons to move the paddle accordingly.

DishBrain learned the game in 10 to 15 rallies, gaming sessions that last for 15 minutes, but it takes 5,000 rallies for an artificial intelligence to learn the game.

"Using this DishBrain system, we have demonstrated that a single layer of in vitro cortical neurons can self-organize and display intelligent and sentient behavior when embodied in a simulated game-world," the researchers said in their published report.

From DailyMail.com  Full Article

AI Learns by Doing More with Less

Interesting thoughts on the efficiency of natural and current AI systems. Biomimicry possibilities for future intelligences? 

AI Learns by Doing More with Less  By Washington University in St. Louis   from ACM   August 12, 2021

 A tiny insect brain has less than a million neurons but has a diversity of behaviors and is more energy efficient than current AI systems. These tiny brains serve as models for computing systems that are becoming more sophisticated as billions of silicon neurons can be implemented on hardware.   ... 

New research from Washington University in St. Louis finds that the ability of silicon neurons to learn to communicate and establish networks is key to producing artificial intelligence systems that are as energy-efficient as biological ones.

Researchers previously found that neurons in a computational system act as though they are embedded in a rubber sheet; the new study demonstrates how neurons learn to choose the most energy-efficient perturbations and wave patterns in the rubber sheet. Each neuron adjusts the electrical stiffness of the rubber sheet to vibrate the entire network in the most energy-efficient manner, using only local information.

A silicon neuron, researchers discovered, can test all communications routes at once and identify the most efficient way to connect to complete a specific task.  ... 

From Washington University in St. Louis  Full Article

Hiding Malware in Artificial Neurons

 My areas of interest have always included machine learning, neural networks, steganography and security.    So I read this in interest. Not sure how it would work in practice.  Following up.

(When I say I am following up, I may or may not include findings in latter posts at my discretion.  I will if I think its particularly useful.   Do let me know if you have interest) 

ACM NEWS

Researchers Hid Malware Inside an AI's 'Neurons' And It Worked Scarily Well   July 23, 2021

The authors concluded that a 178MB AlexNet model can have up to 36.9MB of malware embedded into its structure without being detected using a technique called steganography. ... 

Neural networks could be the next frontier for malware campaigns as they become more widely used, according to a new study. 

According to the study, which was posted to the arXiv preprint server  on Monday, malware can be embedded directly into the artificial neurons that make up machine learning models in a way that keeps them from being detected. The neural network would even be able to continue performing its set tasks normally.

"As neural networks become more widely used, this method will be universal in delivering malware in the future," the authors, from the University of the Chinese Academy of Sciences, write.

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Bio Neurons versus Computational

When we first learned of the use of the patterns of brain neurons to potentially use as reasoning devices, we took a course from actual neuroscientists.  We very quickly learned that human bio neurons were very much more complex that neurons in our feeble  'neural networks'.   Always been intrigued by the concept, so how could they be more useful 'reasoners"? Tried to augment  our nets with this new complexity.  Here is another case why they are very different.  We can ask ourselves, can we use these aspects of neurons to improve reasoning?  Note the indication that timing matters.  What embedded information  could make us learn faster?   Still unclear, 

Neurons Unexpectedly Encode Information in the Timing of Their Firing

Elena Renken  Quanta Mag     Contributing Writer

A temporal pattern of activity observed in human brains for the first time may explain how we can learn so quickly.

For decades, neuroscientists have treated the brain somewhat like a Geiger counter: The rate at which neurons fire is taken as a measure of activity, just as a Geiger counter’s click rate indicates the strength of radiation. But new research suggests the brain may be more like a musical instrument. When you play the piano, how often you hit the keys matters, but the precise timing of the notes is also essential to the melody.

“It’s really important not just how many [neuron activations] occur, but when exactly they occur,” said Joshua Jacobs, a neuroscientist and biomedical engineer at Columbia University who reported new evidence for this claim last month in Cell.  ... '