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Science & technology | Neuroscience and AI

Artificial brains are helping scientists study the real thing

No model is perfect. But that doesn't stop them being useful

[20230527_STD001]
May 24th 2023
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The striking progress in artificial intelligence over the past decade
is mostly down to advances in machine learning, whereby computers
teach themselves complicated tasks by crunching large quantities of
data, rather than having to be programmed directly by humans. This
approach has driven rapid progress in computer vision, language
translation and, most recently, the human-like conversational skills
of chatbots such as GPT-4.

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The learning is done by software models called "artificial neural
networks" (ANNs). The standard description of an ANN is that it is
loosely inspired by the networks of neurons in the human brain. It is
de rigueur to follow that description with an immediate disclaimer,
in which both computer scientists and neuroscientists jump in
nervously to point out that the analogy is very rough, that ANNs are
mere cartoons of real brains (if even that) and that they fail to
capture the complexity of the biological organ.

All that is true. But some neuroscientists are beginning to find that
even cartoons can be useful. The inner workings of the best ANN
s--those that are closest to matching human performance on tasks like
identifying objects, or responding to text prompts--appear to have
some remarkable similarities to the workings of brains. Having taken
inspiration from biology, in other words, programmers are now
returning the favour, with their creations telling neuroscientists
useful things about biological brains.

The seminal study comparing brains and ANNs was published in
Proceedings of the National Academy of Sciences in 2014. Daniel
Yamins, a neuroscientist at the Massachusetts Institute of Technology
(MIT), and his colleagues trained an ANN to pick out objects from
photographs--a cat, for instance. The researchers compared what was
going on inside the electronic network to what was happening inside
the brains of macaque monkeys that had been set the same task, and
whose brains had been wired with electrodes.

ANNs are built up from large numbers of artificial neurons that, just
like their natural counterparts, can be on or off; firing or silent.
These neurons are linked together in layered, interconnected
networks. Activity in lower layers can affect how neurons in the
higher layers fire.

Inside the black box

Dr Yamins's test involves image recognition, which in natural brains
proceeds hierarchically. One layer of neurons will detect simple
features such as patches of light or dark. A higher layer organises
those into edges; a still higher layer combines the edges into
shapes. That process of increasing abstraction continues until,
eventually, the brain decides whether it is looking at a cat, a dog
or a banana.

Images that share some characteristics provoke similar clusters of
neurons to fire. If a certain set of neurons fires when looking at a
cat, another, partially overlapping set is likely to fire in response
to a picture of a dog. The neurons that respond to both images are
thought to be representing features--fur, four legs and a tail,
say--that are present in both pictures.

When Dr Yamins and his colleagues compared what was going on inside
the macaque brains to the silicon ones, they found arresting
parallels between how the monkeys represented images and how the
computers did. "The paper was a game-changer," says Nancy Kanwisher,
another professor at MIT who has spent much of her career studying
the human visual system, and who now uses ANNs in some of her
research. "The [artificial] network was not in any way designed to
fit the brain. It was just designed to solve the problem and yet we
see this incredible fit."

Since then, whenever an ANN model has close to human performance on a
task, neuroscientists have been eager to compare it with natural
brains. They have found similarities between ANNs trained to
recognise speech and process language, such as those used in
transcription software, and the human auditory cortex.

The pattern holds for written language too. One paper published in
2021 compared human brain activity against many different commercial
language models. It found that the most sophisticated ANN--at the time
OpenAI's GPT-2--was the closest match for human brain activity. The
better models get at solving certain tasks, the more similar they
seem to get to the human brain doing the same.

Another indication that the analogy between artificial neural
networks and natural ones is useful is that the study of the former
can make testable predictions about the latter. A paper published in
2022, by researchers at Columbia University and MIT, found that an 
ANN trained on image-recognition tasks produced a group of artificial
neurons devoted to classifying foodstuffs specifically. When the
paper was published there was, as far as anyone knew, no analogous
area of the human visual system. But the following year researchers
from the same laboratory announced that they had discovered a region
of the human brain that does indeed contain neurons that fire more
often when a person is shown pictures of food.

Perhaps the strongest evidence for the claim that artificial brains
can reveal useful things about biological ones is the apparent
ability for software and wetware to interact with each other
directly. Nicholas Sexton and Bradley Love, a pair of neuroscientists
of University College London, started out rather sceptical about the
supposed resemblance between natural and artificial neural networks.
Simply seeing similar patterns of activity, they argued, was not
enough to claim that ANNs and brains were solving problems in the
same way. To prove that the correspondence was meaningful, they
suggested investigating whether it was possible to feed brain
activity into an ANN.

In 2022 they published a paper in Science Advances that did just
that. The researchers fed an ANN trained to recognise images data
recorded by an MRI scanner examining human brains. The idea was to
try to let the ANN "see" through human eyes. Sure enough, the
hotwired ANN was able to interpret data from any of the hierarchical
layers of the biological visual system--though it did best with data
from the higher levels, which had already been partly processed by
the brain in question. If the computer model was shown brain activity
from a human that was looking at a picture of a greyhound, for
example, then it would say that it was looking at a greyhound--as
opposed to some other object--almost 70% of the time.

The fact that a silicon brain can happily accept half-chewed data
from a biological one suggests that, on some level, the two systems
are performing the same sort of cognitive task. That insight might
prove useful for brain-computer interfaces, which are devices that
aim to allow biological brains to talk directly to machines. An ANN
linked up to a camera, for instance, might be used to feed partly
processed visual information into the brain. That might help treat
some forms of blindness caused by damage to the brain's visual
system. Several different research groups in Europe and America are
already testing that idea in experiments on macaques.

Models of the mind

Even those most enthusiastic about ANNs do not argue they are perfect
analogues of the human brain. Some make mistakes that humans never
would--give an ANN a picture of a cat but with the skin of an
elephant, for example, and the model is more likely to identify it as
an elephant. But no scientific model is ever perfect. The question is
whether it is useful. One of neuroscience's problems is that
experiments are difficult to run, for both ethical and practical
reasons. Poking and prodding ANNs could offer a useful alternative.

In any case, comparing biology and silicon continues to produce
intriguing results. In a paper published in May researchers from the
University of Texas at Austin used a neural network to monitor brain
signals from participants in an MRI scanner. Using just data from the
mri, the ANN could produce a rough summary of a story that the test
subject was listening to, a description of a film they were watching,
or the gist of a sentence they were imagining. "When I was in
graduate school I would dream about something like this existing,"
says Dr Love. "I thought it would be hundreds of years until we had
something that works this well." #

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newsletter.

This article appeared in the Science & technology section of the
print edition under the headline "Brains in a box"

Science & technology May 27th 2023

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