Brain cell differences could be key to learning in humans and AI

Date:
October 6, 2021
Source:
Imperial College London
Summary:
Researchers have found that variability between brain cells might
speed up learning and improve the performance of the brain and
future AI.



FULL STORY ==========================================================================
The new study found that by tweaking the electrical properties of
individual cells in simulations of brain networks, the networks learned
faster than simulations with identical cells.


==========================================================================
They also found that the networks needed fewer of the tweaked cells to
get the same results, and that the method is less energy intensive than
models with identical cells.

The authors say that their findings could teach us about why our
brains are so good at learning, and might also help us to build better artificially intelligent systems, such as digital assistants that can
recognise voices and faces, or self-driving car technology.

First author Nicolas Perez, a PhD student at Imperial College London's Department of Electrical and Electronic Engineering, said: "The brain
needs to be energy efficient while still being able to excel at solving
complex tasks.

Our work suggests that having a diversity of neurons in both brains and
AI systems fulfils both these requirements and could boost learning."
The research is published in Nature Communications.

Why is a neuron like a snowflake? The brain is made up of billions of
cells called neurons, which are connected by vast 'neural networks'
that allow us to learn about the world. Neurons are like snowflakes:
they look the same from a distance but on further inspection it's clear
that no two are exactly alike.



==========================================================================
By contrast, each cell in an artificial neural network -- the technology
on which AI is based -- is identical, with only their connectivity
varying.

Despite the speed at which AI technology is advancing, their neural
networks do not learn as accurately or quickly as the human brain --
and the researchers wondered if their lack of cell variability might be
a culprit.

They set out to study whether emulating the brain by varying neural
network cell properties could boost learning in AI. They found that
the variability in the cells improved their learning and reduced energy consumption.

Lead author Dr Dan Goodman, of Imperial's Department of Electrical and Electronic Engineering, said: "Evolution has given us incredible brain functions -- most of which we are only just beginning to understand. Our research suggests that we can learn vital lessons from our own biology
to make AI work better for us." Tweaked timing To carry out the study,
the researchers focused on tweaking the "time constant" -- that is,
how quickly each cell decides what it wants to do based on what the
cells connected to it are doing. Some cells will decide very quickly,
looking only at what the connected cells have just done. Other cells
will be slower to react, basing their decision on what other cells have
been doing for a while.



========================================================================== After varying the cells' time constants, they tasked the network with performing some benchmark machine learning tasks: to classify images
of clothing and handwritten digits; to recognise human gestures; and to identify spoken digits and commands.

The results show that by allowing the network to combine slow and fast information, it was better able to solve tasks in more complicated,
real-world settings.

When they changed the amount of variability in the simulated networks,
they found that the ones that performed best matched the amount of
variability seen in the brain, suggesting that the brain may have evolved
to have just the right amount of variability for optimal learning.

Nicolas added: "We demonstrated that AI can be brought closer to how our
brains work by emulating certain brain properties. However, current AI
systems are far from achieving the level of energy efficiency that we
find in biological systems.

"Next, we will look at how to reduce the energy consumption of these
networks to get AI networks closer to performing as efficiently as
the brain." This research was funded by the Engineering and Physical
Sciences Research Council and Imperial College President's PhD Scholarship ========================================================================== Story Source: Materials provided by Imperial_College_London. Original
written by Caroline Brogan. Note: Content may be edited for style
and length.


========================================================================== Journal Reference:
1. Perez-Nieves, N., Leung, V.C.H., Dragotti, P.L. et al. Neural
heterogeneity promotes robust learning. Nat Commun, 2021 DOI:
10.1038/ s41467-021-26022-3 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/10/211006112626.htm

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