How our brains remain active during familiar, repetitive tasks

Date:
July 14, 2020
Source:
University of Cambridge
Summary:
New research, based on earlier results in mice, suggests that our
brains are never at rest, even when we are not learning anything
about the world around us.



FULL STORY ==========================================================================
New research, based on earlier results in mice, suggests that our brains
are never at rest, even when we are not learning anything about the
world around us.


==========================================================================
Our brains are often likened to computers, with learned skills
and memories stored in the activity patterns of billions of nerve
cells. However, new research shows that memories of specific events and experiences may never settle down. Instead, the activity patterns that
store information can continually change, even when we are not learning anything new.

Why does this not cause the brain to forget what it has learned? The
study, from the University of Cambridge, Harvard Medical School and
Stanford University, reveals how the brain can reliably access stored information despite drastic changes in the brain signals that represent
it.

The research, led by Dr Timothy O'Leary from Cambridge's Department
of Engineering, shows that different parts of our brain may need to
relearn and keep track of information in other parts of the brain as it
moves around. Their study, published in the open access journal eLife,
provides some of the first evidence that constant changes in neural
activity are compatible with long term memories of learned skills.

The researchers came to this conclusion through modelling and analysis
of data taken from an experiment in which mice were trained to associate
a visual cue at the start of a 4.5-metre-long virtual reality maze with
turning left or right at a T-junction, before navigating to a reward. The results of the 2017 study showed that single nerve cells in the brain continually changed the information they encoded about this learned task,
even though the behaviour of the mice remained stable over time.

The experimental data consisted of activity patterns from hundreds of
nerve cells recorded simultaneously in a part of the brain that controls
and plans movement, recorded at a resolution that is not yet possible
in humans.



========================================================================== "Finding coherent patterns in this large assembly of cells is challenging,
much like trying to determine the behaviour of a swarm of insects by
watching a random sample of individuals," said O'Leary. "However, in
some respects the brain itself needs to solve a similar task, because
other brain areas need to extract and process information from this same population." Nerve cells connect to hundreds or even thousands of their neighbours and extract information by weighting and pooling it. This
has a direct analogy with the methods used by pollsters in the run up
to an election: survey results from multiple sources are collected and 'weighted' according to their consistency.

In this way a steady pattern can emerge even when individual measurements
vary wildly.

The Cambridge group used this principle to construct a decoding algorithm
that extracted consistent, hidden patterns within the complex activity of hundreds of cells. They found two things. First, that there was indeed
a consistent hidden pattern that could accurately predict the animal's behaviour. Second, this consistent pattern itself gradually changes over
time, but not so drastically that the decoding algorithm couldn't keep
up. This suggests that the brain continually modifies the internal code
that relays information between different internal circuits.

Science fiction explores the possibility of transferring our memories
and experiences into hardware devices directly from our brains. If future technology eventually allows us to upload and download our thoughts and memories, we may find that our brain cannot interpret its own activity
patterns if they are replayed many years later. The concept of an apple
-- its colour, flavour, taste and the memories associated with it --
may remain consistent, but the patterns of activity it evokes in the
brain may change completely over time.

Such conundra will likely remain speculative for the immediate future,
but experimental technology that achieves a limited version of such
mind reading is already a reality, as this study shows. Brain-machine interfaces are a rapidly maturing technology, and human neural interfaces
that can control prosthetics and external hardware have been in clinical
use for over a decade. The work from the Cambridge group highlights a
major open challenge in extracting reliable information from the brain.

"Even though we can now monitor brain activity and relate it directly to memories and experiences, the activity patterns themselves continually
change over a period of several days," said O'Leary, who is a Lecturer in Information Engineering and Medical Neuroscience. "Our study shows that in spite of this change, we can construct and maintain a relatively stable 'dictionary' to read out what an animal is thinking as it navigates a
familiar environment.

"The work suggests that our brains are never at rest, even when we are not learning anything about the external world. This has major implications
for our understanding of the brain and for brain-machine interfaces and
neural prosthetics."

========================================================================== Story Source: Materials provided by University_of_Cambridge. The original
story is licensed under a Creative_Commons_License. Note: Content may
be edited for style and length.


========================================================================== Journal Reference:
1. Michael E Rule, Adrianna R Loback, Dhruva Raman, Laura N Driscoll,
Christopher D Harvey, Timothy O'Leary. Stable task information
from an unstable neural population. eLife, 2020; 9 DOI:
10.7554/eLife.51121 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/07/200714082850.htm

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