Computer algorithm created to encode human memories

Researchers in the US have developed an implant to help a disabled brain encode memories, giving new hope to Alzheimer’s sufferers and wounded soldiers who cannot remember the recent past.

The prosthetic, developed at the University of Southern California and Wake Forest Baptist Medical Centre in a decade-long collaboration, includes a small array of electrodes implanted into the brain.

The key to the research is a computer algorithm that mimics the electrical signalling used by the brain to translate short-term into permanent memories.

This makes it possible to bypass a damaged or diseased region, even though there is no way of “reading” a memory — decoding its content or meaning from its electrical signal.

“It’s like being able to translate from Spanish to French without being able to understand either language,” said Ted Berger of USC, the project leader.

The prosthesis has performed well in tests on rats and monkeys. Now it is being evaluated in human brains, the team told the international conference of the IEEE Engineering in Medicine and Biology Society in Milan.

The project is funded by Darpa, the US Defence Advanced Research Projects Agency, which is interested in new ways to help soldiers recover from memory loss.

But the researchers say findings could eventually help to treat neurodegenerative diseases, including Alzheimer’s, by enabling signals to bypass damaged circuitry in the hippocampus, the brain’s memory centre.

Sensory inputs to the brain — sights, sounds, smells or feelings — create complex electrical signals, known as spike trains, which travel through the hippocampus. This neural process involves re-encoding the signals several times, so they have a quite different electrical signature by the time they are ready for long-term storage.

Damage that interferes with this translation may prevent the formation of long-term memories while old ones survive — which is why some people with brain damage or disease recall events from long ago but not from the recent past.

The translation algorithm, derived first from animal experiments, has been extended into humans by studying nine people with epilepsy who had electrodes implanted in the hippocampus to treat chronic seizures.

The researchers read the electrical input and output signals created in the patients’ brains as they conducted simple tasks, such as remembering the position of different shapes on a computer screen.

These results were used to refine the algorithm until it could predict with 90 per cent accuracy how the signals would be translated.

“Being able to predict neural signals with the USC model suggests that it can be used to design a device to support or replace the function of a damaged part of the brain,” said Robert Hampson of Wake Forest.

The next step will be to send the translated signal back into the brain of a patient with hippocampal damage, in the hope that this will bypass the trouble spot and form an accurate long-term memory.

The project at USC and Wake Forest is a vivid example of the progress being made in neurotechnology by scientists around the world.

Researchers elsewhere are implanting devices that enable people who are paralysed to carry out simple movements with robotic arms or even their own limbs. But no one else is using computers to manipulate memory signals directly in the human brain.

Financial Times

Views: 48

"Destroying the New World Order"

TOP CONTENT THIS WEEK

THANK YOU FOR SUPPORTING THE SITE!

mobile page

12160.info/m

12160 Administrators

 

Latest Activity

cheeki kea commented on Doc Vega's blog post Grooming the New Generation of Assassins
"That's right. Many countries head down that road into a terrorising future of Self ID-ers. (…"
20 hours ago
Doc Vega posted a blog post

Terror on All Hallows Eve Pt. 2 The Aftermath

Elizabeth had just gotten home from Junior High when the doorbell rang. She’d barely put her books…See More
yesterday
Doc Vega commented on Doc Vega's blog post Grooming the New Generation of Assassins
"cheeki kea, I fear that we are headed further down the road of inhumanity institutionalized by the…"
Thursday
omegamann is now a member of 12160 Social Network
Thursday
Doc Vega commented on Doc Vega's blog post Three Must See Movies for Halloween
"cheeki kea Thanks. I watched most of the movie but I'd forgotten until a few minutes into it…"
Wednesday
cheeki kea commented on Doc Vega's blog post Three Must See Movies for Halloween
"That's a fine movie menu you've got Doc V. I love the old days theme. Great to view when…"
Wednesday
Doc Vega posted a blog post

Three Must See Movies for Halloween

Grab Your Popcorn and Settle In!  If you really want to get in the mood for Halloween and you like…See More
Tuesday
Bob of the Family Renner posted photos
Tuesday
Doc Vega posted a blog post

Terror on All Hallows Eve

Chapter IElizabeth was angry. All of her friends were going to be out on Halloween, but her. She…See More
Monday
Jeff favorited Jeff's profile
Monday
Jeff favorited Jeff's profile
Monday
Jeff favorited Doc Vega's profile
Monday
Jeff is now a member of 12160 Social Network
Oct 26
Doc Vega posted blog posts
Oct 24
tjdavis posted a video

How Corporations Are Secretly Poisoning Our Food Supply

Dupont and 3M have been secretly poisoning America for decades. PFAs — also known as forever chemicals—are now in our food, water, clothes, and our blood. Th...
Oct 24
Doc Vega posted a blog post

They Won’t Stop

 The demonically driven left will not stop. Makes no difference how much violence they call for or…See More
Oct 22
Doc Vega posted a blog post

What US Scientist unwittingly helped the Nazis devise the V-2 Missile?

  In the early 1920’s and leading up to World War II German technology outpaces the peace time…See More
Oct 20
tjdavis favorited Sandy's video
Oct 19
tjdavis posted a photo
Oct 19
Christopher Walker is now a member of 12160 Social Network
Oct 19

© 2025   Created by truth.   Powered by

Badges  |  Report an Issue  |  Terms of Service

content and site copyright 12160.info 2007-2019 - all rights reserved. unless otherwise noted