#brain computer interfaces

[gorrus]

[seoteamwxt]

https://www.gtec.at/product/brain-computer-interface-system/

Looking to invest in Brain Measuring Device? Simply approach g.tech medical engineering GmbH! We offer an array of products such as electrical stimulators, biosignal amplifiers, sensors, electrode systems, and many more. For more information, you can visit our website https://www.gtec.at/ or call us at +43 7251 22240

[averycanadianfilm]

Wed 12 May 2021 16.00 BST

[stemgirlchic]

why neuroscience is cool

space & the brain are like the two final frontiers

we know just enough to know we know nothing

there are radically new theories all. the. time. and even just in my research assistant work i've been able to meet with, talk to, and work with the people making them

it's such a philosophical science

potential to do a lot of good in fighting neurological diseases

things like BCI (brain computer interface) and OI (organoid intelligence) are soooooo new and anyone's game - motivation to study hard and be successful so i can take back my field from elon musk

machine learning is going to rapidly increase neuroscience progress i promise you. we get so caught up in AI stealing jobs but yes please steal my job of manually analyzing fMRI scans please i would much prefer to work on the science PLUS computational simulations will soon >>> animal testing to make all drug testing safer and more ethical !! we love ethical AI <3

collab with...everyone under the sun - psychologists, philosophers, ethicists, physicists, molecular biologists, chemists, drug development, machine learning, traditional computing, business, history, education, literally try to name a field we don't work with

it's the brain eeeeee

[scipunk]

Love, Death & Robots - S1E1 - Sonnie's Edge (2019)

[cbirt]

In a groundbreaking advancement, researchers from Stanford University have developed a speech brain-computer interface (BCI) that holds significant promise for individuals with paralysis. By capturing neural signals generated during attempted speech through intracortical microelectrode arrays, this novel BCI achieved remarkable results. A participant afflicted with amyotrophic lateral sclerosis (ALS), rendering them unable to speak coherently, achieved an impressive 9.1% word error rate with a 50-word vocabulary and a 23.8% error rate with a vast 125,000-word vocabulary. This marks the first successful demonstration of decoding speech from a large vocabulary using such technology. Notably, the BCI enabled speech decoding at a rapid pace of 62 words per minute, surpassing prior records by 3.4 times. Encouragingly, the study revealed neural patterns that facilitate accurate decoding from a small cortical region and retained detailed speech representations even after years of paralysis. These findings illuminate a promising path toward restoring efficient communication for paralyzed individuals who have lost the ability to speak.

The organization of orofacial movement and speech production within the motor cortex at a single-neuron resolution is not very well-known. To explore this, neural activity was recorded through four microelectrode arrays, two in the ventral premotor cortex (area 6v) and two in area 44, a component of Broca’s area. The participant, who had bulbar-onset ALS, exhibited restricted orofacial movement and vocalization capabilities but lacked intelligible speech. The findings revealed distinct patterns in area 6v, where strong tuning was observed across all tested movement categories.

This encompassed the successful decoding of various orofacial movements, phonemes, and words with high accuracy. In contrast, area 44, previously linked to higher-order speech aspects, exhibited negligible information related to these categories. Interestingly, speech decoding proved more precise in the ventral array, particularly during the instructed delay phase, aligning with language-associated networks identified through fMRI data.

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[zoanzon]

Perhaps controversial, but: why the hell do people wanna download fics as EPUBs? I'd vastly rather they be PDFs

[recently-reanimated]

Its the year 2050. I think about making a grilled cheese but my neuralink causes 14 ads to start playing over the recipe.

[qwertyfingers]

dads been getting back into making music in a big way and its so funny to have lots of back and forth about cool experimental stuff we've come across and i ask how his own tinkering is going and he tells me he's spent the last 2 days perfecting the setup on his RGB keyboard instead

[the-transhumanist]

Keep Brain Interfaces Open

[transingthoseformers]

*inhale*

i sWEAR TO PRIMUS THIS COMIC FUC—

[sevicia]

I wanted to make a cleaner summary of last week's classes and also review the classes I have this week since the material is already uploaded beforehand but I was feeling so horrible throughout the day that when I sat down I was just gonna look at the ones for tomorrow but I think I'm just gonna go to bed because I just gave my little numbers game a few tries and not even the joy of tribial elementary school-level math games is bringing my brain cells and/or full sentience back

[thepastisalreadywritten]

The patient Gert-Jan Oskam said the breakthrough had given him "a freedom that I did not have" before.

The 40-year-old Dutchman has been paralysed in his legs for more than a decade after suffering a spinal cord injury during a bicycle accident.

However, using a new system, he can now walk "naturally," take on difficult terrain, and even climb stairs, according to a study published in the journal Nature.

The advance is the result of more than a decade of work by a team of researchers in France and Switzerland.

Last year, the team showed that a spinal cord implant -- which sends electrical pulses to stimulate movement in leg muscles -- had allowed three paralysed patients to walk again.

But they needed to press a button to move their legs each time.

Gert-Jan, who also has the spinal implant, said this made it difficult to get into the rhythm of taking a "natural step."

'Digital bridge'

The latest research combines the spinal implant with new technology called a brain-computer interface, which is implanted above the part of the brain that controls leg movement.

"The interface uses algorithms based on artificial intelligence methods to decode brain recordings in real time," the researchers said.

This allows the interface, which was designed by researchers at France's Atomic Energy Commission (CEA), to work out how the patient wants to move their legs at any moment.

The data is transmitted to the spinal cord implant via a portable device that fits in a walker or small backpack, allowing patients to get around without help from others.

The two implants build what the researchers call a "digital bridge" to cross the disconnect between the spinal cord and brain that was created during Gert-Jan's accident.

"Now I can just do what I want -- when I decide to make a step the stimulation will kick in as soon as I think about it," Gert-Jan said.

After undergoing invasive surgery twice to implant both devices, "it has been a long journey to get here," he told a press conference in the Swiss city of Lausanne.

But among other changes, he is now able to stand at a bar again with friends while having a beer.

"This simple pleasure represents a significant change in my life," he said in a statement.

'Radically different'

Gregoire Courtine, a neuroscientist at Switzerland's Ecole Polytechnique Federale de Lausanne and a study co-author, said it was "radically different" from what had been accomplished before.

"Previous patients walked with a lot of effort -- now one just needs to think about walking to take a step," he told a press conference in the Swiss city of Lausanne.

There was another positive sign: following six months of training, Gert-Jan recovered some sensory perception and motor skills that he had lost in the accident.

He was even able to walk with crutches when the "digital bridge" was turned off.

Guillaume Charvet, a researcher at France's CEA, told AFP this suggests "that the establishment of a link between the brain and spinal cord would promote a reorganisation of the neuronal networks at the site of the injury."

So when could this technology be available to paralysed people around the world? Charvet cautioned it will take "many more years of research" to get to that point.

But the team are already preparing a trial to study whether this technology can restore function in arms and hands.

They also hope it could apply to other problems such as paralysis caused by stroke.

(AFP)

24 May 2023

[neophony]

Discover the future with Neuphony& BCI technology. Explore brain computer interfaces, mind-controlled technology, EEG Headsets & more

[jcmarchi]

The Way the Brain Learns is Different from the Way that Artificial Intelligence Systems Learn - Technology Org

New Post has been published on https://thedigitalinsider.com/the-way-the-brain-learns-is-different-from-the-way-that-artificial-intelligence-systems-learn-technology-org/

The Way the Brain Learns is Different from the Way that Artificial Intelligence Systems Learn - Technology Org

Researchers from the MRC Brain Network Dynamics Unit and Oxford University’s Department of Computer Science have set out a new principle to explain how the brain adjusts connections between neurons during learning.

This new insight may guide further research on learning in brain networks and may inspire faster and more robust learning algorithms in artificial intelligence.

Study shows that the way the brain learns is different from the way that artificial intelligence systems learn. Image credit: Pixabay

The essence of learning is to pinpoint which components in the information-processing pipeline are responsible for an error in output. In artificial intelligence, this is achieved by backpropagation: adjusting a model’s parameters to reduce the error in the output. Many researchers believe that the brain employs a similar learning principle.

However, the biological brain is superior to current machine learning systems. For example, we can learn new information by just seeing it once, while artificial systems need to be trained hundreds of times with the same pieces of information to learn them.

Furthermore, we can learn new information while maintaining the knowledge we already have, while learning new information in artificial neural networks often interferes with existing knowledge and degrades it rapidly.

These observations motivated the researchers to identify the fundamental principle employed by the brain during learning. They looked at some existing sets of mathematical equations describing changes in the behaviour of neurons and in the synaptic connections between them.

They analysed and simulated these information-processing models and found that they employ a fundamentally different learning principle from that used by artificial neural networks.

In artificial neural networks, an external algorithm tries to modify synaptic connections in order to reduce error, whereas the researchers propose that the human brain first settles the activity of neurons into an optimal balanced configuration before adjusting synaptic connections.

The researchers posit that this is in fact an efficient feature of the way that human brains learn. This is because it reduces interference by preserving existing knowledge, which in turn speeds up learning.

Writing in Nature Neuroscience, the researchers describe this new learning principle, which they have termed ‘prospective configuration’. They demonstrated in computer simulations that models employing this prospective configuration can learn faster and more effectively than artificial neural networks in tasks that are typically faced by animals and humans in nature.

The authors use the real-life example of a bear fishing for salmon. The bear can see the river and it has learnt that if it can also hear the river and smell the salmon it is likely to catch one. But one day, the bear arrives at the river with a damaged ear, so it can’t hear it.

In an artificial neural network information processing model, this lack of hearing would also result in a lack of smell (because while learning there is no sound, backpropagation would change multiple connections including those between neurons encoding the river and the salmon) and the bear would conclude that there is no salmon, and go hungry.

But in the animal brain, the lack of sound does not interfere with the knowledge that there is still the smell of the salmon, therefore the salmon is still likely to be there for catching.

The researchers developed a mathematical theory showing that letting neurons settle into a prospective configuration reduces interference between information during learning. They demonstrated that prospective configuration explains neural activity and behaviour in multiple learning experiments better than artificial neural networks.

Lead researcher Professor Rafal Bogacz of MRC Brain Network Dynamics Unit and Oxford’s Nuffield Department of Clinical Neurosciences says: ‘There is currently a big gap between abstract models performing prospective configuration, and our detailed knowledge of anatomy of brain networks. Future research by our group aims to bridge the gap between abstract models and real brains, and understand how the algorithm of prospective configuration is implemented in anatomically identified cortical networks.’

The first author of the study Dr Yuhang Song adds: ‘In the case of machine learning, the simulation of prospective configuration on existing computers is slow, because they operate in fundamentally different ways from the biological brain. A new type of computer or dedicated brain-inspired hardware needs to be developed, that will be able to implement prospective configuration rapidly and with little energy use.’

Source: University of Oxford

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