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"Exploiting neuromorphic computing to improve deep brain stimulation for Parkinson’s disease is very innovative. To our knowledge, this is the first effort in the field."Traci Yu, assistant professor of biomedical engineering
“We’ve discovered that neuromorphic chips, including Intel Loihi, outperform other computational platforms in terms of energy-efficiency by 109 times,” An said.Michigan Tech News
Michigan Tech Researchers Develop ‘Smart’ Deep Brain Stimulation Systems for Parkinson’s Patients
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Using Spiking Neural Networks to Detect Symptoms
Neuromorphic computing with memristors, shown here, allows researchers to develop improved deep brain stimulation systems that are more responsive and energy efficient, making life better for people with Parkinson's disease.
By Kimberly Geiger
Published 9:00 a.m., March 22, 2023
Comments (1)
DOI: Beta Oscillation Detector Design for Closed-Loop Deep Brain Stimulation of Parkinson’s Disease with Memristive Spiking Neural Networks
Researchers at Michigan Technological University are applying neuromorphic computing to improve the effectiveness and energy efficiency of deep brain stimulation systems used to treat Parkinson’s disease.
Currently incurable, Parkinson’s disease is a neurodegenerative disorder that affects millions worldwide. Deep brain stimulation (DBS) is an alternative to medications that are effective but lose effectiveness as patients develop drug resistance. Over time, larger doses of medication become necessary to control the condition and with them come potentially serious side effects. DBS is one alternative.
Making Deep Brain Stimulation Systems Better for Patients
DBS systems function like a pacemaker for the brain. They suppress the motor symptoms of Parkinson’s disease, including slowed or delayed movements (called bradykinesia), tremors and stiffness. An electrode, implanted into a specific target in the brain, emits electrical impulses using a battery-powered device in the chest.
DBS systems can be life-changing for people diagnosed with Parkinson’s disease. But battery life is a challenge. Current devices use an implantable pulse generator (IPG), surgically inserted in the chest or abdomen, to send stimulation signals to the brain at a constant frequency, regardless of the clinical state of the patient. Nonchargeable batteries last approximately two to five years. Battery replacement can be disruptive for patients; it requires a surgical procedure. And there can be unwanted side effects caused by the IPG’s continuous stimulation.
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Graduate research assistant Hannah Loughlin, right, works with Traci Yu in the lab. Loughlin earned her biomedical engineering undergraduate degree at Michigan Tech in 2022, with minor in electrical engineering, and is pursuing her master's.
Chunxiu (Traci) Yu, an assistant professor of biomedical engineering, in collaboration with Hongyu An, an assistant professor of electrical and computer engineering, are working with their research teams to develop strategies using a different tool: neuromorphic computing.
“Referred to as brain-inspired computing or neuroscience-powered artificial intelligence, neuromorphic computing emulates a nervous system using microchips and algorithms. It is also highly energy-efficient,” Yu said.
Closed-Loop Smart System Offers Intelligent Adjustments
In both Yu’s Brain Stimulation Engineering Lab in the Department of Biomedical Engineering, and An’s Brain-Inspired AI lab in the Department of Electrical and Computer Engineering research teams are developing strategies to improve DBS systems.
The collaborative project is focused on a closed-loop DBS system that can intelligently adjust stimulus signals according to patient symptoms.
“Most current DBS systems are open-loop. The open-loop DBS is on 24 hours a day, 365 days a year,” said Yu. Open-loop systems are high in energy consumption, providing continuous stimulation to the brain because the real-time symptoms are unknown to the device. “Using a closed-loop system allows us to optimize the energy-efficiency of DBS devices,” Yu explained. “The patient’s brain signals are used to generate a treatment signal — a stimulation — as needed, in real time.”
Using Spiking Neural Networks to Detect Symptoms
The cornerstone of Yu and An’s closed-loop DBS are spiking neural networks, or SNNs, a type of artificial neural network. SNNs can detect Parkinson’s symptoms and generate optimized electric stimulus pulses.
“The communication signals within SNNs are represented with small spike electrical pulses, in volts,” An explained. “In digital systems, data is represented by high and low voltages. For example, a high voltage represents logic one and a low voltage level represents logic zero. In this way, digital systems encode data in binary numbers.”
Data in SNNs can be carried in time, such as the interval between spikes, according to An. “As a result of this, SNN systems have much higher energy-efficiency compared to other artificial neural networks,” he said.
The researchers’ new closed-loop DBS system is able to evaluate the severity of Parkinson’s symptoms by measuring neural activity at a specific brain wave, or oscillation, bandwidth. The areas of the brain that control movement generate beta oscillations.
“We use the beta oscillatory activity as a biomarker because it can be detected much faster than other means, such as tremor signals,” An said. “If the neural activity detected is unusually strong, it indicates the Parkinson’s disease symptoms are more severe.”
SNNs in An’s lab operate using one of the most advanced neuromorphic chips around: Intel Loihi. In a collaboration with Intel, the lab is actively exploring ways to use the chip’s ultra-efficient intelligence to help patients with Parkinson’s disease.
“We’ve discovered that neuromorphic chips, including Intel Loihi, outperform other computational platforms in terms of energy-efficiency by 109 times,” An said.
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Graduate research assistant Noah Zins, left, works with Hongyu An on coding on Intel Loihi chip. In 2021 Zins earned his undergraduate degree in computer engineering with a mathematical sciences minor. The master's student is researching neuromorphic computing applications in robotics.
Another innovation: An and Yu replaced the SNN’s traditional electronic memory with a memristor: an electrical component used in next-generation computers and electronics. A memristor can both store information like a memory chip and resist the flow of electric current, like a resistor in an electrical circuit.
A memristor looks like a resistor. The difference is that its resistance is changeable. “With carefully designed signals, the resistance of a memristor can be changed into multiple or even thousands of different resistances. This feature significantly increases the amount of information that can be stored by individual memristors,” said An.
In simulations, DBS systems using memristors led to smaller chips, faster transmission signals and less energy use.
“This result is highly promising,” An said.
Communicating Their Research
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Designing a Customized DBS Chip is the Next Step
An and Yu plan to collaboratively design their own memristive neuromorphic chip specifically for closed-loop DBS systems.
“Our research on these new, innovative computational paradigms — along with the design of emergent AI chips — will open a new door to greater and faster development of smart medical devices for brain rehabilitation,” said An. “Even wearable medical devices are now well within the realm of possibility.”
For their students at Michigan Tech, the ongoing joint research provides the kind of unique learning experience that comes with working on the cutting edge of chip design, AI, neuromorphic computing and brain-computer interface.
“The chance to discover new deep brain stimulation technologies that could help people suffering from neurological conditions in the future keeps me driven to continue working in the lab and help the advancement of knowledge in this area,” said Jacob Jackson ’23, a biomedical engineering major who conducts research in Yu’s lab. He plans to begin his graduate work at Michigan Tech in the fall. “I am enjoying neural engineering research so much that I knew it was the right path for me,” he said.
Can someone please publish an article addressing this miss informed article please. Someone needs to put MF in their place. …I read the other day that the bum-breathing turtle has extended its habitat range. Now it seems it has learnt to speak:
https://www.fool.com.au/2023/05/28/could-nvidia-destroy-brainchip/
...
Could Nvidia destroy Brainchip?
When it comes to artificial intelligence (AI), Nvidia has proven itself to be the leader in the field.
Unfortunately for Brainchip, the AI behemoth’s operations also cover edge AI. This is the market that Brainchip is targeting with its Akida chip. And while its first chip was a commercial flop, management continues to believe its technology is the best in the market.
But can it really compete with Nvidia? Probably not, for a number of reasons. But first, let’s take a look at Nvidia’s edge AI capabilities.
The Jetsons family
No, it’s not that Jetsons family, it’s a set of chips from Nvidia that look set to dominate the edge AI market. The company explains:
The TX2 4GB Module is of particularly interest here. As it this “embedded computer lets you run neural networks with double the compute performance or double the power efficiency of Jetson TX1.” Sound familiar?
But isn’t Brainchip’s Akida chip supposed to be better? Apparently so. Management claims its chip has better performance metrics than rivals.
However, that doesn’t necessarily guarantee sales. Far from it!
If a trusted company like Nvidia has a product on the market that offers “exceptional speed and power-efficiency in an embedded AI computing device”, that will be more than enough for the majority of users. So, why would you take a risk on a product from a company with no track record and a tiny support team? You probably wouldn’t.
This could mean the future is very bleak for Brainchip and its shares. After all, if the company doesn’t generate meaningful revenue in the near future, it will continue to burn through its cash balance and be forced into raising more funds, diluting shareholders yet again, and putting ever more pressure on its share price. Could it ultimately go to zero? I wouldn’t bet against it.
... as little as 30 times as much power as Akida ...
Here is the get out of jail card. It's worth the readCan someone please publish an article addressing this miss informed article please. Someone needs to put MF in their place. …
He does not need to sell these right away the US tax season is end of December. In reality he needs to raise that money early next year which I would believe our SP be north of where it is today.His tax obligation would equate to 1,932,558 BRN shares @43c.
But perhaps he will be able to pay that debt from some other reserves??
I hope he's thinking that this thing is about to take off and I think I'll hang on for the ride.
Just me hoping.
I thought it was made fairly clear that the tax liability happens the moment you acquire the shares under US law??He does not need to sell these right away the US tax season is end of December. In reality he needs to raise that money early next year which I would believe our SP be north of where it is today.
I’m amazed with those disclosures they have the temerity to ask for payment for their ‘services’Here is the get out of jail card. It's worth the read
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The Motley Fool Disclaimer | The Motley Fool
The Motley Fool provides leading insight and analysis about stocks, helping investors stay informed.www.fool.com
I thought it was made fairly clear that the tax liability happens the moment you acquire the shares under US law??
I believe the individual that wrote the article does not have a formal degree in Electrical Engineering. While it's true that some companies seek out brand recognition or look for solutions that are just "good enough," it does not necessarily mean that they will be engineering a quality product.It is articles like this that cast the seed of doubt in my psyche. If someone has a bit of time and knowledge and would like to cheer me up, could they construct a short rebuttal to that fool? I will love you forever.
Many a true word ...Shorters at Friday after close:
Toss: 'Shit, looks like Brainchip is following the US markets this time after NVIDIA's bullish pump. They are buying'.
Pot: 'What do we do Toss?'
Toss: 'Don't worry. Let's ask Turd.'
Turd: 'What's up Wancas?'
Toss: 'Little worried about our positions mate. 40,000 of the bastards are buying.'
Turd: 'No worries, I will make a phone call Sunday to ask a favour. Could maybe drive a few new holders out'.
----- Sunday morning ------
Ringtone playing 'Don't let me be misunderstood' by Nina Simone.
JM: 'Yooooooo'.
Turd: 'Lucky Pants. We have a problem. We have buying out of control on Friday. Probably due to the NVIDIA pump'.
JM: 'I am on it. Let's say that NVIDIA could nudge Brainchip out the market even though there is no reason that they could. Hahahahahaha.'
Turd: 'No, need it stronger.'
JM: 'Outwit Brainchip'.
Turd: 'Stronger'.
JM: 'Strangle Brainchip'.
Turd: 'C'mon Lucky. You used to be good at this. Stronger'.
JM: 'Destroy Brainchip'.
Turd. Yes'.
-------Evil laughter--------
So anyone could be a fool......Here is the get out of jail card. It's worth the read
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The Motley Fool Disclaimer | The Motley Fool
The Motley Fool provides leading insight and analysis about stocks, helping investors stay informed.www.fool.com
”Tricks and treachery are the practice of fools that don't have brains enough to be honest.”So anyone could be a fool......
He's been using the cafe shibboleth for years.A few out there happy to go on record as shorters of our great company.
This gentlemen's quote being 'BRN has less revenue than some small cafes'.
Can't wait till Peter and the team prove them wrong. I for one have bailed too early on other opportunities in the past. After 5 years invested, I'm pretty sure I can't wait a bit longer.
Fortune favours the brave
Good luck this week chippers.