Food4 thought
Regular
Like we needed that !!
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
Rise from the ashes
Regular
Whatcha reckon a tech that can reveal the invisible in certain cases is worth these days?
This could get expensive real fast bring on CES
This could get expensive real fast bring on CES
thelittleshort
Top Bloke
New tweet
“…VDN can help customers with effective and accurate vision solutions powered with Al and Deep Learning in a faster time to market.”
“…VDN can help customers with effective and accurate vision solutions powered with Al and Deep Learning in a faster time to market.”
Fact Finder
Top 20
Ordinarily I would not repost an article of this type from April but it does go nicely with the excitement theme Rise is decorating TSEx with:
April 26th, 2022 | 12:15 CEST
Chip stocks are having a hard time at the moment, although the market is undersupplied and will probably remain so for some time. This was also highlighted by figures from semiconductor equipment supplier ASML. Orders at the world's largest supplier of lithography systems to semiconductor manufacturers were around EUR 7 billion in the first three months of 2022, well above market expectations. In addition, ASML has indicated targets through 2025. The Company intends to expand production capacities in view of the high demand. Mercedes partner BrainChip should also benefit from these positive industry prospects. And, of course, industry heavyweights such as Nvidia and Infineon.
time to read: 3 minutes | Author:Fabian Lorenz
ISIN: MERCEDES-BENZ GROUP AG | DE0007100000 , BRAINCHIP HOLDINGS LTD | AU000000BRN8 , INFINEON TECH.AG NA O.N. | DE0006231004 , NVIDIA CORP. DL-_001 | US67066G1040
April 26th, 2022 | 12:15 CEST
CHIP INDUSTRY BOOMING: WHAT ARE MERCEDES PARTNERS BRAINCHIP, NVIDIA AND INFINEON DOING?
- AI
- AUTOMOTIVE
- CHIP INDUSTRY
Chip stocks are having a hard time at the moment, although the market is undersupplied and will probably remain so for some time. This was also highlighted by figures from semiconductor equipment supplier ASML. Orders at the world's largest supplier of lithography systems to semiconductor manufacturers were around EUR 7 billion in the first three months of 2022, well above market expectations. In addition, ASML has indicated targets through 2025. The Company intends to expand production capacities in view of the high demand. Mercedes partner BrainChip should also benefit from these positive industry prospects. And, of course, industry heavyweights such as Nvidia and Infineon.
time to read: 3 minutes | Author:Fabian Lorenz
ISIN: MERCEDES-BENZ GROUP AG | DE0007100000 , BRAINCHIP HOLDINGS LTD | AU000000BRN8 , INFINEON TECH.AG NA O.N. | DE0006231004 , NVIDIA CORP. DL-_001 | US67066G1040
TABLE OF CONTENTS:
- MERCEDES PARTNER BRAINCHIP WITH THE NEXT COLLABORATION
- NVIDIA: DEUTSCHE BANK REDUCES PRICE TARGET
- INFINEON: ANALYSTS SKEPTICAL IN THE SHORT TERM
Full interview
MERCEDES PARTNER BRAINCHIP WITH THE NEXT COLLABORATION
Probably one of the most exciting listed chip newcomers is BrainChip. The Australian technology company is working on solutions in the field of artificial intelligence and machine learning. The Akida chip is the Company's current flagship product. It is a neuromorphic processor. It is said to be very close to the workings of the brainand thus, in particular, very energy-efficient. BrainChip sees applications in autonomous driving, IoT devices, robotics, medical diagnostics and security technology. The Australians caused a sensation at the turn of the year when it became known that Akida is installed in the Concept car EQXX from Mercedesand, among other things, makes the "Hey, Mercedes" voice control up to ten times more efficient than conventional voice control. There were also reports of interest on the part of the US Air Force
equanimous
Norse clairvoyant shapeshifter goddess
Icon and role model for motor sports. Sorry to share sad news
Fact Finder
Top 20
Move over @Bravo make room in the naughty corner - Orange only please 300 times - I knew that I was garnering greater interest for your post as it seemed to slip past the keeper.Sorry FF, it was me, I highlighted it in red
Now a reward.
If Kimberly Vaupen thinks this startup is worth noting then perhaps we should keep an eye out at CES2023 as they are presenting and used ARM to get the IP they needed based on my on the fly research just now:
Femtosense
My opinion only DYOR
FF
AKIDA BALLISTA
Rise from the ashes
Regular
Sorry FF, it was me, I highlighted it in red
Sending updated texts to a bunch of people now.
Nearly had a heart attack....
Gates, Bezos invest in Aust 'brain chip'
Billionaires Bill Gates and Jeff Bezos have thrown their weight behind a potentially life-changing paralysis treatment that lets patients control computers with their minds.
Founded by Australian professors Tom Oxley and Nick Opie, New York-based Synchron announced on Friday it had closed a $110 million Series C funding round led by ARCH Venture Partners, with participation from Bezos Expeditions and Gates Frontier.
The product is a tiny device that can be implanted on blood vessels on the surface of the brain via the jugular vein.
Gates, Bezos invest in Aust 'brain chip'
Billionaires Bill Gates and Jeff Bezos have thrown their weight behind a potentially life-changing paralysis treatment that lets patients control computers with their minds.
Founded by Australian professors Tom Oxley and Nick Opie, New York-based Synchron announced on Friday it had closed a $110 million Series C funding round led by ARCH Venture Partners, with participation from Bezos Expeditions and Gates Frontier.
The product is a tiny device that can be implanted on blood vessels on the surface of the brain via the jugular vein.
Labsy
Regular
I can't help but think, how significant an acquisition would brainchip be for Intel... and how much would they need to pay to see this happen?....they need us desperatelyThis one from the WSJ earlier in the year gives an insight into what Intel want to achieve and can see how & why we popped up in the IFS.....pieces of their puzzle they are collecting.
Inside Intel’s Strategy to Compete With Nvidia in the AI-Chip Market
Nvidia has dominated the growing artificial-intelligence business. Intel wants to change that.www.wsj.com
Inside Intel’s Strategy to Compete With Nvidia in the AI-Chip Market
Nvidia has dominated the growing artificial-intelligence business. Intel wants to change that.
By Asa Fitch
Follow
Updated April 9, 2022 12:01 am ET
Intel Corp. INTC 0.84%increase; green up pointing triangle is reworking its artificial-intelligence strategy as it tries to gain ground on Nvidia Corp., the leader in the market for chips designed to excel at AI computations.
Over the past year, under new chief executive Pat Gelsinger, Intel has added staff and introduced new AI software for its expanding lineup of chips to improve AI-driven chatbots, facial recognition and movie recommendations, among other applications.
Intel is known mainly for its dominance in the market for central processing units, the brains behind personal computers and the servers that run corporate networks and the internet. But it has lost some of its sheen for investors over the past decade as Nvidia gobbled up the market for chips specifically designed for AI purposes, especially chips that train AI models.
Nvidia now accounts for about 80% of revenue from AI-specific computation in big data centers, according to Informa PLC’s Omdia unit, a British research and consulting firm, although that doesn’t account for any AI calculations done on Intel’s general-purpose CPUs. That dominance in AI-specific chips helped Nvidia surpass Intel as the most valuable chip company in the U.S. by market capitalization two years ago.
AI chips are a relatively small but rapidly growing segment of the overall chip market. Rising demand for faster, more efficient AI computation has spawned dozens of chip startups, while the leading chip makers have invested heavily. The AI chip market was worth around $8 billion in 2020, but is expected to grow to nearly $200 billion by 2030, according to a report from Allied Market Research, based in Portland, Ore.
The plan
An array of so-called neuromorphic chips Intel is researching. They are built to mimic the structure of the human brain and could eventually be added to Intel’s AI offerings.PHOTO: JASON HENRY FOR THE WALL STREET JOURNAL
Intel’s strategy is to build a stable of chips and open-source software that covers a broad range of computing needs as AI becomes more prevalent. For instance, it could sell customers a package that would allow them to hand off some tasks to specialist chips that excel at things like image recognition, while handling other work on general-purpose chips.
Intel hopes the efficiency of that kind of division of labor could help companies optimize performance for their specific AI tasks and save money by cutting power consumption. That could make sense for customers that have a lot of data and do a lot of AI processing—big corporations and well-funded startups—although Intel also hopes to capture demand for AI computation through sales to the large cloud-computing providers and even products for individual consumers.
One important change Intel has made in pursuit of that strategy is the addition of graphics processing units to its product line. Unveiled more than two years ago, those chips could help it stack up better against Nvidia, which specializes in GPUs initially developed for computer gaming but adapted for machine-learning tasks. Intel in 2019 bought Israeli startup Habana Labs, which makes chips designed specifically for training AI models—systems that spit out realistic-sounding sentences, for example—and for generating output from those models.
Another change is in the way Intel knits together its AI products for customers.
“It isn’t even a question of do we have to invest more—we invest quite a bit in AI,” says Sandra Rivera, a longtime Intel executive whom Mr. Gelsinger tapped to head the data-center business and AI strategy last summer. “But we haven’t gotten the leverage of those investments when we have different strategies and different execution priorities” for various products.
Kavitha Prasad, vice president and general manager of data center, AI and cloud execution and strategy, at Intel headquarters.PHOTO: JASON HENRY FOR THE WALL STREET JOURNAL
Since taking her new role, Ms. Rivera has brought in several new executives, including Kavitha Prasad, who came from a machine-learning startup after an earlier stint at Intel. Ms. Prasad, who directly oversees the AI strategy, is leading a shift in focus she says is centered on using AI to reach customers’ business goals, rather than offering a menu of chips and letting customers figure out the rest.
“Intel has all these technologies, but what is bringing it together to make it cohesive from a customer perspective, so that the customers are able to deploy it at a much faster rate, so that they’re able to get to their business outcomes faster?” she says. “It is not about having the solutions, but it’s about meaningfully bringing them together to make it happen.”
Bringing it all together is largely the job of Intel’s software architects, led by Chief Technology Officer Greg Lavender, whom Mr. Gelsinger hired from VMware Inc., where Mr. Gelsinger was previously CEO.
The biggest challenge
Inside a data center at the Intel headquarters.PHOTO: JASON HENRY FOR THE WALL STREET JOURNAL
Success, of course, isn’t a sure thing. Nvidia, already far ahead of Intel and the rest of the competition, is moving quickly with its own chips, announcing a new generation of superfast processors in March. While analysts say Intel’s strategy could help make it a more formidable competitor to Nvidia, its ability to tap the AI market hinges on delivering AI-targeted chips and related software on schedule. Recent history suggests that could be a challenge. Intel has stumbled in chip-manufacturing technology in recent years, leaving it behind South Korea’s Samsung Electronics Co. and Taiwan Semiconductor Manufacturing Co. in the high-stakes race to make chips with the smallest transistors and best performance. Some of its latest CPU chips for servers have been delayed.
Mr. Gelsinger aims to reverse that trajectory by rededicating the company to manufacturing—he’s announced tens of billions of spending on new chip factories over the next several years—and building up a business making chips on contract according to others’ designs. Whether the company can execute on Mr. Gelsinger’s plan to retake the technological lead from its Asian competitors in the next few years is an open question.
“There are a lot of things they haven’t executed on over the last five or six years,” says Matt Bryson, an analyst at Wedbush Securities. “Clearly under Pat Gelsinger Intel is investing more in product development, and if you put more money into development, you should have a better ability to execute, but it comes back to how do you know until you are showing products and starting to see traction?”
Ms. Rivera says Intel is ready to make that leap. “We have the customer relationships, we have the market position, we have the unique differentiation—we just need to execute our strategy,” she says.
hamilton66
Regular
Ordinarily I would not repost an article of this type from April but it does go nicely with the excitement theme Rise is decorating TSEx with:
April 26th, 2022 | 12:15 CEST
CHIP INDUSTRY BOOMING: WHAT ARE MERCEDES PARTNERS BRAINCHIP, NVIDIA AND INFINEON DOING?
Photo credits: pixabay.com
- AI
- AUTOMOTIVE
- CHIP INDUSTRY
Chip stocks are having a hard time at the moment, although the market is undersupplied and will probably remain so for some time. This was also highlighted by figures from semiconductor equipment supplier ASML. Orders at the world's largest supplier of lithography systems to semiconductor manufacturers were around EUR 7 billion in the first three months of 2022, well above market expectations. In addition, ASML has indicated targets through 2025. The Company intends to expand production capacities in view of the high demand. Mercedes partner BrainChip should also benefit from these positive industry prospects. And, of course, industry heavyweights such as Nvidia and Infineon.
time to read: 3 minutes | Author:Fabian Lorenz
ISIN: MERCEDES-BENZ GROUP AG | DE0007100000 , BRAINCHIP HOLDINGS LTD | AU000000BRN8 , INFINEON TECH.AG NA O.N. | DE0006231004 , NVIDIA CORP. DL-_001 | US67066G1040
TABLE OF CONTENTS:
- MERCEDES PARTNER BRAINCHIP WITH THE NEXT COLLABORATION
- NVIDIA: DEUTSCHE BANK REDUCES PRICE TARGET
- INFINEON: ANALYSTS SKEPTICAL IN THE SHORT TERM
Full interview
MERCEDES PARTNER BRAINCHIP WITH THE NEXT COLLABORATION
Probably one of the most exciting listed chip newcomers is BrainChip. The Australian technology company is working on solutions in the field of artificial intelligence and machine learning. The Akida chip is the Company's current flagship product. It is a neuromorphic processor. It is said to be very close to the workings of the brainand thus, in particular, very energy-efficient. BrainChip sees applications in autonomous driving, IoT devices, robotics, medical diagnostics and security technology. The Australians caused a sensation at the turn of the year when it became known that Akida is installed in the Concept car EQXX from Mercedesand, among other things, makes the "Hey, Mercedes" voice control up to ten times more efficient than conventional voice control. There were also reports of interest on the part of the US Air Force
F/F, the press BRN is achieving at the moment is quite extraordinary. How it plays out, in terms of sales, and s/p, will be very interesting. What is ubiquitous worth ? It's an open question, that no-one can answer. We're entering exciting times. Hold tight.Ordinarily I would not repost an article of this type from April but it does go nicely with the excitement theme Rise is decorating TSEx with:
April 26th, 2022 | 12:15 CEST
CHIP INDUSTRY BOOMING: WHAT ARE MERCEDES PARTNERS BRAINCHIP, NVIDIA AND INFINEON DOING?
Photo credits: pixabay.com
- AI
- AUTOMOTIVE
- CHIP INDUSTRY
Chip stocks are having a hard time at the moment, although the market is undersupplied and will probably remain so for some time. This was also highlighted by figures from semiconductor equipment supplier ASML. Orders at the world's largest supplier of lithography systems to semiconductor manufacturers were around EUR 7 billion in the first three months of 2022, well above market expectations. In addition, ASML has indicated targets through 2025. The Company intends to expand production capacities in view of the high demand. Mercedes partner BrainChip should also benefit from these positive industry prospects. And, of course, industry heavyweights such as Nvidia and Infineon.
time to read: 3 minutes | Author:Fabian Lorenz
ISIN: MERCEDES-BENZ GROUP AG | DE0007100000 , BRAINCHIP HOLDINGS LTD | AU000000BRN8 , INFINEON TECH.AG NA O.N. | DE0006231004 , NVIDIA CORP. DL-_001 | US67066G1040
TABLE OF CONTENTS:
- MERCEDES PARTNER BRAINCHIP WITH THE NEXT COLLABORATION
- NVIDIA: DEUTSCHE BANK REDUCES PRICE TARGET
- INFINEON: ANALYSTS SKEPTICAL IN THE SHORT TERM
Full interview
MERCEDES PARTNER BRAINCHIP WITH THE NEXT COLLABORATION
Probably one of the most exciting listed chip newcomers is BrainChip. The Australian technology company is working on solutions in the field of artificial intelligence and machine learning. The Akida chip is the Company's current flagship product. It is a neuromorphic processor. It is said to be very close to the workings of the brainand thus, in particular, very energy-efficient. BrainChip sees applications in autonomous driving, IoT devices, robotics, medical diagnostics and security technology. The Australians caused a sensation at the turn of the year when it became known that Akida is installed in the Concept car EQXX from Mercedesand, among other things, makes the "Hey, Mercedes" voice control up to ten times more efficient than conventional voice control. There were also reports of interest on the part of the US Air Force
GLTA
Rise from the ashes
Regular
Tothemoon24
Top 20
Why is dough another word for money? Because everyone kneads it.
… so very true , & we are on the Crust of making a shit load
Stable Genius
Regular
I took the bait @Fact Finder:
Edge AI Innovator Femtosense Brings Affordable, Off-the-Shelf Hearing Aids, Other Wearables Closer to Reality with First Silicon
Edge AI innovator Femtosense today announced the arrival and successful testing of its first silicon, substantiating the start-up’s assertions that it will d...
www.businesswire.com
They’re talking about using neuromorphic AI but no company mentioned so fingers crossed they’re using the first and best commercially available NN!
Unfortunately not - they are using these guysI took the bait @Fact Finder:
Edge AI Innovator Femtosense Brings Affordable, Off-the-Shelf Hearing Aids, Other Wearables Closer to Reality with First Silicon
Edge AI innovator Femtosense today announced the arrival and successful testing of its first silicon, substantiating the start-up’s assertions that it will d...www.businesswire.com
They’re talking about using neuromorphic AI but no company mentioned so fingers crossed they’re using the first and best commercially available NN!
AI research | imec
Discover how Imec's co-optimized, modular, and application-driven approach is advancing AI and semiconductor technologies for sustainable innovation.
Edge AI Innovator Femtosense Brings Affordable, Off-the-Shelf Hearing Aids, Other Wearables Closer to Reality with First Silicon | Yiyi Wang, Ph.D.
We are proud to partner with Femtosense to deliver the Edge AI chip!
davidfitz
Regular
Looks likes the Prophesee demo at The Venetian (beautiful hotel by the way) is popular with only a few meetings left for the first couple of days.
www.prophesee.ai
Book a Demo with the Prophesee Team
January 07 - 3:30 a.m., 7:30 a.m., 8 a.m. or 9 a.m.
Book a meeting | PROPHESEE
Book a meeting with our team to discover how Event-Based Vision will get your computer vision applications to the next level
www.prophesee.ai
Book a Demo with the Prophesee Team
January 06 - 7:30 a.m. or 9 a.m.
January 07 - 3:30 a.m., 7:30 a.m., 8 a.m. or 9 a.m.
equanimous
Norse clairvoyant shapeshifter goddess
I can see Dio shaking his head but im eager to hear his replyWhy is dough another word for money? Because everyone kneads it.
Fact Finder
Top 20
IMEC are not supplying the IP they helped produce the actual silicon.Unfortunately not - they are using these guys
AI research | imec
Discover how Imec's co-optimized, modular, and application-driven approach is advancing AI and semiconductor technologies for sustainable innovation.www.imec-int.com
Edge AI Innovator Femtosense Brings Affordable, Off-the-Shelf Hearing Aids, Other Wearables Closer to Reality with First Silicon | Yiyi Wang, Ph.D.
We are proud to partner with Femtosense to deliver the Edge AI chip!www.linkedin.com
ARM is in the sights for the IP.
Also I am puzzled by the claim it is a result of 50 years of research when he looks barely 30 years. Even if he was an in vitro prodigy he is still over 19 years light on:
“Femtosense: Our Ultra-efficient AI Chip, Built with Arm Flexible Access
Sam Fok, co-founder and CEO of silicon startup Femtosense, explains how Arm Flexible Access for Startups enabled the development of a neuromorphic AI chip
Posted on 26th January 2021By Sam Fok, Co-founder and CEO, Femtosense
Artificial Intelligence (AI)Endpoint
Reading Time: 6 mins
One classical pathway to technology success runs from university research to commercial startup. It’s an exciting journey in which a small group of grad students takes a spark of innovation from research and kindles it into a technology that they hope will transform the world.
That journey has its challenges as the technology moves from the lab into the rigorous and sometimes unforgiving world of product development. And it’s a journey my co-founder, Alexander Neckar, and I embarked on two years ago out of Stanford.
Our startup, Femtosense, emerged from work we did in Stanford’s Electrical Engineering graduate program on a project called Braindrop—a mixed-signal neuromorphic system designed to be programmed at a high level of abstraction.
Neuromorphic systems are silicon versions of the neural systems found in neurobiology. It’s a growing field, offering a range of exciting possibilities such as sensory systems that rival human senses in real-time.
Over the past two years, we’ve nurtured our startup to develop the aspects of the technology with commercial potential. We’ve taken the original concept and built a neural network application-specific integrated circuit (ASIC) for the general application area of ultra-efficient AI. As a start, we want to enable ultra-power-efficient inference in embedded endpoint devices everywhere.
Why did we venture down this path? Datacenter technology is well-developed and has different technical challenges than edge technology. Further, consumers and market competition are pushing for edge and endpoint devices with ever-increasing capability. AI can and should be deployed at the endpoints when feasible to reduce latency, lower computing costs, and enhance security.
Area and power major considerations in neural networks
But designing such an ASIC (any ASIC really) is a unique challenge in today’s ultra-deep submicron world. For one thing, area and power considerations loom large. Neural networks are a different animal than classical signal processing. Designing an ASIC or system-on-chip (SoC) for neural networks presents a different challenge than, say, designing an efficient DSP or microcontroller.Neural networks have a lot of parameters, are quite memory intensive, and have a lot of potential for energetically expensive data motion. Of primary concern is the question of on-chip versus off-chip memory. Off-chip memory provides excellent density, allowing for bigger models, but accessing off-chip memory costs a lot of energy, which often puts such systems beyond the power budgets of the envisioned initial applications.
So, when you’re looking at ultra-power-efficient endpoint embedded solutions, you will want to put everything a single die, but then you will hit area and cost issues. This is where algorithm-hardware codesign comes into play; the two really must be done together.
You can’t just naively take an algorithm and map it down to a chip. It would cost too much energy or money or not just perform well. You have to think carefully about how to make that algorithm efficient. That’s the hardware and algorithm co-design challenge. We spend a lot of effort on the algorithm side to fit everything on-chip, and, once it’s on-chip, to map the algorithm onto the chip’s compute fabric.
As we design, we’re exploring many potential applications because we want to apply our technology as widely as possible. The market is not nearly as black-and-white as endpoint-and-cloud terminology suggests.
We see it as a continuum. It’s not like you’re either in datacenters or in tiny battery-powered devices. There are many nodes across the spectrum—everything from on-prem servers, to laptops, phones, smartwatches, earbuds, and even sensors out in the middle of nowhere with no power source could use more efficient neural network compute.
We want to deliver ultra-efficient compute. To us, this is our primary mission. When you’re in tough environments with tight requirements, that’s when you’re going to want to use our technology.
Arm Flexible Access for Startups gave us the design flexibility we needed
We’re a small team for now, so we need to focus on our strengths. There’s the core hardware accelerator, the software that goes with it, and the algorithms. Then, there’s technology to take that value and serve customers. This is where we’re very excited to be working with Arm and the Arm Flexible Access for Startups program.To integrate with customer designs, you need an interface to handle communications and off-load a bit of compute. This is not something we think about day-to-day in terms of our core engineering or IP. This is why it’s paramount to have a well-established, reliable partner like Arm to work with because you don’t have to reinvent everything or spend effort educating the market. Everyone knows Arm, and Arm is a well-known path to integration. Arm solves one of our biggest commercial challenges, and that’s why working with Arm is key.
One of the attractive elements of the Arm Flexible Access for Startups program is the ecosystem support. The ecosystem reduces barriers to adoption, which in turn drives innovation. When you’re planning projects, you need clear and accessible specifications and information about which products do what.
Making actionable information available upfront without huge outlays and with the ability to evaluate different IP and run experiments in the sandbox is huge for us. The program’s pricing models are much more aligned with how startups grow than traditional IP vending.
Standing at the beginning of the SoC integration path, we have an exciting journey ahead, and we’ll have more interesting perspectives the deeper we get into it. We have the initial design and are moving toward an ASIC implementation, making the Arm Flexible Access for Startups program an important tool to have.”
My opinion only DYOR
FF
AKIDA BALLISTA
PS: I suppose his Dad could have started the research before he was born and gave it to him when he was born.
Hi Cardpro,
Thanks for posting this. I think Synchron has been mentioned a couple of times before but this is a timely reminder. There is a clear potential for using Akida in conjunction with this. It ties in nicely with my response to @Bravo's post about brain control of external devices and the brain spike sensor detecting apparently random spikes which N-of-M coding and the JAST rules (from Simon Thorpe's Spikenet company, now owned by BrainChip) can unscramble:
Here are some Synchron patents:
US2022369994A1 NEUROMONITORING DIAGNOSTIC SYSTEMS
A method of facilitating direct interaction between a distributed neural network of a brain of an individual and an external device, the method comprising:
generating a plurality of feedback data from the external device where the plurality of feedback data is related to an activity of the external device;
establishing a connection from the external device to a control unit coupled to the individual, where the control unit includes at a first neural implant previously positioned within a first cytoarchitecture region of the distributed neural network of the brain of the individual; and
transmitting the plurality of feedback data to the control unit, such that the control unit energizes the first neural implant to stimulate the first cytoarchitecture region of the brain, which produces an effect in the individual that is specific to the first cytoarchitecture region such that the individual is able to perceive the effect.
AU2020378095A1 Methods, systems, and apparatus for closed-loop neuromodulation
A method of treating epilepsy, comprising:
detecting, using a first electrode array, an electrophysiological signal of a subject, wherein the first electrode array is coupled to a first endovascular carrier implanted within the subject;
analyzing the electrophysiological signal using a neuromodulation unit implanted within the subject and electrically coupled to the first electrode array; and stimulating an intracorporeal target of the subject using a second electrode array in response to the electrophysiological signal detected, wherein the second electrode array is electrically coupled to the neuromodulation unit, and wherein the second electrode array is coupled to a second endovascular carrier implanted superior to a base of a skull of the subject.
AU2021246506A1 Systems and methods for controlling a device using detected changes in a neural-related signal
Systems and methods of controlling a device using detected changes in a neural-related signal of a subject are disclosed. In one embodiment, a method of controlling a device or software application comprises detecting a first change in a neural-related signal of a subject, detecting a second change in the neural-related signal, and transmitting an input command to the device upon or following the detection of the second change in the neural-related signal. The neural-related signal can be detected using a neural interface implanted within a brain of the subject.
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