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Michigan Tech News
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.
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.
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.
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.”
“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.
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.
“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.
Michigan Tech Researchers Develop ‘Smart’ Deep Brain Stimulation Systems for Parkinson’s Patients
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.
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.
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
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.
Boab
I wish I could paint like Vincent
“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
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.
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.
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
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.
TheFunkMachine
seeds have the potential to become trees.
Can someone please publish an article addressing this miss informed article please. Someone needs to put MF in their place. …
Boab
I wish I could paint like Vincent
Here is the get out of jail card. It's worth the read
The Motley Fool Disclaimer | The Motley Fool
The Motley Fool provides leading insight and analysis about stocks, helping investors stay informed.
www.fool.com
Kachoo
Regular
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.
Boab
I wish I could paint like Vincent
I thought it was made fairly clear that the tax liability happens the moment you acquire the shares under US law??
Labsy
Regular
It is my opinion that all the recent MF article does is put us on par with NVIDIA. This numnut is actually inadvertently advertising us and illustrating we are a substitute for the current NVIDIA solution for edge AI. The nerve of Brainchip to compete with NVIDIA he implies... haha...what a douche bag.
Let that sink in, a small Aussie company is a cheaper, more efficient substitute for multi billion dollar, leading AI company...
Tech scouts will be curious about us...
Let that sink in, a small Aussie company is a cheaper, more efficient substitute for multi billion dollar, leading AI company...
Tech scouts will be curious about us...
Quercuskid
Regular
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
The Motley Fool Disclaimer | The Motley Fool
The Motley Fool provides leading insight and analysis about stocks, helping investors stay informed.
Kachoo
Regular
Fiduciary Trust
Read this and do a bit of searching on US laws but as an individual I believe they are just income. You dont need to pay tax till you do your annual taxes.
Yes it's a liability on receipt so is your paycheck there is no difference. It's just income.
Lots of people like to pay the tax right away as it's based on value or basicly sell and put that away for tax. If you saw future value higher in 8 months you may just what to sell at a later date if price is up you just pay the tax on what you acquired and the capital gains on what price the stock was issued to you plus.
In the end really what Manny does with his shares is his business and I think it has Zero value to the business that Brainchip is engaging in and it's potential success. IMO
JDelekto
Regular
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.
While there are different design considerations based on the application, some common themes Engineering students are taught are to minimize power requirements and costs to produce. Additionally, depending on the system size and various performance metrics come into play.
NVidia's flagship products have been their line of GPUs, which became used for those working with AI because of the speed at which they could crunch matrices required for training CNNs and performing inference. This is why they are still widely used for training models in large server farms, and dedicated workstations are even put together using their Titan line of cards.
NVidia initially delved into the embedded market for their GPUs, such as the ARM-based Tegra line of processors used in tablets and mobile game devices. Another flavor of Tegra was used in the early Jetson line of products. If you look at the newer line of Jetson products, you'll see they have massive gains in operations per second but at higher clock speeds, large amounts of memory, and high power requirements. The latest iteration of the developer boards has large heat sinks and fans.
BrainChip is not competing in this arena. They have designed a product specifically for Edge processing with meager power requirements and exceptional performance (see NVISO's comparisons using their library on various competing AI processors). If you haven't seen BrainChip's PCIe development board, it's small, roughly 3 inches by 1.5 inches. Their silicon package (including the hosting ARM processor) is about the size of a thumbnail, with no heatsink or fan.
If I were creating a wearable or mobile device that needs to be tiny, inexpensive, highly performant, and last a very long time on a charge, BrainChip's technology would be my choice. But that's not my only reason. The ability of the product to have its model updated in the field without connectivity is not getting the attention it deserves.
Aside from a different paradigm shift in how inferencing is done (neuromorphic vs. traditional CNNs), it will take time for adoption. I think that they are on the right track with their approach. The other weapon they have in their arsenal: patents. Because of their patents, it will be difficult for other competitors in the neuromorphic space.
There is always the possibility that some entirely new way of training and inferencing AI models will come along, but look how long it took for neuromorphic to get here today. Some highly innovative inventions are modeled after how things have evolved in nature. People were skeptical about neuromorphic computing's practical use, but BrainChip showed it is commercially viable. Now we need market adoption of the technology.
Last edited:
Easytiger
Regular
AFR ASIC short selling
Hedge funds and investment banks are being ordered to produce documents to prove they are complying with short-selling regulations, as the corporate regulator redoubles its surveillance efforts amid more volatile markets set off by the global banking crisis.
In particular, the Australian Securities and Investments Commission is concerned about short selling occurring without owning or borrowing the underlying shares, an illegal practice known as “naked” short selling.
Hedge funds and investment banks are being ordered to produce documents to prove they are complying with short-selling regulations, as the corporate regulator redoubles its surveillance efforts amid more volatile markets set off by the global banking crisis.
In particular, the Australian Securities and Investments Commission is concerned about short selling occurring without owning or borrowing the underlying shares, an illegal practice known as “naked” short selling.
GazDix
Regular
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--------
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--------
Many a true word ...
So anyone could be a fool......Here is the get out of jail card. It's worth the read
The Motley Fool Disclaimer | The Motley Fool
The Motley Fool provides leading insight and analysis about stocks, helping investors stay informed.
”Tricks and treachery are the practice of fools that don't have brains enough to be honest.”
― Benjamin Franklin
Colorado23
Regular
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.
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.
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