Neuron-computer Interface

Carbon Nanotubes May Enhance Neurotechnology Device

Taking direct electrical measurements from a living brain and even from a single neuron cell requires an invasive connection between the localized electrochemical environment in the cell and a sharp, prickly, prodding metal stake of death.

An electrode might sound harmless, but it can take the form of a gigantic (in the reference frame of a tiny neuron) metallic (or other electrical conducting material) needle that could either damage living tissue, or be rejected by the hosting biological system and quickly bombarded in tissue to effectively disengage the pointy invader.


image courtesy PhysOrg.com

Recently, a collaboration lead by Edward Keefer from the University of Texas Southwestern Medical School, has discovered that coating these harmful–but, necessarily formed–electrical recording devices with the ever popular carbon nanotube is the neuron’s newest fuzzy best friend. The nanotubues act to not only enhance the transmitted signals received from directly implanted electrodes, but they have been shown to be bio-compatible, so that they might even minimize the damage caused to the specimen. In fact, Keefer claims the efficiency of the cell-electrode interface is improved by at least one-thousand times.

The development of neurotechnological devices–hardware that interconnects directly with nervous tissue and even individual neurons–is absolutely dependent on not only the production of electrical connections that will result in highly sensitive signal transmission, but the cells will must also like to have these needles sticking around. The carbon nanotube coating approach could be a critical step in advancing neurotechnology to a future level of high-res recording devices as well as localized, highly-controllable stimulus systems.

“Carbon Nanotube-Coated Electrodes Improve Brain Readouts” :: PhysOrg.com :: August 12, 2008 :: [ READ ]

BASIC for the Brain

Experimental demonstrations of apparent computer control of a living brain is largely based on the software’s ability (and the ability of the corresponding human programmer) to train itself to identify specific, repeatable brain signals and tag these with specific, observed motions of the living test subject. [ READ MORE ] So, the monkey’s arm moves to the left, and a certain picture of brain activity is recorded. The monkey’s arm moves to the left again, and a similar brain activity pattern is again mapped.

There must be a connection, right?

Next, the computer is connected to a robotic arm to simulate the monkey’s arm. The computer records a certain brain activity that was previously tagged when the monkey moved its arm to the left … so, the computer tells the robotic motors to move the simulating arm to the left.

Voilà! Computer control between a monkey’s brain and a robotic arm.

Certainly, this is a magnificent neurotechnological feat, but the experiment is entirely based on a previously mapped out look-up table. There is very little information about the fundamental behavior of the monkey’s active neuron network, which may or may not behave in precisely reproducible ways each time its arm moves to the left.

What if a neuron in the recorded activity dies? Well, the monkey can presumably still move its arm around, but the specific network pattern of electrical activity might change and might not match the look-up table stored in the computer’s memory. The computer might nix the new recorded signal that occurs when the monkey moves its arm to the left because the software code doesn’t understand how the network adapts at a fundamental level.

Developing software that tries to understand the neuron-computer interface at a more basic level is the goal of Lakshminarayan Srinivasan at MIT. This work is essentially the starting point of writing an all-purpose BASIC neurological language for the brain.

The general idea is building a software language that looks at a broader swath of brain activity and links neural action with its probable relation to a specific motor task. This provides for flexibility in the software communication so that it won’t lock up and give the user the Blue Screen of Death just because one neuron didn’t fire the same way it did last week.

So far, Srinivasan’s work is entirely based on simulations, and is currently being expanded to test with interfaces to living test subjects. So, it will be very interesting to watch the results of these new developments to discover if this programming approach is compatible with talking directly with our neuron networks.

I would like to emphasize here that I in no way want to discount the significance and the importance of the successes of “Bionic Monkey” research to date. These new techniques are absolutely critical and very exciting. But, we must be clear that this does not yet reach the notion of a pure neuron-computer interface. There is still a long way to go in continuing the advances of neurotechnology to discover the deeper understanding of neuron network function… and this long road is still very exciting!

“Standardizing the Brain-Machine Interface” :: IEEE Spectrum Online :: April 2008 :: [ READ ]

Srinivasan, L., Eden, U.T., Mitter, S.K, and Brown, E.N. “General purpose filter design for neural prosthetic devices,” Journal of Neurophysiology, 98:2456-2475. [ READ ]

Also take a look at the earlier work…

“Bionic Monkeys!” :: Discover Magazine Blog :: May 29, 2008 :: [ READ ]

“Mind Over Matter: Monkey Feeds Itself Using Its Brain” ::ScienceDaily.com :: May 28, 2008 :: [ READ ]

Googling with Neurons

Google’s algorithms somehow know how to find needles in haystacks. Cyberspace is gigantic and it seems to only take milliseconds to find any little random tidbit of information you might be looking for. In fact, this search finds 378,000 results in 0.20 seconds or less [supercalifragilisticexpialidocious].

Just like the Internet, the brain is a very complicated network. Although the brain is still quite poorly understood, it is very probably that it is significantly more complex than the Internet network of today. Google contains a detailed and efficient map of the Internet, which allows it to quickly zip you along the pathways to your desired destination.

The brain, on the other hand, has a network that might not be so efficient… but it works and works pretty well for our environment. In particular, we store many memories over our lifetime, but we don’t have a font-and-center realization of each and every memory and every point in time. We are able to access certain memories when needed, although we certainly find that having special cues can help bring about certain memories on command.

So, maybe Google could invest in a neuroprosthetic computer chip hat first maps the neural connections in our brain and then takes cues to help us retrieve specific memory information when needed.

The network mapping process is certainly not trivial. Research in this area is vital, and is the key component to furthering the understanding of brain function. But, once the map is known, then Google-type algorithms might be particularly useful for traveling the network’s paths to find information we need … in milliseconds.

Gary Marcus, professor of psychology at New York University, wrote a very interesting review in The New York Times that further explores this idea…

“Total Recall” by Gary Marcus :: The New York Times Idea Lab :: April 13, 2008 :: [ READ ]

NIH Funds Next-generation Neurotechnology

Last year, the National Institute of Health provided funding for another important collaboration between Brown UniversityCyberkinetics, and the Cleveland FES Center. Lead by Arto Nurmikko, a Brown professor of engineering and physics, the academic-industry team is expanding the functionality and portability of BrainGate with a scaled-down, fully-implantable device that records neuronal signals and digitally transmits the information via fiber optic and wireless communication.

The multi-year funding supports a human trial of a prototype being developed, and will continue advancements in implantable devices to bridge communication between the nervous system and computer controlled hardware that can assist with motor skills for paralyzed patients.

“Next-generation neurotechnology possible with NIH grant”
EurekAlert! August 2, 2007 [READ]

“A Microelectrode/Microelectronic Hybrid Device for Brain Implantable Neuroprothesis Applications”
the Overview Research Poster from Nurmikko’s Group [VIEW]

Towards an Artificial Synapse

Neuron devices–little silicon chips that “plug in” to your brain–being developed today tend to function with the idea that electrical pulses can be used to stimulate activity in neurons living either on the chip or in surrounding brain tissue.

The next important step to ensuring a successful neuron device is to have the neurons actually communicate with one another, so that the new electrical stimulation can be processed by the brain. This communication between neurons actually happens by the direct transfer of chemicals from one neuron to the next via very small knobs, called synapses. If stimulated by electrical activity in the neuron, these act like shower heads that spray specific chemicals into a branch, or dendrite, of a neighboring neuron. These chemicals then determine how the neighbor neuron will respond to the first neuron.

Mark Peterman and Harvey Fishman at Stanford University, are working on another approach for neurotechnology by creating a silicon device that contains “artificial synapses” that directly deliver the necessary chemicals of communication to neurons.

Read more about this very interesting development reported in New Scientist.

Read the public release announcement on Eureka Alert ]

Read the article from New Scientist ]

03/27/2003 UPDATE
Read an article from ITWeb in South Africa ]

Plug Your Memories Back In

The first test of an actual silicon device will soon be performed to help restore function to a slice of tissue from a rat’s damaged hippocampus!

This news item has been splashing into headlines all over the world yesterday and today, so we’re trying to keep track of the journalists’ take on the issue. As we keep finding more links, we’ll update this post.

Theodore Berger and his team at the University of Southern California have been working for years to develop mathematical models that represent the appropriate computations performed in the hippocampus of the brain, the area of your brain which is widely accepted as a major storage container and processor of memories. These models would then guide the design and fabrication of an actual silicon chip to be directly integrated with the brain tissue.

However, fully understanding the mechanisms of specific computations in our brain is still beyond scientists’ desperate grasp, so Berger’s team listened to neural activity and attempted to encode what they saw it into silicon.

To accomplish this, a silicon chip is fabricated with tiny electrodes–basically little metal pads and wires–that sit nearby active, non-damaged neurons and receive their electrical signals that are supposed to be providing “input” to damaged neurons. These captured input signals are transmitted by the embedded silicon chip to another chip sitting outside the brain. The external chip is like a mini-computer that processes these electrical signals according to Berger’s model of how the hippocampus is supposed to function. Based on this on-chip processing, it finally sends new electrical signals back down to the non-damaged neurons.

These computer-generated electrical signals are meant to exactly replace the electrical signals that would have resulted from the neurons receiving the original input, but couldn’t do so because they were damaged in some way.

Important note: This prototype brain prosthetic has been specifically programmed to function in a certain way–a way that neurons in a rat’s hippocampus presumably communicate. A successful test of this device will show that if the hippocampus is “turned off” in an animal and the device “turned on”, then normal brain activity resumes.

This would be a great step forward in developing technologies that replace damaged function in our nervous systems. Forgot where your keys are? Can’t find where you placed your presentation material due to your boss in exactly 1.2 minutes? No problem, just don’t forget to turn on your “memory chip”.

Maybe that’s where we are headed, but not by just copying our neural activity from when we were still healthy. One of the articles below mentions the concern that since what we consider to be our “self”, likely has a great deal to do with our memories, then a prosthetic device that has strict and static on-board processing rules, might somehow alter who we really “are”.

Certainly, more must be understood about how our brain functions in general, but this upcoming test is still very exciting!

Read the article from the New Scientist ]

Read the article from Ananova ]

Read the article from MSNBC.com ]

Read the article from BBC News ]

Read the article from the Guardian ]

Last updated October 26, 2021