Neural Engineering System Design programme (DARPA funding award announced)

https://www.darpa.mil/news-events/2017-07-10

 

“The NESD program looks ahead to a future in which advanced neural devices offer improved fidelity, resolution, and precision sensory interface for therapeutic applications,” said Phillip Alvelda, the founding NESD Program Manager. “By increasing the capacity of advanced neural interfaces to engage more than one million neurons in parallel…”

 

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• A Brown University team led by Dr. Arto Nurmikko will seek to decode neural processing of speech, focusing on the tone and vocalization aspects of auditory perception. The team’s proposed interface would be composed of networks of up to 100,000 untethered, submillimeter-sized “neurograin” sensors implanted onto or into the cerebral cortex. A separate RF unit worn or implanted as a flexible electronic patch would passively power the neurograins and serve as the hub for relaying data to and from an external command center that transcodes and processes neural and digital signals.
• A Columbia University team led by Dr. Ken Shepard will study vision and aims to develop a non-penetrating bioelectric interface to the visual cortex. The team envisions layering over the cortex a single, flexible complementary metal-oxide semiconductor (CMOS) integrated circuit containing an integrated electrode array. A relay station transceiver worn on the head would wirelessly power and communicate with the implanted device.
• A Fondation Voir et Entendre team led by Drs. Jose-Alain Sahel and Serge Picaud will study vision. The team aims to apply techniques from the field of optogenetics to enable communication between neurons in the visual cortex and a camera-based, high-definition artificial retina worn over the eyes, facilitated by a system of implanted electronics and micro-LED optical technology.
• A John B. Pierce Laboratory team led by Dr. Vincent Pieribone will study vision. The team will pursue an interface system in which modified neurons capable of bioluminescence and responsive to optogenetic stimulation communicate with an all-optical prosthesis for the visual cortex.
• A Paradromics, Inc., team led by Dr. Matthew Angle aims to create a high-data-rate cortical interface using large arrays of penetrating microwire electrodes for high-resolution recording and stimulation of neurons. As part of the NESD program, the team will seek to build an implantable device to support speech restoration. Paradromics’ microwire array technology exploits the reliability of traditional wire electrodes, but by bonding these wires to specialized CMOS electronics the team seeks to overcome the scalability and bandwidth limitations of previous approaches using wire electrodes.
• A University of California, Berkeley, team led by Dr. Ehud Isacoff aims to develop a novel “light field” holographic microscope that can detect and modulate the activity of up to a million neurons in the cerebral cortex. The team will attempt to create quantitative encoding models to predict the responses of neurons to external visual and tactile stimuli, and then apply those predictions to structure photo-stimulation patterns that elicit sensory percepts in the visual or somatosensory cortices, where the device could replace lost vision or serve as a brain-machine interface for control of an artificial limb.

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See https://www.darpa.mil/attachments/FactsheetNESDKickoffFinal.pdf for more details.

Emotiv gaming headset

We’ve certainly come a long way. (And I never knew about Music Portal behind that thing.)

Download MP3It’s hard to judge the merits of this particular interface but I’m sure this is just the first of many such devices that we’re about to see (demo starts 2:00):

This is an Emotiv headset. More than the gaming application, I like the idea of using it for IM emoticons.

Anyone know if the consumer version will require gel for the scalp electrodes? Hmmm… if gamers are the target audience, I think I have a good idea for a cross-promotional opportunity here.

Optical silencing Cl- channel

Ed strikes again!
Two-Color, Bi-Directional Optical Voltage Control of Genetically-Targeted Neurons

Having found a powerful method for activating neurons with blue light in the protein Channelrhodopsin-2 (ChR2) [1], we sought to augment the toolbox by finding a single-component system capable of mediating light-elicited neuronal inhibition. We identified a powerful tool, the mammalian codon-optimized version of the light-driven chloride pump halorhodopsin, from the archaebacterium Natronobacterium pharaonis (here abbreviated Halo) [2].

So, How Do REAL Neuronal Networks Compute?

What is the right level of biological realism to model neuronal systems in order to understand their computational properties? Some recent papers may help shed some light on the subject. Models of the computational properties of local networks of neurons are starting to come into their own. This year has already seen at least two articles published in experimentalist journals based on the same core of theoretical work.

To bring you up to speed, I need to remind you what is going on in the world of experimental neuroscience.

Experimentalists are now able to record the single-cell activities of a whole population of neurons simultaneously. From Briggman, Abarbanel, Kristan (2006):

By using multi-electrode arrays or optical imaging, investigators can now record from many individual neurons in various parts of nervous systems simultaneously while an animal performs sensory, motor or cognitive tasks. Given the large multidimensional datasets that are now routinely generated, it is often not obvious how to find meaningful results within the data.

This paper goes on to provide a nice overview on mathematical methods that researchers are using to grapple with the challenge of understanding the dynamics of the neural systems they are recording from. They make the case that conceptual progress needs to be made on the interpretation of the data these results yield. How can we understand what computations these neurons are collectively performing?

(Incidentally, this topic is being explored in a conference happening this week at the Los Alamos National Laboratory, which, according to one of the conference session chairs, is intended to help shape future directions for the lab. Hopefully there will be webcasts from this conference.)

Neurotechnology Ventures: New Course

Our brains have a lot of problems that need to be solved — now. And neurotechnology is a hot field. But what knowledge and skills do you study if you want to be a neurotechnologist? What problems are important, but also tractable within a reasonable timeframe? And, can you survive while climbing this possibly-very-high mountain?

A team of three academics at MIT and the University of Hong Kong is launching an international collaboration to create a set of novel courses to address this need. The first one, Neurotechnology Ventures, is being taught in Spring 2007 and focuses on neurotechnologies that are close to solving major human problems. The class explores the problems that neurotechnologists encounter when envisioning, planning, and building startups to bring neuroengineering innovations to the world.

Emphasizing the global nature of any modern neurotechnology, Neurotechnology Ventures will be videoconferenced between the U.S. and China, which is increasingly becoming a major neurotechnology player (including some very daring and scientifically interesting developments in fields such as human spinal cord regenerative medicine). Information will be posted online as the class evolves dynamically, to the web site HTTP://Neuroven.Media.MIT.edu. The goal is to open up this new field to the world, and see if we can solve the major problems of the brain in an open and efficient way.

Ed

Help Please: Future of Neural Engineering: From Job perspective

Dear Members,
I am a prospective graduate student interested in taking up Neural Engineering under EE or Biomedical Engg for research. But I have a lot of concerns and need help from a person who knows about the field well.
1. I have studied VLSI, DSP, Image Processing, Wireless Communication, Control Systems and Embedded Systems as graduate and undergraduate courses and have some research interest in Neural Networks and Machine Learning(That’s how I got interested in Neural Engg and Prosthetics). Which of these subjects will be of help in Neural Engg/Prosthetics research. Which will be of most relevance. Please list them in the order of relevance(high->low).
2. What are the applications of the research ?
3. What is the research and JOB scope for this field? Are there any companies who recruit people with this specialisation? How is the job scene in academia? How many univs are doing research in this field in US? Please let me know about the career progression in academia, like how much time does it take to get full time academic position after PhD?
4. Especially, what are the applications of this research in Robotics?
5. What are the current problems and research themes in universities?
6. What imaging technologies are used in this research?

Though my queries may seem a bit ameteuristic, it is very important for me to get clarity on these doubts.
Hope my queries will be answered.
Thanking all of you in advance,
sudhi

Neuroengineering and the MIT TR35 innovators

Today MIT’s Technology Review magazine released its annual list of innovators under the age of 35 who were nominated for recognition. Interestingly, almost a full quarter are doing work relating to or impacting the field of neuroengineering — including ways to tag synapses with quantum dots, activate neurons remotely, improve machine vision, classify whole-brain states for prosthetic purposes, and make nanowire arrays.

http://www.technologyreview.com/TR35/

Inferring network activity on a MEA from pairwise correlations

Weak pairwise correlations imply strongly correlated network states in a neural population : Nature

Very few MEA studies make it into Nature, so this definitely got my attention.

Often in neuroscience we are confronted with a small sample measurement of a few neurons from a large population. Although many have assumed, few have actually asked: What are we missing here? What does recording a few neurons really tell you about the entire network?

Using an elegant prep (retina on a MEA viewing defined scenes/stimuli), Segev, Bialek, and students show that statistical physics models that assume pairwise correlations (but disregard any higher order phenomena) perform very well in modeling the data. This indicates a certain redundancy exists in the neural code. The results are also replicated with cultured cortical neurons on a MEA.

Some key ideas from the paper are presented after the jump.

Curing blindness, with light-activated ion channels?

How would you cure blindness, if your phototransducing rods and cones had degenerated – as happens in syndromes that affect millions of people worldwide? A lot of investigators have tried to create very complicated electrical stimulators that drive patterned activity in the retina. You need a power source, a camera of sorts, a computational element, and an array of electrodes that can crank out precise, well-timed current pulses, for a long time. It’s a heroic piece of optical and electrical engineering.

But what if you just made other cells in the retina light-sensitive? Channelrhodopsin and other light-activated ion channels have opened up this new kind of endeavor.

Investigators at Wayne State University, the Pennsylvania College of Optometry, and Beijing University have now done this. They expressed Channelrhodopsin in retinal ganglion cells (RGCs) of mice with photoreceptor degeneration. Remarkably, for months afterwards, the RGCs were able to transmit visual information all the way to visual cortex. In mice without channelrhodopsin, these visual evoked responses were never seen. A very impressive piece of systems bioengineering.

Ectopic Expression of a Microbial-Type Rhodopsin Restores Visual Responses in Mice with Photoreceptor Degeneration
Anding Bi, Jinjuan Cui, Yu-Ping Ma, Elena Olshevskaya, Mingliang Pu, Alexander M. Dizhoor, and Zhuo-Hua Pan

— Ed

Jimbo et al '99: plasticity at the network level in culture

Jimbo, Tateno, and Robinson did a network plasticity experiment using cultured networks and a multi-electrode array.

They determine the effect of a tetanus at one electrode in a network on the network. Specifically, they look at how the tetanus potentiates or depresses the ability of a test pulse at another electrode to evoke spike trains at various neurons across the network.

They grew cultures on a MEA for a month. They stimulated each electrode in succession with a test pulse. They recorded the response at all electrodes after each test pulse. They used spike sorting to identify the reponses of individual neurons out of the electrode traces. They found that the network’s response to a given test pulse was reproducable for about 50ms after the test pulse.

Then they applied a strong stimulus (a tetanus) to a single electrode (to make it learn 🙂 ). After that they re-characterized the network’s responses to test pulses at every site.

They found that some electrode sites became more potent (“potentiated response”) after the tetanus was applied. This means that, when a test pulse was applied to this electrode site, neurons in all areas of the network responded either the same, or more strongly than they had before the tetanus.

Other sites became less potent (“depressed response”) after the tetanus was applied.

Surprisingly, it was very rare for any given electrode site to become better at stimulating some neurons and worse at stimulating others as a result of the tetanus.

What determined which electrode sites became potentiated and which ones became depressed? The tetanus potentiated electrodes which evoked spike trains that tended to contain spikes which were within 40ms of the spike trains evoked by the tetanus electrode, and depressed others. That is, it potentiated sites which evoked patterns similar to the patterns evoked by the tetanus site.

However, the spike trains evoked by both potentiated and depressed neurons became more synchronized with the tetanus electrode after applying the tetanus.

See page 5 of “Distributed processing in cultured neuronal networks” for another review of this work.

See this NeuroWiki page for more details (the strange {{}} over there are because we will soon have footnotes).

Jimbo, Y., Tateno, T., and Robinson, H. P. C.,
Simultaneous Induction of Pathway-Specific Potentiation and Depression in Networks of Cortical Neurons. Biophysical Journal, 1999. 76: p. 670-678.