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.
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) , 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) .
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.)
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.
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,
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.
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. Continue reading