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.

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].

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.)

Theorists have worried about what neurons are doing in local populations for some time. Investigations have given rise to all kinds of models of such activity, notably those of Wilson & Cowan (1972), and Hopfield (1982), which viewed a network of neurons as having “attractor states”, invoking the language, for Hopfield, and the mathematics, for Wilson & Cowan, of dynamical systems theory.

In 2002, Maass, Natschlager and Markram proposed a theory of computation for localized populations of neurons that did not require that the population settle into a stable attractor in order to perform computations. The idea is that local populations of interconnected neurons of the right kinds are capable of discriminating temporal input patterns in general, and that this behavior is governed by the network dynamics of those populations. They showed that neuronal circuits can be constructed to create a generalizable computational architecture for continuous analog systems, known as liquid state machines. In addition, these circuit models are some of the first to incorporate short-term synaptic plasticity in a dynamical population model.

This idea, elaborated by Maass & Markram in a book chapter entitled Theory of the computational function of microcircuit dynamics, circa 2005, proposes that the liquid state machine architecture is capable of universal computation, just as Turing machines are.

On the heels of these postulates, two recent papers make a serious effort to combine this theoretical paradigm with experimental data. Haesler & Maass (2007), in Cerebral Cortex, synthesized data from layers of cerebral cortex to build a sophisticated dynamical model and tested to see if it had the kinds of computational properties described by the previous theoretical work (it did). Karmarkar & Buonomano (2007), in Neuron, explored the notion of “clockless computing” by networks of model neurons to understand how neural systems tell time, and used psychophysical experiments to support their theories.

A few concrete things are suggested by these works:

1. Computational models of neuronal networks that take short-term plasticity and other biological details into account can be constructed. They demonstrate relevant computational properties.
2. Testing the computational properties of such networks requires framing experiments in terms of complex analog signal processing.
3. The global dynamical properties of local populations of neurons set the general tone of the computations they perform, while the single cell dynamics shape and refine those computations.

Together, these papers may represent the beginning of a new understanding of the computations that networks of real physiological neurons are capable of. Expectations are high that results from further multi-cellular recordings and from the Blue Brain project will verify and elaborate these ideas. Stay tuned!

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

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

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

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.

To describe the network as a whole, we need to write down a probability distribution for the 2N binary words corresponding to patterns of spiking and silence in the population. The pairwise correlations tell us something about this distribution, but there are an infinite number of models that are consistent with a given set of pairwise correlations. The difficulty thus is to find a distribution that is consistent only with the measured correlations, and does not implicitly assume the existence of unmeasured higher-order interactions.

…

Therefore, the question of whether pairwise correlations provide an effective description of the system becomes the question of whether the reduction in entropy that comes from these correlations, I(2) = S1 – S2, captures all or most of the multi-information IN.

…

We conclude that although the pairwise correlations are small and the multi-neuron deviations from independence are large, the maximum entropy model consistent with the pairwise correlations captures almost all of the structure in the distribution of responses from the full population of neurons. Thus, the weak pairwise correlations imply strongly correlated states. To understand how this happens, it is useful to look at the mathematical structure of the maximum entropy distribution.

…

In a physical system, the maximum entropy distribution is the Boltzmann distribution, and the behaviour of the system depends on the temperature, T. For the network of neurons, there is no real temperature, but the statistical mechanics of the Ising model predicts that when all pairs of elements interact, increasing the number of elements while fixing the typical strength of interactions is equivalent to lowering the temperature, T, in a physical system of fixed size, N. This mapping predicts that correlations will be even more important in larger groups of neurons.

And of note from the Discussion:

The dominance of pairwise interactions means that learning rules based on pairwise correlations could be sufficient to generate nearly optimal internal models for the distribution of ‘codewords’ in the retinal vocabulary, thus allowing the brain to accurately evaluate new events for their degree of surprise.

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

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.

I’d recommend reading this paper if you want to setup a rig to do this; otherwise, I’d recommend reading pages 18 and 19.

S. M. Potter, D. A. Wagenaar, T. B. DeMarse. Closing the loop: Stimulation feedback systems for embodied MEA cultures. In: Advances in network electrophysiology using multi-electrode arrays, M. Taketani, M. Baudry, eds. Kluwer, New York. In press.