Scientists use brain imaging to reveal the movies in our mind.

Atul Gawande also recently wrote a New Yorker article about treating phantom itch with Ramachandran’s mirror box. I found this part of Gawande’s article on statistical inference in perception most interesting:

You can get a sense of this from brain-anatomy studies. If visual sensations were primarily received rather than constructed by the brain, you’d expect that most of the fibres going to the brain’s primary visual cortex would come from the retina. Instead, scientists have found that only twenty per cent do; eighty per cent come downward from regions of the brain governing functions like memory. Richard Gregory, a prominent British neuropsychologist, estimates that visual perception is more than ninety per cent memory and less than ten per cent sensory nerve signals. When Oaklander theorized that M.’s itch was endogenous, rather than generated by peripheral nerve signals, she was onto something important.

I’m not familiar with this field but I wonder if anyone has tried to quantify what percent of our conscious experience that we normally believe to be 100% due to sensory input is actually recall from memory/inference based on past observation. Also, can this percentage adaptively change? Perhaps there are situations where the brain chooses to rely more heavily on memory and other cases where it relies more on primary sensory input.

An amazing paper from Neuron demonstrating adaptive (circadian clock-governed) binning in the retina, based on dopamine modulation of gap junction (electrical) synapses between retinal photodetectors. During the day, abundant dopamine release weakens gap junctions coupling rods and cones together so that visual acuity is high. When light is scarce (at night), there is less dopamine and the electrical coupling between rods and cones is increased. This is analogous to on-chip binning in CCD (digital) cameras. Binning increases signal (in light-limited systems, eg. seeing at night) by increasing optical input area and by reducing single element noise (ie. noise at different photoreceptors should be independent) at the cost of resolution. So, the retina activates photoreceptor binning at night to boost low-light signals and deactivates it during the day to increase resolution. The dopamine comes from cells in the interplexiform layer, whose dopamine release is itself governed by melatonin projections.

Also, I never knew that gap junction strengths were directly modifiable. It looks like the D2 receptors are G-protein coupled to PKA, which acts on the gap junctions.

Not being a hunter, I’m not sure how much I support this, but I must admit this is at least a very interesting application of psychophysics data. Using deer as subjects in a standard battery of visual psychophysics tests, researchers have engineered a new material/pattern (“Gore Optifade”) that is superior to standard camo for evading detection by deer. Looks like deer are red-green colorblind but have higher acuity in the blue end of spectrum than humans.

Once they had assessed the deer’s visual strengths and weaknesses, Dr. Neitz and Dr. O’Neill worked out colors, textures and shapes with Guy Cramer of HyperStealth Biotechnology, a company that designs military camouflage. Mr. Cramer’s computer algorithms create fractal patterns that exploit a couple of ancient tricks used by animal predators.

The first and most obvious trick is to fade into the background, as a leopard’s spots enable it to do while it’s patiently waiting to ambush a prey. The spots aren’t shaped like leaves or branches, but they form an overall “micropattern” matching the colors and overall texture of the woodland background.

That trick, though, won’t work for a predator on the move, which is why a tiger doesn’t have spots. It has a “macropattern” of stripes that break up the shape of its body as it’s stalking or running.

There is a nice demonstration image with the article showing the same scene viewed with human vs. deer vision.

This dramatic example of human neural plasticity is amazing! Someone should go study this kid and his parents and find out more about how he developed his echolocation strategy. Are there other examples of this occurring in the medical literature? I’ve heard that blind people have very good hearing (and other senses) but this seems like a little more than “good hearing.” Also, thanks to Ben Huh for pointing me to this!

Yet the device itself is expensive and rare — they can run from $20,000 to $50,000 or even more, despite the fact that they are, in essence, a coil, a switch, a bank of capacitors, and a power supply. Much of the art lies in making the devices safe and fail-proof. Is it possible to hack/engineer a system that is safe, fault-tolerant, efficacious, and inexpensive? And furthermore, can we facilitate a community that will devise such devices, and share information about protocols and approaches to brain hacking?

This past August at Foo Camp, a hackers’ conference in Northern California, a group of people got together and set out to do just that. We are designing a safe, noninvasive, modular, and “open source” brain stimulator that will open up the field of circuit modulation to a wider audience. Members of the group include therapists and mental health professionals, engineers, programmers, and others interested in either the development of such devices, or the sharing of information on this front. Key to the design is safety — we want to make sure that the devices we create are as safe as devices on the market. Also, all the information is released under the Creative Commons “Attribution and Sharealike” license. This is a new model for “open source” medical device development — which may move it beyond the domain of simply creating “cool toys,” and to creating real devices.

You can find out more information, or contribute to the project, or learn from the project, at
http://transcenmentalism.org/OpenStim/

-Ed

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.

Here’s John Lisman’s review of this paper (from Georgopoulos’s group)… I don’t think I can say it better than him:

If ever there was a paper that would bring tears to one’s eyes, this is it: a previously hidden mental process has now become subject to experimental study. The mental process is the covert movement of attention, the selective focussing of attention to subregions of the visual field, but without eye movement. The movements of covert attention were hypothesized based on psychophysics, but the authors can now follow it using a vector field derived from a population of neurons in the parietal cortex. The monkey has been trained to use covert attentional shifts to solve a maze task. The major finding is that the vector derived from the population of parietal cells follows in time the path through the maze, as the monkey solves the maze.

From the abstract:

We found that the direction of the followed path could be recovered from neuronal population activity.

Yet another scary but cool result…

From a preview of the article:

“A central theme that has emerged in research on face perception therefore is whether or not faces are “special” such that the cognitive and neural mechanisms that underlie their processing are different from those underlying the processing of other visual objects. [...] In this issue of Neuron, Jiang et al. (2006) provide a compelling array of evidence supporting the idea that the processing of faces and objects do not rely on qualitatively different mechanisms. In a series of experiments, Jiang et al. present and integrate findings from neural modeling, behavior, and fMRI, showing that face classification, similarly to object classification, can be achieved by a simple-to-complex architecture, based on hierarchical shape detectors. Furthermore, variations of this model can account for both configural and feature-based processing without qualitative modification of the model’s structure.”

The Riesenhuber lab, from which this work comes, has been working on object recognition in an integrative way. The lab is particularly “at the intersection of neuroscience and AI”.