neurodudes

Echolocating kid, who had both his retinas surgically removed at an early age:

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!

Transcranial magnetic stimulation (TMS) is a popular technology for stimulating human cortical neurons, due to its safety, noninvasiveness, and efficacy. A TMS device is just a little coil of wire, through which 10,000 Amps of current is cranked during a period of only a few hundred microseconds; the resultant rapidly-changing magnetic field induces eddy currents in the brain. Depending on the protocol used, TMS can drive/inhibit a region of cortex corresponding to roughly a cubic centimeter or two, and is being explored for the treatment of depression, the reduction of auditory hallucinations during schizophrenia, and the alleviation of tinnitus and migraines. Thousands of papers on medicine and psychology have been written using this tool.

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

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/

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. (more…)

Neuron : Dynamics of Parietal Neural Activity during Spatial Cognitive Processing

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…

In an impressive integrative effort, a new article in this month’s issue of Neuron describes a robust object and face classification model that is consistent with both behavioral and fMRI experiments.

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”.

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

Looking at static pictures of people running versus pictures of people standing still “evokes a delayed response in an area that overlaps with motionsensitive cortex (hMT+)”. Past studies have indicated a similar response for images depicting a falling cup versus a cup resting on a table.

The paper discusses the role of top-down influence from the temporal lobe as a possible cause for the response. How could this kind of brain activity be influencing our ability to recognize objects in scenes? Is this evidence of the activation of a distributed cortical representation of a moving object?

Should the field of AI be trying to figure out how to replicate a similar top-down influence in next-generation object recognition algorithms?

Abstract from the Journal of Cognitive Neuroscience is available here.

A few places on the internet are talking about a study conducted on Amazon tribespeople which demonstrates that basic concepts about geometry are independant of culture and level of education.

Here’s the story from Science News and here’s the story from Slashdot.

If this is the case, it suggests that there might be something special about geometry that made it evolutionarily advantageous to hard-wire into the brain. Or, from another perspective, some evolutionary adaptation makes geometry easy for our brains to understand. After all, a triangle is just the combination of three bars, which V1 is very good at responding to. As vision research continues to study the brain’s representation of increasingly complex objects, it may shed light on how this works from a systems neuroscience perspective.

–Stephen

This is really weird!

Somebody debunk this before it blows my mind.

–Stephen