NYT: The Pablo Picasso Alzheimer's Therapy

The Pablo Picasso Alzheimer’s Therapy – New York Times

Interesting connection: Art appreciation seems to ease Alzheimer’s symptoms. Less memory loss. Less repetition.

Super interesting… there’s a lot to say here. There are of course many documented cases of particular brain lesions causing marked changes in personality or hobbies. But this appears to be something different. There is both an interest change (ie. people are more interested in art) but the neurological disease itself is somehow lessened (temporarily) by the interest itself.

The article mentions that there is very little research in the area. Does anyone know of any studies? It’s fascinating to think that such a simple, non-invasive therapy could be so powerful.

Either that, or it just means that art critics (“It’s like he’s trying to tell a story using words that don’t exist” — critic or patient?) have something in common with those with neurodegenerative disease. 🙂

Review: Kurzweil's The Singularity is Near

Although Bayle and I are always surprised when we see how many people are actually reading Neurodudes every day (“you really like us! you really do!”), I think we realized we had hit a new milestone when Ray Kurzweil’s book agent called to give us an advance copy of his new book. Let me be clear here: We will gladly review any AI-/neuro-related books you send us. Free books are great! (Heck, we’ll even do an occasional historical biography, if you send us one.)

There’s a lot to say about Kurzweil”s new book, The Singularity is Near (book website; book on Amazon). This book is similar to his previous books (Age of Intelligent Machines, Age of Spiritual Machines) in style and research but the thesis here is that we are on the precipice of a major change in human civilization: We are soon going to create entities of superior intelligence in all aspects to our own selves. This is the Singularity.

Full book review after the jump Continue reading

Basal ganglia activity during task learning, extinction, and reacquisition

Article on what basal ganglia neurons do during learning. In summary,


In Graybiel’s experiments, rats learned via specific cues that there was chocolate at one end of a T-shaped maze. While the rats were still learning, their basal ganglia neurons chattered throughout the maze run
….
As the rats learned to focus in on guiding cues (in the experiment, an audible tone that guided them toward the chocolate), the behavior of the neurons changed. They fired intensely at the beginning and the end, but remained relatively quiet while the rats scurried through the maze.

Subsequently, the reward was removed. While the audible cue became meaningless, everything in the maze from beginning to end became relevant again. The neurons fired throughout the run. But when the reward reappeared, the pattern of beginning and ending spikes separated by downtime reappeared.

Pop sci article

Terra D. Barnes, Yasuo Kubota, Dan Hu1, Dezhe Z. Jin and Ann M. Graybiel1. Activity of striatal neurons reflects dynamic encoding and recoding of procedural memories. Nature 437, 1158-1161 (20 October 2005)

" Schizophrenics Better at Discerning Illusions"


“Normally, contextual processes in the brain help us to focus on what’s relevant and stop our brains being overwhelmed with information. This process seems to be less effective in the schizophrenic brain, possibly due to insufficient inhibition–that is, the process by which cells in the brain switch each other off,” Dakin observes. The mechanism has more to do with vision than with cognition, such as attention span, the researchers report.

Sci Am article

Steven Dakin, Patricia Carlin and David Hemsley. Weak suppression of visual context in chronic schizophrenia. Current Biology, Volume 15, Issue 20, 25 October 2005, Pages R822-R824.

IBM Teams with Brain-Mind Institute To Model Brain

This project was announced several months ago, but I didn’t see a post here so I thought I would add it.

The project, dubbed “Blue Brain“, represents a team up between Henry Markram, (who co-authored the chapter on the neocortex in the acclaimed reference The Synaptic Organization of the Brain), and IBM’s Blue Gene super computer.

From the New Scientist article: For over a decade Markram and his colleagues have been building a database of the neural architecture of the neocortex, the largest and most complex part of mammalian brains.

Using pioneering techniques, they have studied precisely how individual neurons behave electrically and built up a set of rules for how different types of neurons connect to one another.

Very thin slices of mouse brain were kept alive under a microscope and probed electrically before being stained to reveal the synaptic, or nerve, connections. “We have the largest database in the world of single neurons that have been recorded and stained,” says Markram.

–Stephen

Knowledge management software for neuroscientists

Haven’t tried it but this free, open-source software called Neuroscholar seems interesting. The idea seems to be to provide an easy tool for collecting and comparing facts and interpretations across different papers. It uses a SQL database backend and some graph-like data representation for the front-end.

I’d be curious to hear about anyone’s experiences using it or any similar tools. I still have yet to find a good way to organize my PDFs…

From the website:

NeuroScholar is an open-source and is free for download from http://www.neuroscholar.org/ (click on Software > NeuroScholar ). We also have several demonstration movies available from the movies section to show the functionality of the system.

Some thoughts on brain imaging

Can Brain Scans See Depression? – New York Times

At first glance, this doesn’t seem like anything new (imaging-wise) to neuroscientists, but there are some interesting opinions in the article.

Interesting fact:

In a range of studies, researchers have found that people with schizophrenia suffer a progressive loss of their brain cells: a 20-year-old who develops the disorder, for example, might lose 5 percent to 10 percent of overall brain volume over the next decade, studies suggest.

And I like the way this guy thinks:

In an interview, Dr. Amen said that it was unconscionable that the profession of psychiatry was not making more use of brain scans. “Here we are, giving five or six different medications to children without even looking at the organ we’re changing,” he said.

But is this true?

“The thing for people to understand is that right now, the only thing imaging can tell you is whether you have a brain tumor,” or some other neurological damage, said Paul Root Wolpe, a professor of psychiatry and sociology at the University of Pennsylvania’s Center for Bioethics.

Does anyone know of any good work applying machine learning to doing discrimination of neural disease (like ADD, general depression, and anything that’s basically not a giant lesion/tumor) in imaging scans?

a Biologically-Inspired System for Real-time Object Recognition

I skimmed this paper very briefly and it looks cool. Of course, I’m not a computer vision expert so I can’t really tell how state-of-the-art the results are.

Murphy-Chutorian, E., Aboutalib, S., Triesch, J.(2005). Analysis of a Biologically-Inspired System for Real-time Object Recognition. Cognitive Science Online, 3.2, pp. 1-14. http://cogsci-online.ucsd.edu/3/3-3.pdf

“We present a biologically-inspired system for real-time, feed-forward object recognition in cluttered scenes. Our system utilizes a vocabulary of very sparse features that are shared between and within different object models. To detect objects in a novel scene, these features are located in the image, and each detected feature votes for all objects that are consistent with its presence. Due to the sharing of features between object models our approach is more scalable to large object databases than traditional methods. To demonstrate the utility of this approach, we train our system to recognize any of 50 objects in everyday cluttered scenes with substantial occlusion. Without further optimization we also demonstrate near-perfect recognition on a standard 3-D recognition problem. Our system has an interpretation as a sparsely connected feed-forward neural network, making it a viable model for fast, feed-forward object recognition in the primate visual system.”

Postdoc position: "Bayesian inference and neural dynamics"

Below please find an email I received. I removed email addresses from this post to prevent harvesting by spambots — so I guess you’ll have to find a way to contact them yourself if you want the job.

> Subject: Job Annoucement – Theoretical Neuroscience Group in Paris.
> Reply-To: Sophie Deneve
>
> Two postdoctoral research positions are available in the newly created
> Theoretical Neuroscience Group in Ecole Normale Sup?rieure Paris, for a
> project funded by a Marie Curie Team of Excellence grant.?
>
> The overall theme of the project is “Bayesian inference and neural
> dynamics”, and the research will involve building and analyzing
> probabilistic treatments of representation, inference and learning in
> biophysical models of cortical neuron and circuits. To do so we will
> integrate complementary computational neuroscience approaches. The first
> studies neurons and neural networks as biophysical entities. The second
> reinterpret cognitive and neural processes as bayesian computations.
>
> The faculty of this group includes Misha Tsodyks, Boris Gutkin, Sophie
> Deneve and Rava Da Silvera. It is part of the Department of Cognitive
> Science in Ecole Normale Sup?rieure, a unique institution regrouping
> major scientists in computational Neuroscience, Brain imaging,
> Psychology, Philosophy, and Mathematics. We are situated in central
> Paris, at a walking distance to top scientific research and educational
> institutions. We have numerous international collaborations with
> experimental groups, with the goal of understanding the neural basis of
> cognition.
>
> The positions are for two years duration, with attractive salaries,
> including mobility allowance if applicable.? Generous travel support will
> be provided. Candidates should have
> 1- A strong mathematical/biophysical background and a strong interest in
> neuroscience, or
> 2- A strong neuroscience background and good basis in math and/or
> biophysics.?
> 3- Demonstrable interest in experimental collaborations.
> 4- Good communication skills.
>
> Candidates should send a CV, a 1 page research project and the address
> of two referees, to __email omitted__, before the 1st of
> November, 2005. For further information please contact Sophie Deneve (___email omitted___)
> or Boris Gutkin (___email omitted___) .
>