By way of Nextbigfuture, by way of Hackernews.

…where might things have gone wrong? The leaders of the first generation were influenced by the metaphor of the electrical communications devices that where in use in their lifetimes, all of which centered on the sending of signals down wires. If you model information theory on signals going down a wire, you simplify your task in that you only have one point being measured or modified at a time at each end…At the same time, though, you pay by adding complexity at another level….which leads to a particular set of ideas about coding schemes in which the sender and receiver have agreed on a temporal syntactical layer in advance…You stretch information out in time and have past bits give context to future bits in order to create a coding scheme….In order to keep track of a protocol you have to devote huge memory and computational resources to representing the protocol rather than the stuff of ultimate interest. This kind of memory use is populated by software artifacts called data-structures, such as stacks, caches, hash tables, links and so on. They are the first objects in history to be purely syntactical…..With protocols you tend to be drawn into all-or-nothing high wire acts of perfect adherence in at least some aspects of your design….leads to…. brittleness in existing computer software, which means that it breaks before it bends.

The alternative, in which you have a lot of measurements available at one time on a surface, is called pattern classification….The distinction between protocols and patterns is not absolute-one can in theory convert between them. But it’s an important distinction in practice…

….you enter into a different world that has its own tradeoffs and expenses. You’re trying to be an ever better guesser instead of a perfect decoder. You probably start to try to guess ahead, to predict what you are about to see, in order to get more confident about your guesses. You might even start to apply the guessing method between parts of your own guessing process. You rely on feedback to improve your guesses….you enter into a world of approximation rather than perfection. With protocols you tend to be drawn into all-or-nothing high wire acts of perfect adherence in at least some aspects of your design. Pattern recognition, in contrast, assumes the constant minor presence of errors and doesn’t mind them.

I’ve suggested that we call the alternative approach to software that I’ve outlined above “Phenotropic.”…The goal is to have all of the components in the system connect to each other by recognizing and interpreting each other as patterns rather than as followers of a protocol that is vulnerable to catastrophic failures. One day I’d like to build large computers using pattern classification as the most fundamental binding principle, where the different modules of the computer are essentially looking at each other and recognizing states in each other, rather than adhering to codes in order to perfectly match up with each other.

via Edge: WHY GORDIAN SOFTWARE HAS CONVINCED ME TO BELIEVE IN THE REALITY OF CATS AND APPLES by Jaron Lanier.

Semantic wiki:
http://www.connectome.ch/wiki/Main_Page

Desktop viewer:
http://connectomeviewer.org/viewer “Multi-Modal Multi-Level Network and Neuroimaging Visualization and Analysis” (screencasts)

The authors, Cutting, DeLong, and Nothelfer, speculate that the pink noise bit is being driven by some process that is related to attention, since there are some other results (which they cite) showing the relevance of pink noise to attention.

However, IMDB ratings were not positively correlated with the pink-noise-ness of the movie (partial correlation with release date factored out).

Incidentally, this guy did his PhD thesis on cognitive science explanations for film editing techniques.

James E. Cutting, Jordan E. DeLong, and Christine E. Nothelfer. Attention and the Evolution of Hollywood Film. Psychological Science February 2010 , first published on February 5, 2010 doi:10.1177/0956797610361679.

Octave/MATLAB toolkit for analysis of spike train data. Open source. Information theory-y.

A few days later, Henry Markram, leader of the Blue Brain Project at EPFL, sent off an e-mail to IBM CTO Bernard Meyerson harshly criticizing the IBM press release, and cc’ed several reporters. This brought a spate of shock media into the usually placid arena of computational neuroscience reporting, with headlines such as “IBM’s cat-brain sim a ‘scam,’ says Swiss boffin: Neuroscientist hairs on end”, and “Meow! IBM cat brain simulation dissed as ‘hoax’ by rival scientist”.  One reporter chose to highlight the rivalry as cat versus rat, using the different animal model choice of the two researchers as a theme.  Since then, additional criticisms from Markram have appeared online.

Find out more after the jump.

In the aftermath, IBM has stood behind the announcement, citing for Network World their team’s involvement with “Stanford University, University of Wisconsin-Madison, Cornell University, Columbia University Medical Center, University of California-Merced and Lawrence Berkeley National Laboratory” as defense.  Who are the researchers they are standing behind?  According to Modha’s blog, they are:

• Stanford University: Brian A. Wandell (Prof of Psychology, Electrical Engineering), H.-S. Philip Wong (Prof of Electrical Engineering)
• Cornell University: Rajit Manohar (Prof of Electrical Engineering)
• Columbia University Medical Center: Stefano Fusi (Prof of Theoretical Neuroscience)
• University of Wisconsin-Madison: Giulio Tononi (Prof of Psychiatry)
• University of California-Merced: Christopher Kello (Prof of Cognitive Science)

For this neurodude, it is interesting how this disagreement may be symbolic of the gap that still remains between neuroscience and AI.  Markram is a neuroscientist turned technologist, while Modha is a computer engineer who wants to derive technological insight from biological  systems.  They are approaching the ideal of reverse engineering the brain from very different perspectives, and its only natural that they value different milestones.  The IBM team, even with the additional professors on their team, still lacks mainstream neuroscientists to help validate their claims.  That being said, the public realization of this could be a positive thing for both fields.  Although some frustration has resulted from this, this could be a great opportunity for the breakdown of walls between these fields.

In the end though, it does seem like Markram has a point.  The IBM press release clearly went too far.  Whether the angry public e-mail was the best strategic way to make the point remains to be seen.  It will be interesting to see what the next move from the IBM team will look like.

A very cool article on a new open source, online system to crowd source the assemblage of data in neuroscience from the Voice of San Diego.  From the article:

Traditionally, the study of the brain was organized somewhat like an archipelago. Neuroscientists would inhabit their own island or peninsula of the brain, and see little reason to venture elsewhere.

Molecular neuroscientists, who study how DNA and RNA function in the brain, didn’t share their work with cognitive specialists who study how psychological and cognitive functions are produced by the brain, for example.

But there has been an awakening to the idea that brains of humans and mammals should be studied like the complex, and interrelated systems that they are. Neuroscientists realized that they had to start collaborating across disciplines and sharing their data if they wanted to make advances in their own field.

[...]

Ellisman and his UCSD colleagues have devised a solution: crowdsource a brain. And this week they unveiled their years-long project — the Whole Brain Catalog — at the annual convention of the Society for Neuroscience, the largest gathering of brain experts in the world.

You can also see an impressive  artists rendition of the Whole Brain Catalog on YouTube.

UPDATE 10/27: Looks like Voice of San Diego scooped the New York Times, who just posted on this topic in today’s bits blog.

Full disclosure: I am intimately involved with this project.

Watch it online at the TED website.

“The task administered to the salmon involved completing an open-ended mentalizing task. The salmon was shown a series of photographs depicting human individuals in social situations with a specified emotional valence. The salmon was asked to determine what emotion the individual in the photo must have been experiencing.”

Voxels in the data were searched to find voxels which show a statistically significant correlation with the experimental condition. 16 such voxels were found (without doing any correction). As the poster says, “Across the 130,000 voxels in a typical fMRI volume the probability of a false positive is almost certain”. The poster recommends correcting with either “overall false discovery rate (FDR) … based on a method defined by Benjamini and Hochberg (1995)” or “overall familywise error rate (FWER) …. using algorithms originally devised by Friston et al. (1994)” (with either correction, no voxels were found).

http://prefrontal.org/files/posters/Bennett-Salmon-2009.pdf

I’m a fan of it because it is an open-access journal featuring a “tiered system” and more.  From their website:

The Frontiers Journal Series is not just another journal. It is a new approach to scientific publishing. As service to scientists, it is driven by researchers for researchers but it also serves the interests of the general public. Frontiers disseminates research in a tiered system that begins with original articles submitted to Specialty Journals. It evaluates research truly democratically and objectively based on the reading activity of the scientific communities and the public. And it drives the most outstanding and relevant research up to the next tier journals, the Field Journals.

I’m a big fan of the variety of specialty journals they have:

• Aging Neuroscience
• Behavioral Neuroscience
• Cellular Neuroscience
• Computational Neuroscience
• Enteric Neuroscience
• Evolutionary Neuroscience
• Human Neuroscience
• Integrative Neuroscience
• Molecular Neuroscience
• Neural Circuits
• Neuroanatomy
• Neuroenergetics
• Neuroengineering
• Neurogenesis
• Neurogenomics
• Neuroinformatics
• Neuromethods
• Neuropharamacology
• Neuroprosthetics
• Neurorobotics
• Synaptic Neuroscience
• Systems Neuroscience