neurodudes

Although it’s a few months old, Larry Abbott has an excellent article in Neuron on the recent (last 20 years) contributions of theoretical neuroscience. (He came by MIT last week to give a talk and that’s when I found out about the article.) It’s a review that is not too long and provides a good overview with both sufficient (though not overwhelming) detail and original perspective. It’s rare to find a short piece that is so informative. (And for a more experimentally-oriented review with an eye toward the future, see Rafael Yuste’s take on the grand challenges.)

Click on for some of my favorite passages from the Abbott piece. (more…)

Nature is reporting on potential flaw in multiple imaging (fMRI) studies of social neuroscience. Ed Vul (a graduate student in my dept) and colleagues have a paper in press that says that many of the high correlations between brain regions and social behavior are implausible, given the inherent variability/noise in fMRI. Furthermore, based on a survey of methods from individual investigators, they created a list of papers that commit, in their view, a statistical mistake (non-independence). Naturally, the authors named in the paper aren’t happy and, according to the Nature article, several rebuttals are in the works. At the very least, to my non-expert eyes, this seems like an important discussion to have about data analysis and methodology.

NSF:ENG:EFRI:Home Page

NSF’s Emerging Frontiers in Research and Innovation (EFRI) office funded 4 very futuristic neuroengineering grants.

1. Deep learning in mammalian cortex
2. Studying neural networks in vitro with an innovative patch clamp array
3. Determining how the brain controls the hand for robotics
4. In vitro power grid simulation using real neurons

Disclaimer: I was involved with the second proposal on this page.

From March. Actually, the topic of the article is Dehaene, but it talks about some studies too. Excerpts after the break, interspersed with hyperlinks to citations that I looked up.

http://www.newyorker.com/reporting/2008/03/03/080303fa_fact_holt?currentPage=all

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The great Masao Ito, originator of one of the classic theories of cerebellar function, has published a new theory in the recent issue of Nature Neuroscience regarding how the cerebellum may be involved in control of cognition.

The basic idea is that while the cerebellum has evolutionarily had a role of refining motor commands for the purpose of controlling the skeleton, in the human the cerebellum is capable of refining commands from frontal cortex to “control” internal representations of the outside world. Ito uses the increasingly popular language of control theory to describe the effect that the cerebellum may have on different parts of the brain.

From the abstract:

The intricate neuronal circuitry of the cerebellum is thought to encode internal models that reproduce the dynamic properties of body parts. These models are essential for controlling the movement of these body parts: they allow the brain to precisely control the movement without the need for sensory feedback. It is thought that the cerebellum might also encode internal models that reproduce the essential properties of mental representations in the cerebral cortex. This hypothesis suggests a possible mechanism by which intuition and implicit thought might function and explains some of the symptoms that are exhibited by psychiatric patients. This article examines the conceptual bases and experimental evidence for this hypothesis.

Researchers at the University of Nevada, Reno have an interesting and ambitious set-up for doing research in AI that the describe in a recent paper.

From the paper:

We define virtual neurorobotics as follows: a computer-facilitated behavioral loop wherein a human interacts with a projected robot that meets five criteria: (1) the robot is sufficiently embodied for the human to tentatively accept the robot as a social partner, (2) the loop operates in real time, with no pre-specified parcellation into receptive and responsive time windows, (3) the cognitive control is a neuromorphic brain emulation incorporating realistic neuronal dynamics whose time constants reflect synaptic activation and learning, membrane and circuitry properties, and (4) the neuromorphic architecture is expandable to progressively larger scale and complexity to track brain development, (5) the neuromorphic architecture can potentially provide circuitry underlying intrinsic motivation and intentionality, which physiologically is best described as “emotional” rather than rule-based drive.

What’s interesting to me about this is the combination of a embodied robot in a virtual world with a neurally inspired controller for that robot. While there are pros and cons of embodiment in virtual world (some of which have been touched on here before), I think that if your priority is closing the loop from embodiment to research on neural systems, the importance of this kind of approach cannot be ignored.

Neurons come in many shapes and sizes. Frequently, the shape of a neuron is characteristic to its type. Several theoretical papers have demonstrated that the shape of a neuron can crucially determine its pattern of activity, independently of other factors (Mainen & Sejnowski, 1996, for example). Several resources on the web such as neuromorpho.org and the Cell Centered Database are dedicated to maintaining repositories of different neuronal shapes (also known as morphologies).

Any computer scientist worth their salt, noticing this trend, is tempted to say: if neuronal shape is so important, maybe we ought to have good data standards to describe it. That’s just what a paper last year did. It surveyed the popular data standards for modeling, primarily in the NEURON and Genesis simulation packages. The result is a data standard called MorphML, which is part of a larger effort called NeuroML.

Neuronal shape is a weird data type for the computer science world, but I think an incredibly important and fundamental one for deeply coping with the complexity of real brain tissue. It seems to me that many areas of neuroscience research could benefit from the construction of more explicit models of the circuits they study.

Interview with Steve Grand on building human level artificial intelligence at Machines Like Us. Really interesting. Via Chris Chatham at (the excellent) Developing Intelligence.

In particular, MLU asks why his current project to create an android was done as a physical robot rather than as a simulation. The answer, that you can cheat too much in a simulation, is familiar to those from the Brooksian school of embodied intelligence. He says that simulations still aren’t good enough to provide the kinds of physical constraints, like gravity and friction, etc, that you get when building real robots .

However, with the availability of free 3D simulation environments that handle physics, like Breve, we are getting a lot closer. Building a robot within a simulation like this, particularly where you don’t modify the code of the the simulation environment itself, is a terrific way to balance the competing interests of keeping yourself honest and avoiding the painstaking mechanical engineering required to construct complicated robots. This kind of environment allows you to build a body with primary sensory systems and primary motor outputs in a similar fashion as one would with real robots.

Why there aren’t more who have adopted this kind of “in silico embodiment” philosophy I think is the result of taking Brooks’ a bit too seriously. Brooks idea of embodiment is very well founded, but back in the day when he first made those statements, there really were no good ways to simulate the physics of an embodied creature very faithfully. Today that is not the case. Moreover, building real physical robots is great if you have a lot of time, or an engineering team, but it’s a huge investment that distracts from the real problem of understanding the nature of intelligence. The fact that the world has extremely few labs that can make that investment is one of the many reasons there aren’t more serious strong AI researchers any more.

Update: Steve apparently received a few comments along these lines and replies.

Gerstner’s group in Lausanne, Switzerland has announced a competition to predict the electrical behavior of individual neurons in two respects:

1) predict the timing of every spike that a neuron emits with a precision of 2ms.

2) predict the subthreshold membrane potential with a precision of 2mV for arbitrary input.

Details on the competition, including the dataset (released 16 March 2007), are here.

Note that the first prize winner receives:

- 4 nights of hotel in Lausanne at the Lake Geneva, June 23-27.
- Free participation in the Quantitative Neuron Modeling workshop June 25/26
- 35-minute-slot for talk as an Invited Speaker in the workshop.

get coding.

The field of neuroscience naturally focuses its inquiry into neurons. This approach to understanding the brain by studying its parts has been thought to have a greater potential than that of psychology to understand how the brain works, a comment made by no less than Daniel L. Schacter, chair of Harvard’s Department of Psychology, in his book, The Seven Sins of Memory.

However promising the field has been thus far, even the most accomplished neuroscientists will admit that we still do not understand how the brain really works. I would submit that the current reductionist nature of neuroscience has shed much light on the dynamics of how neurons work, but has to a far lesser degree shed light on how neurons process information. The difference between these two lines of inquiry is important for making progress in understanding how the brain works.
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