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.

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

I think he might be right that we can simulate the brain before we understand it, however.

Click on for some of my favorite passages from the Abbott piece.

Abbott uses the following problem of input decoding

Spike counts and neuronal firing rates are positive quantities. This simple fact has important implications for neural coding and neural circuits that provide a framework for thinking about a number of research directions taken over the past 20 years.

to highlight new work in synchrony, dendritic compartments, and balanced excitation-inhibition. This is probably the best part of the whole article. With some simple arithmetic, he motivates and explains solutions to the problem of correlating neural activity with real events.

The successes of circuit models (and principles of circuit models) in primary visual cortex:

We now have plausible mechanisms for how simple and complex cells obtain their basic response characteristics. Although no single consensus abouthow the circuits of primary visual cortex operate has arisen from this body of work, this may simply reflect the fact that multiple mechanisms contribute. In other words, many of these ideas are probably correct in one way or another, and the wealth of ideas in this field should be viewed as a success. Circuit-level modeling is now advancing beyond primary sensory areas (for example, Cadieu etal., 2007) and to the consideration of phenomena such as working memory through sustained activity (Amit and Brunel, 1997,Compte etal., 2000,Seung etal., 2000) and decision making (Wang, 2002,Machens etal., 2005).

And the dangers of an unhealthy obsession with connectomics:

What can we learn from the complete connectome or, indeed, a complete mathematical description of a complex artificial network model?

First, what can’t we learn? It is unlikely, for example, that we could deduce the task that the network was constructed to perform even if we were given the complete equations and connections of the model. If, along with this information, we were told what this task was, it is unlikely that we could figure out how the network performs it. If we somehow managed to make any progress along these lines, the people who constructed the network could probably provide us with another one that performs the same task but has a different connectome. In a similarway, biological systems may operate in a more variable manner than we have suspected, as has been stressed by Eve Marder (Marder etal., 2007). These issues are particularly true of a class of network models known as liquid state or echostate networks (Maass etal., 2002,Jaeger, 2003). In these models, the vast majority of interneuronal connections are not directly related to the task being performed (they are typically chosen randomly and left unchanged), the exceptions being synapses onto the output units of the network. Nevertheless, the tuned values of the synapses onto the output units can only be understood through their relationships to the random synapses. Such systems represent enormous challenges for conventional anatomical and physiological approaches.

The fact that the connectome of an artificial neural network does not typically tell us what the network does or how it does it should not be taken as an indication that this information is useless. Far from it. But we must be willing to be more abstract in our thinking. The important issue for an artificial network is not how it works but how it was constructed, which means what training procedures and modification rules were used to get it to perform a task. Although this information is not provided directly by the connectome, much can be inferred. For example, it is important to know whether the network has a feedforward architecture or has strong feedback loops. Other features of the network layout, whether it has hubs or bottlenecks, how many layers it contains, and its degree of heterogeneity, provide important clues as well. Obtaining a high-resolution connectome in neuroscience will be of great value, but artificial neural networks provide a cautionary tale that reminds us that scientific revolutions tend to render uninteresting as many questions as they answer. We will be fortunate if the connectome project does this for neuroscience, butas we launch ourselves into it we should appreciate that, as artificial neural networks appear to suggest, we may be asking the wrong questions.

Finally a major challenge for the future:

This is where I think the future lies in theoretical investigations of cognitive function. We must learn how to build models that construct hypotheses through their internally generated activity while remaining sensitive to the constraints provided by externally generated sensory evidence.

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.

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.

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.

A computer, at its core, processes information through the physics of transistors, which are essentially switches that are either on or off. What makes transistors such a powerful foundation for modern computing is that they are controlled by electrical signals, which means that transistors can be controlled by other transistors and therefore structured into useful systems. Understanding the physics of transistors, how quickly they can switch from on to off, how their material composition affects their ability to switch, is crucial for building a microchip. However, this level of understanding is not sufficient to build a microchip. For that, one needs to understand how to structure transistors in such a way to produce digital computation.

Single transistors turn on and off. As a medium for constructing computer architectures, they are relatively straightforward to combine into complex circuits that perform useful functions of logic. Single neurons, quite a bit more complicated, have a vast repertoire of behavior that, among other things, involves integrating signals from multiple sources and sending signals to multiple recipients. It is not at all straightforward to construct explanations of how neurons combine into complex circuits to perform useful behavioral functions. Yet, this is the kind of explanation that neuroscience ultimately must seek in order to fulfill the promise of its potential to unlock the secrets of the brain.

Keeping in mind that computers are different that brains in many important ways, in some sense, we are still at the level of understanding the dynamics of the transistors in neuroscience. Cellular neuroscience, as found in journals such as Neuron, has concerned itself with the dynamics of neurons, rather than their role in processing information. This may seems like a bold statement to some; after all, decades of research has been conducted on sensory systems such as vision, and many aspects of the visual pathway are understood. Furthermore, lesion studies have been demonstrating that certain groups of neurons have certain functions throughout the last hundred years. However, despite the current push to apply information theory into the study of sensory systems, even the most cutting edge work in the field of neuroscience is still only just beginning to incorporate the understanding of what single neurons do with a rigorous account of how they carry out the functions they perform.

Other flavors of neuroscience, such as systems neuroscience and cognitive neuroscience have made inroads towards this goal. For example, excellent progress has been made in understanding the olfactory system of the locust. Here is a system where we understand the inputs, we understand the physiology of the neurons in between, and we have ways of analyzing the dynamics of the system that allow us to predict future behavior. And yet, the difficulty of generalizing these findings to more complex neuronal systems looms large as an obstacle to progress. Some of the best accounts of the activity of neurons in the pre-frontal cortex of monkeys still only provide a descriptive model of the data that fits the observations but does not provide a complete explanation for how the system actually carries out the function that is being modeled. This is the rule, rather than the exception in neuroscience.

One of the key difficulties is that processing information does not happen in single transistors by themselves, nor does it happen in single neurons by themselves. Both systems require the coordinated spatiotemporal organization of an complex system. Engineers over the past 60 years have constructed patterns that help organize transistors into useful components that process information. The most basic functions are those of basic logic, AND, OR, and NOT. Using these tools, arithmetic can be carried out to add, subtract, divide, and multiply numbers encoded in ones and zeros. From there, computer programs can be constructed in a straightforward manner and provide the foundation upon which more complex computer programs can be constructed. We have no equivalent explanation for the functions that assemblies of neurons carry out. We know that neurons excite or inhibit one another, and that the influence between two neurons can change. But neuroscience does not yet have the ability to recombine biologically faithful model neurons into novel circuits to perform novel functions. This indicates that the field lacks principles, or at the very least a sufficient set of well-understood patterns, which explain how neurons are organized together to enable an animal to behave in an appropriate manner in its environment.

In summary, while understanding neuronal dynamics is necessary to understanding the brain, it is not sufficient. I would posit that we must understand how the brain processes information in order to understand it as a whole. A prerequisite to understanding how the brain processes information is to describe principles of neural information processing, which a) explain how neurons perform functions collectively, b) help us to explain the functions of those parts of the brain where they are still unknown, and c) are rigorous enough to enable the design of circuits of neurons (model neurons, or eventually real physical neurons) that perform known and novel functions–true neural engineering.