Friday, June 24, 2011

Unraveling Modha & Singh's Map of the Macaque Monkey Brain

(... plus some semi-related AGI musings at the end!)

On July 27 2010, PNAS published a paper entitled "Network architecture of the long-distance pathways in the macaque brain" by Dharmendra Modha and Raghavendra Singh from IBM, which is briefly described here and available in full here. The highlight of the paper is a connectivity diagram of all the regions of the macaque (monkey) brain, reproduced in low res right here:

See here for a hi-res version.

The diagram portrays "a unique network incorporating 410 anatomical tracing studies of the macaque brain from the Collation of Connectivity data on the Macaque brain (CoCoMac) neuroinformatic database. Our network consists of 383 hierarchically organized regions spanning cortex, thalamus, and basal ganglia; models the presence of 6,602 directed long-distance connections; is three times larger than any previously derived brain network; and contains subnetworks corresponding to classic corticocortical, corticosubcortical, and subcortico-subcortical fiber systems."

However, I found that the diagram can be somewhat confusing to browse, if one wants to look at specific brain regions and what they connect to. So my Novamente LLC co-conspirator Eddie Monroe and I went back to the original data files, given in the online supplementary information for the paper, and used this to make a textual version of the information in the diagram, which you can find here.

Our goal in looking at this wiring diagram is as a guide to understanding the interactions between certain human brain regions we're studying (human and monkey brains being rather similar in many respects). But I think it's worth carefully perusing for anyone who's thinking about neuroscience from any aspect, and for anyone who's thinking about AGI from a brain-simulation perspective.

Semi-Related AGI Musings

Complexity such as that revealed in Modha and Singh's diagrams always comes to my mind when I read about someone's "brain inspired" AGI architecture -- say, Hierarchical Temporal Memory architectures (like Numenta or DeSTIN, etc.) that consist of a hierarchy of layers of nodes, passing information up and down in a manner vaguely reminiscent of visual or auditory cortex. Such architectures may be quite valuable and interesting, but each of them captures a teensy weensy fraction of the architectural and dynamical complexity in the brain. Each of the brain regions in Modha and Singh's diagram is its own separate story, with its own separate and important functions and structures and complex dynamics; and each one interacts with a host of others in specially configured ways, to achieve emergent intelligence. In my view, if one wants to make a brain-like AGI, one's going to need to emulate the sort of complexity that the actual brain has -- not just take some brain components (e.g. neurons) and roughly simulate them and wire the simulations together in some clever way; and not just emulate the architecture and dynamics of one little region of the brain and proclaim it to embody the universal principles of brain function.

And of course this is the reason I'm not pursuing brain-like AGI at the moment. If you pick 100 random links from Modha and Singh's diagram, and then search the neuroscience literature for information about the dynamical and informational interactions ensuing from that link, you'll find that in the majority of cases the extant knowledge is mighty sketchy. This is an indicator of how little we still know about the brain.

But can we still learn something from the brain, toward the goal of making loosely brain-inspired but non-brain-like AGI systems? Absolutely. I'm currently interested in understanding how the brain interfaces perceptual and conceptual knowledge -- but not with a goal of emulating how the brain works in any detailed sense (e.g. my AGI approach involves no formal neurons or other elementary brainlike components, and no modules similar in function to specific brain regions), rather just with a goal of seeing what interesting principles can be abstracted therefrom, that may be helpful in designing the interface between OpenCog and DeSTIN (a hierarchical temporal memory designed by Itamar Arel, that we're intending to use for OpenCog's sensorimotor processing).

And so it goes... ;-)


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