Recent support for the basic module of learning’s being a module of dozens of neurons comes from Swiss neuroscientist Henry Markram (born in 1962), whose ambitious Blue Brain Project to simulate the entire human brain I describe in chapter 7. In a 2011 paper he describes how while scanning and analyzing actual mammalian neocortex neurons, he was “search[ing] for evidence of Hebbian assemblies at the most elementary level of the cortex.” What he found instead, he writes, were “elusive assemblies [whose] connectivity and synaptic weights are highly predictable and constrained.” He concludes that “these findings imply that experience cannot easily mold the synaptic connections of these assemblies” and speculates that “they serve as innate, Lego-like building blocks of knowledge for perception and that the acquisition of memories involves the combination of these building blocks into complex constructs.” He continues:
Functional neuronal assemblies have been reported for decades, but direct evidence of clusters of synaptically connected neurons…has been missing…. Since these assemblies will all be similar in topology and synaptic weights, not molded by any specific experience, we consider these to be innate assemblies…. Experience plays only a minor role in determining synaptic connections and weights within these assemblies…. Our study found evidence [of] innate Lego-like assemblies of a few dozen neurons…. Connections between assemblies may combine them into super-assemblies within a neocortical layer, then in higher-order assemblies in a cortical column, even higher-order assemblies in a brain region, and finally in the highest possible order assembly represented by the whole brain…. Acquiring memories is very similar to building with Lego. Each assembly is equivalent to a Lego block holding some piece of elementary innate knowledge about how to process, perceive and respond to the world…. When different blocks come together, they therefore form a unique combination of these innate percepts that represents an individual’s specific knowledge and experience.3
The “Lego blocks” that Markram proposes are fully consistent with the pattern recognition modules that I have described. In an e-mail communication, Markram described these “Lego blocks” as “shared content and innate knowledge.”4 I would articulate that the purpose of these modules is to recognize patterns, to remember them, and to predict them based on partial patterns. Note that Markram’s estimate of each module’s containing “several dozen neurons” is based only on layer V of the neocortex. Layer V is indeed neuron rich, but based on the usual ratio of neuron counts in the six layers, this would translate to an order of magnitude of about 100 neurons per module, which is consistent with my estimates.
The consistent wiring and apparent modularity of the neocortex has been noted for many years, but this study is the first to demonstrate the stability of these modules as the brain undergoes its dynamic processes.
Another recent study, this one from Massachusetts General Hospital, funded by the National Institutes of Health and the National Science Foundation and published in a March 2012 issue of the journal Science, also shows a regular structure of connections across the neocortex.5 The article describes the wiring of the neocortex as following a grid pattern, like orderly city streets: “Basically, the overall structure of the brain ends up resembling Manhattan, where you have a 2-D plan of streets and a third axis, an elevator going in the third dimension,” wrote Van J. Wedeen, a Harvard neuroscientist and physicist and the head of the study.
In a Science magazine podcast, Wedeen described the significance of the research: “This was an investigation of the three-dimensional structure of the pathways of the brain. When scientists have thought about the pathways of the brain for the last hundred years or so, the typical image or model that comes to mind is that these pathways might resemble a bowl of spaghetti—separate pathways that have little particular spatial pattern in relation to one another. Using magnetic resonance imaging, we were able to investigate this question experimentally. And what we found was that rather than being haphazardly arranged or independent pathways, we find that all of the pathways of the brain taken together fit together in a single exceedingly simple structure. They basically look like a cube. They basically run in three perpendicular directions, and in each one of those three directions the pathways are highly parallel to each other and arranged in arrays. So, instead of independent spaghettis, we see that the connectivity of the brain is, in a sense, a single coherent structure.”
Whereas the Markram study shows a module of neurons that repeats itself across the neocortex, the Wedeen study demonstrates a remarkably orderly pattern of connections between modules. The brain starts out with a very large number of “connections-in-waiting” to which the pattern recognition modules can hook up. Thus if a given module wishes to connect to another, it does not need to grow an axon from one and a dendrite from the other to span the entire physical distance between them. It can simply harness one of these axonal connections-in-waiting and just hook up to the ends of the fiber. As Wedeen and his colleagues write, “The pathways of the brain follow a base-plan established by…early embryogenesis. Thus, the pathways of the mature brain present an image of these three primordial gradients, physically deformed by development.” In other words, as we learn and have experiences, the pattern recognition modules of the neocortex are connecting to these preestablished connections that were created when we were embryos.
There is a type of electronic chip called a field programmable gate array (FPGA) that is based on a similar principle. The chip contains millions of modules that implement logic functions along with connections-in-waiting. At the time of use, these connections are either activated or deactivated (through electronic signals) to implement a particular capability.
In the neocortex, those long-distance connections that are not used are eventually pruned away, which is one reason why adapting a nearby region of the neocortex to compensate for one that has become damaged is not quite as effective as using the original region. According to the Wedeen study, the initial connections are extremely orderly and repetitive, just like the modules themselves, and their grid pattern is used to “guide connectivity” in the neocortex. This pattern was found in all of the primate and human brains studied and was evident across the neocortex, from regions that dealt with early sensory patterns up to higher-level emotions. Wedeen’s Science journal article concluded that the “grid structure of cerebral pathways was pervasive, coherent, and continuous with the three principal axes of development.” This again speaks to a common algorithm across all neocortical functions.
It has long been known that at least certain regions of the neocortex are hierarchical. The best-studied region is the visual cortex, which is separated into areas known as V1, V2, and MT (also known as V5). As we advance to higher areas in this region (“higher” in the sense of conceptual processing, not physically, as the neocortex is always just one pattern recognizer thick), the properties that can be recognized become more abstract. V1 recognizes very basic edges and primitive shapes. V2 can recognize contours, the disparity of images presented by each of the eyes, spatial orientation, and whether or not a portion of the image is part of an object or the background.6 Higher-level regions of the neocortex recognize concepts such as the identity of objects and faces and their movement. It has also long been known that communication through this hierarchy is both upward and downward, and that signals can be both excitatory and inhibitory. MIT neuroscientist Tomaso Poggio (born in 1947) has extensively studied vision in the human brain, and his research for the last thirty-five years has been instrumental in establishing hierarchical learning and pattern recognition in the “early” (lowest conceptual) levels of the visual neocortex.7