Review of Whittington, et al. - a theoretical paper on the generalized mapping and learning capabilities of the entorhinal/hippocampal complex that separates memories from a graph-mapping grid.
These are exciting times in neurobiology, as a grand convergence is in the offing with computational artificial intelligence. AI has been gaining powers at a rapid clip, in large part due to the technological evolution of neural networks. But computational theory has also been advancing, on questions of how concepts and relations can be gleaned from data- very basic questions of interest to both data scientists and neuroscientists. On the other hand, neurobiology has benefited from technical advancements as well, if far more modestly, and from the relentless accumulation of experimental and clinical observations. Which is to say, normal science.
One of the hottest areas of neuroscience has been the hippocampus and the closely connected entorhinal cortex, seat of at least recent memory and of navigation maps and other relational knowledge. A recent paper extends this role to a general theory of relational computation in the brain. The basic ingredients of thought are objects and relations. Computer scientists typically represent these as a graph, where the objects are nodes, and the relations are the connecting lines, or edges. Nodes can have a rich set of descriptors (or relations to property nodes that express these descriptions). A key element to get all this off the ground is the ability to chunk, (or abstract, or generalize, or factorize) observations into discrete entities, which then serve as the objects of the relational graph. The ability to say that what you are seeing, in its whirling and colorful reality, is a dog .. is a very significant opening step to conceptualization, and the manipulation of those concepts in useful ways, such as understanding past events and predicting future ones.
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| Gross anatomy of the hippocampus and associated entorhinal cortex, which function together in conceptual binding and memory. |
A particular function of the entorhinal/hippocampal complex is spatial navigation. Reseachers have found place cells, grid cells, and boundary cells (describing when these cells fire) as clear elements of spatial consciousness, which even replay in dreams as the rats re-run their daytime activities. It is evident that these cells are part of an abstraction mechanism that dissociates particular aspects of conceptualized sensory processing from the total scene and puts them back together again in useful ways, i.e. as various maps.
This paper is conducted at a rather abstruse level, so there is little that I can say about it in detail. Yet it and the field it contributes to is so extremely interesting that some extra effort is warranted. By the time the hippocampus is reached, visual (and other sensory data) has already been processed to the conceptual stage. Dogs have been identified, landmarks noted, people recognized. Memories are composed of fully conceptualized, if also sensorily colored, conceptual chunks. The basic idea the authors present is that key areas of the entorhinal cortex provide general and modular mapping services that allow the entorhinal/hippocampal complex to deal with all kinds of relational information and memories, not just physical navigation. Social relations, for example, are mapped similarly.
It is important to note tangentially that conceptualization is an emergent process in the brain, not dictated by pre-existing lists of entities or god-given databases of what exists in the world and beyond. No, all this arises naturally from experience in the world, and it has been of intense interest to computer scientists to figure out how to do this efficiently and accurately, on a computer. Some recent work was cited here and is interesting for its broad implications as well. It is evident that we will in due time be faced with fully conceptualizing, learning, and thinking machines.
"Structural sparsity also brings a new perspective to an old debate in cognitive science between symbolic versus emergent approaches to knowledge representation. The symbolic tradition uses classic knowledge structures including graphs, grammars, and logic, viewing these representations as the most natural route towards the richness of thought. The competing emergent tradition views these structures as epiphenomena: they are approximate characterizations that do not play an active cognitive role. Instead, cognition emerges as the cooperant consequence of simpler processes, often operating over vector spaces and distributed representations. This debate has been particularly lively with regards to conceptual organization, the domain studied here. The structural forms model has been criticized by the emergent camp for lacking the necessary flexibility for many real domains, which often stray from pristine forms. The importance of flexibility has motivated emergent alternatives, such as a connectionist network that maps animals and relations on the input side to attributes on the output side. As this model learns, an implicit tree structure emerges in its distributed representations. But those favoring explicit structure have pointed to difficulties: it becomes hard to incorporate data with direct structural implications like 'A dolphin is not a fish although it looks like one', and latent objects in the structure support the acquisition of superordinate classes such as 'primate' or 'mammal'. Structural sparsity shows how these seemingly incompatible desiderata could be satisfied within a single approach, and how rich and flexible structure can emerge from a preference for sparsity." - from Lake et al., 2017
Getting back the the hippocampus paper, the authors develop a computer model, which they dub the Tolman-Eichenbaum machine [TEM] after key workers in the field. This model implements a three-part system modeled on the physiological situation, plus their theory of how relational processing works. Medial entorhinal cells carry generalized mapping functions (grids, borders, vectors), which can be re-used for any kind of object/concept, supplying relations as originally deduced from sensory processing or possibly other abstract thought. Lateral entorhinal cells carry specific concepts or objects as abstracted from sensory processing, such as landmarks, smells, personal identities, etc. It is then the crossing of these "what" and "where" streams that allows navigation, both in reality and in imagination. This binding is proposed to happen in the hippocampus, as firing that happens when firing from the two separate entorhinal regions happen to synchronize, stating that a part of the conceptual grid or other map and an identified object have been detected in the same place, generating a bound sensory experience, which can be made into a memory, or arise from a memory, or an imaginative event, etc. This is characteristic of "place cells", hippocampal cells that fire when the organism is at a particular place, and not at other times.
"We propose TEM’s [the computational model they call a Tolman-Eichenbaum Machine] abstract location representations (g) as medial entorhinal cells, TEM’s grounded variables (p) as hippocampal cells, and TEM’s sensory input x as lateral entorhinal cells. In other words, TEM’s sensory data (the experience of a state) comes from the ‘what stream’ via lateral entorhinal cortex, and TEM’s abstract location representations are the ‘where stream’ coming from medial entorhinal cortex. TEM’s (hippocampal) conjunctive memory links ‘what’ to ‘where’, such that when we revisit ‘where’ we remember ‘what’."
Given the abstract mapping and a network of relations between each of the components, reasoning or imagining about possible events also becomes feasible, since the system can solve for any of the missing components. If a landmark is seen, a memory can be retrieved that binds the previously known location. If a location is surmised or imagined, then a landmark can be dredged up from memory to predict how that location looks. And if an unfamiliar combination of location and landmark is detected, then either a new memory can be made, or a queasy sense of unreality or hallucination would ensue if one of the two are well-known enough to make the disagreement disorienting.
As one can tell, this allows not only the experience of place, but the imagination of other places, as the generic mapping can be traversed imaginatively, even by paths that the organism has never directly experienced, to figure out what would happen if one, for instance, took a short-cut.
The combination of conceptual abstraction / categorization with generic mapping onto relational graph forms that can model any conceptual scale provides some of the most basic apparatus for cognitive thought. While the system discussed in this paper is mostly demonstrated for spatial navigation, based on the proverbial rat maze, it is claimed, and quite plausible, that the segregation of the mapping from the object identification and binding allows crucial generalization of cognition- the tools we, and someday AI as well, rely on to make sense of the world.
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