What Does the Hippocampus Compute?: 
A Precis of the 1993 NIPS Workshop 
Mark A. Gluck 
Center for Molecular and Behavioral Neuroscience 
Rutgers University 
Newark, NJ 07102 
g luck @pav lov. rutgers. edu 
Computational models of the hippocampal-region provide an important method for 
understanding the functional role of this brain system in learning and memory. The 
presentations in this workshop focused on how modeling can lead to a unified 
understanding of the interplay among hippocampal physiology, anatomy, and 
behavior. Several approaches were presented. One approach can be characterized as 
"top-down" analyses of the neuropsychology of memory, drawing upon brain-lesion 
studies in animals and humans. Other models take a "bottom-up" approach, seeking 
to infer emergent computational and functional properties from detailed analyses of 
circuit connectivity and physiology (see Gluck & Granger, 1993, for a review). 
Among the issues discussed were: (1) integration of physiological and behavioral 
theories of hippocampal function, (2) similarities and differences between animal 
and human studies, (3) representational vs. temporal properties of hippocampal- 
dependent behaviors, (4) rapid vs. incremental learning, (5) multiple vs. unitary 
memory systems, (5) spatial navigation and memory, and (6) hippocampal 
interaction with other brain systems. 
Jay McClelland, of Carnegie-Mellon University, presented one example of a top- 
down approach to theory development in his talk, "Complementary roles of 
neocortex and hippocampus in learning and memory" McCle!land reviewed 
findings indicating that the hippocampus appears necessary for the initial acquisition 
of some forms of memory, but that ultimately all forms of memory are stored 
independently of the hippocampal system. Consolidation in the neocortex appears 
to occur over an extended period -- in humans the process appears to extend over 
several years. McClelland suggested that the cortex uses interleaved learning to 
extract the structure of events and experiences while the hippocampus provides a 
special system for the rapid initial storage of traces of specific events and 
experiences in a form that minimizes mutual interference between memory traces. 
According to this view, the hippocampus is necessary to avoid the catastrophic 
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interference that would result if memories were stored directly in the neocortex. 
Consolidation is slow to allow the gradual integration of new knowledge via 
continuing interleaved learning (McClelland, 1994/in press). 
In another example of top-down modeling, Mark Gluck of Rutgers University 
discussed "Stimulus representation and hippocampal function in animal and human 
learning." He described a computational account of hippocampal-region function in 
classical conditioning (Gluck & Myers, 1993; Myers & Gluck, 1994). In this model, 
the hippocampal region constructs new stimulus representations biased by two 
opponent constraints: first, to differentiate representations of stimuli which predict 
different future events, and second, to compress together representations of co- 
occurring or redundant stimuli. This theory accurately describe the role of the 
hippocampal region in a wide range of conditioning paradigms. Gluck also 
presented an extension of this theory which suggests that stimulus compression may 
arise from the operation of circuitry in the superficial layers of entorhinal cortex, 
whereas stimulus differentiation may arise from the operation of constituent circuits 
of the hippocampal formation. 
Discussing more physiologically-motivated "bottom-up" research, Michael 
Hasselmo, of Harvard University, talked about "The septohippocampal system: 
Feedback regulation of cholinergic modulation." Hasselmo presented a model in 
which feedback regulation sets appropriate dynamics for learning of novel input or 
recall of familiar input. This model extends previous work on cholinergic 
modulation of the piriform cortex (Hasselmo, 1993; Hasselmo, 1994). This model 
depends upon a comparison in region CA1 between self-organized input from 
entorhinal cortex and recall of patterns of activity associated with CA3 input. When 
novel afferent input is presented, the inputs to CA1 do not match, and cholinergic 
modulation remains high, allowing storage of a new association. For familiar input, 
the match between input patterns suppresses modulation, allowing recall dynamics 
dominated by input from CA3. 
Michael Recce and Neil Burgess, from England, presented their work on "Using 
phase coding and wave packets to represent places." They are attempting to model 
the spatial behavior of rats in terms of the firing of single cells in the hippocampus. 
A reinforcement signal enables a set of "goal cells" to learn a population vector 
encoding the direction of the rat from the goal. This is achieved by exploiting the 
apparent phase-coding of place cell firing, and the presence of head-direction cells. 
The model shows rapid latent-learning and robust navigation to previously 
encountered goal locations (Burgess, O'Keefe, & Recce, 1993; Burgess, Recce, & 
O'Keefe, 1994). Spatial trajectories and cell firing characteristics compare well with 
experimental data. 
Richard Granger, of U.C. Irvine, was originally scheduled to talk on "Distinct 
biology and computation of entorhinal, dentate, CA3 and CA1 ." Granger and 
colleagues have noted that synaptic changes in each component of the hippocampus 
(i.e., DG, CA3 and CA1) exhibit different time courses, specificities, and 
reversibility. As such, they suggest that subtypes of memory operate serially, in an 
What Does the Hippocampus Compute?: A Precis of the 1993 NIPS Workshop 1175 
"assembly line" of specialized functions, each of which adds a unique aspect to the 
processing of memories (Granger et al, 1994). 
In other talks, Bruce McNaughton of the University of Arizona discussed models of 
spatial navigation (McNaughton et al, 1991) and William Levy from the University 
of Virginia presented a theory of how sparse recurrence of CA3 and several other, 
less direct feedback systems, leads to an ability to learn and compress sequences 
(Levy, 1989). Mathew Shapiro, of McGill University, had been scheduled to talk on 
computing locations and trajectories with simulated hippocampal place fields. 
References 
Burgess N, O'Keefe J & Recce M (1993) Using hippocampal "place cells" for 
navigation, exploiting phase coding, in: Hanson S J, Giles C L & Cowan J D 
(eds.) Advances in Neural Information Processing Systems 5. San Mateo, CA: 
Morgan Kaufmann. 
Burgess N, Recce M and O'Keefe J (1994) A model of hippocampal function, 
Neural Networks, Special Issue on Neurodynamics and Behavior, to be 
published. 
Gluck, M. and Granger, R. (1993). Computational models of the neural bases of 
learning and memory. Annual Review of Neuroscience. 16, 667-706. 
Gluck, M., & Myers, C. (1993). Hippocampal mediation of stimulus 
representation: A computational theory. Hippocampus, 3, 491-516. 
Granger, R., Whitson, J., Larson, J. and Lynch, G. (1994). Non-Hebbian 
properties of LTP enable high-capacity encoding of temporal sequences. Proc. 
Nat'l. Acad. Sci., (in press). 
Hasselmo, M.E. (1993) Acetylcholine and learning in a cortical associative 
memory. Neural Computation 5, 32-44. 
Hasselmo, M.E. (1994) Runaway synaptic modification in models of cortex: 
Implications for Alzheimer's disease. Neural Networks, in press. 
Levy, W. B (1989) A computational approach to hippocampal function. In: 
Computational Models of Learning in Simple Neural Systems. (R.D. Hawkins 
and G.H. Bower, Eds.), New York: Academic Press, pp. 243-305. 
McClelland, J. L. (1994/in press). The organization of memory: A parallel 
distributed processing perspective. Revue Neurologique, Masson, Paris 
McNaughton, B., Chen, L., & Markus, E. (1991). "Dead reckoning", landmark 
learning, and the sense of direction: A neurophysiological and computational 
hypothesis. Journal of Cognitive Neuroscience, 3(2), 190-202. 
