ReviewTracking the flow of hippocampal computation: Pattern separation, pattern completion, and attractor dynamics
Introduction
Although most computational theories of the mnemonic functions of the hippocampus have focused on the CA3 and dentate gyrus (DG) regions, the large majority of single-unit studies of the hippocampus have been recordings from the CA1 region. This emphasis on CA1 is understandable for both functional and practical reasons. CA1 is the region that primarily transmits the output of DG/CA3 processing to the rest of the cerebrum (Witter & Amaral, 2004). Thus, it can serve as a functional readout of the information provided by the hippocampus to other brain areas that are involved in hippocampus-dependent cognition and behavior. Moreover, CA1 is the first cell layer encountered in the rodent hippocampus when an electrode is advanced from the dorsal surface of the brain, and it is by far the easiest layer of the hippocampus to record large ensembles of well-isolated units.
Although recordings from CA1 can illuminate the types of information and representations being sent to other brain regions, these recordings in isolation can inform little about the nature of the information processing that occurs within the hippocampal circuitry. For example, one may know that CA1 place cells form independent spatial representations of two distinct environments (Bostock, Muller, & Kubie, 1991). However, this knowledge alone tells us little about the computational processing that creates these two representations, and whether that processing occurs within CA1, in upstream hippocampal regions like DG or CA3, or even in regions entirely afferent to the hippocampus. To understand the neural computations of the hippocampus, it is necessary to understand the information represented in hippocampal inputs, in its internal processing stages, and in its outputs, as well as the transformations that occur as information is processed through these circuits.
This article will review a series of studies from our lab over the past decade in which we recorded the activity of hippocampal input regions and output regions, as well as the intrahippocampal processing in the DG and CA3 regions. To induce controlled, parametric changes to the animal’s environment, we used a “double rotation” manipulation, in which the local reference frame of the behavioral track was placed in varying degrees of conflict with the global reference frame of the laboratory environment (Knierim, 2002, Shapiro et al., 1997). We investigated how neural populations in the hippocampal system responded to these alterations in order to deduce the neural representations and computations associated with the different regions. In particular, we addressed the questions of whether we can interpret (1) DG responses as evidence for its proposed role of performing pattern separation on its inputs and (2) CA3 responses as evidence for its proposed role of performing pattern completion (or the related concepts of error correction and generalization) on its inputs. We begin with a brief history of the computational theories of pattern separation and completion.
Section snippets
Classic theories of DG function: Pattern separation in DG vs. pattern completion in CA3
The most prominent theory of DG function is the pattern separation theory (Kesner et al., 2000, McNaughton and Morris, 1987, McNaughton and Nadel, 1990, Rolls and Treves, 1998, Yassa and Stark, 2011), which originated in David Marr’s theory of the cerebellum (Marr, 1969). Marr proposed that the cerebellar granule layer created a very sparse representation of incoming sensorimotor input by an expansion recoding strategy; that is, highly overlapping representations encoded by populations of
Double rotation experiments
With these considerations in mind, we will now review a series of neurophysiological recording experiments from hippocampal afferent regions (the MEC, LEC, and anterior thalamus), intrahippocampal regions (DG and CA3), and the hippocampal output layer (CA1). In these experiments, rats ran clockwise on a circular track (Knierim, 2002) (Fig. 3). The track was divided into 4 quadrants, each with a distinct visual and tactile texture. The track was centered in a room with a circular, black curtain
Final comments and caveats
With this review we hope to have demonstrated how a series of experimental studies from our laboratory over the past decade have provided direct, physiological evidence in favor of classic models of hippocampal computation and its relationship to memory. We have taken the approach from the computational literature that the concepts of pattern separation and pattern completion can only be studied directly in terms of input–output transformations of neural representations (McClelland and Goddard,
Acknowledgments
We thank I. Lee, D. Yoganarasimha, F. Savelli, and G. Rao for help in data collection for some of the figures shown in this manuscript. The experiments reviewed here were supported by Public Health Service grants NS039456 and MH094146 and by the Johns Hopkins University Brain Sciences Institute. The funding agency had no role in the design, data collection, analysis, or writing of the paper.
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