Elsevier

Neural Networks

Volume 18, Issue 9, November 2005, Pages 1191-1201
Neural Networks

2005 Special issue
Recall of memory sequences by interaction of the dentate and CA3: A revised model of the phase precession

https://doi.org/10.1016/j.neunet.2005.08.008Get rights and content

Abstract

Behavioral and electrophysiological evidence indicates that the hippocampus has a special role in the encoding and recall of memory sequences. Importantly, the hippocampal phase precession, a phenomenon recorded as a rat moves through place fields, can be interpreted as cued recall of the sequence of upcoming places. The phase precession can be recorded in all hippocampal regions, but the role of each region has been unclear. Here, we suggest how the dentate and CA3 regions can work together to learn sequences, recall sequences, and generate the phase precession. Our proposal is constrained by information regarding synaptic plasticity rules, network connectivity, timing delays and theta/gamma oscillations.

Section snippets

Behavioral evidence that the hippocampus is involved in memory of sequences

Sequences evolve over time and it should therefore not be surprising that special network and synaptic processes are needed to deal with the temporal issues posed by the storage and retrieval of sequences. There is now a substantial body of data indicating that the hippocampus is required to memorize the sequences of events that make up an ‘episode’. For instance, Honey and Good (Honey, Watt, & Good, 1998) trained rats on tone-light sequences and then measured the rat's orienting to changes in

Electrophysiological evidence for the involvement of the hippocampus in sequence replay during sleep

Several laboratories (Lee and Wilson, 2002, Nadasdy et al., 1999, Skaggs et al., 1996) have observed that sequences of CA1 cell firing that occurred during the awake theta state were seen again during the sharp waves of slow wave sleep (SWS). This ‘replay’ during SWS is about 20 times faster than the sequence observed during awake theta (Lee & Wilson, 2002). In contrast, similar replay during REM sleep appears to be in real-time and involves temporal segments on the time scale of minutes. This

Electrophysiological evidence for cued sequence recall during the awake theta state: properties of the ‘phase precession’

If memory sequences are stored in the hippocampus they should be accessible during the awake state in order to guide behavior. Consistent with this assumption, the phase precession (Fig. 2) of hippocampal place cells (O'Keefe & Recce, 1993) can be interpreted as cued sequence recall (Jensen and Lisman, 1996, Skaggs et al., 1996, Tsodyks et al., 1996). For instance, the recall of a stored sequence of positions along a track can be initiated by an environmental cue (the current position), as

Where is the phase precession generated?

The phase precession has been recorded in all hippocampal subfields. It remains possible that the phase precession is already present in the entorhinal cortex layer 2/3 cells and is simply passed on to the hippocampus. Now that methods are available to record with certainty from this class of cells (Brun et al., 2002), it will be important to test this possibility directly. However, given the generation of sharp wave replay activity in CA3 (see earlier comments about sharp waves) and the

Requirements for accurate sequence recall; the roles of autoassociation and heteroassociation

In early ideas about sequence recall, it was thought that learning occurred exclusively by increasing ‘heteroassociative’ weights, i.e. the weights of the synapses between groups of cells encoding sequential memories. The overall sequence is thus encoded by connecting cells that represent memory A to cells that represent memory B, which then connect to memory C, etc.; see Fig. 5 inset). The recall process can then be triggered by the presentation of A, which acts as a memory cue to evoke the

The dentate and CA3 networks are reciprocally connected

Several anatomical and electrophysiological observations provided the starting point for our thinking about the operations of the dentate and CA3 in the storage and recall of sequences. Dentate granule cells have axons called mossy fibers, which powerfully excite CA3 cells. What is less well appreciated is that there is also a pathway for flow of information back from CA3 to the dentate (reviewed in Lisman, 1999). CA3 axons, in addition to exciting other CA3 cells and providing Schaffer

A revised model: network delay determines why autoassociation occurs in CA3, whereas heteroassociation occurs in the dentate

The reciprocally connected dentate and CA3 networks could provide the kind of interaction between an autoassociative and a heteroassociative network needed for accurate sequence recall. A previous model (Lisman, 1999) proposed that the autoassociative step might occur first, and thus in the dentate, and that the heteroassociative step might therefore occur in CA3. The arguments for this assignment were not very strong, and our view of this matter has now changed to the reverse, in line with

The role of gamma in chaining: predicting the magnitude of the phase precession

As we emphasized previously, the recall of sequences is proposed to occur through a cue-initiated chaining process. In principle such chaining could be very rapid, with delays limited only by conduction delays, synaptic delays and postsynaptic integration time. Such delays are short and cumulatively in the range of several milliseconds. Indeed, such rapid chaining is thought to underlie the so-called ‘synfire’ chains in cortex and sharp waves in the hippocampus (Abeles et al., 2004, Buzsaki,

Concluding remarks

The current account of phase precession, based on the bidirectional interaction of the dentate and CA3, provides the first explanation of why phase precession should be seen in both these structures. It also assigns a function to the feedback connections from CA3 to dentate, for which no purpose has been previously proposed. It should be emphasized that this bidirectional interaction has not yet been simulated. Such simulation, including the roles of gamma, feedback delays and plasticity rules,

Acknowledgements

This work has been supported by grant 1 R01 NS50944-01 as part of the NSF/NIH Collaborative Research in Computational Neuroscience Program, and NIH grants 1 R01 MH61975 and 1 R01-NS-27337. We thank Sridhar Raghavachari and Rod Rinkus for reading the manuscript and we gratefully acknowledge the support of the W.M. Keck Foundation.

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