CoChat
Sign Up
Cited by 545Memory and Neural MechanismsNeuroscience and Neuropharmacology ResearchNeural dynamics and brain function

The DOI matches its Crossref record; 184 of the 191 references it cites were found in a registry or an index, and 1 are not checked yet.

The mechanisms for pattern completion and pattern separation in the hippocampus

Open access
Rolls, Edmund T.·2013·Frontiers in Systems Neuroscience
DOI 10.3389/fnsys.2013.00074PMC PMC3812781
Open this paper in CoChat — free accountOpen PDF

Abstract

The mechanisms for pattern completion and pattern separation are described in the context of a theory of hippocampal function in which the hippocampal CA3 system operates as a single attractor or autoassociation network to enable rapid, one-trial, associations between any spatial location (place in rodents, or spatial view in primates) and an object or reward, and to provide for completion of the whole memory during recall from any part. The factors important in the pattern completion in CA3 together with a large number of independent memories stored in CA3 include a sparse distributed representation which is enhanced by the graded firing rates of CA3 neurons, representations that are independent due to the randomizing effect of the mossy fibers, heterosynaptic long-term depression as well as long-term potentiation in the recurrent collateral synapses, and diluted connectivity to minimize the number of multiple synapses between any pair of CA3 neurons which otherwise distort the basins of attraction. Recall of information from CA3 is implemented by the entorhinal cortex perforant path synapses to CA3 cells, which in acting as a pattern associator allow some pattern generalization. Pattern separation is performed in the dentate granule cells using competitive learning to convert grid-like entorhinal cortex firing to place-like fields. Pattern separation in CA3, which is important for completion of any one of the stored patterns from a fragment, is provided for by the randomizing effect of the mossy fiber synapses to which neurogenesis may contribute, by the large number of dentate granule cells each with a sparse representation, and by the sparse independent representations in CA3. Recall to the neocortex is achieved by a reverse hierarchical series of pattern association networks implemented by the hippocampo-cortical backprojections, each one of which performs some pattern generalization, to retrieve a complete pattern of cortical firing in higher-order cortical areas.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3389/fnsys.2013.00074

Cites

184 of 185 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Forward connections (solid lines) from areas of cerebral association neocortex via the parahippocampal gyrus and perirhinal cortex, and entorhinal cortex, to the hippocampus; and backprojections (dashed lines) via the hippocampal CA1 pyramidal cells, subiculum, and parahippocampal gyrus to the neocortex. There is great convergence in the forward connections down to the single network implemented in the CA3 pyramidal cells; and great divergence again in the backprojections. Left: block diagram. Right: more detailed representation of some of the principal excitatory neurons in the pathways. Abbreviations: D, deep pyramidal cells; DG, dentate granule cells; F, forward inputs to areas of the association cortex from preceding cortical areas in the hierarchy; mf, mossy fibres; PHG, parahippocampal gyrus and perirhinal cortex; pp. perforant path; rc, recurrent collateral of the CA3 hippocampal pyramidal cells; S, superficial pyramidal cells; 2, pyramidal cells in layer 2 of the entorhinal cortex; 3, pyramidal cells in layer 3 of the entorhinal cortex. The thick lines above the cell bodies represent the dendrites.
Figure 1 Forward connections (solid lines) from areas of cerebral association neocortex via the parahippocampal gyrus and perirhinal cortex, and entorhinal cortex, to the hippocampus; and backprojections (dashed lines) via the hippocampal CA1 pyramidal cells, subiculum, and parahippocampal gyrus to the neocortex. There is great convergence in the forward connections down to the single network implemented in the CA3 pyramidal cells; and great divergence again in the backprojections. Left: block diagram. Right: more detailed representation of some of the principal excitatory neurons in the pathways. Abbreviations: D, deep pyramidal cells; DG, dentate granule cells; F, forward inputs to areas of the association cortex from preceding cortical areas in the hierarchy; mf, mossy fibres; PHG, parahippocampal gyrus and perirhinal cortex; pp. perforant path; rc, recurrent collateral of the CA3 hippocampal pyramidal cells; S, superficial pyramidal cells; 2, pyramidal cells in layer 2 of the entorhinal cortex; 3, pyramidal cells in layer 3 of the entorhinal cortex. The thick lines above the cell bodies represent the dendrites.CC BY · PubMed Central
Figure 2 The numbers of connections from three different sources onto each CA3 cell from three different sources in the rat. (After Treves and Rolls, 1992; Rolls and Treves, 1998).
Figure 2 The numbers of connections from three different sources onto each CA3 cell from three different sources in the rat. (After Treves and Rolls, 1992; Rolls and Treves, 1998).CC BY · PubMed Central
Figure 3 The types of firing patterns stored in continuous attractor networks are illustrated for the patterns present on neurons 1–1000 for Memory 1 (when the firing is that produced when the spatial state represented is that for location 300), and for Memory 2 (when the firing is that produced when the spatial state represented is that for location 500). The continuous nature of the spatial representation results from the fact that each neuron has a Gaussian firing rate that peaks at its optimal location. This particular mixed network also contains discrete representations that consist of discrete subsets of active binary firing rate neurons in the range 1001–1500. The firing of these latter neurons can be thought of as representing the discrete events that occur at the location. Continuous attractor networks by definition contain only continuous representations, but this particular network can store mixed continuous and discrete representations, and is illustrated to show the difference of the firing patterns normally stored in separate continuous attractor and discrete attractor networks. For this particular mixed network, during learning, Memory 1 is stored in the synaptic weights (A), then Memory 2 (B), etc., and each memory contains part that is continuously distributed to represent physical space, and part that represents a discrete event or object.
Figure 3 The types of firing patterns stored in continuous attractor networks are illustrated for the patterns present on neurons 1–1000 for Memory 1 (when the firing is that produced when the spatial state represented is that for location 300), and for Memory 2 (when the firing is that produced when the spatial state represented is that for location 500). The continuous nature of the spatial representation results from the fact that each neuron has a Gaussian firing rate that peaks at its optimal location. This particular mixed network also contains discrete representations that consist of discrete subsets of active binary firing rate neurons in the range 1001–1500. The firing of these latter neurons can be thought of as representing the discrete events that occur at the location. Continuous attractor networks by definition contain only continuous representations, but this particular network can store mixed continuous and discrete representations, and is illustrated to show the difference of the firing patterns normally stored in separate continuous attractor and discrete attractor networks. For this particular mixed network, during learning, Memory 1 is stored in the synaptic weights (A), then Memory 2 (B), etc., and each memory contains part that is continuously distributed to represent physical space, and part that represents a discrete event or object.CC BY · PubMed Central
Figure 4 Simulation of competitive learning in the dentate gyrus to produce place cells from the entorhinal cortex grid cell inputs. (A and B) Firing rate profiles of two entorhinal cortex (EC) grid cells with frequencies of 4 and 7 cycles. (C and D) Firing rate profiles of two dentate gyrus (DG) cells with no training using competitive learning. (E and F) Firing rate profiles of two dentate gyrus (DG) cells trained using competitive learning. (After Rolls et al., 2006).
Figure 4 Simulation of competitive learning in the dentate gyrus to produce place cells from the entorhinal cortex grid cell inputs. (A and B) Firing rate profiles of two entorhinal cortex (EC) grid cells with frequencies of 4 and 7 cycles. (C and D) Firing rate profiles of two dentate gyrus (DG) cells with no training using competitive learning. (E and F) Firing rate profiles of two dentate gyrus (DG) cells trained using competitive learning. (After Rolls et al., 2006).CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 191 references

Research works printed but not linked: 7

  1. Amaral D. G. Price J. L. Pitkanen A. Carmichael S. T. (1992). Anatomical organization of the primate amygdaloid complex, in The Amygdala, ed Aggleton J. P. (New York, NY: Wiley-Liss), 1– 66
  2. Morris R. G. M. (1989). Does synaptic plasticity play a role in information storage in the vertebrate brain? in Parallel Distributed Processing: Implications for Psychology and Neurobiology, ed Morris R. G. M. (Oxford: Oxford University Press), 248– 285
  3. Rolls E. T. (1987). Information representation, processing and storage in the brain: analysis at the single neuron level, in The Neural and Molecular Bases of Learning, eds Changeux J.-P. Konishi M. (Chichester: Wiley), 503– 540
  4. Rolls E. T. (1989a). Functions of neuronal networks in the hippocampus and cerebral cortex in memory, in Models of Brain Function, ed Cotterill R. M. J. (Cambridge: Cambridge University Press), 15– 33
  5. Schultz S. Panzeri S. Rolls E. T. Treves A. (2000). Chap. 14. Quantitive model analysis of a Schaffer collateral model, in Information Theory and the Brain, ed Hancock P. Foldiak P. Baddeley R. (Cambridge: Cambridge University Press), 257– 272
  6. Storm-Mathiesen J. Zimmer J. Ottersen O. P. (eds.). (1990). Understanding the Brain Through the Hippocampus. Oxford: Elsevier
  7. Witter M. P. Naber P. A. Van Haeften T. Machielsen W. C. Rombouts S. A. Barkhof F. (2000a). Cortico-hippocampal communication by way of parallel parahippocampal-subicular pathways. Hippocampus 10, 398– 410 10.1002/1098-1063(2000)10:4<398::AID-HIPO6>3.3.CO;2-B 10985279 · DOI

Cited by

Sorted by citations

Items 401–500 of 543

Related

See all

Metadata

Authors
Rolls, E. T.
Affiliations
University of Warwick (GB)
Corresponding author
Rolls, Edmund T.
ORCID
Rolls, Edmund T.
Venue
Frontiers in Systems Neuroscience
Publisher
Frontiers Media SA
Published
2013
Volume
7
Pages
74-74
ISSN
1662-5137 (ISSN-L)
DOI
10.3389/fnsys.2013.00074
PMC
PMC3812781
OpenAlex
W2016454630
Cited by
545
References
191
Citations by year
2026: 55 · 2025: 66 · 2024: 49 · 2023: 42 · 2022: 42 · 2021: 77 · 2020: 42 · 2019: 30 · 2018: 34 · 2017: 32 · 2016: 33 · 2015: 29 · 2014: 12 · 2013: 2
FWCI
2.15
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Systems Neuroscience (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Medical Research Council
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Keywords
pattern completion, pattern separation, hippocampus, CA3, attractor networks, autoassociation, sparse distributed representations, mossy fibers, long-term potentiation, dentate gyrus granule cells, entorhinal cortex, perforant path, place cells, grid cells, pattern generalization, neurogenesis, memory retrieval

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review