Long Term Memory and the Densest K-Subgraph Problem

Robert Legenstein, Wolfgang Maass, Christos H. Papapdimitriou, Santosh S. Vempala

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

Abstract

In a recent experiment [9], a cell in the human medial temporal lobe (MTL) encoding one sensory stimulus starts to also respond to a second stimulus following a combined experience associating the two. We develop a theoretical model predicting that an assembly of cells with exceptionally high synaptic intraconnectivity can emerge, in response to a particular sensory experience, to
encode and abstract that experience. We also show that two such assemblies are modified to increase their intersection after a sensory event that associates the two corresponding stimuli. The main technical tools employed are random graph theory, and Bernoulli approximations. Assembly creation must overcome a computational challenge akin to the Densest K-Subgraph problem, namely selecting, from a large population of randomly and sparsely interconnected cells, a subset with exceptionally high density of interconnections. We identify three mechanisms that help achieve this feat in our model: (1) a simple two-stage randomized algorithm, and (2) the “triangle completion bias” in synaptic connectivity [14] and a “birthday paradox”, while (3) the strength of these connections is enhanced through Hebbian plasticity.
Originalspracheenglisch
Titel9th Innovations in Theoretical Computer Science Conference (ITCS 2018)
Herausgeber (Verlag)Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH
Seiten57:1–57:15
Band94
DOIs
PublikationsstatusVeröffentlicht - 2018

Publikationsreihe

NameLIPIcs-Leibniz International Proceedings in Informatics
Herausgeber (Verlag)Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik

Fingerprint

Data storage equipment
Graph theory
Plasticity
Experiments

Schlagwörter

    Dies zitieren

    Legenstein, R., Maass, W., Papapdimitriou, C. H., & Vempala, S. S. (2018). Long Term Memory and the Densest K-Subgraph Problem. in 9th Innovations in Theoretical Computer Science Conference (ITCS 2018) (Band 94, S. 57:1–57:15). [57] (LIPIcs-Leibniz International Proceedings in Informatics ). Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH. https://doi.org/10.4230/LIPIcs.ITCS.2018.57

    Long Term Memory and the Densest K-Subgraph Problem. / Legenstein, Robert; Maass, Wolfgang; Papapdimitriou, Christos H. ; Vempala, Santosh S. .

    9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Band 94 Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH, 2018. S. 57:1–57:15 57 (LIPIcs-Leibniz International Proceedings in Informatics ).

    Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

    Legenstein, R, Maass, W, Papapdimitriou, CH & Vempala, SS 2018, Long Term Memory and the Densest K-Subgraph Problem. in 9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Bd. 94, 57, LIPIcs-Leibniz International Proceedings in Informatics , Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH, S. 57:1–57:15. https://doi.org/10.4230/LIPIcs.ITCS.2018.57
    Legenstein R, Maass W, Papapdimitriou CH, Vempala SS. Long Term Memory and the Densest K-Subgraph Problem. in 9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Band 94. Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH. 2018. S. 57:1–57:15. 57. (LIPIcs-Leibniz International Proceedings in Informatics ). https://doi.org/10.4230/LIPIcs.ITCS.2018.57
    Legenstein, Robert ; Maass, Wolfgang ; Papapdimitriou, Christos H. ; Vempala, Santosh S. . / Long Term Memory and the Densest K-Subgraph Problem. 9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Band 94 Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH, 2018. S. 57:1–57:15 (LIPIcs-Leibniz International Proceedings in Informatics ).
    @inproceedings{c975e0b87bc24bb9b11493f52d50489d,
    title = "Long Term Memory and the Densest K-Subgraph Problem",
    abstract = "In a recent experiment [9], a cell in the human medial temporal lobe (MTL) encoding one sensory stimulus starts to also respond to a second stimulus following a combined experience associating the two. We develop a theoretical model predicting that an assembly of cells with exceptionally high synaptic intraconnectivity can emerge, in response to a particular sensory experience, toencode and abstract that experience. We also show that two such assemblies are modified to increase their intersection after a sensory event that associates the two corresponding stimuli. The main technical tools employed are random graph theory, and Bernoulli approximations. Assembly creation must overcome a computational challenge akin to the Densest K-Subgraph problem, namely selecting, from a large population of randomly and sparsely interconnected cells, a subset with exceptionally high density of interconnections. We identify three mechanisms that help achieve this feat in our model: (1) a simple two-stage randomized algorithm, and (2) the “triangle completion bias” in synaptic connectivity [14] and a “birthday paradox”, while (3) the strength of these connections is enhanced through Hebbian plasticity.",
    keywords = "Brain computation , long term memory, assemblies, association",
    author = "Robert Legenstein and Wolfgang Maass and Papapdimitriou, {Christos H.} and Vempala, {Santosh S.}",
    year = "2018",
    doi = "10.4230/LIPIcs.ITCS.2018.57",
    language = "English",
    volume = "94",
    series = "LIPIcs-Leibniz International Proceedings in Informatics",
    publisher = "Schloss Dagstuhl - Leibniz-Zentrum f{\"u}r Informatik GmbH",
    pages = "57:1–57:15",
    booktitle = "9th Innovations in Theoretical Computer Science Conference (ITCS 2018)",
    address = "Germany",

    }

    TY - GEN

    T1 - Long Term Memory and the Densest K-Subgraph Problem

    AU - Legenstein, Robert

    AU - Maass, Wolfgang

    AU - Papapdimitriou, Christos H.

    AU - Vempala, Santosh S.

    PY - 2018

    Y1 - 2018

    N2 - In a recent experiment [9], a cell in the human medial temporal lobe (MTL) encoding one sensory stimulus starts to also respond to a second stimulus following a combined experience associating the two. We develop a theoretical model predicting that an assembly of cells with exceptionally high synaptic intraconnectivity can emerge, in response to a particular sensory experience, toencode and abstract that experience. We also show that two such assemblies are modified to increase their intersection after a sensory event that associates the two corresponding stimuli. The main technical tools employed are random graph theory, and Bernoulli approximations. Assembly creation must overcome a computational challenge akin to the Densest K-Subgraph problem, namely selecting, from a large population of randomly and sparsely interconnected cells, a subset with exceptionally high density of interconnections. We identify three mechanisms that help achieve this feat in our model: (1) a simple two-stage randomized algorithm, and (2) the “triangle completion bias” in synaptic connectivity [14] and a “birthday paradox”, while (3) the strength of these connections is enhanced through Hebbian plasticity.

    AB - In a recent experiment [9], a cell in the human medial temporal lobe (MTL) encoding one sensory stimulus starts to also respond to a second stimulus following a combined experience associating the two. We develop a theoretical model predicting that an assembly of cells with exceptionally high synaptic intraconnectivity can emerge, in response to a particular sensory experience, toencode and abstract that experience. We also show that two such assemblies are modified to increase their intersection after a sensory event that associates the two corresponding stimuli. The main technical tools employed are random graph theory, and Bernoulli approximations. Assembly creation must overcome a computational challenge akin to the Densest K-Subgraph problem, namely selecting, from a large population of randomly and sparsely interconnected cells, a subset with exceptionally high density of interconnections. We identify three mechanisms that help achieve this feat in our model: (1) a simple two-stage randomized algorithm, and (2) the “triangle completion bias” in synaptic connectivity [14] and a “birthday paradox”, while (3) the strength of these connections is enhanced through Hebbian plasticity.

    KW - Brain computation

    KW - long term memory

    KW - assemblies

    KW - association

    U2 - 10.4230/LIPIcs.ITCS.2018.57

    DO - 10.4230/LIPIcs.ITCS.2018.57

    M3 - Conference contribution

    VL - 94

    T3 - LIPIcs-Leibniz International Proceedings in Informatics

    SP - 57:1–57:15

    BT - 9th Innovations in Theoretical Computer Science Conference (ITCS 2018)

    PB - Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

    ER -