Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-10946
For citation please use:
Main Title: Memory replay in balanced recurrent networks
Author(s): Chenkov, Nikolay
Sprekeler, Henning
Kempter, Richard
Type: Article
URI: https://depositonce.tu-berlin.de/handle/11303/12072
http://dx.doi.org/10.14279/depositonce-10946
License: https://creativecommons.org/licenses/by/4.0/
Abstract: Complex patterns of neural activity appear during up-states in the neocortex and sharp waves in the hippocampus, including sequences that resemble those during prior behavioral experience. The mechanisms underlying this replay are not well understood. How can small synaptic footprints engraved by experience control large-scale network activity during memory retrieval and consolidation? We hypothesize that sparse and weak synaptic connectivity between Hebbian assemblies are boosted by pre-existing recurrent connectivity within them. To investigate this idea, we connect sequences of assemblies in randomly connected spiking neuronal networks with a balance of excitation and inhibition. Simulations and analytical calculations show that recurrent connections within assemblies allow for a fast amplification of signals that indeed reduces the required number of inter-assembly connections. Replay can be evoked by small sensory-like cues or emerge spontaneously by activity fluctuations. Global—potentially neuromodulatory—alterations of neuronal excitability can switch between network states that favor retrieval and consolidation.
Subject(s): network
neuron
neural network
memory
learning
synaptic plasticity
Issue Date: 30-Jan-2017
Date Available: 26-Nov-2020
Language Code: en
DDC Class: 610 Medizin und Gesundheit
Sponsor/Funder: BMBF, 01GQ1001A, Verbundprojekt: Bernstein Zentrum für Computational Neuroscience, Berlin - "Präzision und Variabilität" - Teilprojekt A2, A3, A4, A8, B6, Zentralprojekt und Professur
BMBF, 01GQ0972, Verbundprojekt: Bernstein Fokus Lernen - Zustandsabhängigkeit des Lernens, TP 2 und 3
BMBF, 01GQ1201, Lernen und Gedächtnis in balancierten Systemen
DFG, 103586207, GRK 1589: Verarbeitung sensorischer Informationen in neuronalen Systemen
Journal Title: PLOS Computational Biology
Publisher: Public Library of Science (PLoS)
Volume: 13
Issue: 1
Article Number: e1005359
Publisher DOI: 10.1371/journal.pcbi.1005359
EISSN: 1553-7358
ISSN: 1553-734X
TU Affiliation(s): Fak. 4 Elektrotechnik und Informatik » Inst. Softwaretechnik und Theoretische Informatik » FG Modellierung kognitiver Prozesse
Appears in Collections:Technische Universität Berlin » Publications

Files in This Item:
journal.pcbi.1005359.pdf
Format: Adobe PDF | Size: 6.16 MB
DownloadShow Preview
Thumbnail

Item Export Bar

This item is licensed under a Creative Commons License Creative Commons