Autonomous emergence of connectivity assemblies via spike triplet interactions

  • Non-random connectivity can emerge without structured external input driven by activity-dependent mechanisms of synaptic plasticity based on precise spiking patterns. Here we analyze the emergence of global structures in recurrent networks based on a triplet model of spike timing dependent plasticity (STDP) which depends on the interactions of three precisely-timed spikes and can describe plasticity experiments with varying spike frequency better than the classical pair-based STDP rule. We derive synaptic changes arising from correlations up to third-order and describe them as the sum of structural motifs which determine how any spike in the network influences a given synaptic connection through possible connectivity paths. This motif expansion framework reveals novel structural motifs under the triplet STDP rule, which support the formation of bidirectional connections and ultimately the spontaneous emergence of global network structure in the form of self-connected groups of neurons, or assemblies. We propose that under triplet STDP assembly structure can emerge without the need for externally patterned inputs or assuming a symmetric pair-based STDP rule common in previous studies. The emergence of non-random network structure under triplet STDP occurs through internally-generated higher-order correlations, which are ubiquitous in natural stimuli and neuronal spiking activity, and important for coding. We further demonstrate how neuromodulatory mechanisms that modulate the shape of the triplet STDP rule or the synaptic transmission function differentially promote structural motifs underlying the emergence of assemblies, and quantify the differences using graph theoretic measures.
Author:Lisandro Montangie, Christoph Miehl, Julijana Gjorgjieva
Pubmed Id:
Parent Title (English):PLoS Computational Biology
Publisher:Public Library of Science
Place of publication:San Francisco, Calif.
Contributor(s):Brent Doiron
Document Type:Article
Year of Completion:2020
Date of first Publication:2020/05/08
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2020/05/25
Tag:Action potentials; Clustering coefficients; Fourier analysis; Network motifs; Neural networks; Neuronal plasticity; Neurons; Synaptic plasticity
Issue:(5): e1007835
Page Number:44
First Page:1
Last Page:44
Copyright: © 2020 Montangie et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Institutes:Wissenschaftliche Zentren und koordinierte Programme / Interdisziplinäres Zentrum für Neurowissenschaften Frankfurt (IZNF)
Angeschlossene und kooperierende Institutionen / MPI für Hirnforschung
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Licence (German):License LogoCreative Commons - Namensnennung 4.0