TY  - JOUR
A1  - Montangie, Lisandro
A1  - Miehl, Christoph
A1  - Gjorgjieva, Julijana
T1  - Autonomous emergence of connectivity assemblies via spike triplet interactions
T2  - PLoS Computational Biology
N2  - 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.
KW  - Neurons
KW  - Action potentials
KW  - Network motifs
KW  - Synaptic plasticity
KW  - Neuronal plasticity
KW  - Neural networks
KW  - Clustering coefficients
KW  - Fourier analysis
Y1  - 2020
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/53533
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-535335
SN  - 1553-7358
SN  - 1553-734X
N1  - 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.
VL  - 16
IS  - (5): e1007835
SP  - 1
EP  - 44
PB  - Public Library of Science
CY  - San Francisco, Calif.
ER  -