ArticlePLoS computational biology2020
Autonomous emergence of connectivity assemblies via spike triplet interactions.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Homeostatic Plasticity Enables Stable yet Tunable Neuronal Assemblies.bioRxiv : the preprint server for biology · 2026Article
- Bioinspired high-order in-sensor spatiotemporal enhancement in van der Waals optoelectronic neuromorphic electronics.Nature communications · 2025Article
- Predictive learning rules generate a cortical-like replay of probabilistic sensory experiences.eLife · 2025Article
- Learning what matters: Synaptic plasticity with invariance to second-order input correlations.PLoS computational biology · 2024Article
- Mean-Field Approximations With Adaptive Coupling for Networks With Spike-Timing-Dependent Plasticity.Neural computation · 2023Article
- Review
- Purely STDP-based assembly dynamics: Stability, learning, overlaps, drift and aging.PLoS computational biology · 2023Article
- Adaptive control of synaptic plasticity integrates micro- and macroscopic network function.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2023Review
- Stability and learning in excitatory synapses by nonlinear inhibitory plasticity.PLoS computational biology · 2022Article
- Drifting assemblies for persistent memory: Neuron transitions and unsupervised compensation.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Memories in a network with excitatory and inhibitory plasticity are encoded in the spiking irregularity.PLoS computational biology · 2021Article
- Article
- From synapse to network: models of information storage and retrieval in neural circuits.Current opinion in neurobiology · 2021Review
- Balanced networks under spike-time dependent plasticity.PLoS computational biology · 2021Article
- Homeostatic mechanisms regulate distinct aspects of cortical circuit dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
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3 authors.
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Abstract
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.
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