ArticlePLoS computational biology2019
From space to time: Spatial inhomogeneities lead to the emergence of spatiotemporal sequences in spiking neuronal networks.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Spatial and network principles behind neural generation of locomotion.Nature communications · 2026Article
- Controlling Spatio-Temporal Sequences of Neural Activity by Local Synaptic Changes.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Correction: From space to time: Spatial inhomogeneities lead to the emergence of spatiotemporal sequences in spiking neuronal networks.PLoS computational biology · 2025Article
- Competition and cooperation of assembly sequences in recurrent neural networks.PLoS computational biology · 2025Article
- Navigation and the efficiency of spatial coding: insights from closed-loop simulations.Brain structure & function · 2024Article
- Modeling the three-dimensional connectivity of in vitro cortical ensembles coupled to Micro-Electrode Arrays.PLoS computational biology · 2023Article
- Article
- Brian2Loihi: An emulator for the neuromorphic chip Loihi using the spiking neural network simulator Brian.Frontiers in neuroinformatics · 2022Article
- Spatiotemporal dynamics of self-assembled structures in enzymatically induced agonistic and antagonistic conditions.Chemical science · 2021Article
- Spatially structured inhibition defined by polarized parvalbumin interneuron axons promotes head direction tuning.Science advances · 2021Article
- Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons.PLoS computational biology · 2021Article
- Robust Trajectory Generation for Robotic Control on the Neuromorphic Research Chip Loihi.Frontiers in neurorobotics · 2020Article
- Learning spatiotemporal signals using a recurrent spiking network that discretizes time.PLoS computational biology · 2020Article
- Article
- Probabilistic associative learning suffices for learning the temporal structure of multiple sequences.PloS one · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatio-temporal sequences of neuronal activity are observed in many brain regions in a variety of tasks and are thought to form the basis of meaningful behavior. However, mechanisms by which a neuronal network can generate spatio-temporal activity sequences have remained obscure. Existing models are biologically untenable because they either require manual embedding of a feedforward network within a random network or supervised learning to train the connectivity of a network to generate sequences. Here, we propose a biologically plausible, generative rule to create spatio-temporal activity sequences in a network of spiking neurons with distance-dependent connectivity. We show that the emergence of spatio-temporal activity sequences requires: (1) individual neurons preferentially project a small fraction of their axons in a specific direction, and (2) the preferential projection direction of neighboring neurons is similar. Thus, an anisotropic but correlated connectivity of neuron groups suffices to generate spatio-temporal activity sequences in an otherwise random neuronal network model.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.