ArticleNetwork neuroscience (Cambridge, Mass.)2019
Topological exploration of artificial neuronal network dynamics.
Article in Network neuroscience (Cambridge, Mass.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Quantifying spike train synchrony and directionality: Measures and Applications.Biological cybernetics · 2026Review
- Topological Neuroscience: Linking Circuits to Function.Annual review of neuroscience · 2025Review
- Topological features of spike trains in recurrent spiking neural networks that are trained to generate spatiotemporal patterns.Frontiers in computational neuroscience · 2024Article
- Living on the edge: network neuroscience beyond nodes.Trends in cognitive sciences · 2023Review
- Topological insights into the neural basis of flexible behavior.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Topological data analysis of the firings of a network of stochastic spiking neurons.Frontiers in neural circuits · 2023Article
- Geometry of spiking patterns in early visual cortex: a topological data analytic approach.Journal of the Royal Society, Interface · 2022Article
- From calcium imaging to graph topology.Network neuroscience (Cambridge, Mass.) · 2022Review
- Finding the neural correlates of collaboration using a three-person fMRI hyperscanning paradigm.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Editorial: Topological Neuroscience.Network neuroscience (Cambridge, Mass.) · 2019Article
- Topological exploration of artificial neuronal network dynamics.Network neuroscience (Cambridge, Mass.) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the paramount challenges in neuroscience is to understand the dynamics of individual neurons and how they give rise to network dynamics when interconnected. Historically, researchers have resorted to graph theory, statistics, and statistical mechanics to describe the spatiotemporal structure of such network dynamics. Our novel approach employs tools from algebraic topology to characterize the global properties of network structure and dynamics. We propose a method based on persistent homology to automatically classify network dynamics using topological features of spaces built from various spike train distances. We investigate the efficacy of our method by simulating activity in three small artificial neural networks with different sets of parameters, giving rise to dynamics that can be classified into four regimes. We then compute three measures of spike train similarity and use persistent homology to extract topological features that are fundamentally different from those used in traditional methods. Our results show that a machine learning classifier trained on these features can accurately predict the regime of the network it was trained on and also generalize to other networks that were not presented during training. Moreover, we demonstrate that using features extracted from multiple spike train distances systematically improves the performance of our method.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.