ArticlePLoS computational biology2018
Emergence of spontaneous assembly activity in developing neural networks without afferent input.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Predictive learning rules generate a cortical-like replay of probabilistic sensory experiences.eLife · 2025Article
- Embedding stochastic dynamics of the environment in spontaneous activity by prediction-based plasticity.eLife · 2025Article
- Embryonically active piriform cortex neurons promote intracortical recurrent connectivity during development.Neuron · 2024Article
- Piezo1-mediated spontaneous calcium transients in satellite glia impact dorsal root ganglia development.PLoS biology · 2023Article
- Purely STDP-based assembly dynamics: Stability, learning, overlaps, drift and aging.PLoS computational biology · 2023Article
- Modeling cholinergic retinal waves: starburst amacrine cells shape wave generation, propagation, and direction bias.Scientific reports · 2023Article
- Drifting assemblies for persistent memory: Neuron transitions and unsupervised compensation.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Model-based decoupling of evoked and spontaneous neural activity in calcium imaging data.PLoS computational biology · 2020Article
- Autonomous emergence of connectivity assemblies via spike triplet interactions.PLoS computational biology · 2020Article
- Topological reinforcement as a principle of modularity emergence in brain networks.Network neuroscience (Cambridge, Mass.) · 2019Article
- Theoretical Models of Neural Development.iScience · 2018Review
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3 authors.
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Abstract
Spontaneous activity is a fundamental characteristic of the developing nervous system. Intriguingly, it often takes the form of multiple structured assemblies of neurons. Such assemblies can form even in the absence of afferent input, for instance in the zebrafish optic tectum after bilateral enucleation early in life. While the development of neural assemblies based on structured afferent input has been theoretically well-studied, it is less clear how they could arise in systems without afferent input. Here we show that a recurrent network of binary threshold neurons with initially random weights can form neural assemblies based on a simple Hebbian learning rule. Over development the network becomes increasingly modular while being driven by initially unstructured spontaneous activity, leading to the emergence of neural assemblies. Surprisingly, the set of neurons making up each assembly then continues to evolve, despite the number of assemblies remaining roughly constant. In the mature network assembly activity builds over several timesteps before the activation of the full assembly, as recently observed in calcium-imaging experiments. Our results show that Hebbian learning is sufficient to explain the emergence of highly structured patterns of neural activity in the absence of structured input.
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