PUBLICATION
Emergence of spontaneous assembly activity in developing neural networks without afferent input
- Authors
- Triplett, M.A., Avitan, L., Goodhill, G.J.
- ID
- ZDB-PUB-180929-7
- Date
- 2018
- Source
- PLoS Computational Biology 14: e1006421 (Journal)
- Registered Authors
- Keywords
- none
- MeSH Terms
-
- Animals
- Calcium/metabolism
- Female
- Male
- Models, Neurological
- Models, Statistical
- Neural Networks, Computer*
- Neurogenesis
- Neuronal Plasticity*
- Retina/physiology
- Sensory Receptor Cells/physiology
- Software
- Stochastic Processes
- Superior Colliculi/metabolism
- Zebrafish
- PubMed
- 30265665 Full text @ PLoS Comput. Biol.
Citation
Triplett, M.A., Avitan, L., Goodhill, G.J. (2018) Emergence of spontaneous assembly activity in developing neural networks without afferent input. PLoS Computational Biology. 14:e1006421.
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.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping