ArticleJournal of computational neuroscience2018
Feedforward architectures driven by inhibitory interactions.
Article in Journal of computational neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons.PLoS computational biology · 2021Article
- Learning spatiotemporal signals using a recurrent spiking network that discretizes time.PLoS computational biology · 2020Article
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Authors and funding
2 authors.
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Abstract
Directed information transmission is paramount for many social, physical, and biological systems. For neural systems, scientists have studied this problem under the paradigm of feedforward networks for decades. In most models of feedforward networks, activity is exclusively driven by excitatory neurons and the wiring patterns between them, while inhibitory neurons play only a stabilizing role for the network dynamics. Motivated by recent experimental discoveries of hippocampal circuitry, cortical circuitry, and the diversity of inhibitory neurons throughout the brain, here we illustrate that one can construct such networks even if the connectivity between the excitatory units in the system remains random. This is achieved by endowing inhibitory nodes with a more active role in the network. Our findings demonstrate that apparent feedforward activity can be caused by a much broader network-architectural basis than often assumed.
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Registered trials
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