Open AccessCell-type–specific neuromodulation guides synaptic credit assignment in a spiking neural network
Author(s) -
Yuhan Helena Liu,
Stephen J Smith,
Ştefan Mihalaş,
Eric SheaBrown,
Uygar Sümbül
Publication year - 2021
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2111821118
Subject(s) - neuromodulation , neuroscience , hebbian theory , synaptic plasticity , computer science , artificial neural network , metaplasticity , neuron , artificial intelligence , biology , central nervous system , biochemistry , receptor
Significance Synaptic connectivity provides the foundation for our present understanding of neuronal network function, but static connectivity cannot explain learning and memory. We propose a computational role for the diversity of cortical neuronal types and their associated cell-type–specific neuromodulators in improving the efficiency of synaptic weight adjustments for task learning in neuronal networks.