A model of operant learning based on chaotically varying synaptic strength. (December 2018)

• Record Type: Journal Article
• Authors: Wei, Tianqi; Webb, Barbara
• Is Part Of: Neural networks. Volume 108(2018)
• Page Start: 114
• Page End: 127
• Publication Date: 2018-12
• Subjects: Dynamic Synapse -- Operant learning -- Chaos -- Receptor Trafficking; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2018.08.006
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Operant learning is learning based on reinforcement of behaviours. We propose a new hypothesis for operant learning at the single neuron level based on spontaneous fluctuations of synaptic strength caused by receptor dynamics. These fluctuations allow the neural system to explore a space of outputs. If the receptor dynamics are altered by a reinforcement signal the neural system settles to better states, i.e., to match the environmental dynamics that determine reward. Simulations show that this mechanism can support operant learning in a feed-forward neural circuit, a recurrent neural circuit, and a spiking neural circuit controlling an agent learning in a dynamic reward and punishment situation. We discuss how the new principle relates to existing learning rules and observed phenomena of short and long-term potentiation.