Wei T, Webb B. A model of operant learning based on chaotically varying synaptic strength.
Neural Netw 2018;
108:114-127. [PMID:
30176514 DOI:
10.1016/j.neunet.2018.08.006]
[Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 07/12/2018] [Accepted: 08/02/2018] [Indexed: 11/17/2022]
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.
Collapse