2024/12/07 by Danil Tyulmankov, Tyulmankov, Danil · 1 voice
Biochemistry, Genetics and Molecular Biology · Computer Science · #FOS: Biological sciences #FOS: Computer and information sciences #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE) #Neurons and Cognition (q-bio.NC) #cs.NE #q-bio.NC
paper · pdf · doi:10.48550/arxiv.2412.05501
openalex publication_date 2024/12/07 · arxiv published 2024/12/07 · arxiv updated 2024/12/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many mathematical models of synaptic plasticity have been proposed to explain the diversity of plasticity phenomena observed in biological organisms. These models range from simple interpretations of Hebb's postulate, which suggests that correlated neural activity leads to increases in synaptic strength, to more complex rules that allow bidirectional synaptic updates, ensure stability, or incorporate additional signals like reward or error. At the same time, a range of learning paradigms can be observed behaviorally, from Pavlovian conditioning to motor learning and memory recall. Although it is difficult to directly link synaptic updates to learning outcomes experimentally, computational models provide a valuable tool for building evidence of this connection. In this chapter, we discuss several fundamental learning paradigms, along with the synaptic plasticity rules that might be used to implement them.