Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning.

ABSTRACT: Sequential activity has been observed in multiple neuronal circuits across species, neural structures, and behaviors. It has been hypothesized that sequences could arise from learning processes. However, it is still unclear whether biologically plausible synaptic plasticity rules can organize neuronal activity to form sequences whose statistics match experimental observations. Here, we investigate temporally asymmetric Hebbian rules in sparsely connected recurrent rate networks and develop a theory of the transient sequential activity observed after learning. These rules transform a sequence of random input patterns into synaptic weight updates. After learning, recalled sequential activity is reflected in the transient correlation of network activity with each of the stored input patterns.

SUBMITTER: Gillett M 

PROVIDER: S-EPMC7703604 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature