Error-based or target-based? A unified framework for learning in recurrent spiking networks.

ABSTRACT: The field of recurrent neural networks is over-populated by a variety of proposed learning rules and protocols. The scope of this work is to define a generalized framework, to move a step forward towards the unification of this fragmented scenario. In the field of supervised learning, two opposite approaches stand out, error-based and target-based. This duality gave rise to a scientific debate on which learning framework is the most likely to be implemented in biological networks of neurons. Moreover, the existence of spikes raises the question of whether the coding of information is rate-based or spike-based. To face these questions, we proposed a learning model with two main parameters, the rank of the feedback learning matrix [Formula: see text] and the tolerance to spike timing τ⋆. We

SUBMITTER: Capone C 

PROVIDER: S-EPMC9249234 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature