Skip to Main content Skip to Navigation
Conference papers

Input Prediction Using Consensus Driven SOMs

Abstract : The motivation of our work is the instantiation of a computational view of the cerebral cortex. Kohonen's early definition of self-organizing maps was inspired by the cortical substrate on a local scale and is now a widely used learning algorithm. Following the same path, from biology to computation, the cortex can be interpreted as an architecture made of similar self-organizing modules connected together. To our knowledge, there are no such algorithmic derivation of large architectures of self-organizing modules. This paper presents the behavior of several maps connected one to another as a step towards wider networks of self-organizing maps and shows that this architecture learns a model of inputs and generates predictions in a map without using an additional algorithm. This prediction ability is applied to the control of a quadcopter flying in a corridor.
Document type :
Conference papers
Complete list of metadata
Contributor : Noémie Gonnier Connect in order to contact the contributor
Submitted on : Tuesday, October 12, 2021 - 3:24:29 PM
Last modification on : Saturday, October 16, 2021 - 11:26:10 AM


Files produced by the author(s)


  • HAL Id : hal-03375134, version 1


Noémie Gonnier, Yann Boniface, Hervé Frezza-Buet. Input Prediction Using Consensus Driven SOMs. ISCMI 2021:8th Intl. Conference on Soft Computing & Machine Intelligence, Nov 2021, Cairo, Egypt. ⟨hal-03375134⟩



Record views


Files downloads