Recurrent neural network architecture with pre-synaptic inhibition for incremental learning

Hiroyuki Ohta*, Yukio Pegio Gunji

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


We propose a recurrent neural network architecture that is capable of incremental learning and test the performance of the network. In incremental learning, the consistency between the existing internal representation and a new sequence is unknown, so it is not appropriate to overwrite the existing internal representation on each new sequence. In the proposed model, the parallel pathways from input to output are preserved as possible, and the pathway which has emitted the wrong output is inhibited by the previously fired pathway. Accordingly, the network begins to try other pathways ad hoc. This modeling approach is based on the concept of the parallel pathways from input to output, instead of the view of the brain as the integration of the state spaces. We discuss the extension of this approach to building a model of the higher functions such as decision making.

Original languageEnglish
Pages (from-to)1106-1119
Number of pages14
JournalNeural Networks
Issue number8
Publication statusPublished - 2006 Oct 1
Externally publishedYes


  • Affection
  • Anticipation
  • Decision making
  • Incremental learning
  • Realization problem
  • Recurrent neural network

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence


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