Cumulative inhibition in neural networks

Research output: Contribution to journalArticle


We show how a multi-resolution network can model the development of acuity and coarse-to-fine processing in the mammalian visual cortex. The network adapts to input statistics in an unsupervised manner, and learns a coarse-to-fine representation by using cumulative inhibition of nodes within a network layer. We show that a system of such layers can represent input by hierarchically composing larger parts from smaller components. It can also model aspects of top-down processes, such as image regeneration.


Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • General Language Studies and Linguistics


  • Cumulative inhibition, Multi-resolution, Coarse-to-fine processing, Unsupervised learning, Acuity, Cortical microcolumn, Visual cortex
Original languageEnglish
Pages (from-to)87-102
JournalCognitive Processing
Issue number1
Early online date2018 Nov 3
Publication statusPublished - 2019
Publication categoryResearch

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Andreas Stephens, Trond Arild Tjöstheim, Maximilian Roszko, Erik J Olsson, Andrey Anikin & Theoretical Philosophy, University of Zurich Arthur Schwaninger

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