Cumulative inhibition in neural networks

Research output: Contribution to journalArticlepeer-review

3 Citations (SciVal)

Abstract

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.
Original languageEnglish
Pages (from-to)87-102
JournalCognitive Processing
Volume20
Issue number1
Early online date2018 Nov 3
DOIs
Publication statusPublished - 2019

Subject classification (UKÄ)

  • General Language Studies and Linguistics

Keywords

  • Cumulative inhibition
  • Multi-resolution
  • Coarse-to-fine processing
  • Unsupervised learning
  • Acuity
  • Cortical microcolumn
  • Visual cortex

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