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Nonlinear Dynamics

, Volume 95, Issue 3, pp 1999–2017 | Cite as

A CNN-based neuromorphic model for classification and decision control

  • Paolo Arena
  • Marco Calí
  • Luca PatanéEmail author
  • Agnese Portera
  • Angelo G. Spinosa
Original Paper
  • 215 Downloads

Abstract

In this paper, an insect brain-inspired computational structure was developed. The peculiarity of the core processing layer is the local connectivity among the spiking neurons, which allows for a representation under the cellular nonlinear network paradigm. Moreover, the processing layer works as a liquid state network with fixed internal connections and trainable output weights. Learning was accomplished by adopting a simple supervised, batch approach based on the calculation of the Moore–Penrose matrix. The architecture, taking inspiration from a specific neuropile of the insect brain, the mushroom bodies, is evaluated and compared with other standard and bio-inspired solutions present in the literature, referring to three different scenarios.

Keywords

Cellular neural networks Insect brain Drosophila melanogaster Neural gas Mushroom bodies Classification Decision-making 

Notes

Funding

This study was funded by MIUR Project CLARA—Cloud platform for LAndslide Risk Assessment (Grant Number SNC_00451).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Paolo Arena
    • 1
    • 2
  • Marco Calí
    • 1
  • Luca Patané
    • 1
    Email author
  • Agnese Portera
    • 1
  • Angelo G. Spinosa
    • 1
  1. 1.DIEEIUniversity of CataniaCataniaItaly
  2. 2.National Institute of Biostructures and Biosystems (INBB)RomeItaly

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