An adaptive locally connected neuron model: Focusing neuron

Creative Commons License

Tek F. B.

NEUROCOMPUTING, vol.419, pp.306-321, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 419
  • Publication Date: 2021
  • Doi Number: 10.1016/j.neucom.2020.08.008
  • Journal Name: NEUROCOMPUTING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, zbMATH
  • Page Numbers: pp.306-321
  • Keywords: Adaptive locally connected neuron, Adaptive receptive field, Attention, Focusing neuron, Pruning, RECEPTIVE-FIELDS, VISUAL-ATTENTION, RECOGNITION, CODE
  • Istanbul Technical University Affiliated: No


This paper presents a new artificial neuron model capable of learning its receptive field in the topological domain of inputs. The experiments include tests of focusing neuron networks of one or two hidden layers on synthetic and well-known image recognition data sets. The results demonstrated that the focusing neurons can move their receptive fields towards more informative inputs. In the simple two-hidden layer networks, the focusing layers outperformed the dense layers in the classification of the 2D spatial data sets. Moreover, the focusing networks performed better than the dense networks even when 70% of the weights were pruned. The tests on convolutional networks revealed that using focusing layers instead of dense layers for the classification of convolutional features may work better in some data sets. (c) 2020 Elsevier B.V. All rights reserved.