Emergence of Hierarchical Layers in a Single Sheet of Self-Organizing Spiking Neurons

Paul Bertens, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Traditionally convolutional neural network architectures have been designed by stacking layers on top of each other to form deeper hierarchical networks. The cortex in the brain however does not just stack layers as done in standard convolution neural networks, instead different regions are organized next to each other in a large single sheet of neurons. Biological neurons self organize to form topographic maps, where neurons encoding similar stimuli group together to form logical clusters. Here we propose new self-organization principles that allow for the formation of hierarchical cortical regions (i.e. layers) in a completely unsupervised manner without requiring any predefined architecture. Synaptic connections are dynamically grown and pruned, which allows us to actively constrain the number of incoming and outgoing connections. This way we can minimize the wiring cost by taking into account both the synaptic strength and the connection length. The proposed method uses purely local learning rules in the form of spike-timing-dependent plasticity (STDP) with lateral excitation and inhibition. We show experimentally that these self-organization rules are sufficient for topographic maps and hierarchical layers to emerge. Our proposed Self-Organizing Neural Sheet (SONS) model can thus form traditional neural network layers in a completely unsupervised manner from just a single large pool of unstructured spiking neurons.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022
EditorsS. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh
PublisherNeural information processing systems foundation
ISBN (Electronic)9781713871088
Publication statusPublished - 2022
Event36th Conference on Neural Information Processing Systems, NeurIPS 2022 - New Orleans, United States
Duration: 2022 Nov 282022 Dec 9

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258


Conference36th Conference on Neural Information Processing Systems, NeurIPS 2022
Country/TerritoryUnited States
CityNew Orleans

Bibliographical note

Funding Information:
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grants funded by the Korea government(MSIT) (No. 2019-0-00079, Artificial Intelligence Graduate School Program, Korea University, No. 2019-0-01371, Development of Brain-inspired AI with Human-like Intelligence, No. 2021-0-02068, Artificial Intelligence Innovation Hub, and No. 2022-0-00984, Development of Artificial Intelligence Technology for Personalized Plug-and-Play Explanation and Verification of Explanation).

Publisher Copyright:
© 2022 Neural information processing systems foundation. All rights reserved.

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


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