ArticleBiological cybernetics2019
An unsupervised neuromorphic clustering algorithm.
Article in Biological cybernetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Clinical phenotypes of comorbidities in end-stage knee osteoarthritis: a cluster analysis.BMC musculoskeletal disorders · 2024Article
- The coming decade of digital brain research: A vision for neuroscience at the intersection of technology and computing.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Construction and verification of atopic dermatitis diagnostic model based on pyroptosis related biological markers using machine learning methods.BMC medical genomics · 2023Article
- Article
- Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts.Sensors (Basel, Switzerland) · 2022Article
- Performance Evaluation of Hospital Economic Management with the Clustering Algorithm Oriented towards Electronic Health Management.Journal of healthcare engineering · 2022Article
- Automotive Radar Processing With Spiking Neural Networks: Concepts and Challenges.Frontiers in neuroscience · 2022Article
- A Hardware-Deployable Neuromorphic Solution for Encoding and Classification of Electronic Nose Data.Sensors (Basel, Switzerland) · 2019Article
- A Spike Time-Dependent Online Learning Algorithm Derived From Biological Olfaction.Frontiers in neuroscience · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brains perform complex tasks using a fraction of the power that would be required to do the same on a conventional computer. New neuromorphic hardware systems are now becoming widely available that are intended to emulate the more power efficient, highly parallel operation of brains. However, to use these systems in applications, we need "neuromorphic algorithms" that can run on them. Here we develop a spiking neural network model for neuromorphic hardware that uses spike timing-dependent plasticity and lateral inhibition to perform unsupervised clustering. With this model, time-invariant, rate-coded datasets can be mapped into a feature space with a specified resolution, i.e., number of clusters, using exclusively neuromorphic hardware. We developed and tested implementations on the SpiNNaker neuromorphic system and on GPUs using the GeNN framework. We show that our neuromorphic clustering algorithm achieves results comparable to those of conventional clustering algorithms such as self-organizing maps, neural gas or k-means clustering. We then combine it with a previously reported supervised neuromorphic classifier network to demonstrate its practical use as a neuromorphic preprocessing module.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.