Unsupervised Feature Vector Clustering Using Temporally Coded Spiking Networks
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 International Joint Conference on Neural Networks (IJCNN), 2023, 2023-June
- Issue Date:
- 2023-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Unsupervised_Feature_Vector_Clustering_Using_Temporally_Coded_Spiking_Networks.pdf | Accepted version | 2.64 MB |
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Spiking Neural Networks SNNs remain on the fringe of machine learning research despite their potential for fast low power performance and fully local operation including rapid online learning on edge computing devices In SNNs encoding information in the timing of individual spikes is more efficient than using spiking rates for which many spikes are required However combining spike time coding with unsupervised learning has proven somewhat challenging Here we use spike latency coding with local unsupervised spike timing dependent plasticity and several biologically inspired local homeostatic mechanisms that maintain network stability We show that when trained on sequences of characters from text the network rapidly and effectively self organizes to learn a latent space mapping of character attributes similar to word2vec but for characters i e char2vec forming clusters of vowels consonants and punctuation for example It does so with no explicit objective function and no error signal showing that time encoded unsupervised SNNs STUNNs can maintain dynamical stability while self organizing to extract complex input relationships using only local learning rules
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