Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task.
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| Title: | Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task. |
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| Authors: | Forno E; Politecnico di Torino, Electronic Design Automation (EDA) Group, Turin, Italy., Fra V; Politecnico di Torino, Electronic Design Automation (EDA) Group, Turin, Italy., Pignari R; Politecnico di Torino, Electronic Design Automation (EDA) Group, Turin, Italy., Macii E; Politecnico di Torino, Electronic Design Automation (EDA) Group, Turin, Italy., Urgese G; Politecnico di Torino, Electronic Design Automation (EDA) Group, Turin, Italy. |
| Source: | Frontiers in neuroscience [Front Neurosci] 2022 Dec 21; Vol. 16, pp. 999029. Date of Electronic Publication: 2022 Dec 21 (Print Publication: 2022). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101478481 Publication Model: eCollection Cited Medium: Print ISSN: 1662-4548 (Print) Linking ISSN: 1662453X NLM ISO Abbreviation: Front Neurosci Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
| ISSN: | 1662-4548 |
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| DOI: | 10.3389/fnins.2022.999029 |
