Waves and symbols in neuromorphic hardware: from analog signal processing to digital computing on the same computational substrate
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866910848497221632 |
|---|---|
| author | Zendrikov, Dmitrii Franci, Alessio Indiveri, Giacomo |
| author_facet | Zendrikov, Dmitrii Franci, Alessio Indiveri, Giacomo |
| contents | Neural systems use the same underlying computational substrate to carry out analog filtering and signal processing operations, as well as discrete symbol manipulation and digital computation. Inspired by the computational principles of canonical cortical microcircuits, we propose a framework for using recurrent spiking neural networks to seamlessly and robustly switch between analog signal processing and categorical and discrete computation. We provide theoretical analysis and practical neural network design tools to formally determine the conditions for inducing this switch. We demonstrate the robustness of this framework experimentally with hardware soft Winner-Take-All and mixed-feedback recurrent spiking neural networks, implemented by appropriately configuring the analog neuron and synapse circuits of a mixed-signal neuromorphic processor chip. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20381 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Waves and symbols in neuromorphic hardware: from analog signal processing to digital computing on the same computational substrate Zendrikov, Dmitrii Franci, Alessio Indiveri, Giacomo Neural and Evolutionary Computing Neural systems use the same underlying computational substrate to carry out analog filtering and signal processing operations, as well as discrete symbol manipulation and digital computation. Inspired by the computational principles of canonical cortical microcircuits, we propose a framework for using recurrent spiking neural networks to seamlessly and robustly switch between analog signal processing and categorical and discrete computation. We provide theoretical analysis and practical neural network design tools to formally determine the conditions for inducing this switch. We demonstrate the robustness of this framework experimentally with hardware soft Winner-Take-All and mixed-feedback recurrent spiking neural networks, implemented by appropriately configuring the analog neuron and synapse circuits of a mixed-signal neuromorphic processor chip. |
| title | Waves and symbols in neuromorphic hardware: from analog signal processing to digital computing on the same computational substrate |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2502.20381 |