The discrete Langevin machine: bridging the gap between thermodynamic and neuromorphic systems
A formulation of Langevin dynamics for discrete systems is derived as a new class of generic stochastic processes. The dynamics simplify for a two-state system and suggest a novel network architecture which is implemented by the Langevin machine. The Langevin machine represents a promising approach...
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| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) Kapitel/Artikel |
| Sprache: | Englisch |
| Veröffentlicht: |
18 Apr 2019
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| In: |
Arxiv
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| Online-Zugang: | Verlag, Volltext: http://arxiv.org/abs/1901.05214
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| Verfasserangaben: | Lukas Kades and Jan M. Pawlowski |
| Zusammenfassung: | A formulation of Langevin dynamics for discrete systems is derived as a new class of generic stochastic processes. The dynamics simplify for a two-state system and suggest a novel network architecture which is implemented by the Langevin machine. The Langevin machine represents a promising approach to compute successfully quantitative exact results of Boltzmann distributed systems by LIF neurons. Besides a detailed introduction of the new dynamics, different simplified models of a neuromorphic hardware system are studied with respect to a control of emerging sources of errors. |
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| Beschreibung: | Gesehen am 11.09.2020 |
| Beschreibung: | Online Resource |