Biologically inspired and energy-efficient neurons
- Silicon neurons represent different levels of biological details and accuracies as a trade-off between complexity and power consumption. With respect to this trade-off and high similarity to neuron behaviour models, relaxation-type oscillator circuits often yield a good compromise to emulate neurons. In this chapter, two exemplified relaxation-type silicon neurons are presented that emulate neural behaviour with energy consumption under the scale of nJ/spike. The first proposed fully CMOS relaxation SiN is based on mathematical Izhikevich model and can mimic a broad range of physiologically observable spike patterns. The results of kinds of biologically plausible output patterns and coupling process of two SiNs are presented in 0.35 μm CMOS technology. The second type is a novel ultra-low-frequency hybrid CMOS-memristive SiN based on relaxation oscillators and analog memristive devices. The hybrid SiN directly emulates neuron behaviour in the range of physiological spiking frequencies (less than 100 Hz). The relaxation oscillator is implemented and fabricated in 0.13 μm CMOS technology. An autonomous neuronal synchronization process is demonstrated with two relaxation oscillators coupled by an analog memristive device in the measurement to emulate the synchronous behaviour between spiking neurons.
Author of HS Reutlingen | Hennig, Eckhard; Wolfer, Tobias |
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URN: | urn:nbn:de:bsz:rt2-opus4-45448 |
DOI: | https://doi.org/10.1007/978-3-031-36705-2_15 |
ISBN: | 978-3-031-36704-5 |
ISBN: | 978-3-031-36707-6 |
ISBN: | 978-3-031-36705-2 |
Erschienen in: | Bio-inspired information pathways : From neuroscience to neurotronics (SSBN ; 16) |
Publisher: | Springer |
Place of publication: | Cham |
Editor: | Martin Ziegler, Thomas Mussenbrock, Hermann Kohlstedt |
Document Type: | Book chapter |
Language: | English |
Publication year: | 2023 |
Tag: | Izhikevich model; analog memristive device; artificial synapse; hybrid CMOS-memristive; integrate-and-fire; neuronal synchronization; silicon neuron; ultra-low-frequency relaxation oscillator |
Page Number: | 28 |
First Page: | 357 |
Last Page: | 384 |
PPN: | Im Katalog der Hochschule Reutlingen ansehen |
DDC classes: | 600 Technik |
Open access?: | Ja |
Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |