2023
DOI: 10.1088/2634-4386/accd90
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Spiking neural networks compensate for weight drift in organic neuromorphic device networks

Abstract: Organic neuromorphic devices can accelerate neural networks and integrate with biological systems. Devices based on the biocompatible and conductive polymer PEDOT:PSS are fast, require low amounts of energy and perform well in crossbar simulations. However, parasitic electrochemical reactions lead to self-discharge and the fading of learned conductance states over time. This limits a neural network's operating time and requires complex compensation mechanisms. Spiking neural networks take inspiration from biol… Show more

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Cited by 3 publications
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“…Using a similar network of organic ECRAMs, which is traditionally implemented for in-memory computing, the same authors show in Felder et al [2] that it is also possible to run spiking neural networks. They demonstrate that their non-ideal devices that 'forget' their state, are actually 'reminded' by the spikes, and thus keep the high classification accuracy.…”
mentioning
confidence: 95%
“…Using a similar network of organic ECRAMs, which is traditionally implemented for in-memory computing, the same authors show in Felder et al [2] that it is also possible to run spiking neural networks. They demonstrate that their non-ideal devices that 'forget' their state, are actually 'reminded' by the spikes, and thus keep the high classification accuracy.…”
mentioning
confidence: 95%