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Enabling an integrated rate-temporal learning scheme on memristor.

ABSTRACT: Learning scheme is the key to the utilization of spike-based computation and the emulation of neural/synaptic behaviors toward realization of cognition. The biological observations reveal an integrated spike time- and spike rate-dependent plasticity as a function of presynaptic firing frequency. However, this integrated rate-temporal learning scheme has not been realized on any nano devices. In this paper, such scheme is successfully demonstrated on a memristor. Great robustness against the spiking rate fluctuation is achieved by waveform engineering with the aid of good analog properties exhibited by the iron oxide-based memristor. The spike-time-dependence plasticity (STDP) occurs at moderate presynaptic firing frequencies and spike-rate-dependence plasticity (SRDP) dominates other regions. This demonstration provides a novel approach in neural coding implementation, which facilitates the development of bio-inspired computing systems.

SUBMITTER: He W

PROVIDER: S-EPMC3996481 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Enabling an integrated rate-temporal learning scheme on memristor.

He Wei W Huang Kejie K Ning Ning N Ramanathan Kiruthika K Li Guoqi G Jiang Yu Y Sze Jiayin J Shi Luping L Zhao Rong R Pei Jing J

Scientific reports 20140423

Learning scheme is the key to the utilization of spike-based computation and the emulation of neural/synaptic behaviors toward realization of cognition. The biological observations reveal an integrated spike time- and spike rate-dependent plasticity as a function of presynaptic firing frequency. However, this integrated rate-temporal learning scheme has not been realized on any nano devices. In this paper, such scheme is successfully demonstrated on a memristor. Great robustness against the spik ...[more]

PMID: 24755608

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