Abstract
In the literature, various pulse -based programming schemes have been used to mimic typical spike time-dependent plasticity (STDP)-based learning rule observed in biological synapses. In this letter, we demonstrate the capability to generated arbitrary STDP behaviors by using analog programming waveforms inspired by neuronal action potential. First, we propose a simple algorithm to generate any arbitrary form of STDP. Second, we show the feasibility of a range of spike correlation time scales for STDP, e.g., biological (~100 ms) to accelerated ( $\sim 20\mu {s})$ , based on W/Pr0.7Ca0.3MnO3/Pt based memristor. Third, we experimentally demonstrate several forms of STDP behaviors, where the pre- and post-neuronal waveforms are randomly spaced in time, akin to operational conditions. STDP shape corresponds well to waveforms. Thus, we show that artificial synapses can achieve the richness observed in biology as well as a range of STDP timescales for biologically compatible to accelerated neural network applications.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
