TY - JOUR
T1 - Arbitrary Spike Time Dependent Plasticity (STDP) in memristor by analog waveform engineering
AU - Panwar, Neeraj
AU - Rajendran, Bipin
AU - Ganguly, Udayan
N1 - Funding Information:
This work was supported in part by the Science and Engineering Research Board, in part by the Department of Science and Technology, and in part by the Department of Electronics and IT,Government of India.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/6
Y1 - 2017/6
N2 - 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 (∼20μs), based on W/ r0.7Ca0.3MnO3/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.
AB - 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 (∼20μs), based on W/ r0.7Ca0.3MnO3/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.
KW - PCMO
KW - RRAM.
KW - exponential waveform
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U2 - 10.1109/LED.2017.2696023
DO - 10.1109/LED.2017.2696023
M3 - Article
AN - SCOPUS:85021770026
SN - 0741-3106
VL - 38
SP - 740
EP - 743
JO - IEEE Electron Device Letters
JF - IEEE Electron Device Letters
IS - 6
M1 - 7904675
ER -