1 research outputs found
Fast learning synapses with molecular spin valves via selective magnetic potentiation
We studied LSMO/Alq3/AlOx/Co molecular spin valves in view of their use as
synapses in neuromorphic computing. In neuromorphic computing, the learning
ability is embodied in specific changes of the synaptic weight. In this
perspective, the relevant parameter is the conductance of the molecular spin
valve, which plays the role of the synaptic weight. In this work we
demonstrated that the conductance can be changes by the repeated application of
voltage pulses. We studied the parameter space of the pulses in order to
determine the most effective voltage and duration of the pulses. The
conductance could also be modified by aligning the magnetizations of the
ferromagnetic electrodes parallel or anti parallel to each other. This
phenomenon, known as magnetoresistance, affects high conductance devices while
leaving low conductance devices unaffected. We studied how this weight update
rule affected the speed of reward-based learning in an actor-critic framework,
compared to a linear update rule. This nonlinear update performed significantly
better (50 learning trials; Epochs to reach a performance goal of 0.975 was
896+/-301 in the nonlinear case and 1076+/-484 in the nonlinear case; Welch
t-test: p<0.05). The linear update resulted in more learning trails with very
long convergence times, which was largely absent in the nonlinear update