# Fast learning synapses with molecular spin valves via selective magnetic potentiation

## TL;DR

This paper demonstrates that molecular spin valves can be used as fast, tunable synapses in neuromorphic computing, with conductance changes achieved through voltage pulses and magnetic alignment, improving learning speed.

## Contribution

It introduces a novel method for synaptic weight modulation in molecular spin valves using voltage pulses and magnetic states, enhancing neuromorphic learning performance.

## Key findings

- Conductance can be modulated by voltage pulses with optimized parameters.
- Magnetoresistance affects high conductance devices, enabling additional weight updates.
- Nonlinear update rule significantly improves learning speed and reduces convergence time.

## Abstract

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.

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Source: https://tomesphere.com/paper/1903.08624