Results for the first simulation in Experiment 1.

<p>A: The average of 500 weight vectors obtained by sparse representation. B: The average of 500 weight vectors obtained by SVM.</p

The three subplots are shown for the two classes, i.e., horizontal axis-of-motion stimuli and vertical axis-of-motion stimuli.

<p>A: a difference map between the two stimulus conditions. B: the reconstructed condition difference map between the two stimulus conditions using our approach. C: the iterative curve of decoding accuracy rates in Experiment 2.</p

Four ROC curves obtained by our sparse representation-based SPL algorithm (black curve with stars), SVM-based SPL algorithm (red curve with circles), SVM method (blue curve with triangles) and correlation method (green curve with diamonds).

<p>Four ROC curves obtained by our sparse representation-based SPL algorithm (black curve with stars), SVM-based SPL algorithm (red curve with circles), SVM method (blue curve with triangles) and correlation method (green curve with diamonds).</p

SPL algorithm diagram.

<p>SPL algorithm diagram.</p

Results of our SPL algorithm with a permutation test at the group level in the second simulation of Experiment 1.

<p>A and B: with sparse representation-based weight determination; C and D: with SVM-based weight determination. Left: for the first pattern. Right: for the second pattern. In each subplot, there are an average probability density function with stars (circled or non-circled) indicating probability values, and a horizontal dash-dotted red line representing a threshold (significance level: 0.001). Stars higher than the threshold correspond to the indices of nonzeros of a predicted pattern. Those stars with circles represent the indices of the nonzeros of the true pattern.</p

Results of our SPL algorithm at different noise levels.

<p>A: accuracy curve for localizing informative features obtained by the sparse representation-based SPL algorithm with different noise levels. B: 8 ROC curves corresponding to 8 noise levels respectively. C: accuracy curve for predicting the labels of the independent test sets generated at different noise levels.</p

Voxels selected by our method with a significance level of 0.05 (corrected with cluster size 10) in Experiment 3.

<p>The red clusters corresponded to the “old people” stimulus condition, and the blue clusters corresponded to the “young people” stimulus condition.</p

Iterative curve of average decoding accuracy rates across all the 9 subjects in Experiment 3.

<p>Iterative curve of average decoding accuracy rates across all the 9 subjects in Experiment 3.</p

The algorithm diagram for the recursive feature search in a fold of cross-validation.

<p>The algorithm diagram for the recursive feature search in a fold of cross-validation.</p

Ferroelectric domain wall memristor

A domain wall-enabled memristor is created, in thin film lithium niobate capacitors, which shows up to twelve orders of magnitude variation in resistance. Such dramatic changes are caused by the injection of strongly inclined conducting ferroelectric domain walls, which provide conduits for current flow between electrodes. Varying the magnitude of the applied electric-field pulse, used to induce switching, alters the extent to which polarization reversal occurs; this systematically changes the density of the injected conducting domain walls in the ferroelectric layer and hence the resistivity of the capacitor structure as a whole. Hundreds of distinct conductance states can be produced, with current maxima achieved around the coercive voltage, where domain wall density is greatest, and minima associated with the almost fully switched ferroelectric (few domain walls). Significantly, this “domain wall memristor” demonstrates a plasticity effect: when a succession of voltage pulses of constant magnitude is applied, the resistance changes. Resistance plasticity opens the way for the domain wall memristor to be considered for artificial synapse applications in neuromorphic circuit