Deep learning for neural decoding in motor cortex.

. Neural decoding is an important tool in neural engineering and neural data analysis. Of various machine learning algorithms adopted for neural decoding, the recently introduced deep learning is promising to excel.

FARCI: Fast and Robust Connectome Inference.

The inference of neuronal connectome from large-scale neuronal activity recordings, such as two-photon Calcium imaging, represents an active area of research in computational neuroscience. In this work, we developed FARCI (Fast and Robust Connectome Inference), a MATLAB package for neuronal connectome inference from high-dimensional two-photon Calcium fluorescence data. We employed partial correlations as a measure of the functional association strength between pairs of neurons to reconstruct a neuronal connectome.

Element-Specific Detection of Sub-Nanosecond Spin-Transfer Torque in a Nanomagnet Ensemble.

Spin currents can exert spin-transfer torques on magnetic systems even in the limit of vanishingly small net magnetization, as recently shown for antiferromagnets. Here, we experimentally show that a spin-transfer torque is operative in a macroscopic ensemble of weakly interacting, randomly magnetized Co nanomagnets. We employ element- and time-resolved X-ray ferromagnetic resonance (XFMR) spectroscopy to directly detect subnanosecond dynamics of the Co nanomagnets, excited into precession with cone angle ≳0.

Conductivitylike Gilbert Damping due to Intraband Scattering in Epitaxial Iron.

Confirming the origin of Gilbert damping by experiment has remained a challenge for many decades, even for simple ferromagnetic metals. Here, we experimentally identify Gilbert damping that increases with decreasing electronic scattering in epitaxial thin films of pure Fe. This observation of conductivitylike damping, which cannot be accounted for by classical eddy-current loss, is in excellent quantitative agreement with theoretical predictions of Gilbert damping due to intraband scattering.