In the brain, the membrane potential of many neurons oscillates in a subthreshold damped fashion and fire when excited by an input frequency that nearly equals their eigen frequency. In this work, we investigate theoretically the artificial implementation of such "resonate-and-fire" neurons by utilizing the magnetization dynamics of a fixed magnetic skyrmion in the free layer of a magnetic tunnel junction (MTJ). To realize firing of this nanomagnetic implementation of an artificial neuron, we propose to employ voltage control of magnetic anisotropy or voltage generated strain as an input (spike or sinusoidal) signal, which modulates the perpendicular magnetic anisotropy (PMA). This results in continual expansion and shrinking (i.e. breathing) of a skyrmion core that mimics the subthreshold oscillation. Any subsequent input pulse having an interval close to the breathing period or a sinusoidal input close to the eigen frequency drives the magnetization dynamics of the fixed skyrmion in a resonant manner. The time varying electrical resistance of the MTJ layer due to this resonant oscillation of the skyrmion core is used to drive a Complementary Metal Oxide Semiconductor (CMOS) buffer circuit, which produces spike outputs. By rigorous micromagnetic simulation, we investigate the interspike timing dependence and response to different excitatory and inhibitory incoming input pulses. Finally, we show that such resonate and fire neurons have potential application in coupled nanomagnetic oscillator based associative memory arrays.

In this paper, a completely new algorithm based on Stimulated Annealing technique for MPPT is proposed to optimize standalone PV panel operating point even under rapidly and significantly changing weather pattern. The proposed algorithm is as simple as traditional P&O and Hill-climbing algorithm, yet it can successfully encounter the oscillation problem and steady state error problem even under worst case scenario with efficiency more than 99.99%. The algorithm eliminates hardware complexity and can be implemented using programmable microcontrollers. This paper presents performance comparison of stimulated annealing algorithm with three of the most popular artificially intelligent algorithms such as fuzzy logic algorithm, particle swarm optimization algorithm and Genetic algorithm.Step by step procedure for practical implementation of the proposed algorithm is included at the later part of this paper.

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