Recently, a great deal of scientific endeavour has been devoted to developing spin-based neuromorphic platforms owing to the ultra-low-power benefits offered by spin devices and the inherent correspondence between spintronic phenomena and the desired neuronal, synaptic behavior. While domain wall motion-based threshold activation unit has previously been demonstrated for neuromorphic circuits, it remains well known that neurons with threshold activation cannot completely learn nonlinearly separable functions. This paper addresses this fundamental limitation by proposing a novel domain wall motion-based dual-threshold activation unit with additional nonlinearity in its function. Furthermore, a new learning algorithm is formulated for a neuron with this activation function. We perform 100 trials of tenfold training and testing of our neural networks on real-world datasets taken from the UCI machine learning repository. On an average, the proposed algorithm achieves [Formula: see text] lower misclassification rate (MCR) than the traditional perceptron learning algorithm. In a circuit-level simulation, the neural networks with the proposed activation unit are observed to outperform the perceptron networks by as much as [Formula: see text] MCR. The energy consumption of a neuron having the proposed domain wall motion-based activation unit averages to [Formula: see text] approximately.