  Neuromorphic computing mimics the organizational principles of the brain in
its quest to replicate the brain's intellectual abilities. An impressive
ability of the brain is its adaptive intelligence, which allows the brain to
regulate its functions "on the fly" to cope with myriad and ever-changing
situations. In particular, the brain displays three adaptive and advanced
intelligence abilities of context-awareness, cross frequency coupling and
feature binding. To mimic these adaptive cognitive abilities, we design and
simulate a novel, hardware-based adaptive oscillatory neuron using a lattice of
magnetic skyrmions. Charge current fed to the neuron reconfigures the skyrmion
lattice, thereby modulating the neuron's state, its dynamics and its transfer
function "on the fly". This adaptive neuron is used to demonstrate the three
cognitive abilities, of which context-awareness and cross-frequency coupling
have not been previously realized in hardware neurons. Additionally, the neuron
is used to construct an adaptive artificial neural network (ANN) and perform
context-aware diagnosis of breast cancer. Simulations show that the adaptive
ANN diagnoses cancer with higher accuracy while learning faster from smaller
amounts of data and using a more compact and energy-efficient network than the
state-of-the-art non-adaptive ANNs used for cancer diagnosis. The work further
describes how hardware-based adaptive neurons can mitigate several critical
challenges facing contemporary ANNs. Modern ANNs require large amounts of
training data, energy and chip area and are highly task-specific; conversely,
hardware-based ANNs built with adaptive neurons show faster learning from
smaller datasets, compact architectures, energy-efficiency, fault-tolerance and
can lead to the realization of general artificial intelligence.

Applied Physics;Mesoscale and Nanoscale Physics;Materials Science