John Wade

TL;DR: A synaptic weight association training (SWAT) algorithm for spiking neural networks (SNNs) that merges the Bienenstock-Cooper-Munro (BCM) learning rule with spike timing dependent plasticity (STDP) and yields a unimodal weight distribution.

TL;DR: Results show that slow inward currents cause synchronized postsynaptic activity in remote neurons and subsequently allow Spike-Timing-Dependent Plasticity based learning to occur at the associated synapses, and that bidirectional communication between neurons and astrocytes underpins dynamic coordination between neuron clusters.

TL;DR: A multi-compartmental mathematical model is developed which proposes a novel mechanism whereby the flow of cations in thin processes is restricted due to negatively charged membrane lipids which result in the formation of deep potential wells near the dipole heads.

TL;DR: This model of self-repair is based on recent research showing that retrograde signaling via astrocytes can increase the PR of neurotransmitter release at damaged or low transmission PR synapses and provides a biologically inspired basis for developing highly adaptive, distributed computing systems that can self- repair autonomously without the traditional constraint of a central fault detect/repair unit.

TL;DR: This paper demonstrates that the hardware can self-detect and self-repair synaptic faults without the conventional components for the fault detection and fault repairing.