PhoenixMR: A GPU-based MRI simulation framework with runtime-dynamic code execution.

Background: Simulations of physical processes and behavior can provide unique insights and understanding of real-world problems. Magnetic Resonance Imaging (MRI) is an imaging technique with several components of complexity. Several of these components have been characterized and simulated in the past.

Experimental Demonstration of Reservoir Computing with Self-Assembled Percolating Networks of Nanoparticles.

The complex self-assembled network of neurons and synapses that comprises the biological brain enables natural information processing with remarkable efficiency. Percolating networks of nanoparticles (PNNs) are complex self-assembled nanoscale systems that have been shown to possess many promising brain-like attributes and which are therefore appealing systems for neuromorphic computation. Here experiments are performed that show that PNNs can be utilized as physical reservoirs within a nanoelectronic reservoir computing framework and demonstrate successful computation for several benchmark tasks (chaotic time series prediction, nonlinear transformation, and memory capacity).

Computation via Neuron-like Spiking in Percolating Networks of Nanoparticles.

The biological brain is a highly efficient computational system in which information processing is performed via electrical spikes. Neuromorphic computing systems that work on similar principles could support the development of the next generation of artificial intelligence and, in particular, enable low-power edge computing. Percolating networks of nanoparticles (PNNs) have previously been shown to exhibit critical spiking behavior, with promise for highly efficient natural computation.

Reservoir computing using networks of memristors: effects of topology and heterogeneity.

Reservoir computing (RC) has attracted significant interest as a framework for the implementation of novel neuromorphic computing architectures. Previously attention has been focussed on software-based reservoirs, where it has been demonstrated that reservoir topology plays a role in task performance, and functional advantage has been attributed to small-world and scale-free connectivity. However in hardware systems, such as electronic memristor networks, the mechanisms responsible for the reservoir dynamics are very different and the role of reservoir topology is largely unknown.

Reservoir computing with 3D nanowire networks.

Networks of nanowires are currently being explored for a range of applications in brain-like (or neuromorphic) computing, and especially in reservoir computing (RC). Fabrication of real-world computing devices requires that the nanowires are deposited sequentially, leading to stacking of the wires on top of each other. However, most simulations of computational tasks using these systems treat the nanowires as 1D objects lying in a perfectly 2D plane - the effect of stacking on RC performance has not yet been established.