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The biological brain is a highly plastic system within which the efficacy and structure of synaptic connections are constantly changing in response to internal and external stimuli. While numerous models of this plastic behavior exist at various levels of abstraction, how these mechanisms allow the brain to learn meaningful values is unclear. The Neural Engineering Framework (NEF) is a hypothesis...
SpiNNaker is a many-core supercomputer — designed for the simulation of large neural-networks — in which cores communicate with multicast packets. Routing within SpiNNaker is controlled by Ternary Content Addressable Memories (TCAMs) of quite limited size. As not all neural-network applications will result in routing tables sufficiently small to fit in TCAM some minimization is necessary. In this...
Background Our understanding of osseous morphology and pathology of the patellofemoral joint continues to improve with the use of magnetic resonance imaging (MRI), but a paucity of data currently exists in the pediatric population. Objective We aim to formulate a reproducible means of quantitative assessment of patellofemoral morphology in children using MRI and to describe morphological changes...
By building and simulating neural systems we hope to understand how the brain may work and use this knowledge to build neural and cognitive systems to tackle engineering problems. The Neural Engineering Framework (NEF) is a hypothesis about how such systems may be constructed and has recently been used to build the world's first functional brain model, Spaun. However, while the NEF simplifies the...
The analysis of large scale, complex networks using dynamic programming is of great use in many scientific and engineering disciplines. Current applications often require the analysis of scale-free networks with many millions of nodes and edges, presenting a huge computational challenge. Employing a distributed networks-on-chip infrastructure presents a unique opportunity of delivering power efficient...
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