Xuan Choo

TL;DR: A 2.5-million-neuron model of the brain (called “Spaun”) is presented that bridges the gap between neural activity and biological function by exhibiting many different behaviors and is presented only with visual image sequences.

TL;DR: Nengo 2.0 is described, which is implemented in Python and uses simple and extendable syntax, simulates a benchmark model on the scale of Spaun 50 times faster than Nengo 1.4, and has a flexible mechanism for collecting simulation results.

TL;DR: An analysis of tradeoffs between network size, inference speed, and energy cost indicates that Loihi's comparative advantage over other low-power computing devices improves for larger networks.

TL;DR: In this article, the inference speed, dynamic power consumption, and energy cost per inference of a two-layer neural network keyword spotter trained to recognize a single phrase were analyzed using Intel's Loihi neuromorphic research chip and ABR's Nengo Deep Learning toolkit.

TL;DR: A model of symbol manipulation implemented using spiking neurons and closely tied to the anatomy of the cortex, basal ganglia, and thalamus is presented, suggesting modifications to the standard structure of production system rules, and offering a neurological explanation for the 50 millisecond cognitive cycle time.