Affordable emerging computer hardware for neuromorphic computing applications

We are pursuing an investigation of neuromorphic computational models and architectures in order to leverage present understanding of how the estimated 10
¹¹
neurons and 10
¹⁵
neuron connections in the mammalian brain are able to do some of the things a human does, and as quickly as it does it, using slow base components, while consuming very little power on affordable synthetic non-biological computing hardware. Understanding and harvesting neurologically based methods is a promising approach with great potential that may help us achieve massively parallel computation far beyond the scope of traditional computing.