Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture.
We evaluate the proposed HM2-BP algorithm by training deep fully connected and convolutional SNNs based on the static MNIST  and dynamic neuromorphic N-MNIST .
Using Intel's Loihi neuromorphic research chip and ABR's Nengo Deep Learning toolkit, we analyze 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.
A relatively smaller body of work, however, discusses similarities between learning dynamics employed in deep artificial neural networks and synaptic plasticity in spiking neural networks.
To our knowledge, this is the first deep learning -- based solution to the problem of dynamic obstacle avoidance using event cameras on a quadrotor.
Convolutional Neural Networks (CNNs) are powerful and highly ubiquitous tools for extracting features from large datasets for applications such as computer vision and natural language processing.
Nonlinear interactions in the dendritic tree play a key role in neural computation.
We study fault tolerance of neural networks subject to small random neuron/weight crash failures in a probabilistic setting.