  We train spiking deep networks using leaky integrate-and-fire (LIF) neurons,
and achieve state-of-the-art results for spiking networks on the CIFAR-10 and
MNIST datasets. This demonstrates that biologically-plausible spiking LIF
neurons can be integrated into deep networks can perform as well as other
spiking models (e.g. integrate-and-fire). We achieved this result by softening
the LIF response function, such that its derivative remains bounded, and by
training the network with noise to provide robustness against the variability
introduced by spikes. Our method is general and could be applied to other
neuron types, including those used on modern neuromorphic hardware. Our work
brings more biological realism into modern image classification models, with
the hope that these models can inform how the brain performs this difficult
task. It also provides new methods for training deep networks to run on
neuromorphic hardware, with the aim of fast, power-efficient image
classification for robotics applications.
