  Spiking neural networks are biologically plausible counterparts of the
artificial neural networks, artificial neural networks are usually trained with
stochastic gradient descent and spiking neural networks are trained with spike
timing dependant plasticity. Training deep convolutional neural networks is a
memory and power intensive job. Spiking networks could potentially help in
reducing the power usage. There is a large pool of tools for one to chose to
train artificial neural networks of any size, on the other hand all the
available tools to simulate spiking neural networks are geared towards
computational neuroscience applications and they are not suitable for real life
applications. In this work we focus on implementing a spiking CNN using
Tensorflow to examine behaviour of the network and empirically study the effect
of various parameters on learning capabilities and also study catastrophic
forgetting in the spiking CNN and weight initialization problem in R-STDP using
MNIST and N-MNIST data sets.
