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.

Speaker(s): Ruthvik Vaila,

Location:
Room: 114
Bldg: Micron Engineering Building
MEC 114
Boise State University
Boise, Idaho
83706