  Brain-inspired learning models attempt to mimic the cortical architecture and
computations performed in the neurons and synapses constituting the human brain
to achieve its efficiency in cognitive tasks. In this work, we present
convolutional spike timing dependent plasticity based feature learning with
biologically plausible leaky-integrate-and-fire neurons in Spiking Neural
Networks (SNNs). We use shared weight kernels that are trained to encode
representative features underlying the input patterns thereby improving the
sparsity as well as the robustness of the learning model. We demonstrate that
the proposed unsupervised learning methodology learns several visual categories
for object recognition with fewer number of examples and outperforms
traditional fully-connected SNN architectures while yielding competitive
accuracy. Additionally, we observe that the learning model performs out-of-set
generalization further making the proposed biologically plausible framework a
viable and efficient architecture for future neuromorphic applications.
