STDP Learning of Image Patches with Convolutional Spiking Neural Networks
This work addresses image feature learning for machine learning applications, but it is incremental as it builds on existing spiking neural network methods.
The paper introduced convolutional spiking neural networks trained with unsupervised competitive learning to detect image features, achieving performance and convergence speed improvements over a baseline spiking network on the MNIST dataset.
Spiking neural networks are motivated from principles of neural systems and may possess unexplored advantages in the context of machine learning. A class of \textit{convolutional spiking neural networks} is introduced, trained to detect image features with an unsupervised, competitive learning mechanism. Image features can be shared within subpopulations of neurons, or each may evolve independently to capture different features in different regions of input space. We analyze the time and memory requirements of learning with and operating such networks. The MNIST dataset is used as an experimental testbed, and comparisons are made between the performance and convergence speed of a baseline spiking neural network.