Lattice Map Spiking Neural Networks (LM-SNNs) for Clustering and Classifying Image Data
This work addresses image classification challenges for researchers in neuromorphic computing, but it is incremental as it builds on existing SNN and SOM techniques.
The paper tackled the problem of clustering and classifying image data by introducing Lattice Map Spiking Neural Networks (LM-SNNs) that combine SNNs and self-organized maps with unsupervised learning rules, achieving effectiveness on the MNIST dataset and Atari Breakout images.
Spiking neural networks (SNNs) with a lattice architecture are introduced in this work, combining several desirable properties of SNNs and self-organized maps (SOMs). Networks are trained with biologically motivated, unsupervised learning rules to obtain a self-organized grid of filters via cooperative and competitive excitatory-inhibitory interactions. Several inhibition strategies are developed and tested, such as (i) incrementally increasing inhibition level over the course of network training, and (ii) switching the inhibition level from low to high (two-level) after an initial training segment. During the labeling phase, the spiking activity generated by data with known labels is used to assign neurons to categories of data, which are then used to evaluate the network's classification ability on a held-out set of test data. Several biologically plausible evaluation rules are proposed and compared, including a population-level confidence rating, and an $n$-gram inspired method. The effectiveness of the proposed self-organized learning mechanism is tested using the MNIST benchmark dataset, as well as using images produced by playing the Atari Breakout game.