Spatiotemporal Classification Using Neuroscience-Inspired Dynamic Architectures

We discuss a neuroscience-inspired dynamic architecture (NIDA) and associated design method based on evolutionary optimization. NIDA networks designed to perform anomaly detection tasks and control tasks have been shown to be successful in previous work. In particular, NIDA networks perform well on tasks that have a temporal component. We present methods for using NIDA networks on classification tasks in which there is no temporal component, in particular, the handwritten digit classification task. The approach we use for both methods produces useful subnetworks that can be combined to produce a final network or combined to produce results using an ensemble method. We discuss how a similar approach can be applied to other problem types.