Oscillatory neural network for pattern recognition: trajectory based classification and supervised learning

Abstract

Computer algorithms that match human performance in recognizing written text or spoken conversation remain elusive. The reasons why the human brain far exceeds any existing recognition scheme to date in the ability to generalize and to extract invariant characteristics relevant to category matching are not clear. However, it has been postulated that the dynamic distribution of brain activity (spatiotemporal activation patterns) is the mechanism by which stimuli are encoded and matched to categories. This research focuses on supervised learning using a trajectory based distance metric for category discrimination in an oscillatory neural network model. Classification is accomplished using a trajectory based distance metric. Since the distance metric is differentiable, a supervised learning algorithm based on gradient descent is demonstrated. Classification of spatiotemporal frequency transitions and their relation to a priori assessed categories is shown along with the improved classification results after supervised training. The results indicate that this spatiotemporal representation of stimuli and the associated distance metric is useful for simple pattern recognition tasks and that supervised learning improves classification results.

Automatic Conversational Scene Analysis in Children with Asperger Syndrome/High-Functioning Autism and Typically Developing Peers

“…Simply, rational inference now becomes a local-value strategy, capturing the mechanics of some single frames within the dynamic, effortless and kaleidoscopic flow of conversational speech, which we just began to tackle. For example, much research is needed on how the brain effectively manages the inherent complexity that our analysis highlighted [58]. From a neurocognitive viewpoint, the concept of mutual knowledge implies that information from multiple sources must be at the same time flexibly integrated within an individual's perceptual focus [59], [60], as well as shared with the interlocutor.…”