Relative Time Scales in the Self-Organization of Pattern Classification: From "One-Shot" to Statistical Learning

. We propose a biologically plausible learning scheme which enables a system to classify patterns based on the presentation of one single example. During a learning mode, the system recognizes whether a category for a presented pattern has been instantiated before, or whether it must be classified as unknown. In this case a new category is created autonomously. The proposed "one-shot" learning rules are characterized by certain time scale relations between system parameter dynamics and input dynamics. We show that reversing these relations (leading to a statistical learning regime), the learning dynamics can be reduced to a Kohonen learning scheme. Our results show that both "one-shot" and statistical learning in biological systems might be governed by identical laws. 1 Introduction The visual system of higher mammals is able to categorize patterns based on single or few examples. When classifying a pattern as "unknown", a new category can be created which is used to classify similar ..