Abstract
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.
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© 1997 Springer-Verlag Berlin Heidelberg
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Kopecz, K., Mohraz, K. (1997). Relative time scales in the self-organization of pattern classification: From “one-shot” to statistical learning. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020164
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DOI: https://doi.org/10.1007/BFb0020164
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