Abstract
We present a new self-organized neural model that we term ReST (Resilient Self-organizing Tissue). ReST can be run as a convolutional neural network (CNN), possesses a \(C^\infty \) energy function as well as a probabilistic interpretation of neural activities, which arises from the constraint of log-normal activity distribution over time that is enforced during learning. We discuss the advantages of a \(C^\infty \) energy function and present experiments demonstrating the self-organization and self-adaptation capabilities of ReST. In addition, we provide a performance benchmark for the publicly available TensorFlow-implementation.
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Gepperth, A., Sarkar, A., Kopinski, T. (2018). An Energy-Based Convolutional SOM Model with Self-adaptation Capabilities. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11140. Springer, Cham. https://doi.org/10.1007/978-3-030-01421-6_41
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DOI: https://doi.org/10.1007/978-3-030-01421-6_41
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