Dresden 2020 – scientific programme

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DY: Fachverband Dynamik und Statistische Physik

DY 58: Poster: Nonlinear Dynamics; Pattern Formation; Networks; Delay Systems; Synchronization

DY 58.8: Poster

Thursday, March 19, 2020, 15:00–18:00, P1C

Learning in Simple Heteroclinic Networks — •Maximilian Voit and Hildegard Meyer-Ortmanns — Jacobs University Bremen, Bremen, Deutschland

Heteroclinic networks provide a promising candidate attractor to generate reproducible sequential series of metastable states. From an engineering point of view it is known how to construct heteroclinic networks to achieve certain dynamics, but a data based approach for the inference of heteroclinic dynamics is still missing. We present a method by which a template system dynamically learns to mimic an input sequence of metastable states. For this purpose, the template is unidirectionally, linearly coupled to the input. At the same time, the learning dynamics causes an adaptation of the eigenvalues of the template in order to minimize the difference of template dynamics and input sequence. Thus, after the learning procedure, the trained template constitutes a model with dynamics that are most similar to the training data. We demonstrate the capabilities and possible difficulties of this method at different examples. Our approach may be applied to infer the topology and the connection strength of a heteroclinic network from data in a dynamic fashion and may serve as a model for learning in the context of winnerless competition.