Neuromorphic Engineering
Autoencoders are a type of artificial neural network used to learn efficient representations of data in an unsupervised manner. They consist of an encoder that compresses the input data into a lower-dimensional representation and a decoder that reconstructs the original data from this compressed form. This architecture is particularly valuable for tasks like dimensionality reduction, feature learning, and anomaly detection, making them integral to concepts of self-organization.
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