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Learning to be attractive: probabilistic computation with dynamic attractor networks

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Academic year: 2021

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Figure

Fig. 1. Illustration of the basic approximation process proposed in this article: the approximation of a full probability distribution by its argmax (the MAP estimate), and its associated probability
Fig. 2. Functionality of the proposed neural model. Given an afferent synaptic input (left), supposed to be population-coded so that activity at a certain place codes for a certain quantity/concept, a neural layer converges to an attractor state (right) wi
Fig. 3. Schematic sketch of the learning of kernel matrices, i.e., lateral connection weights
Fig. 4. 100x100 pixel input patterns used for training the lateral connection weights of the model (axes are scaled differently).
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