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The Hopfield network is a binary, fully recurrent network that, when started on a random activation state, settles the activation over time into a state that represents a solution (Hopfield & Tank, 1986). This architecture has been analyzed thoroughly using tools from statistical physics. In particular, with symmetric weights, no self-connections, and asynchronous neuron activation updates, a Lyapunov function exists for the network, which means that the network activity will eventually settle. The Hopfield network can be used as an associate memory or as a general optimizer. When used as an associative memory, the weight values are computed from the set of patterns to be stored. During retrieval, part of the pattern to be retrieved is activated, and the network settles into the complete pattern. When used as an optimizer, the function to be optimized is mapped into the Lyapunov function of the network, which is then solved for the...
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Hopfield, J. J., & Tank, D. W. (1986). Computing with neural circuits: A model. Science,233, 624–633.
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Miikkulainen, R. (2011). Hopfield Network. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_371
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DOI: https://doi.org/10.1007/978-0-387-30164-8_371
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30768-8
Online ISBN: 978-0-387-30164-8
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