Hopfield Network with State-Dependent Threshold

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In a Hopfield network model, the two states of a neuron (firing or at rest) are denoted by the values . The future state of a neuron is determined by the present state of all the other neurons via the synaptic matrix, but is independent of its own present state. The overlap between the original and the time-evolved state of the network is a measure of "memory recall"; it depends upon the fractional memory load and a state-dependent neuron firing threshold . This Demonstration obtains the average time-evolution of patterns stored in a network of neurons () with a neuron-state-dependent firing threshold . The distribution of resulting overlaps between the time-evolved and original states, , shifts from a peak at one to a broad distribution at lower values of as the memory load increases, indicating the threshold memory capacity of the neural network.

Contributed by: Vaibhav Vavilala and Yogesh Joglekar (March 2013)
Open content licensed under CC BY-NC-SA



Snapshot 1: at memory load , the recall distribution is broad, thus showing a lack of near-perfect recall when the threshold is near zero

Snapshot 2: as the threshold strength is increased, for the same memory load , the probability of recall and thus the memory capacity of the network are increased

Snapshot 3: conversely, when is decreased, even for a smaller memory load , the distribution becomes broad, showing a diminished memory capacity for the network


[1] J. J. Hopfield, "Neural Networks and Physical Systems with Emergent Collective Computational Abilities," Proceedings of the National Academy of Sciences, 79(8), 1982 pp. 2554–2558. www.pnas.org/content/79/8/2554.full.pdf.

[2] D. J. Amit, Modeling Brain Function, New York: Cambridge University Press, 1992.

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