Abstract
The capacities of a specially designed neural network for familiarity recognition and recollection have been compared. Recognition is based on calculating “image familiarity” as a modified Hopfield energy function in which the value of the inner sum is replaced by the sign of this value. This replacement makes the calculation of familiarity compatible with the basic dynamic equations of the Hopfield network and is in fact reduced to calculating the scalar product of the neuronet state vectors at two successive time steps.
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Original Russian Text © E.V. Budilova, M.P. Karpenko, L.M. Kachalova, A.T. Terekhin, 2009, published in Biofizika, 2009, Vol. 54, No. 3, pp. 500–507.
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Budilova, E.V., Karpenko, M.P., Kachalova, L.M. et al. Familiarity recognition and recollection: A neural network model. BIOPHYSICS 54, 349–354 (2009). https://doi.org/10.1134/S0006350909030178
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DOI: https://doi.org/10.1134/S0006350909030178