One component of recognition memory is the recognition of familiarity, where the subject remembers the fact that a particular object or event has been encountered previously but is unable to remember the details of the object or event, or the context in which the object or event was experienced. Experiments reported by Standing [1970, 1973] identified a paradoxically large memory capacity for recognition of the familiarity of natural images, words, and musical melodies. Existing neural network models for recognition of familiarity have demonstrated the potential for recognition of familiarity with memory of the order of n2, where n is the number of neurons in the model. In the present study we propose a new model for the recognition of familiarity oriented to the recognition of the familiarity of time sequences (especially number sequences), which is characterized by sparse encoding of input patterns. Computer experiments showed that specific memory capacity in this model in certain conditions of errorless recognition is greater than that in known Hopfield-type models.
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Translated from Zhurnal Vysshei Nervnoi Deyatel’nosti imeni I. P. Pavlova, Vol. 70, No. 3, pp. 383–393, May–June, 2020.
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Kazanovich, Y.B. A Neural Network Model of the Recognition of the Familiarity of Number Sequences. Neurosci Behav Physi 51, 65–72 (2021). https://doi.org/10.1007/s11055-020-01040-8
- recognition memory
- recognition of familiarity
- neural network models
- memory capacity