A striking example of this deficiency is provided by classical studies of free recall, where participants are asked to recall lists of unrelated words after a quick exposure ( Murdock, 1962 Kahana, 1996). Yet recalling this information is often challenging, especially when no precise cues are available. Human long-term memory capacity for names, facts, episodes and other aspects of our lives is practically unlimited. Overall, we propose a neural network model of information retrieval broadly compatible with experimental observations and is consistent with our recent graphical model ( Romani et al., 2013). It shows that items having larger number of neurons in their representation are statistically easier to recall and reveals possible bottlenecks in our ability of retrieving memories. The network dynamics qualitatively predicts the distribution of time intervals required to recall new memory items observed in experiments. We show that oscillating feedback inhibition in the presence of noise induces transitions between these states triggering the retrieval of different memories. Meanfield analysis of the model reveals stable states of the network corresponding (1) to single memory representations and (2) intersection between memory representations. We present a model for memory retrieval based on a Hopfield neural network where transition between items are determined by similarities in their long-term memory representations. In a classical free recall paradigm, where participants are asked to repeat a briefly presented list of words, people make mistakes for lists as short as 5 words. Nevertheless, recalling is often a challenging task. Human memory can store large amount of information. 3Department of Neurotechnologies, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.2Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.1Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel.Stefano Recanatesi 1, Mikhail Katkov 1, Sandro Romani 2 and Misha Tsodyks 1,3 *
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