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Neural network models of free recall and spatial navigation

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Two different models will be described during the seminar. A Model of free recall addressing the capability human memory in retrieving similar memories to a just retrieved one. This associative ability is at the base of our everyday processing of information. Current models of memory have not been able to underpin the mechanism that the brain could use in order to actively exploit similarities between memories. We introduce a novel mechanism capable to induce transitions between memories where similarities between memories are actively exploited by the neural dynamics to retrieve a new memory. The so generated spontaneous retrieval is compared to experiments of free recall. A Model of spatial navigation motivated by recent theoretical work trying to reconcile the declarative memory view and the spatial navigation view of hippocampal functioning, we investigate a recurrent neural network model that shows how the hippocampus could integrate episodic memories into generic “semantic relational networks”. We propose an explicit computational mechanism for the learning of such relational networks: predictive coding. The model learns to generate recurrent neural activations that are reminiscent of place cells and border cells in a simulated navigation environment, and can naturally account for context-specific representations and “time cells”.

This talk is part of the Computational Neuroscience series.

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