A model for navigation in unknown environments based on a reservoir of hippocampal sequences. (April 2020)

• Record Type: Journal Article
• Authors: Leibold, Christian
• Is Part Of: Neural networks. Volume 124(2020)
• Page Start: 328
• Page End: 342
• Publication Date: 2020-04
• Subjects: Hippocampus -- Sequences -- Reinforcement learning -- Replay -- Theta oscillations; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2020.01.014
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Hippocampal place cell populations are activated in sequences on multiple time scales during active behavior, resting and sleep states, suggesting that these sequences are the genuine dynamical motifs of the hippocampal circuit. Recently, prewired hippocampal place cell sequences have even been reported to correlate to future behaviors, but so far there is no explanation of what could be the computational benefits of such a mapping between intrinsic dynamical structure and external sensory inputs. Here, I propose a computational model in which a set of predefined internal sequences is used as a dynamical reservoir to construct a spatial map of a large unknown maze based on only a small number of salient landmarks. The model is based on a new variant of temporal difference learning and implements a simultaneous localization and mapping algorithm. As a result sequences during intermittent replay periods can be decoded as spatial trajectories and improve navigation performance, which supports the functional interpretation of replay to consolidate memories of motor actions.