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Poster B90 in Poster Session B - Thursday, August 8, 2024, 1:30 – 3:30 pm, Johnson Ice Rink
Predictive Learning for Self-Supervised Mapping and Localization
William Chapman3,1 (), Andrew Alexander2, Frances Chance1, Michael Hasselmo3; 1Sandia National Labs, 2University of California Santa Barbara, 3Boston University
Spatial navigation involves the formation of coherent representations of a map-like space, while simultaneously tracking current location in a primarily unsupervised manner. Despite a plethora of neurophysiological experiments revealing spatially tuned neurons across the mammalian neocortex and subcortical structures, it remains unclear how such representations are acquired in the absence of explicit allocentric targets. Drawing upon the concept of predictive learning, we utilize a biologically plausible learning rule which utilizes sensory-driven observations with internally driven expectations and learns through a contrastive manner to better predict sensory information. We implement this learning rule in a network with the feedforward and feedback pathways known to be necessary for spatial navigation. After training, we find that the receptive fields of the modeled units resemble experimental findings, with allocentric and egocentric representations in the expected order along processing streams.These findings suggest that a self-supervised prediction of sensory information can extract latent structure from the environment.
Keywords: Learning Rule Spatial Navigation Hippocampus