Abstract
SummarySpatial learning requires estimates of location that may be obtained by path integration or from positional cues. Grid and other spatial firing patterns of neurons in the superficial medial entorhinal cortex (MEC) suggest roles in behavioral estimation of location. However, distinguishing the contributions of path integration and cue-based signals to spatial behaviors is challenging, and the roles of identified MEC neurons are unclear. We use virtual reality to dissociate linear path integration from other strategies for behavioral estimation of location. We find that mice learn to path integrate using motor-related self-motion signals, with accuracy that decreases steeply as a function of distance. We show that inactivation of stellate cells in superficial MEC impairs spatial learning in virtual reality and in a real world object location recognition task. Our results quantify contributions of path integration to behavior and corroborate key predictions of models in which stellate cells contribute to location estimation.
Highlights
The ability to learn and update estimates of location during movement is central to theories of animal and artificial navigation (Durrant-Whyte and Bailey, 2006; McNaughton et al, 1996, 2006)
A Behavioral Task for Quantitative Investigation of Cueand Path Integration-Based Estimation of Location To be able to selectively investigate neural mechanisms for beaconing and path integration, we developed a behavioral test that, depending on the task configuration, can be effectively solved either using local cues or by estimating location from self-motion signals, but in which potentially confounding external spatial cues are not available (Figure 1A; Movie S1)
L2SC Output Is Required for Object Location Recognition to establish whether the deficits we identified in virtual reality-based tests of location estimation extend to real world behaviors, we investigated the effects on location recognition of expressing TeLC in L2SCs
Summary
The ability to learn and update estimates of location during movement is central to theories of animal and artificial navigation (Durrant-Whyte and Bailey, 2006; McNaughton et al, 1996, 2006) In mammals, this core cognitive function may be achieved either using spatial cues, for example, through triangulation or beaconing strategies (Geva-Sagiv et al, 2015), or by path integration mechanisms, which generate representations of location from information about direction and speed of movement (Etienne and Jeffery, 2004). While grid cells encode representations of an animal’s current location and theoretical models predict they may be used to plan trajectories to future locations (Burak and Fiete, 2009; Bush et al, 2015; Stemmler et al, 2015), it is not clear whether output from L2SCs is required for behaviors that require estimation of specific locations
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