How landmarks and self-motion cues combine to build the brain's spatial maps and guide behavior

The brain builds its spatial maps by combining input from external landmarks with cues arising from the animal's own movement. In this dissertation, I used behavioral tasks, virtual reality manipulations, in vivo electrophysiological recording, and theoretical modeling to dissect how these inputs generate spatial representations in the entorhinal cortex of the mouse. First, I asked how entorhinal grid, border, and speed cells combine multisensory land-mark and self-motion information (Campbell et al. 2018). To study this question, I recorded entorhinal neurons with tetrodes while mice navigated in head-fixed virtual reality. I then manipulated the virtual environments to tease apart the contributions of visual and motor cues to the firing patterns of these cells. Manipulating the gain between the animal's locomotion and the movement of the visual scene revealed that border cells responded to landmark cues while grid and speed cells responded to combinations of locomotion, optic flow, and landmark cues in a context-dependent manner, with optic flow becoming more influential when it was faster than expected. A network model explained these results, revealing a phase transition between two regimes in which grid cells remain coherent with or break away from the landmark reference frame. These dynamics were mathematically equivalent to a coupled oscillator system in which land-marks and path integration compete to drive grid cell firing. During path integration-based navigation, mice estimated their position following principles predicted by the electrophysiological recordings. These results provided a theoretical framework for understanding how landmark and self-motion cues combine during navigation to generate spatial representations and guide behavior. Next, I tested the predictions of the coupled oscillator model by recording large populations of neurons along the whole dorsal-ventral axis of medial entorhinal cortex using Neuropixels probes (Campbell et al. 2019, in preparation). These data sets contain the largest number of co-recorded entorhinal cells to date (maximum 537; total of 19,468 units). First, we showed that the main prediction of the model holds in large populations of entorhinal neurons: manipulating the gain between the animal's running speed and its progression down the virtual hallway caused maps to shift for small changes in gain (sub-critical response), and remap for gain changes larger than a critical threshold (super-critical response). We then experimentally manipulated the strength of the landmark input to entorhinal cortex by reducing the contrast of the visual cues. In agreement with the model's predictions, decreased contrast reduced the stability of entorhinal spatial maps and amplified cells' responses to gain changes. Finally, we extended our theoretical work by showing that the sub-critical regime of our model reduces to a Kalman filter that optimally combines the position estimate from path integration with the position estimate from landmarks. Together, these results advance our understanding of the algorithms by which the mammalian brain combines diverse sensory and motor cues during navigation.

Advisor: Giocomo Lisa
Hestrin Shaul
Baccus Stephen A. 
Clandinin Thomas R. (Thomas Robert)
Huguenard John