Distinct codes for environment structure and symmetry in postrhinal and retrosplenial cortices

How does the response of egocentric boundary cells depend upon the coordinate system of environmental features?

Neurons in the retrosplenial (RSC) (Alexander et al., 2020a; LaChance and Hasselmo, 2024) and postrhinal cortex (POR) respond to environmental boundaries and configurations in egocentric coordinates relative to an animals current position. Neurons in these structures and adjacent structures also respond to spatial dimensions of self- motion such as running velocity (Carstensen et al., 2021; Robinson et al., 2024). Data and modeling suggest that these responses could be essential for guiding behaviors such as barrier avoidance and goal finding (Erdem and Hasselmo, 2012; 2014). However, these findings still leave the unanswered question: What are the features and what are the coordinate systems of these features that drive these egocentric neural responses? Here we present models of the potential circuit mechanisms generating egocentric responses in RSC. These can be generated based on coding of internal representations of barriers in head-centered coordinates of distance and angle that are transformed based on current running velocity for trajectory planning and obstacle avoidance. This hypothesis is compared with an alternate potentially complementary hypothesis that neurons in the same regions might respond to retinotopic position of features at the top, bottom or edges of walls as a precursor to head-centered coordinates. Alternate hypotheses include the forward scanning of trajectories (ray tracing) to test for collision with barriers, or the comparison of optic flow on different sides of the animal. These hypotheses generate complementary modeling predictions about how changes in environmental parameters could alter the neural responses of egocentric boundary cells that are presented here.

Development of the SPEAR Model: Separate Phases of Encoding and Retrieval Are Necessary for Storing Multiple Overlapping Associative Memories

In keeping with the historical focus of this special issue of Hippocampus, this paper reviews the history of my development of the SPEAR model. The SPEAR model proposes that separate phases of encoding and retrieval (SPEAR) allow effective storage of multiple overlapping associative memories in the hippocampal formation and other cortical structures. The separate phases for encoding and retrieval are proposed to occur within different phases of theta rhythm with a cycle time on the order of 125 ms. The same framework applies to the slower transition between encoding and consolidation dynamics regulated by acetylcholine. The review includes description of the experimental data on acetylcholine and theta rhythm that motivated this model, the realization that existing associative memory models require these different dynamics, and the subsequent experimental data supporting these dynamics. The review also includes discussion of my work on the encoding of episodic memories as spatiotemporal trajectories, and some personal description of the episodic memories from my own spatiotemporal trajectory as I worked on this model.

Distinct cortical spatial representations learned along disparate visual pathways

Recent experimental studies have discovered diverse spatial properties, such as head direction tuning and egocentric tuning, of neurons in the postrhinal cortex (POR) and revealed how the POR spatial representation is distinct from the retrosplenial cortex (RSC). However, how these spatial properties of POR neurons emerge is unknown, and the cause of distinct cortical spatial representations is also unclear. Here, we build a learning model of POR based on the pathway from the superior colliculus (SC) that has been shown to have motion processing within the visual input. Our designed SC-POR model demonstrates that diverse spatial properties of POR neurons can emerge from a learning process based on visual input that incorporates motion processing. Moreover, combining SC-POR model with our previously proposed V1-RSC model, we show that distinct cortical spatial representations in POR and RSC can be learnt along disparate visual pathways (originating in SC and V1), suggesting that the varying features encoded in different visual pathways contribute to the distinct spatial properties in downstream cortical areas.