Retrosplenial cortex
Related papers: 3
About
The retrosplenial cortex (RSC) is a brain region located in the posterior medial cortex that plays a critical role in spatial navigation, memory, and the integration of egocentric (self-centered) and allocentric (world-centered) reference frames. In biological systems, the RSC acts as a hub connecting the hippocampus, posterior parietal cortex, and visual areas, enabling animals to translate between viewpoint-dependent and viewpoint-independent representations of space. It is particularly involved in landmark recognition, path integration, and scene perception at variable distances. In robotics and AI, the RSC serves as inspiration for computational navigation architectures that mimic mammalian spatial reasoning, often paired with models of grid cells and place cells to build biologically plausible localization and mapping systems. Hardware implementations on platforms such as FPGAs have demonstrated that RSC-inspired reference frame transformations can enable efficient, robust robot navigation. Understanding the RSC matters because it offers principled, brain-derived solutions to core robotics challenges like flexible coordinate transformation, context-dependent navigation, and scalable spatial memory.
Top Researchers
Jeffrey L. Krichmar
Institution: —
Florian Röhrbein
Institution: —
Andrew S. Persichetti
Institution: —
Daniel D. Dilks
Institution: —
Timo Oess
Institution: —
Adithya Krishna
Institution: —
Chetan Singh Thakur
Institution: —
Divyansh Mittal
Institution: —
Siri Garudanagiri Virupaksha
Institution: —
Abhishek Ramdas Nair
Institution: —
Top Institutes
Top Cited Papers
Perceived egocentric distance sensitivity and invariance across scene-selective cortex
Andrew S. Persichetti, Daniel D. Dilks
Citations: 72 • 2016
A Computational Model for Spatial Navigation Based on Reference Frames in the Hippocampus, Retrosplenial Cortex, and Posterior Parietal Cortex
Timo Oess, Jeffrey L. Krichmar, Florian Röhrbein
Citations: 53 • 2017
Biomimetic FPGA-based spatial navigation model with grid cells and place cells
Adithya Krishna, Divyansh Mittal, Siri Garudanagiri Virupaksha, Abhishek Ramdas Nair, Rishikesh Narayanan, Chetan Singh Thakur
Citations: 9 • 2021