Uncovering rat hippocampal population codes: topological vs. topographic maps

Zhe Chen
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology

(March 6, 2013 3:00 PM - 3:50 PM)

Uncovering rat hippocampal population codes: topological vs. topographic maps

Abstract

The hippocampus plays an important role in representing space (for spatial navigation) and time (for episodic memory). Spatial representation of the environment is pivotal for navigation in rodents and primates. Two types of maps, topographical and topological, may be used for spatial representation.  Rodent hippocampal place cells exhibit spatially-selective firing patterns in an environment that can be decoded to determine the animal’s location, heading, and past and future trajectory. We recorded ensembles of hippocampal neurons as rodents freely foraged in one and two-dimensional spatial environments, and  we used a ``decode-to-uncover''  strategy to examine the temporally structured patterns embedded in the ensemble spiking activity in the absence of observed spatial correlates during rodent navigation.  Specifically, the spatial environment was represented by a finite discrete state space.

Trajectories across spatial  locations (``states'') were associated with consistent hippocampal ensemble spiking patterns, which were characterized by a state transition matrix of a hidden Markov model. From this state transition matrix, we inferred a topology graph that defined the connectivity in the state space.  In contrast to a topographic code, our results support the efficiency of topological coding in the presence of sparse sample size and fuzzy space mapping.