Zhaoze Wang

The vertebrate hippocampus is thought to use recurrent connectivity in area CA3 to support episodic memory recall from partial cues. This brain area also contains place cells, whose location-selective firing fields implement maps supporting spatial memory. Here we show that place cells emerge in networks trained to remember temporally continuous sensory episodes. We model CA3 as a recurrent autoencoder that recalls and reconstructs sensory experiences from noisy and partially occluded observations by agents traversing simulated arenas. The agents move in realistic trajectories modeled from rodents and environments are modeled as continuously varying, high-dimensional, sensory experience maps (spatially smoothed Gaussian random fields). Training our autoencoder to accurately pattern-complete and reconstruct sensory experiences with a constraint on total activity causes spatially localized firing fields, i.e., place cells, to emerge in the encoding layer. The emergent place fields reproduce key aspects of hippocampal phenomenology: a) remapping (maintenance of and reversion to distinct learned maps in different environments), implemented via repositioning of experience manifolds in the network’s hidden layer, b) orthogonality of spatial representations in different arenas, c) robust place field emergence in differently shaped rooms, with single units showing multiple place fields in large or complex spaces, and (d) slow representational drift of place fields. We argue that these results arise because continuous traversal of space makes sensory experience temporally continuous. We make testable predictions: a) rapidly changing sensory context will disrupt place fields, b) place fields will form even if recurrent connections are blocked, but reversion to previously learned representations upon remapping will be abolished, c) the dimension of temporally smooth experience sets the dimensionality of place fields, including during virtual navigation of abstract spaces.

Researchers have long theorized that animals are capable learning cognitive maps of their environment - a simultaneous representation of context, experience, and position. Since their discovery in the early 1970’s, place cells have been assumed to be the neural substrate of these maps. Individual place cells explicitly encode position and appear to encode experience and context through global, population level firing properties. Context, in particular, appears to be encoded through remapping, a process in which subpopulations of place cells change their tuning in response to changes in sensory cues. While many studies have looked at the physiological basis of remapping, the field still lacks explicit calculations of contextual capacity as a function of place field firing properties. Here, we tackle such calculations. First, we construct a geometric approach for understanding population level activity of place cells, assembled from known firing field statistics. We treat different contexts as low dimensional structures embedded in the high dimensional space of firing rates and the distance between these structures as reflective of discriminability between the underlying contexts. Accordingly, we investigate how changes to place cell firing properties effect the distances between representations of different environments within this rate space. Using this approach, we find that the number of contexts storable by the hippocampus grows exponentially with the number of place cells, and calculate this exponent for environments of different sizes. We further identify a fundamental tradeoff between high resolution encoding of position and the number of storable contexts. This tradeoff is tuned by place cell width, which might explain the change in firing field scale along the ventral dorsal axis of the Hippocampus. Finally, we demonstrate that clustering of place cells near likely points of confusion, such as boundaries, increases the contextual capacity of the place system within our framework.