A unified model of hippocampal spatial and object cells involving bidirectionally coupled Lateral and Medial Entorhinal Cortical layers

Abstract

Popularly referred to as the GPS of the brain, the hippocampus has a variety of neurons that encode spatial properties of the environment. These spatial cells of the hippocampus may be broadly placed under two categories - those that encode spatial locations (e.g. place cells, grid cells etc) and those that encode spatial objects (eg. Object-sensitive cells. Object-trace cells etc). There are computational models that explain emergence of specific types of spatial cells, but it is challenging to construct integrative models that can demonstrate the emergence of the complete range of spatial cells both space and object type. We present a simple, unified computational model that explains the emergence of a wide variety of object- and spatially-sensitive neurons in the hippocampus. The model is essentially a deep neural network that combines visual and path integration information. The visual information is received by a part of the model that is analogous to Lateral Entorhinal Cortex (LEC) and path integration information is received by a layer analogous to Medial Entorhinal Cortex (MEC). In order to arrive at a consistent estimate of position, LEC and MEC in the model are connected laterally using a Graph Neural Network. The model is trained to predict position, orientation and reward of a simulated agent. The agent explores a box-like environment with colored walls and objects on the floor and is rewarded based on its encounters with objects. The model demonstrates the emergence of the following 7 types of spatial and object cells - place, grid, border, object, object-sensitive, object-vector and, object-trace cells. The model findings compare favorably with a large body of experimental literature on hippocampal spatial cells.