  Humans construct internal cognitive maps of their environment directly from
sensory inputs without access to a system of explicit coordinates or distance
measurements. While machine learning algorithms like SLAM utilize specialized
visual inference procedures to identify visual features and construct spatial
maps from visual and odometry data, the general nature of cognitive maps in the
brain suggests a unified mapping algorithmic strategy that can generalize to
auditory, tactile, and linguistic inputs. Here, we demonstrate that predictive
coding provides a natural and versatile neural network algorithm for
constructing spatial maps using sensory data. We introduce a framework in which
an agent navigates a virtual environment while engaging in visual predictive
coding using a self-attention-equipped convolutional neural network. While
learning a next image prediction task, the agent automatically constructs an
internal representation of the environment that quantitatively reflects
distances. The internal map enables the agent to pinpoint its location relative
to landmarks using only visual information.The predictive coding network
generates a vectorized encoding of the environment that supports vector
navigation where individual latent space units delineate localized, overlapping
neighborhoods in the environment. Broadly, our work introduces predictive
coding as a unified algorithmic framework for constructing cognitive maps that
can naturally extend to the mapping of auditory, sensorimotor, and linguistic
inputs.
