This poster introduces a proposal that the brain uses grid cells to perform unsupervised learning of landmark locations. It shows the results of a network model trained on 1000 environments, compared to a bag-of-features model. It also lays out discussion topics for future extensions of this work.
This poster highlights one of the foundational topics of Numenta research: sparse distributed representations, or SDRs for short. SDRs are how the brain represents information. The mathematical properties of SDRs are essential components of biological intelligence. This poster examines how accurately neurons can recognize sparse patterns.
In this poster, we show how the brain might use a grid cell code to represent 1) sensed structures at locations in viewer-centric coordinates and 2) sensed features and locations in object-centric coordinates. We lay out a mechanism that shows the transform routes between grid cells that enable object recognition.
This poster highlights sparse distributed representations, a method the brain uses to represent information. Sparse distributed representations and their mathematical properties are essential components of biological intelligence. This poster examines the robust dendritic computations in the neocortex with sparse distributed representations.
In this poster, we propose a neural mechanism for determining allocentric locations of sensed features. We show how cortical columns can use multiple independent moving sensors to identify and locate objects. We lay out a model inspired by grid cell modules that describes how the brain computes and represents locations.