At this point in time there is no consensus in neuroscience literature on how grid cells are involved in the representation of 3D location, and their contribution to coding variables beyond 2 or 3 dimensions is completely uncharted territory. This poster explores how grid cells can encode N-dimensional variables, using random velocity projections. The poster covers path integration, relation to band cells, and capacity and tuning curve.
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.