Posters
SfN 2018: The predictive neuron, how active dendrites enable spatiotemporal computation in the neocortexThis poster shows a model where patterns detected on active basal dendrites act as predictions by slightly depolarizing the soma without generating an action potential. A single neuron can then predict its activation in hundreds of independent contexts. The predictive network mechanism can learn both pure external temporal sequences as well as sensorimotor sequences. When the contextual input includes information derived from efference motor copies, the cells learn sensorimotor sequences. If the contextual input consists of nearby cellular activity, the cells learn temporal sequences....
Subutai Ahmad, Jeff Hawkins•Numenta
SfN 2018: A mechanism for sensorimotor object recognition using cortical grid cellsThis poster describes a two-layer network model that uses cortical grid cells and path integration to learn and recognize objects through movement. In our model, one layer contains several grid cell-like modules and provides a location signal for each learned object such that features can be associated with a specific location in the reference frame of that object. A second layer, a sensory input layer, receives the location representation as context, and uses it to encode the sensory input in the context of a location in the object’s reference frame....
Marcus Lewis, Scott Purdy, Subutai Ahmad, Mirko Klukas, Jeff Hawkins•Numenta
SfN 2018: Grid Cells in the Neocortex, a Framework for Cortical ComputationAt 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....
Jeff Hawkins, Subutai Ahmad, Marcus Lewis, Mirko Klukas, Scott Purdy•Numenta
Bernstein 2018: Representing N-dimensional cognitive variables with grid cellsAt 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....
Mirko Klukas•Numenta
CNS 2018: Learning Relative Landmark LocationsThis 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....
Scott Purdy, Subutai Ahmad•Numenta