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Posters
Mon, May 01, 2017
NCM 2017: A Cortical Circuit for Sensorimotor Learning and Recognition
In this poster, we describe a network model of cortical circuits that learns sensorimotor representations of objects. Extending previous work, the cortical circuit network integrates motor representations and feed-forward sensory information to build predictive models of objects.
Fri, Feb 24, 2017
Cosyne 2017: How Do Cortical Columns Learn 3D Sensorimotor Models?
We propose that cortical columns learn 3D sensorimotor models of the world by combining sensory inputs with allocentric location. We found that a simulated robot hand can grasp and recognize any object, and that each cortical column can store more objects, and recognize them faster, by using cross-column connections.
Wed, Sep 21, 2016
Bernstein Conference 2016: HTM Sequence Memory for Sequence Learning
This poster explains HTM Sequence Memory, a neural mechanism for sequence learning, which is ubiquitous in the cortex and has the following characteristics: 1) neurons learn to recognize patterns; 2) pattern recognition acts as predictions; 3) a neuron network forms a sequence memory, and 4) sparse presentations lead to robust recognition.
Thu, Feb 25, 2016
Cosyne 2016: Introducing HTM Sequence Memory
This poster introduces a theory of sequence memory in the neocortex called HTM Sequence Memory. The three characteristics of HTM Sequence Memory are: 1) Neurons learn to recognize hundreds of patters; 2) Pattern recognition acts as predictions; and 3) a network of neurons forms a powerful sequence memory.
Thu, Mar 05, 2015
Cosyne 2015: How The Cortex Builds a Sensorimotor Model of The World
How can the cortex build a sensorimotor model of the world? First, we propose a biologically detailed model of sensorimotor inference. Then, we build a model with a premise that predictable transitions lead to invariant representations. We then tested the model, measuring neuron activations over time.
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