Thumbnail
Access Restriction
Open

Author Cui, Yuwei ♦ Hawkins, Jeff ♦ Ahmad, Subutai
Source Directory of Open Access Journals (DOAJ)
Content type Text
Publisher Frontiers Media S.A.
File Format HTM / HTML
Date Created 2017-10-25
Copyright Year ©2017
Language English
Subject Domain (in LCC) RC321-571
Subject Keyword Cortical columns ♦ Neuropsychiatry ♦ Cortical layers ♦ Biological psychiatry ♦ Neurosciences ♦ Internal medicine ♦ Medicine ♦ Neocortex ♦ Sensorimotor learning ♦ Hierarchical temporal memory
Abstract Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete predictive models of observed objects. Excitatory lateral connections across columns allow the network to more rapidly infer objects based on the partial knowledge of adjacent columns. Because columns integrate input over time and space, the network learns models of complex objects that extend well beyond the receptive field of individual cells. Our network model introduces a new feature to cortical columns. We propose that a representation of location relative to the object being sensed is calculated within the sub-granular layers of each column. The location signal is provided as an input to the network, where it is combined with sensory data. Our model contains two layers and one or more columns. Simulations show that using Hebbian-like learning rules small single-column networks can learn to recognize hundreds of objects, with each object containing tens of features. Multi-column networks recognize objects with significantly fewer movements of the sensory receptors. Given the ubiquity of columnar and laminar connectivity patterns throughout the neocortex, we propose that columns and regions have more powerful recognition and modeling capabilities than previously assumed.
ISSN 16625110
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2017-10-01
e-ISSN 16625110
Journal Frontiers in Neural Circuits
Volume Number 11


Source: Directory of Open Access Journals (DOAJ)