loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Atif G. Hashmi and Mikko H. Lipasti

Affiliation: University of Wisconsin - Madison, United States

ISBN: 978-989-8425-32-4

Keyword(s): Cortical columns, Unsupervised learning, Invariant representation, Supervised feedback, Inherent fault tolerance.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computational Neuroscience ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Supervised and Unsupervised Learning ; Theory and Methods

Abstract: We describe a cortical architecture inspired by the structural and functional properties of the cortical columns distributed and hierarchically organized throughout the mammalian neocortex. This results in a model which is both computationally efficient and biologically plausible. The strength and robustness of our cortical architecture is ascribed to its distributed and uniformly structured processing units and their local update rules. Since our architecture avoids complexities involved in modeling individual neurons and their synaptic connections, we can study other interesting neocortical properties like independent feature detection, feedback, plasticity, invariant representation, etc. with ease. Using feedback, plasticity, object permanence, and temporal associations, our architecture creates invariant representations for various similar patterns occurring within its receptive field. We trained and tested our cortical architecture using a subset of handwritten digit images obtai ned from the MNIST database. Our initial results show that our architecture uses unsupervised feedforward processing as well as supervised feedback processing to differentiate handwritten digits from one another and at the same time pools variations of the same digit together to generate invariant representations. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.215.182.36

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
G. Hashmi, A. and H. Lipasti, M. (2010). DISCOVERING CORTICAL ALGORITHMS.In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010) ISBN 978-989-8425-32-4, pages 196-204. DOI: 10.5220/0003079301960204

@conference{icnc10,
author={Atif G. Hashmi. and Mikko H. Lipasti.},
title={DISCOVERING CORTICAL ALGORITHMS},
booktitle={Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)},
year={2010},
pages={196-204},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003079301960204},
isbn={978-989-8425-32-4},
}

TY - CONF

JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)
TI - DISCOVERING CORTICAL ALGORITHMS
SN - 978-989-8425-32-4
AU - G. Hashmi, A.
AU - H. Lipasti, M.
PY - 2010
SP - 196
EP - 204
DO - 10.5220/0003079301960204

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.