NDLI: Population coding for a reward-modulated Hebbian learning of vergence control

Content Provider	IEEE Xplore Digital Library
Author	Gibaldi, A. Canessa, A. Chessa, M. Solari, F. Sabatini, S.P.
Copyright Year	2013
Description	Author affiliation: Dept. of Inf., Bioeng., Univ. of Genoa, Genoa, Italy (Gibaldi, A.; Canessa, A.; Chessa, M.; Solari, F.; Sabatini, S.P.)
Abstract	We show how a cortical model of early disparity detectors is able to autonomously learn effective control signals in order to drive the vergence eye movements of a binocular active vision system. The proposed approach employs early binocular mechanisms of vision and basic learning processes such as synaptic plasticity and reward modulation. The computational substrate consists of a population of modeled V1 complex cells, that provides a distributed representation of binocular disparity information. The population response also provides a global signal to describe the state of the system and thus its deviation from the desired vergence position. The proposed network, by taking into account the modification of its internal state as a consequence of the action performed, evolves following a differential Hebbian rule. Furthermore, the weights update is driven by an intrinsic signal derived by the overall activity of the population. Exploiting this signal implies a maximization of the population activity itself, thus providing an highly effective reward for the developing of a stable and accurate vergence behaviour. The efficacy of the proposed intrinsic reward signal is comparatively assessed against the ground-truth signal (the actual disparity) providing equivalent results, and thus validating the approach. Experimental tests in a simulated environment demonstrate that the proposed network is able to cope with vergent geometry and thus to learn effective vergence movements for static and moving visual targets in realistic situations.
Starting Page	1
Ending Page	8
File Size	481892
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781467361293
DOI	10.1109/IJCNN.2013.6706821
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sociology Statistics Tuning Detectors Computer architecture Substrates Visualization
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Vergence control learning through real V1 disparity tuning curves

Joint development of disparity tuning and vergence control

Deep Reinforcement Learning with Modulated Hebbian plus Q Network Architecture

Development/Plasticity/Repair A Reward-Modulated Hebbian Learning Rule Can Explain Experimentally Observed Network Reorganization in a Brain Control Task

Improved Binocular Vergence Control via a Neural Network That Maximizes an Internally Defined Reward

Development/Plasticity/Repair A Reward-Modulated Hebbian Learning Rule Can Explain Experimentally Observed Network Reorganization in a Brain Control Task

A multi-objective auto-tuning framework for parallel codes

A reward-modulated Hebbian learning rule can explain experimentally observed network reorganization in a brain control task

RM-SORN: a reward-modulated self-organizing recurrent neural network

Population coding for a reward-modulated Hebbian learning of vergence control

Similar Documents

Vergence control learning through real V1 disparity tuning curves

Joint development of disparity tuning and vergence control

Deep Reinforcement Learning with Modulated Hebbian plus Q Network Architecture

Development/Plasticity/Repair A Reward-Modulated Hebbian Learning Rule Can Explain Experimentally Observed Network Reorganization in a Brain Control Task

Improved Binocular Vergence Control via a Neural Network That Maximizes an Internally Defined Reward

Development/Plasticity/Repair A Reward-Modulated Hebbian Learning Rule Can Explain Experimentally Observed Network Reorganization in a Brain Control Task

A multi-objective auto-tuning framework for parallel codes

A reward-modulated Hebbian learning rule can explain experimentally observed network reorganization in a brain control task

RM-SORN: a reward-modulated self-organizing recurrent neural network

Population coding for a reward-modulated Hebbian learning of vergence control