NDLI: Perception Evolution Network Based on Cognition Deepening Model—Adapting to the Emergence of New Sensory Receptor

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Year : 2016 Volume : 27

Issue 3

Table of contents

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information

Neural Network-Based Event-Triggered State Feedback Control of Nonlinear Continuous-Time Systems

Robust Adaptive Neural Tracking Control for a Class of Stochastic Nonlinear Interconnected Systems

Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models

Comparison of Seven Methods for Boolean Factor Analysis and Their Evaluation by Information Gain

A New Stochastic Computing Methodology for Efficient Neural Network Implementation

Hierarchical Theme and Topic Modeling

A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method

Global Exponential Stability for Complex-Valued Recurrent Neural Networks With Asynchronous Time Delays

Perception Evolution Network Based on Cognition Deepening Model—Adapting to the Emergence of New Sensory Receptor

A Spiking Neural Network System for Robust Sequence Recognition

DC Proximal Newton for Nonconvex Optimization Problems

Relevance Vector Machine for Survival Analysis

Analog Programmable Distance Calculation Circuit for Winner Takes All Neural Network Realized in the CMOS Technology

Image Categorization by Learning a Propagated Graphlet Path

Lag Synchronization of Memristor-Based Coupled Neural Networks via

$omega $

-Measure

$L_{1}$

-Minimization Algorithms for Sparse Signal Reconstruction Based on a Projection Neural Network

IEEE Transactions on Cognitive and Developmental Systems

IEEE Computational Intelligence Society Information

IEEE Transactions on Neural Networks information for authors

Issue 2

Issue 1

Year : 2015 Volume : 26

Year : 2014 Volume : 25

Year : 2013 Volume : 24

Year : 2012 Volume : 23

Perception Evolution Network Based on Cognition Deepening Model—Adapting to the Emergence of New Sensory Receptor

Content Provider	IEEE Xplore Digital Library
Author	Xing, Y. Shen, F. Zhao, J.
Copyright Year	2012
Abstract	The proposed perception evolution network (PEN) is a biologically inspired neural network model for unsupervised learning and online incremental learning. It is able to automatically learn suitable prototypes from learning data in an incremental way, and it does not require the predefined prototype number or the predefined similarity threshold. Meanwhile, being more advanced than the existing unsupervised neural network model, PEN permits the emergence of a new dimension of perception in the perception field of the network. When a new dimension of perception is introduced, PEN is able to integrate the new dimensional sensory inputs with the learned prototypes, i.e., the prototypes are mapped to a high-dimensional space, which consists of both the original dimension and the new dimension of the sensory inputs. In the experiment, artificial data and real-world data are used to test the proposed PEN, and the results show that PEN can work effectively.
Page Count	14
File Size	3153154
Starting Page	607
Ending Page	620
File Format	PDF
ISSN	2162237X
Volume Number	27
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2016-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Prototypes Neurons Lead Biological neural networks Robot sensing systems Mice unsupervised learning. New dimension of perception online incremental learning perception evolution network (PEN) stability-plasticity dilemma
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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

On the Stability-Plasticity Dilemma of Class-Incremental Learning

Controlled Forgetting: Targeted Stimulation and Dopaminergic Plasticity Modulation for Unsupervised Lifelong Learning in Spiking Neural Networks

New Insights for the Stability-Plasticity Dilemma in Online Continual Learning

Online Control for Biped Robot with Incremental Learning Mechanism

Continuous online sequence learning with an unsupervised neural network model

Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning

Information-Theoretic Intrinsic Plasticity for Online Unsupervised Learning in Spiking Neural Networks

Developmental Perception of the Self and Action

RAISE: Rank-Aware Incremental Learning for Remote Sensing Object Detection

Perception Evolution Network Based on Cognition Deepening Model—Adapting to the Emergence of New Sensory Receptor

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On the Stability-Plasticity Dilemma of Class-Incremental Learning

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New Insights for the Stability-Plasticity Dilemma in Online Continual Learning

Online Control for Biped Robot with Incremental Learning Mechanism

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