Spatio-temporal adaptation in the unsupervised development of networked visual neurons

Authors:
Dongyue Chen;Liming Zhang;Juyang Weng
Affiliations:
College of Information Science and Engineering, Northeastern University, Shenyang, China and Department of Electronic Engineering, Fudan University, Shanghai, China;Department of Electronic Engineering, Fudan University, Shanghai, China;Department of Computer Science and Engineering, Michigan State University, East Lansing, MI
Venue:
IEEE Transactions on Neural Networks
Year:
2009

Citing 14
Cited 3

What is the goal of sensory coding?

Neural Computation
Modeling visual attention via selective tuning

Artificial Intelligence - Special volume on computer vision
Self-Organizing Maps

Self-Organizing Maps
Object-based visual attention for computer vision

Artificial Intelligence
A Simple Hebbian/Anti-Hebbian Network Learns the Sparse, Independent Components of Natural Images

Neural Computation
Topographic Independent Component Analysis

Neural Computation
VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)

VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems

Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
A Local Learning Rule That Enables Information Maximization for Arbitrary Input Distributions

Neural Computation
2008 Special Issue: Multilayer in-place learning networks for modeling functional layers in the laminar cortex

Neural Networks
A "nonnegative PCA" algorithm for independent component analysis

IEEE Transactions on Neural Networks
Dynamics of Winner-Take-All Competition in Recurrent Neural Networks With Lateral Inhibition

IEEE Transactions on Neural Networks
Neuromorphic Excitable Maps for Visual Processing

IEEE Transactions on Neural Networks
Fast-Learning Adaptive-Subspace Self-Organizing Map: An Application to Saliency-Based Invariant Image Feature Construction

IEEE Transactions on Neural Networks

Artificial neural networks capable of learning spatiotemporal chemical diffusion in the cortical brain

Pattern Recognition
Frequency spectrum modification: a new model for visual saliency detection

ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Object recognition using a bio-inspired neuron model with bottom-up and top-down pathways

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

There have been many computational models mimicking the visual cortex that are based on spatial adaptations of unsupervised neural networks. In this paper, we present a new model called neuronal cluster which includes spatial as well as temporal weights in its unified adaptation scheme. The "in-place" nature of the model is based on two biologically plausible learning rules, Hebbian rule and lateral inhibition. We present the mathematical demonstration that the temporal weights are derived from the delay in lateral inhibition. By training with the natural videos, this model can develop spatio-temporal features such as orientation selective cells, motion sensitive cells, and spatio-temporal complex cells. The unified nature of the adaption scheme allows us to construct a multilayered and task-independent attention selection network which uses the same learning rule for edge, motion, and color detection, and we can use this network to engage in attention selection in both static and dynamic scenes.