AIMC Topic: Learning

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Motor-related signals support localization invariance for stable visual perception.

PLoS computational biology
Our ability to perceive a stable visual world in the presence of continuous movements of the body, head, and eyes has puzzled researchers in the neuroscience field for a long time. We reformulated this problem in the context of hierarchical convoluti...

Optimal Control of Whole Network Control System Using Improved Genetic Algorithm and Information Integrity Scale.

Computational intelligence and neuroscience
WNCS (Whole network control system) is a network-based distributed control system. The control loop formed by the serial network usually includes several subcontrol systems. WNCS optimal control is a complex and multiparameter coupled highly nonlinea...

Multi-landmark environment analysis with reinforcement learning for pelvic abnormality detection and quantification.

Medical image analysis
Morphological abnormalities of the femoroacetabular (hip) joint are among the most common human musculoskeletal disorders and often develop asymptomatically at early easily treatable stages. In this paper, we propose an automated framework for landma...

Event-Driven Off-Policy Reinforcement Learning for Control of Interconnected Systems.

IEEE transactions on cybernetics
In this article, we introduce a novel approximate optimal decentralized control scheme for uncertain input-affine nonlinear-interconnected systems. In the proposed scheme, we design a controller and an event-triggering mechanism (ETM) at each subsyst...

Toward Efficient Processing and Learning With Spikes: New Approaches for Multispike Learning.

IEEE transactions on cybernetics
Spikes are the currency in central nervous systems for information transmission and processing. They are also believed to play an essential role in low-power consumption of the biological systems, whose efficiency attracts increasing attentions to th...

A differential Hebbian framework for biologically-plausible motor control.

Neural networks : the official journal of the International Neural Network Society
In this paper we explore a neural control architecture that is both biologically plausible, and capable of fully autonomous learning. It consists of feedback controllers that learn to achieve a desired state by selecting the errors that should drive ...

Augmented Graph Neural Network with hierarchical global-based residual connections.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Networks (GNNs) are powerful architectures for learning on graphs. They are efficient for predicting nodes, links and graphs properties. Standard GNN variants follow a message passing schema to update nodes representations using informat...

Test Strategy Optimization Based on Soft Sensing and Ensemble Belief Measurement.

Sensors (Basel, Switzerland)
Resulting from the short production cycle and rapid design technology development, traditional prognostic and health management (PHM) approaches become impractical and fail to match the requirement of systems with structural and functional complexity...

Lifelong 3D object recognition and grasp synthesis using dual memory recurrent self-organization networks.

Neural networks : the official journal of the International Neural Network Society
Humans learn to recognize and manipulate new objects in lifelong settings without forgetting the previously gained knowledge under non-stationary and sequential conditions. In autonomous systems, the agents also need to mitigate similar behaviour to ...

Learning online visual invariances for novel objects via supervised and self-supervised training.

Neural networks : the official journal of the International Neural Network Society
Humans can identify objects following various spatial transformations such as scale and viewpoint. This extends to novel objects, after a single presentation at a single pose, sometimes referred to as online invariance. CNNs have been proposed as a c...