AIMC Journal:
IEEE transactions on neural networks and learning systems

Showing 371 to 380 of 780 articles

Target Convergence Analysis of Cancer-Inspired Swarms for Early Disease Diagnosis and Targeted Collective Therapy.

IEEE transactions on neural networks and learning systems May 2, 2022
Sensing and perception is generally a challenging aspect of decision-making. In the nanoscale, however, these processes face further complications due to the physical limitations of devising the nanomachines with more limited perception, more noise, ...
Neoplasms Neural Networks, Computer Models, Theoretical Computer Simulation Humans
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Toward Cognitive Navigation: Design and Implementation of a Biologically Inspired Head Direction Cell Network.

IEEE transactions on neural networks and learning systems May 2, 2022
As a vital cognitive function of animals, the navigation skill is first built on the accurate perception of the directional heading in the environment. Head direction cells (HDCs), found in the limbic system of animals, are proven to play an importan...
Neural Networks, Computer Animals Cognition
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Continuous Online Adaptation of Bioinspired Adaptive Neuroendocrine Control for Autonomous Walking Robots.

IEEE transactions on neural networks and learning systems May 2, 2022
Walking animals can continuously adapt their locomotion to deal with unpredictable changing environments. They can also take proactive steps to avoid colliding with an obstacle. In this study, we aim to realize such features for autonomous walking ro...
Adaptation, Physiological Locomotion Neural Networks, Computer Robotics Walking Animals
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Robust Transcoding Sensory Information With Neural Spikes.

IEEE transactions on neural networks and learning systems May 2, 2022
Neural coding, including encoding and decoding, is one of the key problems in neuroscience for understanding how the brain uses neural signals to relate sensory perception and motor behaviors with neural systems. However, most of the existed studies ...
Brain Brain-Computer Interfaces Neural Networks, Computer Action Potentials Neurons Models, Neurological
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Deep Reinforcement Learning With Modulated Hebbian Plus Q-Network Architecture.

IEEE transactions on neural networks and learning systems May 2, 2022
In this article, we consider a subclass of partially observable Markov decision process (POMDP) problems which we termed confounding POMDPs. In these types of POMDPs, temporal difference (TD)-based reinforcement learning (RL) algorithms struggle, as ...
Reward Algorithms Reinforcement, Psychology Markov Chains Neural Networks, Computer
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Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks.

IEEE transactions on neural networks and learning systems May 2, 2022
Spiking neural networks (SNNs) use spatiotemporal spike patterns to represent and transmit information, which are not only biologically realistic but also suitable for ultralow-power event-driven neuromorphic implementation. Just like other deep lear...
Neurons Synaptic Potentials Algorithms Neuronal Plasticity Neural Networks, Computer
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Triple-Memory Networks: A Brain-Inspired Method for Continual Learning.

IEEE transactions on neural networks and learning systems May 2, 2022
Continual acquisition of novel experience without interfering with previously learned knowledge, i.e., continual learning, is critical for artificial neural networks, while limited by catastrophic forgetting. A neural network adjusts its parameters w...
Brain Learning Algorithms Knowledge Neural Networks, Computer
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A Brain-Inspired Approach for Collision-Free Movement Planning in the Small Operational Space.

IEEE transactions on neural networks and learning systems May 2, 2022
In a small operational space, e.g., mesoscale or microscale, we need to control movements carefully because of fragile objects. This article proposes a novel structure based on spiking neural networks to imitate the joint function of multiple brain r...
Neural Networks, Computer Brain Synapses Movement Learning
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An Event-Based Digital Time Difference Encoder Model Implementation for Neuromorphic Systems.

IEEE transactions on neural networks and learning systems May 2, 2022
Neuromorphic systems are a viable alternative to conventional systems for real-time tasks with constrained resources. Their low power consumption, compact hardware realization, and low-latency response characteristics are the key ingredients of such ...
Neurons Neural Networks, Computer Signal Processing, Computer-Assisted Computers
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How Frequency Injection Locking Can Train Oscillatory Neural Networks to Compute in Phase.

IEEE transactions on neural networks and learning systems May 2, 2022
Brain-inspired computing employs devices and architectures that emulate biological functions for more adaptive and energy-efficient systems. Oscillatory neural networks (ONNs) are an alternative approach in emulating biological functions of the human...
Humans Neural Networks, Computer Models, Neurological Nerve Net Computer Simulation Brain
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