AIMC Topic: Memory

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Iterative neural networks for improving memory capacity.

Neural networks : the official journal of the International Neural Network Society Nov 23, 2024
In recent years, the problem of the multistability of neural networks has been studied extensively. From the research results obtained, the number of stable equilibrium points depends only on a power form of the network dimension. However, in practic...
Neural Networks, Computer Algorithms Computer Simulation Humans Memory
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Brain imaging and machine learning reveal uncoupled functional network for contextual threat memory in long sepsis.

Scientific reports Nov 12, 2024
Positron emission tomography (PET) utilizes radiotracers like [F]fluorodeoxyglucose (FDG) to measure brain activity in health and disease. Performing behavioral tasks between the FDG injection and the PET scan allows the FDG signal to reflect task-re...
Fluorodeoxyglucose F18 Memory Brain Mapping Positron-Emission Tomography Male Brain Sepsis Animals Mice Machine Learning Disease Models, Animal Mice, Inbred C57BL
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Lifelong Learning With Cycle Memory Networks.

IEEE transactions on neural networks and learning systems Oct 29, 2024
Learning from a sequence of tasks for a lifetime is essential for an agent toward artificial general intelligence. Despite the explosion of this research field in recent years, most work focuses on the well-known catastrophic forgetting issue. In con...
Humans Learning Memory Memory, Long-Term Neural Networks, Computer Artificial Intelligence
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Operant Conditioning Neuromorphic Circuit With Addictiveness and Time Memory for Automatic Learning.

IEEE transactions on biomedical circuits and systems Sep 26, 2024
Most operant conditioning circuits predominantly focus on simple feedback process, few studies consider the intricacies of feedback outcomes and the uncertainty of feedback time. This paper proposes a neuromorphic circuit based on operant conditionin...
Learning Neurons Memory Humans Neural Networks, Computer Conditioning, Operant
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A neural network model of differentiation and integration of competing memories.

eLife Sep 25, 2024
What determines when neural representations of memories move together (integrate) or apart (differentiate)? Classic supervised learning models posit that, when two stimuli predict similar outcomes, their representations should integrate. However, the...
Neural Networks, Computer Brain Models, Neurological Memory Learning Humans
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Grasp and remember: the impact of human and robotic actions on object preference and memory.

Scientific reports Aug 27, 2024
Goal contagion, the tendency to adopt others' goals, significantly impacts cognitive processes, which gains particular importance in the emerging field of human-robot interactions. The present study explored how observing human versus robotic actions...
Memory Robotics Psychomotor Performance Female Young Adult Male Hand Strength Adult Humans
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Learning sequence attractors in recurrent networks with hidden neurons.

Neural networks : the official journal of the International Neural Network Society Jun 22, 2024
The brain is targeted for processing temporal sequence information. It remains largely unclear how the brain learns to store and retrieve sequence memories. Here, we study how recurrent networks of binary neurons learn sequence attractors to store pr...
Neurons Memory Humans Neural Networks, Computer Learning Brain Algorithms Models, Neurological
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Bio-inspired computational memory model of the Hippocampus: An approach to a neuromorphic spike-based Content-Addressable Memory.

Neural networks : the official journal of the International Neural Network Society Jun 20, 2024
The brain has computational capabilities that surpass those of modern systems, being able to solve complex problems efficiently in a simple way. Neuromorphic engineering aims to mimic biology in order to develop new systems capable of incorporating s...
Computer Simulation Learning Models, Neurological Action Potentials Animals Humans Neural Networks, Computer Memory CA3 Region, Hippocampal Neurons Hippocampus
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A robust multi-scale feature extraction framework with dual memory module for multivariate time series anomaly detection.

Neural networks : the official journal of the International Neural Network Society May 20, 2024
Although existing reconstruction-based multivariate time series anomaly detection (MTSAD) methods have shown advanced performance, most assume the training data is clean. When faced with noise or contamination in training data, they can also reconstr...
Multivariate Analysis Algorithms Humans Memory Time Factors Neural Networks, Computer
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SSTE: Syllable-Specific Temporal Encoding to FORCE-learn audio sequences with an associative memory approach.

Neural networks : the official journal of the International Neural Network Society May 7, 2024
The circuitry and pathways in the brains of humans and other species have long inspired researchers and system designers to develop accurate and efficient systems capable of solving real-world problems and responding in real-time. We propose the Syll...
Memory Auditory Perception Neural Networks, Computer Humans Models, Neurological Learning Neurons
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