AIMC Topic: Memory

Clear Filters Showing 21 to 30 of 210 articles

Situation-Based Neuromorphic Memory in Spiking Neuron-Astrocyte Network.

IEEE transactions on neural networks and learning systems
Mammalian brains operate in very special surroundings: to survive they have to react quickly and effectively to the pool of stimuli patterns previously recognized as danger. Many learning tasks often encountered by living organisms involve a specific...

Pattern memory cannot be completely and truly realized in deep neural networks.

Scientific reports
The unknown boundary issue, between superior computational capability of deep neural networks (DNNs) and human cognitive ability, has becoming crucial and foundational theoretical problem in AI evolution. Undoubtedly, DNN-empowered AI capability is i...

H control for fractional order neural networks with uncertainties subject to deception attacks via Improved memory-event-triggered scheme and Its application.

Neural networks : the official journal of the International Neural Network Society
The article discusses an improved memory-event-triggered strategy for H control class of fractional-order neural networks (FONNs) with uncertainties, which are vulnerable to deception attacks. The system under consideration is simultaneously influenc...

A multi-memory-augmented network with a curvy metric method for video anomaly detection.

Neural networks : the official journal of the International Neural Network Society
Anomaly detection task in video mainly refers to identifying anomalous events that do not conform to the learned normal patterns in the inferring phase. However, the Euclidean metric used in the learning and inferring phase by the most of the existin...

Iterative neural networks for improving memory capacity.

Neural networks : the official journal of the International Neural Network Society
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...

Brain imaging and machine learning reveal uncoupled functional network for contextual threat memory in long sepsis.

Scientific reports
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...

Lifelong Learning With Cycle Memory Networks.

IEEE transactions on neural networks and learning systems
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...

Operant Conditioning Neuromorphic Circuit With Addictiveness and Time Memory for Automatic Learning.

IEEE transactions on biomedical circuits and systems
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...

A neural network model of differentiation and integration of competing memories.

eLife
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...

Grasp and remember: the impact of human and robotic actions on object preference and memory.

Scientific reports
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...