AIMC Topic: Nerve Net

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Exploring continual learning strategies in artificial neural networks through graph-based analysis of connectivity: Insights from a brain-inspired perspective.

Neural networks : the official journal of the International Neural Network Society
Artificial Neural Networks (ANNs) aim at mimicking information processing in biological networks. In cognitive neuroscience, graph modeling is a powerful framework widely used to study brain structural and functional connectivity. Yet, the extension ...

Neural mechanisms of relational learning and fast knowledge reassembly in plastic neural networks.

Nature neuroscience
Humans and animals have a striking ability to learn relationships between items in experience (such as stimuli, objects and events), enabling structured generalization and rapid assimilation of new information. A fundamental type of such relational l...

Multiple constraint network classification reveals functional brain networks distinguishing 0-back and 2-back task.

Canadian journal of experimental psychology = Revue canadienne de psychologie experimentale
Working memory is associated with general intelligence and is crucial for performing complex cognitive tasks. Neuroimaging investigations have recognized that working memory is supported by a distribution of activity in regions across the entire brai...

EEG microstate analysis and machine learning classification in patients with obsessive-compulsive disorder.

Journal of psychiatric research
BACKGROUND: Microstate characterization of electroencephalogram (EEG) is a data-driven approach to explore the functional changes and interrelationships of multiple brain networks on a millisecond scale. This study aimed to explore the pathological c...

Spatial Craving Patterns in Marijuana Users: Insights From fMRI Brain Connectivity Analysis With High-Order Graph Attention Neural Networks.

IEEE journal of biomedical and health informatics
The excessive consumption of marijuana can induce substantial psychological and social consequences. In this investigation, we propose an elucidative framework termed high-order graph attention neural networks (HOGANN) for the classification of Marij...

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...

Classification of Irritable Bowel Syndrome Using Brain Functional Connectivity Strength and Machine Learning.

Neurogastroenterology and motility
BACKGROUND: Irritable Bowel Syndrome (IBS) is a prevalent condition characterized by dysregulated brain-gut interactions. Despite its widespread impact, the brain mechanism of IBS remains incompletely understood, and there is a lack of objective diag...

Dynamics of specialization in neural modules under resource constraints.

Nature communications
The brain is structurally and functionally modular, although recent evidence has raised questions about the extent of both types of modularity. Using a simple, toy artificial neural network setup that allows for precise control, we find that structur...

Biologically plausible gated recurrent neural networks for working memory and learning-to-learn.

PloS one
The acquisition of knowledge and skills does not occur in isolation but learning experiences amalgamate within and across domains. The process through which learning can accelerate over time is referred to as learning-to-learn or meta-learning. While...

Deep learning models reveal the link between dynamic brain connectivity patterns and states of consciousness.

Scientific reports
Decoding states of consciousness from brain activity is a central challenge in neuroscience. Dynamic functional connectivity (dFC) allows the study of short-term temporal changes in functional connectivity (FC) between distributed brain areas. By clu...