AIMC Topic: Models, Neurological

Clear Filters Showing 81 to 90 of 1162 articles

Random noise promotes slow heterogeneous synaptic dynamics important for robust working memory computation.

Proceedings of the National Academy of Sciences of the United States of America Jan 16, 2025
Recurrent neural networks (RNNs) based on model neurons that communicate via continuous signals have been widely used to study how cortical neural circuits perform cognitive tasks. Training such networks to perform tasks that require information main...
Nerve Net Cognition Models, Neurological Neurons Synapses Neural Networks, Computer Memory, Short-Term Humans Animals Prefrontal Cortex
View on PubMed DOI

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 Jan 15, 2025
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 ...
Models, Neurological Nerve Net Brain Learning Humans Neural Networks, Computer
View on PubMed DOI

Working-memory load decoding model inspired by brain cognition based on cross-frequency coupling.

Brain research bulletin Jan 15, 2025
Working memory, a fundamental cognitive function of the brain, necessitates the evaluation of cognitive load intensity due to limited cognitive resources. Optimizing cognitive load can enhance task performance efficiency by preventing resource waste ...
Neural Networks, Computer Brain Models, Neurological Electroencephalography Cognition Humans Memory, Short-Term
View on PubMed DOI

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

Nature neuroscience Jan 15, 2025
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...
Neuronal Plasticity Models, Neurological Nerve Net Knowledge Neurons Neural Networks, Computer Learning Animals Humans
View on PubMed DOI

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

IEEE transactions on neural networks and learning systems Jan 7, 2025
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...
Computer Simulation Animals Models, Neurological Humans Neurons Neural Networks, Computer Nerve Net Memory Action Potentials Astrocytes
View on PubMed DOI

Recurrent models of orientation selectivity enable robust early-vision processing in mixed-signal neuromorphic hardware.

Nature communications Jan 2, 2025
Mixed signal analog/digital neuromorphic circuits represent an ideal medium for reproducing bio-physically realistic dynamics of biological neural systems in real-time. However, similar to their biological counterparts, these circuits have limited re...
Neural Networks, Computer Models, Neurological Neurons Humans Visual Cortex Vision, Ocular Orientation Photic Stimulation Visual Pathways
View on PubMed DOI

Dynamics of specialization in neural modules under resource constraints.

Nature communications Jan 2, 2025
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...
Models, Neurological Nerve Net Brain Humans Neural Networks, Computer
View on PubMed DOI

CS-QCFS: Bridging the performance gap in ultra-low latency spiking neural networks.

Neural networks : the official journal of the International Neural Network Society Jan 1, 2025
Spiking Neural Networks (SNNs) are at the forefront of computational neuroscience, emulating the nuanced dynamics of biological systems. In the realm of SNN training methods, the conversion from ANNs to SNNs has generated significant interest due to ...
Algorithms Models, Neurological Action Potentials Humans Neural Networks, Computer Neurons
View on PubMed DOI

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

PloS one Dec 31, 2024
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...
Models, Neurological Memory, Short-Term Nerve Net Brain Humans Learning Neural Networks, Computer Animals
View on PubMed DOI

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

Scientific reports Dec 30, 2024
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...
Magnetic Resonance Imaging Models, Neurological Brain Mapping Humans Male Nerve Net Brain Consciousness Deep Learning Connectome
View on PubMed DOI
Popular Topics
Recent Journals