AIMC Topic: Nerve Net

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Self-Lateral Propagation Elevates Synaptic Modifications in Spiking Neural Networks for the Efficient Spatial and Temporal Classification.

IEEE transactions on neural networks and learning systems
The brain's mystery for efficient and intelligent computation hides in the neuronal encoding, functional circuits, and plasticity principles in natural neural networks. However, many plasticity principles have not been fully incorporated into artific...

Subcortical and insula functional connectivity aberrations and clinical implications in first-episode schizophrenia.

Asian journal of psychiatry
INTRODUCTION: Schizophrenia is a complex mental disorder whose pathophysiology remains elusive, particularly in the roles of subcortex. This study aims to explore the role of subcortex and insula and their relationship with symptom changes in first-e...

The phobic brain: Morphometric features correctly classify individuals with small animal phobia.

Psychophysiology
Specific phobia represents an anxiety disorder category characterized by intense fear generated by specific stimuli. Among specific phobias, small animal phobia (SAP) denotes a particular condition that has been poorly investigated in the neuroscient...

Exploring potential ADHD biomarkers through advanced machine learning: An examination of audiovisual integration networks.

Computers in biology and medicine
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition marked by inattention and impulsivity, linked to disruptions in functional brain connectivity and structural alterations in large-scale brain networks. Although sensory...

Detection of Low Resilience Using Data-Driven Effective Connectivity Measures.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Conventional thresholding techniques for graph theory analysis, such as absolute, proportional and mean degree, have often been used in characterizing human brain networks under different mental disorders, such as mental stress. However, these approa...

MMF-NNs: Multi-modal Multi-granularity Fusion Neural Networks for brain networks and its application to epilepsy identification.

Artificial intelligence in medicine
Structural and functional brain networks are generated from two scan sequences of magnetic resonance imaging data, which can provide different perspectives for describing pathological changes caused by brain diseases. Recent studies found that fusing...

Dissociative and prioritized modeling of behaviorally relevant neural dynamics using recurrent neural networks.

Nature neuroscience
Understanding the dynamical transformation of neural activity to behavior requires new capabilities to nonlinearly model, dissociate and prioritize behaviorally relevant neural dynamics and test hypotheses about the origin of nonlinearity. We present...

Selective consistency of recurrent neural networks induced by plasticity as a mechanism of unsupervised perceptual learning.

PLoS computational biology
Understanding the mechanism by which the brain achieves relatively consistent information processing contrary to its inherent inconsistency in activity is one of the major challenges in neuroscience. Recently, it has been reported that the consistenc...

Memristive Circuit Implementation of Caenorhabditis Elegans Mechanism for Neuromorphic Computing.

IEEE transactions on neural networks and learning systems
To overcome the energy efficiency bottleneck of the von Neumann architecture and scaling limit of silicon transistors, an emerging but promising solution is neuromorphic computing, a new computing paradigm inspired by how biological neural networks h...

Learnable Brain Connectivity Structures for Identifying Neurological Disorders.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Brain networks/graphs have been widely recognized as powerful and efficient tools for identifying neurological disorders. In recent years, various graph neural network models have been developed to automatically extract features from brain networks. ...