AIMC Topic: Models, Neurological

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Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network.

Scientific reports Feb 3, 2025
Previous deep learning-based brain network research has made significant progress in understanding the pathophysiology of schizophrenia. However, it ignores the three-dimensional spatial characteristics of EEG signals and cannot dynamically learn the...
Humans Algorithms Female Models, Neurological Electroencephalography Schizophrenia Adult Male Convolutional Neural Networks Nerve Net Graph Neural Networks
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Hybrid neural networks for continual learning inspired by corticohippocampal circuits.

Nature communications Feb 2, 2025
Current artificial systems suffer from catastrophic forgetting during continual learning, a limitation absent in biological systems. Biological mechanisms leverage the dual representation of specific and generalized memories within corticohippocampal...
Memory Hippocampus Animals Cerebral Cortex Humans Nerve Net Models, Neurological Neural Networks, Computer Neurons Learning
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Temporal pavlovian conditioning of a model spiking neural network for discrimination sequences of short time intervals.

Journal of computational neuroscience Feb 1, 2025
The brain's ability to learn and distinguish rapid sequences of events is essential for timing-dependent tasks, such as those in sports and music. However, the mechanisms underlying this ability remain an active area of research. Here, we present a P...
Animals Time Factors Models, Neurological Humans Conditioning, Classical Neural Networks, Computer Time Perception Discrimination, Psychological Action Potentials Neurons Nerve Net
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The calcitron: A simple neuron model that implements many learning rules via the calcium control hypothesis.

PLoS computational biology Jan 29, 2025
Theoretical neuroscientists and machine learning researchers have proposed a variety of learning rules to enable artificial neural networks to effectively perform both supervised and unsupervised learning tasks. It is not always clear, however, how t...
Computational Biology Machine Learning Neurons Humans Neuronal Plasticity Neural Networks, Computer Calcium Models, Neurological Learning Animals Synapses
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Recurrent neural networks with transient trajectory explain working memory encoding mechanisms.

Communications biology Jan 28, 2025
Whether working memory (WM) is encoded by persistent activity using attractors or by dynamic activity using transient trajectories has been debated for decades in both experimental and modeling studies, and a consensus has not been reached. Even thou...
Animals Humans Models, Neurological Nerve Net Memory, Short-Term Neural Networks, Computer Recurrent Neural Networks
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Low-power artificial neuron networks with enhanced synaptic functionality using dual transistor and dual memristor.

PloS one Jan 27, 2025
Artificial neurons with bio-inspired firing patterns have the potential to significantly improve the performance of neural network computing. The most significant component of an artificial neuron circuit is a large amount of energy consumption. Rece...
Models, Neurological Action Potentials Neurons Neural Networks, Computer Synapses Transistors, Electronic
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Self-supervised learning of scale-invariant neural representations of space and time.

Journal of computational neuroscience Jan 22, 2025
Hippocampal representations of space and time seem to share a common coding scheme characterized by neurons with bell-shaped tuning curves called place and time cells. The properties of the tuning curves are consistent with Weber's law, such that, in...
Neural Networks, Computer Learning Animals Models, Neurological Supervised Machine Learning Space Perception Neurons Time Perception Hippocampus
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Investigating the intrinsic top-down dynamics of deep generative models.

Scientific reports Jan 22, 2025
Hierarchical generative models can produce data samples based on the statistical structure of their training distribution. This capability can be linked to current theories in computational neuroscience, which propose that spontaneous brain activity ...
Deep Learning Models, Neurological Humans Neural Networks, Computer Brain
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Incremental accumulation of linguistic context in artificial and biological neural networks.

Nature communications Jan 18, 2025
Large Language Models (LLMs) have shown success in predicting neural signals associated with narrative processing, but their approach to integrating context over large timescales differs fundamentally from that of the human brain. In this study, we s...
Speech Perception Linguistics Brain Language Female Humans Magnetic Resonance Imaging Young Adult Neural Networks, Computer Models, Neurological Adult Brain Mapping Male
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Towards parameter-free attentional spiking neural networks.

Neural networks : the official journal of the International Neural Network Society Jan 16, 2025
Brain-inspired spiking neural networks (SNNs) are increasingly explored for their potential in spatiotemporal information modeling and energy efficiency on emerging neuromorphic hardware. Recent works incorporate attentional modules into SNNs, greatl...
Neural Networks, Computer Brain Models, Neurological Humans Action Potentials Neurons Attention
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