AIMC Topic: Action Potentials

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Adapting to time: Why nature may have evolved a diverse set of neurons.

PLoS computational biology Dec 13, 2024
Brains have evolved diverse neurons with varying morphologies and dynamics that impact temporal information processing. In contrast, most neural network models use homogeneous units that vary only in spatial parameters (weights and biases). To explor...
Action Potentials Neurons Nerve Net Adaptation, Physiological Biological Evolution Humans Neural Networks, Computer Brain Animals Computational Biology Models, Neurological
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Neural networks with optimized single-neuron adaptation uncover biologically plausible regularization.

PLoS computational biology Dec 13, 2024
Neurons in the brain have rich and adaptive input-output properties. Features such as heterogeneous f-I curves and spike frequency adaptation are known to place single neurons in optimal coding regimes when facing changing stimuli. Yet, it is still u...
Action Potentials Neurons Nerve Net Adaptation, Physiological Animals Computational Biology Models, Neurological Algorithms Brain Computer Simulation Humans Neural Networks, Computer
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Similarity-based context aware continual learning for spiking neural networks.

Neural networks : the official journal of the International Neural Network Society Dec 12, 2024
Biological brains have the capability to adaptively coordinate relevant neuronal populations based on the task context to learn continuously changing tasks in real-world environments. However, existing spiking neural network-based continual learning ...
Action Potentials Neurons Algorithms Humans Learning Models, Neurological Neural Networks, Computer
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Two-Terminal Neuromorphic Devices for Spiking Neural Networks: Neurons, Synapses, and Array Integration.

ACS nano Dec 12, 2024
The ever-increasing volume of complex data poses significant challenges to conventional sequential global processing methods, highlighting their inherent limitations. This computational burden has catalyzed interest in neuromorphic computing, particu...
Biomimetics Synapses Neural Networks, Computer Action Potentials Neurons Humans
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Stabilizing sequence learning in stochastic spiking networks with GABA-Modulated STDP.

Neural networks : the official journal of the International Neural Network Society Dec 7, 2024
Cortical networks are capable of unsupervised learning and spontaneous replay of complex temporal sequences. Endowing artificial spiking neural networks with similar learning abilities remains a challenge. In particular, it is unresolved how differen...
Neurons gamma-Aminobutyric Acid Stochastic Processes Neuronal Plasticity Models, Neurological Action Potentials Neural Networks, Computer Learning Humans
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Temporal spiking generative adversarial networks for heading direction decoding.

Neural networks : the official journal of the International Neural Network Society Dec 5, 2024
The spike-based neuronal responses within the ventral intraparietal area (VIP) exhibit intricate spatial and temporal dynamics in the posterior parietal cortex, presenting decoding challenges such as limited data availability at the biological popula...
Animals Models, Neurological Action Potentials Macaca mulatta Generative Adversarial Networks Neurons Parietal Lobe Neural Networks, Computer
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Low-power and lightweight spiking transformer for EEG-based auditory attention detection.

Neural networks : the official journal of the International Neural Network Society Dec 4, 2024
EEG signal analysis can be used to study brain activity and the function and structure of neural networks, helping to understand neural mechanisms such as cognition, emotion, and behavior. EEG-based auditory attention detection is using EEG signals t...
Electroencephalography Signal Processing, Computer-Assisted Neurons Attention Humans Neural Networks, Computer Action Potentials Acoustic Stimulation Auditory Perception
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Developmental Plasticity-Inspired Adaptive Pruning for Deep Spiking and Artificial Neural Networks.

IEEE transactions on pattern analysis and machine intelligence Dec 4, 2024
Developmental plasticity plays a prominent role in shaping the brain's structure during ongoing learning in response to dynamically changing environments. However, the existing network compression methods for deep artificial neural networks (ANNs) an...
Action Potentials Humans Neurons Brain Deep Learning Animals Neuronal Plasticity Neural Networks, Computer Models, Neurological Algorithms
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Low Latency and Sparse Computing Spiking Neural Networks With Self-Driven Adaptive Threshold Plasticity.

IEEE transactions on neural networks and learning systems Dec 2, 2024
Spiking neural networks (SNNs) have captivated the attention worldwide owing to their compelling advantages in low power consumption, high biological plausibility, and strong robustness. However, the intrinsic latency associated with SNNs during infe...
Humans Neuronal Plasticity Models, Neurological Action Potentials Neurons Neural Networks, Computer Algorithms Animals
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Left Atrial Wall Thickness Measured by a Machine Learning Method Predicts AF Recurrence After Pulmonary Vein Isolation.

Journal of cardiovascular electrophysiology Nov 29, 2024
BACKGROUND: Left atrial (LA) remodeling plays a significant role in the progression of atrial fibrillation (AF). Although LA wall thickness (LAWT) has emerged as an indicator of structural remodeling, its impact on AF outcomes remains unclear. We aim...
Atrial Fibrillation Heart Atria Heart Rate Pulmonary Veins Predictive Value of Tests Male Catheter Ablation Atrial Remodeling Registries Middle Aged Recurrence Female Fibrosis Risk Assessment Time Factors Humans Atrial Function, Left Risk Factors Machine Learning Treatment Outcome Aged Retrospective Studies Action Potentials
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