A research team from the Xinjiang Astronomical Observatory (XAO) of the Chinese Academy of Sciences has developed an interpretable artificial intelligence (AI) framework named Convolutional Kolmogorov ...
This study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...
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Neural network activation functions explained simply
Confused about activation functions in neural networks? This video breaks down what they are, why they matter, and the most common types — including ReLU, Sigmoid, Tanh, and more! #NeuralNetworks ...
Confused about cost functions in neural networks? In this video, we break down what cost functions are, why they matter, and which types are best for different applications—from classification to ...
Neuroscience continually strives to unravel the intricate relationship between neural network morphology, spiking dynamics, and their resulting functional ...
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Kolmogorov-Arnold networks bridge AI and scientific discovery by increasing interpretability
AI has successfully been applied in many areas of science, advancing technologies like weather prediction and protein folding ...
Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...
Recent developments in machine learning techniques have been supported by the continuous increase in availability of high-performance computational resources and data. While large volumes of data are ...
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Biomimetic brain modeling mirrors animal learning and neural dynamics
A new computational model of the brain based closely on its biology and physiology not only learned a simple visual category learning task exactly as well as lab animals, but even enabled the ...
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