Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2024; 33rd International C Michael Wand,Kristína Malinovská,Igor V. Tetko Conferenc

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Serial Order Codes for Dimensionality Reduction in the Learning of Higher-Order Rules and Compositiok for neural networks. One of the mechanisms that allows to capture hierarchical dependencies between items within sequences is ordinal coding. Ordinal patterns create a grammar, or a set of rules, that reduces the dimensionality of the search space and that can be used in a generative manner to com

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Sparsity Aware Learning in Feedback-Driven Differential Recurrent Neural Networks effective learning of variable information gain makes training d-RNNs important for their inherent derivative of states property. In addition to training readout weights, the optimization of the intrinsic recurrent connection of the d-RNNs prove significant for performance enhancement. We introduce

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Towards Scalable GPU-Accelerated SNN Training via Temporal Fusionosely emulating the complex dynamics of biological neural networks. While SNNs show promising efficiency on specialized sparse-computational hardware, their practical training often relies on conventional GPUs. This reliance frequently leads to extended computation times when contrasted with traditi

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Dynamic Graph for Biological Memory Modeling: A System-Level Validation, but traditional graph models are static, lack the dynamic and autonomous behaviors of biological neural networks, rely on algorithms with a global view. This study introduces a novel dynamic directed graph model that simulates the brain’s memory process by empowering each node with adaptive learni

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Revealing Functions of Extra-Large Excitatory Postsynaptic Potentials: Insights from Dynamical Chara-tailed excitatory postsynaptic potentials (EPSPs), involving a minority of extra-large (XL) EPSPs, are currently garnering much attention, which strongly relates to cognitive functions. In addition to physiological studies, mathematical modeling approaches are effective in neuroscience because they