networksartificialintelligencecognitionmodelsNeural networks are models of the brain and have been used within Artificial Intelligence to provide alternative explanations to the symbolic explanations of cognition in which one assumes that an intelligent system has certain explicit representations of some aspect...
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks.Communications of the ACM. Kurth-Nelson, Z., Economides, M., Dolan, R. J., & Dayan, P. (2016). Fast Sequences of Non-spatial State Representations in Humans.Neuro...
Spiking neural networks (SNNs) that mimic information transmission in the brain can energy-efficiently process spatio-temporal information through discrete and sparse spikes, thereby receiving considerable attention. To improve accuracy and energy efficiency of SNNs, most previous studies have focused solely...
Neural networks have been a topic of great interest and debate in the field of artificial intelligence. These complex systems, inspired by the human brain, are designed to learn and adapt to data, making them a powerful tool for various applications such as image and speech recognition, natural...
the GNN on data from an earlier study. We conclude that the proposed multi-modal GNN framework can provide a novel perspective on the structure-function relationship in the brain. Accordingly this approach appears to be promising for the characterization of the information flow in brain networks. ...
Neural Networks What they are & why they matter Neural networks are computing systems with interconnected nodes that work much like neurons in the human brain. Usingalgorithms, they can recognize hidden patterns and correlations in raw data, cluster and classify it, and – over time – ...
In biological neural systems, different neurons are capable of self-organizing to form different neural circuits for achieving a variety of cognitive functions. However, the current design paradigm of spiking neural networks is based on structures derived from deep learning. Such structures are dominated...
An artificial neural network (so called to distinguish it from the actual neural networks in the brain) has a different structure. It’s interconnected. This allows it to process data vary, learn from that data, and update its own internal structure to improve performance. ...
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