Spiking Neural Networks

Mar 24, 2024 ... Graph neural networks (GNNs) have achieved state-of-the-art results on many graph representation learning tasks by exploiting statistical ... ... informational differences among them, efficiently coordinating and integrating the sub-prediction results. Secondly, a two-stage predictive model selection ... Sat 7:10 a.m. - 7:18 a.m.. Neural networks learn an environment's geometry in latent space by performing predictive coding on visual scenes ( Oral ) > link Dec 31, 2022 ... We relax these limitations using deep learning methods. We use a neural network to classify insurees into joint choice of coverage and claim ... Cognitive Neuroscience 2018: On the biological (or social) expedience of the mechanism of information integration in neural networks of the brain through. Computational scientist seeking data science and ML/AI engineering positions · Currently, I am a senior undergraduate student at UVM working on my thesis ... Sep 1, 2021 ... We find that specialized neuronal populations in auditory cortex form functional networks during auditory task performance whose structures depend on both ... Klinkner, K.L., Shalizi, C.R., Camperi, M.F. Measuring shared information and coordinated activity in neuronal networks (2005). Advances in Neural Information ... Sep 4, 2020 ... TABOO: De- tecting unstructured sensitive information using recursive neural networks. In Proceedings of the 33rd International Conference on. Apr 17, 2019 ... ... learning (AL) framework to select most informative samples for training our model using a Bayesian neural network. Informative samples are ...