A neural network is a stack of mathematical layers that learn to map inputs to outputs by adjusting millions of small numbers. Despite the name, it doesn't work like biological brains — but the metaphor was sticky and the math turned out to work
Brain networks exist within the confines of resource limitations. As a result, a brain network must overcome the metabolic costs of growing and sustaining the network within its physical space, while simultaneously implementing its required information processing. Here, to observe the effect of these processes, we introduce the spatially embedded recurrent neural network (seRNN). seRNNs learn basic task-related inferences while existing within a three-dimensional Euclidean space, where the communication of
The widespread finding of neural populations apparently tuned to specific, identifiable fea- tures of our external environment (e.g., faces, places, speech) suggests that brains may favor certain representational axes over others. But despite decades of research, we have no formal understanding of whether and why brains use privileged bases for representing the natural world. Here, we develop a formal framework for investigating the extent to which a repre- sentational system has privileged axes. First, we
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← When Analytic Calculus Cracks AdaBoost Code BrainNPT: Pre-training of Transformer networks for brain network classification → # Simulation-based inference using surjective sequential neural likelihood estimation 投稿日: 2023年8月3日 作成者: jarxiv 我々は、尤度関数の評価が難しく
Collective Behavior in Animal Groups, Artificial Agents, and Deep Networks
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NeurIPS Proceedings Search Bayesian Deep Ensembles via the Neural Tangent Kernel Bobby He, Balaji Lakshminarayanan, Yee Whye Teh Advances in Neural Information Processing Systems 33 (NeurIPS 2020) Abstract We explore the link between deep ensembles and Gaussian processes (GPs) through the lens of the Neural Tangent Kernel (NTK): a recent development in understanding the training dynamics of wide neural networks (NNs). Previous work has shown that even in the infinite width limit, when NNs become GPs, there
--> Login Home A technique for determining relevance scores of process activities using graph-based neural networks Stierle M, Weinzierl S, Harl M, Matzner M (2021) Publication Language: English Publication Type: Journal article, Original article Publication year: 2021 Journal Decision Support Systems Elsevier Book Volume: 144 Article Number: 113511 URI: http://www.sciencedirect.com/science/article/pii/S016792362100021X DOI: 10.1016/j.dss.2021.113511 Open Access Link: https://doi.org/10.1016/j.dss.2021.1135
Recurrent spiking neural networks (RSNNs) are notoriously difficult to train because of the vanishing gradient problem that is enhanced by the binary nature of the spikes. In this paper, we review the ability of the current state-of-the-art RSNNs to solve long-term memory tasks, and show that they have strong constraints both in performance, and for their implementation on hardware analog neuromorphic processors. We present a novel spiking neural network that circumvents these limitations. Our biologically