Results 31 to 40 of about 2,000,131 (360)
Deformation prediction programming based on MATLAB and BP neural network [PDF]
E3S Web of Conferences, 2022 In order to solve land tension problems, high-rise buildings become more and more common in the modern big cities. Meanwhile, it is necessary to monitor the deformation of the high-rise buildings for their safety. Based on the deformation monitoring data,
Zheng Haoxin, Chang Zhanqiang, Liu Jiaxi
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Biologically Informed Neural Networks Predict Drug Responses [PDF]
Cancer Cell, 2020 Deep neural networks often achieve high predictive accuracy on biological problems, but it can be hard to contextualize how and explain why predictions are made. In this issue, Kuenzi et al. model the sensitivity of cancers to drugs using deep neural networks with a hierarchical structure derived from the Gene Ontology.
Casey S, Greene, James C, Costello
openaire +2 more sources
Anterior cingulate cortex and its input to the basolateral amygdala control innate fear response
Nature Communications, 2018 Brain circuits that control innate fear response are essential for an animal’s survival. Here, the authors report how the anterior cingulate cortex and its projection to amygdala control the innate fear response in mice.
Jinho Jhang +5 more
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ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, 2020 : An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the biological nervous systems, such as the brain, which process information.
Satchidananda Dehuri +2 more
semanticscholar +1 more source
On the role of synaptic stochasticity in training low-precision neural networks [PDF]
, 2018 Stochasticity and limited precision of synaptic weights in neural network models are key aspects of both biological and hardware modeling of learning processes.
Baldassi, Carlo +6 more
core +4 more sources
A biomimetic neural encoder for spiking neural network
Nature Communications, 2021 Spiking neural networks (SNNs) promise to bridge the gap between artificial neural networks (ANNs) and biological neural networks (BNNs) by exploiting biologically plausible neurons that offer faster inference, lower energy expenditure, and event-driven ...
Shiva Subbulakshmi Radhakrishnan +4 more
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ARTIFICIAL NEURAL NETWORK FOR MODELS OF HUMAN OPERATOR
Acta Polytechnica CTU Proceedings, 2017 This paper presents a new approach to mental functions modeling with the use of artificial neural networks. The artificial neural networks seems to be a promising method for the modeling of a human operator because the architecture of the ANN is directly
Martin Ruzek
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Artificial neural network in diagnostic cytology
CytoJournal, 2022 The artificial neural network (ANN) is a computer software design or model that simulates the biological neural network of the human brain. Instead of biological neurons, ANN is composed of many layers of nodes that carry the signal and process it to ...
P. Dey
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Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor [PDF]
, 2018 Neuromorphic computing is a new paradigm for design of both the computing hardware and algorithms inspired by biological neural networks. The event-based nature and the inherent parallelism make neuromorphic computing a promising paradigm for building ...
Glatz, Sebastian +4 more
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Graph Neural Network for Protein–Protein Interaction Prediction: A Comparative Study
Molecules, 2022 Proteins are the fundamental biological macromolecules which underline practically all biological activities. Protein–protein interactions (PPIs), as they are known, are how proteins interact with other proteins in their environment to perform biological
Hang Zhou +4 more
semanticscholar +1 more source