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10 results for “perceptrons”
Data set for "Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2"
<p>Data sets for the publication "Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS<sub>2</sub>", doi:10.1021/acsnano.1c07065</p>
SDUST2023BCO: a global seafloor model determined from multi-layer perceptron neural network using multi-source differential marine geodetic data
<div> <p>SDUST2023BCO.nc is the global marine bathymetric model covering 80°S~80°N and 0°~360°E on 1′×1′ grids. The dataset contains geospatial information (latitude, longitude), SDUST2023BCO bathymetric model and an attachment data.</p> </div>
Replication Data for: Geometry-Complete Perceptron Networks for 3D Molecular Graphs
<p>Included are preprocessed data files for the Newtonian many-body systems modeling task described in our accompanying manuscript.</p>
The reconstructed three-dimensional nitrate field dataset for the pan-European ocean using a continual learning-based multilayer perceptron
<p>Based on a newly developed continual learning-based multilayer perceptron model and environmental features, we reconstructed the pan-European 3D ocean nitrate field from 2010 to 2023. The reconstructed field features a monthly temporal resolution, a horizontal spatial resolution of 0.25 degrees, and 63 depth levels, with vertical intervals ranging from 5 to 50 m.</p>
Hardware perceptron and ReLU measurements and results
<p>Recently, in-memory analog computing through memristive crossbar arrays attracted a lot of attention due to its efficient power consumption, area, and computing throughput. Using this computing method, different types of neural networks can be implemented for different applications. In such neural networks, memristors represent the synapses. However, in previous work, digital processors have been used to implement the activation functions or neurons. Implementing neurons using analog-based hardware further improves the power consumption, area, and throughput by removing unnecessary data conversions and communication. </p> <p>In this study, we designed a ReLU activation function and built a fully hardware-based two-layer fully connected perceptron using memristive arrays, and verified the operation by classifying downsampled MNIST images. We measured the DC and AC characteristics of our designed ReLU, forming, set, and reset behavior of the memristive arrays, and the perceptron behavior during training and inference in the classification task. We also studied the non-idealities related to both the ReLU design and memristors which is significantly critical in future integrated designs. Moreover, the downsampled 8*8 MNIST images that we generated from the original MNIST dataset are included in the data which can be used in future studies with the limited size of the network.</p>
Hardware perceptron and ReLU measurements and results
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Data from: Probability matching in perceptrons: effects of conditional dependence and linear nonseparability
Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent's environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit) learn to match probability when presented different cues in isolation. The current paper extends this research by showing that perceptrons can match probabilities when presented simultaneous cues, with each cue signaling different reward likelihoods. In our first simulation, we presented up to four different cues simultaneously; the likelihood of reward signaled by the presence of one cue was independent of the likelihood of reward signaled by other cues. Perceptrons learned to match reward probabilities by treating each cue as an independent source of information about the likelihood of reward. In a second simulation, we violated the independence between cues by making some reward probabilities depend upon cue interactions. We did so by basing reward probabilities on a logical combination (AND or XOR) of two of the four possible cues. We also varied the size of the reward associated with the logical combination. We discovered that this latter manipulation was a much better predictor of perceptron performance than was the logical structure of the interaction between cues. This indicates that when perceptrons learn to match probabilities, they do so by assuming that each signal of a reward is independent of any other; the best predictor of perceptron performance is a quantitative measure of the independence of these input signals, and not the logical structure of the problem being learned.
Multi-layer-perceptron trained with supervised and PPO RL
<p>Download of a supervised + RL trained policy</p>
Data from: Probability matching in perceptrons: effects of conditional dependence and linear nonseparability
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Utilização do classificador Perceptron multicamadas em um dataset para classificação de veiculos
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.