Results 41 to 50 of about 4,010,683 (211)

Integrating convolutional neural networks into a sparse distributed representation model based on mammalian cortical learning

, 2016
Biological brains exhibit a remarkable capacity to recognise real-world patterns effectively. Despite major advances in neuroscience over the last few decades, an understanding of the brain's underlying mechanisms for pattern recognition remains ...
Daniel E. Padilla, McDonnell, M.D., Mark D. McDonnell, Padilla, D.E.   +3 more
core   +1 more source

Studying the capacity of cellular encoding to generate feedforward neural network topologies [PDF]

, 2004
Proceeding of: IEEE International Joint Conference on Neural Networks, IJCNN 2004, Budapest, 25-29 July 2004Many methods to codify artificial neural networks have been developed to avoid the disadvantages of direct encoding schema, improving the search ...
Gutiérrez Sánchez, Germán, Galván, Inés M., Molina, José M., Sanchis de Miguel, María Araceli   +3 more
core   +1 more source

Robust Local Cluster Neural Networks (ESANN) [PDF]

, 2006
Eickhoff R, Sitte J, Rückert U. Robust Local Cluster Neural Networks (ESANN). In: Proceedings of the 14th European Symposium on Artificial Neural Networks (ESANN).
Eickhoff, Ralf, Rueckert, Ulrich, Sitte, Joaquin, Rückert, Ulrich   +3 more
core  

Taming the reservoir : feedforward training for recurrent neural networks

, 2012
Recurrent neural networks are successfully used for tasks like time series processing and system identification. Many of the approaches to train these networks, however, are often regarded as too slow, too complicated, or both.
Obst, Oliver, Obst, Oliver (R18459), Martin Riedmiller, Riedmiller, Martin, Oliver Obst   +4 more
core   +1 more source

Correlational Neural Networks [PDF]

Neural Computation, 2016
Common representation learning (CRL), wherein different descriptions (or views) of the data are embedded in a common subspace, has been receiving a lot of attention recently. Two popular paradigms here are canonical correlation analysis (CCA)–based approaches and autoencoder (AE)–based approaches.
Sarath Chandar, Mitesh M. Khapra, Hugo Larochelle, Balaraman Ravindran   +3 more
openaire   +6 more sources

Brain imaging and machine learning reveal uncoupled functional network for contextual threat memory in long sepsis

Scientific Reports
Positron emission tomography (PET) utilizes radiotracers like [18F]fluorodeoxyglucose (FDG) to measure brain activity in health and disease. Performing behavioral tasks between the FDG injection and the PET scan allows the FDG signal to reflect task ...
Joshua J. Strohl, Joseph Carrión, Patricio T. Huerta   +2 more
doaj   +1 more source

Olfactory Stimulation Modulates Visual Perception Without Training

Frontiers in Neuroscience, 2021
Considerable research shows that olfactory stimulations affect other modalities in high-level cognitive functions such as emotion. However, little known fact is that olfaction modulates low-level perception of other sensory modalities.
Yoshiaki Tsushima, Yurie Nishino, Hiroshi Ando   +2 more
doaj   +1 more source

Boosting Neural Networks [PDF]

Neural Computation, 2000
Boosting is a general method for improving the performance of learning algorithms. A recently proposed boosting algorithm, Ada Boost, has been applied with great success to several benchmark machine learning problems using mainly decision trees as base classifiers.
Holger Schwenk, Yoshua Bengio
openaire   +3 more sources

Assessment of glutamatergic synaptic transmission and plasticity in brain slices: relevance to bioelectronic approaches

Bioelectronic Medicine, 2019
Background Glutamatergic neurons represent the largest neuronal class in the brain and are responsible for the bulk of excitatory synaptic transmission and plasticity. Abnormalities in glutamatergic neurons are linked to several brain disorders and their
Eric H. Chang, Samantha T. Carreiro, Stephen A. Frattini, Patricio T. Huerta   +3 more
doaj   +1 more source

A message of the majority with scientific evidence encourages young people to show their prosocial nature in COVID-19 vaccination

Scientific Reports, 2021
The most promising way to prevent the explosive spread of COVID-19 infection is to achieve herd immunity through vaccination. It is therefore important to motivate those who are less willing to be vaccinated. To address this issue, we conducted an online
Toshiko Tanaka, Tsuyoshi Nihonsugi, Fumio Ohtake, Masahiko Haruno   +3 more
doaj   +1 more source