Generative Adversarial Networks (GAN): Another complex network, a GAN trains two neural networks that compete against each ...

Artificially engineered biological processes, such as perception systems, remain an elusive target for organic electronics ...

in an attempt to create “synthetic biological intelligence”. The general idea is that biological neurons have far more complexity and capability than any neural networks simulated in software.

Access to AI intelligence should be equal to all. That means building collaborative systems of learning, like the Thames ...

A Generative Adversarial Network (GAN) is a type of machine learning model that’s used to generate fake data that resembles real data. Since its inception in 2014 with Ian Goodfellow’s ‘Generative ...

Fiducial markers play a vital role in guiding robots by helping them navigate their environment and recognize objects. These ...

How can we characterize the dynamics of neural networks with recurrent connections? How do the time-varying activities of populations of neurons represent things? How are synapse strengths ...

A model developed by the University of Cordoba uses neural networks to optimize the decoding of markers used by machines to detect and ascertain the location of objects ...

A dedicated neural network was created for COSY, HMBC, and HSQC spectra to guarantee optimum detection of real NMR peaks for 2D experiments. For training, 5000 synthetic spectra were used.