is a major transcriptional activator in the core gene expression network of animal cells. "By understanding fundamental processes such as cancer development and metastasis, tissue differentiation from ...

Kanter. "In artificial neural networks, each object needs its own output unit to be recognized, whereas the brain can use one neuronal output, where its activity as a function of time differentiates ...

This message comes to life across every touchpoint—from the AI technologies powering its flagship products, to immersive ...

A collaborative study led by Professor Vittoria Raffa at the University of Pisa and Assistant Professor Fabian Raudzus ...

To solve the time-variant Sylvester equation, in 2013, Li et al. proposed the zeroing neural network with sign-bi-power function (ZNN-SBPF) model via constructing a nonlinear activation function. In ...

A team of researchers from APL and the Johns Hopkins Bloomberg School of Public Health is developing next-generation brain ...

This useful study presents a biologically realistic, large-scale cortical model of the rat's non-barrel somatosensory cortex, investigating synaptic plasticity of excitatory connections under varying ...

The problem of asymptotic stability analysis is investigated about a class of delayed neural networks with homogeneous right-hands sides. Under the assumption that the trivial solution of delay free ...