Deep learning models can be understood as chains of functions, thus making a functional approach look potentially interesting.
These functional domains have very broad and general functions, thus explaining why they appear in all types of fungi and also in the human kinome.
Here we show that astrocytes are also capable of performing these functions, thus suggesting that they might contribute to a key computational property of complex nervous systems.
The process Dr. Krebs discovered in the 1950s with Edmond H. Fischer, a colleague at the University of Washington, activates proteins that can change the entire character of cell functions, thus regulating them.
The targeted genes could have a wide range of functions, thus documenting the versatility of the technique.
This allows to exclude functions (thus sites) that do not contribute locally to the lower envelope of the lifted diagram.
Relaxation measurements can be fitted with a set of exponential decay functions, thus defining three different relaxation times.
It is proposed that by transforming from a basal to a reactive state, astrocytes neglect their neurosupportive functions, thus rendering neurons vulnerable to excitotoxicity and oxidative stress.
On each circuit board, FPGA is designed to provide cell header translation, buffering, and routing functions, thus fast prototyping can be achieved.
We show that this extension can be efficiently evaluated for a number of useful submodular functions, thus making these otherwise impractical algorithms viable for real-world machine learning problems.
First, the constrained states are mapped by smooth functions, thus, the considered systems become nonlinear systems without state constraints subject to unknown approximation error.
