The phrase "a neural system" is correct and usable in written English.
It can be used when referring to a system that mimics or is inspired by the neural networks found in biological brains, often in the context of artificial intelligence or neuroscience.
Example: "Researchers are developing a neural system that can learn and adapt to new information in real-time."
Alternatives: "a neural network" or "a neural architecture".
The only way to quantify it precisely is to consider the exponentially large number of ways a neural system might be arranged, and to compare every possible whole with every conceivable configuration of its parts; the more complicated the system, the harder it is to evaluate.
Then, a neural system can be easily identified to model this low-dimensional temporal data.
A neural system to estimate crack detection probability has been designed, implemented, tested and integrated into an adaptive control system.
This trigger is based on a neural system that extracts the interesting features of the incoming images and rejects the background more efficiently than classical solutions.
The proposed trigger scheme is based on a neural system that extracts the interesting features of the incoming images and rejects the background more efficiently than classical solutions.
Additionally, noise not only impacts the total transmitted information, but it also affects which frequencies of the sensory signal are preferably encoded by a neural system.
Next, we describe recent investigations conducted in our laboratory in which we explicitly adopt a neural-system perspective in examining the relations among these networks in MDD.
In other words, a fuzzy neural system is a fuzzy system that to determine its parameters used from training samples processed by the learning algorithm that derive or inspired by neural network theory.
And an animal with even a rudimentary neural system would therefore be expected to use a substantial proportion of its processing ability to find a sexual partner — up to a point of course.
LiGABAR is developed for specifically probing a defined GABAAR subtype within a complex neural system.
Each time-step of the model represents 1 ms, equivalent to a 1 kHz sampling rate of a realistic neural system.
