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The industrial need is now moving towards a desire to manage requirements in a plausible architecture.
According to this biologically plausible architecture, a popular bottom-up attention model is proposed by Itti et al. [1].
We propose this model provides a biologically plausible architecture that learns from and uses multimodal spatio-temporal working memories to develop and refine reward-eliciting behaviors.
Using visual spatial attention and visual short-term memory as case studies, we suggest that competitive maps are a concrete and plausible architecture that limits cognitive capacity across many domains.
Finally, the functional role of the NeGOI model is examined from the perspective of a biologically plausible architecture which provides a parsimonious set of high-level and view-independent visual features as input to mirror systems.
A plausible architecture of an ancient genetic code is derived from an extended base triplet vector space over the Galois field of the extended base alphabet {D, A, C, G, U}, where symbol D represents one or more hypothetical bases with unspecific pairings.
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Our model suggests plausible network architecture, provides interesting model predictions that can be experimentally tested, and offers a sufficiently realistic multi-scale model for 5-HT neuromodulation simulations.
While there are many models of reaction time performance, these results fall naturally out of a biologically and computationally plausible cognitive architecture.
We also constructed β-solenoid-based insulin fibril models and conducted fiber diffraction simulation to identify plausible fibril architectures of insulin amyloid.
From the results of experiments using both synthetic and real MR datasets we conclude that the denoising algorithm produces anatomically plausible fiber architectures from diffusion tensor images with a wide range of signal-to-noise ratios.
Based on this, computer simulations show that only a small subset of possible arrangements of these input elements maps onto a recently proposed algorithmic three-input model in a way that generates a highly direction-selective motion detector, suggesting plausible network architectures.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com