Neural representation of current and intended task sets during sequential judgements on human faces

Cortical Short-Range Fiber Connectivity and Its Association With Deep Brain White Matter Hyperintensities in Older Diabetic People With Low Serum Vitamin B12

Although previous studies have indicated that older people with diabetes mellitus (DM) had an approximately two times larger white matter hyperintensity (WMH) load than those without DM, the influence of WMHs on cognition is uncertain and inconsistent in the literature. It is unclear whether the short-range fibers in the juxtacortical region, traditionally considered to be spared from WMH pathology, are enhanced as an adaptive response to deep WM degeneration in older diabetic people with normal cognition. Moreover, the specific effect of vitamin B₁₂ deficiency, commonly accompanied by DM, remains to be investigated. This study implemented a specialized analysis of the superficial cortical short-range fiber connectivity density (SFiCD) based on a data-driven framework in 70 older individuals with DM and low serum vitamin B₁₂. Moreover, the effects of time and vitamin B₁₂ supplementation were assessed based on a randomized placebo-controlled trial in 59 individuals. The results demonstrated a higher SFiCD in diabetic individuals with a higher deep WMH load. Additionally, a significant interaction between DWMH load and homocysteine on SFiCD was found. During the 27-month follow-up period, a longitudinal increase in the SFiCD was observed in the bilateral frontal cortices. However, the observed longitudinal SFiCD change was not dependent on vitamin B₁₂ supplementation; thus, the specific reason for the longitudinal cortical short fiber densification may need further study. Overall, these findings may help us better understand the neurobiology of brain plasticity in older patients with DM, as well as the interplay among DM, WMH, and vitamin B₁₂ deficiency.

Is the Free-Energy Principle a Formal Theory of Semantics? From Variational Density Dynamics to Neural and Phenotypic Representations

The aim of this paper is twofold: (1) to assess whether the construct of neural representations plays an explanatory role under the variational free-energy principle and its corollary process theory, active inference; and (2) if so, to assess which philosophical stance—in relation to the ontological and epistemological status of representations—is most appropriate. We focus on non-realist (deflationary and fictionalist-instrumentalist) approaches. We consider a deflationary account of mental representation, according to which the explanatorily relevant contents of neural representations are mathematical, rather than cognitive, and a fictionalist or instrumentalist account, according to which representations are scientifically useful fictions that serve explanatory (and other) aims. After reviewing the free-energy principle and active inference, we argue that the model of adaptive phenotypes under the free-energy principle can be used to furnish a formal semantics, enabling us to assign semantic content to specific phenotypic states (the internal states of a Markovian system that exists far from equilibrium). We propose a modified fictionalist account—an organism-centered fictionalism or instrumentalism. We argue that, under the free-energy principle, pursuing even a deflationary account of the content of neural representations licenses the appeal to the kind of semantic content involved in the ‘aboutness’ or intentionality of cognitive systems; our position is thus coherent with, but rests on distinct assumptions from, the realist position. We argue that the free-energy principle thereby explains the aboutness or intentionality in living systems and hence their capacity to parse their sensory stream using an ontology or set of semantic factors.

The Neural Network Revamping the Process’s Reliability in Deep Lean via Internet of Things

Deep lean is a novel approach that is concerned with the profound analysis for waste’s behavior at hidden layers in manufacturing processes to enhance processes’ reliability level at the upstream. Ideal Standard Co. for bathtubs suffered from defects and cost losses in the spraying section, due to differences in the painting cover thickness due to bubbles, caused by eddies, which move toward the bathtubs through hoses. These bubbles and their movement are considered as a form of lean’s waste. The spraying liquid inside the tanks and hoses must move with uniform velocity, viscosity, pressure, feed rate and suitable Reynolds circulation values to eliminate the eddy causes. These factors are tackled through the adoption Internet of Things (IoT) technologies that are aided by neural networks (NN) when an abnormal flow rate is detected using sensor data in real-time that can reduce the defects. The NN aimed at forecasting eddies’ movement lines that carry bubbles and works on being blasted before entering the hoses through using Design of Experiment (DOE). This paper illustrates a deep lean perspective as driven by the define, measure, analysis, improvement and control (DMAIC) methodology to improve reliability. The eddy moves downstream slowly with an anti-clockwise flow for some of the optimal values for the influencing factors, whereas the circulation of Ω increases, whether for vertical or horizontal travel.