Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor

Neuromorphic computing is a new paradigm for design of both the computing hardware and algorithms inspired by biological neural networks. The event-based nature and the inherent parallelism make neuromorphic computing a promising paradigm for building efficient neural network based architectures for control of fast and agile robots. In this paper, we present a spiking neural network architecture that uses sensory feedback to control rotational velocity of a robotic vehicle. When the velocity reaches the target value, the mapping from the target velocity of the vehicle to the correct motor command, both represented in the spiking neural network on the neuromorphic device, is autonomously stored on the device using on-chip plastic synaptic weights. We validate the controller using a wheel motor of a miniature mobile vehicle and inertia measurement unit as the sensory feedback and demonstrate online learning of a simple 'inverse model' in a two-layer spiking neural network on the neuromorphic chip. The prototype neuromorphic device that features 256 spiking neurons allows us to realise a simple proof of concept architecture for the purely neuromorphic motor control and learning. The architecture can be easily scaled-up if a larger neuromorphic device is available.Comment: 6+1 pages, 4 figures, will appear in one of the Robotics conference

Thermoelectric performance of weakly coupled granular materials

We study thermoelectric properties of inhomogeneous nanogranular materials for weak tunneling conductance between the grains, g_t < 1. We calculate the thermopower and figure of merit taking into account the shift of the chemical potential and the asymmetry of the density of states in the vicinity of the Fermi surface. We show that the weak coupling between the grains leads to a high thermopower and low thermal conductivity resulting in relatively high values of the figure of merit on the order of one. We estimate the temperature at which the figure of merit has its maximum value for two- and three-dimensional samples. Our results are applicable for many emerging materials, including artificially self-assembled nanoparticle arrays.Comment: 4 pages, 3 figure

From fat to FAT (CD36/SR-B2):Understanding the regulation of cellular fatty acid uptake

The molecular mechanisms underlying the cellular uptake of long-chain fatty acids and the regulation of this process have been elucidated in appreciable detail in the last decades. Two main players in this field, each discovered in the early 1990s, are (i) a membrane-associated protein first identified in adipose ('fat') tissue and referred to as putative fatty acid translocase (FAT)/CD36 (now officially designated as SR-B2) which facilitates the transport of fatty acids across the plasma membrane, and (ii) the family of transcription factors designated peroxisome proliferator-activated receptors (PPAR alpha, PPAR gamma, and PPAR(beta/delta) for which fatty acids and fatty acid metabolites are the preferred ligand. CD36/SR-B2 is the predominant membrane protein involved in fatty acid uptake into intestinal enterocytes, adipocytes and cardiac and skeletal myocytes. The rate of cellular fatty acid uptake is regulated by the subcellular vesicular recycling of CD36/SR-B2 from endosomes to the plasma membrane. Fatty acid-induced activation of PPARs results in the upregulation of the expression of genes encoding various proteins and enzymes involved in cellular fatty acid utilization. Both CD36/SR-B2 and the PPARs have been implicated in the derangements in fatty acid and lipid metabolism occurring with the development of pathophysiological conditions, such as high fat diet-induced insulin resistance and diabetic cardiomyopathy, and have been suggested as targets for metabolic intervention. In this brief review we discuss the discovery and current understanding of both CD36/SR-B2 and the PPARs in metabolic homeostasis. (C) 2016 Elsevier B.V. and Societe Francaise de Biochimie et Biologie Moleculaire (SFBBM). All rights reserved

Dysbiosis of skin microbiota with increased fungal diversity is associated with severity of disease in atopic dermatitis

Background: Atopic dermatitis (AD) is a multifactorial inflammatory skin disease and an altered skin microbiota with an increase of Staphylococcus aureus has been reported. However, the role of fungi remains poorly investigated. Objectives: We aimed to improve the understanding of the fungal skin microbiota, the mycobiota, in AD in relation to the bacterial colonization. Methods: Skin swabs of 16 AD patients and 16 healthy controls (HC) from four different skin sites, that is antecubital crease, dorsal neck, glabella and vertex from multiple time points were analysed by DNA sequencing of the internal transcribed spacer region 1 (ITS1) and 16S rRNA gene for fungi and bacteria, respectively. Results: Malassezia spp. were the predominant fungi in all subjects but with a decreased dominance in severe AD patients in favour of non-Malassezia fungi, for example Candida spp. For bacteria, a decrease of Cutibacterium spp. in AD patients in favour of Staphylococcus spp., particularly S. aureus, was observed. Further, both bacterial and fungal community compositions of severe AD patients significantly differed from mild-to-moderate AD patients and HC with the latter two having overall similar microbiota showing some distinctions in bacterial communities. Conclusions: We conclude that severe AD is associated with a pronounced dysbiosis of the microbiota with increased fungal diversity. Potentially infectious agents, for example Staphylococcus and Candida, were increased in severe AD. Keywords: atopic dermatitis; bacteria; disease severity; fungi; skin microbiot

Thermoelectric performance of granular semiconductors

We study thermoelectric properties of granular semiconductors with weak tunneling conductance between the grains, g_t < 1. We calculate the thermopower and figure of merit taking into account the shift of the chemical potential and the asymmetry of the density of states in the vicinity of the Fermi surface due to n- or p-type doping in the Efros-Shklovskii regime for temperatures less than the charging energy. We show that for weakly coupled semiconducting grains the figure of merit is optimized for grain sizes of order 5nm for typical materials and its values can be larger than one. We also study the case of compensated granular semiconductors and show that in this case the thermopower can be still finite, although two to three orders of magnitude smaller than in the uncompensated regime.Comment: 4 pages, 4 figure