Reminding Forgetful Organic Neuromorphic Device Networks

Organic neuromorphic device networks can accelerate neural network algorithms and directly integrate with microfluidic systems or living tissues. Proposed devices based on the bio-compatible conductive polymer PEDOT:PSS have shown high switching speeds and low energy demand. However, as electrochemical systems, they are prone to self-discharge through parasitic electrochemical reactions. Therefore, the network's synapses forget their trained conductance states over time. This work integrates single-device high-resolution charge transport models to simulate neuromorphic device networks and analyze the impact of self-discharge on network performance. Simulation of a single-layer nine-pixel image classification network reveals no significant impact of self-discharge on training efficiency. And, even though the network's weights drift significantly during self-discharge, its predictions remain 100\% accurate for over ten hours. On the other hand, a multi-layer network for the approximation of the circle function is shown to degrade significantly over twenty minutes with a final mean-squared-error loss of 0.4. We propose to counter the effect by periodically reminding the network based on a map between a synapse's current state, the time since the last reminder, and the weight drift. We show that this method with a map obtained through validated simulations can reduce the effective loss to below 0.1 even with worst-case assumptions. Finally, while the training of this network is affected by self-discharge, a good classification is still obtained. Electrochemical organic neuromorphic devices have not been integrated into larger device networks. This work predicts their behavior under nonideal conditions, mitigates the worst-case effects of parasitic self-discharge, and opens the path toward implementing fast and efficient neural networks on organic neuromorphic hardware

Wetting-Induced Polyelectrolyte Pore Bridging

Active layers of ion separation membranes often consist of charged layers that retain ions based on electrostatic repulsion. Conventional fabrication of these layers, such as polyelectrolyte deposition, can in some cases lead to excess coating to prevent defects in the active layer. This excess deposition increases the overall membrane transport resistance. The study at hand presents a manufacturing procedure for controlled polyelectrolyte complexation in and on porous supports by support wetting control. Pre-wetting of the microfiltration membrane support, or even supports with larger pore sizes, leads to ternary phase boundaries of the support, the coating solution, and the pre-wetting agent. At these phase boundaries, polyelectrolytes can be complexated to form partially freestanding selective structures bridging the pores. This polyelectrolyte complex formation control allows the production of membranes with evenly distributed polyelectrolyte layers, providing (1) fewer coating steps needed for defect-free active layers, (2) larger support diameters that can be bridged, and (3) a precise position control of the formed polyelectrolyte multilayers. We further analyze the formed structures regarding their position, composition, and diffusion dialysis performance

Representativeness of personality and involvement preferences in a web-based survey on healthcare decision-making

Background: Obtaining a sample that is representative of the group of interest is of utmost importance in questionnaire studies. In a survey using a state authorized web-portal for citizen communication with authorities, we wanted to investigate the view of adult men on patient involvement in health care decision-making regarding Prostate-Specific Antigen test for prostatic cancer. In this paper, we report on sample characteristics and representativeness of our sample in terms of personality and baseline involvement preferences. Methods: We compared personality profiles (BFI-10) and baseline healthcare decision-making preferences (CPS) in our sample (n = 6756) to internationally available datasets. Pooled data from a) US, UK, Canada, Australia, and New Zealand (n = 1512), b) Germany, Netherlands, Switzerland, and Belgium (n = 1136), and c) Norway, Sweden, Finland, and Denmark (n = 1313) were used for BFI-10 comparisons. Regarding CPS, we compared our sample with three previous datasets relating to decision-making in cancer (n = 425, 387, and 199). Results: Although statistically significant differences particularly appeared in large dataset comparisons, sample BFI-10 and CPS profiles mostly were within the range of those previously reported. Similarity was greatest in BFI-10 comparisons with group a) where no statistically significant difference could be established in factors 'agreeableness' and 'neuroticism' (p = .095 and .578, respectively). Conclusion: Despite some variation, our sample displays personality and baseline preference profiles that are generally similar to those described in previous international studies. For example, this was the case with the BFI-10 'agreeableness' measure (incl. trust and fault-finding items), an important factor in healthcare decision-making

3D Nanofabrication inside rapid prototyped microfluidic channels showcased by wet-spinning of single micrometre fibres

Microfluidics is an established multidisciplinary research domain with widespread applications in the fields of medicine, biotechnology and engineering. Conventional production methods of microfluidic chips have been limited to planar structures, preventing the exploitation of truly three-dimensional architectures for applications such as multi-phase droplet preparation or wet-phase fibre spinning. Here the challenge of nanofabrication inside a microfluidic chip is tackled for the showcase of a spider-inspired spinneret. Multiphoton lithography, an additive manufacturing method, was used to produce free-form microfluidic masters, subsequently replicated by soft lithography. Into the resulting microfluidic device, a threedimensional spider-inspired spinneret was directly fabricated in-chip via multiphoton lithography. Applying this unprecedented fabrication strategy, the to date smallest printed spinneret nozzle is produced. This spinneret resides tightly sealed, connecting it to the macroscopic world. Its functionality is demonstrated by wet-spinning of single-digit micron fibres through a polyacrylonitrile coagulation process induced by a water sheath layer. The methodology developed here demonstrates fabrication strategies to interface complex architectures into classical microfluidic platforms. Using multiphoton lithography for in-chip fabrication adopts a high spatial resolution technology for improving geometry and thus flow control inside microfluidic chips. The showcased fabrication methodology is generic and will be applicable to multiple challenges in fluid control and beyond

Biocompatible Micron-Scale Silk Fibers Fabricated by Microfluidic Wet Spinning

For successful material deployment in tissue engineering, the material itself, its mechanical properties, and the microscopic geometry of the product are of particular interest. While silk is a widely applied protein-based tissue engineering material with strong mechanical properties, the size and shape of artificially spun silk fibers are limited by existing processes. This study adjusts a microfluidic spinneret to manufacture micron-sized wet-spun fibers with three different materials enabling diverse geometries for tissue engineering applications. The spinneret is direct laser written (DLW) inside a microfluidic polydimethylsiloxane (PDMS) chip using two-photon lithography, applying a novel surface treatment that enables a tight print-channel sealing. Alginate, polyacrylonitrile, and silk fibers with diameters down to 1 µm are spun, while the spinneret geometry controls the shape of the silk fiber, and the spinning process tailors the mechanical property. Cell-cultivation experiments affirm bio-compatibility and showcase an interplay between the cell-sized fibers and cells. The presented spinning process pushes the boundaries of fiber fabrication toward smaller diameters and more complex shapes with increased surface-to-volume ratio and will substantially contribute to future tailored tissue engineering materials for healthcare applications. © 2021 The Authors. Advanced Healthcare Materials published by Wiley-VCH Gmb

Porous PEDOT:PSS Particles and their Application as Tunable Cell Culture Substrate

Due to its biocompatibility, electrical conductivity, and tissue-like elasticity, poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) constitutes a highly promising material regarding the fabrication of smart cell culture substrates. However, until now, high-throughput synthesis of pure PEDOT:PSS geometries was restricted to flat sheets and fibers. In this publication, the first microfluidic process for the synthesis of spherical, highly porous, pure PEDOT:PSS particles of adjustable material properties is presented. The particles are synthesized by the generation of PEDOT:PSS emulsion droplets within a 1-octanol continuous phase and their subsequent coagulation and partial crystallization in an isopropanol (IPA)/sulfuric acid (SA) bath. The process allows to tailor central particle characteristics such as crystallinity, particle diameter, pore size as well as electrochemical and mechanical properties by simply adjusting the IPA:SA ratio during droplet coagulation. To demonstrate the applicability of PEDOT:PSS particles as potential cell culture substrate, cultivations of L929 mouse fibroblast cells and MRC-5 human fibroblast cells are conducted. Both cell lines present exponential growth on PEDOT:PSS particles and reach confluency with cell viabilities above 95 vol.% on culture day 9. Single cell analysis could moreover reveal that mechanotransduction and cell infiltration can be controlled by the adjustment of particle crystallinity

Transformative Materials to Create 3D Functional Human Tissue Models In Vitro in a Reproducible Manner

Recreating human tissues and organs in the petri dish to establish models as tools in biomedical sciences has gained momentum. These models can provide insight into mechanisms of human physiology, disease onset, and progression, and improve drug target validation, as well as the development of new medical therapeutics. Transformative materials play an important role in this evolution, as they can be programmed to direct cell behavior and fate by controlling the activity of bioactive molecules and material properties. Using nature as an inspiration, scientists are creating materials that incorporate specific biological processes observed during human organogenesis and tissue regeneration. This article presents the reader with state-of-the-art developments in the field of in vitro tissue engineering and the challenges related to the design, production, and translation of these transformative materials. Advances regarding (stem) cell sources, expansion, and differentiation, and how novel responsive materials, automated and large-scale fabrication processes, culture conditions, in situ monitoring systems, and computer simulations are required to create functional human tissue models that are relevant and efficient for drug discovery, are described. This paper illustrates how these different technologies need to converge to generate in vitro life-like human tissue models that provide a platform to answer health-based scientific questions.</p

Spiking neural networks compensate for weight drift in organic neuromorphic device networks

Organic neuromorphic devices can accelerate neural networks and integrate with biological systems. Devices based on the biocompatible and conductive polymer PEDOT:PSS are fast, require low amounts of energy and perform well in crossbar simulations. However, parasitic electrochemical reactions lead to self-discharge and the fading of the learned conductance states over time. This limits a neural network’s operating time and requires complex compensation mechanisms. Spiking neural networks (SNNs) take inspiration from biology to implement local and always-on learning. We show that these SNNs can function on organic neuromorphic hardware and compensate for self-discharge by continuously relearning and reinforcing forgotten states. In this work, we use a high-resolution charge transport model to describe the behavior of organic neuromorphic devices and create a computationally efficient surrogate model. By integrating the surrogate model into a Brian 2 simulation, we can describe the behavior of SNNs on organic neuromorphic hardware. A biologically plausible two-layer network for recognizing $28\times28$ pixel MNIST images is trained and observed during self-discharge. The network achieves, for its size, competitive recognition results of up to 82.5%. Building a network with forgetful devices yields superior accuracy during training with 84.5% compared to ideal devices. However, trained networks without active spike-timing-dependent plasticity quickly lose their predictive performance. We show that online learning can keep the performance at a steady level close to the initial accuracy, even for idle rates of up to 90%. This performance is maintained when the output neuron’s labels are not revalidated for up to 24 h. These findings reconfirm the potential of organic neuromorphic devices for brain-inspired computing. Their biocompatibility and the demonstrated adaptability to SNNs open the path towards close integration with multi-electrode arrays, drug-delivery devices, and other bio-interfacing systems as either fully organic or hybrid organic-inorganic systems