Investigation of neural circuit dynamics is crucial for deciphering the functional connections among regions of the brain and understanding the mechanism of brain dysfunction. Despite the advancements of neural circuit models in vitro, technologies for both precisely monitoring and modulating neural activities within three-dimensional (3D) neural circuit models have yet to be developed. Specifically, no existing 3D microelectrode arrays (MEAs) have integrated capabilities to stimulate surrounding neurons and to monitor the temporal evolution of the formation of a neural network in real time. Herein, we present a 3D high-density multifunctional MEA with optical stimulation and drug delivery for investigating neural circuit dynamics within engineered 3D neural tissues. We demonstrate precise measurements of synaptic latencies in 3D neural networks. We expect our 3D multifunctional MEA to open up opportunities for studies of neural circuits through precise, in vitro investigations of neural circuit dynamics with 3D brain models.

Traditional fluid simulations require large computational resources even for an average sized scene with the main bottleneck being a very small time step size, required to guarantee the stability of the solution. Despite a large progress in parallel computing and efficient algorithms for pressure computation in the recent years, realtime fluid simulations have been possible only under very restricted conditions. In this paper we propose a novel machine learning based approach, that formulates physics-based fluid simulation as a regression problem, estimating the acceleration of every particle for each frame. We designed a feature vector, directly modelling individual forces and constraints from the Navier-Stokes equations, giving the method strong generalization properties to reliably predict positions and velocities of particles in a large time step setting on yet unseen test videos. We used a regression forest to approximate the behaviour of particles observed in the large training set of simulations obtained using a traditional solver. Our GPU implementation led to a speed-up of one to three orders of magnitude compared to the state-of-the-art position-based fluid solver and runs in real-time for systems with up to 2 million particles.

In native tissues, cellular and acellular components are anisotropically organized and often aligned in specific directions, providing structural and mechanical properties for actuating biological functions. Thus, engineering alignment not only allows for emulation of native tissue structures but might also enable implementation of specific functionalities. However, achieving desired alignment is challenging, especially in three-dimensional constructs. By exploiting the elastomeric property of polydimethylsiloxane and fibrillogenesis kinetics of collagen, here we introduce a simple yet effective method to assemble and align fibrous structures in a multi-modular three-dimensional conglomerate. Applying this method, we have reconstructed the CA3–CA1 hippocampal neural circuit three-dimensionally in a monolithic gel, in which CA3 neurons extend parallel axons to and synapse with CA1 neurons. Furthermore, we show that alignment of the fibrous scaffold facilitates the establishment of functional connectivity. This method can be applied for reconstructing other neural circuits or tissue units where anisotropic organization in a multi-modular structure is desired.

We report on plasma damage-free sputtering of an indium tin oxide (ITO) cathode layer, which was grown by a mirror shape target sputtering (MSTS) technique, for use in top-emitting organic light-emitting diodes (TOLEDs). It is shown that OLEDs with ITO cathodes deposited by MSTS show much lower leakage current (9.2×10−5mA∕cm2) at reverse bias of −6V as compared to that (1×10−1–10−2mA∕cm2 at −6V) of OLEDs with ITO cathodes grown by conventional dc magnetron sputtering. Based on high-resolution electron microcopy, x-ray diffraction, and scanning electron microscopy results, we describe a possible mechanism by which plasma damage-free ITO films are grown and their application for TOLEDs.

Since the advent of organ-on-a-chip, many researchers have tried to mimic the physiology of human tissue on an engineered platform. In the case of brain tissue, structural connections and cell–cell interactions are important factors for brain function. The recent development of brain-on-a-chip is an effort to mimic those structural and functional aspects of brain tissue within a miniaturized engineered platform. From this perspective, we provide an overview of trace of brain-on-a-chip development, especially in terms of complexity and high-content/high-throughput screening capabilities, and future perspectives on more in vivo-like brain-on-a-chip development.

Titanium nitride thin film was deposited on a silicon wafer by the Atomic Layer Deposition (ALD) method using TiCl 4 and NH 3 as source chemicals. Nitrogen gas was used for carrying the TiCl 4 and purging the reactants. The gases were introduced into the reaction chamber in the sequence of TiCl 4 -N 2 -NH 3 -N 2 for the saturated surface reaction on the wafer. TiN film was grown with [100] preferred orientation at 350 o C, while with [111] preferred orientation at 450 o C and higher temperatures. The deposition rate was constant as 0.17 Å/ cycle irrespective of deposition temperature, which demonstrates TiN film was grown by the ALD growth mechanism. TiN thin films grown at a temperature higher than 450 o C with thickness of 320 Å showed electrical resistivity as low as 72×10 −6 Ωcm.

With the rapid increase in the use of optogenetics to investigate the nervous system, there is a high demand for a neural interface that enables 2D mapping of electrophysiological neural signals with high precision during simultaneous light stimulation. Here, a gold nanonetwork (Au NN)-based transparent neural electrocorticogram (ECoG) monitoring system is proposed as implantable neural electronics. The neural interface enables accurate 2D mapping of ECoG neural signals without any photoelectric artifact during light stimulation. By using the Au NN, not only the transmittance of the microelectrodes is increased by 81% but also a low electrochemical impedance of 33.9 kΩ at 1 kHz with improved mechanical stability is achieved. It is demonstrated that the transparent microelectrode array records multichannel in vivo neural activities with no photoelectric artifact and a high signal-tonoise ratio. Propagation of neural dynamics of optically driven neural activities is also clearly visualized using the 2D Au NN microelectrode array. This transparent, flexible ECoG microelectrode array is a promising candidate for next-generation in vitro and in vivo neural interface for 2D mapping of neural dynamics.

Development of polymeric memristors is of great interest for wearable and implantable applications because of its flexibility and transparency. One of distinguished polymeric memristor materials is silk fibroin, which is a biodegradable protein extracted from Bombyx mori cocoons. However, fibroin memristors demonstrated earlier show low memory cell density (<100 cells mm−2). In this paper, silk fibroin memristors with cell density 25 times higher than that of the previously reported silk fibroin memristors by utilizing wafer‐scale UV photolithography are reported. Unlike shadow masking technique adopted in the previous researches, UV photolithography allows single‐micrometer‐scale alignment between layers to obtain a much denser structure. Memristive characteristics of the silk fibroin memristors are studied under different values of compliance current during SET process with different thickness of silk fibroin films. The ON/OFF ratio of the fabricated memristors is maintained over 1000 s of measurement, and remains the same during bending over a cylinder with a radius of curvature of 1.3 mm. It is shown that silk fibroin remains biocompatible and biodegradable after undergoing the fabrication process. Demonstrated transparency and lightweight characteristics are desirable for electronic components for wearable and implantable systems.