Numerical Simulation of Single-Electron Tunneling in Random Arrays of Small Tunnel Junctions Formed by Percolation of Conductive Nanoparticles

We numerically simulated electrical properties, i.e., the resistance and Coulomb blockade threshold, of randomly-placed conductive nanoparticles. In simulation, tunnel junctions were assumed to be formed between neighboring particle-particle and particle-electrode connections. On a plane of triangle 100×100 grids, three electrodes, the drain, source, and gate, were defined. After random placements of conductive particles, the connection between the drain and source electrodes were evaluated with keeping the gate electrode disconnected. The resistance was obtained by use of a SPICE-like simulator, whereas the Coulomb blockade threshold was determined from the current-voltage characteristics simulated using a Monte-Carlo simulator. Strong linear correlation between the resistance and threshold voltage was confirmed, which agreed with results for uniform one-dimensional arrays

Fabrication of resistively-coupled single-electron device using an array of gold nanoparticles

We demonstrated one type of single-electron device that exhibited electrical characteristics similar to those of resistively-coupled SE transistor (R-SET) at 77 K and room temperature (287 K). Three Au electrodes on an oxidized Si chip served as drain, source, and gate electrodes were formed using electron-beam lithography and evaporation techniques. A narrow (70-nm-wide) gate electrode was patterned using thermal evaporation, whereas wide (800-nm-wide) drain and source electrodes were made using shadow evaporation. Subsequently, aqueous solution of citric acid and 15-nm-diameter gold nanoparticles (Au NPs) and toluene solution of 3-nm-diameter Au NPs chemisorbed via decanethiol were dropped on the chip to make the connections between the electrodes. Current–voltage characteristics between the drain and source electrodes exhibited Coulomb blockade (CB) at both 77 and 287 K. Dependence of the CB region on the gate voltage was similar to that of an R-SET. Simulation results of the model based on the scanning electron microscopy image of the device could reproduce the characteristics like the R-SET

Gate-tuned negative differential resistance observed at room temperature in an array of gold nanoparticles

We fabricated a single-electron (SE) device using gold nanoparticles (Au NPs). Drain, source, and gate electrodes on a SiO2/Si substrate were formed using electron beam lithography (EBL) and thermal evaporation of Au. Subsequently, solutions of 3-nm-diameter and 5-nm-diameter Au NPs were dropped on the device to make current paths through Au NPs among the electrodes. Measurements of the device exhibited negative differential resistance (NDR) in the current-voltage characteristics between the drain and source electrodes at room temperature (298 K). The NDR behavior was tuned by applying a gate voltage

Microfluidic cell engineering on high-density microelectrode arrays for assessing structure-function relationships in living neuronal networks

Neuronal networks in dissociated culture combined with cell engineering technology offer a pivotal platform to constructively explore the relationship between structure and function in living neuronal networks. Here, we fabricated defined neuronal networks possessing a modular architecture on high-density microelectrode arrays (HD-MEAs), a state-of-the-art electrophysiological tool for recording neural activity with high spatial and temporal resolutions. We first established a surface coating protocol using a cell-permissive hydrogel to stably attach polydimethylsiloxane microfluidic film on the HD-MEA. We then recorded the spontaneous neural activity of the engineered neuronal network, which revealed an important portrait of the engineered neuronal network--modular architecture enhances functional complexity by reducing the excessive neural correlation between spatially segregated modules. The results of this study highlight the impact of HD-MEA recordings combined with cell engineering technologies as a novel tool in neuroscience to constructively assess the structure-function relationships in neuronal networks.Comment: 18 pages, 5 figure

One-dimensional array of small tunnel junctions fabricated using 30-nm-diameter gold nanoparticles placed in a 140-nm-wide resist groove

We present percolative arrays of gold nanoparticles (NPs) formed in a resist groove. To enhance the con nection probability, the width of the resist groove (140 nm) was designed to be approximately five times larger than the diameter of gold NPs (30 nm). Two-stage deposition of gold NPs was employed to form bridge connections between the source and drain electrodes. Dithiol molecules coated on surfaces of gold NPs worked as tunnel barriers. 5 of 12 samples exhibited Coulomb blockade characteristics, in one of which the gate response was confirmed

Suppression of hypersynchronous network activity in cultured cortical neurons using an ultrasoft silicone scaffold

The spontaneous activity pattern of cortical neurons in dissociated culture is characterized by burst firing that is highly synchronized among a wide population of cells. The degree of synchrony, however, is excessively higher than that in cortical tissues. Here, we employed polydimethylsiloxane (PDMS) elastomers to establish a novel system for culturing neurons on a scaffold with an elastic modulus resembling brain tissue, and investigated the effect of the scaffold's elasticity on network activity patterns in cultured rat cortical neurons. Using whole-cell patch clamp to assess the scaffold effect on the development of synaptic connections, we found that the amplitude of excitatory postsynaptic current, as well as the frequency of spontaneous transmissions, was reduced in neuronal networks grown on an ultrasoft PDMS with an elastic modulus of 0.5 kPa. Furthermore, the ultrasoft scaffold was found to suppress neural correlations in the spontaneous activity of the cultured neuronal network. The dose of GsMTx-4, an antagonist of stretch-activated cation channels (SACs), required to reduce the generation of the events below 1.0 event/min on PDMS substrates was lower than that for neurons on a glass substrate. This suggests that the difference in the baseline level of SAC activation is a molecular mechanism underlying the alteration in neuronal network activity depending on scaffold stiffness. Our results demonstrate the potential application of PDMS with biomimetic elasticity as cell-culture scaffold for bridging the in vivo-in vitro gap in neuronal systems.Comment: 23 pages, 6 figure

Model-Free Idealization: Adaptive Integrated Approach for Idealization of Ion Channel Currents (AI2)

Single-channel electrophysiological recordings provide insights into transmembrane ion permeation and channel gating mechanisms. The first step in the analysis of the recorded currents involves an "idealization" process, in which noisy raw data are classified into two discrete levels corresponding to the open and closed states of channels. This provides valuable information on the gating kinetics of ion channels. However, the idealization step is often challenging in cases of currents with poor signal-to-noise ratios (SNR) and baseline drifts, especially when the gating model of the target channel is not identified. We report herein on a highly robust model-free idealization method for achieving this goal. The algorithm, called AI2 (Adaptive Integrated Approach for the Idealization of Ion Channel Currents), is composed of Kalman filter and Gaussian Mixture Model (GMM) clustering and functions without user input. AI2 automatically determines the noise reduction setting based on the degree of separation between the open and closed levels. We validated the method on pseudo-channel-current datasets which contain either computed or experimentally recorded noise. The AI2 algorithm was then tested on actual experimental data for biological channels including gramicidin A, a voltage-gated sodium channel, and other unidentified channels. We compared the idealization results with those obtained by the conventional methods, including the 50%-threshold-crossing method

Biological neurons act as generalization filters in reservoir computing

Reservoir computing is a machine learning paradigm that transforms the transient dynamics of high-dimensional nonlinear systems for processing time-series data. Although reservoir computing was initially proposed to model information processing in the mammalian cortex, it remains unclear how the non-random network architecture, such as the modular architecture, in the cortex integrates with the biophysics of living neurons to characterize the function of biological neuronal networks (BNNs). Here, we used optogenetics and fluorescent calcium imaging to record the multicellular responses of cultured BNNs and employed the reservoir computing framework to decode their computational capabilities. Micropatterned substrates were used to embed the modular architecture in the BNNs. We first show that modular BNNs can be used to classify static input patterns with a linear decoder and that the modularity of the BNNs positively correlates with the classification accuracy. We then used a timer task to verify that BNNs possess a short-term memory of ~1 s and finally show that this property can be exploited for spoken digit classification. Interestingly, BNN-based reservoirs allow transfer learning, wherein a network trained on one dataset can be used to classify separate datasets of the same category. Such classification was not possible when the input patterns were directly decoded by a linear decoder, suggesting that BNNs act as a generalization filter to improve reservoir computing performance. Our findings pave the way toward a mechanistic understanding of information processing within BNNs and, simultaneously, build future expectations toward the realization of physical reservoir computing systems based on BNNs.Comment: 31 pages, 5 figures, 3 supplementary figure