Aptamer-Functionalized Interface Nanopores Enable Amino Acid-Specific Peptide Detection

Single-molecule proteomics based on nanopore technology has made significant advances in recent years. However, to achieve nanopore sensing with single amino acid resolution, several bottlenecks must be tackled: controlling nanopore sizes with nanoscale precision and slowing molecular translocation events. Herein, we address these challenges by integrating amino acid-specific DNA aptamers into interface nanopores with dynamically tunable pore sizes. A phenylalanine aptamer was used as a proof-of-concept: aptamer recognition of phenylalanine moieties led to the retention of specific peptides, slowing translocation speeds. Importantly, while phenylalanine aptamers were isolated against the free amino acid, the aptamers were determined to recognize the combination of the benzyl or phenyl and the carbonyl group in the peptide backbone, enabling binding to specific phenylalanine-containing peptides. We decoupled specific binding between aptamers and phenylalanine-containing peptides from nonspecific interactions (e.g., electrostatics and hydrophobic interactions) using optical waveguide lightmode spectroscopy. Aptamer-modified interface nanopores differentiated peptides containing phenylalanine vs. control peptides with structurally similar amino acids (i.e., tyrosine and tryptophan). When the duration of aptamer-target interactions inside the nanopore were prolonged by lowering the applied voltage, discrete ionic current levels with repetitive motifs were observed. Such reoccurring signatures in the measured signal suggest that the proposed method has the possibility to resolve amino acid-specific aptamer recognition, a step toward single-molecule proteomics.

Investigation of the input-output relationship of engineered neural networks using high-density microelectrode arrays

Bottom-up neuroscience utilizes small, engineered biological neural networks to study neuronal activity in systems of reduced complexity. We present a platform that establishes up to six independent networks formed by primary rat neurons on planar complementary metal-oxide-semiconductor (CMOS) microelectrode arrays (MEAs). We introduce an approach that allows repetitive stimulation and recording of network activity at any of the over 700 electrodes underlying a network. We demonstrate that the continuous application of a repetitive super-threshold stimulus yields a reproducible network answer within a 15 ms post-stimulus window. This response can be tracked with high spatiotemporal resolution across the whole extent of the network. Moreover, we show that the location of the stimulation plays a significant role in the networks' early response to the stimulus. By applying a stimulation pattern to all network-underlying electrodes in sequence, the sensitivity of the whole network to the stimulus can be visualized. We demonstrate that microchannels reduce the voltage stimulation threshold and induce the strongest network response. By varying the stimulation amplitude and frequency we reveal discrete network transition points. Finally, we introduce vector fields to follow stimulation-induced spike propagation pathways within the network. Overall we show that our defined neural networks on CMOS MEAs enable us to elicit highly reproducible activity patterns that can be precisely modulated by stimulation amplitude, stimulation frequency and the site of stimulation.

Engineering circuits of human iPSC-derived neurons and rat primary glia

Novel in vitro platforms based on human neurons are needed to improve early drug testing and address the stalling drug discovery in neurological disorders. Topologically controlled circuits of human induced pluripotent stem cell (iPSC)-derived neurons have the potential to become such a testing system. In this work, we build in vitro co-cultured circuits of human iPSC-derived neurons and rat primary glial cells using microfabricated polydimethylsiloxane (PDMS) structures on microelectrode arrays (MEAs). Our PDMS microstructures are designed in the shape of a stomach, which guides axons in one direction and thereby facilitates the unidirectional flow of information. Such circuits are created by seeding either dissociated cells or pre-aggregated spheroids at different neuron-to-glia ratios. Furthermore, an antifouling coating is developed to prevent axonal overgrowth in undesired locations of the microstructure. We assess the electrophysiological properties of different types of circuits over more than 50 days, including their stimulation-induced neural activity. Finally, we demonstrate the inhibitory effect of magnesium chloride on the electrical activity of our iPSC circuits as a proof-of-concept for screening of neuroactive compounds.

Nanoscale Patterning of In Vitro Neuronal Circuits

Methods for patterning neurons in vitro have gradually improved and are used to investigate questions that are difficult to address in or ex vivo. Though these techniques guide axons between groups of neurons, multiscale control of neuronal connectivity, from circuits to synapses, is yet to be achieved in vitro. As studying neuronal circuits with synaptic resolution in vivo poses significant challenges, we present an in vitro alternative to validate biophysical and computational models. In this work we use a combination of electron beam lithography and photolithography to create polydimethylsiloxane (PDMS) structures with features ranging from 150 nm to a few millimeters. Leveraging the difference between average axon and dendritic spine diameters, we restrict axon growth while allowing spines to pass through nanochannels to guide synapse formation between small groups of neurons (i.e., nodes). We show this technique can be used to generate large numbers of isolated feed-forward circuits where connections between nodes are restricted to regions connected by nanochannels. Using a genetically encoded calcium indicator in combination with fluorescently tagged postsynaptic protein, PSD-95, we demonstrate functional synapses can form in this region.

Topologically controlled circuits of human iPSC-derived neurons for electrophysiology recordings

Bottom-up neuroscience, which consists of building and studying controlled networks of neurons in vitro, is a promising method to investigate information processing at the neuronal level. However, in vitro studies tend to use cells of animal origin rather than human neurons, leading to conclusions that might not be generalizable to humans and limiting the possibilities for relevant studies on neurological disorders. Here we present a method to build arrays of topologically controlled circuits of human induced pluripotent stem cell (iPSC)-derived neurons. The circuits consist of 4 to 50 neurons with well-defined connections, confined by microfabricated polydimethylsiloxane (PDMS) membranes. Such circuits were characterized using optical imaging and microelectrode arrays (MEAs), suggesting the formation of functional connections between the neurons of a circuit. Electrophysiology recordings were performed on circuits of human iPSC-derived neurons for at least 4.5 months. We believe that the capacity to build small and controlled circuits of human iPSC-derived neurons holds great promise to better understand the fundamental principles of information processing and storing in the brain.

Engineered Biological Neural Networks on High Density CMOS Microelectrode Arrays

In bottom-up neuroscience, questions on neural information processing are addressed by engineering small but reproducible biological neural networks of defined network topology in vitro. The network topology can be controlled by culturing neurons within polydimethylsiloxane (PDMS) microstructures that are combined with microelectrode arrays (MEAs) for electric access to the network. However, currently used glass MEAs are limited to 256 electrodes and pose a limitation to the spatial resolution as well as the design of more complex microstructures. The use of high density complementary metal-oxide-semiconductor (CMOS) MEAs greatly increases the spatial resolution, enabling sub-cellular readout and stimulation of neurons in defined neural networks. Unfortunately, the non-planar surface of CMOS MEAs complicates the attachment of PDMS microstructures. To overcome the problem of axons escaping the microstructures through the ridges of the CMOS MEA, we stamp-transferred a thin film of hexane-diluted PDMS onto the array such that the PDMS filled the ridges at the contact surface of the microstructures without clogging the axon guidance channels. This method resulted in 23 % of structurally fully connected but sealed networks on the CMOS MEA of which about 45 % showed spiking activity in all channels. Moreover, we provide an impedance-based method to visualize the exact location of the microstructures on the MEA and show that our method can confine axonal growth within the PDMS microstructures. Finally, the high spatial resolution of the CMOS MEA enabled us to show that action potentials follow the unidirectional topology of our circular multi-node microstructure.

Magnetic Manipulation of Nanowires for Engineered Stretchable Electronics

Nanowires are often key ingredients of high-tech composite materials. The properties and performance of devices created using these, depend heavily on the structure and density of the embedded nanowires. Despite significant efforts, a process that can be adapted to different materials, compatible with current nanowire deposition methods, and that is able to control both variables simultaneously has not been achieved yet. In this work, we show that we can use low magnetic fields (80 mT) to manipulate nanowires by electrostatically coating them with superparamagnetic iron oxide nanoparticles in an aqueous solution. Monolayers, multilayers, and hierarchical structures of oriented nanowires were achieved in a highly ordered manner using vacuum filtration for two types of nanowires: silver and gold-coated titanium dioxide nanowires. The produced films were embedded in an elastomer, and the strain-dependent electrical properties of the resulting composites were investigated. The orientation of the assembly with respect to the tensile strain heavily impacts the performance of the composites. Composites containing nanowires perpendicular to the strain direction exhibit an extremely low gauge factor. On the other hand, when nanowires are arranged parallel to the strain direction, the composites have a high gauge factor. The possibility to orient nanowires during the processing steps is not only interesting for the shown strain sensing application but also expected to be useful in many other areas of material science.

An experimental paradigm to investigate stimulation dependent activity in topologically constrained neuronal networks

We present a stimulate and record paradigm to examine the behavior of multiple neuronal networks with controlled topology in vitro. Our approach enabled us to electrically induce and record neuronal activity from 60 independent networks in parallel over multiple weeks. We investigated the network performance of neuronal networks of primary hippocampal neurons until 29 days in vitro. We introduced a systematic stimulate and record protocol during which well-defined 4-node neural networks were stimulated electrically 4 times per second (4Hz) and their response was recorded over many days. We found that the network response pattern to a stimulus remained fairly stable for at least 12 h. Moreover, continuous stimulation over multiple weeks did not cause a significant change in the stimulation-induced mean spiking frequency of a circuit. We investigated the effect of stimulation amplitude and stimulation timing on the detailed network response. Finally, we could show that our setup can apply complex stimulation protocols with 125 different stimulation patterns. We used these patterns to perform basic addition tasks with the network, revealing the highly non-linear nature of biological networks' responses to complex stimuli.

Coevolution between MHC class I and Antigen Processing Genes in salamanders

Proteins encoded by antigen-processing genes (APGs) provide major histocompatibility complex (MHC) class I (MHC-I) with antigenic peptides. In mammals, polymorphic multigenic MHC-I family is served by monomorphic APGs, whereas in certain nonmammalian species both MHC-I and APGs are polymorphic and coevolve within stable haplotypes. Coevolution was suggested as an ancestral gnathostome feature, presumably enabling only a single highly expressed classical MHC-I gene. In this view coevolution, while optimizing some aspects of adaptive immunity, would also limit its flexibility by preventing the expansion of classical MHC-I into a multigene family. However, some nonmammalian taxa, such as salamanders, have multiple highly expressed MHC-I genes, suggesting either that coevolution is relaxed or that it does not prevent the establishment of multigene MHC-I. To distinguish between these two alternatives, we use salamanders (30 species from 16 genera representing six families) to test, within a comparative framework, a major prediction of the coevolution hypothesis: the positive correlation between MHC-I and APG diversity. We found that MHC-I diversity explained both within-individual and species-wide diversity of two APGs, TAP1 and TAP2, supporting their coevolution with MHC-I, whereas no consistent effect was detected for the other three APGs (PSMB8, PSMB9, and TAPBP). Our results imply that although coevolution occurs in salamanders, it does not preclude the expansion of the MHC-I gene family. Contrary to the previous suggestions, nonmammalian vertebrates thus may be able to accommodate diverse selection pressures with flexibility granted by rapid expansion or contraction of the MHC-I family, while retaining the benefits of coevolution between MHC-I and TAPs.

Sensing serotonin secreted from human serotonergic neurons using aptamer-modified nanopipettes

The serotonergic system in the human brain modulates several physiological processes, and altered serotonergic neurotransmission has been implicated in the neuropathology of several psychiatric disorders. The study of serotonergic neurotransmission in psychiatry has long been restricted to animal models, but advances in cell reprogramming technology have enabled the generation of serotonergic neurons from patient-induced pluripotent stem cells (iPSCs). While iPSC-derived human serotonergic neurons offer the possibility to study serotonin (5-HT) release and uptake, particularly by 5-HT-modulating drugs such as selective serotonin reuptake inhibitors (SSRIs), a major limitation is the inability to reliably quantify 5-HT secreted from neurons in vitro. Herein, we address this technical gap via a novel sensing technology that couples 5-HT-specific DNA aptamers into nanopores (glass nanopipettes) with orifices of ~10 nm to detect 5-HT in complex neuronal culture medium with higher selectivity, sensitivity, and stability than existing methods. The 5-HT aptamers undergo conformational rearrangement upon target capture and serve as gatekeepers of ionic flux through the nanopipette opening. We generated human serotonergic neurons in vitro and detected secreted 5-HT using aptamer-coated nanopipettes in a low nanomolar range, with the possibility of detecting significantly lower (picomolar) concentrations. Furthermore, as a proof of concept, we treated human serotonergic neurons in vitro with the SSRI citalopram and detected a significant increase in extracellular 5-HT using the aptamer-modified nanopipettes. We demonstrate the utility of such methods for 5-HT detection, raising the possibility of fast quantification of neurotransmitters secreted from patient-derived live neuronal cells.

Nonspecific Binding-Fundamental Concepts and Consequences for Biosensing Applications

Nature achieves differentiation of specific and nonspecific binding in molecular interactions through precise control of biomolecules in space and time. Artificial systems such as biosensors that rely on distinguishing specific molecular binding events in a sea of nonspecific interactions have struggled to overcome this issue. Despite the numerous technological advancements in biosensor technologies, nonspecific binding has remained a critical bottleneck due to the lack of a fundamental understanding of the phenomenon. To date, the identity, cause, and influence of nonspecific binding remain topics of debate within the scientific community. In this review, we discuss the evolution of the concept of nonspecific binding over the past five decades based upon the thermodynamic, intermolecular, and structural perspectives to provide classification frameworks for biomolecular interactions. Further, we introduce various theoretical models that predict the expected behavior of biosensors in physiologically relevant environments to calculate the theoretical detection limit and to optimize sensor performance. We conclude by discussing existing practical approaches to tackle the nonspecific binding challenge in vitro for biosensing platforms and how we can both address and harness nonspecific interactions for in vivo systems.

Investigating Complex Samples with Molograms of Low-Affinity Binders

In vitro diagnostics relies on the quantification of minute amounts of a specific biomolecule, called biomarker, from a biological sample. The majority of clinically relevant biomarkers for conditions beyond infectious diseases are detected by means of binding assays, where target biomarkers bind to a solid phase and are detected by biochemical or physical means. Nonspecifically bound biomolecules, the main source of variation in such assays, need to be washed away in a laborious process, restricting the development of widespread point-of-care diagnostics. Here, we show that a diffractometric assay provides a new, label-free possibility to investigate complex samples, such as blood plasma. A coherently arranged sub-micron pattern, that is, a peptide mologram, is created to demonstrate the insensitivity of this diffractometric assay to the unwanted masking effect of nonspecific interactions. In addition, using an array of low-affinity binders, we also demonstrate the feasibility of molecular profiling of blood plasma in real time and show that individual patients can be differentiated based on the binding kinetics of circulating proteins.

Aptamer Conformational Change Enables Serotonin Biosensing with Nanopipettes

We report artificial nanopores in the form of quartz nanopipettes with ca. 10 nm orifices functionalized with molecular recognition elements termed aptamers that reversibly recognize serotonin with high specificity and selectivity. Nanoscale confinement of ion fluxes, analyte-specific aptamer conformational changes, and related surface charge variations enable serotonin sensing. We demonstrate detection of physiologically relevant serotonin amounts in complex environments such as neurobasal media, in which neurons are cultured in vitro. In addition to sensing in physiologically relevant matrices with high sensitivity (picomolar detection limits), we interrogate the detection mechanism via complementary techniques such as quartz crystal microbalance with dissipation monitoring and electrochemical impedance spectroscopy. Moreover, we provide a novel theoretical model for structure-switching aptamer-modified nanopipette systems that supports experimental findings. Validation of specific and selective small-molecule detection, in parallel with mechanistic investigations, demonstrates the potential of conformationally changing aptamer-modified nanopipettes as rapid, label-free, and translatable nanotools for diverse biological systems.

Soft Electronics Based on Stretchable and Conductive Nanocomposites for Biomedical Applications

Research on the field of implantable electronic devices that can be directly applied in the body with various functionalities is increasingly intensifying due to its great potential for various therapeutic applications. While conventional implantable electronics generally include rigid and hard conductive materials, their surrounding biological objects are soft and dynamic. The mechanical mismatch between implanted devices and biological environments induces damages in the body especially for long-term applications. Stretchable electronics with outstanding mechanical compliance with biological objects effectively improve such limitations of existing rigid implantable electronics. In this article, the recent progress of implantable soft electronics based on various conductive nanocomposites is systematically described. In particular, representative fabrication approaches of conductive and stretchable nanocomposites for implantable soft electronics and various in vivo applications of implantable soft electronics are focused on. To conclude, challenges and perspectives of current implantable soft electronics that should be considered for further advances are discussed.

Ultra-Stable Molecular Sensors by Sub-Micron Referencing and Why They Should Be Interrogated by Optical Diffraction-Part I. The Concept of a Spatial Affinity Lock-in Amplifier

Label-free optical biosensors, such as surface plasmon resonance, are sensitive and well-established for the characterization of molecular interactions. Yet, these sensors require stabilization and constant conditions even with the use of reference channels. In this paper, we use tools from signal processing to show why these sensors are so cross-sensitive and how to overcome their drawbacks. In particular, we conceptualize the spatial affinity lock-in as a universal design principle for sensitive molecular sensors even in the complete absence of stabilization. The spatial affinity lock-in is analogous to the well-established time-domain lock-in. Instead of a time-domain signal, it modulates the binding signal at a high spatial frequency to separate it from the low spatial frequency environmental noise in Fourier space. In addition, direct sampling of the locked-in sensor's response in Fourier space enabled by diffraction has advantages over sampling in real space as done by surface plasmon resonance sensors using the distributed reference principle. This paper and part II hint at the potential of spatially locked-in diffractometric biosensors to surpass state-of-the-art temperature-stabilized refractometric biosensors. Even simple, miniaturized and non-stabilized sensors might achieve the performance of bulky lab instruments. This may enable new applications in label-free analysis of molecular binding and point-of-care diagnostics.

Ultra Stable Molecular Sensors by Submicron Referencing and Why They Should Be Interrogated by Optical Diffraction-Part II. Experimental Demonstration

Label-free optical biosensors are an invaluable tool for molecular interaction analysis. Over the past 30 years, refractometric biosensors and, in particular, surface plasmon resonance have matured to the de facto standard of this field despite a significant cross reactivity to environmental and experimental noise sources. In this paper, we demonstrate that sensors that apply the spatial affinity lock-in principle (part I) and perform readout by diffraction overcome the drawbacks of established refractometric biosensors. We show this with a direct comparison of the cover refractive index jump sensitivity as well as the surface mass resolution of an unstabilized diffractometric biosensor with a state-of-the-art Biacore 8k. A combined refractometric diffractometric biosensor demonstrates that a refractometric sensor requires a much higher measurement precision than the diffractometric to achieve the same resolution. In a conceptual and quantitative discussion, we elucidate the physical reasons behind and define the figure of merit of diffractometric biosensors. Because low-precision unstabilized diffractometric devices achieve the same resolution as bulky stabilized refractometric sensors, we believe that label-free optical sensors might soon move beyond the drug discovery lab as miniaturized, mass-produced environmental/medical sensors. In fact, combined with the right surface chemistry and recognition element, they might even bring the senses of smell/taste to our smart devices.

Force-Controlled Formation of Dynamic Nanopores for Single-Biomolecule Sensing and Single-Cell Secretomics

Nanopore sensing of single nucleotides has emerged as a promising single-molecule technology for DNA sequencing and proteomics. Despite the conceptual simplicity of nanopores, adoption of this technology for practical applications has been limited by a lack of pore size adjustability and an inability to perform long-term recordings in complex solutions. Here we introduce a method for fast and precise on-demand formation of a nanopore with controllable size between 2 and 20 nm through force-controlled adjustment of the nanospace formed between the opening of a microfluidic device (made of silicon nitride) and a soft polymeric substrate. The introduced nanopore system enables stable measurements at arbitrary locations. By accurately positioning the nanopore in the proximity of single neurons and continuously recording single-molecule translations over several hours, we have demonstrated this is a powerful approach for single-cell proteomics and secretomics.

Continuous Heart Volume Monitoring by Fully Implantable Soft Strain Sensor

Cardiothoracic open-heart surgery has revolutionized the treatment of cardiovascular disease, the leading cause of death worldwide. After the surgery, hemodynamic and volume management can be complicated, for example in case of vasoplegia after endocarditis. Timely treatment is crucial for outcomes. Currently, treatment decisions are made based on heart volume, which needs to be measured manually by the clinician each time using ultrasound. Alternatively, implantable sensors offer a real-time window into the dynamic function of our body. Here it is shown that a soft flexible sensor, made with biocompatible materials, implanted on the surface of the heart, can provide continuous information of the heart volume after surgery. The sensor works robustly for a period of two days on a tensile machine. The accuracy of measuring heart volume is improved compared to the clinical gold standard in vivo, with an error of 7.1 mL for the strain sensor versus impedance and 14.0 mL versus ultrasound. Implanting such a sensor would provide essential, continuous information on heart volume in the critical time following the surgery, allowing early identification of complications, facilitating treatment, and hence potentially improving patient outcome.

Additive Manufacturing of Sub-Micron to Sub-mm Metal Structures with Hollow AFM Cantilevers

We describe our force-controlled 3D printing method for layer-by-layer additive micromanufacturing (µAM) of metal microstructures. Hollow atomic force microscopy cantilevers are utilized to locally dispense metal ions in a standard 3-electrode electrochemical cell, enabling a confined electroplating reaction. The deflection feedback signal enables the live monitoring of the voxel growth and the consequent automation of the printing protocol in a layer-by-layer fashion for the fabrication of arbitrary-shaped geometries. In a second step, we investigated the effect of the free parameters (aperture diameter, applied pressure, and applied plating potential) on the voxel size, which enabled us to tune the voxel dimensions on-the-fly, as well as to produce objects spanning at least two orders of magnitude in each direction. As a concrete example, we printed two different replicas of Michelangelo's David. Copper was used as metal, but the process can in principle be extended to all metals that are macroscopically electroplated in a standard way.

Localized detection of ions and biomolecules with a force-controlled scanning nanopore microscope

Proteins, nucleic acids and ions secreted from single cells are the key signalling factors that determine the interaction of cells with their environment and the neighbouring cells. It is possible to study individual ion channels by pipette clamping, but it is difficult to dynamically monitor the activity of ion channels and transporters across the cellular membrane. Here we show that a solid-state nanopore integrated in an atomic force microscope can be used for the stochastic sensing of secreted molecules and the activity of ion channels in arbitrary locations both inside and outside a cell. The translocation of biomolecules and ions through the nanopore is observed in real time in live cells. The versatile nature of this approach allows us to detect specific biomolecules under controlled mechanical confinement and to monitor the ion-channel activities of single cells. Moreover, the nanopore microscope was used to image the surface of the nuclear membrane via high-resolution scanning ion conductance measurements.

Dark-Field Microwells toward High-Throughput Direct miRNA Sensing with Gold Nanoparticles

MicroRNA (miRNA) is a class of short RNA that is emerging as an ideal biomarker, as its expression level has been found to correlate with different types of diseases including diabetes and cancer. The detection of miRNA is highly beneficial for early diagnostics and disease monitoring. However, miRNA sensing remains difficult because of its small size and low expression levels. Common techniques such as quantitative real-time polymerase chain reaction (qRT-PCR), in situ hybridization and Northern blotting have been developed to quantify miRNA in a given sample. Nevertheless, these methods face common challenges in point-of-care practice as they either require complicated sample handling and expensive equipment, or suffer from low sensitivity. Here we present a new tool based on dark-field microwells to overcome these challenges in miRNA sensing. This miniaturized device enables the readout of a gold nanoparticle assay without the need of a dark-field microscope. We demonstrate the feasibility of the dark-field microwells to detect miRNA in both buffer solution and cell lysate. The dark-field microwells allow affordable miRNA sensing at a high throughput which make them a promising tool for point-of-care diagnostics.

A Versatile Protein and Cell Patterning Method Suitable for Long-Term Neural Cultures

Herein, we present an easy-to-use protein and cell patterning method relying solely on pipetting, rinsing steps and illumination with a desktop lamp, which does not require any expensive laboratory equipment, custom-built hardware or delicate chemistry. This method is based on the adhesion promoter poly(allylamine)-grafted perfluorophenyl azide, which allows UV-induced cross-linking with proteins and the antifouling molecule poly(vinylpyrrolidone). Versatility is demonstrated by creating patterns with two different proteins and a polysaccharide directly on plastic well plates and on glass slides, and by subsequently seeding primary neurons and C2C12 myoblasts on the patterns to form islands and mini-networks. Patterning characterization is done via immunohistochemistry, Congo red staining, ellipsometry, and infrared spectroscopy. Using a pragmatic setup, patterning contrasts down to 5 μm and statistically significant long-term stability superior to the gold standard poly(l-lysine)-grafted poly(ethylene glycol) could be obtained. This simple method can be used in any laboratory or even in classrooms and its outstanding stability is especially interesting for long-term cell experiments, e.g., for bottom-up neuroscience, where well-defined microislands and microcircuits of primary neurons are studied over weeks.

Predictive Model for the Electrical Transport within Nanowire Networks

Thin networks of high aspect ratio conductive nanowires can combine high electrical conductivity with excellent optical transparency, which has led to a widespread use of nanowires in transparent electrodes, transistors, sensors, and flexible and stretchable conductors. Although the material and application aspects of conductive nanowire films have been thoroughly explored, there is still no model which can relate fundamental physical quantities, like wire resistance, contact resistance, and nanowire density, to the sheet resistance of the film. Here, we derive an analytical model for the electrical conduction within nanowire networks based on an analysis of the parallel resistor network. The model captures the transport characteristics and fits a wide range of experimental data, allowing for the determination of physical parameters and performance-limiting factors, in sharp contrast to the commonly employed percolation theory. The model thus constitutes a useful tool with predictive power for the evaluation and optimization of nanowire networks in various applications.

Monolayer Graphene Coupled to a Flexible Plasmonic Nanograting for Ultrasensitive Strain Monitoring

Plasmonically coupled graphene structures have shown great promise for sensing applications. Their complex and cumbersome fabrication, however, has prohibited their widespread application and limited their use to rigid, planar surfaces. Here, a plasmonic sensor based on gold nanowire arrays on an elastomer with an added graphene monolayer is introduced. The stretchable plasmonic nanostructures not only significantly enhance the Raman signal from graphene, but can also be used by themselves as a sensor platform for 2D strain sensing. These nanowire arrays on an elastomer are fabricated by template-stripping based nanotransfer printing, which enables a simple and fast production of stable nanogratings. The ultrasmooth surfaces of such transferred structures facilitate reliable large-area transfers of graphene monolayers. The resulting coupled graphene-nanograting construct exhibits ultrahigh sensitivity to applied strain, which can be detected by shifts in the plasmonic-enhanced Raman spectrum. Furthermore, this sensor enables the detection of adsorbed molecules on nonplanar surfaces through graphene-assisted surface enhanced Raman spectroscopy (SERS). The simple fabrication of the plasmonic nanowire array platform and the graphene-coupled devices have the potential to trigger widespread SERS applications and open up new opportunities for high-sensitivity strain sensing applications.

High-Density Stretchable Electrode Grids for Chronic Neural Recording

Electrical interfacing with neural tissue is key to advancing diagnosis and therapies for neurological disorders, as well as providing detailed information about neural signals. A challenge for creating long-term stable interfaces between electronics and neural tissue is the huge mechanical mismatch between the systems. So far, materials and fabrication processes have restricted the development of soft electrode grids able to combine high performance, long-term stability, and high electrode density, aspects all essential for neural interfacing. Here, this challenge is addressed by developing a soft, high-density, stretchable electrode grid based on an inert, high-performance composite material comprising gold-coated titanium dioxide nanowires embedded in a silicone matrix. The developed grid can resolve high spatiotemporal neural signals from the surface of the cortex in freely moving rats with stable neural recording quality and preserved electrode signal coherence during 3 months of implantation. Due to its flexible and stretchable nature, it is possible to minimize the size of the craniotomy required for placement, further reducing the level of invasiveness. The material and device technology presented herein have potential for a wide range of emerging biomedical applications.

Fast and Versatile Multiscale Patterning by Combining Template-Stripping with Nanotransfer Printing

Metal nanostructures are widely used in plasmonic and electronic applications due to their inherent properties. Often, the fabrication of such nanostructures is limited to small areas, as the processing is costly, low-throughput, and comprises harsh fabrication conditions. Here, we introduce a template-stripping based nanotransfer printing method to overcome these limitations. This versatile technique enables the transfer of arbitrary thin film metal structures onto a variety of substrates, including glass, Kapton, silicon, and PDMS. Structures can range from tens of nanometers to hundreds of micrometers over a wafer scale area. The process is organic solvent-free, multilayer compatible, and only takes minutes to perform. The stability of the transferred gold structures on glass exceeds by far those fabricated by e-beam evaporation. Therefore, an adhesion layer is no longer needed, enabling a faster and cheaper fabrication as well as the production of superior nanostructures. Structures can be transferred onto curved substrates, and the technique is compatible with roll-to-roll fabrication; thus, the process is suitable for flexible and stretchable electronics.

Simple and Inexpensive Paper-Based Astrocyte Co-culture to Improve Survival of Low-Density Neuronal Networks

Bottom-up neuroscience aims to engineer well-defined networks of neurons to investigate the functions of the brain. By reducing the complexity of the brain to achievable target questions, such in vitro bioassays better control experimental variables and can serve as a versatile tool for fundamental and pharmacological research. Astrocytes are a cell type critical to neuronal function, and the addition of astrocytes to neuron cultures can improve the quality of in vitro assays. Here, we present cellulose as an astrocyte culture substrate. Astrocytes cultured on the cellulose fiber matrix thrived and formed a dense 3D network. We devised a novel co-culture platform by suspending the easy-to-handle astrocytic paper cultures above neuronal networks of low densities typically needed for bottom-up neuroscience. There was significant improvement in neuronal viability after 5 days in vitro at densities ranging from 50,000 cells/cm² down to isolated cells at 1,000 cells/cm². Cultures exhibited spontaneous spiking even at the very low densities, with a significantly greater spike frequency per cell compared to control mono-cultures. Applying the co-culture platform to an engineered network of neurons on a patterned substrate resulted in significantly improved viability and almost doubled the density of live cells. Lastly, the shape of the cellulose substrate can easily be customized to a wide range of culture vessels, making the platform versatile for different applications that will further enable research in bottom-up neuroscience and drug development.

Easy to Apply Polyoxazoline-Based Coating for Precise and Long-Term Control of Neural Patterns

Arranging cultured cells in patterns via surface modification is a tool used by biologists to answer questions in a specific and controlled manner. In the past decade, bottom-up neuroscience emerged as a new application, which aims to get a better understanding of the brain via reverse engineering and analyzing elementary circuitry in vitro. Building well-defined neural networks is the ultimate goal. Antifouling coatings are often used to control neurite outgrowth. Because erroneous connectivity alters the entire topology and functionality of minicircuits, the requirements are demanding. Current state-of-the-art coating solutions such as widely used poly(l-lysine)-g-poly(ethylene glycol) (PLL-g-PEG) fail to prevent primary neurons from making undesired connections in long-term cultures. In this study, a new copolymer with greatly enhanced antifouling properties is developed, characterized, and evaluated for its reliability, stability, and versatility. To this end, the following components are grafted to a poly(acrylamide) (PAcrAm) backbone: hexaneamine, to support spontaneous electrostatic adsorption in buffered aqueous solutions, and propyldimethylethoxysilane, to increase the durability via covalent bonding to hydroxylated culture surfaces and antifouling polymer poly(2-methyl-2-oxazoline) (PMOXA). In an assay for neural connectivity control, the new copolymer's ability to effectively prevent unwanted neurite outgrowth is compared to the gold standard, PLL-g-PEG. Additionally, its versatility is evaluated on polystyrene, glass, and poly(dimethylsiloxane) using primary hippocampal and cortical rat neurons as well as C2C12 myoblasts, and human fibroblasts. PAcrAm-g-(PMOXA, NH₂, Si) consistently outperforms PLL-g-PEG with all tested culture surfaces and cell types, and it is the first surface coating which reliably prevents arranged nodes of primary neurons from forming undesired connections over the long term. Whereas the presented work focuses on the proof of concept for the new antifouling coating to successfully and sustainably prevent unwanted connectivity, it is an important milestone for in vitro neuroscience, enabling follow-up studies to engineer neurologically relevant networks. Furthermore, because PAcrAm-g-(PMOXA, NH₂, Si) can be quickly applied and used with various surfaces and cell types, it is an attractive extension to the toolbox for in vitro biology and biomedical engineering.

Improving FoRe: A New Inlet Design for Filtering Samples through Individual Microarray Spots

In this publication we present an improvement to our previously introduced vertical flow microarray, the FoRe array, which capitalizes on the fusion of immunofiltration and densely packed micron test sites. Filtering samples through individual microarray spots allows us to rapidly analyze dilute samples with high-throughput and high signal-to-noise. Unlike other flowthrough microarrays, in the FoRe design samples are injected into micron channels and sequentially exposed to different targets. This arrangement makes it possible to increase the sensitivity of the microarray by simply increasing the sample volume or to rapidly reconcentrate samples after preprocessing steps dilute the analyte. Here we present a new inlet system which allows us to increase the analyzed sample volume without compromising the micron spot size and dense layout. We combined this with a model assay to demonstrate that the device is sensitive to the amount of antigen, and as a result, sample volume directly correlates to sensitivity. We introduced a simple technique for analysis of blood, which previously clogged the nanometer-sized pores, requiring only microliter volumes expected from an infant heel prick. A drop of blood is mixed with buffer to separate the plasma before reconcentrating the sample on the microarray spot. We demonstrated the success of this procedure by spiking TNF-α into blood and achieved a limit of detection of 18 pM. Compared to traditional protein microarrays, the FoRe array is still inexpensive, customizable, and simple to use, and thanks to these improvements has a broad range of applications from small animal studies to environmental monitoring.

Soft Hydrogels Featuring In-Depth Surface Density Gradients for the Simple Establishment of 3D Tissue Models for Screening Applications

Three-dimensional (3D) cell culture models are gaining increasing interest for use in drug development pipelines due to their closer resemblance to human tissues. Hydrogels are the first-choice class of materials to recreate in vitro the 3D extra-cellular matrix (ECM) environment, important in studying cell-ECM interactions and 3D cellular organization and leading to physiologically relevant in vitro tissue models. Here we propose a novel hydrogel platform consisting of a 96-well plate containing pre-cast synthetic PEG-based hydrogels for the simple establishment of 3D (co-)culture systems without the need for the standard encapsulation method. The in-depth density gradient at the surface of the hydrogel promotes the infiltration of cells deposited on top of it. The ability to decouple hydrogel production and cell seeding is intended to simplify the use of hydrogel-based platforms and thus increase their accessibility. Using this platform, we established 3D cultures relevant for studying stem cell differentiation, angiogenesis, and neural and cancer models.

Paper-based patterned 3D neural cultures as a tool to study network activity on multielectrode arrays

High-throughput platform targeting activity patterns of 3D neural cultures with arbitrary topology, by combining network-wide intracellular and local extracellular signals.

Serial weighting of micro-objects with resonant microchanneled cantilevers

Atomic force microscopy (AFM) cantilevers have proven to be very effective mass sensors. The attachment of a small mass to a vibrating cantilever produces a resonance frequency shift that can be monitored, providing the ability to measure mass changes down to a few molecules resolution. Nevertheless, the lack of a practical method to handle the catch and release process required for dynamic weighting of microobjects strongly hindered the application of the technology beyond proof of concept measurements. Here, a method is proposed in which FluidFM hollow cantilevers are exploited to overcome the standard limitations of AFM-based mass sensors, providing high throughput single object weighting with picogram accuracy. The extension of the dynamic models of AFM cantilevers to hollow cantilevers was discussed and the effectiveness of mass weighting in air was validated on test samples.

25th Annual Computational Neuroscience Meeting: CNS-2016

A1 Functional advantages of cell-type heterogeneity in neural circuits

Tatyana O. Sharpee

A2 Mesoscopic modeling of propagating waves in visual cortex

Alain Destexhe

A3 Dynamics and biomarkers of mental disorders

Mitsuo Kawato

F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons

Vladislav Sekulić, Frances K. Skinner

F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains

Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári

F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks.

Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić

O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators

Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona

O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain

Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi

O3 Modeling auditory stream segregation, build-up and bistability

James Rankin, Pamela Osborn Popp, John Rinzel

O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields

Alejandro Tabas, André Rupp, Emili Balaguer-Ballester

O5 A simple model of retinal response to multi-electrode stimulation

Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin

O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task

Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer

O7 Input-location dependent gain modulation in cerebellar nucleus neurons

Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Nielsen, Volker Steuber

O8 Analytic solution of cable energy function for cortical axons and dendrites

Huiwen Ju, Jiao Yu, Michael L. Hines, Liang Chen, Yuguo Yu

O9 C. elegans interactome: interactive visualization of Caenorhabditis elegans worm neuronal network

Jimin Kim, Will Leahy, Eli Shlizerman

O10 Is the model any good? Objective criteria for computational neuroscience model selection

Justas Birgiolas, Richard C. Gerkin, Sharon M. Crook

O11 Cooperation and competition of gamma oscillation mechanisms

Atthaphon Viriyopase, Raoul-Martin Memmesheimer, Stan Gielen

O12 A discrete structure of the brain waves

Yuri Dabaghian, Justin DeVito, Luca Perotti

O13 Direction-specific silencing of the Drosophila gaze stabilization system

Anmo J. Kim, Lisa M. Fenk, Cheng Lyu, Gaby Maimon

O14 What does the fruit fly think about values? A model of olfactory associative learning

Chang Zhao, Yves Widmer, Simon Sprecher,Walter Senn

O15 Effects of ionic diffusion on power spectra of local field potentials (LFP)

Geir Halnes, Tuomo Mäki-Marttunen, Daniel Keller, Klas H. Pettersen,Ole A. Andreassen, Gaute T. Einevoll

O16 Large-scale cortical models towards understanding relationship between brain structure abnormalities and cognitive deficits

Yasunori Yamada

O17 Spatial coarse-graining the brain: origin of minicolumns

Moira L. Steyn-Ross, D. Alistair Steyn-Ross

O18 Modeling large-scale cortical networks with laminar structure

Jorge F. Mejias, John D. Murray, Henry Kennedy, Xiao-Jing Wang

O19 Information filtering by partial synchronous spikes in a neural population

Alexandra Kruscha, Jan Grewe, Jan Benda, Benjamin Lindner

O20 Decoding context-dependent olfactory valence in Drosophila

Laurent Badel, Kazumi Ohta, Yoshiko Tsuchimoto, Hokto Kazama

P1 Neural network as a scale-free network: the role of a hub

B. Kahng

P2 Hemodynamic responses to emotions and decisions using near-infrared spectroscopy optical imaging

Nicoladie D. Tam

P3 Phase space analysis of hemodynamic responses to intentional movement directions using functional near-infrared spectroscopy (fNIRS) optical imaging technique

Nicoladie D.Tam, Luca Pollonini, George Zouridakis

P4 Modeling jamming avoidance of weakly electric fish

Jaehyun Soh, DaeEun Kim

P5 Synergy and redundancy of retinal ganglion cells in prediction

Minsu Yoo, S. E. Palmer

P6 A neural field model with a third dimension representing cortical depth

Viviana Culmone, Ingo Bojak

P7 Network analysis of a probabilistic connectivity model of the Xenopus tadpole spinal cord

Andrea Ferrario, Robert Merrison-Hort, Roman Borisyuk

P8 The recognition dynamics in the brain

Chang Sub Kim

P9 Multivariate spike train analysis using a positive definite kernel

Taro Tezuka

P10 Synchronization of burst periods may govern slow brain dynamics during general anesthesia

Pangyu Joo

P11 The ionic basis of heterogeneity affects stochastic synchrony

Young-Ah Rho, Shawn D. Burton, G. Bard Ermentrout, Jaeseung Jeong, Nathaniel N. Urban

P12 Circular statistics of noise in spike trains with a periodic component

Petr Marsalek

P14 Representations of directions in EEG-BCI using Gaussian readouts

Hoon-Hee Kim, Seok-hyun Moon, Do-won Lee, Sung-beom Lee, Ji-yong Lee, Jaeseung Jeong

P15 Action selection and reinforcement learning in basal ganglia during reaching movements

Yaroslav I. Molkov, Khaldoun Hamade, Wondimu Teka, William H. Barnett, Taegyo Kim, Sergey Markin, Ilya A. Rybak

P17 Axon guidance: modeling axonal growth in T-Junction assay

Csaba Forro, Harald Dermutz, László Demkó, János Vörös

P19 Transient cell assembly networks encode persistent spatial memories

Yuri Dabaghian, Andrey Babichev

P20 Theory of population coupling and applications to describe high order correlations in large populations of interacting neurons

Haiping Huang

P21 Design of biologically-realistic simulations for motor control

Sergio Verduzco-Flores

P22 Towards understanding the functional impact of the behavioural variability of neurons

Filipa Dos Santos, Peter Andras

P23 Different oscillatory dynamics underlying gamma entrainment deficits in schizophrenia

Christoph Metzner, Achim Schweikard, Bartosz Zurowski

P24 Memory recall and spike frequency adaptation

James P. Roach, Leonard M. Sander, Michal R. Zochowski

P25 Stability of neural networks and memory consolidation preferentially occur near criticality

Quinton M. Skilling, Nicolette Ognjanovski, Sara J. Aton, Michal Zochowski

P26 Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems

Sheng-Jun Wang, Guang Ouyang, Jing Guang, Mingsha Zhang, K. Y. Michael Wong, Changsong Zhou

P27 Neurofield: a C++ library for fast simulation of 2D neural field models

Peter A. Robinson, Paula Sanz-Leon, Peter M. Drysdale, Felix Fung, Romesh G. Abeysuriya, Chris J. Rennie, Xuelong Zhao

P28 Action-based grounding: Beyond encoding/decoding in neural code

Yoonsuck Choe, Huei-Fang Yang

P29 Neural computation in a dynamical system with multiple time scales

Yuanyuan Mi, Xiaohan Lin, Si Wu

P30 Maximum entropy models for 3D layouts of orientation selectivity

Joscha Liedtke, Manuel Schottdorf, Fred Wolf

P31 A behavioral assay for probing computations underlying curiosity in rodents

Yoriko Yamamura, Jeffery R. Wickens

P32 Using statistical sampling to balance error function contributions to optimization of conductance-based models

Timothy Rumbell, Julia Ramsey, Amy Reyes, Danel Draguljić, Patrick R. Hof, Jennifer Luebke, Christina M. Weaver

P33 Exploration and implementation of a self-growing and self-organizing neuron network building algorithm

Hu He, Xu Yang, Hailin Ma, Zhiheng Xu, Yuzhe Wang

P34 Disrupted resting state brain network in obese subjects: a data-driven graph theory analysis

Kwangyeol Baek, Laurel S. Morris, Prantik Kundu, Valerie Voon

P35 Dynamics of cooperative excitatory and inhibitory plasticity

Everton J. Agnes, Tim P. Vogels

P36 Frequency-dependent oscillatory signal gating in feed-forward networks of integrate-and-fire neurons

William F. Podlaski, Tim P. Vogels

P37 Phenomenological neural model for adaptation of neurons in area IT

Martin Giese, Pradeep Kuravi, Rufin Vogels

P38 ICGenealogy: towards a common topology of neuronal ion channel function and genealogy in model and experiment

Alexander Seeholzer, William Podlaski, Rajnish Ranjan, Tim Vogels

P39 Temporal input discrimination from the interaction between dynamic synapses and neural subthreshold oscillations

Joaquin J. Torres, Fabiano Baroni, Roberto Latorre, Pablo Varona

P40 Different roles for transient and sustained activity during active visual processing

Bart Gips, Eric Lowet, Mark J. Roberts, Peter de Weerd, Ole Jensen, Jan van der Eerden

P41 Scale-free functional networks of 2D Ising model are highly robust against structural defects: neuroscience implications

Abdorreza Goodarzinick, Mohammad D. Niry, Alireza Valizadeh

P42 High frequency neuron can facilitate propagation of signal in neural networks

Aref Pariz, Shervin S. Parsi, Alireza Valizadeh

P43 Investigating the effect of Alzheimer’s disease related amyloidopathy on gamma oscillations in the CA1 region of the hippocampus

Julia M. Warburton, Lucia Marucci, Francesco Tamagnini, Jon Brown, Krasimira Tsaneva-Atanasova

P44 Long-tailed distributions of inhibitory and excitatory weights in a balanced network with eSTDP and iSTDP

Florence I. Kleberg, Jochen Triesch

P45 Simulation of EMG recording from hand muscle due to TMS of motor cortex

Bahar Moezzi, Nicolangelo Iannella, Natalie Schaworonkow, Lukas Plogmacher, Mitchell R. Goldsworthy, Brenton Hordacre, Mark D. McDonnell, Michael C. Ridding, Jochen Triesch

P46 Structure and dynamics of axon network formed in primary cell culture

Martin Zapotocky, Daniel Smit, Coralie Fouquet, Alain Trembleau

P47 Efficient signal processing and sampling in random networks that generate variability

Sakyasingha Dasgupta, Isao Nishikawa, Kazuyuki Aihara, Taro Toyoizumi

P48 Modeling the effect of riluzole on bursting in respiratory neural networks

Daniel T. Robb, Nick Mellen, Natalia Toporikova

P49 Mapping relaxation training using effective connectivity analysis

Rongxiang Tang, Yi-Yuan Tang

P50 Modeling neuron oscillation of implicit sequence learning

Guangsheng Liang, Seth A. Kiser, James H. Howard, Jr., Yi-Yuan Tang

P51 The role of cerebellar short-term synaptic plasticity in the pathology and medication of downbeat nystagmus

Julia Goncharenko, Neil Davey, Maria Schilstra, Volker Steuber

P52 Nonlinear response of noisy neurons

Sergej O. Voronenko, Benjamin Lindner

P53 Behavioral embedding suggests multiple chaotic dimensions underlie C. elegans locomotion

Tosif Ahamed, Greg Stephens

P54 Fast and scalable spike sorting for large and dense multi-electrodes recordings

Pierre Yger, Baptiste Lefebvre, Giulia Lia Beatrice Spampinato, Elric Esposito, Marcel Stimberg et Olivier Marre

P55 Sufficient sampling rates for fast hand motion tracking

Hansol Choi, Min-Ho Song

P56 Linear readout of object manifolds

SueYeon Chung, Dan D. Lee, Haim Sompolinsky

P57 Differentiating models of intrinsic bursting and rhythm generation of the respiratory pre-Bötzinger complex using phase response curves

Ryan S. Phillips, Jeffrey Smith

P58 The effect of inhibitory cell network interactions during theta rhythms on extracellular field potentials in CA1 hippocampus

Alexandra Pierri Chatzikalymniou, Katie Ferguson, Frances K. Skinner

P59 Expansion recoding through sparse sampling in the cerebellar input layer speeds learning

N. Alex Cayco Gajic, Claudia Clopath, R. Angus Silver

P60 A set of curated cortical models at multiple scales on Open Source Brain

Padraig Gleeson, Boris Marin, Sadra Sadeh, Adrian Quintana, Matteo Cantarelli, Salvador Dura-Bernal, William W. Lytton, Andrew Davison, R. Angus Silver

P61 A synaptic story of dynamical information encoding in neural adaptation

Luozheng Li, Wenhao Zhang, Yuanyuan Mi, Dahui Wang, Si Wu

P62 Physical modeling of rule-observant rodent behavior

Youngjo Song, Sol Park, Ilhwan Choi, Jaeseung Jeong, Hee-sup Shin

P64 Predictive coding in area V4 and prefrontal cortex explains dynamic discrimination of partially occluded shapes

Hannah Choi, Anitha Pasupathy, Eric Shea-Brown

P65 Stability of FORCE learning on spiking and rate-based networks

Dongsung Huh, Terrence J. Sejnowski

P66 Stabilising STDP in striatal neurons for reliable fast state recognition in noisy environments

Simon M. Vogt, Arvind Kumar, Robert Schmidt

P67 Electrodiffusion in one- and two-compartment neuron models for characterizing cellular effects of electrical stimulation

Stephen Van Wert, Steven J. Schiff

P68 STDP improves speech recognition capabilities in spiking recurrent circuits parameterized via differential evolution Markov Chain Monte Carlo

Richard Veale, Matthias Scheutz

P69 Bidirectional transformation between dominant cortical neural activities and phase difference distributions

Sang Wan Lee

P70 Maturation of sensory networks through homeostatic structural plasticity

Júlia Gallinaro, Stefan Rotter

P71 Corticothalamic dynamics: structure, number of solutions and stability of steady-state solutions in the space of synaptic couplings

Paula Sanz-Leon, Peter A. Robinson

P72 Optogenetic versus electrical stimulation of the parkinsonian basal ganglia. Computational study

Leonid L. Rubchinsky, Chung Ching Cheung, Shivakeshavan Ratnadurai-Giridharan

P73 Exact spike-timing distribution reveals higher-order interactions of neurons

Safura Rashid Shomali, Majid Nili Ahmadabadi, Hideaki Shimazaki, S. Nader Rasuli

P74 Neural mechanism of visual perceptual learning using a multi-layered neural network

Xiaochen Zhao, Malte J. Rasch

P75 Inferring collective spiking dynamics from mostly unobserved systems

Jens Wilting, Viola Priesemann

P76 How to infer distributions in the brain from subsampled observations

Anna Levina, Viola Priesemann

P77 Influences of embedding and estimation strategies on the inferred memory of single spiking neurons

Lucas Rudelt, Joseph T. Lizier, Viola Priesemann

P78 A nearest-neighbours based estimator for transfer entropy between spike trains

Joseph T. Lizier, Richard E. Spinney, Mikail Rubinov, Michael Wibral, Viola Priesemann

P79 Active learning of psychometric functions with multinomial logistic models

Ji Hyun Bak, Jonathan Pillow

P81 Inferring low-dimensional network dynamics with variational latent Gaussian process

Yuan Zaho, Il Memming Park

P82 Computational investigation of energy landscapes in the resting state subcortical brain network

Jiyoung Kang, Hae-Jeong Park

P83 Local repulsive interaction between retinal ganglion cells can generate a consistent spatial periodicity of orientation map

Jaeson Jang, Se-Bum Paik

P84 Phase duration of bistable perception reveals intrinsic time scale of perceptual decision under noisy condition

Woochul Choi, Se-Bum Paik

P85 Feedforward convergence between retina and primary visual cortex can determine the structure of orientation map

Changju Lee, Jaeson Jang, Se-Bum Paik

P86 Computational method classifying neural network activity patterns for imaging data

Min Song, Hyeonsu Lee, Se-Bum Paik

P87 Symmetry of spike-timing-dependent-plasticity kernels regulates volatility of memory

Youngjin Park, Woochul Choi^, Se-Bum Paik

P88 Effects of time-periodic coupling strength on the first-spike latency dynamics of a scale-free network of stochastic Hodgkin-Huxley neurons

Ergin Yilmaz, Veli Baysal, Mahmut Ozer

P89 Spectral properties of spiking responses in V1 and V4 change within the trial and are highly relevant for behavioral performance

Veronika Koren, Klaus Obermayer

P90 Methods for building accurate models of individual neurons

Daniel Saska, Thomas Nowotny

P91 A full size mathematical model of the early olfactory system of honeybees

Ho Ka Chan, Alan Diamond, Thomas Nowotny

P92 Stimulation-induced tuning of ongoing oscillations in spiking neural networks

Christoph S. Herrmann, Micah M. Murray, Silvio Ionta, Axel Hutt, Jérémie Lefebvre

P93 Decision-specific sequences of neural activity in balanced random networks driven by structured sensory input

Philipp Weidel, Renato Duarte, Abigail Morrison

P94 Modulation of tuning induced by abrupt reduction of SST cell activity

Jung H. Lee, Ramakrishnan Iyer, Stefan Mihalas

P95 The functional role of VIP cell activation during locomotion

Jung H. Lee, Ramakrishnan Iyer, Christof Koch, Stefan Mihalas

P96 Stochastic inference with spiking neural networks

Mihai A. Petrovici, Luziwei Leng, Oliver Breitwieser, David Stöckel, Ilja Bytschok, Roman Martel, Johannes Bill, Johannes Schemmel, Karlheinz Meier

P97 Modeling orientation-selective electrical stimulation with retinal prostheses

Timothy B. Esler, Anthony N. Burkitt, David B. Grayden, Robert R. Kerr, Bahman Tahayori, Hamish Meffin

P98 Ion channel noise can explain firing correlation in auditory nerves

Bahar Moezzi, Nicolangelo Iannella, Mark D. McDonnell

P99 Limits of temporal encoding of thalamocortical inputs in a neocortical microcircuit

Max Nolte, Michael W. Reimann, Eilif Muller, Henry Markram

P100 On the representation of arm reaching movements: a computational model

Antonio Parziale, Rosa Senatore, Angelo Marcelli

P101 A computational model for investigating the role of cerebellum in acquisition and retention of motor behavior

Rosa Senatore, Antonio Parziale, Angelo Marcelli

P102 The emergence of semantic categories from a large-scale brain network of semantic knowledge

K. Skiker, M. Maouene

P103 Multiscale modeling of M1 multitarget pharmacotherapy for dystonia

Samuel A. Neymotin, Salvador Dura-Bernal, Alexandra Seidenstein, Peter Lakatos, Terence D. Sanger, William W. Lytton

P104 Effect of network size on computational capacity

Salvador Dura-Bernal, Rosemary J. Menzies, Campbell McLauchlan, Sacha J. van Albada, David J. Kedziora, Samuel Neymotin, William W. Lytton, Cliff C. Kerr

P105 NetPyNE: a Python package for NEURON to facilitate development and parallel simulation of biological neuronal networks

Salvador Dura-Bernal, Benjamin A. Suter, Samuel A. Neymotin, Cliff C. Kerr, Adrian Quintana, Padraig Gleeson, Gordon M. G. Shepherd, William W. Lytton

P107 Inter-areal and inter-regional inhomogeneity in co-axial anisotropy of Cortical Point Spread in human visual areas

Juhyoung Ryu, Sang-Hun Lee

P108 Two bayesian quanta of uncertainty explain the temporal dynamics of cortical activity in the non-sensory areas during bistable perception

Joonwon Lee, Sang-Hun Lee

P109 Optimal and suboptimal integration of sensory and value information in perceptual decision making

Hyang Jung Lee, Sang-Hun Lee

P110 A Bayesian algorithm for phoneme Perception and its neural implementation

Daeseob Lim, Sang-Hun Lee

P111 Complexity of EEG signals is reduced during unconsciousness induced by ketamine and propofol

Jisung Wang, Heonsoo Lee

P112 Self-organized criticality of neural avalanche in a neural model on complex networks

Nam Jung, Le Anh Quang, Seung Eun Maeng, Tae Ho Lee, Jae Woo Lee

P113 Dynamic alterations in connection topology of the hippocampal network during ictal-like epileptiform activity in an in vitro rat model

Chang-hyun Park, Sora Ahn, Jangsup Moon, Yun Seo Choi, Juhee Kim, Sang Beom Jun, Seungjun Lee, Hyang Woon Lee

P114 Computational model to replicate seizure suppression effect by electrical stimulation

Sora Ahn, Sumin Jo, Eunji Jun, Suin Yu, Hyang Woon Lee, Sang Beom Jun, Seungjun Lee

P115 Identifying excitatory and inhibitory synapses in neuronal networks from spike trains using sorted local transfer entropy

Felix Goetze, Pik-Yin Lai

P116 Neural network model for obstacle avoidance based on neuromorphic computational model of boundary vector cell and head direction cell

Seonghyun Kim, Jeehyun Kwag

P117 Dynamic gating of spike pattern propagation by Hebbian and anti-Hebbian spike timing-dependent plasticity in excitatory feedforward network model

Hyun Jae Jang, Jeehyun Kwag

P118 Inferring characteristics of input correlations of cells exhibiting up-down state transitions in the rat striatum

Marko Filipović, Ramon Reig, Ad Aertsen, Gilad Silberberg, Arvind Kumar

P119 Graph properties of the functional connected brain under the influence of Alzheimer’s disease

Claudia Bachmann, Simone Buttler, Heidi Jacobs, Kim Dillen, Gereon R. Fink, Juraj Kukolja, Abigail Morrison

P120 Learning sparse representations in the olfactory bulb

Daniel Kepple, Hamza Giaffar, Dima Rinberg, Steven Shea, Alex Koulakov

P121 Functional classification of homologous basal-ganglia networks

Jyotika Bahuguna,Tom Tetzlaff, Abigail Morrison, Arvind Kumar, Jeanette Hellgren Kotaleski

P122 Short term memory based on multistability

Tim Kunze, Andre Peterson, Thomas Knösche

P123 A physiologically plausible, computationally efficient model and simulation software for mammalian motor units

Minjung Kim, Hojeong Kim

P125 Decoding laser-induced somatosensory information from EEG

Ji Sung Park, Ji Won Yeon, Sung-Phil Kim

P126 Phase synchronization of alpha activity for EEG-based personal authentication

Jae-Hwan Kang, Chungho Lee, Sung-Phil Kim

P129 Investigating phase-lags in sEEG data using spatially distributed time delays in a large-scale brain network model

Andreas Spiegler, Spase Petkoski, Matias J. Palva, Viktor K. Jirsa

P130 Epileptic seizures in the unfolding of a codimension-3 singularity

Maria L. Saggio, Silvan F. Siep, Andreas Spiegler, William C. Stacey, Christophe Bernard, Viktor K. Jirsa

P131 Incremental dimensional exploratory reasoning under multi-dimensional environment

Oh-hyeon Choung, Yong Jeong

P132 A low-cost model of eye movements and memory in personal visual cognition

Yong-il Lee, Jaeseung Jeong

P133 Complex network analysis of structural connectome of autism spectrum disorder patients

Su Hyun Kim, Mir Jeong, Jaeseung Jeong

P134 Cognitive motives and the neural correlates underlying human social information transmission, gossip

Jeungmin Lee, Jaehyung Kwon, Jerald D. Kralik, Jaeseung Jeong

P135 EEG hyperscanning detects neural oscillation for the social interaction during the economic decision-making

Jaehwan Jahng, Dong-Uk Hwang, Jaeseung Jeong

P136 Detecting purchase decision based on hyperfrontality of the EEG

Jae-Hyung Kwon, Sang-Min Park, Jaeseung Jeong

P137 Vulnerability-based critical neurons, synapses, and pathways in the Caenorhabditis elegans connectome

Seongkyun Kim, Hyoungkyu Kim, Jerald D. Kralik, Jaeseung Jeong

P138 Motif analysis reveals functionally asymmetrical neurons in C. elegans

Pyeong Soo Kim, Seongkyun Kim, Hyoungkyu Kim, Jaeseung Jeong

P139 Computational approach to preference-based serial decision dynamics: do temporal discounting and working memory affect it?

Sangsup Yoon, Jaehyung Kwon, Sewoong Lim, Jaeseung Jeong

P141 Social stress induced neural network reconfiguration affects decision making and learning in zebrafish

Choongseok Park, Thomas Miller, Katie Clements, Sungwoo Ahn, Eoon Hye Ji, Fadi A. Issa

P142 Descriptive, generative, and hybrid approaches for neural connectivity inference from neural activity data

JeongHun Baek, Shigeyuki Oba, Junichiro Yoshimoto, Kenji Doya, Shin Ishii

P145 Divergent-convergent synaptic connectivities accelerate coding in multilayered sensory systems

Thiago S. Mosqueiro, Martin F. Strube-Bloss, Brian Smith, Ramon Huerta

P146 Swinging networks

Michal Hadrava, Jaroslav Hlinka

P147 Inferring dynamically relevant motifs from oscillatory stimuli: challenges, pitfalls, and solutions

Hannah Bos, Moritz Helias

P148 Spatiotemporal mapping of brain network dynamics during cognitive tasks using magnetoencephalography and deep learning

Charles M. Welzig, Zachary J. Harper

P149 Multiscale complexity analysis for the segmentation of MRI images

Won Sup Kim, In-Seob Shin, Hyeon-Man Baek, Seung Kee Han

P150 A neuro-computational model of emotional attention

René Richter, Julien Vitay, Frederick Beuth, Fred H. Hamker

P151 Multi-site delayed feedback stimulation in parkinsonian networks

Kelly Toppin, Yixin Guo

P152 Bistability in Hodgkin–Huxley-type equations

Tatiana Kameneva, Hamish Meffin, Anthony N. Burkitt, David B. Grayden

P153 Phase changes in postsynaptic spiking due to synaptic connectivity and short term plasticity: mathematical analysis of frequency dependency

Mark D. McDonnell, Bruce P. Graham

P154 Quantifying resilience patterns in brain networks: the importance of directionality

Penelope J. Kale, Leonardo L. Gollo

P155 Dynamics of rate-model networks with separate excitatory and inhibitory populations

Merav Stern, L. F. Abbott

P156 A model for multi-stable dynamics in action recognition modulated by integration of silhouette and shading cues

Leonid A. Fedorov, Martin A. Giese

P157 Spiking model for the interaction between action recognition and action execution

Mohammad Hovaidi Ardestani, Martin Giese

P158 Surprise-modulated belief update: how to learn within changing environments?

Mohammad Javad Faraji, Kerstin Preuschoff, Wulfram Gerstner

P159 A fast, stochastic and adaptive model of auditory nerve responses to cochlear implant stimulation

Margriet J. van Gendt, Jeroen J. Briaire, Randy K. Kalkman, Johan H. M. Frijns

P160 Quantitative comparison of graph theoretical measures of simulated and empirical functional brain networks

Won Hee Lee, Sophia Frangou

P161 Determining discriminative properties of fMRI signals in schizophrenia using highly comparative time-series analysis

Ben D. Fulcher, Patricia H. P. Tran, Alex Fornito

P162 Emergence of narrowband LFP oscillations from completely asynchronous activity during seizures and high-frequency oscillations

Stephen V. Gliske, William C. Stacey, Eugene Lim, Katherine A. Holman, Christian G. Fink

P163 Neuronal diversity in structure and function: cross-validation of anatomical and physiological classification of retinal ganglion cells in the mouse

Jinseop S. Kim, Shang Mu, Kevin L. Briggman, H. Sebastian Seung, the EyeWirers

P164 Analysis and modelling of transient firing rate changes in area MT in response to rapid stimulus feature changes

Detlef Wegener, Lisa Bohnenkamp, Udo A. Ernst

P165 Step-wise model fitting accounting for high-resolution spatial measurements: construction of a layer V pyramidal cell model with reduced morphology

Tuomo Mäki-Marttunen, Geir Halnes, Anna Devor, Christoph Metzner, Anders M. Dale, Ole A. Andreassen, Gaute T. Einevoll

P166 Contributions of schizophrenia-associated genes to neuron firing and cardiac pacemaking: a polygenic modeling approach

Tuomo Mäki-Marttunen, Glenn T. Lines, Andy Edwards, Aslak Tveito, Anders M. Dale, Gaute T. Einevoll, Ole A. Andreassen

P167 Local field potentials in a 4 × 4 mm² multi-layered network model

Espen Hagen, Johanna Senk, Sacha J. van Albada, Markus Diesmann

P168 A spiking network model explains multi-scale properties of cortical dynamics

Maximilian Schmidt, Rembrandt Bakker, Kelly Shen, Gleb Bezgin, Claus-Christian Hilgetag, Markus Diesmann, Sacha Jennifer van Albada

P169 Using joint weight-delay spike-timing dependent plasticity to find polychronous neuronal groups

Haoqi Sun, Olga Sourina, Guang-Bin Huang, Felix Klanner, Cornelia Denk

P170 Tensor decomposition reveals RSNs in simulated resting state fMRI

Katharina Glomb, Adrián Ponce-Alvarez, Matthieu Gilson, Petra Ritter, Gustavo Deco

P171 Getting in the groove: testing a new model-based method for comparing task-evoked vs resting-state activity in fMRI data on music listening

Matthieu Gilson, Maria AG Witek, Eric F. Clarke, Mads Hansen, Mikkel Wallentin, Gustavo Deco, Morten L. Kringelbach^, Peter Vuust

P172 STochastic engine for pathway simulation (STEPS) on massively parallel processors

Guido Klingbeil, Erik De Schutter

P173 Toolkit support for complex parallel spatial stochastic reaction–diffusion simulation in STEPS

Weiliang Chen, Erik De Schutter

P174 Modeling the generation and propagation of Purkinje cell dendritic spikes caused by parallel fiber synaptic input

Yunliang Zang, Erik De Schutter

P175 Dendritic morphology determines how dendrites are organized into functional subunits

Sungho Hong, Akira Takashima, Erik De Schutter

P176 A model of Ca²⁺/calmodulin-dependent protein kinase II activity in long term depression at Purkinje cells

Criseida Zamora, Andrew R. Gallimore, Erik De Schutter

P177 Reward-modulated learning of population-encoded vectors for insect-like navigation in embodied agents

Dennis Goldschmidt, Poramate Manoonpong, Sakyasingha Dasgupta

P178 Data-driven neural models part II: connectivity patterns of human seizures

Philippa J. Karoly, Dean R. Freestone, Daniel Soundry, Levin Kuhlmann, Liam Paninski, Mark Cook

P179 Data-driven neural models part I: state and parameter estimation

Dean R. Freestone, Philippa J. Karoly, Daniel Soundry, Levin Kuhlmann, Mark Cook

P180 Spectral and spatial information processing in human auditory streaming

Jaejin Lee, Yonatan I. Fishman, Yale E. Cohen

P181 A tuning curve for the global effects of local perturbations in neural activity: Mapping the systems-level susceptibility of the brain

Leonardo L. Gollo, James A. Roberts, Luca Cocchi

P182 Diverse homeostatic responses to visual deprivation mediated by neural ensembles

Yann Sweeney, Claudia Clopath

P183 Opto-EEG: a novel method for investigating functional connectome in mouse brain based on optogenetics and high density electroencephalography

Soohyun Lee, Woo-Sung Jung, Jee Hyun Choi

P184 Biphasic responses of frontal gamma network to repetitive sleep deprivation during REM sleep

Bowon Kim, Youngsoo Kim, Eunjin Hwang, Jee Hyun Choi

P185 Brain-state correlate and cortical connectivity for frontal gamma oscillations in top-down fashion assessed by auditory steady-state response

Younginha Jung, Eunjin Hwang, Yoon-Kyu Song, Jee Hyun Choi

P186 Neural field model of localized orientation selective activation in V1

James Rankin, Frédéric Chavane

P187 An oscillatory network model of Head direction and Grid cells using locomotor inputs

Karthik Soman, Vignesh Muralidharan, V. Srinivasa Chakravarthy

P188 A computational model of hippocampus inspired by the functional architecture of basal ganglia

Karthik Soman, Vignesh Muralidharan, V. Srinivasa Chakravarthy

P189 A computational architecture to model the microanatomy of the striatum and its functional properties

Sabyasachi Shivkumar, Vignesh Muralidharan, V. Srinivasa Chakravarthy

P190 A scalable cortico-basal ganglia model to understand the neural dynamics of targeted reaching

Vignesh Muralidharan, Alekhya Mandali, B. Pragathi Priyadharsini, Hima Mehta, V. Srinivasa Chakravarthy

P191 Emergence of radial orientation selectivity from synaptic plasticity

Catherine E. Davey, David B. Grayden, Anthony N. Burkitt

P192 How do hidden units shape effective connections between neurons?

Braden A. W. Brinkman, Tyler Kekona, Fred Rieke, Eric Shea-Brown, Michael Buice

P193 Characterization of neural firing in the presence of astrocyte-synapse signaling

Maurizio De Pittà, Hugues Berry, Nicolas Brunel

P194 Metastability of spatiotemporal patterns in a large-scale network model of brain dynamics

James A. Roberts, Leonardo L. Gollo, Michael Breakspear

P195 Comparison of three methods to quantify detection and discrimination capacity estimated from neural population recordings

Gary Marsat, Jordan Drew, Phillip D. Chapman, Kevin C. Daly, Samual P. Bradley

P196 Quantifying the constraints for independent evoked and spontaneous NMDA receptor mediated synaptic transmission at individual synapses

Sat Byul Seo, Jianzhong Su, Ege T. Kavalali, Justin Blackwell

P199 Gamma oscillation via adaptive exponential integrate-and-fire neurons

LieJune Shiau, Laure Buhry, Kanishka Basnayake

P200 Visual face representations during memory retrieval compared to perception

Sue-Hyun Lee, Brandon A. Levy, Chris I. Baker

P201 Top-down modulation of sequential activity within packets modeled using avalanche dynamics

Timothée Leleu, Kazuyuki Aihara

Q28 An auto-encoder network realizes sparse features under the influence of desynchronized vascular dynamics

Ryan T. Philips, Karishma Chhabria, V. Srinivasa Chakravarthy

Controlled single-cell deposition and patterning by highly flexible hollow cantilevers

Single-cell patterning represents a key approach to decouple and better understand the role and mechanisms of individual cells of a given population. In particular, the bottom-up approach of engineering neuronal circuits with a controlled topology holds immense promises to perceive the relationships between connectivity and function. In order to accommodate these efforts, highly flexible SU-8 cantilevers with integrated microchannels have been fabricated for both additive and subtractive patterning. By directly squeezing out single cells onto adhesive surfaces, controlled deposition with a spatial accuracy of 5 μm could be achieved, while subtractive patterning has been realized by selective removal of targeted single cells. Complex cell patterns were created on substrates pre-patterned with cell-adhesive and repulsive areas, preserving the original pattern geometry for long-term studies. For example, a circular loop with a diameter of 530 μm has been realized using primary hippocampal neurons, which were fully connected to their respective neighbors along the loop. Using the same cantilevers, the versatility of the technique has also been demonstrated via in situ modification of already mature neuronal cultures by both detaching individual cells of the population and adding fresh ones, incorporating them into the culture.

Template-Free 3D Microprinting of Metals Using a Force-Controlled Nanopipette for Layer-by-Layer Electrodeposition

A novel 3D printing method for voxel-by-voxel metal printing is presented. Hollow atomic force microscopy (AFM) cantilevers are used to locally supply metal ions in an electrochemical cell, enabling a localized electroplating reaction. By exploiting the deflection feedback of these probes, electrochemical 3D metal printing is, for the first time, demonstrated in a layer-by-layer fashion, enabling the fabrication of arbitrary-shaped geometries.

Simultaneous Scanning Ion Conductance Microscopy and Atomic Force Microscopy with Microchanneled Cantilevers

We combined scanning ion conductance microscopy (SICM) and atomic force microscopy (AFM) into a single tool using AFM cantilevers with an embedded microchannel flowing into the nanosized aperture at the apex of the hollow pyramid. An electrode was positioned in the AFM fluidic circuit connected to a second electrode in the bath. We could thus simultaneously measure the ionic current and the cantilever bending (in optical beam deflection mode). First, we quantitatively compared the SICM and AFM contact points on the approach curves. Second, we estimated where the probe in SICM mode touches the sample during scanning on a calibration grid and applied the finding to image a network of neurites on a Petri dish. Finally, we assessed the feasibility of a double controller using both the ionic current and the deflection as input signals of the piezofeedback. The experimental data were rationalized in the framework of finite elements simulations.

Femtomolar oligonucleotide detection by a one-step gold nanoparticle-based assay

A sequence-specific oligonucleotide detection method based on the tail-to-tail aggregation of functionalized gold nanoparticles in the presence of target analytes is presented together with its optimization and capabilities for detection of single nucleotide polymorphisms (SNPs). In this single-step method, capture probes are freely accessible for hybridization, resulting in an improved assay performance compared to substrate-based assays. The analytes bring the nanoparticles close to each other via hybridization, causing a red shift of the nanoparticle plasmon peak detected by a spectrophotometer or CCD camera coupled to a darkfield imaging system. Optimal conditions for the assay were found to be (i) use of capture probes complementary to the target without any gap, (ii) maximum possible probe density on the gold nanoparticles, and (iii) 1M ionic strength buffer. The optimized assay has a 1 fM limit of detection and fM to 10 pM dynamic range, with detection of perfect match sequences being three orders of magnitude more sensitive than targets with single nucleotide mismatches.

Patterning gold nanoparticles in liquid environment with high ionic strength for local fabrication of up to 100 μm long metallic interconnections

Metallic interconnections were fabricated in situ using the FluidFM as scanning probe lithography tool. In contrast to other SPL tools, the closed fluidic circuit of the FluidFM enables a pressure-controlled deposition of metallic nanoparticles in liquid environment. Taking advantage of the salt concentration of the liquid environment (i.e. the ionic strength) to tailor the resulting particle density in the deposited layer, a protocol was established for direct patterning of conductive interconnecting structures. The FluidFM microchannel was filled with an aqueous solution of negatively charged gold nanoparticles (AuNPs) to be delivered onto a glass surface coated with a polycation favoring electrostatic adhesion. The deposited structures were analyzed both topographically and electrically to optimize the external parameters such as contact time, salt concentration of the liquid environment and size of the AuNPs. Using this optimized protocol we succeeded in the local fabrication of conductive metallic wires between two prefabricated macroelectrodes in liquid environment. In a subsequent step, the conductivity of the deposited structure was improved by gold annealing.

Force-controlled patch clamp of beating cardiac cells

From its invention in the 1970s, the patch clamp technique is the gold standard in electrophysiology research and drug screening because it is the only tool enabling accurate investigation of voltage-gated ion channels, which are responsible for action potentials. Because of its key role in drug screening, innovation efforts are being made to reduce its complexity toward more automated systems. While some of these new approaches are being adopted in pharmaceutical companies, conventional patch-clamp remains unmatched in fundamental research due to its versatility. Here, we merged the patch clamp and atomic force microscope (AFM) techniques, thus equipping the patch-clamp with the sensitive AFM force control. This was possible using the FluidFM, a force-controlled nanopipette based on microchanneled AFM cantilevers. First, the compatibility of the system with patch-clamp electronics and its ability to record the activity of voltage-gated ion channels in whole-cell configuration was demonstrated with sodium (NaV1.5) channels. Second, we showed the feasibility of simultaneous recording of membrane current and force development during contraction of isolated cardiomyocytes. Force feedback allowed for a gentle and stable contact between AFM tip and cell membrane enabling serial patch clamping and injection without apparent cell damage.

Iodide sensing via electrochemical etching of ultrathin gold films

Iodide is an essential element for humans and animals and insufficient intake is still a major problem. Affordable and accurate methods are required to quantify iodide concentrations in biological and environmental fluids. A simple and low cost sensing device is presented which is based on iodide induced electrochemical etching of ultrathin gold films. The sensitivity of resistance measurements to film thickness changes is increased by using films with a thickness smaller than the electron mean free path. The underlying mechanism is demonstrated by simultaneous cyclic voltammetry experiments and resistance change measurements in a buffer solution. Iodide sensing is conducted in buffer solutions as well as in lake water with limits of detection in the range of 1 μM (127 μg L(-1)) and 2 μM (254 μg L(-1)), respectively. In addition, nanoholes embedded in the thin films are tested for suitability of optical iodide sensing based on localized surface plasmon resonance.