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Surface topography on the scale of tens of nanometers to several micrometers substantially affects cell adhesion, migration, and differentiation. Recent studies using electron microscopy and super-resolution microscopy provide insight into how cells interact with surface nanotopography; however, the complex sample preparation and expensive imaging equipment required for these methods makes them not easily accessible. Expansion microscopy (ExM) is an affordable approach to image beyond the diffraction limit, but ExM cannot be readily applied to image the cell-material interface as most materials do not expand. Here, we develop a protocol that allows the use of ExM to resolve the cell-material interface with high resolution. We apply the technique to image the interface between U2OS cells and nanostructured substrates as well as the interface between primary osteoblasts with titanium dental implants. The high spatial resolution enabled by ExM reveals that although AP2 and F-actin both accumulate at curved membranes induced by vertical nanostructures, they are spatially segregated. Using ExM, we also reliably image how osteoblasts interact with roughened titanium implant surfaces below the diffraction limit; this is of great interest to understand osseointegration of the implants but has up to now been a significant technical challenge due to the irregular shape, the large volume, and the opacity of the titanium implants that have rendered them incompatible with other super-resolution techniques. We believe that our protocol will enable the use of ExM as a powerful tool for cell-material interface studies.

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., Lab on a Chip, 22 (7), ISSN:1473-0197, ISSN:1473-0189

Neuromorphic systems that display synaptic conditioning based on biochemical signaling activity have recently been introduced in the form of artificial synapses that are model devices to develop tissue-interfaced platforms. In this regard, biohybrid synapses promise adaptive neuron-integrated functions. However, these systems suffer from both molecular cross-talk as biological neural circuits signal transmission typically involves more than one neuromodulator, and unstable electronics wirings as complex architectures are required to interface the tissues. Moreover, whilst novel spiking circuits can work as artificial neurons, they only recreate the biological electrical signaling pathway while electrochemical signal transduction is required for inter-neuron communication. As such, artificial chemically-mediated synapses are essential to perform memory/learning computing functions. Herein, an electrochemical neuromorphic organic device (ENODe) working as an artificial synapse that overcomes electrochemical and readout interferences while it emulates two neurotransmitters synaptic weight modulation and their recycling machinery at the synaptic cleft is shown. Neuronal short- and long-term plasticity are replicated by transducing two separate neurotransmitter-mediated chemical signals into reversible and nonreversible variations of PEDOT:PSS conductance. By exploiting the electrochromic properties of PEDOT:PSS, an alternative optical monitoring strategy is introduced which promises stable multidevice readout from complex bio-hybrid interfaces. The platform emulates high-order biological processes such as intrinsic forgetting, memory consolidation, and neurotransmitter co-modulation. These brain-inspired functionalities herald the development of tissue-integrated neuromorphic systems that combine spiking (electrical neurons) and nonspiking (electrochemical synapses) elements, thus envisioning prosthetic bridges for neural engineering and regenerative medicine.

Optical recording based on voltage-sensitive fluorescent reporters allows for spatial flexibility of measuring from desired cells, but photobleaching and phototoxicity of the fluorescent labels often limit their sensitivity and recording duration. Voltage-dependent optical absorption, rather than fluorescence, of electrochromic materials, would overcome these limitations to achieve long-term optical recording of bioelectrical signals. Electrochromic materials such as PEDOT:PSS possess the property that an applied voltage can either increase or decrease the light absorption depending on the wavelength. In this work, we harness this anticorrelated light absorption at two different wavelengths to significantly improve the signal detection. With dual-color detection, electrical activity from cells produces signals of opposite polarity, while artifacts, mechanical motions, and technical noises are uncorrelated or positively correlated. Using this technique, we are able to optically record cardiac action potentials with a high signal-to-noise ratio, 10 kHz sampling rate,15 min recording duration, and no time-dependent degradation of the signal. Furthermore, we can reliably perform multiple recording sessions from the same culture for over 25 days.

Implantable sensors can be used to monitor biomechanical strain continuously. However, three key challenges need to be addressed before they can be of use in clinical practice: the structural mismatch between the sensors and tissue or organs should be eliminated; a practical suturing attachment process should be developed; and the sensors should be equipped with wireless readout. Here, we report a wireless and suturable fibre strain-sensing system created by combining a capacitive fibre strain sensor with an inductive coil for wireless readout. The sensor is composed of two stretchable conductive fibres organized in a double helical structure with an empty core, and has a sensitivity of around 12. Mathematical analysis and simulation of the sensor can effectively predict its capacitive response and can be used to modulate performance according to the intended application. To illustrate the capabilities of the system, we use it to perform strain measurements on the Achilles tendon and knee ligament in an ex vivo and in vivo porcine leg. A capacitive, fibre-like stretchable strain sensor, formed of two conductors in a double helical structure, can be combined with an inductive coil to create a wireless strain-sensing system for biomedical applications.

Bioelectronics aims to design electronic devices which can be fully integrated within tissues to monitor or stimulate specific cell functions. The main challenge is the engineering of the cell–chip interface and diverse materials, and devices have been developed to recapitulate biological architectures and functionalities. In this Prospective article, the authors give an overview on how the bioelectronics community has exploited biomimetic approaches to emulate cell morphologies, interactions, and functions to design optimal electrical platforms to be coupled to living cells.

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 super-paramagnetic 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. ISSN:1936-0851 ISSN:1936-086X

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., Frontiers in Neuroscience, 16, ISSN:1662-453X, ISSN:1662-4548

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 mostly unidirectional connections, confined by microfabricated polydimethylsiloxane (PDMS) membranes. Such circuits were characterized using optical imaging and microelectrode arrays (MEAs). 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.

T cells sense and respond to their local environment at the nanoscale by forming small actin-rich protrusions, called microvilli, which play critical roles in signaling and antigen recognition, particularly at the interface with the antigen presenting cells. However, the mechanism by which microvilli contribute to cell signaling and activation is largely unknown. Here, we present a tunable engineered system that promotes microvilli formation and T cell signaling via physical stimuli. We discovered that nanoporous surfaces favored microvilli formation and markedly altered gene expression in T cells and promoted their activation. Mechanistically, confinement of microvilli inside of nanopores leads to size-dependent sorting of membrane-anchored proteins, specifically segregating CD45 phosphatases and T cell receptors (TCR) from the tip of the protrusions when microvilli are confined in 200-nm pores but not in 400-nm pores. Consequently, formation of TCR nanoclustered hotspots within 200-nm pores allows sustained and augmented signaling that prompts T cell activation even in the absence of TCR agonists. The synergistic combination of mechanical and biochemical signals on porous surfaces presents a straightforward strategy to investigate the role of microvilli in T cell signaling as well as to boost T cell activation and expansion for application in the growing field of adoptive immunotherapy.

Drug-induced cardiotoxicity arises primarily when a compound alters the electrophysiological properties of cardiomyocytes. Features of intracellular action potentials (iAPs) are powerful biomarkers that predict proarrhythmic risks. However, the conventional patch clamp techniques for measuring iAPs are either laborious and low throughput or not suitable for measuring electrically connected cardiomyocytes. In the last decade, a number of vertical nanoelectrodes have been demonstrated to achieve parallel and minimally-invasive iAP recordings. Nanoelectrodes show great promise, but the large variability in success rate, signal strength, and the low throughput of device fabrication have hindered them from being broadly adopted for proarrhythmia drug assessment. In this work, we developed vertically-aligned and semi-hollow nanocrown electrodes that are mechanically robust and made through a scalable fabrication process. Nanocrown electrodes achieve >99% success rates in obtaining intracellular access through electroporation, allowing reliable and simultaneous iAP recordings from up to 57 human pluripotent stem-cell-derived cardiomyocytes (hPSC-CMs). The accuracy of nanocrown electrode recordings is validated by simultaneous patch clamp recording from the same cell. Nanocrown electrodes enable prolonged iAP recording for continual monitoring of the same cells upon the sequential addition of four to five incremental drug doses. In this way, the dose-response data is self-referencing, which avoids the cell-to-cell variations inherent to hPSC-CMs. We are hopeful that this technology development is a step towards establishing an iAP screening assay for preclinical evaluation of drug-induced arrhythmogenicity.

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. ISSN:0956-5663 ISSN:1873-4235

Studying particle–particle interactions is important in understanding the performance and limits of nanoparticle-based biosensors. Gold nanoparticles (AuNPs) are among the most popular nanomaterial...

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.

T cells sense and respond to their local environment at the nanoscale by forming small actin-rich protrusions, called microvilli, which play critical roles in signaling and antigen recognition, particularly at the interface with the antigen presenting cells. However, the mechanisms by which microvilli contribute to cell signaling and activation is largely unknown. Here, we present a tunable engineered system that promotes microvilli formation and T cell signaling via physical stimuli. We discovered that nanoporous surfaces favored microvilli formation, and markedly altered gene expression in T cells and promoted their activation. Mechanistically, confinement of microvilli inside of nanopores leads to size-dependent sorting of membrane-anchored proteins, specifically segregating CD45 phosphatases and T cell receptors (TCR) from the tip of the protrusions when microvilli are confined in 200 nm pores, but not in 400 nm pores. Consequently, formation of TCR nanoclustered hotspots within 200 nm pores, allows sustained and augmented signaling that prompts T cell activation even in the absence of TCR agonists. The synergistic combination of mechanical and biochemical signals on porous surfaces presents a straightforward strategy to investigate the role of microvilli in T cell signaling as well as to boost T cell activation and expansion for application in the growing field of adoptive immunotherapy.

Brain-on-Chip (BoC) biotechnology is emerging as a promising tool for biomedical and pharmaceutical research applied to the neurosciences. At the convergence between lab-on-chip and cell biology, BoC couples in vitro three-dimensional brain-like systems to an engineered microfluidics platform designed to provide an in vivo-like extrinsic microenvironment with the aim of replicating tissue- or organ-level physiological functions. BoC therefore offers the advantage of an in vitro reproduction of brain structures that is more faithful to the native correlate than what is obtained with conventional cell culture techniques. As brain function ultimately results in the generation of electrical signals, electrophysiology techniques are paramount for studying brain activity in health and disease. However, as BoC is still in its infancy, the availability of combined BoC–electrophysiology platforms is still limited. Here, we summarize the available biological substrates for BoC, starting with a historical perspective. We then describe the available tools enabling BoC electrophysiology studies, detailing their fabrication process and technical features, along with their advantages and limitations. We discuss the current and future applications of BoC electrophysiology, also expanding to complementary approaches. We conclude with an evaluation of the potential translational applications and prospective technology developments.

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., Analytical Chemistry, 93 (8), ISSN:1520-6882, ISSN:0003-2700

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.

Physiological communication between neurons is dependent on the exchange of neurotransmitters at the synapses. Although this chemical signal transmission targets specific receptors and allows for subtle adaptation of the action potential, in vitro neuroscience typically relies on electrical currents and potentials to stimulate neurons. The electric stimulus is unspecific and the confinement of the stimuli within the media is technically difficult to control and introduces large artifacts in electric recordings of the activity. Here, we present a local chemical stimulation platform that resembles in vivo physiological conditions and can be used to target specific receptors of synapses. Neurotransmitters were dispensed using the force-controlled fluidic force microscope (FluidFM) nanopipette, which provides exact positioning and precise liquid delivery. We show that controlled release of the excitatory neurotransmitter glutamate induces spiking activity in primary rat hippocampal neurons, as measured by concurrent electrical and optical recordings using a microelectrode array and a calcium-sensitive dye, respectively. Furthermore, we characterized the glutamate dose response of neurons by applying stimulation pulses of glutamate with concentrations from 0 to 0.5 mm. This new stimulation approach, which combines FluidFM for gentle and precise positioning with a microelectrode array read-out, makes it possible to modulate the activity of individual neurons chemically and simultaneously record their induced activity across the entire neuronal network. The presented platform not only offers a more physiological alternative compared with electrical stimulation, but also provides the possibility to study the effects of the local application of neuromodulators and other drugs.

The segmentation of images is a common task in a broad range of research fields. To tackle increasingly complex images, artificial intelligence-based approaches have emerged to overcome the shortcomings of traditional feature detection methods. Owing to the fact that most artificial intelligence research is made publicly accessible and programming the required algorithms is now possible in many popular languages, the use of such approaches is becoming widespread. However, these methods often require data labelled by the researcher to provide a training target for the algorithms to converge to the desired result. This labelling is a limiting factor in many cases and can become prohibitively time consuming. Inspired by the ability of cycle-consistent generative adversarial networks to perform style transfer, we outline a method whereby a computer-generated set of images is used to segment the true images. We benchmark our unsupervised approach against a state-of-the-art supervised cell-counting network on the VGG Cells dataset and show that it is not only competitive but also able to precisely locate individual cells. We demonstrate the power of this method by segmenting bright-field images of cell cultures, images from a live/dead assay of C. elegans, and X-ray computed tomography of metallic nanowire meshes. Labelling training data to train machine learning models is very time intense. A new method shows that content transformation can be effectively learned from generated data, avoiding the need for any manual labelling in segmentation and classification tasks.

DNA functionalized gold nanoparticles (DNA-AuNPs) have shown great potential for biosensing as they combine the excellent optical properties of gold nanoparticles and the molecular recognition function of DNA. Since the DNA density determines the assay performance and the stability of the conjugate, a precise control of the surface density of DNA-AuNP is crucial for an optimized biosensor. Here we report a simple assay for quantifying multiple unlabeled DNAs on AuNPs. The assay relies on potassium cyanide (KCN) to first dissolve the AuNPs, which then releases surface bound DNA for quantification through a double-stranded DNA dye. Using this analytical quantification method, we investigated several strategies to control the surface density of DNA-AuNPs. Besides the precise control of DNA density, the stability of DNA-AuNPs after conjugation is also important in developing a biosensor with optimal performance. Without proper storing conditions, DNA-AuNPs are unstable and aggregate over time. To overcome this problem, we developed a long-term storage solution to ensure the stability and quality of DNA-AuNPs after conjugation which would benefit any DNA-AuNP-based biosensor.

The segmentation of images is a common task in a broad range of research fields. To tackle increasingly complex images, artificial intelligence (AI) based approaches have emerged to overcome the shortcomings of traditional feature detection methods. Owing to the fact that most AI research is made publicly accessible and programming the required algorithms is now possible in many popular languages, the use of such approaches is becoming widespread. However, these methods often require data labeled by the researcher to provide a training target for the algorithms to converge to the desired result. This labeling is a limiting factor in many cases and can become prohibitively time consuming. Inspired by Cycle-consistent Generative Adversarial Networks’ (cycleGAN) ability to perform style transfer, we outline a method whereby a computer generated set of images is used to segment the true images. We benchmark our unsupervised approach against a state-of-the-art supervised cell-counting network on the VGG Cells dataset and show that it is not only competitive but can also precisely locate individual cells. We demonstrate the power of this method by segmenting bright-field images of cell cultures, a live-dead assay of C.Elegans and X-ray-computed tomography of metallic nanowire meshes.

Label-free biosensors enable the monitoring of biomolecular interactions in real-time, which is key to the analysis of the binding characteristics of biomolecules. While refractometric optical biosensors are sensitive and well-established, they are susceptible to any change of the refractive index in the sensing volume caused by minute variations in composition of the sample buffer, temperature drifts and nonspecific binding to the sensor surface. Refractometric biosensors require reference channels as well as temperature stabilisation and their applicability in complex fluids such as blood is limited by nonspecific bindings. Focal molography does not measure the refractive index of the entire sensing volume but detects the diffracted light from a coherent assembly of analyte molecules. Thus, it does not suffer from the limitations of refractometric sensors since they stem from non-coherent processes and therefore do not add to the coherent molographic signal. The coherent assembly is generated by selective binding of the analyte molecules to a synthetic binding pattern - the mologram. Focal Molography has been introduced theoretically and verified experimentally in previous papers. However, further understanding of the underlying physics and a diffraction-limited readout is needed to unveil its full potential. This paper introduces refined theoretical models which can accurately quantify the amount of matter bound to the mologram from the diffracted intensity. In addition, it presents measurements of diffraction-limited molographic foci. These improvements enabled us to demonstrate a resolution in real-time binding experiments comparable to the best SPR sensors, without the need of temperature stabilisation or drift correction and to detect small molecules label-free in an endpoint format. The presented experiments exemplify the robustness and sensitivity of diffractometric sensors., 19 pages, 9 figures and a supplemental material of 26 pages

We established a process for the microfabrication of SU-8 AFM cantilevers with an embedded microchannel and an integrated layer of silver nanowires as piezoresistive element. The self-sensing detection is based on the electrical resistance increase of the silver nanowire network associated with the strain applied upon deflection of the cantilever. We observed a sensitivity of 1.4 × 10−6 nm−1 at large strains of 4 % (gauge factor of 10) and a sensitivity of 0.1 × 10−6 nm−1 at small strains of 0.6 % (gauge factor of 1). We obtained a still higher sensitivity (gauge factor of 28) by coating the silver nanowires network with a thin (∼10 nm) aluminum oxide layer acting as an insulating barrier. As application, we studied the cantilever deformation as a function of the overpressure applied in the microchannel while the cantilever was kept in contact with a glass surface confirming their large deflection detection capabilities. The results were qualitatively rationalized in the percolation framework. The presented self-sensing micropipettes could be used for experiments that require remarkably important bending, such as localized single cell deposition by squeezing the micropipette against the substrate, as well as force-controlled electrochemical additive manufacturing at the sub-mm scale.

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. Funding Agencies|ETH Zurich; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoping University (Faculty Grant SFO Mat LiU) [2009 00971]; Swedish Foundation for Strategic Research

Nature Nanotechnology, 14 (8), ISSN:1748-3387, ISSN:1748-3395

Cells in vitro behave differently if cultured in a 2D or 3D environment. In spite of the continuous progress over the recent years, methods available for realizing 3D cultures of primary neurons are still fairly complex, limited in throughput and especially limited in compatibility with other techniques like multielectrode arrays (MEAs) for recording and stimulating the network activity with high temporal precision. In this manuscript, a paper-based approach is presented using cellulose filter paper as a mobile substrate for 3D cultures of primary rat hippocampal and cortical neurons. Acting as 3D scaffolds for network development, filter membranes with different surface treatments were prepared to control network homogeneity and laser cut to change the network topology through spatial confinement. The viability of the prepared cultures was comparable to that of reference 2D cultures for over 4 weeks, and the mechanical stability of the paper substrates made it possible to transfer the cultures to MEA chips in an on-demand manner. Once the cultures were successfully transduced with a gene-encoded calcium indicator and transferred to a MEA chip, the optical and electrical signals of neuronal activity were simultaneously recorded and combined to study the different activity patterns with high spatiotemporal resolution. The high-throughput nature of the presented approach makes it a valuable tool for investigating the intimate relationship between topology and function, by studying the intrinsic parameters influencing network synchronization and signal propagation through the different activity patterns of 3D neural cultures with arbitrary topology. The developed platform provides a robust and simple alternative to existing 3D culturing technologies for neurons., RSC Advances, 7 (62), ISSN:2046-2069

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, NH

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.

The fundamental mechanisms of complex neural computation remain largely unknown, especially in respect to the characteristics of distinct neural circuits within the mammalian brain. The bottom-up approach of building well-defined neural networks with controlled topology has immense promise for improved reproducibility and increased target selectivity and response of drug action, along with hopes to unravel the relationships between functional connectivity and its imprinted physiological and pathological functions. In this review, we summarize the different approaches available for engineering neural networks treated analogously to a mathematical graph consisting of cell bodies and axons as nodes and edges, respectively. After discussing the advances and limitations of the current techniques in terms of cell placement to the nodes and guiding the growth of axons to connect them, the basic properties of patterned networks are analyzed in respect to cell survival and activity dynamics, and compared to that of in vivo and random in vitro cultures. Besides the fundamental scientific interest and relevance to drug and toxicology tests, we also visualize the possible applications of such engineered networks. The review concludes by comparing the possibilities and limitations of the different methods for realizing in vitro engineered neural networks in 2D.

Scanning ion conductance microscopy (SICM) is a technique for high-resolution non-contact imaging, particularly powerful for live cell studies. Despite debates on its lateral resolution, consensus is that a probe presenting a tip with small opening aperture, large opening angle, and large outer-to-inner radius ratio will offer a SICM current signal more sensitive to tip-sample separation, ultimately impacting the image resolution. We report here the design of such a probe, integrating a nano-opening (

We introduce a photolithography process compatible with soft and rigid substrates, enabling the fabrication of complex 3D interconnected patterns of silver nanowire (AgNW) networks embedded in polydimethylsiloxane (PDMS). Dimensions of the AgNW micropatterns are controlled within the film plane by photolithography, whereas thickness is controlled via a novel and uniform deposition technique using centrifugation. We report the first systematic characterization of the electromechanical properties of such microelectrodes with finest stretchable feature of 15 μm. We observe a geometry-dependent behavior of the gauge factor not only by changing the thickness of the microelectrodes, as it has been commonly reported so far, but also by varying their lateral dimensions. The presented nanocomposites exhibited sheet resistances down to 0.6 Ω/sq, gauge factors ranging from 0.01 to 100, and stretchability above 50% uniaxial strain. This versatile process allows for the production of highly sensitive strain sensors and robust high-density stretchable conductors on a wafer scale with direct implications in mass production of stretchable electronic devices.

A green laser pointer shone through a glass of cola will appear red when looking from above the glass. Lowering the laser so that it is deeper in the cola, the colour will vary from orange to deep red. Fluorescence and absorption spectra were recorded consistent with the reddening of the laser. The depth dependence of the colour of the immersed laser is attributed to a more significant absorption of the short wavelengths by the cola, making the laser look redder as it is lowered deeper into the cola. To confirm this hypothesis, we simulated the fluorescence spectrum we would get at various depths by applying the Lambert law on our measured fluorescence spectrum. These spectra were then converted into red, green and blue (RGB) values. These values were compared to those measured on images of the immersed laser beam at corresponding depths.

Table of contents 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. 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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. 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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