Synthetic Nanoelectronic Probes for Biological Cells and Tissues

Research at the interface between nanoscience and biology could yield breakthroughs in fundamental science and lead to revolutionary technologies. In this review, we focus on the interfaces between nanoelectronics and biology. First, we discuss nanoscale field effect transistors (nanoFETs) as probes to study cellular systems; specifically, we describe the development of nanoFETs that are comparable in size to biological nanostructures involved in communication through synthesized nanowires. Second, we review current progress in multiplexed extracellular sensing using planar nanoFET arrays. Third, we describe the designs and implementation of three distinct nanoFETs used to perform the first intracellular electrical recording from single cells. Fourth, we present recent progress in merging electronic and biological systems at the three-dimensional tissue level by use of macro-porous nanoelectronic scaffolds. Finally, we discuss future developments in this research area, unique challenges and opportunities, and the tremendous impact these nanoFET-based technologies might have on biological and medical sciences.Chemistry and Chemical BiologyEngineering and Applied Science

Nanoelectronics-Biology Frontier: From Nanoscopic Probes for Action Potential Recording in Live Cells to Three-Dimensional Cyborg Tissues

Semiconductor nanowires configured as the active channels of field-effect transistors (FETs) have been used as detectors for high-resolution electrical recording from single live cells, cell networks, tissues and organs. Extracellular measurements with substrate supported silicon nanowire (SiNW) FETs, which have projected active areas orders of magnitude smaller than conventional microfabricated multielectrode arrays (MEAs) and planar FETs, recorded action potential and field potential signals with high signal-to-noise ratio and temporal resolution from cultured neurons, cultured cardiomyocytes, acute brain slices and whole animal hearts. Measurements made with modulation-doped nanoscale active channel SiNW FETs demonstrate that signals recorded from cardiomyocytes are highly localized and have improved time resolution compared to larger planar detectors. In addition, several novel three-dimensional (3D) transistor probes, which were realized using advanced nanowire synthesis methods, have been implemented for intracellular recording. These novel probes include (i) flexible 3D kinked nanowire FETs, (ii) branched intracellular nanotube SiNW FETs, and (iii) active silicon nanotube FETs. Following phospholipid modification of the probes to mimic the cell membrane, the kinked nanowire, branched intracellular nanotube and active silicon nanotube FET probes recorded full-amplitude intracellular action potentials from spontaneously firing cardiomyocytes. Moreover, these probes demonstrated the capability of reversible, stable, and long-term intracellular recording, thus indicating the minimal invasiveness of the new nanoscale structures and suggesting biomimetic internalization via the phospholipid modification. Simultaneous, multi-site intracellular recording from both single cells and cell networks were also readily achieved by interfacing independently addressable nanoprobe devices with cells. Finally, electronic and biological systems have been seamlessly merged in 3D for the first time using macroporous nanoelectronic scaffolds that are analogous to synthetic tissue scaffold and the extracellular matrix in tissue. Free-standing 3D nanoelectronic scaffolds were cultured with neurons, cardiomyocytes and smooth muscle cells to yield electronically-innervated synthetic or ‘cyborg’ tissues. Measurements demonstrate that innervated tissues exhibit similar cell viability as with conventional tissue scaffolds, and importantly, demonstrate that the real-time response to drugs and pH changes can be mapped in 3D through the tissues. These results open up a new field of research, wherein nanoelectronics are merged with biological systems in 3D thereby providing broad opportunities, ranging from a nanoelectronic/tissue platform for real-time pharmacological screening in 3D to implantable ‘cyborg’ tissues enabling closed-loop monitoring and treatment of diseases. Furthermore, the capability of high density scale-up of the above extra- and intracellular nanoscopic probes for action potential recording provide important tools for large-scale high spatio-temporal resolution electrical neural activity mapping in both 2D and 3D, which promises to have a profound impact on many research areas, including the mapping of activity within the brain.Chemistry and Chemical BiologyEngineering and Applied Science

Experimental study of artificial neural networks using a digital memristor simulator

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a fully digital implementation of a memristor hardware simulator, as the core of an emulator, based on a behavioral model of voltage-controlled threshold-type bipolar memristors. Compared to other analog solutions, the proposed digital design is compact, easily reconfigurable, demonstrates very good matching with the mathematical model on which it is based, and complies with all the required features for memristor emulators. We validated its functionality using Altera Quartus II and ModelSim tools targeting low-cost yet powerful field programmable gate array (FPGA) families. We tested its suitability for complex memristive circuits as well as its synapse functioning in artificial neural networks (ANNs), implementing examples of associative memory and unsupervised learning of spatio-temporal correlations in parallel input streams using a simplified STDP. We provide the full circuit schematics of all our digital circuit designs and comment on the required hardware resources and their scaling trends, thus presenting a design framework for applications based on our hardware simulator.Peer ReviewedPostprint (author's final draft

Magnetic Cellular Nonlinear Network with Spin Wave Bus for Image Processing

We describe and analyze a cellular nonlinear network based on magnetic nanostructures for image processing. The network consists of magneto-electric cells integrated onto a common ferromagnetic film - spin wave bus. The magneto-electric cell is an artificial two-phase multiferroic structure comprising piezoelectric and ferromagnetic materials. A bit of information is assigned to the cell's magnetic polarization, which can be controlled by the applied voltage. The information exchange among the cells is via the spin waves propagating in the spin wave bus. Each cell changes its state as a combined effect of two: the magneto-electric coupling and the interaction with the spin waves. The distinct feature of the network with spin wave bus is the ability to control the inter-cell communication by an external global parameter - magnetic field. The latter makes possible to realize different image processing functions on the same template without rewiring or reconfiguration. We present the results of numerical simulations illustrating image filtering, erosion, dilation, horizontal and vertical line detection, inversion and edge detection accomplished on one template by the proper choice of the strength and direction of the external magnetic field. We also present numerical assets on the major network parameters such as cell density, power dissipation and functional throughput, and compare them with the parameters projected for other nano-architectures such as CMOL-CrossNet, Quantum Dot Cellular Automata, and Quantum Dot Image Processor. Potentially, the utilization of spin waves phenomena at the nanometer scale may provide a route to low-power consuming and functional logic circuits for special task data processing

Nanotools for Neuroscience and Brain Activity Mapping

Neuroscience is at a crossroads. Great effort is being invested into deciphering specific neural interactions and circuits. At the same time, there exist few general theories or principles that explain brain function. We attribute this disparity, in part, to limitations in current methodologies. Traditional neurophysiological approaches record the activities of one neuron or a few neurons at a time. Neurochemical approaches focus on single neurotransmitters. Yet, there is an increasing realization that neural circuits operate at emergent levels, where the interactions between hundreds or thousands of neurons, utilizing multiple chemical transmitters, generate functional states. Brains function at the nanoscale, so tools to study brains must ultimately operate at this scale, as well. Nanoscience and nanotechnology are poised to provide a rich toolkit of novel methods to explore brain function by enabling simultaneous measurement and manipulation of activity of thousands or even millions of neurons. We and others refer to this goal as the Brain Activity Mapping Project. In this Nano Focus, we discuss how recent developments in nanoscale analysis tools and in the design and synthesis of nanomaterials have generated optical, electrical, and chemical methods that can readily be adapted for use in neuroscience. These approaches represent exciting areas of technical development and research. Moreover, unique opportunities exist for nanoscientists, nanotechnologists, and other physical scientists and engineers to contribute to tackling the challenging problems involved in understanding the fundamentals of brain function

Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing