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Sample records for analog neural circuit

  1. Analog electronic neural network circuits

    Energy Technology Data Exchange (ETDEWEB)

    Graf, H.P.; Jackel, L.D. (AT and T Bell Labs., Holmdel, NJ (USA))

    1989-07-01

    The large interconnectivity and moderate precision required in neural network models present new opportunities for analog computing. This paper discusses analog circuits for a variety of problems such as pattern matching, optimization, and learning. Most of the circuits build so far are relatively small, exploratory designs. The most mature circuits are those for template matching. Chips performing this function are now being applied to pattern recognition problems.

  2. Analog VLSI neural network integrated circuits

    Science.gov (United States)

    Kub, F. J.; Moon, K. K.; Just, E. A.

    1991-01-01

    Two analog very large scale integration (VLSI) vector matrix multiplier integrated circuit chips were designed, fabricated, and partially tested. They can perform both vector-matrix and matrix-matrix multiplication operations at high speeds. The 32 by 32 vector-matrix multiplier chip and the 128 by 64 vector-matrix multiplier chip were designed to perform 300 million and 3 billion multiplications per second, respectively. An additional circuit that has been developed is a continuous-time adaptive learning circuit. The performance achieved thus far for this circuit is an adaptivity of 28 dB at 300 KHz and 11 dB at 15 MHz. This circuit has demonstrated greater than two orders of magnitude higher frequency of operation than any previous adaptive learning circuit.

  3. Implementing neural architectures using analog VLSI circuits

    Science.gov (United States)

    Maher, Mary Ann C.; Deweerth, Stephen P.; Mahowald, Misha A.; Mead, Carver A.

    1989-05-01

    Analog very large-scale integrated (VLSI) technology can be used not only to study and simulate biological systems, but also to emulate them in designing artificial sensory systems. A methodology for building these systems in CMOS VLSI technology has been developed using analog micropower circuit elements that can be hierarchically combined. Using this methodology, experimental VLSI chips of visual and motor subsystems have been designed and fabricated. These chips exhibit behavior similar to that of biological systems, and perform computations useful for artificial sensory systems.

  4. Wavelet neural network based fault diagnosis in nonlinear analog circuits

    Institute of Scientific and Technical Information of China (English)

    Yin Shirong; Chen Guangju; Xie Yongle

    2006-01-01

    The theories of diagnosing nonlinear analog circuits by means of the transient response testing are studied. Wavelet analysis is made to extract the transient response signature of nonlinear circuits and compress the signature dada. The best wavelet function is selected based on the between-category total scatter of signature. The fault dictionary of nonlinear circuits is constructed based on improved back-propagation(BP) neural network. Experimental results demonstrate that the method proposed has high diagnostic sensitivity and fast fault identification and deducibility.

  5. A Neural Network Appraoch to Fault Diagnosis in Analog Circuits

    Institute of Scientific and Technical Information of China (English)

    尉乃红; 杨士元; 等

    1996-01-01

    Thia paper presents a neural network based fault diagnosis approach for analog circuits,taking the tolerances of circuit elements into account.Specifically,a normalization rule of input information,a pseudo-fault domain border(PFDB)pattern selection method and a new output error function are proposed for training the backpropagation(BP) network to be a fault diagnoser.Experimental results demonstrate that the diagnoser performs as well as or better than any classical approaches in terms of accuracy,and provides at least an order-of-magnitude improvement in post-fault diagnostic speed.

  6. Gray Code ADC Based on an Analog Neural Circuit

    Directory of Open Access Journals (Sweden)

    L. Michaeli

    1995-04-01

    Full Text Available In this paper a new neural ADC design is presented, which is based on the idea to replace all functional components needed in the ADC block scheme by a simple connection of neurons. Transformation of ADC functional scheme into an analog neural structure and its computer simulation is one of the main results of this paper. Furthermore, a discrete component prototype of the proposed A/D converter is discussed and experimental results are also given.

  7. Feature evaluation and extraction based on neural network in analog circuit fault diagnosis

    Institute of Scientific and Technical Information of China (English)

    Yuan Haiying; Chen Guangju; Xie Yongle

    2007-01-01

    Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit.The feature evaluation and extraction methods based on neural network are presented.Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently.The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency.A fault diagnosis illustration validated this method.

  8. Analog and VLSI circuits

    CERN Document Server

    Chen, Wai-Kai

    2009-01-01

    Featuring hundreds of illustrations and references, this book provides the information on analog and VLSI circuits. It focuses on analog integrated circuits, presenting the knowledge on monolithic device models, analog circuit cells, high performance analog circuits, RF communication circuits, and PLL circuits.

  9. Current-mode subthreshold MOS circuits for analog VLSI neural systems

    Science.gov (United States)

    Andreou, Andreas G.; Boahen, Kwabena A.; Pouliquen, Philippe O.; Pavasovic, Aleksandra; Jenkins, Robert E.

    1991-03-01

    An overview of the current-mode approach for designing analog VLSI neural systems in subthreshold CMOS technology is presented. Emphasis is given to design techniques at the device level using the current-controlled current conveyor and the translinear principle. Circuits for associative memory and silicon retina systems are used as examples. The design methodology and how it relates to actual biological microcircuits are discussed.

  10. Current-mode subthreshold MOS circuits for analog VLSI neural systems.

    Science.gov (United States)

    Andreou, A G; Boahen, K A; Pouliquen, P O; Pavasovic, A; Jenkins, R E; Strohbehn, K

    1991-01-01

    An overview of the current-mode approach for designing analog VLSI neural systems in subthreshold CMOS technology is presented. Emphasis is given to design techniques at the device level using the current-controlled current conveyor and the translinear principle. Circuits for associative memory and silicon retina systems are used as examples. The design methodology and how it relates to actual biological microcircuits are discussed.

  11. Intuitive analog circuit design

    CERN Document Server

    Thompson, Marc

    2013-01-01

    Intuitive Analog Circuit Design outlines ways of thinking about analog circuits and systems that let you develop a feel for what a good, working analog circuit design should be. This book reflects author Marc Thompson's 30 years of experience designing analog and power electronics circuits and teaching graduate-level analog circuit design, and is the ideal reference for anyone who needs a straightforward introduction to the subject. In this book, Dr. Thompson describes intuitive and ""back-of-the-envelope"" techniques for designing and analyzing analog circuits, including transistor amplifi

  12. Analog circuit design

    CERN Document Server

    Dobkin, Bob

    2012-01-01

    Analog circuit and system design today is more essential than ever before. With the growth of digital systems, wireless communications, complex industrial and automotive systems, designers are being challenged to develop sophisticated analog solutions. This comprehensive source book of circuit design solutions aids engineers with elegant and practical design techniques that focus on common analog challenges. The book's in-depth application examples provide insight into circuit design and application solutions that you can apply in today's demanding designs. <

  13. Electrical Circuits and Water Analogies

    Science.gov (United States)

    Smith, Frederick A.; Wilson, Jerry D.

    1974-01-01

    Briefly describes water analogies for electrical circuits and presents plans for the construction of apparatus to demonstrate these analogies. Demonstrations include series circuits, parallel circuits, and capacitors. (GS)

  14. Analog circuits cookbook

    CERN Document Server

    Hickman, Ian

    2013-01-01

    Analog Circuits Cookbook presents articles about advanced circuit techniques, components and concepts, useful IC for analog signal processing in the audio range, direct digital synthesis, and ingenious video op-amp. The book also includes articles about amplitude measurements on RF signals, linear optical imager, power supplies and devices, and RF circuits and techniques. Professionals and students of electrical engineering will find the book informative and useful.

  15. Hierarchical Neural Networks Method for Fault Diagnosis of Large-Scale Analog Circuits

    Institute of Scientific and Technical Information of China (English)

    TAN Yanghong; HE Yigang; FANG Gefeng

    2007-01-01

    A novel hierarchical neural networks (HNNs) method for fault diagnosis of large-scale circuits is proposed. The presented techniques using neural networks(NNs) approaches require a large amount of computation for simulating various faulty component possibilities. For large scale circuits, the number of possible faults, and hence the simulations, grow rapidly and become tedious and sometimes even impractical. Some NNs are distributed to the torn sub-blocks according to the proposed torn principles of large scale circuits. And the NNs are trained in batches by different patterns in the light of the presented rules of various patterns when the DC, AC and transient responses of the circuit are available. The method is characterized by decreasing the over-lapped feasible domains of responses of circuits with tolerance and leads to better performance and higher correct classification. The methodology is illustrated by means of diagnosis examples.

  16. Troubleshooting analog circuits

    CERN Document Server

    Pease, Robert A

    1991-01-01

    Troubleshooting Analog Circuits is a guidebook for solving product or process related problems in analog circuits. The book also provides advice in selecting equipment, preventing problems, and general tips. The coverage of the book includes the philosophy of troubleshooting; the modes of failure of various components; and preventive measures. The text also deals with the active components of analog circuits, including diodes and rectifiers, optically coupled devices, solar cells, and batteries. The book will be of great use to both students and practitioners of electronics engineering. Other

  17. Analog integrated circuits for the Lotka-Volterra competitive neural networks.

    Science.gov (United States)

    Asai, T; Ohtani, M; Yonezu, H

    1999-01-01

    A subthreshold MOS integrated circuit (IC) is designed and fabricated for implementing a competitive neural network of the Lotka-Volterra (LV) type which is derived from conventional membrane dynamics of neurons and is used for the selection of external inputs. The steady-state solutions to the LV equation can be classified into three types, each of which represents qualitatively different selection behavior. Among the solutions, the winners-share-all (WSA) solution in which a certain number of neurons remain activated in steady states is particularly useful owing to robustness in the selection of inputs from a noisy environment. The measured results of the fabricated LV IC's agree well with the theoretical prediction as long as the influence of device mismatches is small. Furthermore, results of extensive circuit simulations prove that the large-scale LV circuit producing the WSA solution does exhibit a reliable selection compared with winner-take-all circuits, in the possible presence of device mismatches.

  18. Feedback in analog circuits

    CERN Document Server

    Ochoa, Agustin

    2016-01-01

    This book describes a consistent and direct methodology to the analysis and design of analog circuits with particular application to circuits containing feedback. The analysis and design of circuits containing feedback is generally presented by either following a series of examples where each circuit is simplified through the use of insight or experience (someone else’s), or a complete nodal-matrix analysis generating lots of algebra. Neither of these approaches leads to gaining insight into the design process easily. The author develops a systematic approach to circuit analysis, the Driving Point Impedance and Signal Flow Graphs (DPI/SFG) method that does not require a-priori insight to the circuit being considered and results in factored analysis supporting the design function. This approach enables designers to account fully for loading and the bi-directional nature of elements both in the feedback path and in the amplifier itself, properties many times assumed negligible and ignored. Feedback circuits a...

  19. ESD analog circuits and design

    CERN Document Server

    Voldman, Steven H

    2014-01-01

    A comprehensive and in-depth review of analog circuit layout, schematic architecture, device, power network and ESD design This book will provide a balanced overview of analog circuit design layout, analog circuit schematic development, architecture of chips, and ESD design.  It will start at an introductory level and will bring the reader right up to the state-of-the-art. Two critical design aspects for analog and power integrated circuits are combined. The first design aspect covers analog circuit design techniques to achieve the desired circuit performance. The second and main aspect pres

  20. A Hardware-Implementation-Friendly Pulse-Coupled Neural Network Algorithm for Analog Image-Feature-Generation Circuits

    Science.gov (United States)

    Chen, Jun; Shibata, Tadashi

    2007-04-01

    Pulse-coupled neural networks (PCNNs) are biologically inspired algorithms that have been shown to be highly effective for image feature generation. However, conventional PCNNs are software-oriented algorithms that are too complicated to implement as very-large-scale integration (VLSI) hardware. To employ PCNNs in image-feature-generation VLSIs, a hardware-implementation-friendly PCNN is proposed here. By introducing the concepts of exponentially decaying output and a one-branch dendritic tree, the new PCNN eliminates the large number of convolution operators and floating-point multipliers in conventional PCNNs without compromising its performance at image feature generation. As an analog VLSI implementation of the new PCNN, an image-feature-generation circuit is proposed. By employing floating-gate metal-oxide-semiconductor (MOS) technology, the circuit achieves a full voltage-mode implementation of the PCNN in a compact structure. Inheriting the merits of the PCNN, the circuit is capable of generating rotation-independent and translation-independent features for input patterns, which has been verified by SPICE simulation.

  1. Analog circuit design designing waveform processing circuits

    CERN Document Server

    Feucht, Dennis

    2010-01-01

    The fourth volume in the set Designing Waveform-Processing Circuits builds on the previous 3 volumes and presents a variety of analog non-amplifier circuits, including voltage references, current sources, filters, hysteresis switches and oscilloscope trigger and sweep circuitry, function generation, absolute-value circuits, and peak detectors.

  2. Optimal neural computations require analog processors

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V.

    1998-12-31

    This paper discusses some of the limitations of hardware implementations of neural networks. The authors start by presenting neural structures and their biological inspirations, while mentioning the simplifications leading to artificial neural networks. Further, the focus will be on hardware imposed constraints. They will present recent results for three different alternatives of parallel implementations of neural networks: digital circuits, threshold gate circuits, and analog circuits. The area and the delay will be related to the neurons` fan-in and to the precision of their synaptic weights. The main conclusion is that hardware-efficient solutions require analog computations, and suggests the following two alternatives: (i) cope with the limitations imposed by silicon, by speeding up the computation of the elementary silicon neurons; (2) investigate solutions which would allow the use of the third dimension (e.g. using optical interconnections).

  3. Diagnosis Method for Analog Circuit Hard fault and Soft Fault

    Directory of Open Access Journals (Sweden)

    Baoru Han

    2013-09-01

    Full Text Available Because the traditional BP neural network slow convergence speed, easily falling in local minimum and the learning process will appear oscillation phenomena. This paper introduces a tolerance analog circuit hard fault and soft fault diagnosis method based on adaptive learning rate and the additional momentum algorithm BP neural network. Firstly, tolerance analog circuit is simulated by OrCAD / Pspice circuit simulation software, accurately extracts fault waveform data by matlab program automatically. Secondly, using the adaptive learning rate and momentum BP algorithm to train neural network, and then applies it to analog circuit hard fault and soft fault diagnosis. With shorter training time, high precision and global convergence effectively reduces the misjudgment, missing, it can improve the accuracy of fault diagnosis and fast.  

  4. Analog circuit design art, science, and personalities

    CERN Document Server

    Williams, Jim

    1991-01-01

    Analog Circuit Design: Art, Science, and Personalities discusses the many approaches and styles in the practice of analog circuit design. The book is written in an informal yet informative manner, making it easily understandable to those new in the field. The selection covers the definition, history, current practice, and future direction of analog design; the practice proper; and the styles in analog circuit design. The book also includes the problems usually encountered in analog circuit design; approach to feedback loop design; and other different techniques and applications. The text is

  5. Integrated Circuits for Analog Signal Processing

    CERN Document Server

    2013-01-01

      This book presents theory, design methods and novel applications for integrated circuits for analog signal processing.  The discussion covers a wide variety of active devices, active elements and amplifiers, working in voltage mode, current mode and mixed mode.  This includes voltage operational amplifiers, current operational amplifiers, operational transconductance amplifiers, operational transresistance amplifiers, current conveyors, current differencing transconductance amplifiers, etc.  Design methods and challenges posed by nanometer technology are discussed and applications described, including signal amplification, filtering, data acquisition systems such as neural recording, sensor conditioning such as biomedical implants, actuator conditioning, noise generators, oscillators, mixers, etc.   Presents analysis and synthesis methods to generate all circuit topologies from which the designer can select the best one for the desired application; Includes design guidelines for active devices/elements...

  6. 秀丽线虫接触感知神经网络的电路实现%Analog circuit implementation and application of neural network for touch sensitivity in Caenorhabditis elegans

    Institute of Scientific and Technical Information of China (English)

    申蛟隆; 陈焕文; 刘泽文

    2014-01-01

    To overcome bad real time effect and weak parallel processing ability of neural network simulated by software,using analog circuit to implement a neural network for touch sensitivity in Caenorhabditis elegans was proposed,and the nematode’s withdrawal behaviour was reproduced with analog circuit at the same time.All parameters included in the neural network imple-mented by analog circuit were converted from the parameters acquired by using the real-coded genetic algorithm to train the neu-ral network for touch sensitivity in Caenorhabditis elegans.The analog circuit was simulated by Hspice.The results of circuit simulated by Hspice were consistent with the numerical results obtained from the neural network model,which showed the vali-dity and the correctness of analog circuit.%为克服神经网络软件仿真实时性差、并行处理能力弱等缺点,提出了采用电路的方法实现秀丽线虫的接触感知神经网络,模拟秀丽线虫的回撤行为。其中所有参数由实数编码遗传算法训练秀丽线虫接触感知神经网络模型所得参数转化而来。通过Hspice仿真器进行仿真,Hspice仿真结果和秀丽线虫接触感知神经网络模型的数值仿真结果相符,验证了该电路的有效性和正确性。

  7. Analog circuit design designing dynamic circuit response

    CERN Document Server

    Feucht, Dennis

    2010-01-01

    This second volume, Designing Dynamic Circuit Response builds upon the first volume Designing Amplifier Circuits by extending coverage to include reactances and their time- and frequency-related behavioral consequences.

  8. Analog circuit design art, science and personalities

    CERN Document Server

    Williams, Jim

    1991-01-01

    This book is far more than just another tutorial or reference guide - it's a tour through the world of analog design, combining theory and applications with the philosophies behind the design process. Readers will learn how leading analog circuit designers approach problems and how they think about solutions to those problems. They'll also learn about the `analog way' - a broad, flexible method of thinking about analog design tasks.A comprehensive and useful guide to analog theory and applications. Covers visualizing the operation of analog circuits. Looks at how to rap

  9. A radial basis function neurocomputer implemented with analog VLSI circuits

    Science.gov (United States)

    Watkins, Steven S.; Chau, Paul M.; Tawel, Raoul

    1992-01-01

    An electronic neurocomputer which implements a radial basis function neural network (RBFNN) is described. The RBFNN is a network that utilizes a radial basis function as the transfer function. The key advantages of RBFNNs over existing neural network architectures include reduced learning time and the ease of VLSI implementation. This neurocomputer is based on an analog/digital hybrid design and has been constructed with both custom analog VLSI circuits and a commercially available digital signal processor. The hybrid architecture is selected because it offers high computational performance while compensating for analog inaccuracies, and it features the ability to model large problems.

  10. Analysis of Recurrent Analog Neural Networks

    Directory of Open Access Journals (Sweden)

    Z. Raida

    1998-06-01

    Full Text Available In this paper, an original rigorous analysis of recurrent analog neural networks, which are built from opamp neurons, is presented. The analysis, which comes from the approximate model of the operational amplifier, reveals causes of possible non-stable states and enables to determine convergence properties of the network. Results of the analysis are discussed in order to enable development of original robust and fast analog networks. In the analysis, the special attention is turned to the examination of the influence of real circuit elements and of the statistical parameters of processed signals to the parameters of the network.

  11. Selective Manipulation of Neural Circuits.

    Science.gov (United States)

    Park, Hong Geun; Carmel, Jason B

    2016-04-01

    Unraveling the complex network of neural circuits that form the nervous system demands tools that can manipulate specific circuits. The recent evolution of genetic tools to target neural circuits allows an unprecedented precision in elucidating their function. Here we describe two general approaches for achieving circuit specificity. The first uses the genetic identity of a cell, such as a transcription factor unique to a circuit, to drive expression of a molecule that can manipulate cell function. The second uses the spatial connectivity of a circuit to achieve specificity: one genetic element is introduced at the origin of a circuit and the other at its termination. When the two genetic elements combine within a neuron, they can alter its function. These two general approaches can be combined to allow manipulation of neurons with a specific genetic identity by introducing a regulatory gene into the origin or termination of the circuit. We consider the advantages and disadvantages of both these general approaches with regard to specificity and efficacy of the manipulations. We also review the genetic techniques that allow gain- and loss-of-function within specific neural circuits. These approaches introduce light-sensitive channels (optogenetic) or drug sensitive channels (chemogenetic) into neurons that form specific circuits. We compare these tools with others developed for circuit-specific manipulation and describe the advantages of each. Finally, we discuss how these tools might be applied for identification of the neural circuits that mediate behavior and for repair of neural connections.

  12. Analog baseband circuits for sensor systems

    OpenAIRE

    2008-01-01

    This thesis is composed of six publications and an overview of the research topic, which also summarizes the work. The research presented in this thesis focuses on research into analog baseband circuits for sensor systems. The research is divided into three different topics: the integration of analog baseband circuits into a radio receiver for sensor applications; the integration of an ΔΣ modulator A/D converter into a GSM/WCDMA radio receiver for mobile phones, and the integration of algorit...

  13. Advances in Analog Circuit Design 2015

    CERN Document Server

    Baschirotto, Andrea; Harpe, Pieter

    2016-01-01

    This book is based on the 18 tutorials presented during the 24th workshop on Advances in Analog Circuit Design. Expert designers present readers with information about a variety of topics at the frontier of analog circuit design, including low-power and energy-efficient analog electronics, with specific contributions focusing on the design of efficient sensor interfaces and low-power RF systems. This book serves as a valuable reference to the state-of-the-art, for anyone involved in analog circuit research and development. ·         Provides a state-of-the-art reference in analog circuit design, written by experts from industry and academia; ·         Presents material in a tutorial-based format; ·         Includes coverage of high-performance analog-to-digital and digital to analog converters, integrated circuit design in scaled technologies, and time-domain signal processing.

  14. Design of analog circuits through symbolic analysis

    CERN Document Server

    Fakhfakh, Mourad; V Fernández, Francisco

    2012-01-01

    Symbolic analyzers have the potential to offer knowledge to sophomores as well as practitioners of analog circuit design. Actually, they are an essential complement to numerical simulators, since they provide insight into circuit behavior which numerical analyzers do not provide. Symbolic analysis of electronic circuits addresses the generation of symbolic expressions for the parameters that describe the performance of linear and nonlinear circuits in three domains: DC, AC and time; some or all the circuit parameters can be kept as symbols. Due to the fact that these expressions remain va

  15. CMOS circuits for analog signal processing

    NARCIS (Netherlands)

    Wallinga, Hans

    1988-01-01

    Design choices in CMOS analog signal processing circuits are presented. Special attention is focussed on continuous-time filter technologies. The basics of MOSFET-C continuous-time filters and CMOS Square Law Circuits are explained at the hand of a graphical MOST characteristics representation.

  16. Analog neural network for support vector machine learning.

    Science.gov (United States)

    Perfetti, Renzo; Ricci, Elisa

    2006-07-01

    An analog neural network for support vector machine learning is proposed, based on a partially dual formulation of the quadratic programming problem. It results in a simpler circuit implementation with respect to existing neural solutions for the same application. The effectiveness of the proposed network is shown through some computer simulations concerning benchmark problems.

  17. Analog approach to mixed analog-digital circuit simulation

    Science.gov (United States)

    Ogrodzki, Jan

    2013-10-01

    Logic simulation of digital circuits is a well explored research area. Most up-to-date CAD tools for digital circuits simulation use an event driven, selective trace algorithm and Hardware Description Languages (HDL), e.g. the VHDL. This techniques enable simulation of mixed circuits, as well, where an analog part is connected to the digital one through D/A and A/D converters. The event-driven mixed simulation applies a unified, digital-circuits dedicated method to both digital and analog subsystems. In recent years HDL techniques have been also applied to mixed domains, as e.g. in the VHDL-AMS. This paper presents an approach dual to the event-driven one, where an analog part together with a digital one and with converters is treated as the analog subsystem and is simulated by means of circuit simulation techniques. In our problem an analog solver used yields some numerical problems caused by nonlinearities of digital elements. Efficient methods for overriding these difficulties have been proposed.

  18. Analog circuit design for communication SOC

    CERN Document Server

    Tu, Steve Hung-Lung

    2012-01-01

    This e-book provides several state-of-the-art analog circuit design techniques. It presents both empirical and theoretical materials for system-on-a-chip (SOC) circuit design. Fundamental communication concepts are used to explain a variety of topics including data conversion (ADC, DAC, S-? oversampling data converters), clock data recovery, phase-locked loops for system timing synthesis, supply voltage regulation, power amplifier design, and mixer design. This is an excellent reference book for both circuit designers and researchers who are interested in the field of design of analog communic

  19. DESIGN AND ANALOG VLSI IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK

    OpenAIRE

    2011-01-01

    Nature has evolved highly advanced systems capable of performing complex computations, adoption and learning using analog computations. Furthermore nature has evolved techniques to deal with imprecise analog computations by using redundancy and massive connectivity. In this paper we are making use of Artificial Neural Network to demonstrate the way in which the biological system processes in analog domain. We are using 180nm CMOS VLSI technology for implementing circuits which ...

  20. Analog Computation by DNA Strand Displacement Circuits.

    Science.gov (United States)

    Song, Tianqi; Garg, Sudhanshu; Mokhtar, Reem; Bui, Hieu; Reif, John

    2016-08-19

    DNA circuits have been widely used to develop biological computing devices because of their high programmability and versatility. Here, we propose an architecture for the systematic construction of DNA circuits for analog computation based on DNA strand displacement. The elementary gates in our architecture include addition, subtraction, and multiplication gates. The input and output of these gates are analog, which means that they are directly represented by the concentrations of the input and output DNA strands, respectively, without requiring a threshold for converting to Boolean signals. We provide detailed domain designs and kinetic simulations of the gates to demonstrate their expected performance. On the basis of these gates, we describe how DNA circuits to compute polynomial functions of inputs can be built. Using Taylor Series and Newton Iteration methods, functions beyond the scope of polynomials can also be computed by DNA circuits built upon our architecture.

  1. Neural Circuits on a Chip

    Directory of Open Access Journals (Sweden)

    Md. Fayad Hasan

    2016-09-01

    Full Text Available Neural circuits are responsible for the brain’s ability to process and store information. Reductionist approaches to understanding the brain include isolation of individual neurons for detailed characterization. When maintained in vitro for several days or weeks, dissociated neurons self-assemble into randomly connected networks that produce synchronized activity and are capable of learning. This review focuses on efforts to control neuronal connectivity in vitro and construct living neural circuits of increasing complexity and precision. Microfabrication-based methods have been developed to guide network self-assembly, accomplishing control over in vitro circuit size and connectivity. The ability to control neural connectivity and synchronized activity led to the implementation of logic functions using living neurons. Techniques to construct and control three-dimensional circuits have also been established. Advances in multiple electrode arrays as well as genetically encoded, optical activity sensors and transducers enabled highly specific interfaces to circuits composed of thousands of neurons. Further advances in on-chip neural circuits may lead to better understanding of the brain.

  2. Associative Pattern Recognition In Analog VLSI Circuits

    Science.gov (United States)

    Tawel, Raoul

    1995-01-01

    Winner-take-all circuit selects best-match stored pattern. Prototype cascadable very-large-scale integrated (VLSI) circuit chips built and tested to demonstrate concept of electronic associative pattern recognition. Based on low-power, sub-threshold analog complementary oxide/semiconductor (CMOS) VLSI circuitry, each chip can store 128 sets (vectors) of 16 analog values (vector components), vectors representing known patterns as diverse as spectra, histograms, graphs, or brightnesses of pixels in images. Chips exploit parallel nature of vector quantization architecture to implement highly parallel processing in relatively simple computational cells. Through collective action, cells classify input pattern in fraction of microsecond while consuming power of few microwatts.

  3. ENHANCING THE SYMBOLIC ANALYSIS OF ANALOG CIRCUITS

    Directory of Open Access Journals (Sweden)

    E. Tlelo-Cuautle

    2005-08-01

    Full Text Available A new symbollc-method is introduced to enhance the calculation of symbolic expressions of analog circults. First, the analog circuit is transformed to a nullor equivalent circuit. Second, a new method is introduced to the formulation of a compact system of equations (CSES. Third, a new method is introduced to the solution of theCSES, by avoiding multiplications by zero to Improve the evaluation of determlnants. Flnally, two eXamples are given to show the usefulness of the proposed methods to calculate fully symbolic transfer functions.

  4. On analog implementations of discrete neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V.; Moore, K.R.

    1998-12-01

    The paper will show that in order to obtain minimum size neural networks (i.e., size-optimal) for implementing any Boolean function, the nonlinear activation function of the neutrons has to be the identity function. The authors shall shortly present many results dealing with the approximation capabilities of neural networks, and detail several bounds on the size of threshold gate circuits. Based on a constructive solution for Kolmogorov`s superpositions they will show that implementing Boolean functions can be done using neurons having an identity nonlinear function. It follows that size-optimal solutions can be obtained only using analog circuitry. Conclusions, and several comments on the required precision are ending the paper.

  5. Quantum Electric Circuits Analogous to Ballistic Conductors

    OpenAIRE

    2007-01-01

    The conductance steps in a constricted two-dimensional electron gas and the minimum conductivity in graphene are related to a new uncertainty relation between electric charge and conductance in a quantized electric circuit that mimics the electric transport in mesoscopic systems. This uncertainty relation makes specific use of the discreteness of electric charge. Quantum electric circuits analogous to both constricted two-dimensional electron gas and graphene are introduced. In the latter cas...

  6. Synthetic Biology: A Unifying View and Review Using Analog Circuits.

    Science.gov (United States)

    Teo, Jonathan J Y; Woo, Sung Sik; Sarpeshkar, Rahul

    2015-08-01

    We review the field of synthetic biology from an analog circuits and analog computation perspective, focusing on circuits that have been built in living cells. This perspective is well suited to pictorially, symbolically, and quantitatively representing the nonlinear, dynamic, and stochastic (noisy) ordinary and partial differential equations that rigorously describe the molecular circuits of synthetic biology. This perspective enables us to construct a canonical analog circuit schematic that helps unify and review the operation of many fundamental circuits that have been built in synthetic biology at the DNA, RNA, protein, and small-molecule levels over nearly two decades. We review 17 circuits in the literature as particular examples of feedforward and feedback analog circuits that arise from special topological cases of the canonical analog circuit schematic. Digital circuit operation of these circuits represents a special case of saturated analog circuit behavior and is automatically incorporated as well. Many issues that have prevented synthetic biology from scaling are naturally represented in analog circuit schematics. Furthermore, the deep similarity between the Boltzmann thermodynamic equations that describe noisy electronic current flow in subthreshold transistors and noisy molecular flux in biochemical reactions has helped map analog circuit motifs in electronics to analog circuit motifs in cells and vice versa via a `cytomorphic' approach. Thus, a body of knowledge in analog electronic circuit design, analysis, simulation, and implementation may also be useful in the robust and efficient design of molecular circuits in synthetic biology, helping it to scale to more complex circuits in the future.

  7. Document analysis with neural net circuits

    Science.gov (United States)

    Graf, Hans Peter

    1994-01-01

    Document analysis is one of the main applications of machine vision today and offers great opportunities for neural net circuits. Despite more and more data processing with computers, the number of paper documents is still increasing rapidly. A fast translation of data from paper into electronic format is needed almost everywhere, and when done manually, this is a time consuming process. Markets range from small scanners for personal use to high-volume document analysis systems, such as address readers for the postal service or check processing systems for banks. A major concern with present systems is the accuracy of the automatic interpretation. Today's algorithms fail miserably when noise is present, when print quality is poor, or when the layout is complex. A common approach to circumvent these problems is to restrict the variations of the documents handled by a system. In our laboratory, we had the best luck with circuits implementing basic functions, such as convolutions, that can be used in many different algorithms. To illustrate the flexibility of this approach, three applications of the NET32K circuit are described in this short viewgraph presentation: locating address blocks, cleaning document images by removing noise, and locating areas of interest in personal checks to improve image compression. Several of the ideas realized in this circuit that were inspired by neural nets, such as analog computation with a low resolution, resulted in a chip that is well suited for real-world document analysis applications and that compares favorably with alternative, 'conventional' circuits.

  8. Analog circuit for controlling acoustic transducer arrays

    Energy Technology Data Exchange (ETDEWEB)

    Drumheller, Douglas S. (Cedar Crest, NM)

    1991-01-01

    A simplified ananlog circuit is presented for controlling electromechanical transducer pairs in an acoustic telemetry system. The analog circuit of this invention comprises a single electrical resistor which replaces all of the digital components in a known digital circuit. In accordance with this invention, a first transducer in a transducer pair of array is driven in series with the resistor. The voltage drop across this resistor is then amplified and used to drive the second transducer. The voltage drop across the resistor is proportional and in phase with the current to the transducer. This current is approximately 90 degrees out of phase with the driving voltage to the transducer. This phase shift replaces the digital delay required by the digital control circuit of the prior art.

  9. Analog implementation of pulse-coupled neural networks.

    Science.gov (United States)

    Ota, Y; Wilamowski, B M

    1999-01-01

    This paper presents a compact architecture for analog CMOS hardware implementation of voltage-mode pulse-coupled neural networks (PCNN's). The hardware implementation methods shows inherent fault tolerance specialties and high speed, which is usually more than an order of magnitude over the software counterpart. A computational style described in this article mimics a biological neural network using pulse-stream signaling and analog summation and multiplication. Pulse-stream encoding technique uses pulse streams to carry information and control analog circuitry, while storing further analog information on the time axis. The main feature of the proposed neuron circuit is that the structure is compact, yet exhibiting all the basic properties of natural biological neurons. Functional and structural forms of neural and synaptic functions are presented along with simulation results. Finally, the proposed design is applied to image processing to demonstrate successful restoration of images and their features.

  10. Digital and analog gene circuits for biotechnology.

    Science.gov (United States)

    Roquet, Nathaniel; Lu, Timothy K

    2014-05-01

    Biotechnology offers the promise of valuable chemical production via microbial processing of renewable and inexpensive substrates. Thus far, static metabolic engineering strategies have enabled this field to advance industrial applications. However, the industrial scaling of statically engineered microbes inevitably creates inefficiencies due to variable conditions present in large-scale microbial cultures. Synthetic gene circuits that dynamically sense and regulate different molecules can resolve this issue by enabling cells to continuously adapt to variable conditions. These circuits also have the potential to enable next-generation production programs capable of autonomous transitioning between steps in a bioprocess. Here, we review the design and application of two main classes of dynamic gene circuits, digital and analog, for biotechnology. Within the context of these classes, we also discuss the potential benefits of digital-analog interconversion, memory, and multi-signal integration. Though synthetic gene circuits have largely been applied for cellular computation to date, we envision that utilizing them in biotechnology will enhance the efficiency and scope of biochemical production with living cells.

  11. Method of analog circuit fault diagnosis based on FOA-neural network%基于果蝇-构造小波神经网络模拟电路诊断方法

    Institute of Scientific and Technical Information of China (English)

    于文新; 何怡刚; 吴先明; 高坤

    2015-01-01

    利用果蝇算法优化构造小波神经网络,建立FOA-构造小波神经网络模型,并将模型应用于模拟电路故障分析当中,通过仿真试验可发现该方法在故障诊断中有较高的准确性。%In the paper, FOA and wavelet-neural network are applied to establish a FOA-structure wavelet neural network algorithm. The model is applied to an analog circuit fault analysis by simulation. The method has higher accuracy in fault diagnosis.

  12. Analog circuit design techniques at 0.5V

    CERN Document Server

    Chatterjee, Shouri; Stanic, Nebojša

    2010-01-01

    This book tackles challenges for the design of analog integrated circuits that operate from ultra-low power supply voltages (down to 0.5V). Coverage demonstrates the signal processing circuit and circuit biasing approaches through the design of operational transconductance amplifiers (OTAs). These amplifiers are then used to build analog system functions including continuous time filter and a sample and hold amplifier.

  13. Design and Analog VLSI Implementation of Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Prof. Bapuray.D.Yammenavar

    2011-07-01

    Full Text Available Nature has evolved highly advanced systems capable of performing complex computations, adoption and learning using analog computations. Furthermore nature has evolved techniques to deal with imprecise analog computations by using redundancy and massive connectivity. In this paper we are making use of Artificial Neural Network to demonstrate the way in which the biological system processes in analog domain. We are using 180nm CMOS VLSI technology for implementing circuits which performs arithmetic operations and for implementing Neural Network. The arithmetic circuits presented here are based on MOS transistors operating in subthreshold region. The basic blocks of artificial neuron are multiplier, adder and neuron activation function. The functionality of designed neural network is verified for analog operations like signal amplification and frequency multiplication. The network designed can be adopted for digital operations like AND, OR and NOT. The network realizes its functionality for the trained targets which is verified using simulation results. The schematic, Layout design and verification of proposed Neural Network is carried out using Cadence Virtuoso tool.

  14. Four-gate transistor analog multiplier circuit

    Science.gov (United States)

    Mojarradi, Mohammad M. (Inventor); Blalock, Benjamin (Inventor); Cristoloveanu, Sorin (Inventor); Chen, Suheng (Inventor); Akarvardar, Kerem (Inventor)

    2011-01-01

    A differential output analog multiplier circuit utilizing four G.sup.4-FETs, each source connected to a current source. The four G.sup.4-FETs may be grouped into two pairs of two G.sup.4-FETs each, where one pair has its drains connected to a load, and the other par has its drains connected to another load. The differential output voltage is taken at the two loads. In one embodiment, for each G.sup.4-FET, the first and second junction gates are each connected together, where a first input voltage is applied to the front gates of each pair, and a second input voltage is applied to the first junction gates of each pair. Other embodiments are described and claimed.

  15. Efficient Analog Circuits for Boolean Satisfiability

    CERN Document Server

    Yin, Xunzhao; Varga, Melinda; Ercsey-Ravasz, Maria; Toroczkai, Zoltan; Hu, Xiaobo Sharon

    2016-01-01

    Efficient solutions to NP-complete problems would significantly benefit both science and industry. However, such problems are intractable on digital computers based on the von Neumann architecture, thus creating the need for alternative solutions to tackle such problems. Recently, a deterministic, continuous-time dynamical system (CTDS) was proposed (Nature Physics, 7(12), 966 (2011)) to solve a representative NP-complete problem, Boolean Satisfiability (SAT). This solver shows polynomial analog time-complexity on even the hardest benchmark $k$-SAT ($k \\geq 3$) formulas, but at an energy cost through exponentially driven auxiliary variables. With some modifications to the CTDS equations, here we present a novel analog hardware SAT solver, AC-SAT, implementing the CTDS. AC-SAT is intended to be used as a co-processor, and with its modular design can be readily extended to different problem sizes. The circuit is designed and simulated based on a 32nm CMOS technology. SPICE simulation results show speedup factor...

  16. 23rd workshop on Advances in Analog Circuit Design

    CERN Document Server

    Baschirotto, Andrea; Makinwa, Kofi

    2015-01-01

    This book is based on the 18 tutorials presented during the 23rd workshop on Advances in Analog Circuit Design.  Expert designers present readers with information about a variety of topics at the frontier of analog circuit design, serving as a valuable reference to the state-of-the-art, for anyone involved in analog circuit research and development.    • Includes coverage of high-performance analog-to-digital and digital to analog converters, integrated circuit design in scaled technologies, and time-domain signal processing; • Provides a state-of-the-art reference in analog circuit design, written by experts from industry and academia; • Presents material in a tutorial-based format.

  17. 25th workshop on Advances in Analog Circuit Design

    CERN Document Server

    Harpe, Pieter; Makinwa, Kofi

    2017-01-01

    This book is based on the 18 tutorials presented during the 25th workshop on Advances in Analog Circuit Design. Expert designers present readers with information about a variety of topics at the frontier of analog circuit design, including low-power and energy-efficient analog electronics, with specific contributions focusing on the design of continuous-time sigma-delta modulators, automotive electronics, and power management. This book serves as a valuable reference to the state-of-the-art, for anyone involved in analog circuit research and development.

  18. Energy Harvesting Using an Analog Circuit under Multimodal Vibration

    Directory of Open Access Journals (Sweden)

    Shigeru Shimose

    2013-01-01

    Full Text Available The efficiency of harvesting energy from a vibrating structure using a piezoelectric transducer and a simple analog circuit is investigated experimentally. This analog circuit was originally invented for a synchronized switch damping on inductor (SSDI technique, which enhances the damping of mechanical vibration. In this study, the circuit is used to implement a synchronized switch harvesting on inductor (SSHI technique. A multiple degree of freedom (MDOF structure is excited by single sinusoidal forces at its resonant frequencies and by random forces. The piezoelectric transducer converts this mechanical energy into electrical energy which is harvested using a standard rectifier bridge circuit with and without our analog circuit. Experimental results show that our analog circuit makes it possible to harvest twice as much energy under both single sinusoidal and random vibration excitations.

  19. Configurable analog-digital conversion using the neural engineering framework.

    Science.gov (United States)

    Mayr, Christian G; Partzsch, Johannes; Noack, Marko; Schüffny, Rene

    2014-01-01

    Efficient Analog-Digital Converters (ADC) are one of the mainstays of mixed-signal integrated circuit design. Besides the conventional ADCs used in mainstream ICs, there have been various attempts in the past to utilize neuromorphic networks to accomplish an efficient crossing between analog and digital domains, i.e., to build neurally inspired ADCs. Generally, these have suffered from the same problems as conventional ADCs, that is they require high-precision, handcrafted analog circuits and are thus not technology portable. In this paper, we present an ADC based on the Neural Engineering Framework (NEF). It carries out a large fraction of the overall ADC process in the digital domain, i.e., it is easily portable across technologies. The analog-digital conversion takes full advantage of the high degree of parallelism inherent in neuromorphic networks, making for a very scalable ADC. In addition, it has a number of features not commonly found in conventional ADCs, such as a runtime reconfigurability of the ADC sampling rate, resolution and transfer characteristic.

  20. DESIGN AND ANALOG VLSI IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    D.Yammenavar

    2011-08-01

    Full Text Available Nature has evolved highly advanced systems capable of performing complex computations, adoption andlearning using analog computations. Furthermore nature has evolved techniques to deal with impreciseanalog computations by using redundancy and massive connectivity. In this paper we are making use ofArtificial Neural Network to demonstrate the way in which the biological system processes in analogdomain.We are using 180nm CMOS VLSI technology for implementing circuits which performs arithmeticoperations and for implementing Neural Network. The arithmetic circuits presented here are based onMOS transistors operating in subthreshold region. The basic blocks of artificial neuron are multiplier,adder and neuron activation function.The functionality of designed neural network is verified for analog operations like signal amplificationand frequency multiplication. The network designed can be adopted for digital operations like AND, ORand NOT. The network realizes its functionality for the trained targets which is verified using simulationresults. The schematic, Layout design and verification of proposed Neural Network is carried out usingCadence Virtuoso tool.

  1. 22nd Workshop on Advances in Analog Circuit Design

    CERN Document Server

    Makinwa, Kofi; Harpe, Pieter

    2014-01-01

    This book is based on the 18 tutorials presented during the 22nd workshop on Advances in Analog Circuit Design.  Expert designers present readers with information about a variety of topics at the frontier of analog circuit design, including frequency reference, power management for systems-on-chip, and smart wireless interfaces.  This book serves as a valuable reference to the state-of-the-art, for anyone involved in analog circuit research and development.    ·         Provides a state-of-the-art reference in analog circuit design, written by experts from industry and academia; ·         Presents material in a tutorial-based format; ·         Includes coverage of frequency reference, power management for systems-on-chip, and smart wireless interfaces.

  2. Implementing size-optimal discrete neural networks require analog circuitry

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V.

    1998-12-01

    This paper starts by overviewing results dealing with the approximation capabilities of neural networks, as well as bounds on the size of threshold gate circuits. Based on a constructive solution for Kolmogorov`s superpositions the authors show that implementing Boolean functions can be done using neurons having an identity transfer function. Because in this case the size of the network is minimized, it follows that size-optimal solutions for implementing Boolean functions can be obtained using analog circuitry. Conclusions and several comments on the required precision are ending the paper.

  3. SEAS: A simulated evolution approach for analog circuit synthesis

    NARCIS (Netherlands)

    Ning, Zhen-Qiu; Mouthaan, Ton; Wallinga, Hans

    1991-01-01

    The authors present a simulated evolution approach for analog circuit synthesis based on an analogy with the natural selection process in biological environments and on the iterative improvements in solving engineering problems. A prototype framework based on this idea, called SEAS, has been impleme

  4. Synthetic analog and digital circuits for cellular computation and memory

    OpenAIRE

    Purcell, Oliver; Lu, Timothy K.

    2014-01-01

    Biological computation is a major area of focus in synthetic biology because it has the potential to enable a wide range of applications. Synthetic biologists have applied engineering concepts to biological systems in order to construct progressively more complex gene circuits capable of processing information in living cells. Here, we review the current state of computational genetic circuits and describe artificial gene circuits that perform digital and analog computation. We then discuss r...

  5. Analog circuit design a tutorial guide to applications and solutions

    CERN Document Server

    Williams, Jim

    2011-01-01

    * Covers the fundamentals of linear/analog circuit and system design to guide engineers with their design challenges. * Based on the Application Notes of Linear Technology, the foremost designer of high performance analog products, readers will gain practical insights into design techniques and practice. * Broad range of topics, including power management tutorials, switching regulator design, linear regulator design, data conversion, signal conditioning, and high frequency/RF design. * Contributors include the leading lights in analog design, Robert Dobkin, Jim Willia

  6. CMOS DESIGN OF A MULTI_INPUT ANALOG MULTIPLIER AND DIVIDER CIRCUIT

    OpenAIRE

    2014-01-01

    This paper proposes a CMOS current-mode multi_input analog multiplier and divider circuit based on a new method. Exponential and logarithmic functions are employed to realize the circuit which is used in neural network and fuzzy integrated systems. The major advantages of this multiplier are ability of having multi_input signals, and low Total Harmonic Distortion (THD). The circuit is designed and simulated using MATLAB software and HSPICE simulator by level 49 parameters (BSIM3v3) in 0.35μm ...

  7. Analog integrated circuits design for processing physiological signals.

    Science.gov (United States)

    Li, Yan; Poon, Carmen C Y; Zhang, Yuan-Ting

    2010-01-01

    Analog integrated circuits (ICs) designed for processing physiological signals are important building blocks of wearable and implantable medical devices used for health monitoring or restoring lost body functions. Due to the nature of physiological signals and the corresponding application scenarios, the ICs designed for these applications should have low power consumption, low cutoff frequency, and low input-referred noise. In this paper, techniques for designing the analog front-end circuits with these three characteristics will be reviewed, including subthreshold circuits, bulk-driven MOSFETs, floating gate MOSFETs, and log-domain circuits to reduce power consumption; methods for designing fully integrated low cutoff frequency circuits; as well as chopper stabilization (CHS) and other techniques that can be used to achieve a high signal-to-noise performance. Novel applications using these techniques will also be discussed.

  8. Application of Extension Neural Network Type-1 to Fault Diagnosis of Electronic Circuits

    Directory of Open Access Journals (Sweden)

    Meng-Hui Wang

    2012-01-01

    Full Text Available The values of electronic components are always deviated, but the functions of the modern circuits are more and more precise, which makes the automatic fault diagnosis of analog circuits very complex and difficult. This paper presents an extension-neural-network-type-1-(ENN-1- based method for fault diagnosis of analog circuits. This proposed method combines the extension theory and neural networks to create a novel neural network. Using the matter-element models of fault types and a correlation function, can be calculated the correlation degree between the tested pattern and every fault type; then, the cause of the circuit malfunction can be directly diagnosed by the analysis of the correlation degree. The experimental results show that the proposed method has a high diagnostic accuracy and is more fault tolerant than the multilayer neural network (MNN and the k-means based methods.

  9. Semaphorin signaling in vertebrate neural circuit assembly

    Directory of Open Access Journals (Sweden)

    Yutaka eYoshida

    2012-06-01

    Full Text Available Neural circuit formation requires the coordination of many complex developmental processes. First, neurons project axons over long distances to find their final targets and then establish appropriate connectivity essential for the formation of neuronal circuitry. Growth cones, the leading edges of axons, navigate by interacting with a variety of attractive and repulsive axon guidance cues along their trajectories and at final target regions. In addition to guidance of axons, neuronal polarization, neuronal migration and dendrite development must be precisely regulated during development to establish proper neural circuitry. Semaphorins consist of a large protein family, which includes secreted and cell surface proteins, and they play important roles in many steps of neural circuit formation. The major semaphorin receptors are plexins and neuropilins, however other receptors and co-receptors also mediate signaling by semaphorins. Upon semaphorin binding to their receptors, downstream signaling molecules transduce this event within cells to mediate further events, including alteration of microtubule and actin cytoskeletal dynamics. Here, I review recent studies on semaphorin signaling in vertebrate neural circuit assembly, with the goal of highlighting how this diverse family of cues and receptors imparts exquisite specificity to neural complex connectivity.

  10. On automatic synthesis of analog/digital circuits

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V.

    1998-12-31

    The paper builds on a recent explicit numerical algorithm for Kolmogorov`s superpositions, and will show that in order to synthesize minimum size (i.e., size-optimal) circuits for implementing any Boolean function, the nonlinear activation function of the gates has to be the identity function. Because classical and--or implementations, as well as threshold gate implementations require exponential size, it follows that size-optimal solutions for implementing arbitrary Boolean functions can be obtained using analog (or mixed analog/digital) circuits. Conclusions and several comments are ending the paper.

  11. Accurate and Precise Computation Using Analog VLSI, with Applications to Computer Graphics and Neural Networks.

    Science.gov (United States)

    Kirk, David Blair

    This thesis develops an engineering practice and design methodology to enable us to use CMOS analog VLSI chips to perform more accurate and precise computation. These techniques form the basis of an approach that permits us to build computer graphics and neural network applications using analog VLSI. The nature of the design methodology focuses on defining goals for circuit behavior to be met as part of the design process. To increase the accuracy of analog computation, we develop techniques for creating compensated circuit building blocks, where compensation implies the cancellation of device variations, offsets, and nonlinearities. These compensated building blocks can be used as components in larger and more complex circuits, which can then also be compensated. To this end, we develop techniques for automatically determining appropriate parameters for circuits, using constrained optimization. We also fabricate circuits that implement multi-dimensional gradient estimation for a gradient descent optimization technique. The parameter-setting and optimization tools allow us to automatically choose values for compensating our circuit building blocks, based on our goals for the circuit performance. We can also use the techniques to optimize parameters for larger systems, applying the goal-based techniques hierarchically. We also describe a set of thought experiments involving circuit techniques for increasing the precision of analog computation. Our engineering design methodology is a step toward easier use of analog VLSI to solve problems in computer graphics and neural networks. We provide data measured from compensated multipliers built using these design techniques. To demonstrate the feasibility of using analog VLSI for more quantitative computation, we develop small applications using the goal-based design approach and compensated components. Finally, we conclude by discussing the expected significance of this work for the wider use of analog VLSI for

  12. Electronic Circuit Analog of Synthetic Genetic Networks: Revisited

    CERN Document Server

    Hellen, Edward H

    2016-01-01

    Electronic circuits are useful tools for studying potential dynamical behaviors of synthetic genetic networks. The circuit models are complementary to numerical simulations of the networks, especially providing a framework for verification of dynamical behaviors in the presence of intrinsic and extrinsic noise of the electrical systems. Here we present an improved version of our previous design of an electronic analog of genetic networks that includes the 3-gene Repressilator and we show conversions between model parameters and real circuit component values to mimic the numerical results in experiments. Important features of the circuit design include the incorporation of chemical kinetics representing Hill function inhibition, quorum sensing coupling, and additive noise. Especially, we make a circuit design for a systematic change of initial conditions in experiment, which is critically important for studies of dynamical systems' behavior, particularly, when it shows multistability. This improved electronic ...

  13. Contextual behavior and neural circuits

    Directory of Open Access Journals (Sweden)

    Inah eLee

    2013-05-01

    Full Text Available Animals including humans engage in goal-directed behavior flexibly in response to items and their background, which is called contextual behavior in this review. Although the concept of context has long been studied, there are differences among researchers in defining and experimenting with the concept. The current review aims to provide a categorical framework within which not only the neural mechanisms of contextual information processing but also the contextual behavior can be studied in more concrete ways. For this purpose, we categorize contextual behavior into three subcategories as follows by considering the types of interactions among context, item, and response: contextual response selection, contextual item selection, and contextual item-response selection. Contextual response selection refers to the animal emitting different types of responses to the same item depending on the context in the background. Contextual item selection occurs when there are multiple items that need to be chosen in a contextual manner. Finally, when multiple items and multiple contexts are involved, contextual item-response selection takes place whereby the animal either choose an item or inhibit such a response depending on item-context paired association. The literature suggests that the rhinal cortical regions and the hippocampal formation play key roles in mnemonically categorizing and recognizing contextual representations and the associated items. In addition, it appears that the fronto-striatal cortical loops in connection with the contextual information-processing areas critically control the flexible deployment of adaptive action sets and motor responses for maximizing goals. We suggest that contextual information processing should be investigated in experimental settings where contextual stimuli and resulting behaviors are clearly defined and measurable, considering the dynamic top-down and bottom-up interactions among the neural systems for

  14. Contextual behavior and neural circuits

    Science.gov (United States)

    Lee, Inah; Lee, Choong-Hee

    2013-01-01

    Animals including humans engage in goal-directed behavior flexibly in response to items and their background, which is called contextual behavior in this review. Although the concept of context has long been studied, there are differences among researchers in defining and experimenting with the concept. The current review aims to provide a categorical framework within which not only the neural mechanisms of contextual information processing but also the contextual behavior can be studied in more concrete ways. For this purpose, we categorize contextual behavior into three subcategories as follows by considering the types of interactions among context, item, and response: contextual response selection, contextual item selection, and contextual item–response selection. Contextual response selection refers to the animal emitting different types of responses to the same item depending on the context in the background. Contextual item selection occurs when there are multiple items that need to be chosen in a contextual manner. Finally, when multiple items and multiple contexts are involved, contextual item–response selection takes place whereby the animal either chooses an item or inhibits such a response depending on item–context paired association. The literature suggests that the rhinal cortical regions and the hippocampal formation play key roles in mnemonically categorizing and recognizing contextual representations and the associated items. In addition, it appears that the fronto-striatal cortical loops in connection with the contextual information-processing areas critically control the flexible deployment of adaptive action sets and motor responses for maximizing goals. We suggest that contextual information processing should be investigated in experimental settings where contextual stimuli and resulting behaviors are clearly defined and measurable, considering the dynamic top-down and bottom-up interactions among the neural systems for contextual behavior

  15. Analog Circuits in Ultra-Deep-Submicron CMOS

    NARCIS (Netherlands)

    Annema, Anne-Johan; Nauta, Bram; Langevelde, van Ronald; Tuinhout, Hans

    2005-01-01

    Modern and future ultra-deep-submicron (UDSM) technologies introduce several new problems in analog design. Nonlinear output conductance in combination with reduced voltage gain pose limits in linearity of (feedback) circuits. Gate-leakage mismatch exceeds conventional matching tolerances. Increasin

  16. Grobner Bases for Nonlinear DAE Systems of Analog Circuits

    Directory of Open Access Journals (Sweden)

    Silke J. Spang

    2008-04-01

    Full Text Available Systems of differential equations play an important role in modelling and analysis of many complex systems e.g. in electronics and mechanics. The following article is concerned with a symbolic analysis approach for reduction of the differential index of nonlinear differential algebraic equation (DAE systems, which occur in the modelling and simulation of analog circuits.

  17. An Analog Multilayer Perceptron Neural Network for a Portable Electronic Nose

    Directory of Open Access Journals (Sweden)

    Chih-Heng Pan

    2012-12-01

    Full Text Available This study examines an analog circuit comprising a multilayer perceptron neural network (MLPNN. This study proposes a low-power and small-area analog MLP circuit to implement in an E-nose as a classifier, such that the E-nose would be relatively small, power-efficient, and portable. The analog MLP circuit had only four input neurons, four hidden neurons, and one output neuron. The circuit was designed and fabricated using a 0.18 μm standard CMOS process with a 1.8 V supply. The power consumption was 0.553 mW, and the area was approximately 1.36 × 1.36 mm2. The chip measurements showed that this MLPNN successfully identified the fruit odors of bananas, lemons, and lychees with 91.7% accuracy.

  18. Application of Op-amp Fixators in Analog Circuits

    Directory of Open Access Journals (Sweden)

    R. Rohith Krishnan

    2016-10-01

    Full Text Available Nullor elements have applications not only in analog behaviour modeling but also in analog circuit design and analysis. Fixator- orator pair, the emerging tool in analog design is a combination of a nullor and sources. A method for the realization of fixator- orator pair is discussed in this paper. Application of fixator-norator pair into a circuit makes it possible to perform the AC and DC designs in a linear like way. Fixator fixes a critical biasing spec at the design, whereas the pairing norator finds the value of power conducting components or DC sources that meets the design. A scaling amplifier design, an active load design and a CMOS differential amplifier design are provided as examples to demonstrate the procedure and the methodology.

  19. Analog Circuit Design Optimization Based on Evolutionary Algorithms

    Directory of Open Access Journals (Sweden)

    Mansour Barari

    2014-01-01

    Full Text Available This paper investigates an evolutionary-based designing system for automated sizing of analog integrated circuits (ICs. Two evolutionary algorithms, genetic algorithm and PSO (Parswal particle swarm optimization algorithm, are proposed to design analog ICs with practical user-defined specifications. On the basis of the combination of HSPICE and MATLAB, the system links circuit performances, evaluated through specific electrical simulation, to the optimization system in the MATLAB environment, for the selected topology. The system has been tested by typical and hard-to-design cases, such as complex analog blocks with stringent design requirements. The results show that the design specifications are closely met. Comparisons with available methods like genetic algorithms show that the proposed algorithm offers important advantages in terms of optimization quality and robustness. Moreover, the algorithm is shown to be efficient.

  20. Wireless Neural Recording With Single Low-Power Integrated Circuit

    Science.gov (United States)

    Harrison, Reid R.; Kier, Ryan J.; Chestek, Cynthia A.; Gilja, Vikash; Nuyujukian, Paul; Ryu, Stephen; Greger, Bradley; Solzbacher, Florian; Shenoy, Krishna V.

    2010-01-01

    We present benchtop and in vivo experimental results from an integrated circuit designed for wireless implantable neural recording applications. The chip, which was fabricated in a commercially available 0.6-μm 2P3M BiCMOS process, contains 100 amplifiers, a 10-bit analog-to-digital converter (ADC), 100 threshold-based spike detectors, and a 902–928 MHz frequency-shift-keying (FSK) transmitter. Neural signals from a selected amplifier are sampled by the ADC at 15.7 kSps and telemetered over the FSK wireless data link. Power, clock, and command signals are sent to the chip wirelessly over a 2.765-MHz inductive (coil-to-coil) link. The chip is capable of operating with only two off-chip components: a power/command receiving coil and a 100-nF capacitor. PMID:19497825

  1. Wireless neural recording with single low-power integrated circuit.

    Science.gov (United States)

    Harrison, Reid R; Kier, Ryan J; Chestek, Cynthia A; Gilja, Vikash; Nuyujukian, Paul; Ryu, Stephen; Greger, Bradley; Solzbacher, Florian; Shenoy, Krishna V

    2009-08-01

    We present benchtop and in vivo experimental results from an integrated circuit designed for wireless implantable neural recording applications. The chip, which was fabricated in a commercially available 0.6- mum 2P3M BiCMOS process, contains 100 amplifiers, a 10-bit analog-to-digital converter (ADC), 100 threshold-based spike detectors, and a 902-928 MHz frequency-shift-keying (FSK) transmitter. Neural signals from a selected amplifier are sampled by the ADC at 15.7 kSps and telemetered over the FSK wireless data link. Power, clock, and command signals are sent to the chip wirelessly over a 2.765-MHz inductive (coil-to-coil) link. The chip is capable of operating with only two off-chip components: a power/command receiving coil and a 100-nF capacitor.

  2. Electronic circuit analog of synthetic genetic networks: Revisited

    Science.gov (United States)

    Hellen, Edward H.; Kurths, Jürgen; Dana, Syamal K.

    2017-06-01

    Electronic circuits are useful tools for studying potential dynamical behaviors of synthetic genetic networks. The circuit models are complementary to numerical simulations of the networks, especially providing a framework for verification of dynamical behaviors in the presence of intrinsic and extrinsic noise of the electrical systems. Here we present an improved version of our previous design of an electronic analog of genetic networks that includes the 3-gene Repressilator and we show conversions between model parameters and real circuit component values to mimic the numerical results in experiments. Important features of the circuit design include the incorporation of chemical kinetics representing Hill function inhibition, quorum sensing coupling, and additive noise. Especially, we make a circuit design for a systematic change of initial conditions in experiment, which is critically important for studies of dynamical systems' behavior, particularly, when it shows multistability. This improved electronic analog of the synthetic genetic network allows us to extend our investigations from an isolated Repressilator to coupled Repressilators and to reveal the dynamical behavior's complexity.

  3. Electronic circuits modeling using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Andrejević Miona V.

    2003-01-01

    Full Text Available In this paper artificial neural networks (ANN are applied to modeling of electronic circuits. ANNs are used for application of the black-box modeling concept in the time domain. Modeling process is described, so the topology of the ANN, the testing signal used for excitation, together with the complexity of ANN are considered. The procedure is first exemplified in modeling of resistive circuits. MOS transistor, as a four-terminal device, is modeled. Then nonlinear negative resistive characteristic is modeled in order to be used as a piece-wise linear resistor in Chua's circuit. Examples of modeling nonlinear dynamic circuits are given encompassing a variety of modeling problems. A nonlinear circuit containing quartz oscillator is considered for modeling. Verification of the concept is performed by verifying the ability of the model to generalize i.e. to create acceptable responses to excitations not used during training. Implementation of these models within a behavioral simulator is exemplified. Every model is implemented in realistic surrounding in order to show its interaction, and of course, its usage and purpose.

  4. Sequences of gluing bifurcations in an analog electronic circuit

    Energy Technology Data Exchange (ETDEWEB)

    Akhtanov, Sayat N.; Zhanabaev, Zeinulla Zh. [Physico-Technical Department, Al Farabi Kazakh National University, Al Farabi Av. 71, Almaty, 050038 Kazakhstan (Kazakhstan); Zaks, Michael A., E-mail: zaks@math.hu-berlin.de [Institute of Mathematics, Humboldt University, Rudower Chaussee 25, D-12489 Berlin (Germany)

    2013-10-01

    We report on the experimental investigation of gluing bifurcations in the analog electronic circuit which models a dynamical system of the third order: Lorenz equations with an additional quadratic nonlinearity. Variation of one of the resistances in the circuit changes the coefficient at this nonlinearity and replaces the Lorenz route to chaos by a different scenario which leads, through the sequence of homoclinic bifurcations, from periodic oscillations of the voltage to the irregular ones. Every single bifurcation “glues” in the phase space two stable periodic orbits and creates a new one, with the doubled length: a sequence of such bifurcations results in the birth of the chaotic attractor.

  5. A Multi-Gigahertz Analog Transient Recorder Integrated Circuit

    CERN Document Server

    Kleinfelder, Stuart A

    2015-01-01

    A monolithic multi-channel analog transient recorder, implemented using switched capacitor sample-and-hold circuits and a high-speed analogically-adjustable delay-line-based write clock, has been designed, fabricated and tested. The 2.1 by 6.9 mm layout, in 1.2 micron CMOS, includes over 31,000 transistors and 2048 double polysilicon capacitors. The circuit contains four parallel channels, each with a 512 deep switched-capacitor sample-and-hold system. A 512 deep edge sensitive tapped active delay line uses look-ahead and 16 way interleaving to develop the 512 sample and hold clocks, each as little as 3.2 ns wide and 200 ps apart. Measurements of the device have demonstrated 5 GHz maximum sample rate, at least 350 MHz bandwidth, an extrapolated rms aperture uncertainty per sample of 0.7 ps, and a signal to rms noise ratio of 2000:1.

  6. Dynamical systems, attractors, and neural circuits.

    Science.gov (United States)

    Miller, Paul

    2016-01-01

    Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic-they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.

  7. A Powerful Optimization Tool for Analog Integrated Circuits Design

    Directory of Open Access Journals (Sweden)

    M. Kubar

    2013-09-01

    Full Text Available This paper presents a new optimization tool for analog circuit design. Proposed tool is based on the robust version of the differential evolution optimization method. Corners of technology, temperature, voltage and current supplies are taken into account during the optimization. That ensures robust resulting circuits. Those circuits usually do not need any schematic change and are ready for the layout.. The newly developed tool is implemented directly to the Cadence design environment to achieve very short setup time of the optimization task. The design automation procedure was enhanced by optimization watchdog feature. It was created to control optimization progress and moreover to reduce the search space to produce better design in shorter time. The optimization algorithm presented in this paper was successfully tested on several design examples.

  8. An analog integrated signal processing circuit for on-chip diffusion-based gas analysis

    Science.gov (United States)

    Sadeghi, Hesam; Ghafarinia, Vahid

    2013-07-01

    In diffusion-based gas analysis, the transient of gas diffusion process is recorded by a generic gas sensor to serve as a fingerprint for qualitative and quantitative analysis of gaseous samples. Following the acquisition of these specific signals, any standalone gas analyzer requires a pattern recognition system for pattern classification. The classic digital pattern recognition methods require computing hardware of adequate computational throughput. In this paper, we have followed a straightforward mathematical procedure to relate the signals to their associated target gases. We have shown that the procedure can be implemented by a set of analog functions. Based on the results, we have designed an analog integrated circuit, in 0.18 µm standard CMOS process, for processing the diffusion-based transient signals. The main circuit components are a low-pass filter, the differentiator, the feature extractor and an artificial neural network. The output of the circuit is a 2-bit binary code that specifies the target gas. The circuit successfully classified four alcoholic vapors by processing the experimentally obtained response patterns. The proposed signal processing circuit, the semiconductor gas sensor and the diffusion channel can all be implemented on a single substrate to fabricate an integrated micro gas analyzer.

  9. Review: “Implementation of Feedforward and Feedback Neural Network for Signal Processing Using Analog VLSI Technology”

    Directory of Open Access Journals (Sweden)

    Miss. Rachana R. Patil

    2015-01-01

    Full Text Available Main focus of project is on implementation of Neural Network Architecture (NNA with on chip learning on Analog VLSI Technology for signal processing application. In the proposed paper the analog components like Gilbert Cell Multiplier (GCM, Neuron Activation Function (NAF are used to implement artificial NNA. Analog components used comprises of multiplier, adder and tan sigmoidal function circuit using MOS transistor. This Neural Architecture is trained using Back Propagation (BP Algorithm in analog domain with new techniques of weight storage. Layout design and verification of above design is carried out using VLSI Backend Microwind 3.1 software Tool. The technology used to design layout is 32 nm CMOS Technology

  10. Neural circuit mechanisms of posttraumatic epilepsy

    Directory of Open Access Journals (Sweden)

    Robert F Hunt

    2013-06-01

    Full Text Available Traumatic brain injury (TBI greatly increases the risk for a number of mental health problems and is one of the most common causes of medically intractable epilepsy in humans. Several models of TBI have been developed to investigate the relationship between trauma, seizures, and epilepsy-related changes in neural circuit function. These studies have shown that the brain initiates immediate neuronal and glial responses following an injury, usually leading to significant cell loss in areas of the injured brain. Over time, long-term changes in the organization of neural circuits, particularly in neocortex and hippocampus, lead to an imbalance between excitatory and inhibitory neurotransmission and increased risk for spontaneous seizures. These include alterations to inhibitory interneurons and formation of new, excessive recurrent excitatory synaptic connectivity. Here, we review in vivo models of TBI as well as key cellular mechanisms of synaptic reorganization associated with posttraumatic epilepsy. The potential role of inflammation and increased blood brain barrier permeability in the pathophysiology of posttraumatic epilepsy is also discussed. A better understanding of mechanisms that promote the generation of epileptic activity versus those that promote compensatory brain repair and functional recovery should aid development of successful new therapies for posttraumatic epilepsy.

  11. A neural circuit architecture for angular integration in Drosophila.

    Science.gov (United States)

    Green, Jonathan; Adachi, Atsuko; Shah, Kunal K; Hirokawa, Jonathan D; Magani, Pablo S; Maimon, Gaby

    2017-06-01

    Many animals keep track of their angular heading over time while navigating through their environment. However, a neural-circuit architecture for computing heading has not been experimentally defined in any species. Here we describe a set of clockwise- and anticlockwise-shifting neurons in the Drosophila central complex whose wiring and physiology provide a means to rotate an angular heading estimate based on the fly's angular velocity. We show that each class of shifting neurons exists in two subtypes, with spatiotemporal activity profiles that suggest different roles for each subtype at the start and end of tethered-walking turns. Shifting neurons are required for the heading system to properly track the fly's heading in the dark, and stimulation of these neurons induces predictable shifts in the heading signal. The central features of this biological circuit are analogous to those of computational models proposed for head-direction cells in rodents and may shed light on how neural systems, in general, perform integration.

  12. IIP framework: A tool for reuse-centric analog circuit design

    OpenAIRE

    2016-01-01

    Current design of analog integrated circuits is still a time-consuming manual process resulting in static analog blocks which can hardly be reused. In order to address this problem, a new framework to ease reuse-centric bottom-up design of analog integrated circuits is introduced. Our IIP Framework (IIP: Intelligent Intellectual Property) enables the development of highly technology-independent analog circuit generators applicable in multiple design environments. IIP Generators are parameteri...

  13. Design techniques for low-voltage analog integrated circuits

    Science.gov (United States)

    Rakús, Matej; Stopjaková, Viera; Arbet, Daniel

    2017-08-01

    In this paper, a review and analysis of different design techniques for (ultra) low-voltage integrated circuits (IC) are performed. This analysis shows that the most suitable design methods for low-voltage analog IC design in a standard CMOS process include techniques using bulk-driven MOS transistors, dynamic threshold MOS transistors and MOS transistors operating in weak or moderate inversion regions. The main advantage of such techniques is that there is no need for any modification of standard CMOS structure or process. Basic circuit building blocks like differential amplifiers or current mirrors designed using these approaches are able to operate with the power supply voltage of 600 mV (or even lower), which is the key feature towards integrated systems for modern portable applications.

  14. Analysis and application of analog electronic circuits to biomedical instrumentation

    CERN Document Server

    Northrop, Robert B

    2003-01-01

    This book introduces the basic mathematical tools used to describe noise and its propagation through linear systems and provides a basic description of the improvement of signal-to-noise ratio by signal averaging and linear filtering. The text also demonstrates how op amps are the keystone of modern analog signal conditioning systems design, and illustrates their use in isolation and instrumentation amplifiers, active filters, and numerous biomedical instrumentation systems and subsystems. It examines the properties of the ideal op amp and applies this model to the analysis of various circuits

  15. SEMICONDUCTOR INTEGRATED CIRCUITS: A reconfigurable analog baseband circuit for WLAN, WCDMA, and Bluetooth

    Science.gov (United States)

    Tao, Tong; Baoyong, Chi; Ziqiang, Wang; Ying, Zhang; Hanjun, Jiang; Zhihua, Wang

    2010-05-01

    A reconfigurable analog baseband circuit for WLAN, WCDMA, and Bluetooth in 0.35 μm CMOS is presented. The circuit consists of two variable gain amplifiers (VGA) in cascade and a Gm-C elliptic low-pass filter (LPF). The filter-order and the cut-off frequency of the LPF can be reconfigured to satisfy the requirements of various applications. In order to achieve the optimum power consumption, the bandwidth of the VGAs can also be dynamically reconfigured and some Gm cells can be cut off in the given application. Simulation results show that the analog baseband circuit consumes 16.8 mW for WLAN, 8.9 mW for WCDMA and only 6.5 mW for Bluetooth, all with a 3 V power supply. The analog baseband circuit could provide -10 to +40 dB variable gain, third-order low pass filtering with 1 MHz cut-off frequency for Bluetooth, fourth-order low pass filtering with 2.2 MHz cut-off frequency for WCDMA, and fifth-order low pass filtering with 11 MHz cut-off frequency for WLAN, respectively.

  16. Methodology for the digital calibration of analog circuits and systems with case studies

    CERN Document Server

    Pastre, Marc

    2006-01-01

    Methodology for the Digital Calibration of Analog Circuits and Systems shows how to relax the extreme design constraints in analog circuits, allowing the realization of high-precision systems even with low-performance components. A complete methodology is proposed, and three applications are detailed. To start with, an in-depth analysis of existing compensation techniques for analog circuit imperfections is carried out. The M/2+M sub-binary digital-to-analog converter is thoroughly studied, and the use of this very low-area circuit in conjunction with a successive approximations algorithm for digital compensation is described. A complete methodology based on this compensation circuit and algorithm is then proposed. The detection and correction of analog circuit imperfections is studied, and a simulation tool allowing the transparent simulation of analog circuits with automatic compensation blocks is introduced. The first application shows how the sub-binary M/2+M structure can be employed as a conventional di...

  17. Neural recording front-end IC using action potential detection and analog buffer with digital delay for data compression.

    Science.gov (United States)

    Liu, Lei; Yao, Lei; Zou, Xiaodan; Goh, Wang Ling; Je, Minkyu

    2013-01-01

    This paper presents a neural recording analog front-end IC intended for simultaneous neural recording with action potential (AP) detection for data compression in wireless multichannel neural implants. The proposed neural recording front-end IC detects the neural spikes and sends only the preserved AP information for wireless transmission in order to reduce the overall power consumption of the neural implant. The IC consists of a low-noise neural amplifier, an AP detection circuit and an analog buffer with digital delay. The neural amplifier makes use of a current-reuse technique to maximize the transconductance efficiency for attaining a good noise efficiency factor. The AP detection circuit uses an adaptive threshold voltage to generate an enable signal for the subsequent functional blocks. The analog buffer with digital delay is employed using a finite impulse response (FIR) filter which preserves the AP waveform before the enable signal as well as provides low-pass filtering. The neural recording front-end IC has been designed using standard CMOS 0.18-µm technology occupying a core area of 220 µm by 820 µm.

  18. Nano-scale CMOS analog circuits models and CAD techniques for high-level design

    CERN Document Server

    Pandit, Soumya; Patra, Amit

    2014-01-01

    Reliability concerns and the limitations of process technology can sometimes restrict the innovation process involved in designing nano-scale analog circuits. The success of nano-scale analog circuit design requires repeat experimentation, correct analysis of the device physics, process technology, and adequate use of the knowledge database.Starting with the basics, Nano-Scale CMOS Analog Circuits: Models and CAD Techniques for High-Level Design introduces the essential fundamental concepts for designing analog circuits with optimal performances. This book explains the links between the physic

  19. Challenges of VDD scaling for analog circuits: an amplifier

    Science.gov (United States)

    Bargagli-Stoffi, A.; Sauerbrey, J.; Wang, J.; Schmitt-Landsiedel, D.

    2005-05-01

    With the shrinking of the device dimensions, the power supply voltage value is continuously decreasing. Since the threshold voltage value does not decrease as much as the power supply and the drain source saturation voltage becomes an important fraction of the power supply, many amplifier architectures are no more suitable for modern processes. A transconductance amplifier based on current mirrors is analyzed highlighting the main challenges of a low-voltage analog design. Among the many proposed amplifier architectures, a topology based on current mirrors has been chosen as the most promising to operate with low voltages. Simulations with 90nm CMOS prove the feasibility of circuit operation with satisfactory performance at an operating power supply voltage as low as 0.6V.

  20. A simple structure wavelet transform circuit employing function link neural networks and SI filters

    Science.gov (United States)

    Mu, Li; Yigang, He

    2016-12-01

    Signal processing by means of analog circuits offers advantages from a power consumption viewpoint. Implementing wavelet transform (WT) using analog circuits is of great interest when low-power consumption becomes an important issue. In this article, a novel simple structure WT circuit in analog domain is presented by employing functional link neural network (FLNN) and switched-current (SI) filters. First, the wavelet base is approximated using FLNN algorithms for giving a filter transfer function that is suitable for simple structure WT circuit implementation. Next, the WT circuit is constructed with the wavelet filter bank, whose impulse response is the approximated wavelet and its dilations. The filter design that follows is based on a follow-the-leader feedback (FLF) structure with multiple output bilinear SI integrators and current mirrors as the main building blocks. SI filter is well suited for this application since the dilation constant across different scales of the transform can be precisely implemented and controlled by the clock frequency of the circuit with the same system architecture. Finally, to illustrate the design procedure, a seventh-order FLNN-approximated Gaussian wavelet is implemented as an example. Simulations have successfully verified that the designed simple structure WT circuit has low sensitivity, low-power consumption and litter effect to the imperfections.

  1. Larger bases and mixed analog/digital neural nets

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V.

    1998-12-31

    The paper overviews results dealing with the approximation capabilities of neural networks, and bounds on the size of threshold gate circuits. Based on an explicit numerical algorithm for Kolmogorov`s superpositions the authors show that minimum size neural networks--for implementing any Boolean function--have the identity function as the activation function. Conclusions and several comments on the required precision are ending the paper.

  2. A neural circuit for angular velocity computation

    Directory of Open Access Journals (Sweden)

    Samuel B Snider

    2010-12-01

    Full Text Available In one of the most remarkable feats of motor control in the animal world, some Diptera, such as the housefly, can accurately execute corrective flight maneuvers in tens of milliseconds. These reflexive movements are achieved by the halteres, gyroscopic force sensors, in conjunction with rapidly-tunable wing-steering muscles. Specifically, the mechanosensory campaniform sensilla located at the base of the halteres transduce and transform rotation-induced gyroscopic forces into information about the angular velocity of the fly's body. But how exactly does the fly's neural architecture generate the angular velocity from the lateral strain forces on the left and right halteres? To explore potential algorithms, we built a neuro-mechanical model of the rotation detection circuit. We propose a neurobiologically plausible method by which the fly could accurately separate and measure the three-dimensional components of an imposed angular velocity. Our model assumes a single sign-inverting synapse and formally resembles some models of directional selectivity by the retina. Using multidimensional error analysis, we demonstrate the robustness of our model under a variety of input conditions. Our analysis reveals the maximum information available to the fly given its physical architecture and the mathematics governing the rotation-induced forces at the haltere's end knob.

  3. Implantable neurotechnologies: a review of integrated circuit neural amplifiers.

    Science.gov (United States)

    Ng, Kian Ann; Greenwald, Elliot; Xu, Yong Ping; Thakor, Nitish V

    2016-01-01

    Neural signal recording is critical in modern day neuroscience research and emerging neural prosthesis programs. Neural recording requires the use of precise, low-noise amplifier systems to acquire and condition the weak neural signals that are transduced through electrode interfaces. Neural amplifiers and amplifier-based systems are available commercially or can be designed in-house and fabricated using integrated circuit (IC) technologies, resulting in very large-scale integration or application-specific integrated circuit solutions. IC-based neural amplifiers are now used to acquire untethered/portable neural recordings, as they meet the requirements of a miniaturized form factor, light weight and low power consumption. Furthermore, such miniaturized and low-power IC neural amplifiers are now being used in emerging implantable neural prosthesis technologies. This review focuses on neural amplifier-based devices and is presented in two interrelated parts. First, neural signal recording is reviewed, and practical challenges are highlighted. Current amplifier designs with increased functionality and performance and without penalties in chip size and power are featured. Second, applications of IC-based neural amplifiers in basic science experiments (e.g., cortical studies using animal models), neural prostheses (e.g., brain/nerve machine interfaces) and treatment of neuronal diseases (e.g., DBS for treatment of epilepsy) are highlighted. The review concludes with future outlooks of this technology and important challenges with regard to neural signal amplification.

  4. High-precision analog circuit technology for power supply integrated circuits; Dengen IC yo koseido anarogu kairo gijutsu

    Energy Technology Data Exchange (ETDEWEB)

    Nakamori, A.; Suzuki, T.; Mizoe, K. [Fuji Electric Corporate Research and Development,Ltd., Kanagawa (Japan)

    2000-08-10

    With the recent rapid spread of portable electronic appliances, specification requirements such as compact power supply and long operation with batteries have become severer. Power supply ICs (integrated circuits) are required to reduce power consumption in the circuit and perform high-precision control. To meet these requirements, Fuji Electric develops high-precision CMOS (complementary metal-oxide semiconductor) analog technology. This paper describes three analog circuit technologies of a voltage reference, an operational amplifier and a comparator as circuit components particularly important for the precision of power supply ICs. (author)

  5. Configurable Analog-Digital Conversion Using the Neural EngineeringFramework

    Directory of Open Access Journals (Sweden)

    Christian G Mayr

    2014-07-01

    Full Text Available Efficient Analog-Digital Converters (ADC are one of the mainstays of mixed-signal integrated circuit design. Besides the conventional ADCs used in mainstream ICs, there have been various attempts in the past to utilize neuromorphic networks to accomplish an efficient crossing between analog and digital domains, i.e. to build neurally inspired ADCs. Generally, these have suffered from the same problems as conventional ADCs, that is they require high-precision, handcrafted analog circuits and are thus not technology portable. In this paper, we present an ADC based on the Neural Engineering Framework (NEF. It carries out a large fraction of the overall ADC process in the digital domain, i.e. it is easily portable across technologies. The analog-digital conversion takes full advantage of the high degree of parallelism inherent in neuromorphic networks, making for a very scalable ADC. In addition, it has a number of features not commonly found in conventional ADCs, such as a runtime reconfigurability of the ADC sampling rate, resolution and transfer characteristic.

  6. The neural circuit basis of learning

    Science.gov (United States)

    Patrick, Kaifosh William John

    The astounding capacity for learning ranks among the nervous system's most impressive features. This thesis comprises studies employing varied approaches to improve understanding, at the level of neural circuits, of the brain's capacity for learning. The first part of the thesis contains investigations of hippocampal circuitry -- both theoretical work and experimental work in the mouse Mus musculus -- as a model system for declarative memory. To begin, Chapter 2 presents a theory of hippocampal memory storage and retrieval that reflects nonlinear dendritic processing within hippocampal pyramidal neurons. As a prelude to the experimental work that comprises the remainder of this part, Chapter 3 describes an open source software platform that we have developed for analysis of data acquired with in vivo Ca2+ imaging, the main experimental technique used throughout the remainder of this part of the thesis. As a first application of this technique, Chapter 4 characterizes the content of signaling at synapses between GABAergic neurons of the medial septum and interneurons in stratum oriens of hippocampal area CA1. Chapter 5 then combines these techniques with optogenetic, pharmacogenetic, and pharmacological manipulations to uncover inhibitory circuit mechanisms underlying fear learning. The second part of this thesis focuses on the cerebellum-like electrosensory lobe in the weakly electric mormyrid fish Gnathonemus petersii, as a model system for non-declarative memory. In Chapter 6, we study how short-duration EOD motor commands are recoded into a complex temporal basis in the granule cell layer, which can be used to cancel Purkinje-like cell firing to the longer duration and temporally varying EOD-driven sensory responses. In Chapter 7, we consider not only the temporal aspects of the granule cell code, but also the encoding of body position provided from proprioceptive and efference copy sources. Together these studies clarify how the cerebellum-like circuitry of the

  7. Neural circuit dysfunction in schizophrenia: Insights from animal models.

    Science.gov (United States)

    Sigurdsson, T

    2016-05-03

    Despite decades of research, the neural circuit abnormalities underlying schizophrenia remain elusive. Although studies on schizophrenia patients have yielded important insights they have not been able to fully reveal the details of how neural circuits are disrupted in the disease, which is essential for understanding its pathophysiology and developing new treatment strategies. Animal models of schizophrenia are likely to play an important role in this effort. Such models allow neural circuit dysfunction to be investigated in detail and the role of risk factors and pathophysiological mechanisms to be experimentally assessed. The goal of this review is to summarize what we have learned from electrophysiological studies that have examined neural circuit function in animal models of schizophrenia. Although these studies have revealed diverse manifestations of neural circuit dysfunction spanning multiple levels of analysis, common themes have nevertheless emerged across different studies and animal models, revealing a core set of neural circuit abnormalities. These include an imbalance between excitation and inhibition, deficits in synaptic plasticity, disruptions in local and long-range synchrony and abnormalities in dopaminergic signaling. The relevance of these findings to the pathophysiology of the disease is discussed, as well as outstanding questions for future research.

  8. A Novel Analog Integrated Circuit Design Course Covering Design, Layout, and Resulting Chip Measurement

    Science.gov (United States)

    Lin, Wei-Liang; Cheng, Wang-Chuan; Wu, Chen-Hao; Wu, Hai-Ming; Wu, Chang-Yu; Ho, Kuan-Hsuan; Chan, Chueh-An

    2010-01-01

    This work describes a novel, first-year graduate-level analog integrated circuit (IC) design course. The course teaches students analog circuit design; an external manufacturer then produces their designs in three different silicon chips. The students, working in pairs, then test these chips to verify their success. All work is completed within…

  9. Digitally-assisted analog and RF CMOS circuit design for software-defined radio

    CERN Document Server

    Okada, Kenichi

    2011-01-01

    This book describes the state-of-the-art in RF, analog, and mixed-signal circuit design for Software Defined Radio (SDR). It synthesizes for analog/RF circuit designers the most important general design approaches to take advantage of the most recent CMOS technology, which can integrate millions of transistors, as well as several real examples from the most recent research results.

  10. A Novel Analog Integrated Circuit Design Course Covering Design, Layout, and Resulting Chip Measurement

    Science.gov (United States)

    Lin, Wei-Liang; Cheng, Wang-Chuan; Wu, Chen-Hao; Wu, Hai-Ming; Wu, Chang-Yu; Ho, Kuan-Hsuan; Chan, Chueh-An

    2010-01-01

    This work describes a novel, first-year graduate-level analog integrated circuit (IC) design course. The course teaches students analog circuit design; an external manufacturer then produces their designs in three different silicon chips. The students, working in pairs, then test these chips to verify their success. All work is completed within…

  11. FinFET Based Tunable Analog Circuit: Design and Analysis at 45 nm Technology

    Directory of Open Access Journals (Sweden)

    Ravindra Singh Kushwah

    2013-01-01

    Full Text Available We included a designing of low power tunable analog circuits built using independently driven FinFETs devices, where the controlling of the back gate provide the output on the front gate. We show that this could be an effective solution to conveniently tune the output of bulk CMOS analog circuits particularly for Schmitt trigger and operational transconductance amplifier circuits. FinFET devices can be used to increase the performance by reducing the leakage current and power dissipation, because front and back gates both are independently controlled. FinFET device has a higher controllability, resulting relatively high Ion/Ioff ratio. In this paper, we proposed a tunable analog circuit such as CMOS amplifier circuit, Schmitt trigger circuit, and operational transconductance amplifier circuit, these circuit blocks are necessary for low noise high performance ICs for analog applications. Gain, phase, group delay, and output response of analog tunable circuits have been discussed in this paper. The proposed FinFET based analog tunable circuits have been designed using Cadence Virtuoso tool at 45 nm.

  12. Classes of feedforward neural networks and their circuit complexity

    NARCIS (Netherlands)

    Shawe-Taylor, John S.; Anthony, Martin H.G.; Kern, Walter

    1992-01-01

    This paper aims to place neural networks in the context of boolean circuit complexity. We define appropriate classes of feedforward neural networks with specified fan-in, accuracy of computation and depth and using techniques of communication complexity proceed to show that the classes fit into a

  13. FUZZY NEURAL NETWORK FOR OBJECT IDENTIFICATION ON INTEGRATED CIRCUIT LAYOUTS

    Directory of Open Access Journals (Sweden)

    A. A. Doudkin

    2015-01-01

    Full Text Available Fuzzy neural network model based on neocognitron is proposed to identify layout objects on images of topological layers of integrated circuits. Testing of the model on images of real chip layouts was showed a highеr degree of identification of the proposed neural network in comparison to base neocognitron.

  14. CMOS analog integrated circuit design technology; CMOS anarogu IC sekkei gijutsu

    Energy Technology Data Exchange (ETDEWEB)

    Fujimoto, H.; Fujisawa, A. [Fuji Electric Co. Ltd., Tokyo (Japan)

    2000-08-10

    In the field of the LSI (large scale integrated circuit) in rapid progress toward high integration and advanced functions, CAD (computer-aided design) technology has become indispensable to LSI development within a short period. Fuji Electric has developed design technologies and automatic design system to develop high-quality analog ICs (integrated circuits), including power supply ICs. within a short period. This paper describes CMOS (complementary metal-oxide semiconductor) analog macro cell, circuit simulation, automatic routing, and backannotation technologies. (author)

  15. Illuminating neural circuits and behaviour in Caenorhabditis elegans with optogenetics.

    Science.gov (United States)

    Fang-Yen, Christopher; Alkema, Mark J; Samuel, Aravinthan D T

    2015-09-19

    The development of optogenetics, a family of methods for using light to control neural activity via light-sensitive proteins, has provided a powerful new set of tools for neurobiology. These techniques have been particularly fruitful for dissecting neural circuits and behaviour in the compact and transparent roundworm Caenorhabditis elegans. Researchers have used optogenetic reagents to manipulate numerous excitable cell types in the worm, from sensory neurons, to interneurons, to motor neurons and muscles. Here, we show how optogenetics applied to this transparent roundworm has contributed to our understanding of neural circuits.

  16. An Optimized Device Sizing of Analog Circuits using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    K. Duraiswamy

    2012-01-01

    Full Text Available Problem statement: Day by day more and more products rely on analog circuits to improve the speed and reduce the power consumption(Products rely on analog circuits to improve the speed and reduce the power consumption day by day more and more.. For the VLSI implementation analog circuit design plays an important role. This analog circuit synthesis might be the most challenging and time-consumed task, because it does not only consist of topology and layout synthesis but also of component sizing. Approach: A Particle Swarm Optimization (PSO technique for the optimal design of analog circuits. Analog signal processing finds many applications and widely uses OpAmp based amplifiers, mixers, comparators. and filters. Results: A two-stage opamp (Miller Operational Trans-conductance Amplifier (OTA is considered for the synthesis that satisfies certain design specifications. Performance has been evaluated with the Simulation Program with Integrated Circuit Emphasis (SPICE circuit simulator until optimal sizes of the transistors are found. Conclusion: The output of the simulation for the two-stage opamp shows that the PSO technique is an accurate and promising approach in determining the device sizes in an analog circuit.

  17. Complexity and competition in appetitive and aversive neural circuits

    Directory of Open Access Journals (Sweden)

    Crista L. Barberini

    2012-11-01

    Full Text Available Decision-making often involves using sensory cues to predict possible rewarding or punishing reinforcement outcomes before selecting a course of action. Recent work has revealed complexity in how the brain learns to predict rewards and punishments. Analysis of neural signaling during and after learning in the amygdala and orbitofrontal cortex, two brain areas that process appetitive and aversive stimuli, reveals a dynamic relationship between appetitive and aversive circuits. Specifically, the relationship between signaling in appetitive and aversive circuits in these areas shifts as a function of learning. Furthermore, although appetitive and aversive circuits may often drive opposite behaviors – approaching or avoiding reinforcement depending upon its valence – these circuits can also drive similar behaviors, such as enhanced arousal or attention; these processes also may influence choice behavior. These data highlight the formidable challenges ahead in dissecting how appetitive and aversive neural circuits interact to produce a complex and nuanced range of behaviors.

  18. Event-driven neural integration and synchronicity in analog VLSI.

    Science.gov (United States)

    Yu, Theodore; Park, Jongkil; Joshi, Siddharth; Maier, Christoph; Cauwenberghs, Gert

    2012-01-01

    Synchrony and temporal coding in the central nervous system, as the source of local field potentials and complex neural dynamics, arises from precise timing relationships between spike action population events across neuronal assemblies. Recently it has been shown that coincidence detection based on spike event timing also presents a robust neural code invariant to additive incoherent noise from desynchronized and unrelated inputs. We present spike-based coincidence detection using integrate-and-fire neural membrane dynamics along with pooled conductance-based synaptic dynamics in a hierarchical address-event architecture. Within this architecture, we encode each synaptic event with parameters that govern synaptic connectivity, synaptic strength, and axonal delay with additional global configurable parameters that govern neural and synaptic temporal dynamics. Spike-based coincidence detection is observed and analyzed in measurements on a log-domain analog VLSI implementation of the integrate-and-fire neuron and conductance-based synapse dynamics.

  19. A subthreshold MOS circuit for the Lotka-Volterra neural network producing the winners-share-all solution.

    Science.gov (United States)

    Asai, T; Fukai, T; Tanaka, S

    1999-03-01

    An analog MOS circuit is proposed for implementing a Lotka-Volterra (LV) competitive neural network which produces winners-share-all solutions. The solutions give multiple winners receiving large inputs and are particularly useful for selecting a set of inputs through "decision by majority". We show that the LV network can easily be implemented using subthreshold MOS transistors. Results of extensive circuit simulations prove that the proposed circuit does exhibit a reliable selection compared with winner-take-all circuits, in the possible presence of device mismatches. These results pave a way to future implementation on a real device.

  20. Improvements of Analog Neural Networks Based on Kalman Filter

    Directory of Open Access Journals (Sweden)

    Z. Raida

    2002-04-01

    Full Text Available In the paper, original improvements of recurrent analog neuralnetworks, which are based on Kalman filter, are presented. Theseimprovements eliminate some disadvantages of the classical Kalmanneural network and enable a real time processing of quickly changingsignals, which appear in adaptive antennas and similar applications.This goal is reached using such circuit elements, which increase theconvergence rate of the network and decrease the dependence ofconvergence rate on the ratio of eigenvalues of the correlation matrixof input signals.

  1. Neural circuit mechanisms of short-term memory

    Science.gov (United States)

    Goldman, Mark

    Memory over time scales of seconds to tens of seconds is thought to be maintained by neural activity that is triggered by a memorized stimulus and persists long after the stimulus is turned off. This presents a challenge to current models of memory-storing mechanisms, because the typical time scales associated with cellular and synaptic dynamics are two orders of magnitude smaller than this. While such long time scales can easily be achieved by bistable processes that toggle like a flip-flop between a baseline and elevated-activity state, many neuronal systems have been observed experimentally to be capable of maintaining a continuum of stable states. For example, in neural integrator networks involved in the accumulation of evidence for decision making and in motor control, individual neurons have been recorded whose activity reflects the mathematical integral of their inputs; in the absence of input, these neurons sustain activity at a level proportional to the running total of their inputs. This represents an analog form of memory whose dynamics can be conceptualized through an energy landscape with a continuum of lowest-energy states. Such continuous attractor landscapes are structurally non-robust, in seeming violation of the relative robustness of biological memory systems. In this talk, I will present and compare different biologically motivated circuit motifs for the accumulation and storage of signals in short-term memory. Challenges to generating robust memory maintenance will be highlighted and potential mechanisms for ameliorating the sensitivity of memory networks to perturbations will be discussed. Funding for this work was provided by NIH R01 MH065034, NSF IIS-1208218, Simons Foundation 324260, and a UC Davis Ophthalmology Research to Prevent Blindness Grant.

  2. Nyquist AD Converters, Sensor Interfaces, and Robustness Advances in Analog Circuit Design, 2012

    CERN Document Server

    Baschirotto, Andrea; Steyaert, Michiel

    2013-01-01

    This book is based on the presentations during the 21st workshop on Advances in Analog Circuit Design.  Expert designers provide readers with information about a variety of topics at the frontier of analog circuit design, including Nyquist analog-to-digital converters, capacitive sensor interfaces, reliability, variability, and connectivity.  This book serves as a valuable reference to the state-of-the-art, for anyone involved in analog circuit research and development.  Provides a state-of-the-art reference in analog circuit design, written by experts from industry and academia; Presents material in a tutorial-based format; Includes coverage of Nyquist A/D converters, capacitive sensor interfaces, reliability, variability, and connectivity.

  3. CMOS analog integrated circuits high-speed and power-efficient design

    CERN Document Server

    Ndjountche, Tertulien

    2011-01-01

    High-speed, power-efficient analog integrated circuits can be used as standalone devices or to interface modern digital signal processors and micro-controllers in various applications, including multimedia, communication, instrumentation, and control systems. New architectures and low device geometry of complementary metaloxidesemiconductor (CMOS) technologies have accelerated the movement toward system on a chip design, which merges analog circuits with digital, and radio-frequency components. CMOS: Analog Integrated Circuits: High-Speed and Power-Efficient Design describes the important tren

  4. Modeling and Experimental Demonstration of a Hopfield Network Analog-to-Digital Converter with Hybrid CMOS/Memristor Circuits.

    Science.gov (United States)

    Guo, Xinjie; Merrikh-Bayat, Farnood; Gao, Ligang; Hoskins, Brian D; Alibart, Fabien; Linares-Barranco, Bernabe; Theogarajan, Luke; Teuscher, Christof; Strukov, Dmitri B

    2015-01-01

    The purpose of this work was to demonstrate the feasibility of building recurrent artificial neural networks with hybrid complementary metal oxide semiconductor (CMOS)/memristor circuits. To do so, we modeled a Hopfield network implementing an analog-to-digital converter (ADC) with up to 8 bits of precision. Major shortcomings affecting the ADC's precision, such as the non-ideal behavior of CMOS circuitry and the specific limitations of memristors, were investigated and an effective solution was proposed, capitalizing on the in-field programmability of memristors. The theoretical work was validated experimentally by demonstrating the successful operation of a 4-bit ADC circuit implemented with discrete Pt/TiO2- x /Pt memristors and CMOS integrated circuit components.

  5. Diagnosis of soft faults in analog integrated circuits based on fractional correlation

    Institute of Scientific and Technical Information of China (English)

    Deng Yong; Shi Yibing; Zhang Wei

    2012-01-01

    Aiming at the problem of diagnosing soft faults in analog integrated circuits,an approach based on fractional correlation is proposed.First,the Volterra series of the circuit under test (CUT) decomposed by the fractional wavelet packet are used to calculate the fractional correlation functions.Then,the calculated fractional correlation functions are used to form the fault signatures of the CUT.By comparing the fault signatures,the different soft faulty conditions of the CUT are identified and the faults are located.Simulations of benchmark circuits illustrate the proposed method and validate its effectiveness in diagnosing soft faults in analog integrated circuits.

  6. A feedback neural circuit for calibrating aversive memory strength.

    Science.gov (United States)

    Ozawa, Takaaki; Ycu, Edgar A; Kumar, Ashwani; Yeh, Li-Feng; Ahmed, Touqeer; Koivumaa, Jenny; Johansen, Joshua P

    2017-01-01

    Aversive experiences powerfully regulate memory formation, and memory strength is proportional to the intensity of these experiences. Inhibition of the neural circuits that convey aversive signals when they are predicted by other sensory stimuli is hypothesized to set associative memory strength. However, the neural circuit mechanisms that produce this predictive inhibition to regulate memory formation are unknown. Here we show that predictive sensory cues recruit a descending feedback circuit from the central amygdala that activates a specific population of midbrain periaqueductal gray pain-modulatory neurons to control aversive memory strength. Optogenetic inhibition of this pathway disinhibited predicted aversive responses in lateral amygdala neurons, which store fear memories, resulting in the resetting of fear learning levels. These results reveal a control mechanism for calibrating learning signals to adaptively regulate the strength of behavioral learning. Dysregulation of this circuit could contribute to psychiatric disorders associated with heightened fear responsiveness.

  7. A novel prediction method about single components of analog circuits based on complex field modeling.

    Science.gov (United States)

    Zhou, Jingyu; Tian, Shulin; Yang, Chenglin

    2014-01-01

    Few researches pay attention to prediction about analog circuits. The few methods lack the correlation with circuit analysis during extracting and calculating features so that FI (fault indicator) calculation often lack rationality, thus affecting prognostic performance. To solve the above problem, this paper proposes a novel prediction method about single components of analog circuits based on complex field modeling. Aiming at the feature that faults of single components hold the largest number in analog circuits, the method starts with circuit structure, analyzes transfer function of circuits, and implements complex field modeling. Then, by an established parameter scanning model related to complex field, it analyzes the relationship between parameter variation and degeneration of single components in the model in order to obtain a more reasonable FI feature set via calculation. According to the obtained FI feature set, it establishes a novel model about degeneration trend of analog circuits' single components. At last, it uses particle filter (PF) to update parameters for the model and predicts remaining useful performance (RUP) of analog circuits' single components. Since calculation about the FI feature set is more reasonable, accuracy of prediction is improved to some extent. Finally, the foregoing conclusions are verified by experiments.

  8. Distinct Neural Circuits Subserve Interpersonal and Non-interpersonal Emotions

    OpenAIRE

    Landa, Alla; Wang, Zhishun; Russell, James A.; Posner, Jonathan; Duan, Yunsuo; Kangarlu, Alayar; Huo, Yuankai; Fallon, Brian A.; Peterson, Bradley S.

    2013-01-01

    Emotions elicited by interpersonal versus non-interpersonal experiences have different effects on neurobiological functioning in both animals and humans. However, the extent to which the brain circuits underlying interpersonal and non-interpersonal emotions are distinct still remains unclear. The goal of our study was to assess whether different neural circuits are implicated in the processing of arousal and valence of interpersonal versus non-interpersonal emotions. During functional magneti...

  9. Classical Conditioning with Pulsed Integrated Neural Networks: Circuits and System

    DEFF Research Database (Denmark)

    Lehmann, Torsten

    1998-01-01

    In this paper we investigate on-chip learning for pulsed, integrated neural networks. We discuss the implementational problems the technology imposes on learning systems and we find that abiologically inspired approach using simple circuit structures is most likely to bring success. We develop...... a suitable learning algorithm -- a continuous-time version of a temporal differential Hebbian learning algorithm for pulsed neural systems with non-linear synapses -- as well as circuits for the electronic implementation. Measurements from an experimental CMOS chip are presented. Finally, we use our test...

  10. A multi-channel fully differential programmable integrated circuit for neural recording application

    Science.gov (United States)

    Yun, Gui; Xu, Zhang; Yuan, Wang; Ming, Liu; Weihua, Pei; Kai, Liang; Suibiao, Huang; Bin, Li; Hongda, Chen

    2013-10-01

    A multi-channel, fully differential programmable chip for neural recording application is presented. The integrated circuit incorporates eight neural recording amplifiers with tunable bandwidth and gain, eight 4th-order Bessel switch capacitor filters, an 8-to-1 analog time-division multiplexer, a fully differential successive approximation register analog-to-digital converter (SAR ADC), and a serial peripheral interface for communication. The neural recording amplifier presents a programmable gain from 53 dB to 68 dB, a tunable low cut-off frequency from 0.1 Hz to 300 Hz, and 3.77 μVrms input-referred noise over a 5 kHz bandwidth. The SAR ADC digitizes signals at maximum sampling rate of 20 kS/s per channel and achieves an ENOB of 7.4. The integrated circuit is designed and fabricated in 0.18-μm CMOS mix-signal process. We successfully performed a multi-channel in-vivo recording experiment from a rat cortex using the neural recording chip.

  11. Neural - glial circuits : Can Interneurons stop seizures

    Science.gov (United States)

    Nadkarni, Suhita; Jung, Peter

    2004-03-01

    Recent progress in neurobiology suggests that astrocytes - through calcium excitability - are active partners to the neurons by integrating their activity and, in turn, regulating synaptic transmission. In a similar fashion neurons and interneurons are the 'Yin and Yang' of the hippocampus. The dichotomy of excitation and inhibition between pyramidal neurons and interneurons plays a crucial role in the function of the neuronal circuit.We consider a model of a pyramidal cell in contact with one synaptic astrocytes. It has been shown that such a circuit - triggered by transient stimulation - can exhibit sustained oscillations ("seizures") for strong coupling. The question we are considering is, under what conditions synaptic inhibition can stop these seizures?

  12. Analog CMOS circuit design and characterization for optical coherence tomography signal processing.

    Science.gov (United States)

    Kariya, Rajesh; Mathine, David L; Barton, Jennifer K

    2004-12-01

    We have developed a custom analog CMOS circuit to perform the signal processing for an optical coherence tomography imaging system. The circuit is realized in a 1.5 microm low-noise analog CMOS technology. The circuitry extracts the Doppler frequency from the signal and electrically mixes this with the original signal to provide a filtered A-scan. The circuitry was used to produce a two-dimensional image of an onion.

  13. Neural circuits as computational dynamical systems.

    Science.gov (United States)

    Sussillo, David

    2014-04-01

    Many recent studies of neurons recorded from cortex reveal complex temporal dynamics. How such dynamics embody the computations that ultimately lead to behavior remains a mystery. Approaching this issue requires developing plausible hypotheses couched in terms of neural dynamics. A tool ideally suited to aid in this question is the recurrent neural network (RNN). RNNs straddle the fields of nonlinear dynamical systems and machine learning and have recently seen great advances in both theory and application. I summarize recent theoretical and technological advances and highlight an example of how RNNs helped to explain perplexing high-dimensional neurophysiological data in the prefrontal cortex.

  14. Adaptive Neurotechnology for Making Neural Circuits Functional .

    Science.gov (United States)

    Jung, Ranu

    2008-03-01

    Two of the most important trends in recent technological developments are that technology is increasingly integrated with biological systems and that it is increasingly adaptive in its capabilities. Neuroprosthetic systems that provide lost sensorimotor function after a neural disability offer a platform to investigate this interplay between biological and engineered systems. Adaptive neurotechnology (hardware and software) could be designed to be biomimetic, guided by the physical and programmatic constraints observed in biological systems, and allow for real-time learning, stability, and error correction. An example will present biomimetic neural-network hardware that can be interfaced with the isolated spinal cord of a lower vertebrate to allow phase-locked real-time neural control. Another will present adaptive neural network control algorithms for functional electrical stimulation of the peripheral nervous system to provide desired movements of paralyzed limbs in rodents or people. Ultimately, the frontier lies in being able to utilize the adaptive neurotechnology to promote neuroplasticity in the living system on a long-time scale under co-adaptive conditions.

  15. Genetic control of active neural circuits

    Directory of Open Access Journals (Sweden)

    Leon Reijmers

    2009-12-01

    Full Text Available The use of molecular tools to study the neurobiology of complex behaviors has been hampered by an inability to target the desired changes to relevant groups of neurons. Specific memories and specific sensory representations are sparsely encoded by a small fraction of neurons embedded in a sea of morphologically and functionally similar cells. In this review we discuss genetics techniques that are being developed to address this difficulty. In several studies the use of promoter elements that are responsive to neural activity have been used to drive long lasting genetic alterations into neural ensembles that are activated by natural environmental stimuli. This approach has been used to examine neural activity patterns during learning and retrieval of a memory, to examine the regulation of receptor trafficking following learning and to functionally manipulate a specific memory trace. We suggest that these techniques will provide a general approach to experimentally investigate the link between patterns of environmentally activated neural firing and cognitive processes such as perception and memory.

  16. Fault Diagnosis of Nonlinear Analog Circuits. Volume III. Fault Diagnosis in the Tableau Context.

    Science.gov (United States)

    1983-04-01

    of the limited fault assumption is that of Biernacki and Bandler who developed an approach to multiple fault location for linear networks. Here the...and J. W. Bandler , "Multiple-Fault Location of Analog Circuits," IEEE Trans. on Circuits and Systems, Vol. CAS-28, 361-367, May 1981. [5] R. A. DeCarlo

  17. How to deal with substrate bounce in analog circuits in epi-type CMOS technology

    NARCIS (Netherlands)

    Nauta, Bram; Hoogzaad, Gian; Donnay, S.; Gielen, G.

    2003-01-01

    Substrate noise is one of the key problems in mixed analog/digital ICs. Although measures are known to reduce substrate noise, the noise will never be completely eliminated since this requires larger chip area or exotic packages and thus higher cost. Analog circuits on digital ICs simply have to be

  18. [Dual neural circuit model of reading and writing].

    Science.gov (United States)

    Iwata, Makoto

    2011-08-01

    In the hypothetical neural circuit model of reading and writing that was initially proposed by Dejerine and subsequently confirmed by Geschwind, the left angular gyrus was considered as a unique center for processing letters. Japanese investigators, however, have repeatedly pointed out that this angular gyrus model cannot fully explain the disturbances observed in reading and writing Kanji letters in Japanese patients with various types of alexia with or without agraphia. In 1982, I proposed a dual neural circuit model of reading and writing Japanese on the basis of neuropsychological studies on the various types of alexia with or without agraphia without aphasia. This dual neural circuit model proposes that apart from the left angular gyrus which was thought to be a node for phonological processing of letters, the left posterior inferior temporal area, also acts as a node for semantic processing of letters. Further investigations using O15-PET activation on normal subjects revealed that the left middle occipital gyrus (area 19 of Brodmann) and the posterior portion of the left inferior temporal gyrus (area 37 of Brodmann) are the cortical areas responsible for reading Japanese letters; the former serving for phonological reading and the latter for semantic reading. This duality of the neural circuit in processing letters was later applied to explain disturbances in reading English, and was finally accepted as a valid model for other alphabetic letter systems too.

  19. Integrating Neural Circuits Controlling Female Sexual Behavior

    Science.gov (United States)

    Micevych, Paul E.; Meisel, Robert L.

    2017-01-01

    The hypothalamus is most often associated with innate behaviors such as is hunger, thirst and sex. While the expression of these behaviors important for survival of the individual or the species is nested within the hypothalamus, the desire (i.e., motivation) for them is centered within the mesolimbic reward circuitry. In this review, we will use female sexual behavior as a model to examine the interaction of these circuits. We will examine the evidence for a hypothalamic circuit that regulates consummatory aspects of reproductive behavior, i.e., lordosis behavior, a measure of sexual receptivity that involves estradiol membrane-initiated signaling in the arcuate nucleus (ARH), activating β-endorphin projections to the medial preoptic nucleus (MPN), which in turn modulate ventromedial hypothalamic nucleus (VMH) activity—the common output from the hypothalamus. Estradiol modulates not only a series of neuropeptides, transmitters and receptors but induces dendritic spines that are for estrogenic induction of lordosis behavior. Simultaneously, in the nucleus accumbens of the mesolimbic system, the mating experience produces long term changes in dopamine signaling and structure. Sexual experience sensitizes the response of nucleus accumbens neurons to dopamine signaling through the induction of a long lasting early immediate gene. While estrogen alone increases spines in the ARH, sexual experience increases dendritic spine density in the nucleus accumbens. These two circuits appear to converge onto the medial preoptic area where there is a reciprocal influence of motivational circuits on consummatory behavior and vice versa. While it has not been formally demonstrated in the human, such circuitry is generally highly conserved and thus, understanding the anatomy, neurochemistry and physiology can provide useful insight into the motivation for sexual behavior and other innate behaviors in humans. PMID:28642689

  20. Unraveling the central proopiomelanocortin neural circuits

    Directory of Open Access Journals (Sweden)

    Aaron J. Mercer

    2013-02-01

    Full Text Available Central proopiomelanocortin (POMC neurons form a potent anorexigenic network, but our understanding of the integration of this hypothalamic circuit throughout the central nervous system (CNS remains incomplete. POMC neurons extend projections along the rostrocaudal axis of the brain, and can signal with both POMC-derived peptides and fast amino acid neurotransmitters. Although recent experimental advances in circuit-level manipulation have been applied to POMC neurons, many pivotal questions still remain: How and where do POMC neurons integrate metabolic information? Under what conditions do POMC neurons release bioactive molecules throughout the CNS? Are GABA and glutamate or neuropeptides released from POMC neurons more crucial for modulating feeding and metabolism? Resolving the exact stoichiometry of signals evoked from POMC neurons under different metabolic conditions therefore remains an ongoing endeavor. In this review, we analyze the anatomical atlas of this network juxtaposed to the physiological signaling of POMC neurons both in vitro and in vivo. We also consider novel genetic tools to further characterize the function of the POMC circuit in vivo. Our goal is to synthesize a global view of the POMC network, and to highlight gaps that require further research to expand our knowledge on how these neurons modulate energy balance.

  1. Precision psychiatry: a neural circuit taxonomy for depression and anxiety.

    Science.gov (United States)

    Williams, Leanne M

    2016-05-01

    Although there have been tremendous advances in the understanding of human dysfunctions in the brain circuitry for self-reflection, emotion, and cognitive control, a brain-based taxonomy for mental disease is still lacking. As a result, these advances have not been translated into actionable clinical tools, and the language of brain circuits has not been incorporated into training programmes. To address this gap, I present this synthesis of published work, with a focus on functional imaging of circuit dysfunctions across the spectrum of mood and anxiety disorders. This synthesis provides the foundation for a taxonomy of putative types of dysfunction, which cuts across traditional diagnostic boundaries for depression and anxiety and includes instead distinct types of neural circuit dysfunction that together reflect the heterogeneity of depression and anxiety. This taxonomy is suited to specifying symptoms in terms of underlying neural dysfunction at the individual level and is intended as the foundation for building mechanistic research and ultimately guiding clinical practice.

  2. Phylogenetic plasticity in the evolution of molluscan neural circuits.

    Science.gov (United States)

    Katz, Paul S

    2016-12-01

    Recent research on molluscan nervous systems provides a unique perspective on the evolution of neural circuits. Molluscs evolved large, encephalized nervous systems independently from other phyla. Homologous body-patterning genes were re-specified in molluscs to create a plethora of body plans and nervous system organizations. Octopuses, having the largest brains of any invertebrate, independently evolved a learning circuit similar in organization and function to the mushroom body of insects and the hippocampus of mammals. In gastropods, homologous neurons have been re-specified for different functions. Even species exhibiting similar, possibly homologous behavior have fundamental differences in the connectivity of the neurons underlying that behavior. Thus, molluscan nervous systems provide clear examples of re-purposing of homologous genes and neurons for neural circuits. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. Hox genes: choreographers in neural development, architects of circuit organization.

    Science.gov (United States)

    Philippidou, Polyxeni; Dasen, Jeremy S

    2013-10-02

    The neural circuits governing vital behaviors, such as respiration and locomotion, are comprised of discrete neuronal populations residing within the brainstem and spinal cord. Work over the past decade has provided a fairly comprehensive understanding of the developmental pathways that determine the identity of major neuronal classes within the neural tube. However, the steps through which neurons acquire the subtype diversities necessary for their incorporation into a particular circuit are still poorly defined. Studies on the specification of motor neurons indicate that the large family of Hox transcription factors has a key role in generating the subtypes required for selective muscle innervation. There is also emerging evidence that Hox genes function in multiple neuronal classes to shape synaptic specificity during development, suggesting a broader role in circuit assembly. This Review highlights the functions and mechanisms of Hox gene networks and their multifaceted roles during neuronal specification and connectivity.

  4. Neuronify: An Educational Simulator for Neural Circuits

    Science.gov (United States)

    Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne

    2017-01-01

    Abstract Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker). We aim to provide a low entry point to simulation-based neuroscience by allowing students with no programming experience to create and simulate neural networks. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation, gain control by inhibition, and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt and runs on smart phones (Android, iOS) and tablet computers as well personal computers (Windows, Mac, Linux). PMID:28321440

  5. Neuronify: An Educational Simulator for Neural Circuits.

    Science.gov (United States)

    Dragly, Svenn-Arne; Hobbi Mobarhan, Milad; Våvang Solbrå, Andreas; Tennøe, Simen; Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne; Hafting, Torkel; Einevoll, Gaute T

    2017-01-01

    Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker). We aim to provide a low entry point to simulation-based neuroscience by allowing students with no programming experience to create and simulate neural networks. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation, gain control by inhibition, and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt and runs on smart phones (Android, iOS) and tablet computers as well personal computers (Windows, Mac, Linux).

  6. Complemenary body driving - a low voltage analog circuit technique for SOI

    Science.gov (United States)

    Mojarradi, M. M.; Terry, S.; Blalock, B. J.; Yong, L.; Dufrene, B.

    2002-01-01

    This paper describes several analog circuit primitives that utilize the body terminal as a signal port. A cascode current mirror that can operate with an input and output voltage of 200 mV; and a rail-to-rail, constant transconductance gain block capable of 1 V operation are presented. These circuits have been implemented in a standard 0.351 partially-depleted Silicon-on-Insulator (PDSOI) CMOS process and should find wide application in next-generation analog circuit designs.

  7. Autonomous Traffic Signal Control Model with Neural Network Analogy

    CERN Document Server

    Ohira, T

    1997-01-01

    We propose here an autonomous traffic signal control model based on analogy with neural networks. In this model, the length of cycle time period of traffic lights at each signal is autonomously adapted. We find a self-organizing collective behavior of such a model through simulation on a one-dimensional lattice model road: traffic congestion is greatly diffused when traffic signals have such autonomous adaptability with suitably tuned parameters. We also find that effectiveness of the system emerges through interactions between units and shows a threshold transition as a function of proportion of adaptive signals in the model.

  8. A Novel Prediction Method about Single Components of Analog Circuits Based on Complex Field Modeling

    Directory of Open Access Journals (Sweden)

    Jingyu Zhou

    2014-01-01

    Full Text Available Few researches pay attention to prediction about analog circuits. The few methods lack the correlation with circuit analysis during extracting and calculating features so that FI (fault indicator calculation often lack rationality, thus affecting prognostic performance. To solve the above problem, this paper proposes a novel prediction method about single components of analog circuits based on complex field modeling. Aiming at the feature that faults of single components hold the largest number in analog circuits, the method starts with circuit structure, analyzes transfer function of circuits, and implements complex field modeling. Then, by an established parameter scanning model related to complex field, it analyzes the relationship between parameter variation and degeneration of single components in the model in order to obtain a more reasonable FI feature set via calculation. According to the obtained FI feature set, it establishes a novel model about degeneration trend of analog circuits’ single components. At last, it uses particle filter (PF to update parameters for the model and predicts remaining useful performance (RUP of analog circuits’ single components. Since calculation about the FI feature set is more reasonable, accuracy of prediction is improved to some extent. Finally, the foregoing conclusions are verified by experiments.

  9. A Novel Approach To Diagnosis Of Analog Circuit Incipient Faults Based On KECA And OAO LSSVM

    Directory of Open Access Journals (Sweden)

    Zhang Chaolong

    2015-06-01

    Full Text Available Correct incipient identification of an analog circuit fault is conducive to the health of the analog circuit, yet very difficult. In this paper, a novel approach to analog circuit incipient fault identification is presented. Time responses are acquired by sampling outputs of the circuits under test, and then the responses are decomposed by the wavelet transform in order to generate energy features. Afterwards, lower-dimensional features are produced through the kernel entropy component analysis as samples for training and testing a one-against-one least squares support vector machine. Simulations of the incipient fault diagnosis for a Sallen-Key band-pass filter and a two-stage four-op-amp bi-quad low-pass filter demonstrate the diagnosing procedure of the proposed approach, and also reveal that the proposed approach has higher diagnosis accuracy than the referenced methods.

  10. The neural circuits for arithmetic principles.

    Science.gov (United States)

    Liu, Jie; Zhang, Han; Chen, Chuansheng; Chen, Hui; Cui, Jiaxin; Zhou, Xinlin

    2017-02-15

    Arithmetic principles are the regularities underlying arithmetic computation. Little is known about how the brain supports the processing of arithmetic principles. The current fMRI study examined neural activation and functional connectivity during the processing of verbalized arithmetic principles, as compared to numerical computation and general language processing. As expected, arithmetic principles elicited stronger activation in bilateral horizontal intraparietal sulcus and right supramarginal gyrus than did language processing, and stronger activation in left middle temporal lobe and left orbital part of inferior frontal gyrus than did computation. In contrast, computation elicited greater activation in bilateral horizontal intraparietal sulcus (extending to posterior superior parietal lobule) than did either arithmetic principles or language processing. Functional connectivity analysis with the psychophysiological interaction approach (PPI) showed that left temporal-parietal (MTG-HIPS) connectivity was stronger during the processing of arithmetic principle and language than during computation, whereas parietal-occipital connectivities were stronger during computation than during the processing of arithmetic principles and language. Additionally, the left fronto-parietal (orbital IFG-HIPS) connectivity was stronger during the processing of arithmetic principles than during computation. The results suggest that verbalized arithmetic principles engage a neural network that overlaps but is distinct from the networks for computation and language processing.

  11. Developmental metaplasticity in neural circuit codes of firing and structure.

    Science.gov (United States)

    Baram, Yoram

    2017-01-01

    Firing-rate dynamics have been hypothesized to mediate inter-neural information transfer in the brain. While the Hebbian paradigm, relating learning and memory to firing activity, has put synaptic efficacy variation at the center of cortical plasticity, we suggest that the external expression of plasticity by changes in the firing-rate dynamics represents a more general notion of plasticity. Hypothesizing that time constants of plasticity and firing dynamics increase with age, and employing the filtering property of the neuron, we obtain the elementary code of global attractors associated with the firing-rate dynamics in each developmental stage. We define a neural circuit connectivity code as an indivisible set of circuit structures generated by membrane and synapse activation and silencing. Synchronous firing patterns under parameter uniformity, and asynchronous circuit firing are shown to be driven, respectively, by membrane and synapse silencing and reactivation, and maintained by the neuronal filtering property. Analytic, graphical and simulation representation of the discrete iteration maps and of the global attractor codes of neural firing rate are found to be consistent with previous empirical neurobiological findings, which have lacked, however, a specific correspondence between firing modes, time constants, circuit connectivity and cortical developmental stages.

  12. Controlling chaos in balanced neural circuits with input spike trains

    Science.gov (United States)

    Engelken, Rainer; Wolf, Fred

    The cerebral cortex can be seen as a system of neural circuits driving each other with spike trains. Here we study how the statistics of these spike trains affects chaos in balanced target circuits.Earlier studies of chaos in balanced neural circuits either used a fixed input [van Vreeswijk, Sompolinsky 1996, Monteforte, Wolf 2010] or white noise [Lajoie et al. 2014]. We study dynamical stability of balanced networks driven by input spike trains with variable statistics. The analytically obtained Jacobian enables us to calculate the complete Lyapunov spectrum. We solved the dynamics in event-based simulations and calculated Lyapunov spectra, entropy production rate and attractor dimension. We vary correlations, irregularity, coupling strength and spike rate of the input and action potential onset rapidness of recurrent neurons.We generally find a suppression of chaos by input spike trains. This is strengthened by bursty and correlated input spike trains and increased action potential onset rapidness. We find a link between response reliability and the Lyapunov spectrum. Our study extends findings in chaotic rate models [Molgedey et al. 1992] to spiking neuron models and opens a novel avenue to study the role of projections in shaping the dynamics of large neural circuits.

  13. Utilizing Symbolic Programming in Analog Circuit Synthesis of Arbitrary Rational Transfer Functions

    Directory of Open Access Journals (Sweden)

    Amjad Fuad Hajjar

    2014-11-01

    Full Text Available The employment of symbolic programming in analog circuit design for system interfaces is proposed. Given a rational transfer function with a set of specifications and constraints, one may autonomously synthesize it into an analog circuit. First, a classification of the target transfer function polynomials into 14 classes is performed. The classes include both stable and unstable functions as required. A symbolic exhaustive search algorithm based on a circuit configuration under investigation is then conducted where a polynomial in hand is to be identified. For illustration purposes, a set of complete design equations for the primary rational transfer functions is obtained targeting all classes of second order polynomials based on a proposed general circuit configuration. The design consists of a single active element and four different circuit structures. Finally, an illustrative example with full analysis and simulation is presented.

  14. Hierarchical Symbolic Analysis of Large Analog Circuits with Totally Coded Method

    Institute of Scientific and Technical Information of China (English)

    XU Jing-bo

    2006-01-01

    Symbolic analysis has many applications in the design of analog circuits. Existing approaches rely on two forms of symbolic-expression representation: expanded sum-ofproduct form and arbitrarily nested form. Expanded form suffers the problem that the number of product terms grows exponentially with the size of a circuit. Nested form is neither canonical nor amenable to symbolic manipulation. In this paper, we present a new approach to exact and canonical symbolic analysis by exploiting the sparsity and sharing of product terms. This algorithm, called totally coded method (TCM), consists of representing the symbolic determinant of a circuit matrix by code series and performing symbolic analysis by code manipulation. We describe an efficient code-ordering heuristic and prove that it is optimum for ladder-structured circuits. For practical analog circuits, TCM not only covers all advantages of the algorithm via determinant decision diagrams (DDD) but is more simple and efficient than DDD method.

  15. Analog VLSI Circuits for Short-Term Dynamic Synapses

    Directory of Open Access Journals (Sweden)

    Shih-Chii Liu

    2003-06-01

    Full Text Available Short-term dynamical synapses increase the computational power of neuronal networks. These synapses act as additional filters to the inputs of a neuron before the subsequent integration of these signals at its cell body. In this work, we describe a model of depressing and facilitating synapses derived from a hardware circuit implementation. This model is equivalent to theoretical models of short-term synaptic dynamics in network simulations. These circuits have been added to a network of leaky integrate-and-fire neurons. A cortical model of direction-selectivity that uses short-term dynamic synapses has been implemented with this network.

  16. Genetic dissection of GABAergic neural circuits in mouse neocortex

    Directory of Open Access Journals (Sweden)

    Hiroki eTaniguchi

    2014-01-01

    Full Text Available Diverse and flexible cortical functions rely on the ability of neural circuits to perform multiple types of neuronal computations. GABAergic inhibitory interneurons significantly contribute to this task by regulating the balance of activity, synaptic integration, spiking, synchrony, and oscillation in a neural ensemble. GABAergic interneruons display a high degree of cellular diversity in morphology, physiology, connectivity, and gene expression. A considerable number of subtypes of GABAergic interneurons diversify modes of cortical inhibition, enabling various types of information processing in the cortex. Thus, comprehensively understanding fate specification, circuit assembly and physiological function of GABAergic interneurons is a key to elucidate the principles of cortical wiring and function. Recent advances in genetically encoded molecular tools have made a breakthrough to systematically study cortical circuitry at the molecular, cellular, circuit, and whole animal levels. However, the biggest obstacle to fully applying the power of these to analysis of GABAergic circuits was that there were no efficient and reliable methods to express them in subtypes of GABAergic interneurons. Here, I first summarize cortical interneuron diversity and current understanding of mechanisms, by which distinct classes of GABAergic interneurons are generated. I then review recent development in genetically encoded molecular tools for neural circuit research, and genetic targeting of GABAergic interneuron subtypes, particulary focusing on our recent effort to develop and characterize Cre/CreER knockin lines. Finally, I highlight recent success in genetic targeting of chandelier cells (ChCs, the most unique and distinct GABAergic interneuron subtype, and discuss what kind of questions need to be addressed to understand development and function of cortical inhibitory circuits.

  17. Chaotic phenomena in Josephson circuits coupled quantum cellular neural networks

    Institute of Scientific and Technical Information of China (English)

    Wang Sen; Cai Li; Li Qin; Wu Gang

    2007-01-01

    In this paper the nonlinear dynamical behaviour of a quantum cellular neural network (QCNN) by coupling Josephson circuits was investigated and it was shown that the QCNN using only two of them can cause the onset of chaotic oscillation. The theoretical analysis and simulation for the two Josephson-circuits-coupled QCNN have been done by using the amplitude and phase as state variables. The complex chaotic behaviours can be observed and then proved by calculating Lyapunov exponents. The study provides valuable information about QCNNs for future application in high-parallel signal processing and novel chaotic generators.

  18. Analog/RF Circuit Design Techniques for Nanometerscale IC Technologies

    NARCIS (Netherlands)

    Nauta, Bram; Annema, Anne-Johan

    2005-01-01

    CMOS evolution introduces several problems in analog design. Gate-leakage mismatch exceeds conventional matching tolerances requiring active cancellation techniques or alternative architectures. One strategy to deal with the use of lower supply voltages is to operate critical parts at higher supply

  19. Circuit design and exponential stabilization of memristive neural networks.

    Science.gov (United States)

    Wen, Shiping; Huang, Tingwen; Zeng, Zhigang; Chen, Yiran; Li, Peng

    2015-03-01

    This paper addresses the problem of circuit design and global exponential stabilization of memristive neural networks with time-varying delays and general activation functions. Based on the Lyapunov-Krasovskii functional method and free weighting matrix technique, a delay-dependent criteria for the global exponential stability and stabilization of memristive neural networks are derived in form of linear matrix inequalities (LMIs). Two numerical examples are elaborated to illustrate the characteristics of the results. It is noteworthy that the traditional assumptions on the boundness of the derivative of the time-varying delays are removed.

  20. High-Mixed-Voltage Analog and RF Circuit Techniques for Nanoscale CMOS

    CERN Document Server

    Mak, Pui-In

    2012-01-01

    This book presents high-/mixed-voltage analog and radio frequency (RF) circuit techniques for developing low-cost multistandard wireless receivers in nm-length CMOS processes.  Key benefits of high-/mixed-voltage RF and analog CMOS circuits are explained, state-of-the-art examples are studied, and circuit solutions before and after voltage-conscious design are compared. Three real design examples are included, which demonstrate the feasibility of high-/mixed-voltage circuit techniques.    Provides a valuable summary and real case studies of the state-of-the-art in high-/mixed-voltage circuits and systems; Includes novel high-/mixed-voltage analog and RF circuit techniques – from concept to practice; Describes the first high-voltage-enabled mobile-TVRF front-end in 90nm CMOS and the first mixed-voltage full-band mobile-TV Receiver in 65nm CMOS; Demonstrates the feasibility of high-/mixed-voltage circuit techniques with real design examples.  

  1. Synchrony and neural coding in cerebellar circuits

    Directory of Open Access Journals (Sweden)

    Abigail L Person

    2012-12-01

    circuits.

  2. Breathtaking Songs: Coordinating the Neural Circuits for Breathing and Singing.

    Science.gov (United States)

    Schmidt, Marc F; Goller, Franz

    2016-11-01

    The vocal behavior of birds is remarkable for its diversity, and songs can feature elaborate characteristics such as long duration, rapid temporal pattern, and broad frequency range. The respiratory system plays a central role in generating the complex song patterns that must be integrated with its life-sustaining functions. Here, we explore how precise coordination between the neural circuits for breathing and singing is fundamental to production of these remarkable behaviors. ©2016 Int. Union Physiol. Sci./Am. Physiol. Soc.

  3. A wide bandwidth analog front-end circuit for 60-GHz wireless communication receiver

    Science.gov (United States)

    Furuta, M.; Okuni, H.; Hosoya, M.; Sai, A.; Matsuno, J.; Saigusa, S.; Itakura, T.

    2014-03-01

    This paper presents an analog front-end circuit for a 60-GHz wireless communication receiver. The feature of the proposed analog front-end circuit is a bandwidth more than 1-GHz wide. To expand the bandwidth of a low-pass filter and a voltage gain amplifier, a technique to reduce the parasitic capacitance of a transconductance amplifier is proposed. Since the bandwidth is also limited by on-resistance of the ADC sampling switch, a switch separation technique for reduction of the on-resistance is also proposed. In a high-speed ADC, the SNDR is limited by the sampling jitter. The developed high resolution VCO auto tuning effectively reduces the jitter of PLL. The prototype is fabricated in 65nm CMOS. The analog front-end circuit achieves over 1-GHz bandwidth and 27.2-dB SNDR with 224 mW Power consumption.

  4. Microwave Photonic Architecture for Direction Finding of LPI Emitters: Front End Analog Circuit Design and Component Characterization

    Science.gov (United States)

    2016-09-01

    PHOTONIC ARCHITECTURE FOR DIRECTION FINDING OF LPI EMITTERS: FRONT-END ANALOG CIRCUIT DESIGN AND COMPONENT CHARACTERIZATION by Chew K. Tan...PHOTONIC ARCHITECTURE FOR DIRECTION FINDING OF LPI EMITTERS: FRONT-END ANALOG CIRCUIT DESIGN AND COMPONENT CHARACTERIZATION 5. FUNDING NUMBERS 6. AUTHOR... ANALOG CIRCUIT DESIGN AND COMPONENT CHARACTERIZATION Chew K. Tan Military Expert 6, Republic of Singapore Navy B.E. (Hons), University of New

  5. A Transistor Sizing Tool for Optimization of Analog CMOS Circuits: TSOp

    Directory of Open Access Journals (Sweden)

    Y.C.Wong

    2015-02-01

    Full Text Available Optimization of a circuit by transistor sizing is often a slow, tedious and iterative manual process which relies on designer intuition. It is highly desirable to automate the transistor sizing process towards being able to rapidly design high performance integrated circuit. Presented here is a simple but effective algorithm for automatically optimizing the circuit parameters by exploiting the relationships among the genetic algorithm's coefficient values derived from the analog circuit design variables. Simulation results demonstrate that the proposed algorithm (TSOp converges to optimal solutions efficiently for the circuits which contain discrete or discontinuous parameters constraints in a large search spaces. The robustness of TSOp has been verified by using a cascaded amplifier assisted inverter and an operational amplifier circuitries based on TSMC 0.25um process technology. Even though with a large number of design variables, TSOp successfully converges to a range of optimum solution for the targeted circuit performance. TSOp achieves optimum solutions and simplifies the design steps in developing an analog circuit, thereby significantly improving the time to market for an integrated circuit chip.

  6. Analog and Digital Circuit Design in 65 nm CMOS: End of the Road?

    CERN Document Server

    Gielen, Georges; Christie, Phillip; Draxelmayr, Dieter; Janssens, Edmond; Maex, Karen; Vucurevich, Ted

    2011-01-01

    This special session adresses the problems that designers face when implementing analog and digital circuits in nanometer technologies. An introductory embedded tutorial will give an overview of the design problems at hand : the leakage power and process variability and their implications for digital circuits and memories, and the reducing supply voltages, the design productivity and signal integrity problems for embedded analog blocks. Next, a panel of experts from both industrial semiconductor houses and design companies, EDA vendors and research institutes will present and discuss with the audience their opinions on whether the design road ends at marker "65nm" or not.

  7. New Strategy for Analog Circuit Performance Evaluation under Disturbance and Fault Value

    Directory of Open Access Journals (Sweden)

    Aihua Zhang

    2014-01-01

    Full Text Available Focus on this issue of disturbance and fault value is inevitable in data collection about analog circuit. A novel strategy is developed for analog circuit online performance evaluation based on fuzzy learning and double weighted support vector machine (DWMK-FSVM. First, the double weighted support vector regression machine is employed to be the indirect evaluation means, relied on the college analog electronic technology experiment to evaluate analog circuit. Second, the superiority of fuzzy learning also is addressed to realize active suppression to the fault values and disturbance parameters. Moreover, the multikernel RBF is employed by support vector regression machine to realize more flexibility online such as the bandwidths tuning. Numerical results, supported by the college analog circuit experiments, adopted OTL performance eight indexes, which were obtained via precision instrument evaluation in two years to construct training set and are then to be evaluated online based on DWMK-FSVM. Simulation results presented not only highlight precision of the evaluation strategy derived here but also illustrate its great robustness.

  8. Analysis and application of analog electronic circuits to biomedical instrumentation

    CERN Document Server

    Northrop, Robert B

    2012-01-01

    All chapters include an introduction and chapter summary.Sources and Properties of Biomedical SignalsSources of Endogenous Bioelectric SignalsNerve Action PotentialsMuscle Action PotentialsThe ElectrocardiogramOther BiopotentialsElectrical Properties of BioelectrodesExogenous Bioelectric SignalsProperties and Models of Semiconductor Devices Used in Analog Electronic Systemspn Junction DiodesMidfrequency Models for BJT BehaviorMidfrequency Models for Field-Effect TransistorsHigh-Frequency Models for Transistors and Simple Transistor AmplifiersPhotons, Photodiodes, Photoconductors, LEDs, and Las

  9. Integrated electrofluidic circuits: pressure sensing with analog and digital operation functionalities for microfluidics.

    Science.gov (United States)

    Wu, Chueh-Yu; Lu, Jau-Ching; Liu, Man-Chi; Tung, Yi-Chung

    2012-10-21

    Microfluidic technology plays an essential role in various lab on a chip devices due to its desired advantages. An automated microfluidic system integrated with actuators and sensors can further achieve better controllability. A number of microfluidic actuation schemes have been well developed. In contrast, most of the existing sensing methods still heavily rely on optical observations and external transducers, which have drawbacks including: costly instrumentation, professional operation, tedious interfacing, and difficulties of scaling up and further signal processing. This paper reports the concept of electrofluidic circuits - electrical circuits which are constructed using ionic liquid (IL)-filled fluidic channels. The developed electrofluidic circuits can be fabricated using a well-developed multi-layer soft lithography (MSL) process with polydimethylsiloxane (PDMS) microfluidic channels. Electrofluidic circuits allow seamless integration of pressure sensors with analog and digital operation functions into microfluidic systems and provide electrical readouts for further signal processing. In the experiments, the analog operation device is constructed based on electrofluidic Wheatstone bridge circuits with electrical outputs of the addition and subtraction results of the applied pressures. The digital operation (AND, OR, and XOR) devices are constructed using the electrofluidic pressure controlled switches, and output electrical signals of digital operations of the applied pressures. The experimental results demonstrate the designed functions for analog and digital operations of applied pressures are successfully achieved using the developed electrofluidic circuits, making them promising to develop integrated microfluidic systems with capabilities of precise pressure monitoring and further feedback control for advanced lab on a chip applications.

  10. Digital Operation of Microelectronic Circuits Analogous to Protein Hydrogen Bonding Networks

    Directory of Open Access Journals (Sweden)

    Elitsa Gieva

    2012-12-01

    Full Text Available Two hydrogen bonding networks with water molecules and branching residues extracted from β-lactamase protein are investigated and their proton transfer characteristics are studied by creating analogous electrical circuits consisting of block-elements. The block-elements and their proton transfer are described by polynomials that are coded in Matlab and in Verilog-A for use in the Spectre simulator of Cadence IC design system. DC and digital pulse analyses are performed to demonstrate that some circuit outputs behave as repeaters while other - behave as inverters. The results also showed that the HBN circuits might behave as a D-latch and a demultiplexer.

  11. Biophysical Neural Spiking, Bursting, and Excitability Dynamics in Reconfigurable Analog VLSI.

    Science.gov (United States)

    Yu, T; Sejnowski, T J; Cauwenberghs, G

    2011-10-01

    We study a range of neural dynamics under variations in biophysical parameters underlying extended Morris-Lecar and Hodgkin-Huxley models in three gating variables. The extended models are implemented in NeuroDyn, a four neuron, twelve synapse continuous-time analog VLSI programmable neural emulation platform with generalized channel kinetics and biophysical membrane dynamics. The dynamics exhibit a wide range of time scales extending beyond 100 ms neglected in typical silicon models of tonic spiking neurons. Circuit simulations and measurements show transition from tonic spiking to tonic bursting dynamics through variation of a single conductance parameter governing calcium recovery. We similarly demonstrate transition from graded to all-or-none neural excitability in the onset of spiking dynamics through the variation of channel kinetic parameters governing the speed of potassium activation. Other combinations of variations in conductance and channel kinetic parameters give rise to phasic spiking and spike frequency adaptation dynamics. The NeuroDyn chip consumes 1.29 mW and occupies 3 mm × 3 mm in 0.5 μm CMOS, supporting emerging developments in neuromorphic silicon-neuron interfaces.

  12. An Implantable Mixed Analog/Digital Neural Stimulator Circuit

    DEFF Research Database (Denmark)

    Gudnason, Gunnar; Bruun, Erik; Haugland, Morten

    1999-01-01

    transmitted to the stimulator, the chip is able to generate charge-balanced current pulses with a controllable length and amplitude for stimulation of nerve fibres. The chip has 4 output channels so that it can be employed in a cuff electrode with multiple connections to a nerve. The purpose of the functional...

  13. Design and analysis of CMOS analog signal processing circuits by means of a graphical MOST model

    NARCIS (Netherlands)

    Wallinga, Hans; Bult, Klaas

    1989-01-01

    A graphical representation of a simple MOST (metal-oxide-semiconductor transistor) model for the analysis of analog MOS circuits operating in strong inversion is given. It visualizes the principles of signal-processing techniques depending on the characteristics of an MOS transistor. Several lineari

  14. Neural dynamics and circuit mechanisms of decision-making.

    Science.gov (United States)

    Wang, Xiao-Jing

    2012-12-01

    In this review, I briefly summarize current neurobiological studies of decision-making that bear on two general themes. The first focuses on the nature of neural representation and dynamics in a decision circuit. Experimental and computational results suggest that ramping-to-threshold in the temporal domain and trajectory of population activity in the state space represent a duality of perspectives on a decision process. Moreover, a decision circuit can display several different dynamical regimes, such as the ramping mode and the jumping mode with distinct defining properties. The second is concerned with the relationship between biologically-based mechanistic models and normative-type models. A fruitful interplay between experiments and these models at different levels of abstraction have enabled investigators to pose increasingly refined questions and gain new insights into the neural basis of decision-making. In particular, recent work on multi-alternative decisions suggests that deviations from rational models of choice behavior can be explained by established neural mechanisms.

  15. Grand Research Plan for Neural Circuits of Emotion and Memory-Current status of neural circuit studies in China

    Institute of Scientific and Technical Information of China (English)

    Yuan-Gui Zhu; He-Qi Cao; Er-Dan Dong

    2013-01-01

    During recent years,major advances have been made in neuroscience,i.e.,asynchronous release,three-dimensional structural data sets,saliency maps,magnesium in brain research,and new functional roles of long non-coding RNAs.Especially,the development of optogenetic technology provides access to important information about relevant neural circuits by allowing the activation of specific neurons in awake mammals and directly observing the resulting behavior.The Grand Research Plan for Neural Circuits of Emotion and Memory was launched by the National Natural Science Foundation of China.It takes emotion and memory as its main objects,making the best use of cutting-edge technologies from medical science,life science and information science.In this paper,we outline the current status of neural circuit studies in China and the technologies and methodologies being applied,as well as studies related to the impairments of emotion and memory.In this phase,we are making efforts to repair the current deficiencies by making adjustments,mainly involving four aspects of core scientific issues to investigate these circuits at multiple levels.Five research directions have been taken to solve important scientific problems while the Grand Research Plan is implemented.Future research into this area will be multimodal,incorporating a range of methods and sciences into each project.Addressing these issues will ensure a bright future,major discoveries,and a higher level of treatment for all affected by debilitating brain illnesses.

  16. Digital-analog quantum simulation of generalized Dicke models with superconducting circuits

    Science.gov (United States)

    Lamata, Lucas

    2017-01-01

    We propose a digital-analog quantum simulation of generalized Dicke models with superconducting circuits, including Fermi- Bose condensates, biased and pulsed Dicke models, for all regimes of light-matter coupling. We encode these classes of problems in a set of superconducting qubits coupled with a bosonic mode implemented by a transmission line resonator. Via digital-analog techniques, an efficient quantum simulation can be performed in state-of-the-art circuit quantum electrodynamics platforms, by suitable decomposition into analog qubit-bosonic blocks and collective single-qubit pulses through digital steps. Moreover, just a single global analog block would be needed during the whole protocol in most of the cases, superimposed with fast periodic pulses to rotate and detune the qubits. Therefore, a large number of digital steps may be attained with this approach, providing a reduced digital error. Additionally, the number of gates per digital step does not grow with the number of qubits, rendering the simulation efficient. This strategy paves the way for the scalable digital-analog quantum simulation of many-body dynamics involving bosonic modes and spin degrees of freedom with superconducting circuits. PMID:28256559

  17. Digital-analog quantum simulation of generalized Dicke models with superconducting circuits

    Science.gov (United States)

    Lamata, Lucas

    2017-03-01

    We propose a digital-analog quantum simulation of generalized Dicke models with superconducting circuits, including Fermi- Bose condensates, biased and pulsed Dicke models, for all regimes of light-matter coupling. We encode these classes of problems in a set of superconducting qubits coupled with a bosonic mode implemented by a transmission line resonator. Via digital-analog techniques, an efficient quantum simulation can be performed in state-of-the-art circuit quantum electrodynamics platforms, by suitable decomposition into analog qubit-bosonic blocks and collective single-qubit pulses through digital steps. Moreover, just a single global analog block would be needed during the whole protocol in most of the cases, superimposed with fast periodic pulses to rotate and detune the qubits. Therefore, a large number of digital steps may be attained with this approach, providing a reduced digital error. Additionally, the number of gates per digital step does not grow with the number of qubits, rendering the simulation efficient. This strategy paves the way for the scalable digital-analog quantum simulation of many-body dynamics involving bosonic modes and spin degrees of freedom with superconducting circuits.

  18. A PURE NODAL-ANALYSIS METHOD SUITABLE FOR ANALOG CIRCUITS USING NULLORS

    Directory of Open Access Journals (Sweden)

    E. Tlelo-Cuautle

    2003-09-01

    Full Text Available A novel technique suitable for computer-aided analysis of analog integrated circuits (ICs is introduced. This technique uses the features of both nodal-analysis (NA and symbolic analysis, at nullor level. First, the nullor is used to model the ideal behavior of several analog devices, namely: transistors, opamps, OTAs, and current conveyors. From this modeling approach, it is shown how to transform circuits working in voltage-mode to current-mode and vice-versa. Second, it is demonstrated that using nullors, all non-NA-compatible elements can be transformed into NA-compatible ones, this results in a computationally-improved pure-NA method. Third, the computation of fully-symbolic expressions using , is described. It is demonstrated that a symbolic expression gives more insight in the behavior and performance of the circuit. Finally, several examples demonstrate the suitability and appropriateness of the proposed method to be used in education.

  19. Low Voltage Analog Circuit Design Based on the Flipped Voltage Follower

    Directory of Open Access Journals (Sweden)

    Neeraj Yadav

    2012-03-01

    Full Text Available The desire for portability of electronics equipment generated a need for low power system in battery products like hearing aids, implantable cardiac pacemakers, cell phones and hand held multimedia terminals. Low voltage analog circuit design differs considerably from those of high voltage analog circuit design. This paper present the basic cell knows as “flipped voltage follower” for low voltage/ low power operation. The detailed classification of basic topologies derived from the FVF cell is presented and there is a low voltage current mirror based on FVF cell has been presented. All the Circuit has been simulated using Hspice tool 0.18µm CMOS Technology. Different quality factors such as frequency response, power consumption are considered. A compression also made between previous current mirror and new designed current mirror. The layout of the current mirror has been also designed using Cadence tool.

  20. Analog CMOS contrastive Hebbian networks

    Science.gov (United States)

    Schneider, Christian; Card, Howard

    1992-09-01

    CMOS VLSI circuits implementing an analog neural network with on-chip contrastive Hebbian learning and capacitive synaptic weight storage have been designed and fabricated. Weights are refreshed by periodic repetition of the training data. To evaluate circuit performance in a medium-sized system, these circuits were used to build a 132 synapse neural network. An adaptive neural system, such as the one described in this paper, can compensate for imperfections in the components from which it is constructed, and thus it is possible to build this type of system using simple, silicon area-efficient analog circuits. Because these analog VLSI circuits are far more compact than their digital counterparts, analog VLSI neural network implementations are potentially more efficient than digital ones.

  1. Progress in understanding mood disorders: optogenetic dissection of neural circuits.

    Science.gov (United States)

    Lammel, S; Tye, K M; Warden, M R

    2014-01-01

    Major depression is characterized by a cluster of symptoms that includes hopelessness, low mood, feelings of worthlessness and inability to experience pleasure. The lifetime prevalence of major depression approaches 20%, yet current treatments are often inadequate both because of associated side effects and because they are ineffective for many people. In basic research, animal models are often used to study depression. Typically, experimental animals are exposed to acute or chronic stress to generate a variety of depression-like symptoms. Despite its clinical importance, very little is known about the cellular and neural circuits that mediate these symptoms. Recent advances in circuit-targeted approaches have provided new opportunities to study the neuropathology of mood disorders such as depression and anxiety. We review recent progress and highlight some studies that have begun tracing a functional neuronal circuit diagram that may prove essential in establishing novel treatment strategies in mood disorders. First, we shed light on the complexity of mesocorticolimbic dopamine (DA) responses to stress by discussing two recent studies reporting that optogenetic activation of midbrain DA neurons can induce or reverse depression-related behaviors. Second, we describe the role of the lateral habenula circuitry in the pathophysiology of depression. Finally, we discuss how the prefrontal cortex controls limbic and neuromodulatory circuits in mood disorders.

  2. Olfactory systems and neural circuits that modulate predator odor fear.

    Science.gov (United States)

    Takahashi, Lorey K

    2014-01-01

    When prey animals detect the odor of a predator a constellation of fear-related autonomic, endocrine, and behavioral responses rapidly occur to facilitate survival. How olfactory sensory systems process predator odor and channel that information to specific brain circuits is a fundamental issue that is not clearly understood. However, research in the last 15 years has begun to identify some of the essential features of the sensory detection systems and brain structures that underlie predator odor fear. For instance, the main (MOS) and accessory olfactory systems (AOS) detect predator odors and different types of predator odors are sensed by specific receptors located in either the MOS or AOS. However, complex predator chemosignals may be processed by both the MOS and AOS, which complicate our understanding of the specific neural circuits connected directly and indirectly from the MOS and AOS to activate the physiological and behavioral components of unconditioned and conditioned fear. Studies indicate that brain structures including the dorsal periaqueductal gray (DPAG), paraventricular nucleus (PVN) of the hypothalamus, and the medial amygdala (MeA) appear to be broadly involved in predator odor induced autonomic activity and hypothalamic-pituitary-adrenal (HPA) stress hormone secretion. The MeA also plays a key role in predator odor unconditioned fear behavior and retrieval of contextual fear memory associated with prior predator odor experiences. Other neural structures including the bed nucleus of the stria terminalis and the ventral hippocampus (VHC) appear prominently involved in predator odor fear behavior. The basolateral amygdala (BLA), medial hypothalamic nuclei, and medial prefrontal cortex (mPFC) are also activated by some but not all predator odors. Future research that characterizes how distinct predator odors are uniquely processed in olfactory systems and neural circuits will provide significant insights into the differences of how diverse predator

  3. Design of pressure-driven microfluidic networks using electric circuit analogy.

    Science.gov (United States)

    Oh, Kwang W; Lee, Kangsun; Ahn, Byungwook; Furlani, Edward P

    2012-02-07

    This article reviews the application of electric circuit methods for the analysis of pressure-driven microfluidic networks with an emphasis on concentration- and flow-dependent systems. The application of circuit methods to microfluidics is based on the analogous behaviour of hydraulic and electric circuits with correlations of pressure to voltage, volumetric flow rate to current, and hydraulic to electric resistance. Circuit analysis enables rapid predictions of pressure-driven laminar flow in microchannels and is very useful for designing complex microfluidic networks in advance of fabrication. This article provides a comprehensive overview of the physics of pressure-driven laminar flow, the formal analogy between electric and hydraulic circuits, applications of circuit theory to microfluidic network-based devices, recent development and applications of concentration- and flow-dependent microfluidic networks, and promising future applications. The lab-on-a-chip (LOC) and microfluidics community will gain insightful ideas and practical design strategies for developing unique microfluidic network-based devices to address a broad range of biological, chemical, pharmaceutical, and other scientific and technical challenges.

  4. Local Biasing and the Use of Nullator-Norator Pairs in Analog Circuits Designs

    Directory of Open Access Journals (Sweden)

    Reza Hashemian

    2010-01-01

    Full Text Available A new technique is presented for biasing of analog circuits. The biasing design begins with local biasing of the nonlinear components (transistors, done according to the pre-specified operating points (OPs and for the best performance of the circuit. Next, the transistors are replaced with their linear models to perform the AC design. Upon finishing with the AC design we need to move from the local biasing to global (normal biasing while the OPs are kept unchanged. Here fixators—nullators plus sources—are shown to be very instrumental and with norators—as the place holders for the DC supplies in the circuit—they make pairs. The solution of the circuit so prepared provides the DC supplies at the designated locations in the circuit. The rules to engage in circuit analysis with fixator-norator pairs are discussed, and numerous pitfalls in this line are specified. Finally, two design examples are worked out that clearly demonstrate the capability and power of the proposed technique for biasing any analog circuit.

  5. Generating three-qubit quantum circuits with neural networks

    Science.gov (United States)

    Swaddle, Michael; Noakes, Lyle; Smallbone, Harry; Salter, Liam; Wang, Jingbo

    2017-10-01

    A new method for compiling quantum algorithms is proposed and tested for a three qubit system. The proposed method is to decompose a unitary matrix U, into a product of simpler Uj via a neural network. These Uj can then be decomposed into product of known quantum gates. Key to the effectiveness of this approach is the restriction of the set of training data generated to paths which approximate minimal normal subRiemannian geodesics, as this removes unnecessary redundancy and ensures the products are unique. The two neural networks are shown to work effectively, each individually returning low loss values on validation data after relatively short training periods. The two networks are able to return coefficients that are sufficiently close to the true coefficient values to validate this method as an approach for generating quantum circuits. There is scope for more work in scaling this approach for larger quantum systems.

  6. Improved Estimation and Interpretation of Correlations in Neural Circuits

    Science.gov (United States)

    Yatsenko, Dimitri; Josić, Krešimir; Ecker, Alexander S.; Froudarakis, Emmanouil; Cotton, R. James; Tolias, Andreas S.

    2015-01-01

    Ambitious projects aim to record the activity of ever larger and denser neuronal populations in vivo. Correlations in neural activity measured in such recordings can reveal important aspects of neural circuit organization. However, estimating and interpreting large correlation matrices is statistically challenging. Estimation can be improved by regularization, i.e. by imposing a structure on the estimate. The amount of improvement depends on how closely the assumed structure represents dependencies in the data. Therefore, the selection of the most efficient correlation matrix estimator for a given neural circuit must be determined empirically. Importantly, the identity and structure of the most efficient estimator informs about the types of dominant dependencies governing the system. We sought statistically efficient estimators of neural correlation matrices in recordings from large, dense groups of cortical neurons. Using fast 3D random-access laser scanning microscopy of calcium signals, we recorded the activity of nearly every neuron in volumes 200 μm wide and 100 μm deep (150–350 cells) in mouse visual cortex. We hypothesized that in these densely sampled recordings, the correlation matrix should be best modeled as the combination of a sparse graph of pairwise partial correlations representing local interactions and a low-rank component representing common fluctuations and external inputs. Indeed, in cross-validation tests, the covariance matrix estimator with this structure consistently outperformed other regularized estimators. The sparse component of the estimate defined a graph of interactions. These interactions reflected the physical distances and orientation tuning properties of cells: The density of positive ‘excitatory’ interactions decreased rapidly with geometric distances and with differences in orientation preference whereas negative ‘inhibitory’ interactions were less selective. Because of its superior performance, this

  7. Improved estimation and interpretation of correlations in neural circuits.

    Directory of Open Access Journals (Sweden)

    Dimitri Yatsenko

    2015-03-01

    Full Text Available Ambitious projects aim to record the activity of ever larger and denser neuronal populations in vivo. Correlations in neural activity measured in such recordings can reveal important aspects of neural circuit organization. However, estimating and interpreting large correlation matrices is statistically challenging. Estimation can be improved by regularization, i.e. by imposing a structure on the estimate. The amount of improvement depends on how closely the assumed structure represents dependencies in the data. Therefore, the selection of the most efficient correlation matrix estimator for a given neural circuit must be determined empirically. Importantly, the identity and structure of the most efficient estimator informs about the types of dominant dependencies governing the system. We sought statistically efficient estimators of neural correlation matrices in recordings from large, dense groups of cortical neurons. Using fast 3D random-access laser scanning microscopy of calcium signals, we recorded the activity of nearly every neuron in volumes 200 μm wide and 100 μm deep (150-350 cells in mouse visual cortex. We hypothesized that in these densely sampled recordings, the correlation matrix should be best modeled as the combination of a sparse graph of pairwise partial correlations representing local interactions and a low-rank component representing common fluctuations and external inputs. Indeed, in cross-validation tests, the covariance matrix estimator with this structure consistently outperformed other regularized estimators. The sparse component of the estimate defined a graph of interactions. These interactions reflected the physical distances and orientation tuning properties of cells: The density of positive 'excitatory' interactions decreased rapidly with geometric distances and with differences in orientation preference whereas negative 'inhibitory' interactions were less selective. Because of its superior performance, this

  8. Design of Passive Analog Electronic Circuits Using Hybrid Modified UMDA algorithm

    Directory of Open Access Journals (Sweden)

    J. Slezak

    2015-04-01

    Full Text Available Hybrid evolutionary passive analog circuits synthesis method based on modified Univariate Marginal Distribution Algorithm (UMDA and a local search algorithm is proposed in the paper. The modification of the UMDA algorithm which allows to specify the maximum number of the nodes and the maximum number of the components of the synthesized circuit is proposed. The proposed hybrid approach efficiently reduces the number of the objective function evaluations. The modified UMDA algorithm is used for synthesis of the topology and the local search algorithm is used for determination of the parameters of the components of the designed circuit. As an example the proposed method is applied to a problem of synthesis of the fractional capacitor circuit.

  9. Realization of an analog predistortion circuit for RF optical fiber links

    Institute of Scientific and Technical Information of China (English)

    Tian Xuenong; Wang Zhigong; Li Wei

    2009-01-01

    This paper presents an analog predistortion circuit for RF optical fiber links. The circuit consists of two source-coupled differential transconductance amplifiers which could provide linear and nonlinear transfer charac-teristics by adjusting the bias voltage and the transistor sizes. The circuit was designed and realized in a standard 0.18-μm CMOS technology of SMIC. The chip occupies 0.48 × 0.24 mm~2. The DC supply is 3.3 V. Using this circuit, the third-order intermodulation distortion (IMD) suppression of a directly modulated RF optical fiber link can be improved by 9-16 dBc at relatively low cost.

  10. Operational amplifier speed and accuracy improvement analog circuit design with structural methodology

    CERN Document Server

    Ivanov, Vadim V

    2004-01-01

    Operational Amplifier Speed and Accuracy Improvement proposes a new methodology for the design of analog integrated circuits. The usefulness of this methodology is demonstrated through the design of an operational amplifier. This methodology consists of the following iterative steps: description of the circuit functionality at a high level of abstraction using signal flow graphs; equivalent transformations and modifications of the graph to the form where all important parameters are controlled by dedicated feedback loops; and implementation of the structure using a library of elementary cells. Operational Amplifier Speed and Accuracy Improvement shows how to choose structures and design circuits which improve an operational amplifier's important parameters such as speed to power ratio, open loop gain, common-mode voltage rejection ratio, and power supply rejection ratio. The same approach is used to design clamps and limiting circuits which improve the performance of the amplifier outside of its linear operat...

  11. Activity-dependent modulation of neural circuit synaptic connectivity

    Directory of Open Access Journals (Sweden)

    Charles R Tessier

    2009-07-01

    Full Text Available In many nervous systems, the establishment of neural circuits is known to proceed via a two-stage process; 1 early, activity-independent wiring to produce a rough map characterized by excessive synaptic connections, and 2 subsequent, use-dependent pruning to eliminate inappropriate connections and reinforce maintained synapses. In invertebrates, however, evidence of the activity-dependent phase of synaptic refinement has been elusive, and the dogma has long been that invertebrate circuits are “hard-wired” in a purely activity-independent manner. This conclusion has been challenged recently through the use of new transgenic tools employed in the powerful Drosophila system, which have allowed unprecedented temporal control and single neuron imaging resolution. These recent studies reveal that activity-dependent mechanisms are indeed required to refine circuit maps in Drosophila during precise, restricted windows of late-phase development. Such mechanisms of circuit refinement may be key to understanding a number of human neurological diseases, including developmental disorders such as Fragile X syndrome (FXS and autism, which are hypothesized to result from defects in synaptic connectivity and activity-dependent circuit function. This review focuses on our current understanding of activity-dependent synaptic connectivity in Drosophila, primarily through analyzing the role of the fragile X mental retardation protein (FMRP in the Drosophila FXS disease model. The particular emphasis of this review is on the expanding array of new genetically-encoded tools that are allowing cellular events and molecular players to be dissected with ever greater precision and detail.

  12. A novel analog/digital reconfigurable automatic gain control with a novel DC offset cancellation circuit*

    Institute of Scientific and Technical Information of China (English)

    He Xiaofeng; Mo Taishan; Ma Chengyan; Ye Tianchun

    2011-01-01

    An analog/digital reconfigurable automatic gain control (AGC) circuit with a novel DC offset cancellation circuit for a direct-conversion receiver is presented. The AGC is analog/digital reconfigurable in order to be compatible with different baseband chips. What's more, a novel DC offset cancellation (DCOC) circuit with an HPCF (high pass cutoff frequency) less than 10 kHz is proposed. The AGC is fabricated by a 0.18 μm CMOS process. Under analog control mode, the AGC achieves a 70 dB dynamic range with a 3 dB-bandwidth larger than 60 MHz. Under digital control mode, through a 5-bit digital control word, the AGC shows a 64 dB gain control range by 2 dB each step with a gain error of less than 0.3 dB. The DC offset cancellation circuits can suppress the output DC offset voltage to be less than 1.5 mV, while the offset voltage of 40 mV is introduced into the input. The overall power consumption is less than 3.5 mA, and the die area is 800 × 300μm2.

  13. Extinction of drug seeking: Neural circuits and approaches to augmentation.

    Science.gov (United States)

    McNally, Gavan P

    2014-01-01

    Extinction training can reduce drug seeking behavior. This article reviews the neural circuits that contribute to extinction and approaches to enhancing the efficacy of extinction. Extinction of drug seeking depends on cortical-striatal-hypothalamic and cortical-hypothalamic-thalamic pathways. These pathways interface, in the hypothalamus and thalamus respectively, with the neural circuits controlling reinstatement of drug seeking. The actions of these pathways at lateral hypothalamic orexin neurons, and of perifornical/dorsomedial hypothalamic derived opioid peptides at kappa opioid receptors in the paraventricular thalamus, are important for inhibiting drug seeking. Despite effectively reducing or inhibiting drug seeking in the short term, extinguished drug seeking is prone to relapse. Three different strategies to augment extinction learning or retrieval are reviewed: pharmacological augmentation, retrieval - extinction training, and provision of extinction memory retrieval cues. These strategies have been used in animal models and with human drug users to enhance extinction or cue exposure treatments. They hold promise as novel strategies to promote abstinence from drug seeking. This article is part of a Special Issue entitled 'NIDA 40th Anniversary Issue'.

  14. A New Automated Design Method Based on Machine Learning for CMOS Analog Circuits

    Science.gov (United States)

    Moradi, Behzad; Mirzaei, Abdolreza

    2016-11-01

    A new simulation based automated CMOS analog circuit design method which applies a multi-objective non-Darwinian-type evolutionary algorithm based on Learnable Evolution Model (LEM) is proposed in this article. The multi-objective property of this automated design of CMOS analog circuits is governed by a modified Strength Pareto Evolutionary Algorithm (SPEA) incorporated in the LEM algorithm presented here. LEM includes a machine learning method such as the decision trees that makes a distinction between high- and low-fitness areas in the design space. The learning process can detect the right directions of the evolution and lead to high steps in the evolution of the individuals. The learning phase shortens the evolution process and makes remarkable reduction in the number of individual evaluations. The expert designer's knowledge on circuit is applied in the design process in order to reduce the design space as well as the design time. The circuit evaluation is made by HSPICE simulator. In order to improve the design accuracy, bsim3v3 CMOS transistor model is adopted in this proposed design method. This proposed design method is tested on three different operational amplifier circuits. The performance of this proposed design method is verified by comparing it with the evolutionary strategy algorithm and other similar methods.

  15. High-Precision CMOS Analog Computational Circuits Based on a New Linearly Tunable OTA

    Directory of Open Access Journals (Sweden)

    A. Naderi Saatlo

    2016-06-01

    Full Text Available Implementation of CMOS current-mode analog computational circuits are presented in this paper. A new Linearly Tunable OTA is employed in a modified structure as a basic building block for implementation of the circuits either linear or nonlinear functions. The proposed trans-conductance amplifier provides a constant Gm over a wide range of input voltage which allows the implementation of high precision computational circuits including square rooting, squaring, multiplication and division functions. Layout pattern of the proposed circuit confirms that the circuit can be implemented in 102μm*69μm active area. In order to verify the performance of the circuits, the post layout simulation results are presented through the use of HSPICE and Cadence with TSMC level 49 (BSIM3v3 parameters for 0.18 μm CMOS technology, where under supply voltage of 1.8 V, the maximum relative error of the circuits within 500 µA of input range is about 11 μA (2.2 % error and the THD remains as low as 1.2 % for the worst case. Moreover, the power dissipation of the complete structure is found to be 0.66 mW.

  16. Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks.

    Science.gov (United States)

    de Bruin, Tim; Verbert, Kim; Babuska, Robert

    2017-03-01

    Timely detection and identification of faults in railway track circuits are crucial for the safety and availability of railway networks. In this paper, the use of the long-short-term memory (LSTM) recurrent neural network is proposed to accomplish these tasks based on the commonly available measurement signals. By considering the signals from multiple track circuits in a geographic area, faults are diagnosed from their spatial and temporal dependences. A generative model is used to show that the LSTM network can learn these dependences directly from the data. The network correctly classifies 99.7% of the test input sequences, with no false positive fault detections. In addition, the t-Distributed Stochastic Neighbor Embedding (t-SNE) method is used to examine the resulting network, further showing that it has learned the relevant dependences in the data. Finally, we compare our LSTM network with a convolutional network trained on the same task. From this comparison, we conclude that the LSTM network architecture is better suited for the railway track circuit fault detection and identification tasks than the convolutional network.

  17. Photovoltaic Pixels for Neural Stimulation: Circuit Models and Performance.

    Science.gov (United States)

    Boinagrov, David; Lei, Xin; Goetz, Georges; Kamins, Theodore I; Mathieson, Keith; Galambos, Ludwig; Harris, James S; Palanker, Daniel

    2016-02-01

    Photovoltaic conversion of pulsed light into pulsed electric current enables optically-activated neural stimulation with miniature wireless implants. In photovoltaic retinal prostheses, patterns of near-infrared light projected from video goggles onto subretinal arrays of photovoltaic pixels are converted into patterns of current to stimulate the inner retinal neurons. We describe a model of these devices and evaluate the performance of photovoltaic circuits, including the electrode-electrolyte interface. Characteristics of the electrodes measured in saline with various voltages, pulse durations, and polarities were modeled as voltage-dependent capacitances and Faradaic resistances. The resulting mathematical model of the circuit yielded dynamics of the electric current generated by the photovoltaic pixels illuminated by pulsed light. Voltages measured in saline with a pipette electrode above the pixel closely matched results of the model. Using the circuit model, our pixel design was optimized for maximum charge injection under various lighting conditions and for different stimulation thresholds. To speed discharge of the electrodes between the pulses of light, a shunt resistor was introduced and optimized for high frequency stimulation.

  18. An integrated modelling framework for neural circuits with multiple neuromodulators

    Science.gov (United States)

    Vemana, Vinith

    2017-01-01

    Neuromodulators are endogenous neurochemicals that regulate biophysical and biochemical processes, which control brain function and behaviour, and are often the targets of neuropharmacological drugs. Neuromodulator effects are generally complex partly owing to the involvement of broad innervation, co-release of neuromodulators, complex intra- and extrasynaptic mechanism, existence of multiple receptor subtypes and high interconnectivity within the brain. In this work, we propose an efficient yet sufficiently realistic computational neural modelling framework to study some of these complex behaviours. Specifically, we propose a novel dynamical neural circuit model that integrates the effective neuromodulator-induced currents based on various experimental data (e.g. electrophysiology, neuropharmacology and voltammetry). The model can incorporate multiple interacting brain regions, including neuromodulator sources, simulate efficiently and easily extendable to large-scale brain models, e.g. for neuroimaging purposes. As an example, we model a network of mutually interacting neural populations in the lateral hypothalamus, dorsal raphe nucleus and locus coeruleus, which are major sources of neuromodulator orexin/hypocretin, serotonin and norepinephrine/noradrenaline, respectively, and which play significant roles in regulating many physiological functions. We demonstrate that such a model can provide predictions of systemic drug effects of the popular antidepressants (e.g. reuptake inhibitors), neuromodulator antagonists or their combinations. Finally, we developed user-friendly graphical user interface software for model simulation and visualization for both fundamental sciences and pharmacological studies. PMID:28100828

  19. A novel technique for CAD-optimization of analog circuits with bipolar transistors

    Directory of Open Access Journals (Sweden)

    B. Dimov

    2009-05-01

    Full Text Available In this paper, a novel approach for robust automatic optimization of analog circuits with bipolar transistors is presented. It includes additional formal parameters into the device model cards, which sweep the model parameters smoothly between the different device types. In this way, not only the sizing, but also the choice of the device type is committed to the optimization tool, thus improving the efficiency of the design process significantly.

  20. Simulation Methodology for Analysis of Substrate Noise Impact on Analog / RF Circuits Including Interconnect Resistance

    CERN Document Server

    Soens, C; Wambacq, P; Donnay, S

    2011-01-01

    This paper reports a novel simulation methodology for analysis and prediction of substrate noise impact on analog / RF circuits taking into account the role of the parasitic resistance of the on-chip interconnect in the impact mechanism. This methodology allows investigation of the role of the separate devices (also parasitic devices) in the analog / RF circuit in the overall impact. This way is revealed which devices have to be taken care of (shielding, topology change) to protect the circuit against substrate noise. The developed methodology is used to analyze impact of substrate noise on a 3 GHz LC-tank Voltage Controlled Oscillator (VCO) designed in a high-ohmic 0.18 $\\mu$m 1PM6 CMOS technology. For this VCO (in the investigated frequency range from DC to 15 MHz) impact is mainly caused by resistive coupling of noise from the substrate to the non-ideal on-chip ground interconnect, resulting in analog ground bounce and frequency modulation. Hence, the presented test-case reveals the important role of the o...

  1. A Demonstrator Analog Signal Processing Circuit in a Radiation Hard SOI-CMOS Technology

    CERN Multimedia

    2002-01-01

    % RD-9 A Demonstrator Analog Signal Processing Circuit in a Radiation Hard SOI-CMOS Technology \\\\ \\\\Radiation hardened SOI-CMOS (Silicon-On-Insulator, Complementary Metal-Oxide- \\linebreak Semiconductor planar microelectronic circuit technology) was a likely candidate technology for mixed analog-digital signal processing electronics in experiments at the future high luminosity hadron colliders. We have studied the analog characteristics of circuit designs realized in the Thomson TCS radiation hard technologies HSOI3-HD. The feature size of this technology was 1.2 $\\mu$m. We have irradiated several devices up to 25~Mrad and 3.10$^{14}$ neutrons cm$^{-2}$. Gain, noise characteristics and speed have been measured. Irradiation introduces a degradation which in the interesting bandwidth of 0.01~MHz~-~1~MHz is less than 40\\%. \\\\ \\\\Some specific SOI phenomena have been studied in detail, like the influence on the noise spectrum of series resistence in the thin silicon film that constitutes the body of the transistor...

  2. Design of chaotic analog noise generators with logistic map and MOS QT circuits

    Energy Technology Data Exchange (ETDEWEB)

    Vazquez-Medina, R. [National Polytechnic Institute, IPN-Mexico (Mexico)], E-mail: ruvazquez@ipn.mx; Diaz-Mendez, A. [National Institute of Astrophysics, Optic and Electronics, INAOE-Mexico (Mexico)], E-mail: ajdiaz@inaoep.mx; Rio-Correa, J.L. del [Metropolitan University, UAM-Mexico (Mexico)], E-mail: jlrc@xanum.uam.mx; Lopez-Hernandez, J. [National Institute of Astrophysics, Optic and Electronics, INAOE-Mexico (Mexico)], E-mail: jlopezh@inaoep.mx

    2009-05-30

    In this paper a method to design chaotic analog noise generators using MOS transistors is presented. Two aspects are considered, the determination of operation regime of the MOS circuit and the statistical distribution of its output signal. The operation regime is related with the transconductance linear (TL: translinear) principle. For MOS transistors this principle was originally formulated in weak inversion regime; but, strong inversion regimen is used because in 1991, Seevinck and Wiegerink made the generalization for this principle. The statistical distribution of the output signal on the circuit, which should be a uniform distribution, is related with the parameter value that rules the transfer function of the circuit, the initial condition (seed) in the circuit and its operation as chaotic generator. To show these concepts, the MOS Quadratic Translinear circuit proposed by Wiegerink in 1993 was selected and it is related with the logistic map and its properties. This circuit will operate as noise generator if it works in strong inversion regime using current-mode approach when the parameter that rules the transfer function is higher than the onset chaos value (3.5699456...) for the logistic map.

  3. Design of a high linearity and high gain accuracy analog baseband circuit for DAB receiver

    Science.gov (United States)

    Li, Ma; Zhigong, Wang; Jian, Xu; Yiqiang, Wu; Junliang, Wang; Mi, Tian; Jianping, Chen

    2015-02-01

    An analog baseband circuit of high linearity and high gain accuracy for a digital audio broadcasting receiver is implemented in a 0.18-μm RFCMOS process. The circuit comprises a 3rd-order active-RC complex filter (CF) and a programmable gain amplifier (PGA). An automatic tuning circuit is also designed to tune the CF's pass band. Instead of the class-A fully differential operational amplifier (FDOPA) adopted in the conventional CF and PGA design, a class-AB FDOPA is specially employed in this circuit to achieve a higher linearity and gain accuracy for its large current swing capability with lower static current consumption. In the PGA circuit, a novel DC offset cancellation technique based on the MOS resistor is introduced to reduce the settling time significantly. A reformative switching network is proposed, which can eliminate the switch resistor's influence on the gain accuracy of the PGA. The measurement result shows the gain range of the circuit is 10-50 dB with a 1-dB step size, and the gain accuracy is less than ±0.3 dB. The OIP3 is 23.3 dBm at the gain of 10 dB. Simulation results show that the settling time is reduced from 100 to 1 ms. The image band rejection is about 40 dB. It only draws 4.5 mA current from a 1.8 V supply voltage.

  4. An Inductively-Powered Wireless Neural Recording System with a Charge Sampling Analog Front-End

    OpenAIRE

    Lee, Seung Bae; Lee, Byunghun; Kiani, Mehdi; Mahmoudi, Babak; Gross, Robert; Ghovanloo, Maysam

    2015-01-01

    An inductively-powered wireless integrated neural recording system (WINeR-7) is presented for wireless and battery less neural recording from freely-behaving animal subjects inside a wirelessly-powered standard homecage. The WINeR-7 system employs a novel wide-swing dual slope charge sampling (DSCS) analog front-end (AFE) architecture, which performs amplification, filtering, sampling, and analog-to-time conversion (ATC) with minimal interference and small amount of power. The output of the D...

  5. An analog circuit implementation of a Huber-Braun cold receptor neuron model.

    Science.gov (United States)

    Hermida, Raul; Patrone, Martin; Pijuan, Martin; Monzon, Pablo; Oreggioni, Julián

    2012-01-01

    We present the design and implementation of an electronic device that, using off the shelf discrete analog components, implements the mathematical model of a cold receptor neuron called Huber-Braun. This model describes the electrical behavior of certain kinds of receptors when interacting with their environment, and it consists of a set of differential equations that has only been solved by numeric simulations. By these means a chaotic behavior has been found. An analog computer can be relevant for further analysis and validation of the model. The results obtained by means of numeric simulations and through our analog circuit simulator are consistent. In particular, temperature and external current bifurcation diagrams were successfully built. Finally, the electronic device allows the observation of all relevant variables and most of the expected behavior (tonic firing, chaotic, burst discharge, subthreshold oscillation and steady state).

  6. A demonstrator analog signal processing circuit in a radiation hard SOI-CMOS technology

    CERN Document Server

    Anghinolfi, Francis; Campbell, M; Heijne, Erik H M; Jarron, Pierre; Meddeler, G; CERN. Geneva. Detector Research and Development Committee

    1990-01-01

    It is proposed to develop a demonstrator integrated circuit for particle detector analog signal processing using the advanced 1.2 micron HSOI3-HD Silicon-on-Insulator (SOI) CMOS radiation hard technology of Thomson-TMS, which has recently become accessible for selected civilian applications. The characteristics announced for this process promise survivability after a total dose in excess of 10 Mrad (SiO2) and 10**14 to 10**15 n/cm2, which is probably satisfactory for applications in LHC detector systems. The properties of such a SOI process look promising, in particular regarding speed. In view of the special analog requirements in the particle physics environment,one should verify the analog characteristics before and after irradiation by producing a demonstrator signal processing circuit which incorporates the most vital functional blocks. This demonstrator would consist of a low noise front-end amplifier, a comparator and an analog pipeline element with associated logic, following the scheme of the Hierarc...

  7. 电流模式可重构模拟信号处理电路%Current-Mode Reconfigurable Analog Circuit for Analog Signal Processing

    Institute of Scientific and Technical Information of China (English)

    王友仁; 祝鸣涛; 崔江; 林华; 姜媛媛

    2011-01-01

    There are problems of limited function, low operational speed and insufficient flexibility in the conventional reconfigurable analog circuits. A new current-mode reconfigurable analog circuit is proposed to solve them. The configurable analog block (CAB) based on second-generation current controlled conveyor (CCCII) is designed, which has the advantages of smaller nonlinear distortion, higher operational speed and better anti-interference ability. A new crossbar switch inter-connection network is developed, which can reduce the number of switches, and improve the flexibility and high frequency response of the reconfigurable analog circuit. The forth-order butterworth low-pass filter (LPF) and the analog multiplier are reconfigured with 2X4 reconfigurable analog circuit array. The experimental results show that the proposed reconfigurable analog circuit can effectively realize analog signal processing circuits of different functions through reconfiguration.%针对现有的可重构模拟电路存在功能有限、带宽小、灵活性不足等同题,设计了一种新的电流模式可重构模拟电路.设计了基于二代电流传输控制器的可重构模拟单元,能减小电路非线性失真,提高电路工作速度与抗干扰能力.设计了一种可编程开关数量较少的纵横交叉开关网络结构,提高了电路灵活性和高频性能.在2×4一阵列结构上分别重构实现了四阶低通滤波器和模拟乘法器,实验结果表明所设计可重构模拟电路是有效的.

  8. Relating functional connectivity in V1 neural circuits and 3D natural scenes using Boltzmann machines

    Science.gov (United States)

    Zhang, Yimeng; Li, Xiong; Samonds, Jason M.

    2015-01-01

    Bayesian theory has provided a compelling conceptualization for perceptual inference in the brain. Central to Bayesian inference is the notion of statistical priors. To understand the neural mechanisms of Bayesian inference, we need to understand the neural representation of statistical regularities in the natural environment. In this paper, we investigated empirically how statistical regularities in natural 3D scenes are represented in the functional connectivity of disparity-tuned neurons in the primary visual cortex of primates. We applied a Boltzmann machine model to learn from 3D natural scenes, and found that the units in the model exhibited cooperative and competitive interactions, forming a “disparity association field”, analogous to the contour association field. The cooperative and competitive interactions in the disparity association field are consistent with constraints of computational models for stereo matching. In addition, we simulated neurophysiological experiments on the model, and found the results to be consistent with neurophysiological data in terms of the functional connectivity measurements between disparity-tuned neurons in the macaque primary visual cortex. These findings demonstrate that there is a relationship between the functional connectivity observed in the visual cortex and the statistics of natural scenes. They also suggest that the Boltzmann machine can be a viable model for conceptualizing computations in the visual cortex and, as such, can be used to predict neural circuits in the visual cortex from natural scene statistics. PMID:26712581

  9. Analog circuit design structured mixed-mode design, multi-bit sigma-delta converters, short range RF circuits

    CERN Document Server

    van Roermund, Arthur

    2007-01-01

    Preface. Part I: Structured Mixed-Mode Design. Introduction. Structured Oscillator Design; C. Verhoeven, A. van Staveren. Systematic Design of High-frequency gm-C Filters; E. Lauwers, G. Gielen. Structured LNA Design; E.H. Nordholt. High-Level Simulation and Modeling Tools for Mixed-Signal Front-ends of Wireless Systems; P. Wambacq, et al. Structured Simulation-Based Analog Design Synthesis; R.A. Rutenbar. Structured Analog layout Design; K. Lampaert. Part II: Multi-Bit Sigma Delta Converters. Introduction. Architecture Considerations for Multi-Bit SigmaDelta ADCs; T. Brooks. Multirate Sigma-Delta Modulators, an Alternative to Multibit; F. Colodro, A. Torralba. Circuit Design Aspects of Multi-Bit Delta-Sigma Converters; Y. Geerts, et al. High-speed Digital to Analog Converter Issues with Applications to Sigma Delta Modulators; K. Doris, et al. Correction-Free Multi-Bit Sigma-Delta Modulators for ADSL; R. del Rio, et al. Sigma Delta Converters in Wireline Communications; A. Wiesbauer, et al. Part III: Short Ra...

  10. Neural circuits mediating olfactory-driven behavior in fish

    Directory of Open Access Journals (Sweden)

    Florence eKermen

    2013-04-01

    Full Text Available The fish olfactory system processes odor signals and mediates behaviors that are crucial for survival such as foraging, courtship and alarm response. Although the upstream olfactory brain areas (olfactory epithelium and olfactory bulb are well studied, less is known about their target brain areas and the role they play in generating odor-driven behaviors. Here we review a broad range of literature on the anatomy, physiology and behavioral output of the olfactory system and its target areas in a wide range of teleost fish. Additionally, we discuss how applying recent technological advancements to the zebrafish (Danio rerio could help in understanding the function of these target areas. We hope to provide a framework for elucidating the neural circuit computations underlying the odor-driven behaviors in this small, transparent and genetically amenable vertebrate.

  11. Reconstruction of virtual neural circuits in an insect brain

    Directory of Open Access Journals (Sweden)

    Shigehiro Namiki

    2009-09-01

    Full Text Available The reconstruction of large-scale nervous systems represents a major scientific and engineering challenge in current neuroscience research that needs to be resolved in order to understand the emergent properties of such systems. We focus on insect nervous systems because they represent a good compromise between architectural simplicity and the ability to generate a rich behavioral repertoire. In insects, several sensory maps have been reconstructed so far. We provide an overview over this work including our reconstruction of population activity in the primary olfactory network, the antennal lobe. Our reconstruction approach, that also provides functional connectivity data, will be refined and extended to allow the building of larger scale neural circuits up to entire insect brains, from sensory input to motor output.

  12. A low-power 32-channel digitally programmable neural recording integrated circuit.

    Science.gov (United States)

    Wattanapanitch, W; Sarpeshkar, R

    2011-12-01

    We report the design of an ultra-low-power 32-channel neural-recording integrated circuit (chip) in a 0.18 μ m CMOS technology. The chip consists of eight neural recording modules where each module contains four neural amplifiers, an analog multiplexer, an A/D converter, and a serial programming interface. Each amplifier can be programmed to record either spikes or LFPs with a programmable gain from 49-66 dB. To minimize the total power consumption, an adaptive-biasing scheme is utilized to adjust each amplifier's input-referred noise to suit the background noise at the recording site. The amplifier's input-referred noise can be adjusted from 11.2 μVrms (total power of 5.4 μW) down to 5.4 μVrms (total power of 20 μW) in the spike-recording setting. The ADC in each recording module digitizes the a.c. signal input to each amplifier at 8-bit precision with a sampling rate of 31.25 kS/s per channel, with an average power consumption of 483 nW per channel, and, because of a.c. coupling, allows d.c. operation over a wide dynamic range. It achieves an ENOB of 7.65, resulting in a net efficiency of 77 fJ/State, making it one of the most energy-efficient designs for neural recording applications. The presented chip was successfully tested in an in vivo wireless recording experiment from a behaving primate with an average power dissipation per channel of 10.1 μ W. The neural amplifier and the ADC occupy areas of 0.03 mm(2) and 0.02 mm(2) respectively, making our design simultaneously area efficient and power efficient, thus enabling scaling to high channel-count systems.

  13. Two-photon holographic optogenetics of neural circuits (Conference Presentation)

    Science.gov (United States)

    Yang, Weijian; Carrillo-Reid, Luis; Peterka, Darcy S.; Yuste, Rafael

    2016-03-01

    Optical manipulation of in vivo neural circuits with cellular resolution could be important for understanding cortical function. Despite recent progress, simultaneous optogenetic activation with cellular precision has either been limited to 2D planes, or a very small numbers of neurons over a limited volume. Here we demonstrate a novel paradigm for simultaneous 3D activation using a low repetition rate pulse-amplified fiber laser system and a spatial light modulator (SLM) to project 3D holographic excitation patterns on the cortex of mice in vivo for targeted volumetric 3D photoactivation. This method is compatible with two-photon imaging, and enables the simultaneous activation of multiple cells in 3D, using red-shifted opsins, such as C1V1 or ReaChR, while simultaneously imaging GFP-based sensors such as GCaMP6. This all-optical imaging and 3D manipulation approach achieves simultaneous reading and writing of cortical activity, and should be a powerful tool for the study of neuronal circuits.

  14. [Principles of design of neural-network analog-to-digital converters of bioelectric signals].

    Science.gov (United States)

    Loktiukhin, V N; Chelebaev, S V

    2007-01-01

    A design principle and a procedure for synthesis of neural-network analog-to-digital converters of bioelectric signals are suggested. An example of implementation of an FPGA-based neural-network converter for classification of bioparameters is presented.

  15. Acute Stress Influences Neural Circuits of Reward Processing

    Directory of Open Access Journals (Sweden)

    Anthony John Porcelli

    2012-11-01

    Full Text Available People often make decisions under aversive conditions such as acute stress. Yet, less is known about the process in which acute stress can influence decision-making. A growing body of research has established that reward-related information associated with the outcomes of decisions exerts a powerful influence over the choices people make and that an extensive network of brain regions, prominently featuring the striatum, is involved in the processing of this reward-related information. Thus, an important step in research on the nature of acute stress’ influence over decision-making is to examine how it may modulate responses to rewards and punishments within reward-processing neural circuitry. In the current experiment, we employed a simple reward processing paradigm – where participants received monetary rewards and punishments – known to evoke robust striatal responses. Immediately prior to performing each of two task runs, participants were exposed to acute stress (i.e., cold pressor or a no stress control procedure in a between-subjects fashion. No stress group participants exhibited a pattern of activity within the dorsal striatum and orbitofrontal cortex consistent with past research on outcome processing – specifically, differential responses for monetary rewards over punishments. In contrast, acute stress group participants’ dorsal striatum and orbitofrontal cortex demonstrated decreased sensitivity to monetary outcomes and a lack of differential activity. These findings provide insight into how neural circuits may process rewards and punishments associated with simple decisions under acutely stressful conditions.

  16. Long-Lasting Neural Circuit Dysfunction Following Developmental Ethanol Exposure

    Directory of Open Access Journals (Sweden)

    Mariko Saito

    2013-04-01

    Full Text Available Fetal Alcohol Spectrum Disorder (FASD is a general diagnosis for those exhibiting long-lasting neurobehavioral and cognitive deficiencies as a result of fetal alcohol exposure. It is among the most common causes of mental deficits today. Those impacted are left to rely on advances in our understanding of the nature of early alcohol-induced disorders toward human therapies. Research findings over the last decade have developed a model where ethanol-induced neurodegeneration impacts early neural circuit development, thereby perpetuating subsequent integration and plasticity in vulnerable brain regions. Here we review our current knowledge of FASD neuropathology based on discoveries of long-lasting neurophysiological effects of acute developmental ethanol exposure in animal models. We discuss the important balance between synaptic excitation and inhibition in normal neural network function, and relate the significance of that balance to human FASD as well as related disease states. Finally, we postulate that excitation/inhibition imbalance caused by early ethanol-induced neurodegeneration results in perturbed local and regional network signaling and therefore neurobehavioral pathology.

  17. Neural Networks Integrated Circuit for Biomimetics MEMS Microrobot

    Directory of Open Access Journals (Sweden)

    Ken Saito

    2014-06-01

    Full Text Available In this paper, we will propose the neural networks integrated circuit (NNIC which is the driving waveform generator of the 4.0, 2.7, 2.5 mm, width, length, height in size biomimetics microelectromechanical systems (MEMS microrobot. The microrobot was made from silicon wafer fabricated by micro fabrication technology. The mechanical system of the robot was equipped with small size rotary type actuators, link mechanisms and six legs to realize the ant-like switching behavior. The NNIC generates the driving waveform using synchronization phenomena such as biological neural networks. The driving waveform can operate the actuators of the MEMS microrobot directly. Therefore, the NNIC bare chip realizes the robot control without using any software programs or A/D converters. The microrobot performed forward and backward locomotion, and also changes direction by inputting an external single trigger pulse. The locomotion speed of the microrobot was 26.4 mm/min when the step width was 0.88 mm. The power consumption of the system was 250 mWh when the room temperature was 298 K.

  18. Optimization of cocoa butter analog synthesis variables using neural networks and genetic algorithm

    OpenAIRE

    Shekarchizadeh, Hajar; Tikani, Reza; Kadivar, Mahdi

    2012-01-01

    Cocoa butter analog was prepared from camel hump fat and tristearin by enzymatic interesterification in supercritical carbon dioxide (SC-CO2) using immobilized Thermomyces lanuginosus lipase (Lipozyme TL IM) as a biocatalyst. Optimal process conditions were determined using neural networks and genetic algorithm optimization. Response surfaces methodology was used to design the experiments to collect data for the neural network modelling. A general regression neural network model was developed...

  19. Nano-scale Bias-scalable CMOS Analog Computational Circuits Using Margin Propagation

    Institute of Scientific and Technical Information of China (English)

    GU Ming

    2012-01-01

    Approximation techniques are useful for implementing pattern recognizers,communication decoders and sensory processing algorithms where computational precision is not critical to achieve the desired system level performance.In our previous work,we had proposed margin propagation (MP) as an efficient piece-wise linear (PWL) approximation technique to a Iog-sumexp function and had demonstrated its advantages for implementing probabilistic decoders.In this paper,we present a systematic and a generalized approach for synthesizing analog piecewiselinear (PWL) computing circuits using the MP principle.MP circuits use only addition,subtraction and threshold operations and hence can be implemented using universal conservation principles like the Kirchoff' s current law.Thus,unlike the conventional translinear CMOS currentmode circuits,the operation of the MP circuits are functionally similar in weak,moderate and strong inversion regimes of the MOS transistor making the design approach bias-scalable.This paper presents measured results from MP circuits prototyped in a 0.5 μm standard CMOS process verifying the bias-scalable property.As an example,we apply the synthesis approach towards designing linear classifiers and verify its performance using measured results.

  20. Low-power analog integrated circuits for wireless ECG acquisition systems.

    Science.gov (United States)

    Tsai, Tsung-Heng; Hong, Jia-Hua; Wang, Liang-Hung; Lee, Shuenn-Yuh

    2012-09-01

    This paper presents low-power analog ICs for wireless ECG acquisition systems. Considering the power-efficient communication in the body sensor network, the required low-power analog ICs are developed for a healthcare system through miniaturization and system integration. To acquire the ECG signal, a low-power analog front-end system, including an ECG signal acquisition board, an on-chip low-pass filter, and an on-chip successive-approximation analog-to-digital converter for portable ECG detection devices is presented. A quadrature CMOS voltage-controlled oscillator and a 2.4 GHz direct-conversion transmitter with a power amplifier and upconversion mixer are also developed to transmit the ECG signal through wireless communication. In the receiver, a 2.4 GHz fully integrated CMOS RF front end with a low-noise amplifier, differential power splitter, and quadrature mixer based on current-reused folded architecture is proposed. The circuits have been implemented to meet the specifications of the IEEE 802.15.4 2.4 GHz standard. The low-power ICs of the wireless ECG acquisition systems have been fabricated using a 0.18 μm Taiwan Semiconductor Manufacturing Company (TSMC) CMOS standard process. The measured results on the human body reveal that ECG signals can be acquired effectively by the proposed low-power analog front-end ICs.

  1. Synaptic dynamics in analog VLSI.

    Science.gov (United States)

    Bartolozzi, Chiara; Indiveri, Giacomo

    2007-10-01

    Synapses are crucial elements for computation and information transfer in both real and artificial neural systems. Recent experimental findings and theoretical models of pulse-based neural networks suggest that synaptic dynamics can play a crucial role for learning neural codes and encoding spatiotemporal spike patterns. Within the context of hardware implementations of pulse-based neural networks, several analog VLSI circuits modeling synaptic functionality have been proposed. We present an overview of previously proposed circuits and describe a novel analog VLSI synaptic circuit suitable for integration in large VLSI spike-based neural systems. The circuit proposed is based on a computational model that fits the real postsynaptic currents with exponentials. We present experimental data showing how the circuit exhibits realistic dynamics and show how it can be connected to additional modules for implementing a wide range of synaptic properties.

  2. Fully Parallel Self-Learning Analog Support Vector Machine Employing Compact Gaussian Generation Circuits

    Science.gov (United States)

    Zhang, Renyuan; Shibata, Tadashi

    2012-04-01

    An analog support vector machine (SVM) processor employing a fully parallel self-learning circuitry was developed for the classification of highly dimensional patterns. To implement a highly dimensional Gaussian function, which is the most powerful kernel function in classification algorithms but computationally expensive, a compact analog Gaussian generation circuit was developed. By employing this proposed Gaussian generation circuit, a fully parallel self-learning processor based on an SVM algorithm was built for 64 dimension pattern classification. The chip real estate occupied by the processor is very small. The object images from two classes were converted into 64 dimension vectors using the algorithm developed in a previous work and fed into the processor. The learning process autonomously proceeded without any clock-based control and self-converged within a single clock cycle of the system (at 10 MHz). Some test object images were used to verify the learning performance. According to the circuit simulation results, it was shown that all the test images were classified into correct classes in real time. A proof-of-concept chip was designed in a 0.18 µm complementary metal-oxide-semiconductor (CMOS) technology, and the performance of the proposed SVM processor was confirmed from the measurement results of the fabricated chips.

  3. A Case Study Analysing the Process of Analogy-Based Learning in a Teaching Unit about Simple Electric Circuits

    Science.gov (United States)

    Paatz, Roland; Ryder, James; Schwedes, Hannelore; Scott, Philip

    2004-01-01

    The purpose of this case study is to analyse the learning processes of a 16-year-old student as she learns about simple electric circuits in response to an analogy-based teaching sequence. Analogical thinking processes are modelled by a sequence of four steps according to Gentner's structure mapping theory (activate base domain, postulate local…

  4. Analogy for Drude's Free Electron Model to Promote Students' Understanding of Electric Circuits in Lower Secondary School

    Science.gov (United States)

    de Almeida, Maria José B. M.; Salvador, Andreia; Costa, Maria Margarida R. R.

    2014-01-01

    Aiming at a deep understanding of some basic concepts of electric circuits in lower secondary schools, this work introduces an analogy between the behavior of children playing in a school yard with a central lake, subject to different conditions, rules, and stimuli, and Drude's free electron model of metals. Using this analogy from the first…

  5. An analog front-end circuit for ISO/IEC 15693-compatible RFID transponder IC

    Institute of Scientific and Technical Information of China (English)

    LIU Dong-sheng; ZOU Xue-cheng; YANG Qiu-ping; XIONG Ting-wen

    2006-01-01

    The 13.56 MHz analog front-end circuit for ISO/IEC 15693-compatible radio frequency identification (RFID) transponder IC presented in this paper converts RF power to DC and extracts clock and data from the interrogator by 10% or 100% ASK modulation. The transponder sends data back to the interrogator by load modulation technology. The electrostatic discharge (ESD)protection circuits function to limit RF voltage to a safe level. An inductive coupling simulation modelling for 13.56 MHz RFID system is presented, with simulation results showing that the transponder operates over a wide range of electromagnetic field strength from Hmin (150 mA/m) to Hmax (5 A/m). The transponder IC is implemented in SMIC 0.35-μm three-metal two-poly mixed signal CMOS technology with embedded EEPROM.

  6. SEMICONDUCTOR INTEGRATED CIRCUITS Design of an analog front-end for ambulatory biopotential measurement systems

    Science.gov (United States)

    Jiazhen, Wang; Jun, Xu; Lirong, Zheng; Junyan, Ren

    2010-10-01

    A continuously tunable gain and bandwidth analog front-end for ambulatory biopotential measurement systems is presented. The front-end circuit is capable of amplifying and conditioning different biosignals. To optimize the power consumption and simplify the system architecture, the front-end only adopts two-stage amplifiers. In addition, careful design eliminates the need for chopping circuits. The input-referred noise of the system is only 1.19 μVrms (0.48-2000 Hz). The chip is fabricated via a SMIC 0.18 μm CMOS process. Although the power consumption is only 32.1 μW under a 3 V voltage supply, test results show that the chip can successfully extract biopotential signals.

  7. Analog integrated circuit design automation placement, routing and parasitic extraction techniques

    CERN Document Server

    Martins, Ricardo; Horta, Nuno

    2017-01-01

    This book introduces readers to a variety of tools for analog layout design automation. After discussing the placement and routing problem in electronic design automation (EDA), the authors overview a variety of automatic layout generation tools, as well as the most recent advances in analog layout-aware circuit sizing. The discussion includes different methods for automatic placement (a template-based Placer and an optimization-based Placer), a fully-automatic Router and an empirical-based Parasitic Extractor. The concepts and algorithms of all the modules are thoroughly described, enabling readers to reproduce the methodologies, improve the quality of their designs, or use them as starting point for a new tool. All the methods described are applied to practical examples for a 130nm design process, as well as placement and routing benchmark sets. Introduces readers to hierarchical combination of Pareto fronts of placements; Presents electromigration-aware routing with multilayer multiport terminal structures...

  8. A neural circuit covarying with social hierarchy in macaques.

    Directory of Open Access Journals (Sweden)

    MaryAnn P Noonan

    2014-09-01

    Full Text Available Despite widespread interest in social dominance, little is known of its neural correlates in primates. We hypothesized that social status in primates might be related to individual variation in subcortical brain regions implicated in other aspects of social and emotional behavior in other mammals. To examine this possibility we used magnetic resonance imaging (MRI, which affords the taking of quantitative measurements noninvasively, both of brain structure and of brain function, across many regions simultaneously. We carried out a series of tests of structural and functional MRI (fMRI data in 25 group-living macaques. First, a deformation-based morphometric (DBM approach was used to show that gray matter in the amygdala, brainstem in the vicinity of the raphe nucleus, and reticular formation, hypothalamus, and septum/striatum of the left hemisphere was correlated with social status. Second, similar correlations were found in the same areas in the other hemisphere. Third, similar correlations were found in a second data set acquired several months later from a subset of the same animals. Fourth, the strength of coupling between fMRI-measured activity in the same areas was correlated with social status. The network of subcortical areas, however, had no relationship with the sizes of individuals' social networks, suggesting the areas had a simple and direct relationship with social status. By contrast a second circuit in cortex, comprising the midsuperior temporal sulcus and anterior and dorsal prefrontal cortex, covaried with both individuals' social statuses and the social network sizes they experienced. This cortical circuit may be linked to the social cognitive processes that are taxed by life in more complex social networks and that must also be used if an animal is to achieve a high social status.

  9. Clustered Protocadherins Are Required for Building Functional Neural Circuits

    Directory of Open Access Journals (Sweden)

    Takeshi Yagi

    2017-04-01

    Full Text Available Neuronal identity is generated by the cell-surface expression of clustered protocadherin (Pcdh isoforms. In mice, 58 isoforms from three gene clusters, Pcdhα, Pcdhβ, and Pcdhγ, are differentially expressed in neurons. Since cis-heteromeric Pcdh oligomers on the cell surface interact homophilically with that in other neurons in trans, it has been thought that the Pcdh isoform repertoire determines the binding specificity of synapses. We previously described the cooperative functions of isoforms from all three Pcdh gene clusters in neuronal survival and synapse formation in the spinal cord. However, the neuronal loss and the following neonatal lethality prevented an analysis of the postnatal development and characteristics of the clustered-Pcdh-null (Δαβγ neural circuits. Here, we used two methods, one to generate the chimeric mice that have transplanted Δαβγ neurons into mouse embryos, and the other to generate double mutant mice harboring null alleles of both the Pcdh gene and the proapoptotic gene Bax to prevent neuronal loss. First, our results showed that the surviving chimeric mice that had a high contribution of Δαβγ cells exhibited paralysis and died in the postnatal period. An analysis of neuronal survival in postnatally developing brain regions of chimeric mice clarified that many Δαβγ neurons in the forebrain were spared from apoptosis, unlike those in the reticular formation of the brainstem. Second, in Δαβγ/Bax null double mutants, the central pattern generator (CPG for locomotion failed to create a left-right alternating pattern even in the absence of neurodegeneraton. Third, calcium imaging of cultured hippocampal neurons showed that the network activity of Δαβγ neurons tended to be more synchronized and lost the variability in the number of simultaneously active neurons observed in the control network. Lastly, a comparative analysis for trans-homophilic interactions of the exogenously introduced single

  10. A Fault Dictionary-Based Fault Diagnosis Approach for CMOS Analog Integrated Circuits

    Directory of Open Access Journals (Sweden)

    Mouna Karmani

    2011-09-01

    Full Text Available In this paper, we propose a simulation-before-test (SBT fault diagnosis methodology based on the use of a fault dictionary approach. This technique allows the detection and localization of the most likely defects of open-circuit type occurring in Complementary Metal–Oxide–Semiconductor (CMOS analog integrated circuits (ICs interconnects. The fault dictionary is built by simulating the most likely defects causing the faults to be detected at the layout level. Then, for each injected fault, the spectre’s frequency responses and the power consumption obtained by simulation are stored in a table which constitutes the fault dictionary.In fact, each line in the fault dictionary constitutes a fault signature used to identify and locate a considered defect. When testing, the circuit under test is excited with the same stimulus, and the responses obtained are compared to the stored ones. To prove the efficiency of the proposed technique, a full custom CMOS operational amplifier is implemented in 0.25 μm technology and the most likely faults of open circuit type are deliberately injected and simulated at the layout level.

  11. A 66 MHz, 32-channel analog memory circuit with data selection for fast silicon detectors

    Energy Technology Data Exchange (ETDEWEB)

    Munday, D.; Parker, A. (Cavendish Lab., Univ. Cambridge (United Kingdom)); Anghinolfi, F.; Aspell, P.; Campbell, M.; Jarron, P.; Heijne, E.H.M.; Meddeler, G.; Santiard, J.C.; Verweij, H. (CERN, Geneva (Switzerland)); Goessling, C. (Inst. fuer Physik, Univ. Dortmund (Germany)); Bonino, R.; Clark, A.G.; Couyoumtzelis, C.; La Marra, D.; Wu, X. (DPNC, Geneva Univ. (Switzerland)); Moorhead, G. (School of Physics, Univ. Melbourne, Parkville (Ausralia)); Weidberg, A. (Dept. of Nuclear Physics, Oxford Univ. (United Kingdom)); Campbell, D.; Murray, P.; Seller, P.; Stevens, R. (Rutherford Appleton Lab., Chilton (United Kingdom)); Beuville, E.; Rouger, M.; Teiger, J. (Centre d' Etudes Nucleaires de Saclay, 91 - Gif-sur-Yvette (France)); RD2 Collaboration

    1993-03-01

    An analog memory array with 64 memory cells for each channel has been designed and manufactured in CMOS. A new skip logic controller allows to write at 66 MHz without dead time and to read out at a lower frequency simultaneously. The input circuit is charge-sensitive and integrates continuously. Pedestal nonuniformity is 1.4 mV rms from cell-to-cell and 3.5 mV rms between channels. The linearity range is -2.5 to +1.5 V, which corresponds to 11 bits. The chip has been used in a particle detection test. (orig.).

  12. Efficient Circuit Configuration for Enhancing Resolution of 8-bit flash Analog to Digital Convertor

    Directory of Open Access Journals (Sweden)

    Gururaj Balikatti

    2012-11-01

    Full Text Available The need constantly exists for converters with higher resolution, faster conversion speeds and lower power dissipation. High speed analog to digital converters (ADCs have been based on flash architecture, because all comparators sample the analog input voltage simultaneously, this ADC is thus inherently fast. Unfortunately flash ADC requires 2N - 1 comparators to convert N bit digital code from an analog sample. This makes flash ADCs unsuitable for high resolution applications. The focus of this paper is on efficient circuit configuration to enhance resolution of available 8-bit flash ADC, while maintaining number of comparators only 256 for 12 bit conversion. This technique optimizes the number of comparator requirements. In this approach, an 8-bit flash ADC partitions the analog input range into 256 quantization cells, separated by 255 boundary points. An 8-bit binary code 00000000 to 11111111 is assigned to each cell. The Microcontroller decides within which cell the input sample lies and assigns a 12-bit binary center code 000000000000 to 111111111111 according to the cell value. The exact 12-bit digital code is obtained by successive approximation technique. In this paper the focus will be on all-around efficient circuit for enhancing resolution of 8-bit Flash ADC. It is shown that by adopting this configuration, we can obtain 12-bit digital data just using 256 comparators. Therefore this technique is best suitable when high speed combined with high resolution is required. An experimental prototype of proposed 12-bit ADC was implemented using Philips P89V51RD2BN Microcontroller. Use of Microcontroller has greatly reduced the hardware requirement and cost. An ADC result of 12-bit prototype is presented. The results show that the ADC exhibits a maximum DNL of 0.52LSB and a maximum INL of 0.55LSB.

  13. Layout Design for CMOS Analog Circuit%CMOS模拟集成电路版图设计

    Institute of Scientific and Technical Information of China (English)

    解放; 罗闯

    2012-01-01

    由于模拟集成电路的性能与版图设计密切相关,着重介绍了CMOS模拟电路版图设计的一般思路,优化器件结构和平面布局使寄生效应对电路性能的影响降至最低.%Because the performance of analog circuit have great relationship with layout design, commonconsideration about layout design of CMOS analog circuit is introduced. Optimization of device structure and plane floorplan degrade the effect of parasitical to the minimum for circuit performance.

  14. Dissociable neural systems for analogy and metaphor: implications for the neuroscience of creativity.

    Science.gov (United States)

    Vartanian, Oshin

    2012-08-01

    Two recent reviews of the neuroimaging literature on creativity have pointed to inconsistent findings across studies, calling into question the usefulness of the theoretical constructs motivating the search for its neural bases. However, it is argued that consistent patterns of neural activation do emerge when the cognitive process and the neuroimaging method are kept uniform across studies. To demonstrate this empirically, the activation likelihood estimation (ALE) method was used to conduct quantitative meta-analyses of functional magnetic resonance imaging (fMRI) experiments of analogy and metaphor - two processes related to creativity and included in the recent reviews. The results demonstrated that analogy and metaphor reliably activate consistent but dissociable brain regions across fMRI studies. The implications of the findings for cognitive theories of analogy and metaphor are discussed. Furthermore, these results demonstrate that to the extent that creativity has heterogeneous sources, its neural instantiation will vary as a function of the underlying cognitive processes.

  15. Noise-shaping gradient descent-based online adaptation algorithms for digital calibration of analog circuits.

    Science.gov (United States)

    Chakrabartty, Shantanu; Shaga, Ravi K; Aono, Kenji

    2013-04-01

    Analog circuits that are calibrated using digital-to-analog converters (DACs) use a digital signal processor-based algorithm for real-time adaptation and programming of system parameters. In this paper, we first show that this conventional framework for adaptation yields suboptimal calibration properties because of artifacts introduced by quantization noise. We then propose a novel online stochastic optimization algorithm called noise-shaping or ΣΔ gradient descent, which can shape the quantization noise out of the frequency regions spanning the parameter adaptation trajectories. As a result, the proposed algorithms demonstrate superior parameter search properties compared to floating-point gradient methods and better convergence properties than conventional quantized gradient-methods. In the second part of this paper, we apply the ΣΔ gradient descent algorithm to two examples of real-time digital calibration: 1) balancing and tracking of bias currents, and 2) frequency calibration of a band-pass Gm-C biquad filter biased in weak inversion. For each of these examples, the circuits have been prototyped in a 0.5-μm complementary metal-oxide-semiconductor process, and we demonstrate that the proposed algorithm is able to find the optimal solution even in the presence of spurious local minima, which are introduced by the nonlinear and non-monotonic response of calibration DACs.

  16. An area and power-efficient analog li-ion battery charger circuit.

    Science.gov (United States)

    Do Valle, Bruno; Wentz, Christian T; Sarpeshkar, Rahul

    2011-04-01

    The demand for greater battery life in low-power consumer electronics and implantable medical devices presents a need for improved energy efficiency in the management of small rechargeable cells. This paper describes an ultra-compact analog lithium-ion (Li-ion) battery charger with high energy efficiency. The charger presented here utilizes the tanh basis function of a subthreshold operational transconductance amplifier to smoothly transition between constant-current and constant-voltage charging regimes without the need for additional area- and power-consuming control circuitry. Current-domain circuitry for end-of-charge detection negates the need for precision-sense resistors in either the charging path or control loop. We show theoretically and experimentally that the low-frequency pole-zero nature of most battery impedances leads to inherent stability of the analog control loop. The circuit was fabricated in an AMI 0.5-μm complementary metal-oxide semiconductor process, and achieves 89.7% average power efficiency and an end voltage accuracy of 99.9% relative to the desired target 4.2 V, while consuming 0.16 mm(2) of chip area. To date and to the best of our knowledge, this design represents the most area-efficient and most energy-efficient battery charger circuit reported in the literature.

  17. Wafer-scalable high-performance CVD graphene devices and analog circuits

    Science.gov (United States)

    Tao, Li; Lee, Jongho; Li, Huifeng; Piner, Richard; Ruoff, Rodney; Akinwande, Deji

    2013-03-01

    Graphene field effect transistors (GFETs) will serve as an essential component for functional modules like amplifier and frequency doublers in analog circuits. The performance of these modules is directly related to the mobility of charge carriers in GFETs, which per this study has been greatly improved. Low-field electrostatic measurements show field mobility values up to 12k cm2/Vs at ambient conditions with our newly developed scalable CVD graphene. For both hole and electron transport, fabricated GFETs offer substantial amplification for small and large signals at quasi-static frequencies limited only by external capacitances at high-frequencies. GFETs biased at the peak transconductance point featured high small-signal gain with eventual output power compression similar to conventional transistor amplifiers. GFETs operating around the Dirac voltage afforded positive conversion gain for the first time, to our knowledge, in experimental graphene frequency doublers. This work suggests a realistic prospect for high performance linear and non-linear analog circuits based on the unique electron-hole symmetry and fast transport now accessible in wafer-scalable CVD graphene. *Support from NSF CAREER award (ECCS-1150034) and the W. M. Keck Foundation are appreicated.

  18. Towards Confirming Neural Circuit Inference from Population Calcium Imaging. NIPS Workshop on Connectivity Inference in Neuroimaging

    OpenAIRE

    NeuroData; Mishchenko, Y.; AM, Packer; TA, Machado; Yuste, R.; Paninski, L

    2015-01-01

    Vogelstein JT, Mishchenko Y, Packer AM, Machado TA, Yuste R, Paninski L. Towards Confirming Neural Circuit Inference from Population Calcium Imaging. NIPS Workshop on Connectivity Inference in Neuroimaging, 2009

  19. Use of the Analog Neural Networks in the Adaptive Antenna Control Systems

    Directory of Open Access Journals (Sweden)

    Z. Raida

    2002-09-01

    Full Text Available In the paper, original control system of adaptive antennas, which isbased on Kalman filter, is presented and compared with earlierapproaches to this problem. The designed control circuit eliminatessome disadvantages of the control circuits based on the classicalKalman neural network and the Wang one, and enables a real timeprocessing of quickly changing signals processed by adaptive antennas.Especially, the dependence of the convergence rate on ratio ofeigenvalues and the risk of instability are significantly reduced.

  20. Neural underpinnings of divergent production of rules in numerical analogical reasoning.

    Science.gov (United States)

    Wu, Xiaofei; Jung, Rex E; Zhang, Hao

    2016-05-01

    Creativity plays an important role in numerical problem solving. Although the neural underpinnings of creativity have been studied over decades, very little is known about neural mechanisms of the creative process that relates to numerical problem solving. In the present study, we employed a numerical analogical reasoning task with functional Magnetic Resonance Imaging (fMRI) to investigate the neural correlates of divergent production of rules in numerical analogical reasoning. Participants performed two tasks: a multiple solution analogical reasoning task and a single solution analogical reasoning task. Results revealed that divergent production of rules involves significant activations at Brodmann area (BA) 10 in the right middle frontal cortex, BA 40 in the left inferior parietal lobule, and BA 8 in the superior frontal cortex. The results suggest that right BA 10 and left BA 40 are involved in the generation of novel rules, and BA 8 is associated with the inhibition of initial rules in numerical analogical reasoning. The findings shed light on the neural mechanisms of creativity in numerical processing.

  1. Analog Circuit Design Low Voltage Low Power; Short Range Wireless Front-Ends; Power Management and DC-DC

    CERN Document Server

    Roermund, Arthur; Baschirotto, Andrea

    2012-01-01

    The book contains the contribution of 18 tutorials of the 20th workshop on Advances in Analog Circuit Design.  Each part discusses a specific to-date topic on new and valuable design ideas in the area of analog circuit design. Each part is presented by six experts in that field and state of the art information is shared and overviewed. This book is number 20 in this successful series of Analog Circuit Design, providing valuable information and excellent overviews of Low-Voltage Low-Power Data Converters - Chaired by Prof. Anderea Baschirotto, University of Milan-Bicocca Short Range Wireless Front-Ends - Chaired by Prof. Arthur van Roermund, Eindhoven University of Technology Power management and DC-DC - Chaired by Prof. M. Steyaert, Katholieke University Leuven Analog Circuit Design is an essential reference source for analog circuit designers and researchers wishing to keep abreast with the latest development in the field. The tutorial coverage also makes it suitable for use in an advanced design.

  2. Design of CMOS analog integrated fractional-order circuits applications in medicine and biology

    CERN Document Server

    Tsirimokou, Georgia; Elwakil, Ahmed

    2017-01-01

    This book describes the design and realization of analog fractional-order circuits, which are suitable for on-chip implementation, capable of low-voltage operation and electronic adjustment of their characteristics. The authors provide a brief introduction to fractional-order calculus, followed by design issues for fractional-order circuits of various orders and types. The benefits of this approach are demonstrated with current-mode and voltage-mode filter designs. Electronically tunable emulators of fractional-order capacitors and inductors are presented, where the behavior of the corresponding chips fabricated using the AMS 0.35um CMOS process has been experimentally verified. Applications of fractional-order circuits are demonstrated, including a pre-processing stage suitable for the implementation of the Pan-Tompkins algorithm for detecting the QRS complexes of an electrocardiogram (ECG), a fully tunable implementation of the Cole-Cole model used for the modeling of biological tissues, and a simple, non-i...

  3. A combined noise analysis and power supply current based testing of CMOS analog integrated circuits

    Science.gov (United States)

    Srivastava, Ashok; Pulendra, Vani K.; Yellampalli, Siva

    2005-05-01

    A technique integrating the noise analysis based testing and the conventional power supply current testing of CMOS analog integrated circuits is presented for bridging type faults due to manufacturing defects. The circuit under test (CUT) is a CMOS amplifier designed for operation at +/- 2.5 V and implemented in 1.5 μm CMOS process. The faults simulating possible manufacturing defects have been introduced using the fault injection transistors. The amplifier circuit is analyzed and simulated in SPICE for its performance with and without fault injections. The faults in the CUT are identified by observing the variation in the equivalent noise voltage at the output of CUT. In power supply current testing, the current (IPS) through the power supply voltage, VDD is measured under the application of an AC input stimulus. The effect of parametric variation is taken into consideration by determining the tolerance limit using the Monte-Carlo analysis. The fault is identified if the power supply current, IPS lies outside the deviation given by Monte-Carlo analysis. Simulation results are in close agreement with the corresponding experimental values.

  4. Passive Guaranteed Simulation of Analog Audio Circuits: A Port-Hamiltonian Approach

    Directory of Open Access Journals (Sweden)

    Antoine Falaize

    2016-09-01

    Full Text Available We present a method that generates passive-guaranteed stable simulations of analog audio circuits from electronic schematics for real-time issues. On one hand, this method is based on a continuous-time power-balanced state-space representation structured into its energy-storing parts, dissipative parts, and external sources. On the other hand, a numerical scheme is especially designed to preserve this structure and the power balance. These state-space structures define the class of port-Hamiltonian systems. The derivation of this structured system associated with the electronic circuit is achieved by an automated analysis of the interconnection network combined with a dictionary of models for each elementary component. The numerical scheme is based on the combination of finite differences applied on the state (with respect to the time variable and on the total energy (with respect to the state. This combination provides a discrete-time version of the power balance. This set of algorithms is valid for both the linear and nonlinear case. Finally, three applications of increasing complexities are given: a diode clipper, a common-emitter bipolar-junction transistor amplifier, and a wah pedal. The results are compared to offline simulations obtained from a popular circuit simulator.

  5. A Vague Decision Method for Analog Circuit Fault Diagnosis Based on Description Sphere

    Institute of Scientific and Technical Information of China (English)

    LUO Hui; WANG Youren; CUI Jiang

    2011-01-01

    This paper proposes a vague decision method for analog circuit fault diagnosis based on description sphere.Firstly,the proposed method uses the wavelet transform as the preprocessor to extract fault features from the output voltages of the circuit under test (CUT).And then,each class sample is trained to produce a minimum description sphere.Finally,the test samples are detected by a defined vague decision rule,which is based on the vague weight distance between the test data and the center of description sphere.The defined decision rule fuses the truth and false membership degrees of the test sample and the weight of the description sphere,and it can effectively deal with the uncertain information.The reliability of the defined decision rule is proved theoretically.This new diagnostic method is first applied to testing two actual circuits,and then it is compared with other two diagnostic methods.The experimental results show that the proposed technique can achieve good performance and reduce the diagnostic time.

  6. An analog integrated circuit beamformer for high-frequency medical ultrasound imaging.

    Science.gov (United States)

    Gurun, Gokce; Zahorian, Jaime S; Sisman, Alper; Karaman, Mustafa; Hasler, Paul E; Degertekin, F Levent

    2012-10-01

    We designed and fabricated a dynamic receive beamformer integrated circuit (IC) in 0.35-μm CMOS technology. This beamformer IC is suitable for integration with an annular array transducer for high-frequency (30-50 MHz) intravascular ultrasound (IVUS) imaging. The beamformer IC consists of receive preamplifiers, an analog dynamic delay-and-sum beamformer, and buffers for 8 receive channels. To form an analog dynamic delay line we designed an analog delay cell based on the current-mode first-order all-pass filter topology, as the basic building block. To increase the bandwidth of the delay cell, we explored an enhancement technique on the current mirrors. This technique improved the overall bandwidth of the delay line by a factor of 6. Each delay cell consumes 2.1-mW of power and is capable of generating a tunable time delay between 1.75 ns to 2.5 ns. We successfully integrated the fabricated beamformer IC with an 8-element annular array. Experimental test results demonstrated the desired buffering, preamplification and delaying capabilities of the beamformer.

  7. The universal fuzzy Logical framework of neural circuits and its application in modeling primary visual cortex

    Institute of Scientific and Technical Information of China (English)

    HU Hong; LI Su; WANG YunJiu; QI XiangLin; SHI ZhongZhi

    2008-01-01

    Analytical study of large-scale nonlinear neural circuits is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells' dynamical equations. Al-though there is a close relation between the theories of fuzzy logical systems and neural systems and many papers investigate this subject, most investigations focus on finding new functions of neural systems by hybridizing fuzzy logical and neural system. In this paper, the fuzzy logical framework of neural cells is used to understand the nonlinear dynamic attributes of a common neural system by abstracting the fuzzy logical framework of a neural cell. Our analysis enables the educated design of network models for classes of computation. As an example, a recurrent network model of the primary visual cortex has been built and tested using this approach.

  8. The universal fuzzy logical framework of neural circuits and its application in modeling primary visual cortex

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Analytical study of large-scale nonlinear neural circuits is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells’ dynamical equations. Al- though there is a close relation between the theories of fuzzy logical systems and neural systems and many papers investigate this subject, most investigations focus on finding new functions of neural systems by hybridizing fuzzy logical and neural system. In this paper, the fuzzy logical framework of neural cells is used to understand the nonlinear dynamic attributes of a common neural system by abstracting the fuzzy logical framework of a neural cell. Our analysis enables the educated design of network models for classes of computation. As an example, a recurrent network model of the primary visual cortex has been built and tested using this approach.

  9. The universal fuzzy logical framework of neural circuits and its application in modeling primary visual cortex.

    Science.gov (United States)

    Hu, Hong; Li, Su; Wang, YunJiu; Qi, XiangLin; Shi, ZhongZhi

    2008-10-01

    Analytical study of large-scale nonlinear neural circuits is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells' dynamical equations. Although there is a close relation between the theories of fuzzy logical systems and neural systems and many papers investigate this subject, most investigations focus on finding new functions of neural systems by hybridizing fuzzy logical and neural system. In this paper, the fuzzy logical framework of neural cells is used to understand the nonlinear dynamic attributes of a common neural system by abstracting the fuzzy logical framework of a neural cell. Our analysis enables the educated design of network models for classes of computation. As an example, a recurrent network model of the primary visual cortex has been built and tested using this approach.

  10. Neural correlates of intelligence as revealed by fMRI of fluid analogies.

    Science.gov (United States)

    Geake, John G; Hansen, Peter C

    2005-06-01

    It has been conjectured that the cognitive basis of intelligence is the ability to make fluid or creative analogical relationships between distantly related concepts or pieces of information (Hofstadter, D.R. 1995. Fluid Concepts and Creative Analogies. Basic Books, New York., Hofstadter, D.R. 2001. Analogy as the Core of Cognition. In The Analogical Mind: Perspectives from Cognitive Science (D. Gentner, K. J. Holyoak and B. N. Kokinov, Ed.). pp. 504-537. MIT Press, Cambridge, Mass.). We hypothesised that fluid analogy-making tasks would activate specific regions of frontal cortex that were common to those of previous inferential reasoning tasks. We report here a novel self-paced event-related fMRI study employed to investigate the neural correlates of intelligence associated with undertaking fluid letter string analogy tasks. Stimuli were adapted from items of the AI program Copycat (Mitchell, M. 1993. Analogy-making as Perception: A computer model. The MIT Press, Cambridge MA.). Twelve right-handed adults chose their own "best" completions from four plausible response choices to 55 fluid letter string analogies across a range of analogical depths. An analysis using covariates determined per subject by analogical depth revealed significant bilateral neural activations in the superior, inferior, and middle frontal gyri and in the anterior cingulate/paracingulate cortex. These frontal areas have been previously associated with reasoning tasks involving inductive syllogisms, syntactic hierarchies, and linguistic creativity. A higher-order analysis covarying participants' verbal intelligence measures found correlations with individual BOLD activation strengths in two ROIs within BA 9 and BA 45/46. This is a provocative result given that verbal intelligence is conceptualised as being a measure of crystallised intelligence, while analogy making is conceptualised as requiring fluid intelligence. The results therefore support the conjecture that fluid analogising could

  11. Circuit theorem analysis in analog circuits and its application%电路定理在分析模拟电路中的应用

    Institute of Scientific and Technical Information of China (English)

    邓友娥

    2011-01-01

    Based on the nonlinear analog electronic basic amplifying circuit, the application of circuit theorem is worked out(Kirchhoff's law, superposition theorem, Thevenin's theorem)analyses and solution of analog circuit such as the ideal operational amplifier circuit and amplifying circuit. This method can be used to reduce the difficulty of circuit analysis. And it can also simplify the process of solving problems, shorten the solving time and reduce the analysis of error. Meanwhile, it promotes the students' ability to use the circuit theorem analysis, and to solve practical problems.%基于模拟电子基本放大电路的非线性,研究了应用电路定理(基尔霍夫定律、叠加定理、戴维南定理)分析和求解模拟电路(理想运放电路、放大电路等)的方法.应用该方法可以降低电路的分析难度,简化解题过程,缩短解题时间,减少分析误差.同时,还可提高学生运用电路定理分析、解决实际问题的能力.

  12. Refinement and Pattern Formation in Neural Circuits by the Interaction of Traveling Waves with Spike-Timing Dependent Plasticity

    Science.gov (United States)

    Bennett, James E. M.; Bair, Wyeth

    2015-01-01

    Traveling waves in the developing brain are a prominent source of highly correlated spiking activity that may instruct the refinement of neural circuits. A candidate mechanism for mediating such refinement is spike-timing dependent plasticity (STDP), which translates correlated activity patterns into changes in synaptic strength. To assess the potential of these phenomena to build useful structure in developing neural circuits, we examined the interaction of wave activity with STDP rules in simple, biologically plausible models of spiking neurons. We derive an expression for the synaptic strength dynamics showing that, by mapping the time dependence of STDP into spatial interactions, traveling waves can build periodic synaptic connectivity patterns into feedforward circuits with a broad class of experimentally observed STDP rules. The spatial scale of the connectivity patterns increases with wave speed and STDP time constants. We verify these results with simulations and demonstrate their robustness to likely sources of noise. We show how this pattern formation ability, which is analogous to solutions of reaction-diffusion systems that have been widely applied to biological pattern formation, can be harnessed to instruct the refinement of postsynaptic receptive fields. Our results hold for rich, complex wave patterns in two dimensions and over several orders of magnitude in wave speeds and STDP time constants, and they provide predictions that can be tested under existing experimental paradigms. Our model generalizes across brain areas and STDP rules, allowing broad application to the ubiquitous occurrence of traveling waves and to wave-like activity patterns induced by moving stimuli. PMID:26308406

  13. Optimization of cocoa butter analog synthesis variables using neural networks and genetic algorithm.

    Science.gov (United States)

    Shekarchizadeh, Hajar; Tikani, Reza; Kadivar, Mahdi

    2014-09-01

    Cocoa butter analog was prepared from camel hump fat and tristearin by enzymatic interesterification in supercritical carbon dioxide (SC-CO2) using immobilized Thermomyces lanuginosus lipase (Lipozyme TL IM) as a biocatalyst. Optimal process conditions were determined using neural networks and genetic algorithm optimization. Response surfaces methodology was used to design the experiments to collect data for the neural network modelling. A general regression neural network model was developed to predict the response of triacylglycerol (TAG) distribution of cocoa butter analog from the process pressure, temperature, tristearin/camel hump fat ratio, water content, and incubation time. A genetic algorithm was used to search for a combination of the process variables for production of most similar cocoa butter analog to the corresponding cocoa butter. The combinations of the process variables during genetic algorithm optimization were evaluated using the neural network model. The pressure of 10 MPa; temperature of 40 °C; SSS/CHF ratio of 0.6:1; water content of 13 % (w/w); and incubation time of 4.5 h were found to be the optimum conditions to achieve the most similar cocoa butter analog to the corresponding cocoa butter.

  14. Demonstration of a neural circuit critical for imprinting behavior in chicks.

    Science.gov (United States)

    Nakamori, Tomoharu; Sato, Katsushige; Atoji, Yasuro; Kanamatsu, Tomoyuki; Tanaka, Kohichi; Ohki-Hamazaki, Hiroko

    2010-03-24

    Imprinting behavior in birds is elicited by visual and/or auditory cues. It has been demonstrated previously that visual cues are recognized and processed in the visual Wulst (VW), and imprinting memory is stored in the intermediate medial mesopallium (IMM) of the telencephalon. Alteration of neural responses in these two regions according to imprinting has been reported, yet direct evidence of the neural circuit linking these two regions is lacking. Thus, it remains unclear how memory is formed and expressed in this circuit. Here, we present anatomical as well as physiological evidence of the neural circuit connecting the VW and IMM and show that imprinting training during the critical period strengthens and refines this circuit. A functional connection established by imprint training resulted in an imprinting behavior. After the closure of the critical period, training could not activate this circuit nor induce the imprinting behavior. Glutamatergic neurons in the ventroposterior region of the VW, the core region of the hyperpallium densocellulare (HDCo), sent their axons to the periventricular part of the HD, just dorsal and afferent to the IMM. We found that the HDCo is important in imprinting behavior. The refinement and/or enhancement of this neural circuit are attributed to increased activity of HDCo cells, and the activity depended on NR2B-containing NMDA receptors. These findings show a neural connection in the telencephalon in Aves and demonstrate that NR2B function is indispensable for the plasticity of HDCo cells, which are key mediators of imprinting.

  15. Implementing size-optimal discrete neural networks requires analog circuitry

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V.

    1998-03-01

    Neural networks (NNs) have been experimentally shown to be quite effective in many applications. This success has led researchers to undertake a rigorous analysis of the mathematical properties that enable them to perform so well. It has generated two directions of research: (i) to find existence/constructive proofs for what is now known as the universal approximation problem; (ii) to find tight bounds on the size needed by the approximation problem (or some particular cases). The paper will focus on both aspects, for the particular case when the functions to be implemented are Boolean.

  16. An Analog Circuit Approximation of the Discrete Wavelet Transform for Ultra Low Power Signal Processing in Wearable Sensor Nodes

    Science.gov (United States)

    Casson, Alexander J.

    2015-01-01

    Ultra low power signal processing is an essential part of all sensor nodes, and particularly so in emerging wearable sensors for biomedical applications. Analog signal processing has an important role in these low power, low voltage, low frequency applications, and there is a key drive to decrease the power consumption of existing analog domain signal processing and to map more signal processing approaches into the analog domain. This paper presents an analog domain signal processing circuit which approximates the output of the Discrete Wavelet Transform (DWT) for use in ultra low power wearable sensors. Analog filters are used for the DWT filters and it is demonstrated how these generate analog domain DWT-like information that embeds information from Butterworth and Daubechies maximally flat mother wavelet responses. The Analog DWT is realised in hardware via gmC circuits, designed to operate from a 1.3 V coin cell battery, and provide DWT-like signal processing using under 115 nW of power when implemented in a 0.18 μm CMOS process. Practical examples demonstrate the effective use of the new Analog DWT on ECG (electrocardiogram) and EEG (electroencephalogram) signals recorded from humans. PMID:26694414

  17. An Analog Circuit Approximation of the Discrete Wavelet Transform for Ultra Low Power Signal Processing in Wearable Sensor Nodes.

    Science.gov (United States)

    Casson, Alexander J

    2015-12-17

    Ultra low power signal processing is an essential part of all sensor nodes, and particularly so in emerging wearable sensors for biomedical applications. Analog signal processing has an important role in these low power, low voltage, low frequency applications, and there is a key drive to decrease the power consumption of existing analog domain signal processing and to map more signal processing approaches into the analog domain. This paper presents an analog domain signal processing circuit which approximates the output of the Discrete Wavelet Transform (DWT) for use in ultra low power wearable sensors. Analog filters are used for the DWT filters and it is demonstrated how these generate analog domain DWT-like information that embeds information from Butterworth and Daubechies maximally flat mother wavelet responses. The Analog DWT is realised in hardware via g(m)C circuits, designed to operate from a 1.3 V coin cell battery, and provide DWT-like signal processing using under 115 nW of power when implemented in a 0.18 μm CMOS process. Practical examples demonstrate the effective use of the new Analog DWT on ECG (electrocardiogram) and EEG (electroencephalogram) signals recorded from humans.

  18. An Analog Circuit Approximation of the Discrete Wavelet Transform for Ultra Low Power Signal Processing in Wearable Sensor Nodes

    Directory of Open Access Journals (Sweden)

    Alexander J. Casson

    2015-12-01

    Full Text Available Ultra low power signal processing is an essential part of all sensor nodes, and particularly so in emerging wearable sensors for biomedical applications. Analog signal processing has an important role in these low power, low voltage, low frequency applications, and there is a key drive to decrease the power consumption of existing analog domain signal processing and to map more signal processing approaches into the analog domain. This paper presents an analog domain signal processing circuit which approximates the output of the Discrete Wavelet Transform (DWT for use in ultra low power wearable sensors. Analog filters are used for the DWT filters and it is demonstrated how these generate analog domain DWT-like information that embeds information from Butterworth and Daubechies maximally flat mother wavelet responses. The Analog DWT is realised in hardware via g m C circuits, designed to operate from a 1.3 V coin cell battery, and provide DWT-like signal processing using under 115 nW of power when implemented in a 0.18 μm CMOS process. Practical examples demonstrate the effective use of the new Analog DWT on ECG (electrocardiogram and EEG (electroencephalogram signals recorded from humans.

  19. Development of analog-digital readout integrated circuits for infrared focal plane arrays

    Science.gov (United States)

    Dem'yanenko, M. A.; Kozlov, A. I.; Marchishin, I. V.; Ovsyuk, V. N.

    2016-11-01

    This paper describes the design of readout integrated circuits (ROICs) for hybrid infrared focal plane arrays (IR FPAs). This work contains the estimation of the noise equivalent temperature difference (NETD) of IR FPAs based on frame and row integration of pixel signals in the spectral ranges of 8 to 14 and 3 to 5 μm. This paper also describes the development of ROICs for IR FPAs created with the use of mercury—cadmium—telluride (MCT) photodiodes and quantum well infrared photodetectors (QWIPs). The designed ROICs ensure the use of matrix and linear photodetector chips, including those with increased dark currents, in order to produce IR FPAs with temperature resolution corresponding to the world level of array analogs.

  20. Double negative elastic metamaterial design through electrical-mechanical circuit analogies.

    Science.gov (United States)

    Pope, Simon A

    2013-07-01

    Previous studies into solid elastic metamaterials which have a simultaneously negative effective bulk modulus and density have proposed designs for materials with relatively narrow bandwidths, because of the reliance on resonators to provide the dispersive material properties. Some of the proposed novel applications for metamaterials, such as invisibility cloaks and sub-wavelength lenses, generally require materials with inherently larger bandwidths for practical exploitation. In this paper, a well-known electromagnetic metamaterial design is used together with the electrical-mechanical circuit analogies to propose a simultaneously double negative elastic metamaterial design which does not suffer from the narrow bandwidth constraints of previous designs. An interesting consequence of the proposed design is that it has an effective wavelength which asymptotically goes to infinity with frequency.

  1. A figure of merit for neural electrical stimulation circuits.

    Science.gov (United States)

    Kolbl, Florian; Demosthenous, Andreas

    2015-01-01

    Electrical stimulators are widely used in neuro-prostheses. Many different implementations exist. However, no quantitative ranking criterion is available to allow meaningful comparison of the various stimulation circuits and systems to aid the designer. This paper presents a novel Figure of Merit (FOM) dedicated to stimulation circuits and systems. The proposed optimization performance metric takes into account tissue safety conditions and energy efficiency which can be evaluated by measurement. The FOM is used to rank several stimulator circuits and systems.

  2. A Fault Dictionary-Based Fault Diagnosis Approach for CMOS Analog Integrated Circuits

    Directory of Open Access Journals (Sweden)

    Mouna Karmani

    2011-10-01

    Full Text Available In this paper, we propose a simulation-before-test (SBT fault diagnosis methodology based on the use of afault dictionary approach. This technique allows the detection and localization of the most likely defects ofopen-circuit type occurring in Complementary Metal–Oxide–Semiconductor (CMOS analog integratedcircuits (ICs interconnects. The fault dictionary is built by simulating the most likely defects causing thefaults to be detected at the layout level. Then, for each injected fault, the spectre’s frequency responses andthe power consumption obtained by simulation are stored in a table which constitutes the fault dictionary.In fact, each line in the fault dictionary constitutes a fault signature used to identify and locate aconsidered defect. When testing, the circuit under test is excited with the same stimulus, and the responsesobtained are compared to the stored ones. To prove the efficiency of the proposed technique, a full customCMOS operational amplifier is implemented in 0.25 μm technology and the most likely faults of opencircuittype are deliberately injected and simulated at the layout level.

  3. A Low Power CMOS Analog Circuit Design for Acquiring Multichannel EEG Signals

    Directory of Open Access Journals (Sweden)

    G.Deepika

    2015-02-01

    Full Text Available EEG signals are the signatures of neural activities and are captured by multiple-electrodes and the signals are recorded from pairs of electrodes. To acquire these multichannel signals a low power CMOS circuit was designed and implemented. The design operates in weak inversion region employing sub threshold source coupled logic. A 16 channel differential multiplexer is designed by utilizing a transmission gate with dynamic threshold logic and a 4 to 16 decoder is used to select the individual channels. The ON and OFF resistance of the transmission gate obtained is 27 ohms and 10 M ohms respectively. The power dissipation achieved is around 337nW for a dynamic range of 1µV to 0.4 V.

  4. A LOW POWER CMOS ANALOG CIRCUIT DESIGN FOR ACQUIRING MULTICHANNEL EEG SIGNALS

    Directory of Open Access Journals (Sweden)

    G. Deepika

    2015-02-01

    Full Text Available EEG signals are the signatures of neural activities and are captured by multiple-electrodes and the signals are recorded from pairs of electrodes. To acquire these multichannel signals a low power CMOS circuit was designed and implemented. The design operates in weak inversion region employing sub threshold source coupled logic. A 16 channel differential multiplexer is designed by utilizing a transmission gate with dynamic threshold logic and a 4 to 16 decoder is used to select the individual channels. The ON and OFF resistance of the transmission gate obtained is 27 ohms and 10 M ohms respectively. The power dissipation achieved is around 337nW for a dynamic range of 1µV to 0.4 V.

  5. Time delay along a chained lumped-circuits: for the physical analogy of half-wavelength power transmission lines

    Science.gov (United States)

    Zhan, Rongrong; Li, Yurong; Jiao, Chongqing; Yu, Yue; Meng, Jiangwen; Wang, Bei

    2017-09-01

    Half-wavelength AC power transmission (HWACT) technology is a kind of three-phase AC transmission technology, which can transmit electric power over a distance close to half power-frequency wavelength, i.e. 3000 km (50Hz) or 2500 km (60 Hz). In order to implement physical analogy of HWACT lines, in general, the equivalent lumped-circuits consisting of some chained π-type circuits or T-type circuits are used in laboratory. The number of the chained circuits is the most key parameter to establish good equivalence between the lumped-circuits and the transmission line. In this paper, the time delay of the chained circuits, which is defined as the time of a sine wave propagating from the sending end to the receiving end of the chained circuits, is calculated for different number of the chained circuits and different wave frequencies. Good equivalence requires the time delay equal to 10ms (the time of electromagnetic waves propagating along 3000km). It is shown that the time delay is dependent on the number of the chained circuits, as well as the wave frequency. For 50Hz, 4 chained π-type circuits can ensure that the relative error of the time delay is less than 2.6% and the sending-to-receiving voltage ratio is approximately 1. For frequencies below 400Hz, 30 chained π-type or T-type circuits can ensure that the relative error of the time delay is less than 3.2% and the sending-to-receiving voltage ratio is approximately 1. These works are instructive for the physical analogy of HWACT lines.

  6. Social Status-Dependent Shift in Neural Circuit Activation Affects Decision Making.

    Science.gov (United States)

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

    2017-02-22

    In a social group, animals make behavioral decisions that fit their social ranks. These behavioral choices are dependent on the various social cues experienced during social interactions. In vertebrates, little is known of how social status affects the underlying neural mechanisms regulating decision-making circuits that drive competing behaviors. Here, we demonstrate that social status in zebrafish (Danio rerio) influences behavioral decisions by shifting the balance in neural circuit activation between two competing networks (escape and swim). We show that socially dominant animals enhance activation of the swim circuit. Conversely, social subordinates display a decreased activation of the swim circuit, but an enhanced activation of the escape circuit. In an effort to understand how social status mediates these effects, we constructed a neurocomputational model of the escape and swim circuits. The model replicates our findings and suggests that social status-related shift in circuit dynamics could be mediated by changes in the relative excitability of the escape and swim networks. Together, our results reveal that changes in the excitabilities of the Mauthner command neuron for escape and the inhibitory interneurons that regulate swimming provide a cellular mechanism for the nervous system to adapt to changes in social conditions by permitting the animal to select a socially appropriate behavioral response.SIGNIFICANCE STATEMENT Understanding how social factors influence nervous system function is of great importance. Using zebrafish as a model system, we demonstrate how social experience affects decision making to enable animals to produce socially appropriate behavior. Based on experimental evidence and computational modeling, we show that behavioral decisions reflect the interplay between competing neural circuits whose activation thresholds shift in accordance with social status. We demonstrate this through analysis of the behavior and neural circuit

  7. Low-power transceiver analog front-end circuits for bidirectional high data rate wireless telemetry in medical endoscopy applications.

    Science.gov (United States)

    Chi, Baoyong; Yao, Jinke; Han, Shuguang; Xie, Xiang; Li, Guolin; Wang, Zhihua

    2007-07-01

    State-of-the-art endoscopy systems require electronics allowing for real-time, bidirectional data transfer. Proposed are 2.4-GHz low-power transceiver analog front-end circuits for bidirectional high data rate wireless telemetry in medical endoscopy applications. The prototype integrates a low-IF receiver analog front-end [low noise amplifier (LNA), double balanced down-converter, bandpass-filtered automatic gain controlled (AGC) loop and amplitude-shift keying (ASK) demodulator], and a direct up-conversion transmitter analog front-end [20-MHz IF phase-locked loop (PLL) with well-defined amplitude control circuit, ASK modulator, up-converter, and power amplifier] on a single chip together with an internal radio frequency oscillator and local oscillating (LO) buffers. Design tradeoffs have been made over the boundaries of the different building blocks to optimize the overall system performance. All building blocks feature circuit topologies that enable comfortable operation at low power consumption. The circuits have been implemented in a 0.25-microm CMOS process. The measured sensitivity of the receiver analog front-end is -70 dBm with a data rate of 256 kbps, and the measured output power of the transmitter analog front-end could achieve -23 dBm with a data rate of 1 Mbps. The integrated circuit consumes a current of 6 mA in receiver mode and 5.6 mA in transmitter mode with a power supply of 2.5 V. This paper shows the feasibility of achieving the analog performance required by the wireless endoscopy capsule system in 0.25 microm CMOS.

  8. A Tool for Single-Fault Diagnosis in Linear Analog Circuits with Tolerance Using the T-Vector Approach

    Directory of Open Access Journals (Sweden)

    José A. Soares Augusto

    2008-01-01

    Full Text Available In previous works of these authors, a technique for doing single-fault diagnosis in linear analog circuits was developed. Under certain conditions, one of them assuming nominal values for the circuit parameters, it was shown that only two measurements taken on two selected circuit nodes, at a single frequency, were needed to detect and diagnose any parametric fault. In this paper, the practical value of the technique is improved by extending the application to the diagnosis of faults in circuits with parameters subject to tolerance. With this in mind, single parametric faults with several strengths are randomly injected in the circuit under study and, afterwards, these faults are diagnosed (or the diagnosis fails. Results are reported on a simple active filter. Conclusions are drawn about the robustness and effectiveness of the technique.

  9. A Fault Signature Characterization Based Analog Circuit Testing Scheme and the Extension of IEEE 1149.4 Standard

    Science.gov (United States)

    San-Um, Wimol; Tachibana, Masayoshi

    An analog circuit testing scheme is presented. The testing technique is a sinusoidal fault signature characterization, involving the measurement of DC offset, amplitude, frequency and phase shift, and the realization of two crossing level voltages. The testing system is an extension of the IEEE 1149.4 standard through the modification of an analog boundary module, affording functionalities for both on-chip testing capability, and accessibility to internal components for off-chip testing. A demonstrating circuit-under-test, a 4th-order Gm-C low-pass filter, and the proposed analog testing scheme are implemented in a physical level using 0.18-µm CMOS technology, and simulated using Hspice. Both catastrophic and parametric faults are potentially detectable at the minimum parameter variation of 0.5%. The fault coverage associated with CMOS transconductance operational amplifiers and capacitors are at 94.16% and 100%, respectively. This work offers the enhancement of standardizing test approach, which reduces the complexity of testing circuit and provides non-intrusive analog circuit testing.

  10. A neural command circuit for grooming movement control.

    Science.gov (United States)

    Hampel, Stefanie; Franconville, Romain; Simpson, Julie H; Seeds, Andrew M

    2015-09-07

    Animals perform many stereotyped movements, but how nervous systems are organized for controlling specific movements remains unclear. Here we use anatomical, optogenetic, behavioral, and physiological techniques to identify a circuit in Drosophila melanogaster that can elicit stereotyped leg movements that groom the antennae. Mechanosensory chordotonal neurons detect displacements of the antennae and excite three different classes of functionally connected interneurons, which include two classes of brain interneurons and different parallel descending neurons. This multilayered circuit is organized such that neurons within each layer are sufficient to specifically elicit antennal grooming. However, we find differences in the durations of antennal grooming elicited by neurons in the different layers, suggesting that the circuit is organized to both command antennal grooming and control its duration. As similar features underlie stimulus-induced movements in other animals, we infer the possibility of a common circuit organization for movement control that can be dissected in Drosophila.

  11. CAFFEINE: Template-Free Symbolic Model Generation of Analog Circuits via Canonical Form Functions and Genetic Programming

    CERN Document Server

    Mcconaghy, Trent; Gielen, Georges

    2011-01-01

    This paper presents a method to automatically generate compact symbolic performance models of analog circuits with no prior specification of an equation template. The approach takes SPICE simulation data as input, which enables modeling of any nonlinear circuits and circuit characteristics. Genetic programming is applied as a means of traversing the space of possible symbolic expressions. A grammar is specially designed to constrain the search to a canonical form for functions. Novel evolutionary search operators are designed to exploit the structure of the grammar. The approach generates a set of symbolic models which collectively provide a tradeoff between error and model complexity. Experimental results show that the symbolic models generated are compact and easy to understand, making this an effective method for aiding understanding in analog design. The models also demonstrate better prediction quality than posynomials.

  12. Mixed Analog/Digital Matrix-Vector Multiplier for Neural Network Synapses

    DEFF Research Database (Denmark)

    Lehmann, Torsten; Bruun, Erik; Dietrich, Casper

    1996-01-01

    In this work we present a hardware efficient matrix-vector multiplier architecture for artificial neural networks with digitally stored synapse strengths. We present a novel technique for manipulating bipolar inputs based on an analog two's complements method and an accurate current rectifier....../sign detector. Measurements on a CMOS test chip are presented and validates the techniques. Further, we propose to use an analog extension, based on a simple capacitive storage, for enhancing weight resolution during learning. It is shown that the implementation of Hebbian learning and back-propagation learning...

  13. Circuit Design of On-Chip BP Learning Neural Network with Programmable Neuron Characteristics

    Institute of Scientific and Technical Information of China (English)

    卢纯; 石秉学; 陈卢

    2000-01-01

    A circuit system of on chip BP(Back-Propagation) learning neural network with pro grammable neurons has been designed,which comprises a feedforward network,an error backpropagation network and a weight updating circuit. It has the merits of simplicity,programmability, speedness,low power-consumption and high density. A novel neuron circuit with pro grammable parameters has been proposed. It generates not only the sigmoidal function but also its derivative. HSPICE simulations are done to a neuron circuit with level 47 transistor models as a standard 1.2tμm CMOS process. The results show that both functions are matched with their respec ive ideal functions very well. The non-linear partition problem is used to verify the operation of the network. The simulation result shows the superior performance of this BP neural network with on-chip learning.

  14. Realization of rapid debugging for detection circuit of optical fiber gas sensor: Using an analog signal source

    Science.gov (United States)

    Tian, Changbin; Chang, Jun; Wang, Qiang; Wei, Wei; Zhu, Cunguang

    2015-03-01

    An optical fiber gas sensor mainly consists of two parts: optical part and detection circuit. In the debugging for the detection circuit, the optical part usually serves as a signal source. However, in the debugging condition, the optical part can be easily influenced by many factors, such as the fluctuation of ambient temperature or driving current resulting in instability of the wavelength and intensity for the laser; for dual-beam sensor, the different bends and stresses of the optical fiber will lead to the fluctuation of the intensity and phase; the intensity noise from the collimator, coupler, and other optical devices in the system will also result in the impurity of the optical part based signal source. In order to dramatically improve the debugging efficiency of the detection circuit and shorten the period of research and development, this paper describes an analog signal source, consisting of a single chip microcomputer (SCM), an amplifier circuit, and a voltage-to-current conversion circuit. It can be used to realize the rapid debugging detection circuit of the optical fiber gas sensor instead of optical part based signal source. This analog signal source performs well with many other advantages, such as the simple operation, small size, and light weight.

  15. A bipolar analog front-end integrated circuit for the SDC silicon tracker

    Energy Technology Data Exchange (ETDEWEB)

    Kipnis, I.; Spieler, H.; Collins, T.

    1993-11-01

    A low-noise, low-power, high-bandwidth, radiation hard, silicon bipolar-transistor full-custom integrated circuit (IC) containing 64 channels of analog signal processing has been developed for the SDC silicon tracker. The IC was designed and tested at LBL and was fabricated using AT&T`s CBIC-U2, 4 GHz f{sub T} complementary bipolar technology. Each channel contains the following functions: low-noise preamplification, pulse shaping and threshold discrimination. This is the first iteration of the production analog IC for the SDC silicon tracker. The IC is laid out to directly match the 50 {mu}m pitch double-sided silicon strip detector. The chip measures 6.8 mm {times} 3.1 mm and contains 3,600 transistors. Three stages of amplification provide 180 mV/fC of gain with a 35 nsec peaking time at the comparator input. For a 14 pF detector capacitance, the equivalent noise charge is 1300 el. rms at a power consumption of 1 mW/channel from a single 3.5 V supply. With the discriminator threshold set to 4 times the noise level, a 16 nsec time-walk for 1.25 to 10fC signals is achieved using a time-walk compensation network. Irradiation tests at TRIUMF to a {Phi}=10{sup 14} protons/cm{sup 2} have been performed on the IC, demonstrating the radiation hardness of the complementary bipolar process.

  16. Devices and circuits for nanoelectronic implementation of artificial neural networks

    Science.gov (United States)

    Turel, Ozgur

    Biological neural networks perform complicated information processing tasks at speeds better than conventional computers based on conventional algorithms. This has inspired researchers to look into the way these networks function, and propose artificial networks that mimic their behavior. Unfortunately, most artificial neural networks, either software or hardware, do not provide either the speed or the complexity of a human brain. Nanoelectronics, with high density and low power dissipation that it provides, may be used in developing more efficient artificial neural networks. This work consists of two major contributions in this direction. First is the proposal of the CMOL concept, hybrid CMOS-molecular hardware [1-8]. CMOL may circumvent most of the problems in posed by molecular devices, such as low yield, vet provide high active device density, ˜1012/cm 2. The second contribution is CrossNets, artificial neural networks that are based on CMOL. We showed that CrossNets, with their fault tolerance, exceptional speed (˜ 4 to 6 orders of magnitude faster than biological neural networks) can perform any task any artificial neural network can perform. Moreover, there is a hope that if their integration scale is increased to that of human cerebral cortex (˜ 1010 neurons and ˜ 1014 synapses), they may be capable of performing more advanced tasks.

  17. Distributed dynamical computation in neural circuits with propagating coherent activity patterns.

    Directory of Open Access Journals (Sweden)

    Pulin Gong

    2009-12-01

    Full Text Available Activity in neural circuits is spatiotemporally organized. Its spatial organization consists of multiple, localized coherent patterns, or patchy clusters. These patterns propagate across the circuits over time. This type of collective behavior has ubiquitously been observed, both in spontaneous activity and evoked responses; its function, however, has remained unclear. We construct a spatially extended, spiking neural circuit that generates emergent spatiotemporal activity patterns, thereby capturing some of the complexities of the patterns observed empirically. We elucidate what kind of fundamental function these patterns can serve by showing how they process information. As self-sustained objects, localized coherent patterns can signal information by propagating across the neural circuit. Computational operations occur when these emergent patterns interact, or collide with each other. The ongoing behaviors of these patterns naturally embody both distributed, parallel computation and cascaded logical operations. Such distributed computations enable the system to work in an inherently flexible and efficient way. Our work leads us to propose that propagating coherent activity patterns are the underlying primitives with which neural circuits carry out distributed dynamical computation.

  18. Changes in the Spinal Neural Circuits are Dependent on the Movement Speed of the Visuomotor Task.

    Science.gov (United States)

    Kubota, Shinji; Hirano, Masato; Koizume, Yoshiki; Tanabe, Shigeo; Funase, Kozo

    2015-01-01

    Previous studies have shown that spinal neural circuits are modulated by motor skill training. However, the effects of task movement speed on changes in spinal neural circuits have not been clarified. The aim of this research was to investigate whether spinal neural circuits were affected by task movement speed. Thirty-eight healthy subjects participated in this study. In experiment 1, the effects of task movement speed on the spinal neural circuits were examined. Eighteen subjects performed a visuomotor task involving ankle muscle slow (nine subjects) or fast (nine subjects) movement speed. Another nine subjects performed a non-visuomotor task (controls) in fast movement speed. The motor task training lasted for 20 min. The amounts of D1 inhibition and reciprocal Ia inhibition were measured using H-relfex condition-test paradigm and recorded before, and at 5, 15, and 30 min after the training session. In experiment 2, using transcranial magnetic stimulation (TMS), the effects of corticospinal descending inputs on the presynaptic inhibitory pathway were examined before and after performing either a visuomotor (eight subjects) or a control task (eight subjects). All measurements were taken under resting conditions. The amount of D1 inhibition increased after the visuomotor task irrespective of movement speed (P circuits, and that task movement speed is one of the critical factors for inducing plastic changes in reciprocal Ia inhibition.

  19. Artificial Neural Network-Based Fault Distance Locator for Double-Circuit Transmission Lines

    Directory of Open Access Journals (Sweden)

    Anamika Jain

    2013-01-01

    Full Text Available This paper analyses two different approaches of fault distance location in a double circuit transmission lines, using artificial neural networks. The single and modular artificial neural networks were developed for determining the fault distance location under varying types of faults in both the circuits. The proposed method uses the voltages and currents signals available at only the local end of the line. The model of the example power system is developed using Matlab/Simulink software. Effects of variations in power system parameters, for example, fault inception angle, CT saturation, source strength, its X/R ratios, fault resistance, fault type and distance to fault have been investigated extensively on the performance of the neural network based protection scheme (for all ten faults in both the circuits. Additionally, the effects of network changes: namely, double circuit operation and single circuit operation, have also been considered. Thus, the present work considers the entire range of possible operating conditions, which has not been reported earlier. The comparative results of single and modular neural network indicate that the modular approach gives correct fault location with better accuracy. It is adaptive to variation in power system parameters, network changes and works successfully under a variety of operating conditions.

  20. Implantable neurotechnologies: bidirectional neural interfaces--applications and VLSI circuit implementations.

    Science.gov (United States)

    Greenwald, Elliot; Masters, Matthew R; Thakor, Nitish V

    2016-01-01

    A bidirectional neural interface is a device that transfers information into and out of the nervous system. This class of devices has potential to improve treatment and therapy in several patient populations. Progress in very large-scale integration has advanced the design of complex integrated circuits. System-on-chip devices are capable of recording neural electrical activity and altering natural activity with electrical stimulation. Often, these devices include wireless powering and telemetry functions. This review presents the state of the art of bidirectional circuits as applied to neuroprosthetic, neurorepair, and neurotherapeutic systems.

  1. Circuit-breakers: optical technologies for probing neural signals and systems.

    Science.gov (United States)

    Zhang, Feng; Aravanis, Alexander M; Adamantidis, Antoine; de Lecea, Luis; Deisseroth, Karl

    2007-08-01

    Neuropsychiatric disorders, which arise from a combination of genetic, epigenetic and environmental influences, epitomize the challenges faced in understanding the mammalian brain. Elucidation and treatment of these diseases will benefit from understanding how specific brain cell types are interconnected and signal in neural circuits. Newly developed neuroengineering tools based on two microbial opsins, channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR), enable the investigation of neural circuit function with cell-type-specific, temporally accurate and reversible neuromodulation. These tools could lead to the development of precise neuromodulation technologies for animal models of disease and clinical neuropsychiatry.

  2. Nanowire electrodes for high-density stimulation and measurement of neural circuits

    OpenAIRE

    Robinson, Jacob T.; Jorgolli, Marsela; Park, Hongkun

    2013-01-01

    Brain-machine interfaces (BMIs) that can precisely monitor and control neural activity will likely require new hardware with improved resolution and specificity. New nanofabricated electrodes with feature sizes and densities comparable to neural circuits may lead to such improvements. In this perspective, we review the recent development of vertical nanowire (NW) electrodes that could provide highly parallel single-cell recording and stimulation for future BMIs. We compare the advantages of t...

  3. Analog CMOS circuit implementation of a pulse-coupled phase oscillator system and observation of synchronization phenomena

    Science.gov (United States)

    Matsuzaka, Kenji; Tohara, Takashi; Nakada, Kazuki; Morie, Takashi

    Analog CMOS circuit implementation of a system of pulse-coupled phase oscillators is proposed. A CMOS circuit that achieves the dynamics of pulse-coupled oscillators has been designed and fabricated using a 0.25-µm CMOS technology. The proposed oscillator circuits with continuous-time operation interact with each other via a pulse at each firing time. Update of the oscillator state is achieved by integrating the phase sensitivity function with the pulse width time span. The phase sensitivity function is generated by the combination of binary functions, while the function consists of three-values {-1,0,1}. Introducing a zero-value span in the function leads to fast synchronization and robustness to parameter fluctuation due to LSI device mismatches, which facilitates VLSI implementation. Using the fabricated CMOS circuit, we have observed not only in- and anti-phase but also out-of-phase synchronization.

  4. Design of a reliable PUF circuit based on R-2R ladder digital-to-analog convertor

    Science.gov (United States)

    Pengjun, Wang; Xuelong, Zhang; Yuejun, Zhang; Jianrui, Li

    2015-07-01

    A novel physical unclonable functions (PUF) circuit is proposed, which relies on non-linear characteristic of analog voltage generated by R-2R ladder DAC. After amplifying the deviation signal, the robustness of the DAC-PUF circuit has increased significantly. The DAC-PUF circuit is designed in TSMC 65 nm CMOS technology and the layout occupies 86.06 × 63.56 μm2. Monte Carlo simulation results show that the reliability of the DAC-PUF circuit is above 98% over a comprehensive range of environmental variation, such as temperature and supply voltage. Project supported by the National Natural Science Foundation of China (Nos. 61474068, 61404076, 61274132), the Zhejiang Provincial Natural Science Foundation of China (No. LQ14F040001), and the K. C. Wong Magna Fund in Ningbo University, China.

  5. Symbolic Nodal Analysis of Analog Circuits with Modern Multiport Functional Blocks

    Directory of Open Access Journals (Sweden)

    C. Sanchez-Lopez

    2013-06-01

    Full Text Available This paper proposes admittance matrix models to approach the behavior of six modern multiport functional blocks called: differential difference amplifier, differential difference operational floating amplifier, differential difference operational mirror amplifier, differential difference current conveyor, current backward transconductance amplifier and current differencing transconductance amplifier. The novelty is that the behavior of any active device mentioned before can immediately be introduced in the nodal admittance matrix by using the proposed admittance matrix models and without requiring the use of extra variables. Therefore, a standard nodal analysis is applied to compute fully-symbolic small-signal performance parameters of analog circuits containing any active device mentioned above. This means that not only the size of the admittance matrix is smaller than those generated by applying modified nodal analysis method, for instance, but also, the number of nonzero elements and the generations of cancellation-terms are both reduced. An analysis example for each amplifier is provided in order to show the useful of the proposed stamps.

  6. Analogy for Drude’s free electron model to promote students’ understanding of electric circuits in lower secondary school

    Directory of Open Access Journals (Sweden)

    Maria José BM de Almeida

    2014-09-01

    Full Text Available Aiming at a deep understanding of some basic concepts of electric circuits in lower secondary schools, this work introduces an analogy between the behavior of children playing in a school yard with a central lake, subject to different conditions, rules, and stimuli, and Drude’s free electron model of metals. Using this analogy from the first school contacts with electric phenomena, one can promote students’ understanding of concepts such as electric current, the role of generators, potential difference effects, energy transfer, open and closed circuits, resistances, and their combinations in series and parallel. One believes that through this analogy well-known previous misconceptions of young students about electric circuit behaviors can be overcome. Furthermore, students’ understanding will enable them to predict, and justify with self-constructed arguments, the behavior of different elementary circuits. The students’ predictions can be verified—as a challenge of self-produced understanding schemes—using laboratory experiments. At a preliminary stage, our previsions were confirmed through a pilot study with three classrooms of 9th level Portuguese students.

  7. Sensory processing by neural circuits in Caenorhabditis elegans.

    Science.gov (United States)

    Whittaker, Allyson J; Sternberg, Paul W

    2004-08-01

    The anatomical and developmental constancy of Caenorhabditis elegans belies the complexity of its numerically small nervous system. Indeed, there is an increased appreciation of C. elegans as an organism to study systems level questions. Many recent studies focus on the circuits that control locomotion, egg-laying, and male mating behaviors and their modulation by multiple sensory stimuli.

  8. Synthesis of analog behavioral models of variable complexity for use in simulation of electronic circuits associated with microsystems

    CERN Document Server

    Boucher, J; Ayoub, K I; Cousineau, S M; Ahmadpanah, M; Rakotondrazafy, C; Harchani, N; Andreu, D; Montagner, L; Marin, M

    1999-01-01

    The analog behavioral modeling must constitute a privileged axis of research for a global simulation of systems and micro-systems. This paper presents a research/education (R&E) methodology which has been developed by the authors as a result of many years of experience in the domains of electronic components, circuits and systems, in different university and industrial research laboratories. It concerns the entire constitutive analog functions, used in the processing of energy and information, with different abstraction levels, extending from a simple component to complex macro-functions used in system electronics. (10 refs).

  9. Scaling down DNA circuits with competitive neural networks.

    Science.gov (United States)

    Genot, Anthony J; Fujii, Teruo; Rondelez, Yannick

    2013-08-06

    DNA has proved to be an exquisite substrate to compute at the molecular scale. However, nonlinear computations (such as amplification, comparison or restoration of signals) remain costly in term of strands and are prone to leak. Kim et al. showed how competition for an enzymatic resource could be exploited in hybrid DNA/enzyme circuits to compute a powerful nonlinear primitive: the winner-take-all (WTA) effect. Here, we first show theoretically how the nonlinearity of the WTA effect allows the robust and compact classification of four patterns with only 16 strands and three enzymes. We then generalize this WTA effect to DNA-only circuits and demonstrate similar classification capabilities with only 23 strands.

  10. A new wide range Euclidean distance circuit for neural network hardware implementations.

    Science.gov (United States)

    Gopalan, A; Titus, A H

    2003-01-01

    In this paper, we describe an analog very large-scale integration (VLSI) implementation of a wide range Euclidean distance computation circuit - the key element of many synapse circuits. This circuit is essentially a wide-range absolute value circuit that is designed to be as small as possible (80 /spl times/ 76 /spl mu/m) in order to achieve maximum synapse density while maintaining a wide range of operation (0.5 to 4.5 V) and low power consumption (less than 200 /spl mu/W). The circuit has been fabricated in 1.5-/spl mu/m technology through MOSIS. We present simulated and experimental results of the circuit, and compare these results. Ultimately, this circuit is intended for use as part of a high-density hardware implementation of a self-organizing map (SOM). We describe how this circuit can be used as part of the SOM and how the SOM is going to be used as part of a larger bio-inspired vision system based on the octopus visual system.

  11. Adult Neurogenesis Leads to the Functional Reconstruction of a Telencephalic Neural Circuit

    Science.gov (United States)

    Macedo-Lima, Matheus; Miller, Kimberly E.; Brenowitz, Eliot A.

    2016-01-01

    Seasonally breeding songbirds exhibit pronounced annual changes in song behavior, and in the morphology and physiology of the telencephalic neural circuit underlying production of learned song. Each breeding season, new adult-born neurons are added to the pallial nucleus HVC in response to seasonal changes in steroid hormone levels, and send long axonal projections to their target nucleus, the robust nucleus of the arcopallium (RA). We investigated the role that adult neurogenesis plays in the seasonal reconstruction of this circuit. We labeled newborn HVC neurons with BrdU, and RA-projecting HVC neurons (HVCRA) with retrograde tracer injected in RA of adult male white-crowned sparrows (Zonotrichia leucophrys gambelii) in breeding or nonbreeding conditions. We found that there were many more HVCRA neurons in breeding than nonbreeding birds. Furthermore, we observed that more newborn HVC neurons were back-filled by the tracer in breeding animals. Behaviorally, song structure degraded as the HVC-RA circuit degenerated, and recovered as the circuit regenerated, in close correlation with the number of new HVCRA neurons. These results support the hypothesis that the HVC-RA circuit degenerates in nonbreeding birds, and that newborn neurons reconstruct the circuit in breeding birds, leading to functional recovery of song behavior. SIGNIFICANCE STATEMENT We investigated the role that adult neurogenesis plays in the seasonal reconstruction of a telencephalic neural circuit that controls song behavior in white-crowned sparrows. We showed that nonbreeding birds had a 36%–49% reduction in the number of projection neurons compared with breeding birds, and the regeneration of the circuit in the breeding season is due to the integration of adult-born projection neurons. Additionally, song structure degraded as the circuit degenerated and recovered as the circuit regenerated, in close correlation with new projection neuron number. This study demonstrates that steroid hormones

  12. A neural circuit encoding sexual preference in humans.

    OpenAIRE

    Poeppl, Timm B.; Langguth, Berthold; Rupprecht, Rainer; Laird, Angela R.; Eickhoff, Simon

    2016-01-01

    Sexual preference determines mate choice for reproduction and hence guarantees conservation of species in mammals. Despite this fundamental role in human behavior, current knowledge on its target-specific neurofunctional substrate is based on lesion studies and therefore limited. We used meta-analytic remodeling of neuroimaging data from 364 human subjects with diverse sexual interests during sexual stimulation to quantify neural regions associated with sexual preference manipulations. We fou...

  13. Monitoring activity in neural circuits with genetically encoded indicators

    Directory of Open Access Journals (Sweden)

    Gerard Joseph Broussard

    2014-12-01

    Full Text Available Recent developments in genetically encoded indicators of neural activity (GINAs have greatly advanced the field of systems neuroscience. As they are encoded by DNA, GINAs can be targeted to genetically defined cellular populations. Combined with fluorescence microscopy, most notably multi-photon imaging, GINAs allow chronic simultaneous optical recordings from large populations of neurons or glial cells in awake, behaving mammals, particularly rodents. This large-scale recording of neural activity at multiple temporal and spatial scales has greatly advanced our understanding of the dynamics of neural circuitry underlying behavior—a critical first step toward understanding the complexities of brain function, such as sensorimotor integration and learning.Here, we summarize the recent development and applications of the major classes of GINAs. In particular, we take an in-depth look at the design of available GINA families with a particular focus on genetically encoded calcium indicators, sensors probing synaptic activity, and genetically encoded voltage indicators. Using the family of the genetically encoded calcium indicator GCaMP as an example, we review established sensor optimization pipelines. We also discuss practical considerations for end users of GINAs about experimental methods including approaches for gene delivery, imaging system requirements, and data analysis techniques. With the growing toolbox of GINAs and with new microscopy techniques pushing beyond their current limits, the age of light can finally achieve the goal of broad and dense sampling of neuronal activity across time and brain structures to obtain a dynamic picture of brain function.

  14. Energy efficient neural stimulation: coupling circuit design and membrane biophysics.

    Directory of Open Access Journals (Sweden)

    Thomas J Foutz

    Full Text Available The delivery of therapeutic levels of electrical current to neural tissue is a well-established treatment for numerous indications such as Parkinson's disease and chronic pain. While the neuromodulation medical device industry has experienced steady clinical growth over the last two decades, much of the core technology underlying implanted pulse generators remain unchanged. In this study we propose some new methods for achieving increased energy-efficiency during neural stimulation. The first method exploits the biophysical features of excitable tissue through the use of a centered-triangular stimulation waveform. Neural activation with this waveform is achieved with a statistically significant reduction in energy compared to traditional rectangular waveforms. The second method demonstrates energy savings that could be achieved by advanced circuitry design. We show that the traditional practice of using a fixed compliance voltage for constant-current stimulation results in substantial energy loss. A portion of this energy can be recuperated by adjusting the compliance voltage to real-time requirements. Lastly, we demonstrate the potential impact of axon fiber diameter on defining the energy-optimal pulse-width for stimulation. When designing implantable pulse generators for energy efficiency, we propose that the future combination of a variable compliance system, a centered-triangular stimulus waveform, and an axon diameter specific stimulation pulse-width has great potential to reduce energy consumption and prolong battery life in neuromodulation devices.

  15. A digital-to-analog conversion circuit using third-order polynomial interpolation

    Science.gov (United States)

    Dotson, W. P., Jr.; Wilson, J. H.

    1972-01-01

    Zero- and third-order digital-to-analog conversion techniques are described, and the theoretical error performances are compared. The design equations and procedures for constructing a third-order digital-to-analog converter by using analog design elements are presented. Both a zero- and a third-order digital-to-analog converter were built, and the performances are compared with various signal inputs.

  16. Optogenetic dissection of neural circuits underlying emotional valence and motivated behaviors.

    Science.gov (United States)

    Nieh, Edward H; Kim, Sung-Yon; Namburi, Praneeth; Tye, Kay M

    2013-05-20

    The neural circuits underlying emotional valence and motivated behaviors are several synapses away from both defined sensory inputs and quantifiable motor outputs. Electrophysiology has provided us with a suitable means for observing neural activity during behavior, but methods for controlling activity for the purpose of studying motivated behaviors have been inadequate: electrical stimulation lacks cellular specificity and pharmacological manipulation lacks temporal resolution. The recent emergence of optogenetic tools provides a new means for establishing causal relationships between neural activity and behavior. Optogenetics, the use of genetically-encodable light-activated proteins, permits the modulation of specific neural circuit elements with millisecond precision. The ability to control individual cell types, and even projections between distal regions, allows us to investigate functional connectivity in a causal manner. The greatest consequence of controlling neural activity with finer precision has been the characterization of individual neural circuits within anatomical brain regions as defined functional units. Within the mesolimbic dopamine system, optogenetics has helped separate subsets of dopamine neurons with distinct functions for reward, aversion and salience processing, elucidated GABA neuronal effects on behavior, and characterized connectivity with forebrain and cortical structures. Within the striatum, optogenetics has confirmed the opposing relationship between direct and indirect pathway medium spiny neurons (MSNs), in addition to characterizing the inhibition of MSNs by cholinergic interneurons. Within the hypothalamus, optogenetics has helped overcome the heterogeneity in neuronal cell-type and revealed distinct circuits mediating aggression and feeding. Within the amygdala, optogenetics has allowed the study of intra-amygdala microcircuitry as well as interconnections with distal regions involved in fear and anxiety. In this review, we

  17. The role of fluid intelligence and learning in analogical reasoning: How to become neurally efficient?

    Science.gov (United States)

    Dix, Annika; Wartenburger, Isabell; van der Meer, Elke

    2016-10-01

    This study on analogical reasoning evaluates the impact of fluid intelligence on adaptive changes in neural efficiency over the course of an experiment and specifies the underlying cognitive processes. Grade 10 students (N=80) solved unfamiliar geometric analogy tasks of varying difficulty. Neural efficiency was measured by the event-related desynchronization (ERD) in the alpha band, an indicator of cortical activity. Neural efficiency was defined as a low amount of cortical activity accompanying high performance during problem-solving. Students solved the tasks faster and more accurately the higher their FI was. Moreover, while high FI led to greater cortical activity in the first half of the experiment, high FI was associated with a neurally more efficient processing (i.e., better performance but same amount of cortical activity) in the second half of the experiment. Performance in difficult tasks improved over the course of the experiment for all students while neural efficiency increased for students with higher but decreased for students with lower fluid intelligence. Based on analyses of the alpha sub-bands, we argue that high fluid intelligence was associated with a stronger investment of attentional resource in the integration of information and the encoding of relations in this unfamiliar task in the first half of the experiment (lower-2 alpha band). Students with lower fluid intelligence seem to adapt their applied strategies over the course of the experiment (i.e., focusing on task-relevant information; lower-1 alpha band). Thus, the initially lower cortical activity and its increase in students with lower fluid intelligence might reflect the overcoming of mental overload that was present in the first half of the experiment.

  18. A First Approach to Design Mobility Function and Noise Filter in VLC System Utilizing Low-cost Analog Circuits

    Directory of Open Access Journals (Sweden)

    Syifaul Fuada

    2017-07-01

    Full Text Available Visible Light Communication (VLC as one of wireless technology must be able to offer a good capability as mobile communication system. The signal will be faded when the distance and angle of LED to photo-detector become higher at a certain distance. Other problem at VLC system is light interference noise which is caused by flicker effect from other light sources such as incandescent, fluorescent, DC-lamp (i.e. flashlight and the sunlight. Each of lights have specific carried signal characteristics and it can influences the VLC system. In this paper we offer design of mobile VLC system based on analog domain. We use Automatic Gain Controller (AGC circuit using commercially available IC and it will be placed at analog front-end receiver side. AGC can self-adjust its gain according to the input signal amplitude.  We also design analog filter to eliminate all interferences noise spectrum which is existed under 50 KHz. We design both circuits, analog filter and AGC in VLC receiver system with low-cost. The test data are obtained through the simulation and achieved good results in ideal condition.

  19. Neural circuits in anxiety and stress disorders: a focused review.

    Science.gov (United States)

    Duval, Elizabeth R; Javanbakht, Arash; Liberzon, Israel

    2015-01-01

    Anxiety and stress disorders are among the most prevalent neuropsychiatric disorders. In recent years, multiple studies have examined brain regions and networks involved in anxiety symptomatology in an effort to better understand the mechanisms involved and to develop more effective treatments. However, much remains unknown regarding the specific abnormalities and interactions between networks of regions underlying anxiety disorder presentations. We examined recent neuroimaging literature that aims to identify neural mechanisms underlying anxiety, searching for patterns of neural dysfunction that might be specific to different anxiety disorder categories. Across different anxiety and stress disorders, patterns of hyperactivation in emotion-generating regions and hypoactivation in prefrontal/regulatory regions are common in the literature. Interestingly, evidence of differential patterns is also emerging, such that within a spectrum of disorders ranging from more fear-based to more anxiety-based, greater involvement of emotion-generating regions is reported in panic disorder and specific phobia, and greater involvement of prefrontal regions is reported in generalized anxiety disorder and posttraumatic stress disorder. We summarize the pertinent literature and suggest areas for continued investigation.

  20. Ontogeny of neural circuits underlying spatial memory in the rat

    Directory of Open Access Journals (Sweden)

    James Alexander Ainge

    2012-03-01

    Full Text Available Spatial memory is a well characterised psychological function in both humans and rodents. The combined computations of a network of systems including place cells in the hippocampus, grid cells in the medial entorhinal cortex and head direction cells found in numerous structures in the brain have been suggested to form the neural instantiation of the cognitive map as first described by Tolman in 1948. However, while our understanding of the neural mechanisms underlying spatial representations in adults is relatively sophisticated, we know substantially less about how this network develops in young animals. In this article we review studies examining the developmental timescale that these systems follow. Electrophysiological recordings from very young rats show that directional information is at adult levels at the outset of navigational experience. The systems supporting allocentric memory, however, take longer to mature. This is consistent with behavioural studies of young rats which show that spatial memory based on head direction develops very early but that allocentric spatial memory takes longer to mature. We go on to report new data demonstrating that memory for associations between objects and their spatial locations is slower to develop than memory for objects alone. This is again consistent with previous reports suggesting that adult like spatial representations have a protracted development in rats and also suggests that the systems involved in processing non-spatial stimuli come online earlier.

  1. The design, fabrication, and test of a new VLSI hybrid analog-digital neural processing element

    Science.gov (United States)

    Deyong, Mark R.; Findley, Randall L.; Fields, Chris

    1992-01-01

    A hybrid analog-digital neural processing element with the time-dependent behavior of biological neurons has been developed. The hybrid processing element is designed for VLSI implementation and offers the best attributes of both analog and digital computation. Custom VLSI layout reduces the layout area of the processing element, which in turn increases the expected network density. The hybrid processing element operates at the nanosecond time scale, which enables it to produce real-time solutions to complex spatiotemporal problems found in high-speed signal processing applications. VLSI prototype chips have been designed, fabricated, and tested with encouraging results. Systems utilizing the time-dependent behavior of the hybrid processing element have been simulated and are currently in the fabrication process. Future applications are also discussed.

  2. The design, fabrication, and test of a new VLSI hybrid analog-digital neural processing element

    Science.gov (United States)

    Deyong, Mark R.; Findley, Randall L.; Fields, Chris

    1992-01-01

    A hybrid analog-digital neural processing element with the time-dependent behavior of biological neurons has been developed. The hybrid processing element is designed for VLSI implementation and offers the best attributes of both analog and digital computation. Custom VLSI layout reduces the layout area of the processing element, which in turn increases the expected network density. The hybrid processing element operates at the nanosecond time scale, which enables it to produce real-time solutions to complex spatiotemporal problems found in high-speed signal processing applications. VLSI prototype chips have been designed, fabricated, and tested with encouraging results. Systems utilizing the time-dependent behavior of the hybrid processing element have been simulated and are currently in the fabrication process. Future applications are also discussed.

  3. Nonlocal mechanism for cluster synchronization in neural circuits

    Science.gov (United States)

    Kanter, I.; Kopelowitz, E.; Vardi, R.; Zigzag, M.; Kinzel, W.; Abeles, M.; Cohen, D.

    2011-03-01

    The interplay between the topology of cortical circuits and synchronized activity modes in distinct cortical areas is a key enigma in neuroscience. We present a new nonlocal mechanism governing the periodic activity mode: the greatest common divisor (GCD) of network loops. For a stimulus to one node, the network splits into GCD-clusters in which cluster neurons are in zero-lag synchronization. For complex external stimuli, the number of clusters can be any common divisor. The synchronized mode and the transients to synchronization pinpoint the type of external stimuli. The findings, supported by an information mixing argument and simulations of Hodgkin-Huxley population dynamic networks with unidirectional connectivity and synaptic noise, call for reexamining sources of correlated activity in cortex and shorter information processing time scales.

  4. Neural Circuits Trained with Standard Reinforcement Learning Can Accumulate Probabilistic Information during Decision Making.

    Science.gov (United States)

    Kurzawa, Nils; Summerfield, Christopher; Bogacz, Rafal

    2017-02-01

    Much experimental evidence suggests that during decision making, neural circuits accumulate evidence supporting alternative options. A computational model well describing this accumulation for choices between two options assumes that the brain integrates the log ratios of the likelihoods of the sensory inputs given the two options. Several models have been proposed for how neural circuits can learn these log-likelihood ratios from experience, but all of these models introduced novel and specially dedicated synaptic plasticity rules. Here we show that for a certain wide class of tasks, the log-likelihood ratios are approximately linearly proportional to the expected rewards for selecting actions. Therefore, a simple model based on standard reinforcement learning rules is able to estimate the log-likelihood ratios from experience and on each trial accumulate the log-likelihood ratios associated with presented stimuli while selecting an action. The simulations of the model replicate experimental data on both behavior and neural activity in tasks requiring accumulation of probabilistic cues. Our results suggest that there is no need for the brain to support dedicated plasticity rules, as the standard mechanisms proposed to describe reinforcement learning can enable the neural circuits to perform efficient probabilistic inference.

  5. An analog CMOS chip set for neural networks with arbitrary topologies

    DEFF Research Database (Denmark)

    Lansner, John; Lehmann, Torsten

    1993-01-01

    An analog CMOS chip set for implementations of artificial neural networks (ANNs) has been fabricated and tested. The chip set consists of two cascadable chips: a neuron chip and a synapse chip. Neurons on the neuron chips can be interconnected at random via synapses on the synapse chips thus...... implementing an ANN with arbitrary topology. The neuron test chip contains an array of 4 neurons with well defined hyperbolic tangent activation functions which is implemented by using parasitic lateral bipolar transistors. The synapse test chip is a cascadable 4×4 matrix-vector multiplier with variable, 10-b...

  6. Grid cells generate an analog error-correcting code for singularly precise neural computation.

    Science.gov (United States)

    Sreenivasan, Sameet; Fiete, Ila

    2011-09-11

    Entorhinal grid cells in mammals fire as a function of animal location, with spatially periodic response patterns. This nonlocal periodic representation of location, a local variable, is unlike other neural codes. There is no theoretical explanation for why such a code should exist. We examined how accurately the grid code with noisy neurons allows an ideal observer to estimate location and found this code to be a previously unknown type of population code with unprecedented robustness to noise. In particular, the representational accuracy attained by grid cells over the coding range was in a qualitatively different class from what is possible with observed sensory and motor population codes. We found that a simple neural network can effectively correct the grid code. To the best of our knowledge, these results are the first demonstration that the brain contains, and may exploit, powerful error-correcting codes for analog variables.

  7. Massively parallel neural circuits for stereoscopic color vision: encoding, decoding and identification.

    Science.gov (United States)

    Lazar, Aurel A; Slutskiy, Yevgeniy B; Zhou, Yiyin

    2015-03-01

    Past work demonstrated how monochromatic visual stimuli could be faithfully encoded and decoded under Nyquist-type rate conditions. Color visual stimuli were then traditionally encoded and decoded in multiple separate monochromatic channels. The brain, however, appears to mix information about color channels at the earliest stages of the visual system, including the retina itself. If information about color is mixed and encoded by a common pool of neurons, how can colors be demixed and perceived? We present Color Video Time Encoding Machines (Color Video TEMs) for encoding color visual stimuli that take into account a variety of color representations within a single neural circuit. We then derive a Color Video Time Decoding Machine (Color Video TDM) algorithm for color demixing and reconstruction of color visual scenes from spikes produced by a population of visual neurons. In addition, we formulate Color Video Channel Identification Machines (Color Video CIMs) for functionally identifying color visual processing performed by a spiking neural circuit. Furthermore, we derive a duality between TDMs and CIMs that unifies the two and leads to a general theory of neural information representation for stereoscopic color vision. We provide examples demonstrating that a massively parallel color visual neural circuit can be first identified with arbitrary precision and its spike trains can be subsequently used to reconstruct the encoded stimuli. We argue that evaluation of the functional identification methodology can be effectively and intuitively performed in the stimulus space. In this space, a signal reconstructed from spike trains generated by the identified neural circuit can be compared to the original stimulus.

  8. Consistency and advantage of loop regularization method merging with Bjorken-Drell's analogy between Feynman diagrams and electrical circuits

    Science.gov (United States)

    Huang, Da; Wu, Yue-Liang

    2012-07-01

    The consistency of loop regularization (LORE) method is explored in multiloop calculations. A key concept of the LORE method is the introduction of irreducible loop integrals (ILIs) which are evaluated from the Feynman diagrams by adopting the Feynman parametrization and ultraviolet-divergence-preserving (UVDP) parametrization. It is then inevitable for the ILIs to encounter the divergences in the UVDP parameter space due to the generic overlapping divergences in the four-dimensional momentum space. By computing the so-called αβγ integrals arising from two-loop Feynman diagrams, we show how to deal with the divergences in the parameter space with the LORE method. By identifying the divergences in the UVDP parameter space to those in the subdiagrams, we arrive at the Bjorken-Drell analogy between Feynman diagrams and electrical circuits. The UVDP parameters are shown to correspond to the conductance or resistance in the electrical circuits, and the divergence in Feynman diagrams is ascribed to the infinite conductance or zero resistance. In particular, the sets of conditions required to eliminate the overlapping momentum integrals for obtaining the ILIs are found to be associated with the conservations of electric voltages, and the momentum conservations correspond to the conservations of electrical currents, which are known as the Kirchhoff laws in the electrical circuits analogy. As a practical application, we carry out a detailed calculation for one-loop and two-loop Feynman diagrams in the massive scalar ϕ 4 theory, which enables us to obtain the well-known logarithmic running of the coupling constant and the consistent power-law running of the scalar mass at two-loop level. Especially, we present an explicit demonstration on the general procedure of applying the LORE method to the multiloop calculations of Feynman diagrams when merging with the advantage of Bjorken-Drell's circuit analogy.

  9. In Search of the Neural Circuits of Intrinsic Motivation

    Science.gov (United States)

    Kaplan, Frederic; Oudeyer, Pierre-Yves

    2007-01-01

    Children seem to acquire new know-how in a continuous and open-ended manner. In this paper, we hypothesize that an intrinsic motivation to progress in learning is at the origins of the remarkable structure of children's developmental trajectories. In this view, children engage in exploratory and playful activities for their own sake, not as steps toward other extrinsic goals. The central hypothesis of this paper is that intrinsically motivating activities correspond to expected decrease in prediction error. This motivation system pushes the infant to avoid both predictable and unpredictable situations in order to focus on the ones that are expected to maximize progress in learning. Based on a computational model and a series of robotic experiments, we show how this principle can lead to organized sequences of behavior of increasing complexity characteristic of several behavioral and developmental patterns observed in humans. We then discuss the putative circuitry underlying such an intrinsic motivation system in the brain and formulate two novel hypotheses. The first one is that tonic dopamine acts as a learning progress signal. The second is that this progress signal is directly computed through a hierarchy of microcortical circuits that act both as prediction and metaprediction systems. PMID:18982131

  10. In search of the neural circuits of intrinsic motivation

    Directory of Open Access Journals (Sweden)

    Frederic Kaplan

    2007-10-01

    Full Text Available Children seem to acquire new know-how in a continuous and open-ended manner. In this paper, we hypothesize that an intrinsic motivation to progress in learning is at the origins of the remarkable structure of children's developmental trajectories. In this view, children engage in exploratory and playful activities for their own sake, not as steps toward other extrinsic goals. The central hypothesis of this paper is that intrinsically motivating activities correspond to expected decrease in prediction error. This motivation system pushes the infant to avoid both predictable and unpredictable situations in order to focus on the ones that are expected to maximize progress in learning. Based on a computational model and a series of robotic experiments, we show how this principle can lead to organized sequences of behavior of increasing complexity characteristic of several behavioral and developmental patterns observed in humans. We then discuss the putative circuitry underlying such an intrinsic motivation system in the brain and formulate two novel hypotheses. The first one is that tonic dopamine acts as a learning progress signal. The second is that this progress signal is directly computed through a hierarchy of microcortical circuits that act both as prediction and metaprediction systems.

  11. Diversity of Dopaminergic Neural Circuits in Response to Drug Exposure.

    Science.gov (United States)

    Juarez, Barbara; Han, Ming-Hu

    2016-09-01

    Addictive substances are known to increase dopaminergic signaling in the mesocorticolimbic system. The origin of this dopamine (DA) signaling originates in the ventral tegmental area (VTA), which sends afferents to various targets, including the nucleus accumbens, the medial prefrontal cortex, and the basolateral amygdala. VTA DA neurons mediate stimuli saliency and goal-directed behaviors. These neurons undergo robust drug-induced intrinsic and extrinsic synaptic mechanisms following acute and chronic drug exposure, which are part of brain-wide adaptations that ultimately lead to the transition into a drug-dependent state. Interestingly, recent investigations of the differential subpopulations of VTA DA neurons have revealed projection-specific functional roles in mediating reward, aversion, and stress. It is now critical to view drug-induced neuroadaptations from a circuit-level perspective to gain insight into how differential dopaminergic adaptations and signaling to targets of the mesocorticolimbic system mediates drug reward. This review hopes to describe the projection-specific intrinsic characteristics of these subpopulations, the differential afferent inputs onto these VTA DA neuron subpopulations, and consolidate findings of drug-induced plasticity of VTA DA neurons and highlight the importance of future projection-based studies of this system.

  12. Improved Monosynaptic Neural Circuit Tracing Using Engineered Rabies Virus Glycoproteins

    Directory of Open Access Journals (Sweden)

    Euiseok J. Kim

    2016-04-01

    Full Text Available Monosynaptic rabies virus tracing is a unique and powerful tool used to identify neurons making direct presynaptic connections onto neurons of interest across the entire nervous system. Current methods utilize complementation of glycoprotein gene-deleted rabies of the SAD B19 strain with its glycoprotein, B19G, to mediate retrograde transsynaptic spread across a single synaptic step. In most conditions, this method labels only a fraction of input neurons and would thus benefit from improved efficiency of transsynaptic spread. Here, we report newly engineered glycoprotein variants to improve transsynaptic efficiency. Among them, oG (optimized glycoprotein is a codon-optimized version of a chimeric glycoprotein consisting of the transmembrane/cytoplasmic domain of B19G and the extracellular domain of rabies Pasteur virus strain glycoprotein. We demonstrate that oG increases the tracing efficiency for long-distance input neurons up to 20-fold compared to B19G. oG-mediated rabies tracing will therefore allow identification and study of more complete monosynaptic input neural networks.

  13. Fault Modeling and Testing for Analog Circuits in Complex Space Based on Supply Current and Output Voltage

    Directory of Open Access Journals (Sweden)

    Hongzhi Hu

    2015-01-01

    Full Text Available This paper deals with the modeling of fault for analog circuits. A two-dimensional (2D fault model is first proposed based on collaborative analysis of supply current and output voltage. This model is a family of circle loci on the complex plane, and it simplifies greatly the algorithms for test point selection and potential fault simulations, which are primary difficulties in fault diagnosis of analog circuits. Furthermore, in order to reduce the difficulty of fault location, an improved fault model in three-dimensional (3D complex space is proposed, which achieves a far better fault detection ratio (FDR against measurement error and parametric tolerance. To address the problem of fault masking in both 2D and 3D fault models, this paper proposes an effective design for testability (DFT method. By adding redundant bypassing-components in the circuit under test (CUT, this method achieves excellent fault isolation ratio (FIR in ambiguity group isolation. The efficacy of the proposed model and testing method is validated through experimental results provided in this paper.

  14. Graph-Based Symbolic Technique and Its Application in the Frequency Response Bound Analysis of Analog Integrated Circuits

    Directory of Open Access Journals (Sweden)

    E. Tlelo-Cuautle

    2014-01-01

    Full Text Available A new graph-based symbolic technique (GBST for deriving exact analytical expressions like the transfer function H(s of an analog integrated circuit (IC, is introduced herein. The derived H(s of a given analog IC is used to compute the frequency response bounds (maximum and minimum associated to the magnitude and phase of H(s, subject to some ranges of process variational parameters, and by performing nonlinear constrained optimization. Our simulations demonstrate the usefulness of the new GBST for deriving the exact symbolic expression for H(s, and the last section highlights the good agreement between the frequency response bounds computed by our variational analysis approach versus traditional Monte Carlo simulations. As a conclusion, performing variational analysis using our proposed GBST for computing the frequency response bounds of analog ICs, shows a gain in computing time of 100x for a differential circuit topology and 50x for a 3-stage amplifier, compared to traditional Monte Carlo simulations.

  15. Contribution of visual and circadian neural circuits to memory for prolonged mating induced by rivals.

    Science.gov (United States)

    Kim, Woo Jae; Jan, Lily Yeh; Jan, Yuh Nung

    2012-06-01

    Rival exposure causes Drosophila melanogaster males to prolong mating. Longer mating duration (LMD) may enhance reproductive success, but its underlying mechanism is currently unknown. We found that LMD is context dependent and can be induced solely via visual stimuli. In addition, we found that LMD involves neural circuits that are important for visual memory, including central neurons in the ellipsoid body, but not the mushroom bodies or the fan-shaped bodies, and may rely on the rival exposure memory lasting for several hours. LMD is affected by a subset of learning and memory mutants. LMD depends on the circadian clock genes timeless and period, but not Clock or cycle, and persists in many arrhythmic conditions. Moreover, LMD critically depends on a subset of pigment dispersing factor neurons rather than the entire circadian neural circuit. Our study thus delineates parts of the molecular and cellular basis for LMD, a plastic social behavior elicited by visual cues.

  16. Homology and homoplasy of swimming behaviors and neural circuits in the Nudipleura (Mollusca, Gastropoda, Opisthobranchia)

    Science.gov (United States)

    Newcomb, James M.; Sakurai, Akira; Lillvis, Joshua L.; Gunaratne, Charuni A.; Katz, Paul S.

    2012-01-01

    How neural circuit evolution relates to behavioral evolution is not well understood. Here the relationship between neural circuits and behavior is explored with respect to the swimming behaviors of the Nudipleura (Mollusca, Gastropoda, Opithobranchia). Nudipleura is a diverse monophyletic clade of sea slugs among which only a small percentage of species can swim. Swimming falls into a limited number of categories, the most prevalent of which are rhythmic left–right body flexions (LR) and rhythmic dorsal–ventral body flexions (DV). The phylogenetic distribution of these behaviors suggests a high degree of homoplasy. The central pattern generator (CPG) underlying DV swimming has been well characterized in Tritonia diomedea and in Pleurobranchaea californica. The CPG for LR swimming has been elucidated in Melibe leonina and Dendronotus iris, which are more closely related. The CPGs for the categorically distinct DV and LR swimming behaviors consist of nonoverlapping sets of homologous identified neurons, whereas the categorically similar behaviors share some homologous identified neurons, although the exact composition of neurons and synapses in the neural circuits differ. The roles played by homologous identified neurons in categorically distinct behaviors differ. However, homologous identified neurons also play different roles even in the swim CPGs of the two LR swimming species. Individual neurons can be multifunctional within a species. Some of those functions are shared across species, whereas others are not. The pattern of use and reuse of homologous neurons in various forms of swimming and other behaviors further demonstrates that the composition of neural circuits influences the evolution of behaviors. PMID:22723353

  17. Neural circuit components of the Drosophila OFF motion vision pathway.

    Science.gov (United States)

    Meier, Matthias; Serbe, Etienne; Maisak, Matthew S; Haag, Jürgen; Dickson, Barry J; Borst, Alexander

    2014-02-17

    Detecting the direction of visual motion is an essential task of the early visual system. The Reichardt detector has been proven to be a faithful description of the underlying computation in insects. A series of recent studies addressed the neural implementation of the Reichardt detector in Drosophila revealing the overall layout in parallel ON and OFF channels, its input neurons from the lamina (L1→ON, and L2→OFF), and the respective output neurons to the lobula plate (ON→T4, and OFF→T5). While anatomical studies showed that T4 cells receive input from L1 via Mi1 and Tm3 cells, the neurons connecting L2 to T5 cells have not been identified so far. It is, however, known that L2 contacts, among others, two neurons, called Tm2 and L4, which show a pronounced directionality in their wiring. We characterized the visual response properties of both Tm2 and L4 neurons via Ca(2+) imaging. We found that Tm2 and L4 cells respond with an increase in activity to moving OFF edges in a direction-unselective manner. To investigate their participation in motion vision, we blocked their output while recording from downstream tangential cells in the lobula plate. Silencing of Tm2 and L4 completely abolishes the response to moving OFF edges. Our results demonstrate that both cell types are essential components of the Drosophila OFF motion vision pathway, prior to the computation of directionality in the dendrites of T5 cells. Copyright © 2014 Elsevier Ltd. All rights reserved.

  18. Biologically based neural circuit modelling for the study of fear learning and extinction

    Science.gov (United States)

    Nair, Satish S.; Paré, Denis; Vicentic, Aleksandra

    2016-11-01

    The neuronal systems that promote protective defensive behaviours have been studied extensively using Pavlovian conditioning. In this paradigm, an initially neutral-conditioned stimulus is paired with an aversive unconditioned stimulus leading the subjects to display behavioural signs of fear. Decades of research into the neural bases of this simple behavioural paradigm uncovered that the amygdala, a complex structure comprised of several interconnected nuclei, is an essential part of the neural circuits required for the acquisition, consolidation and expression of fear memory. However, emerging evidence from the confluence of electrophysiological, tract tracing, imaging, molecular, optogenetic and chemogenetic methodologies, reveals that fear learning is mediated by multiple connections between several amygdala nuclei and their distributed targets, dynamical changes in plasticity in local circuit elements as well as neuromodulatory mechanisms that promote synaptic plasticity. To uncover these complex relations and analyse multi-modal data sets acquired from these studies, we argue that biologically realistic computational modelling, in conjunction with experiments, offers an opportunity to advance our understanding of the neural circuit mechanisms of fear learning and to address how their dysfunction may lead to maladaptive fear responses in mental disorders.

  19. Large scale neural circuit mapping data analysis accelerated with the graphical processing unit (GPU)

    Science.gov (United States)

    Shi, Yulin; Veidenbaum, Alexander V.; Nicolau, Alex; Xu, Xiangmin

    2014-01-01

    Background Modern neuroscience research demands computing power. Neural circuit mapping studies such as those using laser scanning photostimulation (LSPS) produce large amounts of data and require intensive computation for post-hoc processing and analysis. New Method Here we report on the design and implementation of a cost-effective desktop computer system for accelerated experimental data processing with recent GPU computing technology. A new version of Matlab software with GPU enabled functions is used to develop programs that run on Nvidia GPUs to harness their parallel computing power. Results We evaluated both the central processing unit (CPU) and GPU-enabled computational performance of our system in benchmark testing and practical applications. The experimental results show that the GPU-CPU co-processing of simulated data and actual LSPS experimental data clearly outperformed the multi-core CPU with up to a 22x speedup, depending on computational tasks. Further, we present a comparison of numerical accuracy between GPU and CPU computation to verify the precision of GPU computation. In addition, we show how GPUs can be effectively adapted to improve the performance of commercial image processing software such as Adobe Photoshop. Comparison with Existing Method(s) To our best knowledge, this is the first demonstration of GPU application in neural circuit mapping and electrophysiology-based data processing. Conclusions Together, GPU enabled computation enhances our ability to process large-scale data sets derived from neural circuit mapping studies, allowing for increased processing speeds while retaining data precision. PMID:25277633

  20. Priming Neural Circuits to Modulate Spinal Reflex Excitability

    Science.gov (United States)

    Estes, Stephen P.; Iddings, Jennifer A.; Field-Fote, Edelle C.

    2017-01-01

    While priming is most often thought of as a strategy for modulating neural excitability to facilitate voluntary motor control, priming stimulation can also be utilized to target spinal reflex excitability. In this application, priming can be used to modulate the involuntary motor output that often follows central nervous system injury. Individuals with spinal cord injury (SCI) often experience spasticity, for which antispasmodic medications are the most common treatment. Physical therapeutic/electroceutic interventions offer an alternative treatment for spasticity, without the deleterious side effects that can accompany pharmacological interventions. While studies of physical therapeutic/electroceutic interventions have been published, a systematic comparison of these approaches has not been performed. The purpose of this study was to compare four non-pharmacological interventions to a sham-control intervention to assess their efficacy for spasticity reduction. Participants were individuals (n = 10) with chronic SCI (≥1 year) who exhibited stretch-induced quadriceps spasticity. Spasticity was quantified using the pendulum test before and at two time points after (immediate, 45 min delayed) each of four different physical therapeutic/electroceutic interventions, plus a sham-control intervention. Interventions included stretching, cyclic passive movement (CPM), transcutaneous spinal cord stimulation (tcSCS), and transcranial direct current stimulation (tDCS). The sham-control intervention consisted of a brief ramp-up and ramp-down of knee and ankle stimulation while reclined with legs extended. The order of interventions was randomized, and each was tested on a separate day with at least 48 h between sessions. Compared to the sham-control intervention, stretching, CPM, and tcSCS were associated with a significantly greater reduction in spasticity immediately after treatment. While the immediate effect was largest for stretching, the reduction persisted

  1. Consistency and Advantage of Loop Regularization Method Merging with Bjorken-Drell's Analogy Between Feynman Diagrams and Electrical Circuits

    CERN Document Server

    Huang, Da

    2011-01-01

    The consistency of loop regularization (LORE) method is explored in multiloop calculations. A key concept of the LORE method is the introduction of irreducible loop integrals (ILIs) which are evaluated from the Feynman diagrams by adopting the Feynman parametrization and ultraviolet-divergence-preserving(UVDP) parametrization. It is then inevitable for the ILIs to encounter the divergences in the UVDP-parameter space due to the generic overlapping divergences in the 4-dimensional momentum space. By computing the so-called $\\alpha\\beta\\gamma$ integrals arising from two loop Feynman diagrams, we show how to deal with the divergences in the parameter space by applying for the LORE method. By identifying the divergences in the UVDP-parameter space to those in the subdiagrams of two loop diagrams, we arrive at the Bjorken-Drell's analogy between Feynman diagrams and electrical circuits, where the UVDP parameters are associated with the conductance or resistance in the electrical circuits. In particular, the sets o...

  2. Accurate SPICE Modeling of Poly-silicon Resistor in 40nm CMOS Technology Process for Analog Circuit Simulation

    Directory of Open Access Journals (Sweden)

    Sun Lijie

    2015-01-01

    Full Text Available In this paper, the SPICE model of poly resistor is accurately developed based on silicon data. To describe the non-linear R-V trend, the new correlation in temperature and voltage is found in non-silicide poly-silicon resistor. A scalable model is developed on the temperature-dependent characteristics (TDC and the temperature-dependent voltage characteristics (TDVC from the R-V data. Besides, the parasitic capacitance between poly and substrate are extracted from real silicon structure in replacing conventional simulation data. The capacitance data are tested through using on-wafer charge-induced-injection error-free charge-based capacitance measurement (CIEF-CBCM technique which is driven by non-overlapping clock generation circuit. All modeling test structures are designed and fabricated through using 40nm CMOS technology process. The new SPICE model of poly-silicon resistor is more accurate to silicon for analog circuit simulation.

  3. CMOS-analogous wafer-scale nanotube-on-insulator approach for submicrometer devices and integrated circuits using aligned nanotubes.

    Science.gov (United States)

    Ryu, Koungmin; Badmaev, Alexander; Wang, Chuan; Lin, Albert; Patil, Nishant; Gomez, Lewis; Kumar, Akshay; Mitra, Subhasish; Wong, H-S Philip; Zhou, Chongwu

    2009-01-01

    Massive aligned carbon nanotubes hold great potential but also face significant integration/assembly challenges for future beyond-silicon nanoelectronics. We report a wafer-scale processing of aligned nanotube devices and integrated circuits, including progress on essential technological components such as wafer-scale synthesis of aligned nanotubes, wafer-scale transfer of nanotubes to silicon wafers, metallic nanotube removal and chemical doping, and defect-tolerant integrated nanotube circuits. We have achieved synthesis of massive aligned nanotubes on complete 4 in. quartz and sapphire substrates, which were then transferred to 4 in. Si/SiO(2) wafers. CMOS analogous fabrication was performed to yield transistors and circuits with features down to 0.5 mum, with high current density approximately 20 muA/mum and good on/off ratios. In addition, chemical doping has been used to build fully integrated complementary inverter with a gain approximately 5, and a defect-tolerant design has been employed for NAND and NOR gates. This full-wafer approach could serve as a critical foundation for future integrated nanotube circuits.

  4. Optogenetic dissection of neural circuit underlying locomotory decision-making in Caenorhabditis Elegans

    Science.gov (United States)

    Kocabas, Askin; Guo, Zengcai; Ramanathan, Sharad

    2011-03-01

    Despite the knowledge of the physical connectivity of the entire nervous system of C.elegans, we know little about how neuronal dynamics results in decision-making. Detailed understanding of functional and dynamic relations of the neural circuitry requires spatiotemporal control of the neuronal activity. Recent discoveries of light gated ion channels have allowed temporal optical control of neural activity. However, excitation of a specific neuron from among many expressing the channel has been a challenge. By combining optogenetic tools, micro mirror array technology and fast real time image processing, we have developed a technique to activate specific single or multiple neurons in an intact crawling animal while tracking its behavior. Using this setup we traced the neural pathway controlling the gradual turning of the animal during the locomotion. We found that the activity of a specific neuronal circuit that receives inputs from sensory neurons is coordinated with head movement. This coordination allows the animal to turn left or right based on the variation of sensory stimulus during head movement. By directly modulating the activity of the neural circuit, we can force the animal to turn in a specific direction independent of sensory stimuli. Human Frontier Science Program.

  5. Amigo Adhesion Protein Regulates Development of Neural Circuits in Zebrafish Brain*

    Science.gov (United States)

    Zhao, Xiang; Kuja-Panula, Juha; Sundvik, Maria; Chen, Yu-Chia; Aho, Vilma; Peltola, Marjaana A.; Porkka-Heiskanen, Tarja; Panula, Pertti; Rauvala, Heikki

    2014-01-01

    The Amigo protein family consists of three transmembrane proteins characterized by six leucine-rich repeat domains and one immunoglobulin-like domain in their extracellular moieties. Previous in vitro studies have suggested a role as homophilic adhesion molecules in brain neurons, but the in vivo functions remain unknown. Here we have cloned all three zebrafish amigos and show that amigo1 is the predominant family member expressed during nervous system development in zebrafish. Knockdown of amigo1 expression using morpholino oligonucleotides impairs the formation of fasciculated tracts in early fiber scaffolds of brain. A similar defect in fiber tract development is caused by mRNA-mediated expression of the Amigo1 ectodomain that inhibits adhesion mediated by the full-length protein. Analysis of differentiated neural circuits reveals defects in the catecholaminergic system. At the behavioral level, the disturbed formation of neural circuitry is reflected in enhanced locomotor activity and in the inability of the larvae to perform normal escape responses. We suggest that Amigo1 is essential for the development of neural circuits of zebrafish, where its mechanism involves homophilic interactions within the developing fiber tracts and regulation of the Kv2.1 potassium channel to form functional neural circuitry that controls locomotion. PMID:24904058

  6. Analog circuits using FinFETs: benefits in speed-accuracy-power trade-off and simulation of parasitic effects

    Directory of Open Access Journals (Sweden)

    M. Fulde

    2007-06-01

    Full Text Available Multi-gate FET, e.g. FinFET devices are the most promising contenders to replace bulk FETs in sub-45 nm CMOS technologies due to their improved sub threshold and short channel behavior, associated with low leakage currents. The introduction of novel gate stack materials (e.g. metal gate, high-k dielectric and modified device architectures (e.g. fully depleted, undoped fins affect the analog device properties significantly. First measurements indicate enhanced intrinsic gain (gm/gDS and promising matching behavior of FinFETs. The resulting benefits regarding the speed-accuracy-power trade-off in analog circuit design will be shown in this work. Additionally novel device specific effects will be discussed. The hysteresis effect caused by charge trapping in high-k dielectrics or self-heating due to the high thermal resistor of the BOX isolation are possible challenges for analog design in these emerging technologies. To gain an early assessment of the impact of such parasitic effects SPICE based models are derived and applied in analog building blocks.

  7. Continuous or discrete attractors in neural circuits? A self-organized switch at maximal entropy

    CERN Document Server

    Bernacchia, Alberto

    2007-01-01

    A recent experiment suggests that neural circuits may alternatively implement continuous or discrete attractors, depending on the training set up. In recurrent neural network models, continuous and discrete attractors are separately modeled by distinct forms of synaptic prescriptions (learning rules). Here, we report a solvable network model, endowed with Hebbian synaptic plasticity, which is able to learn either discrete or continuous attractors, depending on the frequency of presentation of stimuli and on the structure of sensory coding. A continuous attractor is learned when experience matches sensory coding, i.e. when the distribution of experienced stimuli matches the distribution of preferred stimuli of neurons. In that case, there is no processing of sensory information and neural activity displays maximal entropy. If experience goes beyond sensory coding, processing is initiated and the continuous attractor is destabilized into a set of discrete attractors.

  8. Modeling of Modern Active Devices for Simulations of Analog Circuits in PSpice

    Directory of Open Access Journals (Sweden)

    T. Dostal

    2000-09-01

    Full Text Available Suitable models of the modern active components and functional blocks,namely new types of current conveyors, operational and transimpedanceamplifiers, in several appropriate levels, using analog behavioralmodeling are given in this paper.

  9. Phase-locked loops. [in analog and digital circuits communication system

    Science.gov (United States)

    Gupta, S. C.

    1975-01-01

    An attempt to systematically outline the work done in the area of phase-locked loops which are now used in modern communication system design is presented. The analog phase-locked loops are well documented in several books but discrete, analog-digital, and digital phase-locked loop work is scattered. Apart from discussing the various analysis, design, and application aspects of phase-locked loops, a number of references are given in the bibliography.

  10. The Effects of Spaceflight and a Spaceflight Analog on Neurocognitive Perfonnance: Extent, Longevity, and Neural Bases

    Science.gov (United States)

    Seidler, R. D.; Mulavara, A. P.; Koppelmans, V.; Erdeniz, B.; Kofman, I. S.; DeDios, Y. E.; Szecsy, D. L.; Riascos-Castaneda, R. F.; Wood, S. J.; Bloomberg, J. J.

    2014-01-01

    We are conducting ongoing experiments in which we are performing structural and functional magnetic resonance brain imaging to identify the relationships between changes in neurocognitive function and neural structural alterations following a six month International Space Station mission and following 70 days exposure to a spaceflight analog, head down tilt bedrest. Our central hypothesis is that measures of brain structure, function, and network integrity will change from pre to post intervention (spaceflight, bedrest). Moreover, we predict that these changes will correlate with indices of cognitive, sensory, and motor function in a neuroanatomically selective fashion. Our interdisciplinary approach utilizes cutting edge neuroimaging techniques and a broad ranging battery of sensory, motor, and cognitive assessments that will be conducted pre flight, during flight, and post flight to investigate potential neuroplastic and maladaptive brain changes in crewmembers following long-duration spaceflight. Success in this endeavor would 1) result in identification of the underlying neural mechanisms and operational risks of spaceflight-induced changes in behavior, and 2) identify whether a return to normative behavioral function following re-adaptation to Earth's gravitational environment is associated with a restitution of brain structure and function or instead is supported by substitution with compensatory brain processes. With the bedrest study, we will be able to determine the neural and neurocognitive effects of extended duration unloading, reduced sensory inputs, and increased cephalic fluid distribution. This will enable us to parse out the multiple mechanisms contributing to any spaceflight-induced neural structural and behavioral changes that we observe in the flight study. In this presentation I will discuss preliminary results from six participants who have undergone the bed rest protocol. These individuals show decrements in balance and functional mobility

  11. Expediting analog design retargeting by design knowledge re-use and circuit synthesis: a practical example on a Delta-Sigma modulator

    Science.gov (United States)

    Webb, Matthew; Tang, Hua

    2016-08-01

    In the past decade or two, due to constant and rapid technology changes, analog design re-use or design retargeting to newer technologies has been brought to the table in order to expedite the design process and improve time-to-market. If properly conducted, analog design retargeting could significantly cut down design cycle compared to designs starting from the scratch. In this article, we present an empirical and general method for efficient analog design retargeting by design knowledge re-use and circuit synthesis (CS). The method first identifies circuit blocks that compose the source system and extracts the performance parameter specifications of each circuit block. Then, for each circuit block, it scales the values of design variables (DV) from the source design to derive an initial design in the target technology. Depending on the performance of this initial target design, a design space is defined for synthesis. Subsequently, each circuit block is automatically synthesised using state-of-art analog synthesis tools based on a combination of global and local optimisation techniques to achieve comparable performance specifications to those extracted from the source system. Finally, the overall system is composed of those synthesised circuit blocks in the target technology. We illustrate the method using a practical example of a complex Delta-Sigma modulator (DSM) circuit.

  12. Incorporating Artificial Neural Networks in the dynamic thermal-hydraulic model of a controlled cryogenic circuit

    Science.gov (United States)

    Carli, S.; Bonifetto, R.; Savoldi, L.; Zanino, R.

    2015-09-01

    A model based on Artificial Neural Networks (ANNs) is developed for the heated line portion of a cryogenic circuit, where supercritical helium (SHe) flows and that also includes a cold circulator, valves, pipes/cryolines and heat exchangers between the main loop and a saturated liquid helium (LHe) bath. The heated line mimics the heat load coming from the superconducting magnets to their cryogenic cooling circuits during the operation of a tokamak fusion reactor. An ANN is trained, using the output from simulations of the circuit performed with the 4C thermal-hydraulic (TH) code, to reproduce the dynamic behavior of the heated line, including for the first time also scenarios where different types of controls act on the circuit. The ANN is then implemented in the 4C circuit model as a new component, which substitutes the original 4C heated line model. For different operational scenarios and control strategies, a good agreement is shown between the simplified ANN model results and the original 4C results, as well as with experimental data from the HELIOS facility confirming the suitability of this new approach which, extended to an entire magnet systems, can lead to real-time control of the cooling loops and fast assessment of control strategies for heat load smoothing to the cryoplant.

  13. A 4 μW/Ch analog front-end module with moderate inversion and power-scalable sampling operation for 3-D neural microsystems.

    Science.gov (United States)

    Al-Ashmouny, Khaled M; Chang, Sun-Il; Yoon, Euisik

    2012-10-01

    We report an analog front-end prototype designed in 0.25 μm CMOS process for hybrid integration into 3-D neural recording microsystems. For scaling towards massive parallel neural recording, the prototype has investigated some critical circuit challenges in power, area, interface, and modularity. We achieved extremely low power consumption of 4 μW/channel, optimized energy efficiency using moderate inversion in low-noise amplifiers (K of 5.98 × 10⁸ or NEF of 2.9), and minimized asynchronous interface (only 2 per 16 channels) for command and data capturing. We also implemented adaptable operations including programmable-gain amplification, power-scalable sampling (up to 50 kS/s/channel), wide configuration range (9-bit) for programmable gain and bandwidth, and 5-bit site selection capability (selecting 16 out of 128 sites). The implemented front-end module has achieved a reduction in noise-energy-area product by a factor of 5-25 times as compared to the state-of-the-art analog front-end approaches reported to date.

  14. CMOS VLSI Hyperbolic Tangent Function & its Derivative Circuits for Neuron Implementation

    Directory of Open Access Journals (Sweden)

    Hussein CHIBLE,

    2013-10-01

    Full Text Available The hyperbolic tangent function and its derivative are key essential element in analog signal processing and especially in analog VLSI implementation of neuron of artificial neural networks. The main conditions of these types of circuits are the small silicon area, and the low power consumption. The objective of this paper is to study and design CMOS VLSI hyperbolic tangent function and its derivative circuit for neural network implementation. A circuit is designed and the results are presented

  15. Altered behavioral performance and live imaging of circuit-specific neural deficiencies in a zebrafish model for psychomotor retardation.

    Directory of Open Access Journals (Sweden)

    David Zada

    2014-09-01

    Full Text Available The mechanisms and treatment of psychomotor retardation, which includes motor and cognitive impairment, are indefinite. The Allan-Herndon-Dudley syndrome (AHDS is an X-linked psychomotor retardation characterized by delayed development, severe intellectual disability, muscle hypotonia, and spastic paraplegia, in combination with disturbed thyroid hormone (TH parameters. AHDS has been associated with mutations in the monocarboxylate transporter 8 (mct8/slc16a2 gene, which is a TH transporter. In order to determine the pathophysiological mechanisms of AHDS, MCT8 knockout mice were intensively studied. Although these mice faithfully replicated the abnormal serum TH levels, they failed to exhibit the neurological and behavioral symptoms of AHDS patients. Here, we generated an mct8 mutant (mct8-/- zebrafish using zinc-finger nuclease (ZFN-mediated targeted gene editing system. The elimination of MCT8 decreased the expression levels of TH receptors; however, it did not affect the expression of other TH-related genes. Similar to human patients, mct8-/- larvae exhibited neurological and behavioral deficiencies. High-throughput behavioral assays demonstrated that mct8-/- larvae exhibited reduced locomotor activity, altered response to external light and dark transitions and an increase in sleep time. These deficiencies in behavioral performance were associated with altered expression of myelin-related genes and neuron-specific deficiencies in circuit formation. Time-lapse imaging of single-axon arbors and synapses in live mct8-/- larvae revealed a reduction in filopodia dynamics and axon branching in sensory neurons and decreased synaptic density in motor neurons. These phenotypes enable assessment of the therapeutic potential of three TH analogs that can enter the cells in the absence of MCT8. The TH analogs restored the myelin and axon outgrowth deficiencies in mct8-/- larvae. These findings suggest a mechanism by which MCT8 regulates neural circuit

  16. Diagnosis of Soft Spot Short Defects in Analog Circuits Considering the Thermal Behaviour of the Chip

    Directory of Open Access Journals (Sweden)

    Tadeusiewicz Michał

    2016-06-01

    Full Text Available The paper deals with fault diagnosis of nonlinear analogue integrated circuits. Soft spot short defects are analysed taking into account variations of the circuit parameters due to physical imperfections as well as self-heating of the chip. A method enabling to detect, locate and estimate the value of a spot defect has been developed. For this purpose an appropriate objective function was minimized using an optimization procedure based on the Fibonacci method. The proposed approach exploits DC measurements in the test phase, performed at a limited number of accessible points. For illustration three numerical examples are given.

  17. An Inductively-Powered Wireless Neural Recording System with a Charge Sampling Analog Front-End.

    Science.gov (United States)

    Lee, Seung Bae; Lee, Byunghun; Kiani, Mehdi; Mahmoudi, Babak; Gross, Robert; Ghovanloo, Maysam

    2016-01-15

    An inductively-powered wireless integrated neural recording system (WINeR-7) is presented for wireless and battery less neural recording from freely-behaving animal subjects inside a wirelessly-powered standard homecage. The WINeR-7 system employs a novel wide-swing dual slope charge sampling (DSCS) analog front-end (AFE) architecture, which performs amplification, filtering, sampling, and analog-to-time conversion (ATC) with minimal interference and small amount of power. The output of the DSCS-AFE produces a pseudo-digital pulse width modulated (PWM) signal. A circular shift register (CSR) time division multiplexes (TDM) the PWM pulses to create a TDM-PWM signal, which is fed into an on-chip 915 MHz transmitter (Tx). The AFE and Tx are supplied at 1.8 V and 4.2 V, respectively, by a power management block, which includes a high efficiency active rectifier and automatic resonance tuning (ART), operating at 13.56 MHz. The 8-ch system-on-a-chip (SoC) was fabricated in a 0.35-μm CMOS process, occupying 5.0 × 2.5 mm(2) and consumed 51.4 mW. For each channel, the sampling rate is 21.48 kHz and the power consumption is 19.3 μW. In vivo experiments were conducted on freely behaving rats in an energized homecage by continuously delivering 51.4 mW to the WINeR-7 system in a closed-loop fashion and recording local field potentials (LFP).

  18. Dynamical systems, attractors, and neural circuits [version 1; referees: 3 approved

    Directory of Open Access Journals (Sweden)

    Paul Miller

    2016-05-01

    Full Text Available Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic—they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.

  19. A neural circuit transforming temporal periodicity information into a rate-based representation in the mammalian auditory system

    DEFF Research Database (Denmark)

    Dicke, Ulrike; Ewert, Stephan D.; Dau, Torsten;

    2007-01-01

    . In order to investigate the compatibility of the neural circuit with a linear modulation filterbank analysis as proposed in psychophysical studies, complex stimuli such as tones modulated by the sum of two sinusoids, narrowband noise, and iterated rippled noise were processed by the model. The model....... The present study suggests a neural circuit for the transformation from the temporal to the rate-based code. Due to the neural connectivity of the circuit, bandpass shaped rate modulation transfer functions are obtained that correspond to recorded functions of inferior colliculus IC neurons. In contrast...... to previous modeling studies, the present circuit does not employ a continuously changing temporal parameter to obtain different best modulation frequencies BMFs of the IC bandpass units. Instead, different BMFs are yielded from varying the number of input units projecting onto different bandpass units...

  20. Introduction to the special section on the neural substrate of analogical reasoning and metaphor comprehension.

    Science.gov (United States)

    Bassok, Miriam; Dunbar, Kevin N; Holyoak, Keith J

    2012-03-01

    The special section on the neural substrate of relational reasoning includes 4 articles that address the processes and brain regions involved in analogical reasoning (Green, Kraemer, Fugelsang, Gray, & Dunbar, 2011; Maguire, McClelland, Donovan, Tillman, & Krawczyk, 2011) and in metaphor comprehension (Chettih, Durgin, & Grodner, 2011; Prat, Mason, & Just, 2011). We see this work as an example of how neuroscience approaches to cognition can lead to increased understanding of cognitive processes. In this brief introduction, we first situate the 4 articles in the context of prior cognitive neuroscience work on relational reasoning. We then highlight the main issues explored in these articles: different sources of complexity and difficulty in relational processing, potential differences between the roles of the 2 hemispheres, and the impact of individual differences in various cognitive abilities. The 4 articles illustrate a range of methodologies, including functional magnetic resonance imaging (fMRI; Green et al., 2011; Prat et al., 2011), event-related potentials (ERPs; Maguire et al., 2011), and different types of semantic priming (Chettih et al., 2011; Prat et al., 2011). They highlight the connections between research on analogy and on metaphor comprehension and suggest, collectively, that a cognitive neuroscience approach to relational reasoning can lead to converging conclusions.

  1. Advances in two photon scanning and scanless microscopy technologies for functional neural circuit imaging.

    Science.gov (United States)

    Schultz, Simon R; Copeland, Caroline S; Foust, Amanda J; Quicke, Peter; Schuck, Renaud

    2017-01-01

    Recent years have seen substantial developments in technology for imaging neural circuits, raising the prospect of large scale imaging studies of neural populations involved in information processing, with the potential to lead to step changes in our understanding of brain function and dysfunction. In this article we will review some key recent advances: improved fluorophores for single cell resolution functional neuroimaging using a two photon microscope; improved approaches to the problem of scanning active circuits; and the prospect of scanless microscopes which overcome some of the bandwidth limitations of current imaging techniques. These advances in technology for experimental neuroscience have in themselves led to technical challenges, such as the need for the development of novel signal processing and data analysis tools in order to make the most of the new experimental tools. We review recent work in some active topics, such as region of interest segmentation algorithms capable of demixing overlapping signals, and new highly accurate algorithms for calcium transient detection. These advances motivate the development of new data analysis tools capable of dealing with spatial or spatiotemporal patterns of neural activity, that scale well with pattern size.

  2. Dynamic changes in neural circuit topology following mild mechanical injury in vitro.

    Science.gov (United States)

    Patel, Tapan P; Ventre, Scott C; Meaney, David F

    2012-01-01

    Despite its enormous incidence, mild traumatic brain injury is not well understood. One aspect that needs more definition is how the mechanical energy during injury affects neural circuit function. Recent developments in cellular imaging probes provide an opportunity to assess the dynamic state of neural networks with single-cell resolution. In this article, we developed imaging methods to assess the state of dissociated cortical networks exposed to mild injury. We estimated the imaging conditions needed to achieve accurate measures of network properties, and applied these methodologies to evaluate if mild mechanical injury to cortical neurons produces graded changes to either spontaneous network activity or altered network topology. We found that modest injury produced a transient increase in calcium activity that dissipated within 1 h after injury. Alternatively, moderate mechanical injury produced immediate disruption in network synchrony, loss in excitatory tone, and increased modular topology. A calcium-activated neutral protease (calpain) was a key intermediary in these changes; blocking calpain activation restored the network nearly completely to its pre-injury state. Together, these findings show a more complex change in neural circuit behavior than previously reported for mild mechanical injury, and highlight at least one important early mechanism responsible for these changes.

  3. Analysis and Simulation of a Low-Leakage Analog Single Gate and FinFET Circuits

    Science.gov (United States)

    Chauhan, Manorama; Kushwah, Ravindra Singh; Shrivastava, Pavan; Akashe, Shyam

    2014-05-01

    In the world of Integrated Circuits, complementary metal-oxide-semiconductor (CMOS) has lost its ability during scaling beyond 50 nm. Scaling causes severe short channel effects (SCEs) which are difficult to suppress. FinFET devices undertake to replace usual Metal Oxide Semiconductor Field Effect Transistor (MOSFETs) because of their better ability in controlling leakage and diminishing SCEs while delivering a strong drive current. In this paper, we present a relative examination of FinFET with the double gate MOSFET (DGMOSFET) and conventional bulk Si single gate MOSFET (SGMOSFET) by using Cadence Virtuoso simulation tool. Physics-based numerical two-dimensional simulation results for FinFET device, circuit power is presented, and classifying that FinFET technology is an ideal applicant for low power applications. Exclusive FinFET device features resulting from gate-gate coupling are conversed and efficiently exploited for optimal low leakage device design. Design trade-off for FinFET power and performance are suggested for low power and high performance applications. Whole power consumptions of static and dynamic circuits and latches for FinFET device, believing state dependency, show that leakage currents for FinFET circuits are reduced by a factor of over 10X, compared to DGMOSFET and 20X compared with SGMOSFET.

  4. Analog Integrated Circuit and System Design for a Compact, Low-Power Cochlear Implant

    NARCIS (Netherlands)

    Ngamkham, W.

    2015-01-01

    Cochlear Implants (CIs) are prosthetic devices that restore hearing in profoundly deaf patients by bypassing the damaged parts of the inner ear and directly stimulating the remaining auditory nerve fibers in the cochlea with electrical pulses. This thesis describs the electronic circuit design of va

  5. A 97 dB dynamic range CSA-based readout circuit with analog temperature compensation for MEMS capacitive sensors

    Science.gov (United States)

    Tao, Yin; Chong, Zhang; Huanming, Wu; Qisong, Wu; Haigang, Yang

    2013-11-01

    This paper presents a charge-sensitive-amplifier (CSA) based readout circuit for capacitive microelectro-mechanical-system (MEMS) sensors. A continuous-time (CT) readout structure using the chopper technique is adopted to cancel the low frequency noise and improve the resolution of the readout circuits. An operational trans-conductance amplifier (OTA) structure with an auxiliary common-mode-feedback-OTA is proposed in the fully differential CSA to suppress the chopper modulation induced disturbance at the OTA input terminal. An analog temperature compensation method is proposed, which adjusts the chopper signal amplitude with temperature variation to compensate the temperature drift of the CSA readout sensitivity. The chip is designed and implemented in a 0.35 μm CMOS process and is 2.1 × 2.1 mm2 in area. The measurement shows that the readout circuit achieves 0.9 aF / √Hz capacitive resolution, 97 dB dynamic range in 100 Hz signal bandwidth, and 0.8 mV/fF sensitivity with a temperature drift of 35 ppm/°C after optimized compensation.

  6. AgRP Neural Circuits Mediate Adaptive Behaviors in the Starved State

    Science.gov (United States)

    Padilla, Stephanie L.; Qiu, Jian; Soden, Marta E.; Sanz, Elisenda; Nestor, Casey C; Barker, Forrest D.; Quintana, Albert; Zweifel, Larry S.; Rønnekleiv, Oline K.; Kelly, Martin J.; Palmiter, Richard D.

    2016-01-01

    In the face of starvation animals will engage in high-risk behaviors that would normally be considered maladaptive. Starving rodents for example will forage in areas that are more susceptible to predators and will also modulate aggressive behavior within a territory of limited or depleted nutrients. The neural basis of these adaptive behaviors likely involves circuits that link innate feeding, aggression, and fear. Hypothalamic AgRP neurons are critically important for driving feeding and project axons to brain regions implicated in aggression and fear. Using circuit-mapping techniques, we define a disynaptic network originating from a subset of AgRP neurons that project to the medial nucleus of the amygdala and then to the principle bed nucleus of the stria terminalis, which plays a role in suppressing territorial aggression and reducing contextual fear. We propose that AgRP neurons serve as a master switch capable of coordinating behavioral decisions relative to internal state and environmental cues. PMID:27019015

  7. Neural CMOS-integrated circuit and its application to data classification.

    Science.gov (United States)

    Göknar, Izzet Cem; Yildiz, Merih; Minaei, Shahram; Deniz, Engin

    2012-05-01

    Implementation and new applications of a tunable complementary metal-oxide-semiconductor-integrated circuit (CMOS-IC) of a recently proposed classifier core-cell (CC) are presented and tested with two different datasets. With two algorithms-one based on Fisher's linear discriminant analysis and the other based on perceptron learning, used to obtain CCs' tunable parameters-the Haberman and Iris datasets are classified. The parameters so obtained are used for hard-classification of datasets with a neural network structured circuit. Classification performance and coefficient calculation times for both algorithms are given. The CC has 6-ns response time and 1.8-mW power consumption. The fabrication parameters used for the IC are taken from CMOS AMS 0.35-μm technology.

  8. Alzheimer's disease Braak Stage progressions: reexamined and redefined as Borrelia infection transmission through neural circuits.

    Science.gov (United States)

    MacDonald, Alan B

    2007-01-01

    Brain structure in health is a dynamic energized equation incorporating chemistry, neuronal structure, and circuitry components. The chemistry "piece" is represented by multiple neurotransmitters such as Acetylcholine, Serotonin, and Dopamine. The neuronal structure "piece" incorporates synapses and their connections. And finally circuits of neurons establish "architectural blueprints" of anatomic wiring diagrams of the higher order of brain neuron organizations. In Alzheimer's disease, there are progressive losses in all of these components. Brain structure crumbles. The deterioration in Alzheimer's is ordered, reproducible, and stepwise. Drs. Braak and Braak have described stages in the Alzheimer disease continuum. "Progressions" through Braak Stages benchmark "Regressions" in Cognitive function. Under the microscope, the Stages of Braak commence in brain regions near to the hippocampus, and over time, like a tsunami wave of destruction, overturn healthy brain regions, with neurofibrillary tangle damaged neurons "marching" through the temporal lobe, neocortex and occipital cortex. In effect the destruction ascends from the limbic regions to progressively destroy the higher brain centers. Rabies infection also "begins low and finishes high" in its wave of destruction of brain tissue. Herpes Zoster infections offer the paradigm of clinical latency of infection inside of nerves before the "marching commences". Varicella Zoster virus enters neurons in the pediatric years. Dormant virus remains inside the neurons for 50-80 years, tissue damage late in life (shingles) demonstrates the "march of the infection" down neural pathways (dermatomes) as linear areas of painful blisters loaded with virus from a childhood infection. Amalgamation of Zoster with Rabies models produces a hybrid model to explain all of the Braak Stages of Alzheimer's disease under a new paradigm, namely "Alzheimer's neuroborreliosis" in which latent Borrelia infections ascend neural circuits through

  9. Cell biology in neuroscience: Architects in neural circuit design: glia control neuron numbers and connectivity.

    Science.gov (United States)

    Corty, Megan M; Freeman, Marc R

    2013-11-11

    Glia serve many important functions in the mature nervous system. In addition, these diverse cells have emerged as essential participants in nearly all aspects of neural development. Improved techniques to study neurons in the absence of glia, and to visualize and manipulate glia in vivo, have greatly expanded our knowledge of glial biology and neuron-glia interactions during development. Exciting studies in the last decade have begun to identify the cellular and molecular mechanisms by which glia exert control over neuronal circuit formation. Recent findings illustrate the importance of glial cells in shaping the nervous system by controlling the number and connectivity of neurons.

  10. Distinct neural circuits underlie assessment of a diversity of natural dangers by American crows.

    Science.gov (United States)

    Cross, Donna J; Marzluff, John M; Palmquist, Ila; Minoshima, Satoshi; Shimizu, Toru; Miyaoka, Robert

    2013-08-22

    Social animals encountering natural dangers face decisions such as whether to freeze, flee or harass the threat. The American crow, Corvus brachyrhynchos, conspicuously mobs dangers. We used positron emission tomography to test the hypothesis that distinct neuronal substrates underlie the crow's consistent behavioural response to different dangers. We found that crows activated brain regions associated with attention and arousal (nucleus isthmo-opticus/locus coeruleus), and with motor response (arcopallium), as they fixed their gaze on a threat. However, despite this consistent behavioural and neural response, the sight of a person who previously captured the crow, a person holding a dead crow and a taxidermy-mounted hawk activated distinct forebrain regions (amygdala, hippocampus and portion of the caudal nidopallium, respectively). We suggest that aspects of mobbing behaviour are guided by unique neural circuits that respond to differences in mental processing-learning, memory formation and multisensory discrimination-required to appropriately nuance a risky behaviour to specific dangers.

  11. Fault detection in digital and analog circuits using an i(DD) temporal analysis technique

    Science.gov (United States)

    Beasley, J.; Magallanes, D.; Vridhagiri, A.; Ramamurthy, Hema; Deyong, Mark

    1993-01-01

    An i(sub DD) temporal analysis technique which is used to detect defects (faults) and fabrication variations in both digital and analog IC's by pulsing the power supply rails and analyzing the temporal data obtained from the resulting transient rail currents is presented. A simple bias voltage is required for all the inputs, to excite the defects. Data from hardware tests supporting this technique are presented.

  12. Neuromorphic control of stepping pattern generation: a dynamic model with analog circuit implementation.

    Science.gov (United States)

    Yang, Zhijun; Cameron, Katherine; Lewinger, William; Webb, Barbara; Murray, Alan

    2012-03-01

    Animals such as stick insects can adaptively walk on complex terrains by dynamically adjusting their stepping motion patterns. Inspired by the coupled Matsuoka and resonate-and-fire neuron models, we present a nonlinear oscillation model as the neuromorphic central pattern generator (CPG) for rhythmic stepping pattern generation. This dynamic model can also be used to actuate the motoneurons on a leg joint with adjustable driving frequencies and duty cycles by changing a few of the model parameters while operating such that different stepping patterns can be generated. A novel mixed-signal integrated circuit design of this dynamic model is subsequently implemented, which, although simplified, shares the equivalent output performance in terms of the adjustable frequency and duty cycle. Three identical CPG models being used to drive three joints can make an arthropod leg of three degrees of freedom. With appropriate initial circuit parameter settings, and thus suitable phase lags among joints, the leg is expected to walk on a complex terrain with adaptive steps. The adaptation is associated with the circuit parameters mediated both by the higher level nervous system and the lower level sensory signals. The model is realized using a 0.3- complementary metal-oxide-semiconductor process and the results are reported.

  13. Sex differences in behavioral decision-making and the modulation of shared neural circuits

    Directory of Open Access Journals (Sweden)

    Mowrey William R

    2012-03-01

    Full Text Available Abstract Animals prioritize behaviors according to their physiological needs and reproductive goals, selecting a single behavioral strategy from a repertoire of possible responses to any given stimulus. Biological sex influences this decision-making process in significant ways, differentiating the responses animals choose when faced with stimuli ranging from food to conspecifics. We review here recent work in invertebrate models, including C. elegans, Drosophila, and a variety of insects, mollusks and crustaceans, that has begun to offer intriguing insights into the neural mechanisms underlying the sexual modulation of behavioral decision-making. These findings show that an animal's sex can modulate neural function in surprisingly diverse ways, much like internal physiological variables such as hunger or thirst. In the context of homeostatic behaviors such as feeding, an animal's sex and nutritional status may converge on a common physiological mechanism, the functional modulation of shared sensory circuitry, to influence decision-making. Similarly, considerable evidence suggests that decisions on whether to mate or fight with conspecifics are also mediated through sex-specific neuromodulatory control of nominally shared neural circuits. This work offers a new perspective on how sex differences in behavior emerge, in which the regulated function of shared neural circuitry plays a crucial role. Emerging evidence from vertebrates indicates that this paradigm is likely to extend to more complex nervous systems as well. As men and women differ in their susceptibility to a variety of neuropsychiatric disorders affecting shared behaviors, these findings may ultimately have important implications for human health.

  14. Nonlinear Companding Circuits With Thermal Compensation to Enhance Input Dynamic Range in Analog Optical Fiber Links

    Directory of Open Access Journals (Sweden)

    J. Rodríguez-Rodriguez

    2011-04-01

    Full Text Available Measuring systems based on a pair of optical fiber transmitter-receivers are used in medium-voltage testinglaboratories wherein the environment of high electromagnetic interference (EMI is a limitation for using conventionalcabling. Nonlinear compensation techniques have been used to limit the voltage range at the input of optical fiberlinks. However, nonlinear compensation introduces gain and linearity errors caused by thermal drift. This paperpresents a method of thermal compensation for the nonlinear circuit used to improve transient signal handlingcapabilities in measuring system while maintaining low errors in gain and linearity caused by thermal drift.

  15. The Neural Representation of 3-Dimensional Objects in Rodent Memory Circuits

    Science.gov (United States)

    Burke, Sara N.; Barnes, Carol A.

    2014-01-01

    Three-dimensional objects are common stimuli that rodents and other animals encounter in the natural world that contribute to the associations that are the hallmark of explicit memory. Thus, the use of 3-dimensional objects for investigating the circuits that support associative and episodic memories has a long history. In rodents, the neural representation of these types of stimuli is a polymodal process and lesion data suggest that the perirhinal cortex, an area of the medial temporal lobe that receives afferent input from all sensory modalities, is particularly important for integrating sensory information across modalities to support object recognition. Not surprisingly, recent data from in vivo electrophysiological recordings have shown that principal cells within the perirhinal cortex are activated at locations of an environment that contain 3-dimensional objects. Interestingly, it appears that neural activity patterns related to object stimuli are ubiquitous across memory circuits and have now been observed in many medial temporal lobe structures as well as in the anterior cingulate cortex. This review summarizes behavioral and neurophysiological data that examine the representation of 3-dimensional objects across brain regions that are involved in memory. PMID:25205370

  16. An implantable wireless neural interface for recording cortical circuit dynamics in moving primates

    Science.gov (United States)

    Borton, David A.; Yin, Ming; Aceros, Juan; Nurmikko, Arto

    2013-04-01

    Objective. Neural interface technology suitable for clinical translation has the potential to significantly impact the lives of amputees, spinal cord injury victims and those living with severe neuromotor disease. Such systems must be chronically safe, durable and effective. Approach. We have designed and implemented a neural interface microsystem, housed in a compact, subcutaneous and hermetically sealed titanium enclosure. The implanted device interfaces the brain with a 510k-approved, 100-element silicon-based microelectrode array via a custom hermetic feedthrough design. Full spectrum neural signals were amplified (0.1 Hz to 7.8 kHz, 200× gain) and multiplexed by a custom application specific integrated circuit, digitized and then packaged for transmission. The neural data (24 Mbps) were transmitted by a wireless data link carried on a frequency-shift-key-modulated signal at 3.2 and 3.8 GHz to a receiver 1 m away by design as a point-to-point communication link for human clinical use. The system was powered by an embedded medical grade rechargeable Li-ion battery for 7 h continuous operation between recharge via an inductive transcutaneous wireless power link at 2 MHz. Main results. Device verification and early validation were performed in both swine and non-human primate freely-moving animal models and showed that the wireless implant was electrically stable, effective in capturing and delivering broadband neural data, and safe for over one year of testing. In addition, we have used the multichannel data from these mobile animal models to demonstrate the ability to decode neural population dynamics associated with motor activity. Significance. We have developed an implanted wireless broadband neural recording device evaluated in non-human primate and swine. The use of this new implantable neural interface technology can provide insight into how to advance human neuroprostheses beyond the present early clinical trials. Further, such tools enable mobile

  17. Does the capsaicin-sensitive local neural circuit constitutively regulate vagally evoked esophageal striated muscle contraction in rats?

    Science.gov (United States)

    Shima, Takeshi; Shiina, Takahiko; Naitou, Kiyotada; Nakamori, Hiroyuki; Sano, Yuuki; Shimizu, Yasutake

    2016-03-01

    To determine whether a capsaicin-sensitive local neural circuit constitutively modulates vagal neuromuscular transmission in the esophageal striated muscle or whether the neural circuit operates in a stimulus-dependent manner, we compared the motility of esophageal preparations isolated from intact rats with those in which capsaicin-sensitive neurons had been destroyed. Electrical stimulation of the vagus nerve trunk evoked contractile responses in the esophagus isolated from a capsaicin-treated rat in a manner similar to those in the esophagus from a control rat. No obvious differences were observed in the inhibitory effects of D-tubocurarine on intact and capsaicin-treated rat esophageal motility. Destruction of the capsaicin-sensitive neurons did not significantly affect latency, time to peak and duration of a vagally evoked twitch-like contraction. These findings indicate that the capsaicin-sensitive neural circuit does not operate constitutively but rather is activated in response to an applied stimulus.

  18. High performance printed N and P-type OTFTs enabling digital and analog complementary circuits on flexible plastic substrate

    Science.gov (United States)

    Jacob, S.; Abdinia, S.; Benwadih, M.; Bablet, J.; Chartier, I.; Gwoziecki, R.; Cantatore, E.; van Roermund, A. H. M.; Maddiona, L.; Tramontana, F.; Maiellaro, G.; Mariucci, L.; Rapisarda, M.; Palmisano, G.; Coppard, R.

    2013-06-01

    This paper presents a printed organic complementary technology on flexible plastic substrate with high performance N and P-type Organic Thin Film Transistors (OTFTs), based on small-molecule organic semiconductors in solution. Challenges related to the integration of both OTFT types in a common complementary flow are addressed, showing the importance of surface treatments. Stability on single devices and on an elementary complementary digital circuit (ring oscillator) is studied, demonstrating that a robust and reliable flow with high electrical performances can be established for printed organic devices. These devices are used to manufacture several analog and digital building blocks. The design is carried out using a model specifically developed for this technology, and taking into account the parametric variability. High-frequency measurements of printed envelope detectors show improved speed performance, resulting from the high mobility of the OTFTs. In addition, a compact dynamic flip-flop and a low-offset comparator are demonstrated, thanks to availability of both n-type and p-type OTFTs in the technology. Measurement results are in good agreement with the simulations. The circuits presented establish a complete library of building blocks for the realization of a printed RFID tag.

  19. Impaired activity-dependent neural circuit assembly and refinement in autism spectrum disorder genetic models

    Directory of Open Access Journals (Sweden)

    Caleb Andrew Doll

    2014-02-01

    Full Text Available Early-use activity during circuit-specific critical periods refines brain circuitry by the coupled processes of eliminating inappropriate synapses and strengthening maintained synapses. We theorize these activity-dependent developmental processes are specifically impaired in autism spectrum disorders (ASDs. ASD genetic models in both mouse and Drosophila have pioneered our insights into normal activity-dependent neural circuit assembly and consolidation, and how these developmental mechanisms go awry in specific genetic conditions. The monogenic Fragile X syndrome (FXS, a common cause of heritable ASD and intellectual disability, has been particularly well linked to defects in activity-dependent critical period processes. The Fragile X Mental Retardation Protein (FMRP is positively activity-regulated in expression and function, in turn regulates excitability and activity in a negative feedback loop, and appears to be required for the activity-dependent remodeling of synaptic connectivity during early-use critical periods. The Drosophila FXS model has been shown to functionally conserve the roles of human FMRP in synaptogenesis, and has been centrally important in generating our current mechanistic understanding of the FXS disease state. Recent advances in Drosophila optogenetics, transgenic calcium reporters, highly-targeted transgenic drivers for individually-identified neurons, and a vastly improved connectome of the brain are now being combined to provide unparalleled opportunities to both manipulate and monitor activity-dependent processes during critical period brain development in defined neural circuits. The field is now poised to exploit this new Drosophila transgenic toolbox for the systematic dissection of activity-dependent mechanisms in normal versus ASD brain development, particularly utilizing the well-established Drosophila FXS disease model.

  20. Distinct rhythmic locomotor patterns can be generated by a simple adaptive neural circuit: biology, simulation, and VLSI implementation.

    Science.gov (United States)

    Ryckebusch, S; Wehr, M; Laurent, G

    1994-12-01

    Rhythmic motor patterns can be induced in leg motor neurons of isolated locust thoracic ganglia by bath application of pilocarpine. We observed that the relative phases of levators and depressors differed in the three thoracic ganglia. Assuming that the central pattern generating circuits underlying these three segmental rhythms are probably very similar, we developed a simple model circuit that can produce any one of the three activity patterns and characteristic phase relationships by modifying a single synaptic weight. We show results of a computer simulation of this circuit using the neuronal simulator NeuraLOG/Spike. We built and tested an analog VLSI circuit implementation of this model circuit that exhibits the same range of "behaviors" as the computer simulation. This multidisciplinary strategy will be useful to explore the dynamics of central pattern generating networks coupled to physical actuators, and ultimately should allow the design of biologically realistic walking robots.

  1. Path programmable logic: A structured design method for digital and/or mixed analog integrated circuits

    Science.gov (United States)

    Taylor, B.

    1990-01-01

    The design of Integrated Circuits has evolved past the black art practiced by a few semiconductor companies to a world wide community of users. This was basically accomplished by the development of computer aided design tools which were made available to this community. As the tools matured into different components of the design task they were accepted into the community at large. However, the next step in this evolution is being ignored by the large tool vendors hindering the continuation of this process. With system level definition and simulation through the logic specification well understood, why is the physical generation so blatantly ignored. This portion of the development is still treated as an isolated task with information being passed from the designer to the layout function. Some form of result given back but it severely lacks full definition of what has transpired. The level of integration in I.C.'s for tomorrow, whether through new processes or applications will require higher speeds, increased transistor density, and non-digital performance which can only be achieved through attention to the physical implementation.

  2. PSPICE controlled-source models of analogous circuit for Langevin type piezoelectric transducer

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The design and construction of wide-band and high efficiency acoustical projector has long been considered an art beyond the capabilities of many smaller groups. Langevin type piezoelectric transducers have been the most candidate of sonar array system applied in underwater communication. The transducers are fabricated, by bolting head mass and tail mass on both ends of stacked piezoelectric ceramic, to satisfy the multiple, conflicting design for high power transmitting capability. The aim of this research is to study the characteristics of Langevin type piezoelectric transducer that depend on different metal loading. First, the Mason equivalent circuit is used to model the segmented piezoelectric ceramic, then, the impedance network of tail and head masses is deduced by the Newton’s theory. To obtain the optimal solution to a specific design formulation, PSPICE controlled-source programming techniques can be applied. A valid example of the application of PSPICE models for Langevin type transducer analysis is presented and the simulation results are in good agreement with the experimental measurements.

  3. PSPICE controlled-source models of analogous circuit for Langevin type piezoelectric transducer

    Institute of Scientific and Technical Information of China (English)

    CHEN YeongChin; WU MenqJiun; LIU WeiKuo

    2007-01-01

    The design and construction of wide-band and high efficiency acoustical projector has long been considered an art beyond the capabilities of many smaller groups.Langevin type piezoelectric transducers have been the most candidate of sonar array system applied in underwater communication.The transducers are fabricated,by bolting head mass and tail mass on both ends of stacked piezoelectric ceramic,to satisfy the multiple,conflicting design for high power transmitting capability.The aim of this research is to study the characteristics of Langevin type piezoelectric transducer that depend on different metal loading.First,the Mason equivalent circuit is used to model the segmented piezoelectric ceramic,then,the impedance network of tail and head masses is deduced by the Newton's theory.To obtain the optimal solution to a specific design formulation,PSPICE controlled-source programming techniques can be applied.A valid example of the application of PSPICE models for Langevin type transducer analysis is presented and the simulation results are in good agreement with the experimental measurements.

  4. A Novel in Vitro Analog Expressing Learning-Induced Cellular Correlates in Distinct Neural Circuits

    Science.gov (United States)

    Weisz, Harris A.; Wainwright, Marcy L.; Mozzachiodi, Riccardo

    2017-01-01

    When presented with noxious stimuli, "Aplysia" exhibits concurrent sensitization of defensive responses, such as the tail-induced siphon withdrawal reflex (TSWR) and suppression of feeding. At the cellular level, sensitization of the TSWR is accompanied by an increase in the excitability of the tail sensory neurons (TSNs) that elicit the…

  5. Experience in the Development of the CMS Inner Tracker Analog Optohybrid Circuits: Project, Qualification, Volume Production, Quality Assurance and Final Performance

    CERN Document Server

    Ricci, Daniel; Bilei, Gian Mario; Casinini, F; Postolache, Vasile

    2005-01-01

    The Tracker system of the Compact Muon Solenoid (CMS) Experiment, will employ approximately 40,000 analog fibre-optic data and control links. The optical readout system is responsible for converting and transmitting the electrical signals coming out from the front-end to the outside counting room. Concerning the inner part of the Tracker, about 3,600 Analog Optohybrid circuits are involved in this tasks. These circuits have been designed and successfully produced in Italy under the responsibility of INFN Perugia CMS group completing the volume production phase by February 2005. Environmental features, reliability and performances of these circuits have been extensively tested and qualified. This paper reviews the most relevant steps of the manufacturing and quality assurance process: from prototypes to mass-production for the final CMS use.

  6. 模拟PID电路参数自整定温控系统设计%Design of analog PID circuit system with parameter auto-tuning

    Institute of Scientific and Technical Information of China (English)

    刘云芳; 张晓; 李建伟

    2013-01-01

    Based on analog PID control theory, an analog PID circuit with auto-tuning parameter of temperature controlled system was designed on purpose of simple, easy to use, intuitive parameters setting principles and effectively improving the performance of auto-tuning system. The analog PID circuit consisted of temperature measure circuit with constant flow source, main control with MSP430, analog PID circuit with digital potentiometer, serial communication and PC software interface. Experiments with the analog PID circuit show that the auto-tuning system works well, the temperature control system can get a 0.1 ℃ temperature stability degree in measurement precision of 0. 03℃after auto-tuning parameters.%针对模拟PID电路的控制理论和机理,从简单、易用、直观的参数整定原则和切实改善系统控制性能的参数自整定的目的,设计了由电流源搭建的铂电阻测温电路、MSP430主控电路、含数字电位器的模拟PID电路、串口通讯和上位机软件构成的模拟PID电路参数自整定温控系统.试验表明,自整定系统工作正常,整定后的温控系统在测温精度为0.03℃的情况下能获得0.1℃的温度稳定度.

  7. Implementation study of an analog spiking neural network for assisting cardiac delay prediction in a cardiac resynchronization therapy device.

    Science.gov (United States)

    Sun, Qing; Schwartz, François; Michel, Jacques; Herve, Yannick; Dalmolin, Renzo

    2011-06-01

    In this paper, we aim at developing an analog spiking neural network (SNN) for reinforcing the performance of conventional cardiac resynchronization therapy (CRT) devices (also called biventricular pacemakers). Targeting an alternative analog solution in 0.13- μm CMOS technology, this paper proposes an approach to improve cardiac delay predictions in every cardiac period in order to assist the CRT device to provide real-time optimal heartbeats. The primary analog SNN architecture is proposed and its implementation is studied to fulfill the requirement of very low energy consumption. By using the Hebbian learning and reinforcement learning algorithms, the intended adaptive CRT device works with different functional modes. The simulations of both learning algorithms have been carried out, and they were shown to demonstrate the global functionalities. To improve the realism of the system, we introduce various heart behavior models (with constant/variable heart rates) that allow pathologic simulations with/without noise on the signals of the input sensors. The simulations of the global system (pacemaker models coupled with heart models) have been investigated and used to validate the analog spiking neural network implementation.

  8. Microbiota-generated metabolites promote metabolic benefits via gut-brain neural circuits.

    Science.gov (United States)

    De Vadder, Filipe; Kovatcheva-Datchary, Petia; Goncalves, Daisy; Vinera, Jennifer; Zitoun, Carine; Duchampt, Adeline; Bäckhed, Fredrik; Mithieux, Gilles

    2014-01-16

    Soluble dietary fibers promote metabolic benefits on body weight and glucose control, but underlying mechanisms are poorly understood. Recent evidence indicates that intestinal gluconeogenesis (IGN) has beneficial effects on glucose and energy homeostasis. Here, we show that the short-chain fatty acids (SCFAs) propionate and butyrate, which are generated by fermentation of soluble fiber by the gut microbiota, activate IGN via complementary mechanisms. Butyrate activates IGN gene expression through a cAMP-dependent mechanism, while propionate, itself a substrate of IGN, activates IGN gene expression via a gut-brain neural circuit involving the fatty acid receptor FFAR3. The metabolic benefits on body weight and glucose control induced by SCFAs or dietary fiber in normal mice are absent in mice deficient for IGN, despite similar modifications in gut microbiota composition. Thus, the regulation of IGN is necessary for the metabolic benefits associated with SCFAs and soluble fiber.

  9. A Power Conditioning Stage Based on Analog-Circuit MPPT Control and a Superbuck Converter for Thermoelectric Generators in Spacecraft Power Systems

    Science.gov (United States)

    Sun, Kai; Wu, Hongfei; Cai, Yan; Xing, Yan

    2014-06-01

    A thermoelectric generator (TEG) is a very important kind of power supply for spacecraft, especially for deep-space missions, due to its long lifetime and high reliability. To develop a practical TEG power supply for spacecraft, a power conditioning stage is indispensable, being employed to convert the varying output voltage of the TEG modules to a definite voltage for feeding batteries or loads. To enhance the system reliability, a power conditioning stage based on analog-circuit maximum-power-point tracking (MPPT) control and a superbuck converter is proposed in this paper. The input of this power conditioning stage is connected to the output of the TEG modules, and the output of this stage is connected to the battery and loads. The superbuck converter is employed as the main circuit, featuring low input current ripples and high conversion efficiency. Since for spacecraft power systems reliable operation is the key target for control circuits, a reset-set flip-flop-based analog circuit is used as the basic control circuit to implement MPPT, being much simpler than digital control circuits and offering higher reliability. Experiments have verified the feasibility and effectiveness of the proposed power conditioning stage. The results show the advantages of the proposed stage, such as maximum utilization of TEG power, small input ripples, and good stability.

  10. Optical dissection of neural circuits responsible for Drosophila larval locomotion with halorhodopsin.

    Directory of Open Access Journals (Sweden)

    Kengo Inada

    Full Text Available Halorhodopsin (NpHR, a light-driven microbial chloride pump, enables silencing of neuronal function with superb temporal and spatial resolution. Here, we generated a transgenic line of Drosophila that drives expression of NpHR under control of the Gal4/UAS system. Then, we used it to dissect the functional properties of neural circuits that regulate larval peristalsis, a continuous wave of muscular contraction from posterior to anterior segments. We first demonstrate the effectiveness of NpHR by showing that global and continuous NpHR-mediated optical inhibition of motor neurons or sensory feedback neurons induce the same behavioral responses in crawling larvae to those elicited when the function of these neurons are inhibited by Shibire(ts, namely complete paralyses or slowed locomotion, respectively. We then applied transient and/or focused light stimuli to inhibit the activity of motor neurons in a more temporally and spatially restricted manner and studied the effects of the optical inhibition on peristalsis. When a brief light stimulus (1-10 sec was applied to a crawling larva, the wave of muscular contraction stopped transiently but resumed from the halted position when the light was turned off. Similarly, when a focused light stimulus was applied to inhibit motor neurons in one or a few segments which were about to be activated in a dissected larva undergoing fictive locomotion, the propagation of muscular constriction paused during the light stimulus but resumed from the halted position when the inhibition (>5 sec was removed. These results suggest that (1 Firing of motor neurons at the forefront of the wave is required for the wave to proceed to more anterior segments, and (2 The information about the phase of the wave, namely which segment is active at a given time, can be memorized in the neural circuits for several seconds.

  11. A Neural Circuit for Acoustic Navigation combining Heterosynaptic and Non-synaptic Plasticity that learns Stable Trajectories

    DEFF Research Database (Denmark)

    Shaikh, Danish; Manoonpong, Poramate

    2017-01-01

    controllers be resolved in a manner that generates consistent and stable robot trajectories? We propose a neural circuit that minimises this conflict by learning sensorimotor mappings as neuronal transfer functions between the perceived sound direction and wheel velocities of a simulated non-holonomic mobile...

  12. Equivalent Circuit Parameters Estimation for PEM Fuel Cell Using RBF Neural Network and Enhanced Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Wen-Yeau Chang

    2013-01-01

    Full Text Available This paper proposes an equivalent circuit parameters measurement and estimation method for proton exchange membrane fuel cell (PEMFC. The parameters measurement method is based on current loading technique; in current loading test a no load PEMFC is suddenly turned on to obtain the waveform of the transient terminal voltage. After the equivalent circuit parameters were measured, a hybrid method that combines a radial basis function (RBF neural network and enhanced particle swarm optimization (EPSO algorithm is further employed for the equivalent circuit parameters estimation. The RBF neural network is adopted such that the estimation problem can be effectively processed when the considered data have different features and ranges. In the hybrid method, EPSO algorithm is used to tune the connection weights, the centers, and the widths of RBF neural network. Together with the current loading technique, the proposed hybrid estimation method can effectively estimate the equivalent circuit parameters of PEMFC. To verify the proposed approach, experiments were conducted to demonstrate the equivalent circuit parameters estimation of PEMFC. A practical PEMFC stack was purposely created to produce the common current loading activities of PEMFC for the experiments. The practical results of the proposed method were studied in accordance with the conditions for different loading conditions.

  13. Estimating neural background input with controlled and fast perturbations: A bandwidth comparison between inhibitory opsins and neural circuits

    Directory of Open Access Journals (Sweden)

    David Eriksson

    2016-08-01

    Full Text Available To test the importance of a certain cell type or brain area it is common to make a lack of function experiment in which the neuronal population of interest is inhibited. Here we review physiological and methodological constraints for making controlled perturbations using the corticothalamic circuit as an example. The brain with its many types of cells and rich interconnectivity offers many paths through which a perturbation can spread within a short time. To understand the side effects of the perturbation one should record from those paths. We find that ephaptic effects, gap-junctions, and fast chemical synapses are so fast that they can react to the perturbation during the few milliseconds it takes for an opsin to change the membrane potential. The slow chemical synapses, astrocytes, extracellular ions and vascular signals, will continue to give their physiological input for around 20 milliseconds before they also react to the perturbation. Although we show that some pathways can react within milliseconds the strength/speed reported in this review should be seen as an upper bound since we have omitted how polysynaptic signals are attenuated. Thus the number of additional recordings that has to be made to control for the perturbation side effects is expected to be fewer than proposed here. To summarize, the reviewed literature not only suggests that it is possible to make controlled lack of function experiments, but, it also suggests that such a lack of function experiment can be used to measure the context of local neural computations.

  14. Lyapunov exponents from CHUA's circuit time series using artificial neural networks

    Science.gov (United States)

    Gonzalez, J. Jesus; Espinosa, Ismael E.; Fuentes, Alberto M.

    1995-01-01

    In this paper we present the general problem of identifying if a nonlinear dynamic system has a chaotic behavior. If the answer is positive the system will be sensitive to small perturbations in the initial conditions which will imply that there is a chaotic attractor in its state space. A particular problem would be that of identifying a chaotic oscillator. We present an example of three well known different chaotic oscillators where we have knowledge of the equations that govern the dynamical systems and from there we can obtain the corresponding time series. In a similar example we assume that we only know the time series and, finally, in another example we have to take measurements in the Chua's circuit to obtain sample points of the time series. With the knowledge about the time series the phase plane portraits are plotted and from them, by visual inspection, it is concluded whether or not the system is chaotic. This method has the problem of uncertainty and subjectivity and for that reason a different approach is needed. A quantitative approach is the computation of the Lyapunov exponents. We describe several methods for obtaining them and apply a little known method of artificial neural networks to the different examples mentioned above. We end the paper discussing the importance of the Lyapunov exponents in the interpretation of the dynamic behavior of biological neurons and biological neural networks.

  15. Research Domain Criteria: cognitive systems, neural circuits, and dimensions of behavior.

    Science.gov (United States)

    Morris, Sarah E; Cuthbert, Bruce N

    2012-03-01

    Current diagnostic systems for mental disorders were established before the tools of neuroscience were available, and although they have improved the reliability of psychiatric classification, progress toward the discovery of disease etiologies and novel approaches to treatment and prevention may benefit from alternative conceptualizations of mental disorders. The Research Domain Criteria (RDoC) initiative is the centerpiece of NIMH's effort to achieve its strategic goal of developing new methods to classify mental disorders for research purposes. The RDoC matrix provides a research framework that encourages investigators to reorient their research perspective by taking a dimensional approach to the study of the genetic, neural, and behavioral features of mental disorders, RDoCs integrative approach includes cognition along with social processes, arousal/regulatory systems, and negative and positive valence systems as the major domains, because these neurobehavioral systems have all evolved to serve the motivational and adaptive needs of the organism. With its focus on neural circuits informed by the growing evidence of the neurodevelopmental nature of many disorders and its capacity to capture the patterns of co-occurrence of behaviors and symptoms, the RDoC approach holds promise to advance our understanding of the nature of mental disorders.

  16. Analgesic Neural Circuits Are Activated by Electroacupuncture at Two Sets of Acupoints

    Directory of Open Access Journals (Sweden)

    Man-Li Hu

    2016-01-01

    Full Text Available To investigate analgesic neural circuits activated by electroacupuncture (EA at different sets of acupoints in the brain, goats were stimulated by EA at set of Baihui-Santai acupoints or set of Housanli acupoints for 30 min. The pain threshold was measured using the potassium iontophoresis method. The levels of c-Fos were determined with Streptavidin-Biotin Complex immunohistochemistry. The results showed pain threshold induced by EA at set of Baihui-Santai acupoints was 44.74%±4.56% higher than that by EA at set of Housanli acupoints (32.64%±5.04%. Compared with blank control, EA at two sets of acupoints increased c-Fos expression in the medial septal nucleus (MSN, the arcuate nucleus (ARC, the nucleus amygdala basalis (AB, the lateral habenula nucleus (HL, the ventrolateral periaqueductal grey (vlPAG, the locus coeruleus (LC, the nucleus raphe magnus (NRM, the pituitary gland, and spinal cord dorsal horn (SDH. Compared with EA at set of Housanli points, EA at set of Baihui-Santai points induced increased c-Fos expression in AB but decrease in MSN, the paraventricular nucleus of the hypothalamus, HL, and SDH. It suggests that ARC-PAG-NRM/LC-SDH and the hypothalamus-pituitary may be the common activated neural pathways taking part in EA-induced analgesia at the two sets of acupoints.

  17. The neural circuit and synaptic dynamics underlying perceptual decision-making

    Science.gov (United States)

    Liu, Feng

    2015-03-01

    Decision-making with several choice options is central to cognition. To elucidate the neural mechanisms of multiple-choice motion discrimination, we built a continuous recurrent network model to represent a local circuit in the lateral intraparietal area (LIP). The network is composed of pyramidal cells and interneurons, which are directionally tuned. All neurons are reciprocally connected, and the synaptic connectivity strength is heterogeneous. Specifically, we assume two types of inhibitory connectivity to pyramidal cells: opposite-feature and similar-feature inhibition. The model accounted for both physiological and behavioral data from monkey experiments. The network is endowed with slow excitatory reverberation, which subserves the buildup and maintenance of persistent neural activity, and predominant feedback inhibition, which underlies the winner-take-all competition and attractor dynamics. The opposite-feature and opposite-feature inhibition have different effects on decision-making, and only their combination allows for a categorical choice among 12 alternatives. Together, our work highlights the importance of structured synaptic inhibition in multiple-choice decision-making processes.

  18. Information processing in micro and meso-scale neural circuits during normal and disease states

    Science.gov (United States)

    Luongo, Francisco

    Neural computation can occur at multiple spatial and temporal timescales. The sum total of all of these processes is to guide optimal behaviors within the context of the constraints imposed by the physical world. How the circuits of the brain achieves this goal represents a central question in systems neuroscience. Here I explore the many ways in which the circuits of the brain can process information at both the micro and meso scale. Understanding the way information is represented and processed in the brain could shed light on the neuropathology underlying complex neuropsychiatric diseases such as autism and schizophrenia. Chapter 2 establishes an experimental paradigm for assaying patterns of microcircuit activity and examines the role of dopaminergic modulation on prefrontal microcircuits. We find that dopamine type 2 (D2) receptor activation results in an increase in spontaneous activity while dopamine type 1 (D1) activation does not. Chapter 3 of this dissertation presents a study that illustrates how cholingergic activation normally produces what has been suggested as a neural substrate of attention; pairwise decorrelation in microcircuit activity. This study also shows that in two etiologicall distinct mouse models of autism, FMR1 knockout mice and Valproic Acid exposed mice, this ability to decorrelate in the presence of cholinergic activation is lost. This represents a putative microcircuit level biomarker of autism. Chapter 4 examines the structure/function relationship within the prefrontal microcircuit. Spontaneous activity in prefrontal microcircuits is shown to be organized according to a small world architecture. Interestingly, this architecture is important for one concrete function of neuronal microcircuits; the ability to produce temporally stereotyped patterns of activation. In the final chapter, we identify subnetworks in chronic intracranial electrocorticographic (ECoG) recordings using pairwise electrode coherence and dimensionality reduction

  19. A class of analog CMOS circuits based on the square-law characteristic of an MOS transistor in saturation

    NARCIS (Netherlands)

    Bult, Klaas; Wallinga, Hans

    1979-01-01

    The examined class of circuits includes voltage multipliers, current multipliers, linear V-I convertors, linear I-V convertors, current squaring circuits, and current divider circuits. Typical for these circuits is an independent control of the sum as well as the difference between two gate-source v

  20. Component-Level Electronic-Assembly Repair (CLEAR) Spacecraft Circuit Diagnostics by Analog and Complex Signature Analysis

    Science.gov (United States)

    Oeftering, Richard C.; Wade, Raymond P.; Izadnegahdar, Alain

    2011-01-01

    The Component-Level Electronic-Assembly Repair (CLEAR) project at the NASA Glenn Research Center is aimed at developing technologies that will enable space-flight crews to perform in situ component-level repair of electronics on Moon and Mars outposts, where there is no existing infrastructure for logistics spares. These technologies must provide effective repair capabilities yet meet the payload and operational constraints of space facilities. Effective repair depends on a diagnostic capability that is versatile but easy to use by crew members that have limited training in electronics. CLEAR studied two techniques that involve extensive precharacterization of "known good" circuits to produce graphical signatures that provide an easy-to-use comparison method to quickly identify faulty components. Analog Signature Analysis (ASA) allows relatively rapid diagnostics of complex electronics by technicians with limited experience. Because of frequency limits and the growing dependence on broadband technologies, ASA must be augmented with other capabilities. To meet this challenge while preserving ease of use, CLEAR proposed an alternative called Complex Signature Analysis (CSA). Tests of ASA and CSA were used to compare capabilities and to determine if the techniques provided an overlapping or complementary capability. The results showed that the methods are complementary.

  1. Research and Design of Anti-radiation Analog CMOS Integrated Circuits%抗辐射模拟CMOS集成电路研究与设计

    Institute of Scientific and Technical Information of China (English)

    赵源; 徐立新; 赵琦; 金星

    2013-01-01

    The characteristics and effects of analog CMOS integrated circuit on spacecraft were analyzed in the radiation environment.Based on the generation of radiation effect,the main anti-radiation design methods were introduced for the analog CMOS integrated circuit designing and processing.In the outer space,threshold voltage deviation,transdiode decreasing,substrate leakage current increasing and corner noise amplitude increasing occur to the CMOS semiconductor components in the analog CMOS integrated circuit.As a result,there are three kinds of methods proposed to protect against radiation of the analog integrated circuits,including anti-radiation analog CMOS integrated circuit',anti-radiation PCB' and silicon on insulator anti-radiation processing.Accordingly,the designed anti-radiation analog CMOS integrated circuits obtain the ideal effect in anti-radiation function.%为研究宇宙辐射环境中航天器里的模拟互补金属氧化物半导体(Complementary Metal Oxide Semiconductor,CMOS)集成电路性能和各种效应,并在辐射效应所产生机制的基础上,从设计和工艺方面提出了模拟CMOS集成电路主要抗辐射加固设计方法.在宇宙环境中,卫星中的模拟CMOS集成电路存在CMOS半导体元器件阈值电压偏离、线性跨导减小、衬底的漏电流增加和转角1/f噪声幅值增加.所以提出了3种对模拟CMOS集成电路进行抗辐射加固的方法:1)抗辐射模拟CMOS集成电路的设计;2)抗辐射集成电路版图设计;3)单晶半导体硅膜(Silicon on Insulator,SOI)抗辐射工艺与加固设计.根据上面的设计方法研制了抗辐射加固模拟CMOS集成电路,可以取得较好的抗辐射效果.

  2. Induction and discussion on analog circuit analysis method%模拟电路分析方法归纳与探讨

    Institute of Scientific and Technical Information of China (English)

    高琴; 张翠霞; 姚振静

    2012-01-01

      针对《模拟电子技术》课程基本概念抽象、知识点繁多、学习难度大的特点,本文以课程的主要内容“集成电路的单元电路和集成运算放大器的应用电路”为出发点,结合多年的教学经验,从电路的识别到性能指标的估算作了详细介绍,探讨了分析模拟电路方法的原则,为更好的学习该课程提供理论参考。%  In view of the characteristic of concepts, knowledge, learning difficulty of"analog electronic technology"course, the main contents of the course"integrated circuit element circuits and application of operational amplifier circuit"as the starting point, combines with years of teaching experience, introduces from the identification circuit to the estimation of performance index, discusses the method of analog circuit analysis principle, provides the theory reference for better learning this course.

  3. A point-process response model for spike trains from single neurons in neural circuits under optogenetic stimulation.

    Science.gov (United States)

    Luo, X; Gee, S; Sohal, V; Small, D

    2016-02-10

    Optogenetics is a new tool to study neuronal circuits that have been genetically modified to allow stimulation by flashes of light. We study recordings from single neurons within neural circuits under optogenetic stimulation. The data from these experiments present a statistical challenge of modeling a high-frequency point process (neuronal spikes) while the input is another high-frequency point process (light flashes). We further develop a generalized linear model approach to model the relationships between two point processes, employing additive point-process response functions. The resulting model, point-process responses for optogenetics (PRO), provides explicit nonlinear transformations to link the input point process with the output one. Such response functions may provide important and interpretable scientific insights into the properties of the biophysical process that governs neural spiking in response to optogenetic stimulation. We validate and compare the PRO model using a real dataset and simulations, and our model yields a superior area-under-the-curve value as high as 93% for predicting every future spike. For our experiment on the recurrent layer V circuit in the prefrontal cortex, the PRO model provides evidence that neurons integrate their inputs in a sophisticated manner. Another use of the model is that it enables understanding how neural circuits are altered under various disease conditions and/or experimental conditions by comparing the PRO parameters. Copyright © 2015 John Wiley & Sons, Ltd.

  4. Differential regulation of polarized synaptic vesicle trafficking and synapse stability in neural circuit rewiring in Caenorhabditis elegans.

    Directory of Open Access Journals (Sweden)

    Naina Kurup

    2017-06-01

    Full Text Available Neural circuits are dynamic, with activity-dependent changes in synapse density and connectivity peaking during different phases of animal development. In C. elegans, young larvae form mature motor circuits through a dramatic switch in GABAergic neuron connectivity, by concomitant elimination of existing synapses and formation of new synapses that are maintained throughout adulthood. We have previously shown that an increase in microtubule dynamics during motor circuit rewiring facilitates new synapse formation. Here, we further investigate cellular control of circuit rewiring through the analysis of mutants obtained in a forward genetic screen. Using live imaging, we characterize novel mutations that alter cargo binding in the dynein motor complex and enhance anterograde synaptic vesicle movement during remodeling, providing in vivo evidence for the tug-of-war between kinesin and dynein in fast axonal transport. We also find that a casein kinase homolog, TTBK-3, inhibits stabilization of nascent synapses in their new locations, a previously unexplored facet of structural plasticity of synapses. Our study delineates temporally distinct signaling pathways that are required for effective neural circuit refinement.

  5. Navigating Monogamy: Nonapeptide Sensitivity in a Memory Neural Circuit May Shape Social Behavior and Mating Decisions.

    Science.gov (United States)

    Ophir, Alexander G

    2017-01-01

    The role of memory in mating systems is often neglected despite the fact that most mating systems are defined in part by how animals use space. Monogamy, for example, is usually characterized by affiliative (e.g., pairbonding) and defensive (e.g., mate guarding) behaviors, but a high degree of spatial overlap in home range use is the easiest defining feature of monogamous animals in the wild. The nonapeptides vasopressin and oxytocin have been the focus of much attention for their importance in modulating social behavior, however this work has largely overshadowed their roles in learning and memory. To date, the understanding of memory systems and mechanisms governing social behavior have progressed relatively independently. Bridging these two areas will provide a deeper appreciation for understanding behavior, and in particular the mechanisms that mediate reproductive decision-making. Here, I argue that the ability to mate effectively as monogamous individuals is linked to the ability to track conspecifics in space. I discuss the connectivity across some well-known social and spatial memory nuclei, and propose that the nonapeptide receptors within these structures form a putative "socio-spatial memory neural circuit." This purported circuit may function to integrate social and spatial information to shape mating decisions in a context-dependent fashion. The lateral septum and/or the nucleus accumbens, and neuromodulation therein, may act as an intermediary to relate socio-spatial information with social behavior. Identifying mechanisms responsible for relating information about the social world with mechanisms mediating mating tactics is crucial to fully appreciate the suite of factors driving reproductive decisions and social decision-making.

  6. The use of brain imaging to elucidate neural circuit changes in cocaine addiction

    Directory of Open Access Journals (Sweden)

    Hanlon CA

    2012-09-01

    Full Text Available Colleen A Hanlon,1,2 Melanie Canterberry11Department of Psychiatry and Behavioral Sciences, 2Department of Neurosciences Medical University of South Carolina, Charleston, SC, USAAbstract: Within substance abuse, neuroimaging has experienced tremendous growth as both a research method and a clinical tool in the last decade. The application of functional imaging methods to cocaine dependent patients and individuals in treatment programs, has revealed that the effects of cocaine are not limited to dopamine-rich subcortical structures, but that the cortical projection areas are also disrupted in cocaine dependent patients. In this review, we will first describe several of the imaging methods that are actively being used to address functional and structural abnormalities in addiction. This will be followed by an overview of the cortical and subcortical brain regions that are most often cited as dysfunctional in cocaine users. We will also introduce functional connectivity analyses currently being used to investigate interactions between these cortical and subcortical areas in cocaine users and abstainers. Finally, this review will address recent research which demonstrates that alterations in the functional connectivity in cocaine users may be associated with structural pathology in these circuits, as demonstrated through diffusion tensor imaging. Through the use of these tools in both a basic science setting and as applied to treatment seeking individuals, we now have a greater understanding of the complex cortical and subcortical networks which contribute to the stages of initial craving, dependence, abstinence, and relapse. Although the ability to use neuroimaging to predict treatment response or identify vulnerable populations is still in its infancy, the next decade holds tremendous promise for using neuroimaging to tailor either behavioral or pharmacologic treatment interventions to the individual.Keywords: addiction, neural circuit, functional

  7. NEURAL CORRELATES FOR APATHY: FRONTAL - PREFRONTAL AND PARIETAL CORTICAL - SUBCORTICAL CIRCUITS

    Directory of Open Access Journals (Sweden)

    Rita Moretti

    2016-12-01

    Full Text Available Apathy is an uncertain nosographical entity, which includes reduced motivation, abulia, decreased empathy, and lack of emotional invovlement; it is an important and heavy-burden clinical condition which strongly impacts in every day life events, affects the common daily living abilities, reduced the inner goal directed behavior, and gives the heaviest burden on caregivers. Is a quite common comorbidity of many neurological disease, However, there is no definite consensus on the role of apathy in clinical practice, no definite data on anatomical circuits involved in its development, and no definite instrument to detect it at bedside. As a general observation, the occurrence of apathy is connected to damage of prefrontal cortex (PFC and basal ganglia; emotional affective apathy may be related to the orbitomedial PFC and ventral striatum; cognitive apathy may be associated with dysfunction of lateral PFC and dorsal caudate nuclei; deficit of autoactivation may be due to bilateral lesions of the internal portion of globus pallidus, bilateral paramedian thalamic lesions, or the dorsomedial portion of PFC. On the other hand, apathy severity has been connected to neurofibrillary tangles density in the anterior cingulate gyrus and to grey matter atrophy in the anterior cingulate (ACC and in the left medial frontal cortex, confirmed by functional imaging studies. These neural networks are linked to projects, judjing and planning, execution and selection common actions, and through the basolateral amygdala and nucleus accumbens projects to the frontostriatal and to the dorsolateral prefrontal cortex. Therefore, an alteration of these circuitry caused a lack of insight, a reduction of decision-making strategies and a reduced speedness in action decsion, major resposnible for apathy. Emergent role concerns also the parietal cortex, with its direct action motivation control.We will discuss the importance of these circuits in different pathologies

  8. Structural basis for cholinergic regulation of neural circuits in the mouse olfactory bulb.

    Science.gov (United States)

    Hamamoto, Masakazu; Kiyokage, Emi; Sohn, Jaerin; Hioki, Hiroyuki; Harada, Tamotsu; Toida, Kazunori

    2017-02-15

    Odor information is regulated by olfactory inputs, bulbar interneurons, and centrifugal inputs in the olfactory bulb (OB). Cholinergic neurons projecting from the nucleus of the horizontal limb of the diagonal band of Broca and the magnocellular preoptic nucleus are one of the primary centrifugal inputs to the OB. In this study, we focused on cholinergic regulation of the OB and analyzed neural morphology with a particular emphasis on the projection pathways of cholinergic neurons. Single-cell imaging of a specific neuron within dense fibers is critical to evaluate the structure and function of the neural circuits. We labeled cholinergic neurons by infection with virus vector and then reconstructed them three-dimensionally. We also examined the ultramicrostructure of synapses by electron microscopy tomography. To further clarify the function of cholinergic neurons, we performed confocal laser scanning microscopy to investigate whether other neurotransmitters are present within cholinergic axons in the OB. Our results showed the first visualization of complete cholinergic neurons, including axons projecting to the OB, and also revealed frequent axonal branching within the OB where it innervated multiple glomeruli in different areas. Furthermore, electron tomography demonstrated that cholinergic axons formed asymmetrical synapses with a morphological variety of thicknesses of the postsynaptic density. Although we have not yet detected the presence of other neurotransmitters, the range of synaptic morphology suggests multiple modes of transmission. The present study elucidates the ways that cholinergic neurons could contribute to the elaborate mechanisms involved in olfactory processing in the OB. J. Comp. Neurol. 525:574-591, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  9. Uncertainty-Dependent Extinction of Fear Memory in an Amygdala-mPFC Neural Circuit Model

    Science.gov (United States)

    Li, Yuzhe; Nakae, Ken; Ishii, Shin; Naoki, Honda

    2016-01-01

    Uncertainty of fear conditioning is crucial for the acquisition and extinction of fear memory. Fear memory acquired through partial pairings of a conditioned stimulus (CS) and an unconditioned stimulus (US) is more resistant to extinction than that acquired through full pairings; this effect is known as the partial reinforcement extinction effect (PREE). Although the PREE has been explained by psychological theories, the neural mechanisms underlying the PREE remain largely unclear. Here, we developed a neural circuit model based on three distinct types of neurons (fear, persistent and extinction neurons) in the amygdala and medial prefrontal cortex (mPFC). In the model, the fear, persistent and extinction neurons encode predictions of net severity, of unconditioned stimulus (US) intensity, and of net safety, respectively. Our simulation successfully reproduces the PREE. We revealed that unpredictability of the US during extinction was represented by the combined responses of the three types of neurons, which are critical for the PREE. In addition, we extended the model to include amygdala subregions and the mPFC to address a recent finding that the ventral mPFC (vmPFC) is required for consolidating extinction memory but not for memory retrieval. Furthermore, model simulations led us to propose a novel procedure to enhance extinction learning through re-conditioning with a stronger US; strengthened fear memory up-regulates the extinction neuron, which, in turn, further inhibits the fear neuron during re-extinction. Thus, our models increased the understanding of the functional roles of the amygdala and vmPFC in the processing of uncertainty in fear conditioning and extinction. PMID:27617747

  10. 基于边界扫描的模拟电路BIT技术研究%Research on BIT for Analog Circuit Based on Boundary Scan Technology

    Institute of Scientific and Technical Information of China (English)

    冯长江; 薛冰; 李晓峰

    2012-01-01

    Most of the analog circuit testable design based on boundary scan which still uses the analog test bus prescribed by IEEE1149. 4 standard has not been realized the built-in-test design actually. The extraction and comparison of analog signal are difficult. According to these problems, a design of BIT structure for analog circuit based on boundary scan is presented, which takes the rectangular pulse as actuator and uses analog switch as auxiliary bridge. Each module function timing results show the rationality of the design, and to integrate op-amp uA741 as measured circuit, the pulse count of the signal rising edge fully proves the feasibility of the structure.%边界扫描技术在模拟电路的可测性设计大多仍停留在IEEE1149.4标准所规定的模拟测试总线上,并没有做到真正意义上的内建自测试设计;针对模拟电路信号提取和比较相对困难的测试难点,提出采用方波脉冲作为测试激励信号的方法,并以模拟开关为辅助桥梁,设计了基于边界扫描的模拟电路BIT结构;通过各个模块功能的时序仿真证明了该结构设计的合理性,并以集成运放uA741作为被测电路,通过信号上升沿脉冲计数充分证明该结构的可行性.

  11. The primary visual cortex in the neural circuit for visual orienting

    Science.gov (United States)

    Zhaoping, Li

    The primary visual cortex (V1) is traditionally viewed as remote from influencing brain's motor outputs. However, V1 provides the most abundant cortical inputs directly to the sensory layers of superior colliculus (SC), a midbrain structure to command visual orienting such as shifting gaze and turning heads. I will show physiological, anatomical, and behavioral data suggesting that V1 transforms visual input into a saliency map to guide a class of visual orienting that is reflexive or involuntary. In particular, V1 receives a retinotopic map of visual features, such as orientation, color, and motion direction of local visual inputs; local interactions between V1 neurons perform a local-to-global computation to arrive at a saliency map that highlights conspicuous visual locations by higher V1 responses. The conspicuous location are usually, but not always, where visual input statistics changes. The population V1 outputs to SC, which is also retinotopic, enables SC to locate, by lateral inhibition between SC neurons, the most salient location as the saccadic target. Experimental tests of this hypothesis will be shown. Variations of the neural circuit for visual orienting across animal species, with more or less V1 involvement, will be discussed. Supported by the Gatsby Charitable Foundation.

  12. Hybrid Spintronic-CMOS Spiking Neural Network with On-Chip Learning: Devices, Circuits, and Systems

    Science.gov (United States)

    Sengupta, Abhronil; Banerjee, Aparajita; Roy, Kaushik

    2016-12-01

    Over the past decade, spiking neural networks (SNNs) have emerged as one of the popular architectures to emulate the brain. In SNNs, information is temporally encoded and communication between neurons is accomplished by means of spikes. In such networks, spike-timing-dependent plasticity mechanisms require the online programing of synapses based on the temporal information of spikes transmitted by spiking neurons. In this work, we propose a spintronic synapse with decoupled spike-transmission and programing-current paths. The spintronic synapse consists of a ferromagnet-heavy-metal heterostructure where the programing current through the heavy metal generates spin-orbit torque to modulate the device conductance. Low programing energy and fast programing times demonstrate the efficacy of the proposed device as a nanoelectronic synapse. We perform a simulation study based on an experimentally benchmarked device-simulation framework to demonstrate the interfacing of such spintronic synapses with CMOS neurons and learning circuits operating in the transistor subthreshold region to form a network of spiking neurons that can be utilized for pattern-recognition problems.

  13. Serial Section Registration of Axonal Confocal Microscopy Datasets for Long-Range Neural Circuit Reconstruction

    Energy Technology Data Exchange (ETDEWEB)

    Hogrebe, Luke; Paiva, Antonio R.; Jurrus, Elizabeth R.; Christensen, Cameron; Bridge, Michael; Dai, Li; Pfeiffer, Rebecca; Hof, Patrick; Roysam, Badrinath; Korenberg, Julie; Tasdizen, Tolga

    2012-06-15

    In the context of long-range digital neural circuit reconstruction, this paper investigates an approach for registering axons across histological serial sections. Tracing distinctly labeled axons over large distances allows neuroscientists to study very explicit relationships between the brain's complex interconnects and, for example, diseases or aberrant development. Large scale histological analysis requires, however, that the tissue be cut into sections. In immunohistochemical studies thin sections are easily distorted due to the cutting, preparation, and slide mounting processes. In this work we target the registration of thin serial sections containing axons. Sections are first traced to extract axon centerlines, and these traces are used to define registration landmarks where they intersect section boundaries. The trace data also provides distinguishing information regarding an axon's size and orientation within a section. We propose the use of these features when pairing axons across sections in addition to utilizing the spatial relationships amongst the landmarks. The global rotation and translation of an unregistered section are accounted for using a random sample consensus (RANSAC) based technique. An iterative nonrigid refinement process using B-spline warping is then used to reconnect axons and produce the sought after connectivity information.

  14. Consistency and advantage of loop regularization method merging with Bjorken-Drell's analogy between Feynman diagrams and electrical circuits

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Da; Wu, Yue-Liang [Chinese Academy of Science, State Key Laboratory of Theoretical Physics (SKLTP), Kavli Institute for Theoretical Physics China (KITPC), Institute of Theoretical Physics, Beijing (China)

    2012-07-15

    The consistency of loop regularization (LORE) method is explored in multiloop calculations. A key concept of the LORE method is the introduction of irreducible loop integrals (ILIs) which are evaluated from the Feynman diagrams by adopting the Feynman parametrization and ultraviolet-divergence-preserving (UVDP) parametrization. It is then inevitable for the ILIs to encounter the divergences in the UVDP parameter space due to the generic overlapping divergences in the four-dimensional momentum space. By computing the so-called {alpha}{beta}{gamma} integrals arising from two-loop Feynman diagrams, we show how to deal with the divergences in the parameter space with the LORE method. By identifying the divergences in the UVDP parameter space to those in the subdiagrams, we arrive at the Bjorken-Drell analogy between Feynman diagrams and electrical circuits. The UVDP parameters are shown to correspond to the conductance or resistance in the electrical circuits, and the divergence in Feynman diagrams is ascribed to the infinite conductance or zero resistance. In particular, the sets of conditions required to eliminate the overlapping momentum integrals for obtaining the ILIs are found to be associated with the conservations of electric voltages, and the momentum conservations correspond to the conservations of electrical currents, which are known as the Kirchhoff laws in the electrical circuits analogy. As a practical application, we carry out a detailed calculation for one-loop and two-loop Feynman diagrams in the massive scalar {phi}{sup 4} theory, which enables us to obtain the well-known logarithmic running of the coupling constant and the consistent power-law running of the scalar mass at two-loop level. Especially, we present an explicit demonstration on the general procedure of applying the LORE method to the multiloop calculations of Feynman diagrams when merging with the advantage of Bjorken-Drell's circuit analogy. (orig.)

  15. A Survey of Non-conventional Techniques for Low-voltage Low-power Analog Circuit Design

    National Research Council Canada - National Science Library

    F. Khateb; S. Bay Abo Dabbous; S. Vlassis

    2013-01-01

    ...). Therefore, this paper presents the operation principle, the advantages and disadvantages of each of these techniques, enabling circuit designers to choose the proper design technique based on application...

  16. Simulation of control circuit of servo mechanism with DxAnalog%用DxAnalog进行某型伺服机构控制电路的仿真

    Institute of Scientific and Technical Information of China (English)

    张世欣; 郑维斌

    2012-01-01

    为了能更好地理解某型伺服机构控制电路原理并确定参数,采用Mentor公司的DxAnalog软件对电路进行了详尽的仿真分析.通过改变参数,观察不同电路参数对电路性能的影响,实时的观察各实验点的仿真波形并精确测量出量值的变化,极大地缩减了电路调试过程,提高了设计效率.%In order to have a good knowledge of the control circuit principles of a servo mechanism and determine the parameters, the circuit is simulated and analyzed with DxAnalog software of Metor Company. The effect of variable circuit parameters on circuit performance was observed. Through a real-time observation of the simulation waveform at the experimental points and accurate measurement of the variables, the debugging process of the circuit was greatly reduced and the design efficiency was improved.

  17. 现代模拟电路智能故障诊断方法研究与发展%Advance in Modern Analog Circuit Intelligent Fault Diagnosis Methods

    Institute of Scientific and Technical Information of China (English)

    郭珂; 伞冶; 朱奕

    2012-01-01

    The reliability and economy requirements of electronic system are getting higher and higher,which make analog circuit fault diagnosis become more and more important.Based on the introduction of the cause and classification for analog circuit faults,the characteristics of analog circuit faults were analyzed in detail.According to the deficiency of traditional methods,modern diagnosis methods based on artificial intelligence and modern information processing were introduced,including expert system diagnosis methods,neural networks diagnosis methods,fuzzy fault diagnosis methods and kernel-based methods.The principle,advantages and disadvantages,research advance and representative application of each method are introduced systematically at the same time.The issues which modern diagnosis methods are confronted and the future research directions are discussed at last.%对系统可靠性和经济性要求的提高使得模拟电路故障诊断的重要性日益凸显。首先在介绍了模拟电路故障原因及分类的基础上,详细分析了模拟电路故障诊断的特点。针对传统诊断方法的不足之处,介绍了基于人工智能和现代信息信号处理的现代故障诊断方法,包括专家系统诊断方法、神经网络诊断方法、模糊诊断方法和基于核的诊断方法,同时系统地分析了每种方法的基本原理、优缺点、研究进展和典型应用。最后探讨了目前模拟电路故障诊断研究存在的问题和未来的发展方向。

  18. Modulatory Effects of Modafinil on Neural Circuits Regulating Emotion and Cognition

    Science.gov (United States)

    Rasetti, Roberta; Mattay, Venkata S; Stankevich, Beth; Skjei, Kelsey; Blasi, Giuseppe; Sambataro, Fabio; Arrillaga-Romany, Isabel C; Goldberg, Terry E; Callicott, Joseph H; Apud, José A; Weinberger, Daniel R

    2010-01-01

    Modafinil differs from other arousal-enhancing agents in chemical structure, neurochemical profile, and behavioral effects. Most functional neuroimaging studies to date examined the effect of modafinil only on information processing underlying executive cognition, but cognitive enhancers in general have been shown to have pronounced effects on emotional behavior, too. We examined the effect of modafinil on neural circuits underlying affective processing and cognitive functions. Healthy volunteers were enrolled in this double-blinded placebo-controlled trial (100 mg/day for 7 days). They underwent BOLD fMRI while performing an emotion information-processing task that activates the amygdala and two prefrontally dependent cognitive tasks—a working memory (WM) task and a variable attentional control (VAC) task. A clinical assessment that included measurement of blood pressure, heart rate, the Hamilton anxiety scale, and the profile of mood state (POMS) questionnaire was also performed on each test day. BOLD fMRI revealed significantly decreased amygdala reactivity to fearful stimuli on modafinil compared with the placebo condition. During executive cognition tasks, a WM task and a VAC task, modafinil reduced BOLD signal in the prefrontal cortex and anterior cingulate. Although not statistically significant, there were trends for reduced anxiety, for decreased fatigue-inertia and increased vigor-activity, as well as decreased anger-hostility on modafinil. Modafinil in low doses has a unique physiologic profile compared with stimulant drugs: it enhances the efficiency of prefrontal cortical cognitive information processing, while dampening reactivity to threatening stimuli in the amygdala, a brain region implicated in anxiety. PMID:20555311

  19. Distribution of language-related Cntnap2 protein in neural circuits critical for vocal learning.

    Science.gov (United States)

    Condro, Michael C; White, Stephanie A

    2014-01-01

    Variants of the contactin associated protein-like 2 (Cntnap2) gene are risk factors for language-related disorders including autism spectrum disorder, specific language impairment, and stuttering. Songbirds are useful models for study of human speech disorders due to their shared capacity for vocal learning, which relies on similar cortico-basal ganglia circuitry and genetic factors. Here we investigate Cntnap2 protein expression in the brain of the zebra finch, a songbird species in which males, but not females, learn their courtship songs. We hypothesize that Cntnap2 has overlapping functions in vocal learning species, and expect to find protein expression in song-related areas of the zebra finch brain. We further expect that the distribution of this membrane-bound protein may not completely mirror its mRNA distribution due to the distinct subcellular localization of the two molecular species. We find that Cntnap2 protein is enriched in several song control regions relative to surrounding tissues, particularly within the adult male, but not female, robust nucleus of the arcopallium (RA), a cortical song control region analogous to human layer 5 primary motor cortex. The onset of this sexually dimorphic expression coincides with the onset of sensorimotor learning in developing males. Enrichment in male RA appears due to expression in projection neurons within the nucleus, as well as to additional expression in nerve terminals of cortical projections to RA from the lateral magnocellular nucleus of the nidopallium. Cntnap2 protein expression in zebra finch brain supports the hypothesis that this molecule affects neural connectivity critical for vocal learning across taxonomic classes. Copyright © 2013 Wiley Periodicals, Inc.

  20. Axonal Activity in vivo: Technical considerations and implications for the exploration of neural circuits in freely moving animals

    Directory of Open Access Journals (Sweden)

    Jeremy Michael Barry

    2015-05-01

    Full Text Available While extracellular somatic action potentials from freely moving rats have been well characterized, axonal activity has not. We have recently reported extracellular tetrode recordings of short duration waveforms (SDW with an average peak-trough duration less than 172 µs. These waveforms have significantly shorter duration than somatic action potentials and tend to be triphasic. The present review discusses further data that suggests SDWs are representative of axonal activity, how this characterization allows for more accurate classification of somatic activity and could serve as a means of exploring signal integration in neural circuits. The review also discusses how axons may function as more than neural cables and the implications this may have for axonal information processing. While the technical challenges necessary for the exploration of axonal processes in functional neural circuits during behavior are impressive, preliminary evidence suggests that the in vivo study of axons is attainable. The resulting theoretical implications for systems level function make refinement of this approach a necessary goal toward developing a more complete understanding of the processes underlying learning, memory and attention as well as the pathological states underlying mental illness and epilepsy.

  1. A 1.4-V 48-μW current-mode front-end circuit for analog hearing aids with frequency compensation

    Institute of Scientific and Technical Information of China (English)

    Wang Xiaoyu; Yang Haigang; Li Fanyang; Yin Tao; Liu Fei

    2012-01-01

    A current-mode front-end circuit with low voltage and low power for analog hearing aids is presented.The circuit consists of a current-mode AGC (automatic gain control) and a current-mode adaptive filter.Compared with its conventional voltage-mode counterparts,the proposed front-end circuit has the identified features of frequency compensation based on the state space theory and continuous gain with an exponential characteristic.The frequency compensation which appears only in the DSP unit of the digital hearing aid can upgrade the performance of the analog hearing aid in the field of low-frequency hearing loss.The continuous gain should meet the requirement of any input amplitude level,while its exponential characteristic leads to a large input dynamic range in accordance with the dB SPL (sound pressure level).Furthermore,the front-end circuit also provides a discrete knee point and discrete compression ratio to allow for high calibration flexibility.These features can accommodate users whose ears have different pain thresholds.Taking advantage of the current-mode technique,the MOS transistors work in the subthreshold region so that the quiescent current is small.Moreover,the input current can be compressed to a low voltage signal for processing according to the compression principle from the current-domain to the voltage-domain.Therefore,the objective of low voltage and low power (48 μW at 1.4 V) can be easily achieved in a high threshold-voltage CMOS process of 0.35 μm (VToN + |Vrop|≈ 1.35 V).The THD is below -45 dB.The fabricated chip only occupies the area of 1 × 0.5 mm2 and 1 × 1 mm2.

  2. Using NCAR Yellowstone for PhotoVoltaic Power Forecasts with Artificial Neural Networks and an Analog Ensemble

    Science.gov (United States)

    Cervone, G.; Clemente-Harding, L.; Alessandrini, S.; Delle Monache, L.

    2016-12-01

    A methodology based on Artificial Neural Networks (ANN) and an Analog Ensemble (AnEn) is presented to generate 72-hour deterministic and probabilistic forecasts of power generated by photovoltaic (PV) power plants using input from a numerical weather prediction model and computed astronomical variables. ANN and AnEn are used individually and in combination to generate forecasts for three solar power plant located in Italy. The computational scalability of the proposed solution is tested using synthetic data simulating 4,450 PV power stations. The NCAR Yellowstone supercomputer is employed to test the parallel implementation of the proposed solution, ranging from 1 node (32 cores) to 4,450 nodes (141,140 cores). Results show that a combined AnEn + ANN solution yields best results, and that the proposed solution is well suited for massive scale computation.

  3. The neural basis of analogical reasoning: an event-related potential study.

    Science.gov (United States)

    Qiu, Jiang; Li, Hong; Chen, Antao; Zhang, Qinglin

    2008-10-01

    The spatiotemporal analysis of brain activation during the execution of easy analogy (EA) and difficult analogy (DA) tasks was investigated using high-density event-related brain potentials (ERPs). Results showed that reasoning tasks (schema induction) elicited a more negative ERP deflection (N500-1000) than did the baseline task (BS) between 500 and 1000 ms. Dipole source analysis of difference waves (EA-BS and DA-BS) indicated that the negative components were both localized near the left thalamus, possibly associated with the retrieval of alphabetical information. Furthermore, DA elicited a more positive ERP component (P600-1000) than did EA in the same time window. Two generators of P600-1000 were located in the medial prefrontal cortex (BA10) and the left frontal cortex (BA6) which was possibly involved in integrating information in schema abstraction. In the stage of analogy mapping, a greater negativity (N400-600) in the reasoning tasks as compared to BS was found over fronto-central scalp regions. A generator of this effect was located in the left fusiform gyrus and was possibly related to associative memory and activation of schema. Then, a greater negativity in the reasoning tasks, in comparison to BS task, developed between 900-1200 ms (LNC1) and 2000-2500 ms (LNC2). Dipole source analysis (EA-BS) localized the generator of LNC1 in the left prefrontal cortex (BA 10) which was possibly related to mapping the schema to the target problem, and the generator of LNC2 in the left prefrontal cortex (BA 9) which was possibly related to deciding whether a conclusion correctly follows from the schema.

  4. Solid-state reprogrammable analog resistive devices for electronic neural networks

    Science.gov (United States)

    Ramesham, R.; Thakoor, S.; Daud, T.; Thakoor, A. P.

    1990-01-01

    The fabrication and performance of WO3-based, solid-state, three-terminal device configurations as programmable analog memory elements are reported. These transistorlike device structures exhibit good resistance progammability with a remarkable resolution of a few percent of the resistive strength over a four orders of magnitude dynamic range. The most critical component of these devices is an insulating layer between the active WO3 and the cation donor layer. The progamming characteristics and operation mechanisms of the device are described, and probable reaction mechanisms critical to the device stability are discussed.

  5. The Relation between Finger Gnosis and Mathematical Ability: Why Redeployment of Neural Circuits Best Explains the Finding

    Directory of Open Access Journals (Sweden)

    Marcie ePenner-Wilger

    2013-12-01

    Full Text Available This paper elaborates a novel hypothesis regarding the observed predictive relation between finger gnosis and mathematical ability. In brief, we suggest that these two cognitive phenomena have overlapping neural substrates, as the result of the re-use (redeployment of part of the finger gnosis circuit for the purpose of representing numbers. We offer some background on the relation and current explanations for it; an outline of our alternate hypothesis; some evidence supporting redeployment over current views; and a plan for further research.

  6. Imaging neuronal populations in behaving rodents: paradigms for studying neural circuits underlying behavior in the mammalian cortex.

    Science.gov (United States)

    Chen, Jerry L; Andermann, Mark L; Keck, Tara; Xu, Ning-Long; Ziv, Yaniv

    2013-11-06

    Understanding the neural correlates of behavior in the mammalian cortex requires measurements of activity in awake, behaving animals. Rodents have emerged as a powerful model for dissecting the cortical circuits underlying behavior attributable to the convergence of several methods. Genetically encoded calcium indicators combined with viral-mediated or transgenic tools enable chronic monitoring of calcium signals in neuronal populations and subcellular structures of identified cell types. Stable one- and two-photon imaging of neuronal activity in awake, behaving animals is now possible using new behavioral paradigms in head-fixed animals, or using novel miniature head-mounted microscopes in freely moving animals. This mini-symposium will highlight recent applications of these methods for studying sensorimotor integration, decision making, learning, and memory in cortical and subcortical brain areas. We will outline future prospects and challenges for identifying the neural underpinnings of task-dependent behavior using cellular imaging in rodents.

  7. Analog integrated circuit for micro-gyro interface realized by multi-chip service in Japan; Multi chip service ni yoru micro gyro interface shuseki kairo no sekkei to shisaku

    Energy Technology Data Exchange (ETDEWEB)

    Maenaka, K.; Fujita, T.; Okamoto, K.; Maeda, M. [Himeji Institute of Technology, Hyogo (Japan)

    1998-10-01

    This paper deals with an analog integrated circuit for micro-machined gyroscopes with capacitive output. The Integrated circuit was fabricated as a part of the first project from the `Micromachining Multi-Chip Service Cooperative Re-search Committee` organized by The Institute of Electrical Engineers Japan. This multi-chip service project offers a master slice chip with an equivalent of 9 blocks of operational amplifier circuits. Our integrated circuit includes a modulator, demodulator and synchronous rectifier for detecting small changes in the capacitance of a silicon gyroscope. In the paper, the experimental results of fabricated samples will be described. 13 refs., 15 figs.

  8. Analog circuit diagnosis with fault dictionary method based on DAGSVC*%基于DAGSVC的模拟电路故障字典法

    Institute of Scientific and Technical Information of China (English)

    姜媛媛; 韩振云; 崔江

    2011-01-01

    Focusing on the design of problem of fault diagnosis of analog circuit and classifier with support vector machines(SVMs) ,a new method of fault dictionary based on directed acyclic graph SVMs classifier (DAGSVC) is presented, and a specification for estimating the average test complexity of the support vector machine classifier (SVC) is also compared. Two actual analog filter are tested to validate the proposed method,whose performance is proven to be superior to the traditional methods, such as “1-v-r” SVC and “l-v-1” SVC. The proposed method, being proper to perform analog circuit diagnosis and faults isolation,could also achieve almost the same diagnosis rate as the clustering binary tree SVC,whose test structure is not unique.%针对模拟电路的故障诊断和支持向量机分类器的设计问题,讨论了一种基于有向无环图支持向量机分类器(DAGSVC)的故障字典新方法,并比较了几种支持向量机故障分类器的平均测试复杂度指标.通过对2个实际模拟滤波器的实际测试和验证表明:该方法性能要优于"1-v-r"SVC,"1-v-1"SVC等常规的故障分类器,并和聚类二叉树SVC的诊断性能接近,适合模拟电路的故障分类和诊断.

  9. A Survey of Non-conventional Techniques for Low-voltage Low-power Analog Circuit Design

    Directory of Open Access Journals (Sweden)

    F. Khateb

    2013-06-01

    Full Text Available Designing integrated circuits able to work under low-voltage (LV low-power (LP condition is currently undergoing a very considerable boom. Reducing voltage supply and power consumption of integrated circuits is crucial factor since in general it ensures the device reliability, prevents overheating of the circuits and in particular prolongs the operation period for battery powered devices. Recently, non-conventional techniques i.e. bulk-driven (BD, floating-gate (FG and quasi-floating-gate (QFG techniques have been proposed as powerful ways to reduce the design complexity and push the voltage supply towards threshold voltage of the MOS transistors (MOST. Therefore, this paper presents the operation principle, the advantages and disadvantages of each of these techniques, enabling circuit designers to choose the proper design technique based on application requirements. As an example of application three operational transconductance amplifiers (OTA base on these non-conventional techniques are presented, the voltage supply is only ±0.4 V and the power consumption is 23.5 µW. PSpice simulation results using the 0.18 µm CMOS technology from TSMC are included to verify the design functionality and correspondence with theory.

  10. Is there anybody out there? Neural circuits of threat detection in vertebrates.

    Science.gov (United States)

    Pereira, Ana G; Moita, Marta A

    2016-12-01

    Avoiding or escaping a predator is arguably one of the most important functions of a prey's brain, hence of most animals' brains. Studies on fear conditioning have greatly advanced our understanding of the circuits that regulate learned defensive behaviours. However, animals possess a multitude of threat detection mechanisms, from hardwired circuits that ensure innate responses to predator cues, to the use of social information. Surprisingly, only more recently have these circuits captured the attention of a wider range of researchers working on different species and behavioural paradigms. These have shed new light into the mechanisms of threat detection revealing conservation of the kinds of cues animals use and of its underlying detection circuits across vertebrates. As most of these studies focus on single cues, we argue for the need to study multisensory integration, a process that we believe is determinant for the prey's defence responses.

  11. An investigation of the neural circuits underlying reaching and reach-to-grasp movements: from planning to execution.

    Directory of Open Access Journals (Sweden)

    Chiara eBegliomini

    2014-09-01

    Full Text Available Experimental evidence suggests the existence of a sophisticated brain circuit specifically dedicated to reach-to-grasp planning and execution, both in human and non human primates (Castiello, 2005. Studies accomplished by means of neuroimaging techniques suggest the hypothesis of a dichotomy between a reach-to-grasp circuit, involving the intraparietal area (AIP, the dorsal and ventral premotor cortices (PMd and PMv - Castiello and Begliomini, 2008; Filimon, 2010 and a reaching circuit involving the medial intraparietal area (mIP and the Superior Parieto-Occipital Cortex (SPOC (Culham et al., 2006. However, the time course characterizing the involvement of these regions during the planning and execution of these two types of movements has yet to be delineated. A functional magnetic resonance imaging (fMRI study has been conducted, including reach-to grasp and reaching only movements, performed towards either a small or a large stimulus, and Finite Impulse Response model (FIR - Henson, 2003 was adopted to monitor activation patterns from stimulus onset for a time window of 10 seconds duration. Data analysis focused on brain regions belonging either to the reaching or to the grasping network, as suggested by Castiello & Begliomini (2008.Results suggest that reaching and grasping movements planning and execution might share a common brain network, providing further confirmation to the idea that the neural underpinnings of reaching and grasping may overlap in both spatial and temporal terms (Verhagen et al., 2013.

  12. Cerebellar Neural Circuits Involving Executive Control Network Predict Response to Group Cognitive Behavior Therapy in Social Anxiety Disorder.

    Science.gov (United States)

    MinlanYuan; Meng, Yajing; Zhang, Yan; Nie, Xiaojing; Ren, Zhengjia; Zhu, Hongru; Li, Yuchen; Lui, Su; Gong, Qiyong; Qiu, Changjian; Zhang, Wei

    2017-02-02

    Some intrinsic connectivity networks including the default mode network (DMN) and executive control network (ECN) may underlie social anxiety disorder (SAD). Although the cerebellum has been implicated in the pathophysiology of SAD and several networks relevant to higher-order cognition, it remains unknown whether cerebellar areas involved in DMN and ECN exhibit altered resting-state functional connectivity (rsFC) with cortical networks in SAD. Forty-six patients with SAD and 64 healthy controls (HC) were included and submitted to the baseline resting-state functional magnetic resonance imaging (fMRI). Seventeen SAD patients who completed post-treatment clinical assessments were included after group cognitive behavior therapy (CBT). RsFC of three cerebellar subregions in both groups was assessed respectively in a voxel-wise way, and these rsFC maps were compared by two-sample t tests between groups. Whole-brain voxel-wise regression was performed to examine whether cerebellar connectivity networks can predict response to CBT. Lower rsFC circuits of cerebellar subregions compared with HC at baseline (p circuits involving DMN and ECN are possible neuropathologic mechanisms of SAD. Stronger pretreatment cerebellar rsFC circuits involving ECN suggest potential neural markers to predict CBT response.

  13. Millimeter-wave circuits and pulse compression radar baseband/analog signal processing blocks in silicon processes

    OpenAIRE

    2012-01-01

    The power dissipation and cost of the next generation pulse radar beamforming systems needs to be reduced for the imaging and surveillance sensors. This research work aims at developing and innovating the next generation, mobile hand-held, high performance radar systems for outdoor surveillance applications, i.e. pedestrian detection sensor. Integrating the low cost millimeter-wave (mm-wave) imaging array platforms with advanced analog/ baseband signal processing on silicon is proposed for re...

  14. Neural circuits of disgust induced by sexual stimuli in homosexual and heterosexual men: An fMRI study

    Energy Technology Data Exchange (ETDEWEB)

    Zhang Minming [Department of Radiology, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou (China); Hu Shaohua [Department of Mental Health, First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qing Chun Road, Hangzhou, Zhejiang Province 310003 (China); Xu Lijuan [National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing (China); Wang Qidong [Department of Radiology, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou (China); Xu Xiaojun [Department of Radiology, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou (China); Wei Erqing [College of Pharmacology, Zhejiang University (China); Yan Leqin [MD Anderson Cancer Center, Virginia Harris Cockrell Cancer Research Center, University of Texas, Austin (United States); Hu Jianbo; Wei Ning; Zhou Weihua; Huang Manli [Department of Mental Health, First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qing Chun Road, Hangzhou, Zhejiang Province 310003 (China); Xu Yi, E-mail: xuyi61@yahoo.com.cn [Department of Mental Health, First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qing Chun Road, Hangzhou, Zhejiang Province 310003 (China)

    2011-11-15

    Few studies demonstrated neural circuits related to disgust were influenced by internal sexual orientation in male. Here we used fMRI to study the neural responses to disgust in homosexual and heterosexual men to investigate that issue. Thirty-two healthy male volunteers (sixteen homosexual and sixteen heterosexual) were scanned while viewing alternating blocks of three types of erotic film: heterosexual couples (F-M), male homosexual couples (M-M), and female homosexual couples (F-F) engaged in sexual activity. All the participants rated their level of disgust and sexual arousal as well. The F-F and M-M stimuli induced disgust in homosexual and heterosexual men, respectively. The common activations related to disgusting stimuli included: bilateral frontal gyrus and occipital gyrus, right middle temporal gyrus, left superior temporal gyrus, right cerebellum, and right thalamus. Homosexual men had greater neural responses in the left medial frontal gyrus than did heterosexual men to the sexual disgusting stimuli; in contrast, heterosexual men showed significantly greater activation than homosexual men in the left cuneus. ROI analysis showed that negative correlation were found between the magnitude of MRI signals in the left medial frontal gyrus and scores of disgust in homosexual subjects (p < 0.05). This study indicated that there were regions in common as well as regions specific for each type of erotic stimuli during disgust of homosexual and heterosexual men.

  15. Neural circuits of disgust induced by sexual stimuli in homosexual and heterosexual men: an fMRI study.

    Science.gov (United States)

    Zhang, Minming; Hu, Shaohua; Xu, Lijuan; Wang, Qidong; Xu, Xiaojun; Wei, Erqing; Yan, Leqin; Hu, Jianbo; Wei, Ning; Zhou, Weihua; Huang, Manli; Xu, Yi

    2011-11-01

    Few studies demonstrated neural circuits related to disgust were influenced by internal sexual orientation in male. Here we used fMRI to study the neural responses to disgust in homosexual and heterosexual men to investigate that issue. Thirty-two healthy male volunteers (sixteen homosexual and sixteen heterosexual) were scanned while viewing alternating blocks of three types of erotic film: heterosexual couples (F-M), male homosexual couples (M-M), and female homosexual couples (F-F) engaged in sexual activity. All the participants rated their level of disgust and sexual arousal as well. The F-F and M-M stimuli induced disgust in homosexual and heterosexual men, respectively. The common activations related to disgusting stimuli included: bilateral frontal gyrus and occipital gyrus, right middle temporal gyrus, left superior temporal gyrus, right cerebellum, and right thalamus. Homosexual men had greater neural responses in the left medial frontal gyrus than did heterosexual men to the sexual disgusting stimuli; in contrast, heterosexual men showed significantly greater activation than homosexual men in the left cuneus. ROI analysis showed that negative correlation were found between the magnitude of MRI signals in the left medial frontal gyrus and scores of disgust in homosexual subjects (ptype of erotic stimuli during disgust of homosexual and heterosexual men.

  16. 基于乘法器的模拟电路参数测量系统的设计与实现%Design of measurement system for the analog circuits parameters based on the multiplier

    Institute of Scientific and Technical Information of China (English)

    王永喜; 胡玫; 马胜前

    2012-01-01

    针对现有模拟电路参数测量方法复杂、测量结果精度低的缺点,构建了基于乘法器的模拟电路参数测量系统.系统中由信号源产生2路正交同频正弦模拟信号,取出一路信号通过待测模拟电路与原信号源产生的2路信号做乘法、滤波处理,产生两路直流信号,之后通过DAQ、LABVIEW采集,得到模拟电路相位差和幅度.经测量,幅度和相位的误差均小于3%,表明该系统具有电路简单、易于实现、误差小、运算速度快等优点,为模拟电路参数测量的研究提出一种可行性方案.%For the more complicated principle and the lower accuracy for the existing methods of the measurement of the analog circuits' parameters) a measurement system for the analog circuits' parameters based on the multiplier is put forward. The signal source generates two orthogonal sinusoidal analog signals with same frequency and takes a signal through the analog circuits. Then the multiplication and filtering are completed with the two DC signals. At last the phase and amplitude of the analog circuit are obtained through the DAQ and LABVIEW. The amplitude and phase errors are less than 3%. The results show that the system has many advantages of simple circuit, fast speed and high accuracy. So it is a feasible plan for the measurement system of the analog circuit parameters at present.

  17. A New Approach for Modeling Darrieus-Type Vertical Axis Wind Turbine Rotors Using Electrical Equivalent Circuit Analogy: Basis of Theoretical Formulations and Model Development

    Directory of Open Access Journals (Sweden)

    Pierre Tchakoua

    2015-09-01

    Full Text Available Models are crucial in the engineering design process because they can be used for both the optimization of design parameters and the prediction of performance. Thus, models can significantly reduce design, development and optimization costs. This paper proposes a novel equivalent electrical model for Darrieus-type vertical axis wind turbines (DTVAWTs. The proposed model was built from the mechanical description given by the Paraschivoiu double-multiple streamtube model and is based on the analogy between mechanical and electrical circuits. This work addresses the physical concepts and theoretical formulations underpinning the development of the model. After highlighting the working principle of the DTVAWT, the step-by-step development of the model is presented. For assessment purposes, simulations of aerodynamic characteristics and those of corresponding electrical components are performed and compared.

  18. Switched-Current Technology Based Reconfigurable Analog Circuit for Multi-Function Analog Signal Processing%面向多功能模拟信号处理的开关电流型可重构模拟电路研究

    Institute of Scientific and Technical Information of China (English)

    王友仁; 祝鸣涛; 任晋华; 崔江; 林华

    2011-01-01

    现有的离散时间型可重构模拟电路采用开关电容技术,存在功能有限、带宽低、与数字CMOS工艺不兼容等问题.本文提出了一种基于电流模取样数据技术的可重构模拟电路,能够与数字CMOS工艺技术兼容.设计了细粒度开关电流型可重构模拟单元,设计了面向开关电流型CAB互连的可编程网络结构.在4×2规模的可重构模拟阵列上,重构实现了三个典型模拟电路应用实例,实验结果证明了所设计开关电流型可重构模拟电路是有效的,能重构实现多种功能的模拟电路.%Conventional discrete time mode reconfigurable analog circuits are designed with the switched capacitor teclnology,which have the disadvantage of function limitation,tower band pass,ani being not compatible with digital CMOS process technology in hybrid integrated circuits. This paper presented a Reconfigurable Analog Circuit (RAC) based on Current Mode Sampled Data Technology (CMSDT), which was fully compatible with the digital CMOS process. The Configurable Analog Block (CAB) based on switched current technology was developed,and the progrannable intercnnect network structure for the switched currenyCAB was proposed. Three analog circuits for application examples have been achieved respectively by reconfiguration in the 4 × 2 recongurable analog array. The simulation experimental results show that the designed reconfigurable analog circuit is effective and can realize multi-function analog circuit with reconfiguration.

  19. Neural circuit changes mediating lasting brain and behavioral response to predator stress.

    Science.gov (United States)

    Adamec, Robert E; Blundell, Jacqueline; Burton, Paul

    2005-01-01

    This paper reviews recent work which points to critical neural circuitry involved in lasting changes in anxiety like behavior following unprotected exposure of rats to cats (predator stress). Predator stress may increase anxiety like behavior in a variety of behavioral tests including: elevated plus maze, light dark box, acoustic startle, and social interaction. Studies of neural transmission in two limbic pathways, combined with path and covariance analysis relating physiology to behavior, suggest long term potentiation like changes in one or both of these pathways in the right hemisphere accounts for stress induced changes in all behaviors changed by predator stress except light dark box and social interaction. Findings will be discussed within the context of what is known about neural substrates activated by predator odor.

  20. A multichannel integrated circuit for neural spike detection based on EC-PC threshold estimation.

    Science.gov (United States)

    Wu, Tong; Yang, Zhi

    2013-01-01

    In extracellular neural recording experiments, spike detection is an important step for information decoding of neuronal activities. An ASIC implementation of detection algorithms can provide substantial data-rate reduction and facilitate wireless operations. In this paper, we present a 16-channel neural spike detection ASIC. The chip takes raw data as inputs, and outputs three data streams simultaneously: field potentials down sampled at 1.25 KHz, band-pass filtered neural data, and spiking probability maps sampled at 40 KHz. The functionality and the performance of the chip have been verified in both in-vivo and benchtop experiments. Fabricated in a 0.13 µm CMOS process, the chip has a peak power dissipation of 85 µW per channel and achieves a data-rate reduction of 98.44%.

  1. The design of CMOS general-purpose analog front-end circuit with tunable gain and bandwidth for biopotential signal recording systems.

    Science.gov (United States)

    Chen, Wei-Ming; Yang, Wen-Chia; Tsai, Tzung-Yun; Chiueh, Herming; Wu, Chung-Yu

    2011-01-01

    In this paper an 8-channel CMOS general-purpose analog front-end (AFE) circuit with tunable gain and bandwidth for biopotential signal recording systems is presented. The proposed AFE consists of eight chopper stabilized pre-amplifiers, an 8-to-1 analog multiplexer, and a programmable gain amplifier. It can be used to sense and amplify different kinds of biopotential signals, such as electrocorticogram (ECoG), electrocardiogram (ECG) and electromyogram (EMG). The AFE chip is designed and fabricated in 0.18-μm CMOS technology. The measured maximum gain of AFE is 60.8 dB. The low cutoff frequency can achieve as low as 0.8 Hz and high cutoff frequency can be adjusted from 200 Hz to 10 kHz to suit for different kinds of biopotential signals. The measured input-referred noise is 0.9 μV(rms), with the power consumption of 18μW per channel at 1.8-V power supply. And the noise efficiency factor (NEF) is only 1.3 for pre-amplifier.

  2. A Low-cost 4 Bit, 10 Giga-samples-per-second Analog-to-digital Converter Printed Circuit Board Assembly for FPGA-based Backends

    Science.gov (United States)

    Jiang, Homin; Yu, Chen-Yu; Kubo, Derek; Chen, Ming-Tang; Guzzino, Kim

    2016-11-01

    In this study, a 4 bit, 10 giga-samples-per-second analog-to-digital converter (ADC) printed circuit board assembly (PCBA) was designed, manufactured, and characterized for digitizing radio telescopes. For this purpose, an Adsantec ANST7120A-KMA flash ADC chip was used. Together with the field-programmable gate array platform, developed by the Collaboration for Astronomy Signal Processing and Electronics Research community, the PCBA enables data acquisition with a wide bandwidth and simplifies the intermediate frequency section. In the current version, the PCBA and the chip exhibit an analog bandwidth of 10 GHz (3 dB loss) and 20 GHz, respectively, which facilitates second, third, and even fourth Nyquist sampling. The following average performance parameters were obtained from the first and second Nyquist zones of the three boards: a spurious-free dynamic range of 31.35/30.45 dB, a signal-to-noise and distortion ratio of 22.95/21.83 dB, and an effective number of bits of 3.65/3.43, respectively.

  3. Fault Diagnosis of Analog Circuit Based on Extension Theory%基于可拓理论的模拟电路故障诊断方法

    Institute of Scientific and Technical Information of China (English)

    杜占龙; 谭业双; 甘彤

    2011-01-01

    针对模拟电路存在较多故障模式的诊断中易出现分类混叠的问题,提出一种基于可拓理论的故障诊断方法;建立定性地描述模拟电路故障诊断的物元模型,引入可拓集合中的关联函数和相关度;将响应信号进行小波分解提取其各层能量作为故障特征,并利用变尺度的混沌遗传算法优化各故障特征的权重系数,最后定量地计算各故障状态的可能程度;利用实验电路将该方法与另外两种诊断方法比较,实验结果表明,该方法故障分类正确率最高,耗时最短,从而可以证明该方法的有效性.%Aiming at overlapped recognition on analog circuit fault diagnosis with large number of fault categories, this paper presented a fault identification based on extension theory. A matter-element model for analog circuit was established and the correlation functions and correlation value in extension set was introduced. The response signal was decomposed by wavelet and the energy of each layer from wavelet was distilled. Weight parameters were optimized by chaotic genetic algorithm of mutative scale. The failure probability of any possible fault state was calculated. The proposed method and another two methods have been tested on a test circuit. Results show that compared with another two methods, classified rate of the proposed method was the highest and the time used was the least. It confirmed the proposed method effective.

  4. Matching tutor to student: rules and mechanisms for efficient two-stage learning in neural circuits

    CERN Document Server

    Tesileanu, Tiberiu; Balasubramanian, Vijay

    2016-01-01

    Existing models of birdsong learning assume that brain area LMAN introduces variability into song for trial-and-error learning. Recent data suggest that LMAN also encodes a corrective bias driving short-term improvements in song. These later consolidate in area RA, a motor cortex analogue downstream of LMAN. We develop a new model of such two-stage learning. Using a stochastic gradient descent approach, we derive how 'tutor' circuits should match plasticity mechanisms in 'student' circuits for efficient learning. We further describe a reinforcement learning framework with which the tutor can build its teaching signal. We show that mismatching the tutor signal and plasticity mechanism can impair or abolish learning. Applied to birdsong, our results predict the temporal structure of the corrective bias from LMAN given a plasticity rule in RA. Our framework can be applied predictively to other paired brain areas showing two-stage learning.

  5. Analog VLSI implementation of resonate-and-fire neuron.

    Science.gov (United States)

    Nakada, Kazuki; Asai, Tetsuya; Hayashi, Hatsuo

    2006-12-01

    We propose an analog integrated circuit that implements a resonate-and-fire neuron (RFN) model based on the Lotka-Volterra (LV) system. The RFN model is a spiking neuron model that has second-order membrane dynamics, and thus exhibits fast damped subthreshold oscillation, resulting in the coincidence detection, frequency preference, and post-inhibitory rebound. The RFN circuit has been derived from the LV system to mimic such dynamical behavior of the RFN model. Through circuit simulations, we demonstrate that the RFN circuit can act as a coincidence detector and a band-pass filter at circuit level even in the presence of additive white noise and background random activity. These results show that our circuit is expected to be useful for very large-scale integration (VLSI) implementation of functional spiking neural networks.

  6. Rules and mechanisms for efficient two-stage learning in neural circuits

    Science.gov (United States)

    Teşileanu, Tiberiu; Ölveczky, Bence; Balasubramanian, Vijay

    2017-01-01

    Trial-and-error learning requires evaluating variable actions and reinforcing successful variants. In songbirds, vocal exploration is induced by LMAN, the output of a basal ganglia-related circuit that also contributes a corrective bias to the vocal output. This bias is gradually consolidated in RA, a motor cortex analogue downstream of LMAN. We develop a new model of such two-stage learning. Using stochastic gradient descent, we derive how the activity in ‘tutor’ circuits (e.g., LMAN) should match plasticity mechanisms in ‘student’ circuits (e.g., RA) to achieve efficient learning. We further describe a reinforcement learning framework through which the tutor can build its teaching signal. We show that mismatches between the tutor signal and the plasticity mechanism can impair learning. Applied to birdsong, our results predict the temporal structure of the corrective bias from LMAN given a plasticity rule in RA. Our framework can be applied predictively to other paired brain areas showing two-stage learning. DOI: http://dx.doi.org/10.7554/eLife.20944.001 PMID:28374674

  7. Fluorescence-based monitoring of in vivo neural activity using a circuit-tracing pseudorabies virus.

    Directory of Open Access Journals (Sweden)

    Andrea E Granstedt

    Full Text Available The study of coordinated activity in neuronal circuits has been challenging without a method to simultaneously report activity and connectivity. Here we present the first use of pseudorabies virus (PRV, which spreads through synaptically connected neurons, to express a fluorescent calcium indicator protein and monitor neuronal activity in a living animal. Fluorescence signals were proportional to action potential number and could reliably detect single action potentials in vitro. With two-photon imaging in vivo, we observed both spontaneous and stimulated activity in neurons of infected murine peripheral autonomic submandibular ganglia (SMG. We optically recorded the SMG response in the salivary circuit to direct electrical stimulation of the presynaptic axons and to physiologically relevant sensory stimulation of the oral cavity. During a time window of 48 hours after inoculation, few spontaneous transients occurred. By 72 hours, we identified more frequent and prolonged spontaneous calcium transients, suggestive of neuronal or tissue responses to infection that influence calcium signaling. Our work establishes in vivo investigation of physiological neuronal circuit activity and subsequent effects of infection with single cell resolution.

  8. Calcium imaging of neural circuits with extended depth-of-field light-sheet microscopy.

    Science.gov (United States)

    Quirin, Sean; Vladimirov, Nikita; Yang, Chao-Tsung; Peterka, Darcy S; Yuste, Rafael; Ahrens, Misha B

    2016-03-01

    Increasing the volumetric imaging speed of light-sheet microscopy will improve its ability to detect fast changes in neural activity. Here, a system is introduced for brain-wide imaging of neural activity in the larval zebrafish by coupling structured illumination with cubic phase extended depth-of-field (EDoF) pupil encoding. This microscope enables faster light-sheet imaging and facilitates arbitrary plane scanning-removing constraints on acquisition speed, alignment tolerances, and physical motion near the sample. The usefulness of this method is demonstrated by performing multi-plane calcium imaging in the fish brain with a 416×832×160  μm field of view at 33 Hz. The optomotor response behavior of the zebrafish is monitored at high speeds, and time-locked correlations of neuronal activity are resolved across its brain.

  9. Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity

    OpenAIRE

    Wiebke ePotjans; Abigail Morrison; Markus Diesmann

    2010-01-01

    A major puzzle in the field of computational neuroscience is how to relate system-level learning in higher organisms to synaptic plasticity. Recently, plasticity rules depending not only on pre- and post-synaptic activity but also on a third, non-local neuromodulatory signal have emerged as key candidates to bridge the gap between the macroscopic and the microscopic level of learning. Crucial insights into this topic are expected to be gained from simulations of neural systems, as these allow...

  10. A multichannel integrated circuit for electrical recording of neural activity, with independent channel programmability.

    Science.gov (United States)

    Mora Lopez, Carolina; Prodanov, Dimiter; Braeken, Dries; Gligorijevic, Ivan; Eberle, Wolfgang; Bartic, Carmen; Puers, Robert; Gielen, Georges

    2012-04-01

    Since a few decades, micro-fabricated neural probes are being used, together with microelectronic interfaces, to get more insight in the activity of neuronal networks. The need for higher temporal and spatial recording resolutions imposes new challenges on the design of integrated neural interfaces with respect to power consumption, data handling and versatility. In this paper, we present an integrated acquisition system for in vitro and in vivo recording of neural activity. The ASIC consists of 16 low-noise, fully-differential input channels with independent programmability of its amplification (from 100 to 6000 V/V) and filtering (1-6000 Hz range) capabilities. Each channel is AC-coupled and implements a fourth-order band-pass filter in order to steeply attenuate out-of-band noise and DC input offsets. The system achieves an input-referred noise density of 37 nV/√Hz, a NEF of 5.1, a CMRR > 60 dB, a THD < 1% and a sampling rate of 30 kS/s per channel, while consuming a maximum of 70 μA per channel from a single 3.3 V. The ASIC was implemented in a 0.35 μm CMOS technology and has a total area of 5.6 × 4.5 mm². The recording system was successfully validated in in vitro and in vivo experiments, achieving simultaneous multichannel recordings of cell activity with satisfactory signal-to-noise ratios.

  11. Neural circuits underlying mother's voice perception predict social communication abilities in children.

    Science.gov (United States)

    Abrams, Daniel A; Chen, Tianwen; Odriozola, Paola; Cheng, Katherine M; Baker, Amanda E; Padmanabhan, Aarthi; Ryali, Srikanth; Kochalka, John; Feinstein, Carl; Menon, Vinod

    2016-05-31

    The human voice is a critical social cue, and listeners are extremely sensitive to the voices in their environment. One of the most salient voices in a child's life is mother's voice: Infants discriminate their mother's voice from the first days of life, and this stimulus is associated with guiding emotional and social function during development. Little is known regarding the functional circuits that are selectively engaged in children by biologically salient voices such as mother's voice or whether this brain activity is related to children's social communication abilities. We used functional MRI to measure brain activity in 24 healthy children (mean age, 10.2 y) while they attended to brief (social function. Compared to female control voices, mother's voice elicited greater activity in primary auditory regions in the midbrain and cortex; voice-selective superior temporal sulcus (STS); the amygdala, which is crucial for processing of affect; nucleus accumbens and orbitofrontal cortex of the reward circuit; anterior insula and cingulate of the salience network; and a subregion of fusiform gyrus associated with face perception. The strength of brain connectivity between voice-selective STS and reward, affective, salience, memory, and face-processing regions during mother's voice perception predicted social communication skills. Our findings provide a novel neurobiological template for investigation of typical social development as well as clinical disorders, such as autism, in which perception of biologically and socially salient voices may be impaired.

  12. Genetic control of encoding strategy in a food-sensing neural circuit

    Science.gov (United States)

    Diana, Giovanni; Patel, Dhaval S; Entchev, Eugeni V; Zhan, Mei; Lu, Hang; Ch'ng, QueeLim

    2017-01-01

    Neuroendocrine circuits encode environmental information via changes in gene expression and other biochemical activities to regulate physiological responses. Previously, we showed that daf-7 TGFβ and tph-1 tryptophan hydroxylase expression in specific neurons encode food abundance to modulate lifespan in Caenorhabditis elegans, and uncovered cross- and self-regulation among these genes (Entchev et al., 2015). Here, we now extend these findings by showing that these interactions between daf-7 and tph-1 regulate redundancy and synergy among neurons in food encoding through coordinated control of circuit-level signal and noise properties. Our analysis further shows that daf-7 and tph-1 contribute to most of the food-responsiveness in the modulation of lifespan. We applied a computational model to capture the general coding features of this system. This model agrees with our previous genetic analysis and highlights the consequences of redundancy and synergy during information transmission, suggesting a rationale for the regulation of these information processing features. DOI: http://dx.doi.org/10.7554/eLife.24040.001 PMID:28166866

  13. 基于跨导运算放大器的可重构模拟电路及应用设计%Reconfigurable Analog Circuit and Application Design Based on Operational Transconductance Amplifier

    Institute of Scientific and Technical Information of China (English)

    马伟伟; 王友仁; 石玉; 崔江

    2009-01-01

    常规的粗粒度可重构模拟电路灵活性不高,而且可重构模拟单元(CAB)结构较为复杂.针对此类问题,该文改进并设计了一种新的基于OTA的可重构模拟电路.该电路设计方案降低了CAB的复杂度,提高了CAB的使用效率.该文方法的有效性通过3个模拟设计实例(二阶低通滤波器、高通滤波器和三阶巴特沃思低通滤波器)的设计加以验证.实验结果表明,所提出的方法正确有效,可以较好地兼顾CAB资源与所要求功能的平衡.%The conventional coarse-grained reconfigurable analog circuits have bad flexibility, and the reConfigurable Analog Block (CAB) structure is complicated. In order to solve such problems, a new OTA-based reconfigurable analog circuit is presented in this paper. In this circuit scheme, the complexity of CAB is reduced and the CAB utilization rate is increased. To verify the effectiveness, three examples of analog circuit design (a second-order low pass filter, a second-order high pass filter and a low-pass third-order Butterworth filter) are presented. The experimental results indicate that the proposed circuits is effective and feasible, which make a better balance between circuit resources and functions.

  14. SEMICONDUCTOR INTEGRATED CIRCUITS: Low power CMOS preamplifier for neural recording applications

    Science.gov (United States)

    Xu, Zhang; Weihua, Pei; Beiju, Huang; Hongda, Chen

    2010-04-01

    A fully-differential bandpass CMOS (complementary metal oxide semiconductor) preamplifier for extracellular neural recording is presented. The capacitive-coupled and capacitive-feedback topology is adopted. The preamplifier has a midband gain of 20.4 dB and a DC gain of 0. The -3 dB upper cut-off frequency of the preamplifier is 6.7 kHz. The lower cut-off frequency can be adjusted for amplifying the field or action potentials located in different bands. It has an input-referred noise of 8.2 μVrms integrated from 0.15 Hz to 6.7 kHz for recording the local field potentials and the mixed neural spikes with a power dissipation of 23.1 μW from a 3.3 V supply. A bandgap reference circuitry is also designed for providing the biasing voltage and current. The 0.22 mm2 prototype chip, including the preamplifier and its biasing circuitry, is fabricated in the 0.35-μm N-well CMOS 2P4M process.

  15. Single-Cell Memory Regulates a Neural Circuit for Sensory Behavior

    Directory of Open Access Journals (Sweden)

    Kyogo Kobayashi

    2016-01-01

    Full Text Available Unveiling the molecular and cellular mechanisms underlying memory has been a challenge for the past few decades. Although synaptic plasticity is proven to be essential for memory formation, the significance of “single-cell memory” still remains elusive. Here, we exploited a primary culture system for the analysis of C. elegans neurons and show that a single thermosensory neuron has an ability to form, retain, and reset a temperature memory. Genetic and proteomic analyses found that the expression of the single-cell memory exhibits inter-individual variability, which is controlled by the evolutionarily conserved CaMKI/IV and Raf pathway. The variable responses of a sensory neuron influenced the neural activity of downstream interneurons, suggesting that modulation of the sensory neurons ultimately determines the behavioral output in C. elegans. Our results provide proof of single-cell memory and suggest that the individual differences in neural responses at the single-cell level can confer individuality.

  16. A dopamine-modulated neural circuit regulating aversive taste memory in Drosophila.

    Science.gov (United States)

    Masek, Pavel; Worden, Kurtresha; Aso, Yoshinori; Rubin, Gerald M; Keene, Alex C

    2015-06-01

    Taste memories allow animals to modulate feeding behavior in accordance with past experience and avoid the consumption of potentially harmful food [1]. We have developed a single-fly taste memory assay to functionally interrogate the neural circuitry encoding taste memories [2]. Here, we screen a collection of Split-GAL4 lines that label small populations of neurons associated with the fly memory center-the mushroom bodies (MBs) [3]. Genetic silencing of PPL1 dopamine neurons disrupts conditioned, but not naive, feeding behavior, suggesting these neurons are selectively involved in the conditioned taste response. We identify two PPL1 subpopulations that innervate the MB α lobe and are essential for aversive taste memory. Thermogenetic activation of these dopamine neurons during training induces memory, indicating these neurons are sufficient for the reinforcing properties of bitter tastant to the MBs. Silencing of either the intrinsic MB neurons or the output neurons from the α lobe disrupts taste conditioning. Thermogenetic manipulation of these output neurons alters naive feeding response, suggesting that dopamine neurons modulate the threshold of response to appetitive tastants. Taken together, these findings detail a neural mechanism underlying the formation of taste memory and provide a functional model for dopamine-dependent plasticity in Drosophila.

  17. Neural circuits in anxiety and stress disorders: a focused review

    Directory of Open Access Journals (Sweden)

    Duval ER

    2015-01-01

    Full Text Available Elizabeth R Duval, Arash Javanbakht, Israel LiberzonDepartment of Psychiatry, University of Michigan Health System, Ann Arbor, MI, USAAbstract: Anxiety and stress disorders are among the most prevalent neuropsychiatric disorders. In recent years, multiple studies have examined brain regions and networks involved in anxiety symptomatology in an effort to better understand the mechanisms involved and to develop more effective treatments. However, much remains unknown regarding the specific abnormalities and interactions between networks of regions underlying anxiety disorder presentations. We examined recent neuroimaging literature that aims to identify neural mechanisms underlying anxiety, searching for patterns of neural dysfunction that might be specific to different anxiety disorder categories. Across different anxiety and stress disorders, patterns of hyperactivation in emotion-generating regions and hypoactivation in prefrontal/regulatory regions are common in the literature. Interestingly, evidence of differential patterns is also emerging, such that within a spectrum of disorders ranging from more fear-based to more anxiety-based, greater involvement of emotion-generating regions is reported in panic disorder and specific phobia, and greater involvement of prefrontal regions is reported in generalized anxiety disorder and posttraumatic stress disorder. We summarize the pertinent literature and suggest areas for continued investigation.Keywords: fear, anxiety, neuroimaging

  18. Neural Correlates of Inflexible Behavior in the Orbitofrontal–Amygdalar Circuit after Cocaine Exposure

    Science.gov (United States)

    STALNAKER, THOMAS A.; ROESCH, MATTHEW R.; CALU, DONNA J.; BURKE, KATHRYN A.; SINGH, TEGHPAL; SCHOENBAUM, GEOFFREY

    2008-01-01

    Addiction is characterized by compulsive or inflexible behavior, observed both in the context of drug-seeking and in contexts unrelated to drugs. One possible contributor to these inflexible behaviors may be drug-induced dysfunction within circuits that support behavioral flexibility, including the basolateral amygdala (ABL) and the orbitofrontal cortex (OFC). Here we describe data demonstrating that chronic cocaine exposure causes long-lasting changes in encoding properties in the ABL and the OFC during learning and reversal in an odor-guided task. In particular, these data suggest that inflexible encoding in ABL neurons may be the proximal cause of cocaine-induced behavioral inflexibility, and that a loss of outcome-expectant encoding in OFC neurons could be a more distal contributor to this impairment. A similar mechanism of drug-induced orbitofrontal–amygdalar dysfunction may cause inflexible behavior when animals and addicts are exposed to drug-associated cues and contexts. PMID:17846156

  19. SEMICONDUCTOR INTEGRATED CIRCUITS: A four-channel microelectronic system for neural signal regeneration

    Science.gov (United States)

    Shushan, Xie; Zhigong, Wang; Xiaoying, Lü; Wenyuan, Li; Haixian, Pan

    2009-12-01

    This paper presents a microelectronic system which is capable of making a signal record and functional electric stimulation of an injured spinal cord. As a requirement of implantable engineering for the regeneration microelectronic system, the system is of low noise, low power, small size and high performance. A front-end circuit and two high performance OPAs (operational amplifiers) have been designed for the system with different functions, and the two OPAs are a low-noise low-power two-stage OPA and a constant-gm RTR input and output OPA. The system has been realized in CSMC 0.5-μm CMOS technology. The test results show that the system satisfies the demands of neuron signal regeneration.

  20. PDF-1 neuropeptide signaling modulates a neural circuit for mate-searching behavior in C. elegans.

    Science.gov (United States)

    Barrios, Arantza; Ghosh, Rajarshi; Fang, Chunhui; Emmons, Scott W; Barr, Maureen M

    2012-12-01

    Appetitive behaviors require complex decision making that involves the integration of environmental stimuli and physiological needs. C. elegans mate searching is a male-specific exploratory behavior regulated by two competing needs: food and reproductive appetite. We found that the pigment dispersing factor receptor (PDFR-1) modulates the circuit that encodes the male reproductive drive that promotes male exploration following mate deprivation. PDFR-1 and its ligand, PDF-1, stimulated mate searching in the male, but not in the hermaphrodite. pdf-1 was required in the gender-shared interneuron AIM, and the receptor acted in internal and external environment-sensing neurons of the shared nervous system (URY, PQR and PHA) to produce mate-searching behavior. Thus, the pdf-1 and pdfr-1 pathway functions in non-sex-specific neurons to produce a male-specific, goal-oriented exploratory behavior. Our results indicate that secretin neuropeptidergic signaling is involved in regulating motivational internal states.

  1. A Framework for Quantitative Modeling of Neural Circuits Involved in Sleep-to-Wake Transition

    Directory of Open Access Journals (Sweden)

    Siamak eSorooshyari

    2015-02-01

    Full Text Available Identifying the neuronal circuits and dynamics of sleep-to-wake transition is essential to understanding brain regulation of behavioral states, including sleep-wake cycles, arousal, and hyperarousal. Recent work by different laboratories has used optogenetics to determine the role of individual neuromodulators in state transitions. The optogenetically-driven data does not yet provide a multi-dimensional schematic of the mechanisms underlying changes in vigilance states. This work presents a modeling framework to interpret, assist, and drive research on the sleep-regulatory network. We identify feedback, redundancy, and gating hierarchy as three fundamental aspects of this model. The presented model is expected to expand as additional data on the contribution of each transmitter to a vigilance state becomes available. Incorporation of conductance-based models of neuronal ensembles into this model and existing models of cortical excitability will provide more comprehensive insight into sleep dynamics as well as sleep and arousal-related disorders.

  2. A Framework for Quantitative Modeling of Neural Circuits Involved in Sleep-to-Wake Transition

    Science.gov (United States)

    Sorooshyari, Siamak; Huerta, Ramón; de Lecea, Luis

    2015-01-01

    Identifying the neuronal circuits and dynamics of sleep-to-wake transition is essential to understanding brain regulation of behavioral states, including sleep–wake cycles, arousal, and hyperarousal. Recent work by different laboratories has used optogenetics to determine the role of individual neuromodulators in state transitions. The optogenetically driven data do not yet provide a multi-dimensional schematic of the mechanisms underlying changes in vigilance states. This work presents a modeling framework to interpret, assist, and drive research on the sleep-regulatory network. We identify feedback, redundancy, and gating hierarchy as three fundamental aspects of this model. The presented model is expected to expand as additional data on the contribution of each transmitter to a vigilance state becomes available. Incorporation of conductance-based models of neuronal ensembles into this model and existing models of cortical excitability will provide more comprehensive insight into sleep dynamics as well as sleep and arousal-related disorders. PMID:25767461

  3. Sex differences in the neural circuit that mediates female sexual receptivity

    Science.gov (United States)

    Flanagan-Cato, Loretta M.

    2011-01-01

    Female sexual behavior in rodents, typified by the lordosis posture, is hormone-dependent and sex-specific. Ovarian hormones control this behavior via receptors in the hypothalamic ventromedial nucleus (VMH). This review considers the sex differences in the morphology, neurochemistry and neural circuitry of the VMH to gain insights into the mechanisms that control lordosis. The VMH is larger in males compared with females, due to more synaptic connections. Another sex difference is the responsiveness to estradiol, with males exhibiting muted, and in some cases reverse, effects compared with females. The lack of lordosis in males may be explained by differences in synaptic organization or estrogen responsiveness, or both, in the VMH. However, given that damage to other brain regions unmasks lordosis behavior in males, a male-typical VMH is unlikely the main factor that prevents lordosis. In females, key questions remain regarding the mechanisms whereby ovarian hormones modulate VMH function to promote lordosis. PMID:21338620

  4. Neural circuit of verbal humor comprehension in schizophrenia - an fMRI study.

    Science.gov (United States)

    Adamczyk, Przemysław; Wyczesany, Miroslaw; Domagalik, Aleksandra; Daren, Artur; Cepuch, Kamil; Błądziński, Piotr; Cechnicki, Andrzej; Marek, Tadeusz

    2017-01-01

    Individuals with schizophrenia exhibit problems with understanding the figurative meaning of language. This study evaluates neural correlates of diminished humor comprehension observed in schizophrenia. The study included chronic schizophrenia (SCH) outpatients (n = 20), and sex, age and education level matched healthy controls (n = 20). The fMRI punchline based humor comprehension task consisted of 60 stories of which 20 had funny, 20 nonsensical and 20 neutral (not funny) punchlines. After the punchlines were presented, the participants were asked to indicate whether the story was comprehensible and how funny it was. Three contrasts were analyzed in both groups reflecting stages of humor processing: abstract vs neutral stories - incongruity detection; funny vs abstract - incongruity resolution and elaboration; and funny vs neutral - complete humor processing. Additionally, parametric modulation analysis was performed using both subjective ratings separately. Between-group comparisons revealed that the SCH subjects had attenuated activation in the right posterior superior temporal gyrus (BA 41) in case of irresolvable incongruity processing of nonsensical puns; in the left dorsomedial middle and superior frontal gyri (BA 8/9) in case of incongruity resolution and elaboration processing of funny puns; and in the interhemispheric dorsal anterior cingulate cortex (BA 24) in case of complete processing of funny puns. Additionally, during comprehensibility ratings the SCH group showed a suppressed activity in the left dorsomedial middle and superior frontal gyri (BA 8/9) and revealed weaker activation during funniness ratings in the left dorsal anterior cingulate cortex (BA 24). Interestingly, these differences in the SCH group were accompanied behaviorally by a protraction of time in both types of rating responses and by indicating funny punchlines less comprehensible. Summarizing, our results indicate neural substrates of humor comprehension processing

  5. The design and simulation of a titanium oxide memristor-based programmable analog filter in a simulation program with integrated circuit emphasis

    Institute of Scientific and Technical Information of China (English)

    Tian Xiao-Bo; Xu Hui

    2013-01-01

    In many communication and signal routing applications,it is desirable to have a programmable analog filter.According to this practical demand,we consider the titanium oxide memristor,which is a kind of nano-scale electron device with low power dissipation and nonvolatile memory.Such characteristics could be suitable for designing the desired filter.However,both the non-analytical relation between the memristance and the charges that pass through it,and the changeable V-I characteristics in physical tests make it difficult to accurately set the memristance to the target value.In this paper,the conductive mechanism of the memristor is analyzed,a method of continuously programming the memristance is proposed and simulated in a simulation program with integrated circuit emphasis,and its feasibility and compatibility,both in simulations and physical realizations,are demonstrated.This method is then utilized in a first-order active filter as an example to show its applications in programmable filters.This work also provides a practical tool for utilizing memristors as resistance programmable devices.

  6. 基于衬底驱动技术的模拟电路设计%Analog circuit design based on the bulk-driven technique

    Institute of Scientific and Technical Information of China (English)

    张长青; 朱猛

    2011-01-01

    在进行低电压低功耗模拟电路设计的众多技术中,衬底驱动(BD)技术由于设计简单和使用传统MOS工艺实现的特点,而被很多的设计所采用。本文利用这一原理,在标准CMOS工艺和±0.7V电源电压前提下设计低电压低功耗标准模块,最后在TSMC0.25umCMOS工艺模型下,用Spice模拟器验证了模拟仿真结果。%Among many techniques used for the design of LV-LP analog circuits, the Bulk-driven principle offers a promising route towards this design for many aspects mainly the simplicity and using the conventional MOS technology to implement these designs. This paper is devoted to the Bulk-driven(BD) principle and utilizing this principle to design LV LP building blocks in standard CMOS processes and supply voltage ±0.7V. The simulation results have been carried out by the Spice simulator using the 0.25 um CMOS technology from TSMC.

  7. Remediation of Childhood Math Anxiety and Associated Neural Circuits through Cognitive Tutoring.

    Science.gov (United States)

    Supekar, Kaustubh; Iuculano, Teresa; Chen, Lang; Menon, Vinod

    2015-09-09

    Math anxiety is a negative emotional reaction that is characterized by feelings of stress and anxiety in situations involving mathematical problem solving. High math-anxious individuals tend to avoid situations involving mathematics and are less likely to pursue science, technology, engineering, and math-related careers than those with low math anxiety. Math anxiety during childhood, in particular, has adverse long-term consequences for academic and professional success. Identifying cognitive interventions and brain mechanisms by which math anxiety can be ameliorated in children is therefore critical. Here we investigate whether an intensive 8 week one-to-one cognitive tutoring program designed to improve mathematical skills reduces childhood math anxiety, and we identify the neurobiological mechanisms by which math anxiety can be reduced in affected children. Forty-six children in grade 3, a critical early-onset period for math anxiety, participated in the cognitive tutoring program. High math-anxious children showed a significant reduction in math anxiety after tutoring. Remarkably, tutoring remediated aberrant functional responses and connectivity in emotion-related circuits anchored in the basolateral amygdala. Crucially, children with greater tutoring-induced decreases in amygdala reactivity had larger reductions in math anxiety. Our study demonstrates that sustained exposure to mathematical stimuli can reduce math anxiety and highlights the key role of the amygdala in this process. Our findings are consistent with models of exposure-based therapy for anxiety disorders and have the potential to inform the early treatment of a disability that, if left untreated in childhood, can lead to significant lifelong educational and socioeconomic consequences in affected individuals. Significance statement: Math anxiety during early childhood has adverse long-term consequences for academic and professional success. It is therefore important to identify ways to alleviate

  8. 动态自适应遗传算法在模拟电路中的应用%Dynamic Adaptive Genetic Algorithm in the Application of the Analog Circuits

    Institute of Scientific and Technical Information of China (English)

    王硕

    2016-01-01

    In order to resolve the problems of parameter adjustment in analog circuit design , by combination of analog design with genetic algorithm , this paper proposes a dynamic adaptive genetic algorithm as the op-amp method of analog circuit design , so as to obtain the op-amp best parameters in analog circuit by adding the op -amp parameters to the improvement of the genetic algo-rithm.%针对在模拟电路设计中参数调整出现的问题,将模拟电路设计与遗传算法相结合,提出一种利用动态自适应遗传算法设计模拟电路的运放的方法。通过将运放参数融入到遗传算法的改进中,实现模拟电路中运放的参数最佳。

  9. Anatomical characterization of cre driver mice for neural circuit mapping and manipulation

    Directory of Open Access Journals (Sweden)

    Julie Ann Harris

    2014-07-01

    Full Text Available Significant advances in circuit-level analyses of the brain require tools that allow for labeling, modulation of gene expression, and monitoring and manipulation of cellular activity in specific cell types and/or anatomical regions. Large-scale projects and individual laboratories have produced hundreds of gene-specific promoter-driven Cre mouse lines invaluable for enabling genetic access to subpopulations of cells in the brain. However, the potential utility of each line may not be fully realized without systematic whole brain characterization of transgene expression patterns. We established a high-throughput in situ hybridization, imaging and data processing pipeline to describe whole brain gene expression patterns in Cre driver mice. Currently, anatomical data from over 100 Cre driver lines are publicly available via the Allen Institute’s Transgenic Characterization database, which can be used to assist researchers in choosing the appropriate Cre drivers for functional, molecular, or connectional studies of different regions and/or cell types in the brain.

  10. Disrupted insula-based neural circuit organization and conflict interference in trauma-exposed youth

    Science.gov (United States)

    Marusak, Hilary A.; Etkin, Amit; Thomason, Moriah E.

    2015-01-01

    Childhood trauma exposure is a potent risk factor for psychopathology. Emerging research suggests that aberrant saliency processing underlies the link between early trauma exposure and later cognitive and socioemotional deficits that are hallmark of several psychiatric disorders. Here, we examine brain and behavioral responses during a face categorization conflict task, and relate these to intrinsic connectivity of the salience network (SN). The results demonstrate a unique pattern of SN dysfunction in youth exposed to trauma (n = 14) relative to comparison youth (n = 19) matched on age, sex, IQ, and sociodemographic risk. We find that trauma-exposed youth are more susceptible to conflict interference and this correlates with higher fronto-insular responses during conflict. Resting-state functional connectivity data collected in the same participants reveal increased connectivity of the insula to SN seed regions that is associated with diminished reward sensitivity, a critical risk/resilience trait following stress. In addition to altered intrinsic connectivity of the SN, we observed altered connectivity between the SN and default mode network (DMN) in trauma-exposed youth. These data uncover network-level disruptions in brain organization following one of the strongest predictors of illness, early life trauma, and demonstrate the relevance of observed neural effects for behavior and specific symptom dimensions. SN dysfunction may serve as a diathesis that contributes to illness and negative outcomes following childhood trauma. PMID:26199869

  11. Disrupted insula-based neural circuit organization and conflict interference in trauma-exposed youth

    Directory of Open Access Journals (Sweden)

    Hilary A. Marusak

    2015-01-01

    Full Text Available Childhood trauma exposure is a potent risk factor for psychopathology. Emerging research suggests that aberrant saliency processing underlies the link between early trauma exposure and later cognitive and socioemotional deficits that are hallmark of several psychiatric disorders. Here, we examine brain and behavioral responses during a face categorization conflict task, and relate these to intrinsic connectivity of the salience network (SN. The results demonstrate a unique pattern of SN dysfunction in youth exposed to trauma (n = 14 relative to comparison youth (n = 19 matched on age, sex, IQ, and sociodemographic risk. We find that trauma-exposed youth are more susceptible to conflict interference and this correlates with higher fronto-insular responses during conflict. Resting-state functional connectivity data collected in the same participants reveal increased connectivity of the insula to SN seed regions that is associated with diminished reward sensitivity, a critical risk/resilience trait following stress. In addition to altered intrinsic connectivity of the SN, we observed altered connectivity between the SN and default mode network (DMN in trauma-exposed youth. These data uncover network-level disruptions in brain organization following one of the strongest predictors of illness, early life trauma, and demonstrate the relevance of observed neural effects for behavior and specific symptom dimensions. SN dysfunction may serve as a diathesis that contributes to illness and negative outcomes following childhood trauma.

  12. Neural circuit mechanism for learning dependent on dopamine transmission: roles of striatal direct and indirect pathways in sensory discrimination.

    Science.gov (United States)

    Kobayashi, Kazuto; Fukabori, Ryoji; Nishizawa, Kayo

    2013-01-01

    The dorsal striatum in basal ganglia circuit mediates learning processes contributing to instrumental motor actions. The striatum receives excitatory inputs from many cortical areas and the thalamic nuclei and dopaminergic inputs from the ventral midbrain and projects to the output nuclei through direct and indirect pathways. The neural mechanism remains unclear as to how these striatofugal pathways control the learning processes of instrumental actions. Here, we addressed the behavioral roles of the two striatofugal pathways in the performance of sensory discrimination by using immunotoxin (IT)-mediated cell targeting. IT targeting of the striatal direct pathway in mutant mice lengthened the response time but did not affect the accuracy of the response selection in visual discrimination. Subregion-specific pathway targeting revealed a delay in motor responses generated by elimination of the direct pathway arising from the dorsomedial striatum (DMS) but not from the dorsolateral striatum (DLS). These findings indicate that the direct pathway, in particular that from the DMS, contributes to the regulation of the response time in visual discrimination. In addition, IT targeting of the striatal indirect pathway originating from the DLS in transgenic rats impaired the accuracy of response selection in auditory discrimination, whereas the response time remained normal. These data demonstrate that the DLS-derived indirect pathway plays an essential role in the control of the selection accuracy of learned motor responses. Our results suggest that striatal direct and indirect pathways act cooperatively to regulate the selection accuracy and response time of learned motor actions in the performance of discriminative learning.

  13. Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity.

    Science.gov (United States)

    Potjans, Wiebke; Morrison, Abigail; Diesmann, Markus

    2010-01-01

    A major puzzle in the field of computational neuroscience is how to relate system-level learning in higher organisms to synaptic plasticity. Recently, plasticity rules depending not only on pre- and post-synaptic activity but also on a third, non-local neuromodulatory signal have emerged as key candidates to bridge the gap between the macroscopic and the microscopic level of learning. Crucial insights into this topic are expected to be gained from simulations of neural systems, as these allow the simultaneous study of the multiple spatial and temporal scales that are involved in the problem. In particular, synaptic plasticity can be studied during the whole learning process, i.e., on a time scale of minutes to hours and across multiple brain areas. Implementing neuromodulated plasticity in large-scale network simulations where the neuromodulatory signal is dynamically generated by the network itself is challenging, because the network structure is commonly defined purely by the connectivity graph without explicit reference to the embedding of the nodes in physical space. Furthermore, the simulation of networks with realistic connectivity entails the use of distributed computing. A neuromodulated synapse must therefore be informed in an efficient way about the neuromodulatory signal, which is typically generated by a population of neurons located on different machines than either the pre- or post-synaptic neuron. Here, we develop a general framework to solve the problem of implementing neuromodulated plasticity in a time-driven distributed simulation, without reference to a particular implementation language, neuromodulator, or neuromodulated plasticity mechanism. We implement our framework in the simulator NEST and demonstrate excellent scaling up to 1024 processors for simulations of a recurrent network incorporating neuromodulated spike-timing dependent plasticity.

  14. Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity

    Directory of Open Access Journals (Sweden)

    Wiebke ePotjans

    2010-11-01

    Full Text Available A major puzzle in the field of computational neuroscience is how to relate system-level learning in higher organisms to synaptic plasticity. Recently, plasticity rules depending not only on pre- and post-synaptic activity but also on a third, non-local neuromodulatory signal have emerged as key candidates to bridge the gap between the macroscopic and the microscopic level of learning. Crucial insights into this topic are expected to be gained from simulations of neural systems, as these allow the simultaneous study of the multiple spatial and temporal scales that are involved in the problem. In particular, synaptic plasticity can be studied during the whole learning process, i.e. on a time scale of minutes to hours and across multiple brain areas. Implementing neuromodulated plasticity in large-scale network simulations where the neuromodulatory signal is dynamically generated by the network itself is challenging, because the network structure is commonly defined purely by the connectivity graph without explicit reference to the embedding of the nodes in physical space. Furthermore, the simulation of networks with realistic connectivity entails the use of distributed computing. A neuromodulated synapse must therefore be informed in an efficient way about the neuromodulatory signal, which is typically generated by a population of neurons located on different machines than either the pre- or post-synaptic neuron. Here, we develop a general framework to solve the problem of implementing neuromodulated plasticity in a time-driven distributed simulation, without reference to a particular implementation language, neuromodulator or neuromodulated plasticity mechanism. We implement our framework in the simulator NEST and demonstrate excellent scaling up to 1024 processors for simulations of a recurrent network incorporating neuromodulated spike-timing dependent plasticity.

  15. 基于支撑向量机的CMOS运放可行域模型%Feasible Performance Modeling of Analog Circuit Based on Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    梁瑞花; 郭裕顺

    2014-01-01

    在进行模拟与混合信号集成电路的行为级设计时,需要各种基本单元与功能电路的性能可行域模型。可行域模型构造可以看作是性能参数空间中的一个二分类问题。研究了采用支撑向量机进行电路可行域模型构造的方法,给出了建模过程;并以一个常用的Miller补偿CMOS两级运算放大器为例,建立基于支撑向量机的可行域模型,通过数值实验验证了模型的正确性。%There need all kinds of the performance feasible region model to basic unit and function circuit in the analog and mixed-signal IC behavior design level .The modeling of feasible domain can be looked as a binary classification problem in the space of performance parameters .This paper mainly studies the method using support vector machine ( SVM) to make the feasible region model , and gives its process;then, it takes a common Miller compensation CMOS two-stage operational amplifier as example , and gets the feasible domain model based on SVM .At the last, it verifies the correctness of the model through the numerical experiment .

  16. Critical phenomena at a first-order phase transition in a lattice of glow lamps: Experimental findings and analogy to neural activity

    Science.gov (United States)

    Minati, Ludovico; de Candia, Antonio; Scarpetta, Silvia

    2016-07-01

    Networks of non-linear electronic oscillators have shown potential as physical models of neural dynamics. However, two properties of brain activity, namely, criticality and metastability, remain under-investigated with this approach. Here, we present a simple circuit that exhibits both phenomena. The apparatus consists of a two-dimensional square lattice of capacitively coupled glow (neon) lamps. The dynamics of lamp breakdown (flash) events are controlled by a DC voltage globally connected to all nodes via fixed resistors. Depending on this parameter, two phases having distinct event rate and degree of spatiotemporal order are observed. The transition between them is hysteretic, thus a first-order one, and it is possible to enter a metastability region, wherein, approaching a spinodal point, critical phenomena emerge. Avalanches of events occur according to power-law distributions having exponents ≈3/2 for size and ≈2 for duration, and fractal structure is evident as power-law scaling of the Fano factor. These critical exponents overlap observations in biological neural networks; hence, this circuit may have value as building block to realize corresponding physical models.

  17. 阻抗匹配电路在电子测量仪器前端模拟通道中的应用%Application of impedance matching circuit in analog input channels of electronic measuring instruments

    Institute of Scientific and Technical Information of China (English)

    唐金元; 王翠珍; 于潞

    2011-01-01

    阻抗匹配电路是电子测量仪器前端模拟通道中的关键电路.在介绍电子测量仪器前端模拟通道组成、功能的基础七,阐述了阻抗匹配的作用,即实现信号最大功率传输和实现信号无反射传输.分析了选频匹配网络中常用的倒L型网络、T型网络和Ⅱ型网络的电路模型和电路参数设计计算方法,给出了实现阻抗变换的各种电压跟随器的电路结构形式,并对电子测量仪器前端T型阻抗匹配电路参数的计算进行了举例说明.%Impedance matching circuit is the key circuit in analog input channels of electronic measuring instruments.The paper firstly introduced the composition and functions of analog input channels in instruments, and expounded the roles of impedance matching, that is, it can transmit signal with maximum power or without reflecting.In the same time, it also analyzed the circuit models and the calculating methods of overturning L, T and Ⅱ style network, and proposed the circuits of each voltage follower to realize impedance change.Finally, the paper illustrate the parameter calculation of T impedance matching circuit in analog input channels of electronic measuring instruments.

  18. Properties and application of a multichannel integrated circuit for low-artifact, patterned electrical stimulation of neural tissue

    Science.gov (United States)

    Hottowy, Paweł; Skoczeń, Andrzej; Gunning, Deborah E.; Kachiguine, Sergei; Mathieson, Keith; Sher, Alexander; Wiącek, Piotr; Litke, Alan M.; Dąbrowski, Władysław

    2012-01-01

    Objective Modern multielectrode array (MEA) systems can record the neuronal activity from thousands of electrodes, but their ability to provide spatio-temporal patterns of electrical stimulation is very limited. Furthermore, the stimulus-related artifacts significantly limit the ability to record the neuronal responses to the stimulation. To address these issues, we designed a multichannel integrated circuit for patterned MEA-based electrical stimulation and evaluated its performance in experiments with isolated mouse and rat retina. Approach The Stimchip includes 64 independent stimulation channels. Each channel comprises an internal digital-to-analog converter that can be configured as a current or voltage source. The shape of the stimulation waveform is defined independently for each channel by the real-time data stream. In addition, each channel is equipped with circuitry for reduction of the stimulus artifact. Main results Using a high-density MEA stimulation/recording system, we effectively stimulated individual retinal ganglion cells (RGCs) and recorded the neuronal responses with minimal distortion, even on the stimulating electrodes. We independently stimulated a population of RGCs in rat retina and, using a complex spatio-temporal pattern of electrical stimulation pulses, we replicated visually-evoked spiking activity of a subset of these cells with high fidelity. Significance Compared with current state-of-the-art MEA systems, the Stimchip is able to stimulate neuronal cells with much more complex sequences of electrical pulses and with significantly reduced artifacts. This opens up new possibilities for studies of neuronal responses to electrical stimulation, both in the context of neuroscience research and in the development of neuroprosthetic devices. PMID:23160018

  19. Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks.

    Science.gov (United States)

    Pu, Yi-Fei; Yi, Zhang; Zhou, Ji-Liu

    2016-07-14

    This paper mainly discusses a novel conceptual framework: fractional Hopfield neural networks (FHNN). As is commonly known, fractional calculus has been incorporated into artificial neural networks, mainly because of its long-term memory and nonlocality. Some researchers have made interesting attempts at fractional neural networks and gained competitive advantages over integer-order neural networks. Therefore, it is naturally makes one ponder how to generalize the first-order Hopfield neural networks to the fractional-order ones, and how to implement FHNN by means of fractional calculus. We propose to introduce a novel mathematical method: fractional calculus to implement FHNN. First, we implement fractor in the form of an analog circuit. Second, we implement FHNN by utilizing fractor and the fractional steepest descent approach, construct its Lyapunov function, and further analyze its attractors. Third, we perform experiments to analyze the stability and convergence of FHNN, and further discuss its applications to the defense against chip cloning attacks for anticounterfeiting. The main contribution of our work is to propose FHNN in the form of an analog circuit by utilizing a fractor and the fractional steepest descent approach, construct its Lyapunov function, prove its Lyapunov stability, analyze its attractors, and apply FHNN to the defense against chip cloning attacks for anticounterfeiting. A significant advantage of FHNN is that its attractors essentially relate to the neuron's fractional order. FHNN possesses the fractional-order-stability and fractional-order-sensitivity characteristics.

  20. Functional lateralization of the baso-lateral amygdala neural circuits modulating the motivated exploratory behaviour in rats: role of histamine.

    Science.gov (United States)

    Alvarez, Edgardo O; Banzan, Arturo M

    2011-03-17

    Functional laterality appears to be present in many brain functions in man and animals. The existence of paired neural circuits which act differentially to modulate a specific behavioural function seems to be an evolutionary successful strategy in animal evolution. In spite of many examples described in mammals, birds and other vertebrates and invertebrates, still its intrinsic mechanism is not completely understood. In this work the participation of the baso-lateral amygdala (BLA) on lateralized motivated exploratory behaviour and the possible influence of histamine neurons in these mechanisms were studied in rats. Different groups of animals under xylacine-ketamine anesthesia were implanted with microinjection guide cannulae into the right or left BLA. 72 h after implantation, animals were tested in hole-board cage (OVM) with a novelty object positioned in the center of the arena, as a model of exploration of a non-conflictive environment, and 24h later they were tested in the Elevated Asymmetric Plus Maze (APM) as a model of conflictive exploration. In the day of the experiment, lidocaine was applied into the left, or right BLA in order to block the electrical activity of BLA neurons. Saline in the contralateral BLA was considered control. Results showed that exploratory activity in the OVM was significantly inhibited when lidocaine was microinjected into the left BLA, and no changes were observed when lidocaine was applied into the right BLA. When histamine was microinjected into the right BLA and lidocaine into the contralateral BLA, head-dipping, rearing, and focalized exploration behaviour were significantly inhibited. In the APM, lidocaine treatment increased equally the exploration of the "single wall" and "high and low walls" arms of the labyrinth, independently if blocking of electrical activity of the BLA neurons was performed in the left or right amygdala. Histamine treatment inhibited significantly exploration of the lesser fear-inducing arms of the

  1. High-speed low-power analog ASICs for a 3D neuroprocessor

    Science.gov (United States)

    Duong, Tuan A.; Kemeny, Sabrina E.; Tran, Mua D.; Daud, Taher; Thakoor, Anilkumar P.

    1995-03-01

    A particularly challenging neural network application requiring high-speed and intensive image processing capability is target acquisition and discrimination. It requires spatio-temporal recognition of point and resolved targets at high speeds. A reconfigurable neural architecture may discriminate targets from clutter or classify targets once resolved. By mating a 64 X 64 pixel array infrared (IR) image sensor to a 3-D stack (cube) of 64 neural-net ICs along respective edges, every pixel would directly input to a neural network, thereby processing the information with full parallelism. However, the `cube' has to operate at 90 degree(s)K with processing speed and approximately 2 watts of power dissipation. Analog circuitry, where the spatially parallel input to the neural networks is also analog, would make this possible. Digital neural processing would require analog-to-digital converters on each IC, impractical with the power constraint. A versatile reconfigurable circuit is presented that offers a variety of neural architectures: multilayer perceptron, cascade backpropagation, and template matching with winner-take-all (WTA) circuitry. Special designs of analog neuron and synapse implemented in VLSI are presented which bear out high speed response both at room and low temperatures with synapse-neuron signal propagation times of approximately 100 ns.

  2. Classification of correlated patterns with a configurable analog VLSI neural network of spiking neurons and self-regulating plastic synapses.

    Science.gov (United States)

    Giulioni, Massimilian; Pannunzi, Mario; Badoni, Davide; Dante, Vittorio; Del Giudice, Paolo

    2009-11-01

    We describe the implementation and illustrate the learning performance of an analog VLSI network of 32 integrate-and-fire neurons with spike-frequency adaptation and 2016 Hebbian bistable spike-driven stochastic synapses, endowed with a self-regulating plasticity mechanism, which avoids unnecessary synaptic changes. The synaptic matrix can be flexibly configured and provides both recurrent and external connectivity with address-event representation compliant devices. We demonstrate a marked improvement in the efficiency of the network in classifying correlated patterns, owing to the self-regulating mechanism.

  3. Analysis and compensation of the effects of analog VLSI arithmetic on the LMS algorithm.

    Science.gov (United States)

    Carvajal, Gonzalo; Figueroa, Miguel; Sbarbaro, Daniel; Valenzuela, Waldo

    2011-07-01

    Analog very large scale integration implementations of neural networks can compute using a fraction of the size and power required by their digital counterparts. However, intrinsic limitations of analog hardware, such as device mismatch, charge leakage, and noise, reduce the accuracy of analog arithmetic circuits, degrading the performance of large-scale adaptive systems. In this paper, we present a detailed mathematical analysis that relates different parameters of the hardware limitations to specific effects on the convergence properties of linear perceptrons trained with the least-mean-square (LMS) algorithm. Using this analysis, we derive design guidelines and introduce simple on-chip calibration techniques to improve the accuracy of analog neural networks with a small cost in die area and power dissipation. We validate our analysis by evaluating the performance of a mixed-signal complementary metal-oxide-semiconductor implementation of a 32-input perceptron trained with LMS.

  4. Analog Front End Circuit Design of CSNS Beam Loss Monitor System%CSNS 束流损失监测系统前端模拟电路设计

    Institute of Scientific and Technical Information of China (English)

    肖帅; 郭娴; 田建民; 曾磊; 徐韬光; 傅世年

    2013-01-01

    中国散裂中子源(CSNS)束流损失监测系统利用气体电离室来探测束流损失,电离室输出信号需在前端模拟电路中进行信号处理。本工作自主设计开发了束流损失测量系统前端模拟电路,采用跨导放大的方式实现了低重复频率、低占空比、弱电离室信号的电流-电压(I-V )变换测量。同时,电路还实现了对较大束流损失的快速响应,保障加速器设备的安全运行。联机测试结果表明,该电路满足系统要求。%The China Spallation Neutron Source (CSNS) beam loss monitor system uses gas ionization chamber to detect beam losses . The output signals from ionization chamber need to be processed in the analog front end circuit ,which has been designed and developed independently .The way of transimpedance amplifier was used to achieve current-voltage (I-V ) conversion measurement of signal with low repetition rate ,low duty cycle and low amplitude .The analog front end circuit also realized rapid response to the larger beam loss in order to protect the safe operation of the accelerator equipment . The testing results show that the analog front end circuit meets the requirements of beam loss monitor system .

  5. A design method in CMOS analog circuit optimization based on an adaptive genetic algorithm%基于自适应遗传算法的模拟电路的优化设计方法

    Institute of Scientific and Technical Information of China (English)

    于健海; 毛志刚; 陈伟平

    2011-01-01

    针对在模拟电路设计中参数调整复杂性带来的困难,提出了一种新的针对CMOS模拟运算放大器参数优化方法.其特点是把模拟电路设计知识与遗传算法相结合,通过对遗传算法的自适应改进帮助其解决多目标优化和收敛的问题,并根据不同的性能指标要求,在相同结构下优化出不同用途的运算放大器.实验结果证明,该方法在相同结构下与其他优化方法相比较可以精确而有效地优化出高增益、高带宽、低噪声的运算放大器.该方法适用于模拟电路优化设计:由于其基于Hspice仿真结果,更贴近于实际电路设计,具有实用价值.%A new method for optimizing the parameters of a CMOS operational amplifier based on an adaptive GA ( genetic algorithm) was presented in order to solve the difficulty caused by parameter optimization in analog circuit design. The main advantage of the method is that the problems of convergence and multiple objective optimization tasks can be solved through combining the useful features of manual analog circuit design, and adjusting the GA with the evolution process. Operational amplifiers for different uses can also be developed depending on various performance specifications. The simulation results show that this method can accurately achieve high DC-gain, high bandwidth, low noise, and low power operational amplification, and it efficiently compares with other optimizing methods having the same circuit structure. The method is suitable for CMOS analog circuit optimization. Because it is based on the simulation results of Hspice, it is much more similar to actual circuit design and therefore more useful.

  6. Alterations in the neural circuits from peripheral afferents to the spinal cord: possible implications for diabetic polyneuropathy in streptozotocin-induced type 1 diabetic rats

    Directory of Open Access Journals (Sweden)

    Zhen-Zhen eKou

    2014-01-01

    Full Text Available Diabetic polyneuropathy (DPN presents as a wide variety of sensorimotor symptoms and affects approximately 50% of diabetic patients. Changes in the neural circuits may occur in the early stages in diabetes and are implicated in the development of DPN. Therefore, we aimed to detect changes in the expression of isolectin B4 (IB4, the marker for nonpeptidergic unmyelinated fibers and their cell bodies and calcitonin gene-related peptide (CGRP, the marker for peptidergic fibers and their cell bodies in the dorsal root ganglion (DRG and spinal cord of streptozotocin (STZ-induced type 1 diabetic rats showing alterations in sensory and motor function. We also used cholera toxin B subunit (CTB to show the morphological changes of the myelinated fibers and motor neurons. STZ-induced diabetic rats exhibited hyperglycemia, decreased body weight gain, mechanical allodynia and impaired locomotor activity. In the DRG and spinal dorsal horn, IB4-labeled structures decreased, but both CGRP immunostaining and CTB labeling increased from day 14 to day 28 in diabetic rats. In spinal ventral horn, CTB labeling decreased in motor neurons in diabetic rats. Treatment with intrathecal injection of insulin at the early stages of DPN could alleviate mechanical allodynia and impaired locomotor activity in diabetic rats. The results suggest that the alterations of the neural circuits between spinal nerve and spinal cord via the DRG and ventral root might be involved in DPN.

  7. 虚拟仿真技术在构建模拟电路高效课堂中的应用%Application of Virtual Simulation Technology in Constructing Highly Efficient Classroom of Analog Circuit

    Institute of Scientific and Technical Information of China (English)

    王春娟

    2014-01-01

    高效课堂的最终目标是实现学生的“三维”发展。在模拟电路的理论教学中,学生参与课堂的主动性不高,将虚拟仿真技术应用在该课程教学中,通过使用仿真软件Multisim 9来讲授模拟电路的理论知识,能够发动学生,让学生主动参与实际的学习过程,自己获得所学的理论知识,实现高效课堂的目标。%The ultimate goal of highly efficient classroom is to realize "three-dimensional"development. In the theory teach⁃ing of analog circuit, it is not high of students' active participation in the class, the virtual simulation technology is applied in the teaching of this course, the knowledge of analog circuit can be taught by using simulation software Multisim 9, students can be mobilized、participate in the actual learning actively and obtain the theory of knowledge, the efficient classroom can be realized.

  8. 基于融合特权信息支持向量机的模拟电路故障诊断新方法%Novel analog circuit fault diagnosis method based on SVM of learning using privileged information

    Institute of Scientific and Technical Information of China (English)

    李涛柱; 李红波; 曾繁景; 李铁峰

    2012-01-01

    This paper proposed a novel fault diagnosis method based on SVM of learning using privileged information (LUPI-SVM),aiming at solving the problem of correctly identifying fault classes in analog circuit fault diagnosis. Firstly, the fault feature vectors were extracted by PCA (principal component analysis) feature extraction method. Then, after training the LUPI-SVM by faulty feature vectors, the LUPI-SVM model of the circuit fault diagnosis system was built. Finally, input the lest samples' feature vectors into the trained LUPI-SVM model to identify the different fault cases. The simulation results for analog and mixed-signal lest benchmark Sallen-Key filter circuits demonstrate that the proposed method improves classification ability. It correctly classifies not only the single hard fault classes with a highly average classification success rate more than 99% , but also the multiple fault classes. The method develops a new direction for the fault diagnosis of analog circuit.%针对模拟电路故障诊断复杂多样难于辨识的问题,提出了基于融合特权信息支持向量机的模拟电路故障诊断新方法.首先对采集的信号进行主成分分析( PCA)——特征提取;然后将训练集输入融合特权信息支持向量机进行训练获得故障诊断模型;最后将测试集输入训练好的支持向量机分类模型,实现对不同故障类型的识别.Sallen-Key滤波电路故障诊断仿真实验结果表明,该方法有效提高了分类的性能,不仅能够正确分类单故障而且能够有效分类多故障,其中单硬故障情况下平均故障诊断率达到了99%以上,为模拟电路故障诊断提供了新的途径.

  9. A Parallel Genetic Algorithm for Automated Electronic Circuit Design

    Science.gov (United States)

    Lohn, Jason D.; Colombano, Silvano P.; Haith, Gary L.; Stassinopoulos, Dimitris; Norvig, Peter (Technical Monitor)

    2000-01-01

    We describe a parallel genetic algorithm (GA) that automatically generates circuit designs using evolutionary search. A circuit-construction programming language is introduced and we show how evolution can generate practical analog circuit designs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. We present experimental results as applied to analog filter and amplifier design tasks.

  10. The Effect of Combining Analogy-Based Simulation and Laboratory Activities on Turkish Elementary School Students' Understanding of Simple Electric Circuits

    Science.gov (United States)

    Unlu, Zeynep Koyunlu; Dokme, Ibilge

    2011-01-01

    The purpose of this study was to investigate whether the combination of both analogy-based simulation and laboratory activities as a teaching tool was more effective than utilizing them separately in teaching the concepts of simple electricity. The quasi-experimental design that involved 66 seventh grade students from urban Turkish elementary…

  11. Brain micro-inflammation at specific vessels dysregulates organ-homeostasis via the activation of a new neural circuit.

    Science.gov (United States)

    Arima, Yasunobu; Ohki, Takuto; Nishikawa, Naoki; Higuchi, Kotaro; Ota, Mitsutoshi; Tanaka, Yuki; Nio-Kobayashi, Junko; Elfeky, Mohamed; Sakai, Ryota; Mori, Yuki; Kawamoto, Tadafumi; Stofkova, Andrea; Sakashita, Yukihiro; Morimoto, Yuji; Kuwatani, Masaki; Iwanaga, Toshihiko; Yoshioka, Yoshichika; Sakamoto, Naoya; Yoshimura, Akihiko; Takiguchi, Mitsuyoshi; Sakoda, Saburo; Prinz, Marco; Kamimura, Daisuke; Murakami, Masaaki

    2017-08-15

    Impact of stress on diseases including gastrointestinal failure is well-known, but molecular mechanism is not understood. Here we show underlying molecular mechanism using EAE mice. Under stress conditions, EAE caused severe gastrointestinal failure with high-mortality. Mechanistically, autoreactive-pathogenic CD4+ T cells accumulated at specific vessels of boundary area of third-ventricle, thalamus, and dentate-gyrus to establish brain micro-inflammation via stress-gateway reflex. Importantly, induction of brain micro-inflammation at specific vessels by cytokine injection was sufficient to establish fatal gastrointestinal failure. Resulting micro-inflammation activated new neural pathway including neurons in paraventricular-nucleus, dorsomedial-nucleus-of-hypothalamus, and also vagal neurons to cause fatal gastrointestinal failure. Suppression of the brain micro-inflammation or blockage of these neural pathways inhibited the gastrointestinal failure. These results demonstrate direct link between brain micro-inflammation and fatal gastrointestinal disease via establishment of a new neural pathway under stress. They further suggest that brain micro-inflammation around specific vessels could be switch to activate new neural pathway(s) to regulate organ homeostasis.

  12. A Novel STACK Generation Technique for MOS Analog Cell Circuit Layout%一个新的MOS模拟单元电路版图的STACK生成方法

    Institute of Scientific and Technical Information of China (English)

    李明原; 曾璇; 唐璞山; 周电

    2001-01-01

    This paper proposes a new technique to automatically generateSTACK layout for MOS analog cell circuits. The circuit net-list is first mapped into a diffusion graph. Based on the diffusion graph, circuit partition, pattern recognition and symmetry searching are carried out to generate sub-graphs, each of which can be implemented by a STACK. The proposed symmetry searching algorithm can find the maximal symmetry structures in a none fully symmetric circuit. To guarantee the generation of a Eularian trail, the Atallah Eularian trail generation algorithm is improved by employing the dummy adding technique. In order to evaluate the performance of a generated STACK, a distributed parasitic capacitance model is applied to calculate the STACK node parasitic capacitance, as well as the calculation of STACK area and shape.%提出了一种新的MOS模拟单元电路的STACK版图自动生成方法.该方法将电路网表映射为扩散图,基于扩散图进行电路划分、模板匹配和对称查找.提出的对称查找算法适用于非全对称电路的最大匹配对称结构查找.文中改进了Atallah欧拉路径生成算法,通过增加哑元条保证欧拉路径的生成.对生成的STACK,采用分布式寄生电容模型计算各个节点的寄生电容,并计算STACK的面积和形状,以确保其能够满足设计要求.

  13. 模拟电路符号法可测度分析的改进%Improvement of Analog Circuit Testability Analysis Based on Symbolic Method

    Institute of Scientific and Technical Information of China (English)

    郑致刚; 胡云安

    2012-01-01

    Testability is an important concept in circuit test and fault diagnosis field, and it is a quantity measure of test node selection. Symbolic method is used to evaluate circuit testability. To resolve the problem that the symbolic method can't apply to the diagnosis equation whose coefficient of the item with highest power in the denominator is not equal to 1, a new method is proposed to transform the original diagnosis equation into a new one, which is then used to evaluate the circuit testability. The example circuit analysis shows that the improved method can do with any form fault diagnosis equation, it eliminates error introduction during calculation and has the feature of simple and accurate.%可测度是电路测试和故障诊断中一个重要概念,是测试节点选择的一个量化指标;使用符号分析的方法进行电路可测度计算;为了解决当诊断方程分母多项式的最高项系数不等于1时,符号法不能应用的问题,提出了一种原始诊断方程变换的新方法,利用符号法计算变换后诊断方程的可测度;电路实例分析表明,改进后的方法可以处理任意形式的电路诊断方程,计算中避免了误差的引入,具有计算简单、结果准确的特点.

  14. Detecting the single line to ground short circuit fault in the submarine’s power system using the artificial neural network

    Directory of Open Access Journals (Sweden)

    Behniafar Ali

    2013-01-01

    Full Text Available The electric marine instruments are newly inserted in the trade and industry, for which the existence of an equipped and reliable power system is necessitated. One of the features of such a power system is that it cannot have an earth system causing the protection relays not to be able to detect the single line to ground short circuit fault. While on the other hand, the occurrence of another similar fault at the same time can lead to the double line fault and thereby the tripping of relays and shortening of vital loads. This in turn endangers the personals' security and causes the loss of military plans. From the above considerations, it is inferred that detecting the single line to ground fault in the marine instruments is of a special importance. In this way, this paper intends to detect the single line to ground fault in the power systems of the marine instruments using the wavelet transform and Multi-Layer Perceptron (MLP neural network. In the numerical analysis, several different types of short circuit faults are simulated on several marine power systems and the proposed approach is applied to detect the single line to ground fault. The results are of a high quality and preciseness and perfectly demonstrate the effectiveness of the proposed approach.

  15. Visual motion imagery neurofeedback based on the hMT+/V5 complex: evidence for a feedback-specific neural circuit involving neocortical and cerebellar regions

    Science.gov (United States)

    Banca, Paula; Sousa, Teresa; Catarina Duarte, Isabel; Castelo-Branco, Miguel

    2015-12-01

    Objective. Current approaches in neurofeedback/brain-computer interface research often focus on identifying, on a subject-by-subject basis, the neural regions that are best suited for self-driven modulation. It is known that the hMT+/V5 complex, an early visual cortical region, is recruited during explicit and implicit motion imagery, in addition to real motion perception. This study tests the feasibility of training healthy volunteers to regulate the level of activation in their hMT+/V5 complex using real-time fMRI neurofeedback and visual motion imagery strategies. Approach. We functionally localized the hMT+/V5 complex to further use as a target region for neurofeedback. An uniform strategy based on motion imagery was used to guide subjects to neuromodulate hMT+/V5. Main results. We found that 15/20 participants achieved successful neurofeedback. This modulation led to the recruitment of a specific network as further assessed by psychophysiological interaction analysis. This specific circuit, including hMT+/V5, putative V6 and medial cerebellum was activated for successful neurofeedback runs. The putamen and anterior insula were recruited for both successful and non-successful runs. Significance. Our findings indicate that hMT+/V5 is a region that can be modulated by focused imagery and that a specific cortico-cerebellar circuit is recruited during visual motion imagery leading to successful neurofeedback. These findings contribute to the debate on the relative potential of extrinsic (sensory) versus intrinsic (default-mode) brain regions in the clinical application of neurofeedback paradigms. This novel circuit might be a good target for future neurofeedback approaches that aim, for example, the training of focused attention in disorders such as ADHD.

  16. Analog VLSI Models of Range-Tuned Neurons in the Bat Echolocation System

    Directory of Open Access Journals (Sweden)

    Horiuchi Timothy

    2003-01-01

    Full Text Available Bat echolocation is a fascinating topic of research for both neuroscientists and engineers, due to the complex and extremely time-constrained nature of the problem and its potential for application to engineered systems. In the bat's brainstem and midbrain exist neural circuits that are sensitive to the specific difference in time between the outgoing sonar vocalization and the returning echo. While some of the details of the neural mechanisms are known to be species-specific, a basic model of reafference-triggered, postinhibitory rebound timing is reasonably well supported by available data. We have designed low-power, analog VLSI circuits to mimic this mechanism and have demonstrated range-dependent outputs for use in a real-time sonar system. These circuits are being used to implement range-dependent vocalization amplitude, vocalization rate, and closest target isolation.

  17. A comparative examination of neural circuit and brain patterning between the lamprey and amphioxus reveals the evolutionary origin of the vertebrate visual center.

    Science.gov (United States)

    Suzuki, Daichi G; Murakami, Yasunori; Escriva, Hector; Wada, Hiroshi

    2015-02-01

    Vertebrates are equipped with so-called camera eyes, which provide them with image-forming vision. Vertebrate image-forming vision evolved independently from that of other animals and is regarded as a key innovation for enhancing predatory ability and ecological success. Evolutionary changes in the neural circuits, particularly the visual center, were central for the acquisition of image-forming vision. However, the evolutionary steps, from protochordates to jaw-less primitive vertebrates and then to jawed vertebrates, remain largely unknown. To bridge this gap, we present the detailed development of retinofugal projections in the lamprey, the neuroarchitecture in amphioxus, and the brain patterning in both animals. Both the lateral eye in larval lamprey and the frontal eye in amphioxus project to a light-detecting visual center in the caudal prosencephalic region marked by Pax6, which possibly represents the ancestral state of the chordate visual system. Our results indicate that the visual system of the larval lamprey represents an evolutionarily primitive state, forming a link from protochordates to vertebrates and providing a new perspective of brain evolution based on developmental mechanisms and neural functions. © 2014 Wiley Periodicals, Inc.

  18. Curcumin Alters Neural Plasticity and Viability of Intact Hippocampal Circuits and Attenuates Behavioral Despair and COX-2 Expression in Chronically Stressed Rats

    Science.gov (United States)

    Choi, Ga-Young; Kim, Hyun-Bum; Hwang, Eun-Sang; Lee, Seok; Kim, Min-Ji; Choi, Ji-Young; Lee, Sung-Ok

    2017-01-01

    Curcumin is a major diarylheptanoid component of Curcuma longa with traditional usage for anxiety and depression. It has been known for the anti-inflammatory, antistress, and neurotropic effects. Here we examined curcumin effect in neural plasticity and cell viability. 60-channel multielectrode array was applied on organotypic hippocampal slice cultures (OHSCs) to monitor the effect of 10 μM curcumin in long-term depression (LTD) through low-frequency stimulation (LFS) to the Schaffer collaterals and commissural pathways. Cell viability was assayed by propidium iodide uptake test in OHSCs. In addition, the influence of oral curcumin administration on rat behavior was assessed with the forced swim test (FST). Finally, protein expression levels of brain-derived neurotrophic factor (BDNF) and cyclooxygenase-2 (COX-2) were measured by Western blot in chronically stressed rats. Our results demonstrated that 10 μM curcumin attenuated LTD and reduced cell death. It also recovered the behavior immobility of FST, rescued the attenuated BDNF expression, and inhibited the enhancement of COX-2 expression in stressed animals. These findings indicate that curcumin can enhance postsynaptic electrical reactivity and cell viability in intact neural circuits with antidepressant-like effects, possibly through the upregulation of BDNF and reduction of inflammatory factors in the brain. PMID:28167853

  19. Macro-micro imaging of cardiac-neural circuits in co-cultures from normal and diseased hearts.

    Science.gov (United States)

    Bub, Gil; Burton, Rebecca-Ann B

    2015-07-15

    The autonomic nervous system plays an important role in the modulation of normal cardiac rhythm, but is also implicated in modulating the heart's susceptibility to re-entrant ventricular and atrial arrhythmias. The mechanisms by which the autonomic nervous system is pro-arrhythmic or anti-arrhythmic is multifaceted and varies for different types of arrhythmia and their cardiac substrates. Despite decades of research in this area, fundamental questions related to how neuron density and spatial organization modulate cardiac wave dynamics remain unanswered. These questions may be ill-posed in intact tissues where the activity of individual cells is often experimentally inaccessible. Development of simplified biological models that would allow us to better understand the influence of neural activation on cardiac activity can be beneficial. This Symposium Review summarizes the development of in vitro cardiomyocyte cell culture models of re-entrant activity, as well as challenges associated with extending these models to include the effects of neural activation.

  20. High level cognitive information processing in neural networks

    Science.gov (United States)

    Barnden, John A.; Fields, Christopher A.

    1992-01-01

    Two related research efforts were addressed: (1) high-level connectionist cognitive modeling; and (2) local neural circuit modeling. The goals of the first effort were to develop connectionist models of high-level cognitive processes such as problem solving or natural language understanding, and to understand the computational requirements of such models. The goals of the second effort were to develop biologically-realistic model of local neural circuits, and to understand the computational behavior of such models. In keeping with the nature of NASA's Innovative Research Program, all the work conducted under the grant was highly innovative. For instance, the following ideas, all summarized, are contributions to the study of connectionist/neural networks: (1) the temporal-winner-take-all, relative-position encoding, and pattern-similarity association techniques; (2) the importation of logical combinators into connection; (3) the use of analogy-based reasoning as a bridge across the gap between the traditional symbolic paradigm and the connectionist paradigm; and (4) the application of connectionism to the domain of belief representation/reasoning. The work on local neural circuit modeling also departs significantly from the work of related researchers. In particular, its concentration on low-level neural phenomena that could support high-level cognitive processing is unusual within the area of biological local circuit modeling, and also serves to expand the horizons of the artificial neural net field.

  1. Analog Circuit Fault Classification Based on All Samples Support Vector Data Description%全样本支持矢量数据描述模拟电路故障分类

    Institute of Scientific and Technical Information of China (English)

    李传亮; 王友仁; 罗慧; 崔江

    2012-01-01

    传统支持矢量数据描述(Support Vector Domain Description,SVDD)分类规则完全忽略非支持矢量所包含的样本信息,为此本文提出了一种全样本SVDD分类方法,并应用到模拟电路故障诊断中。该方法以贝叶斯理论和分类器模糊融合思想为基础,利用核密度估计得到的类条件概率密度和先验概率的乘积对SVDD相对距离进行加权。实验结果表明,与SVM扩展的多分类器相比,本文方法能够有效提高模拟电路故障诊断的准确率,且全样本SVDD分类模型对参数变化具有较强的稳健性。%The information included in the non-support vectors is completely ignored for the classification rules of the classical support vector domain description(SVDD),so an all samples SVDD method is proposed in this paper,and it is applied to analog circuit fault diagnosis.The new method is based on Bayes theory and classifier fuzzy fusion strategy.The relative distances of this classifier are weighted by the product of prior probability value and conditional probability value,which are calculated by kernel density estimation.The simulation results show that,compared with the multi-class SVM classifiers,the introduced method improves the fault diagnosis accuracy of analog circuit.Moreover,the all samples SVDD classifier is robust against the changes of classifier parameter.

  2. Modeling and Optimization of Microwave Circuits Based on Neural Networks%基于神经网络的微波电路建模与优化

    Institute of Scientific and Technical Information of China (English)

    刘荧; 林嘉宇; 毛钧杰

    2000-01-01

    本文讨论用神经网络对微波电路进行建模、优化。借助电磁场理论计算或基于实际测量,可得到微波电路的输入、输出样本数据,从而可训练神经网络,在兼顾它的推广性能的基础上,对微波电路建模。进一步,通过优化神经网络对应参数,可优化微波电路。文章用RBF(RadialBasis Function)神经网络对微带变阻器建模、优化,以此为例,进行了较为详细的阐述。%[1] A.H. Zaabab. et al. A neural network model ing approach to circuit optimization and statis tical design, IEEE Trans. MTT , 1995; 43 (6): 1349~1358. [2] P.M. Watson,K. C. Gupta. EM-ANN models for microstrip vias and interconnects in dataset circuits. IEEE Trans. MTT, 1996; 44(12): 2495~2503. [3] P.M. Watson,K. C. Gupta. Design and opti mization of CPW circuits using EM-ANN models for CPW components. IEEE Trans. MTT, 1997 ; 45(12): 2515~2535. [4] D.C. Montgomery. Design and Analysis of Experiments. New York :Wiley, 1991. [5] Acosta F. RBF and related models: an overview. Signal Processing, 1995; 45:37~ 58. [6] D.R. Huh,B. G. Horne. Progress in super- vised neural networks :what′.s new since lipp mann?. IEEE SP Magazine, 1993 ;10(1 ):8~ 39. [7] J. Park,I. Sandberg. Approximation and RBF networks. Neural Comput, 1993; 5:305~316. [8] S. Chen,et al. Orthogonal least squares learn ing algorithm for radial basis function net works. IEEE Trans. Neural Networks, 1991; 2(2) :302~309. [9] 陈尚勤,李晓峰.快速自适应信息处理.北京:人民邮电出版社,1993. [10] I. Cha, S. A. Kassam. Channel equalization using adaptive complex radial basis function networks. IEEE J. SAC, 1995;13(1):122 ~131. [11] E.S. Chng, et al. Orthogonal least-square learning algorithm with local adaptation pro cess for the radial basis function networks. IEEE SP Letters, 1996;3(8):253~255. [12] M.J. Orr. Local Smoothing of RBF Net works. http://www. cns. ed. ac. uk/people/ mark

  3. Design and integration of a high accuracy multichannel analog CMOS peak detect and hold circuit for APD-based PET imaging.

    Science.gov (United States)

    Fang, Xiaochao; Brasse, David; Hu-Guo, Christine; Hu, Yann

    2012-04-01

    This paper presents the design of a high accuracy multichannel peak detect and hold (PDH) circuit. This PDH measures the energy of an event and is one part of a readout chain for avalanche photo diodes (APD)-based positron emission tomography (PET) imaging. The circuit is designed in a 0.35μm CMOS process. The proposed PDH is dedicated to ultra low amplitude, large amplitude range from several tens millivolts to 1.1 V, and fast peaking time (190 ns) semi-Gaussian pulses. The two-phase technique has been used to cancel the major error source of the classical CMOS PDH: offset. A two-gain OTA is applied to minimize the DC error. A peak error less 1% for a small input signal (amplitude is between 40 mV and 300 mV) and a peak error less than 0.2% for a large input signal (amplitude is between 300 mV and 1.1 V) have been obtained from test. The area of a PDH is equal to about 200 μm × 40 μm. In our PDH system, the drop rate is negligible.

  4. 基于0.18μm CMOS工艺的电流型模拟运算电路%Current mode analog operational circuit based on 0.18 μm CMOS process

    Institute of Scientific and Technical Information of China (English)

    卢锦川; 詹小英

    2016-01-01

    A new CMOS current mode multifunction analog operational circuit with low voltage and low power consumption is proposed,which can run multiplier,squarer,divider and different types of controllable gain amplifiers. The design is based on translinear principle,in which MOSFET is adopted and runs in the subthreshold region. The proposed circuit is composed of six inter⁃matching transistors to form two overlapping tranlinear loops. The circuit is based on 0.18 μm CMOS process,and sup⁃plied with ± 0.6 V low voltage DC source. The circuit was verified by simulation of Tanner TSpice software. The simulation re⁃sults show that when the circuit is configured as an amplifier,its frequency is about 1.5 MHz at -3 dB,linear error is 0.63%, total harmonic distortion is 0.08%,and maximum power consumption is 1.16 μW.%提出了一种新的低电压低功耗CMOS电流型模拟多功能运算电路,该电路能够运行乘法器、求平方器、除法器以及不同类型的可控增益放大器。该设计基于跨导线性原则,使用场效晶体管(MOSFET)且运行在亚阈值区,其由6个相匹配的晶体管组成,并形成两个重叠的跨导线性回路,电路设计采用0.18μm CMOS技术,使用±0.6 V低压直流电源供电。通过Tanner TSpice软件进行了仿真验证,仿真结果表明,当将其配置为一个放大器时,-3 dB频率约为1.5 MHz,线性误差为0.63%,总谐波失真为0.08%,最大功耗为1.16μW。

  5. Lunar Analog

    Science.gov (United States)

    Cromwell, Ronita L.

    2009-01-01

    In this viewgraph presentation, a ground-based lunar analog is developed for the return of manned space flight to the Moon. The contents include: 1) Digital Astronaut; 2) Bed Design; 3) Lunar Analog Feasibility Study; 4) Preliminary Data; 5) Pre-pilot Study; 6) Selection of Stockings; 7) Lunar Analog Pilot Study; 8) Bed Design for Lunar Analog Pilot.

  6. Circuits and filters handbook

    CERN Document Server

    Chen, Wai-Kai

    2003-01-01

    A bestseller in its first edition, The Circuits and Filters Handbook has been thoroughly updated to provide the most current, most comprehensive information available in both the classical and emerging fields of circuits and filters, both analog and digital. This edition contains 29 new chapters, with significant additions in the areas of computer-aided design, circuit simulation, VLSI circuits, design automation, and active and digital filters. It will undoubtedly take its place as the engineer's first choice in looking for solutions to problems encountered in the design, analysis, and behavi

  7. 基于选择性SVM集成的模拟电路故障诊断方法%A Method of Analog Circuit Fault Diagnosis Based on Selective SVM Ensemble

    Institute of Scientific and Technical Information of China (English)

    吴杰长; 刘海松; 陈国钧

    2011-01-01

    为克服支持向量机在故障诊断应用中存在的不足,设计了基于聚类分析的选择性支持向量机集成学习算法,并应用于模拟电路故障诊断.该方法采用K-means聚类算法去除相似冗余个体,提高剩余个体学习机的差异性,增强了支持向量机集成模型的泛化能力.以ITC' 97标准电路中的Leap-Frog滤波电路为诊断实例进行了仿真实验.%A method of analog circuit fault diagnosis based on selective SVM ensemble is pres-ented in this paper. K - means clustering algorithm is used to improve the diversity of individuals in SVM ensemble, he method overcomes disadvantages of single SVM and greatly improves the generation ability. Simulation experiments on a Leap -Frog filter circuit are carried out.

  8. Study of G-S/D underlap for enhanced analog performance and RF/circuit analysis of UTB InAs-OI-Si MOSFET using NQS small signal model

    Science.gov (United States)

    Maity, Subir Kumar; Pandit, Soumya

    2017-01-01

    InGaAs (and its variant) appears to be a promising channel material for high-performance, low-power scaled CMOS applications due to its excellent carrier transport properties. However, MOS transistors made of this suffer from poor electrostatic integrity. In this work, we consider an underlap ultra thin body (UTB) InAs-on-Insulator n-channel MOS transistor, and study the effect of varying the gate-source/drain (G-S/D) underlap length on the analog performance of the device with the help of technology computer-aided design (TCAD) simulation, calibrated with Schrodinger-Poisson solver and experimental results. The underlap technique improves the gate electrostatic integrity which in turn improves the analog performance. We develop a non-quasi-static (NQS) small signal equivalent circuit model of the device which is used for study of the RF performance. With increase of the underlap length, the unity gain cut-off frequency degrades and the maximum oscillation frequency improves beyond a certain value of the underlap length. We further study the gain-frequency response of a common source amplifier using the NQS model, through SPICE simulation and observe that the voltage gain and the gain bandwidth improves.

  9. Functionality and Robustness of Injured Connectomic Dynamics in C. elegans: Linking Behavioral Deficits to Neural Circuit Damage

    Science.gov (United States)

    Kunert, James M.; Maia, Pedro D.; Kutz, J. Nathan

    2017-01-01

    Using a model for the dynamics of the full somatic nervous system of the nematode C. elegans, we address how biological network architectures and their functionality are degraded in the presence of focal axonal swellings (FAS) arising from neurodegenerative disease and/or traumatic brain injury. Using biophysically measured FAS distributions and swelling sizes, we are able to simulate the effects of injuries on the neural dynamics of C. elegans, showing how damaging the network degrades its low-dimensional dynamical responses. We visualize these injured neural dynamics by mapping them onto the worm’s low-dimensional postures, i.e. eigenworm modes. We show that a diversity of functional deficits arise from the same level of injury on a connectomic network. Functional deficits are quantified using a statistical shape analysis, a procrustes analysis, for deformations of the limit cycles that characterize key behaviors such as forward crawling. This procrustes metric carries information on the functional outcome of injuries in the model. Furthermore, we apply classification trees to relate injury structure to the behavioral outcome. This makes testable predictions for the structure of an injury given a defined functional deficit. More critically, this study demonstrates the potential role of computational simulation studies in understanding how neuronal networks process biological signals, and how this processing is impacted by network injury. PMID:28056097

  10. Forgetting the best when predicting the worst: Preliminary observations on neural circuit function in adolescent social anxiety

    Directory of Open Access Journals (Sweden)

    Johanna M. Jarcho

    2015-06-01

    Full Text Available Social anxiety disorder typically begins in adolescence, a sensitive period for brain development, when increased complexity and salience of peer relationships requires novel forms of social learning. Disordered social learning in adolescence may explain how brain dysfunction promotes social anxiety. Socially anxious adolescents (n = 15 and adults (n = 19 and non-anxious adolescents (n = 24 and adults (n = 32 predicted, then received, social feedback from high and low-value peers while undergoing functional magnetic resonance imaging (fMRI. A surprise recall task assessed memory biases for feedback. Neural correlates of social evaluation prediction errors (PEs were assessed by comparing engagement to expected and unexpected positive and negative feedback. For socially anxious adolescents, but not adults or healthy participants of either age group, PEs elicited heightened striatal activity and negative fronto-striatal functional connectivity. This occurred selectively to unexpected positive feedback from high-value peers and corresponded with impaired memory for social feedback. While impaired memory also occurred in socially-anxious adults, this impairment was unrelated to brain-based PE activity. Thus, social anxiety in adolescence may relate to altered neural correlates of PEs that contribute to impaired learning about social feedback. Small samples necessitate replication. Nevertheless, results suggest that the relationship between learning and fronto-striatal function may attenuate as development progresses.

  11. Analog multivariate counting analyzers

    CERN Document Server

    Nikitin, A V; Armstrong, T P

    2003-01-01

    Characterizing rates of occurrence of various features of a signal is of great importance in numerous types of physical measurements. Such signal features can be defined as certain discrete coincidence events, e.g. crossings of a signal with a given threshold, or occurrence of extrema of a certain amplitude. We describe measuring rates of such events by means of analog multivariate counting analyzers. Given a continuous scalar or multicomponent (vector) input signal, an analog counting analyzer outputs a continuous signal with the instantaneous magnitude equal to the rate of occurrence of certain coincidence events. The analog nature of the proposed analyzers allows us to reformulate many problems of the traditional counting measurements, and cast them in a form which is readily addressed by methods of differential calculus rather than by algebraic or logical means of digital signal processing. Analog counting analyzers can be easily implemented in discrete or integrated electronic circuits, do not suffer fro...

  12. Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System

    Science.gov (United States)

    Milde, Moritz B.; Blum, Hermann; Dietmüller, Alexander; Sumislawska, Dora; Conradt, Jörg; Indiveri, Giacomo; Sandamirskaya, Yulia

    2017-01-01

    Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement neural-network based robotic controllers in an energy-efficient way with low latency, but requires solving the problem of device variability, characteristic for analog electronic circuits. In this work, we interfaced a mixed-signal analog-digital neuromorphic processor ROLLS to a neuromorphic dynamic vision sensor (DVS) mounted on a robotic vehicle and developed an autonomous neuromorphic agent that is able to perform neurally inspired obstacle-avoidance and target acquisition. We developed a neural network architecture that can cope with device variability and verified its robustness in different environmental situations, e.g., moving obstacles, moving target, clutter, and poor light conditions. We demonstrate how this network, combined with the properties of the DVS, allows the robot to avoid obstacles using a simple biologically-inspired dynamics. We also show how a Dynamic Neural Field for target acquisition can be implemented in spiking neuromorphic hardware. This work demonstrates an implementation of working obstacle avoidance and target acquisition using mixed signal analog/digital neuromorphic hardware. PMID:28747883

  13. A dynamic CMOS multiplier for analog VLSI based on exponential pulse-decay modulation

    Science.gov (United States)

    Massengill, Lloyd W.

    1991-03-01

    A clocked, charge-based, CMOS modulator circuit is presented. The circuit, which performs a semilinear multiplication function, has applications in arrayed analog VLSI architectures such as parallel filters and neural network systems. The design presented is simple in structure, uses no operational amplifiers for the actual multiplication function, and uses no power in the static mode. Two-quadrant weighting of an input signal is accomplished by control of the magnitude and decay time of an exponential current pulse, resulting in the delivery of charge packets to a shared capacitive summing bus. The cell is modular in structure and can be fabricated in a standard CMOS process. An analytical derivation of the operation of the circuit, SPICE simulations, and MOSIS fabrication results are presented. The simulation studies indicate that the circuit is inherently tolerant to temperature effects, absolute device sizing errors, and clock-feedthrough transients.

  14. Automated Integrated Analog Filter Design Issues

    OpenAIRE

    2015-01-01

    An analysis of modern automated integrated analog circuits design methods and their use in integrated filter design is done. Current modern analog circuits automated tools are based on optimization algorithms and/or new circuit generation methods. Most automated integrated filter design methods are only suited to gmC and switched current filter topologies. Here, an algorithm for an active RC integrated filter design is proposed, that can be used in automated filter designs. The algorithm is t...

  15. Neural circuit architecture defects in a Drosophila model of Fragile X syndrome are alleviated by minocycline treatment and genetic removal of matrix metalloproteinase

    Directory of Open Access Journals (Sweden)

    Saul S. Siller

    2011-09-01

    Fragile X syndrome (FXS, caused by loss of the fragile X mental retardation 1 (FMR1 product (FMRP, is the most common cause of inherited intellectual disability and autism spectrum disorders. FXS patients suffer multiple behavioral symptoms, including hyperactivity, disrupted circadian cycles, and learning and memory deficits. Recently, a study in the mouse FXS model showed that the tetracycline derivative minocycline effectively remediates the disease state via a proposed matrix metalloproteinase (MMP inhibition mechanism. Here, we use the well-characterized Drosophila FXS model to assess the effects of minocycline treatment on multiple neural circuit morphological defects and to investigate the MMP hypothesis. We first treat Drosophila Fmr1 (dfmr1 null animals with minocycline to assay the effects on mutant synaptic architecture in three disparate locations: the neuromuscular junction (NMJ, clock neurons in the circadian activity circuit and Kenyon cells in the mushroom body learning and memory center. We find that minocycline effectively restores normal synaptic structure in all three circuits, promising therapeutic potential for FXS treatment. We next tested the MMP hypothesis by assaying the effects of overexpressing the sole Drosophila tissue inhibitor of MMP (TIMP in dfmr1 null mutants. We find that TIMP overexpression effectively prevents defects in the NMJ synaptic architecture in dfmr1 mutants. Moreover, co-removal of dfmr1 similarly rescues TIMP overexpression phenotypes, including cellular tracheal defects and lethality. To further test the MMP hypothesis, we generated dfmr1;mmp1 double null mutants. Null mmp1 mutants are 100% lethal and display cellular tracheal defects, but co-removal of dfmr1 allows adult viability and prevents tracheal defects. Conversely, co-removal of mmp1 ameliorates the NMJ synaptic architecture defects in dfmr1 null mutants, despite the lack of detectable difference in MMP1 expression or gelatinase activity between the single

  16. An optogenetics- and imaging-assisted simultaneous multiple patch-clamp recording system for decoding complex neural circuits.

    Science.gov (United States)

    Wang, Guangfu; Wyskiel, Daniel R; Yang, Weiguo; Wang, Yiqing; Milbern, Lana C; Lalanne, Txomin; Jiang, Xiaolong; Shen, Ying; Sun, Qian-Quan; Zhu, J Julius

    2015-03-01

    Deciphering neuronal circuitry is central to understanding brain function and dysfunction, yet it remains a daunting task. To facilitate the dissection of neuronal circuits, a process requiring functional analysis of synaptic connections and morphological identification of interconnected neurons, we present here a method for stable simultaneous octuple patch-clamp recordings. This method allows physiological analysis of synaptic interconnections among 4-8 simultaneously recorded neurons and/or 10-30 sequentially recorded neurons, and it allows anatomical identification of >85% of recorded interneurons and >99% of recorded principal neurons. We describe how to apply the method to rodent tissue slices; however, it can be used on other model organisms. We also describe the latest refinements and optimizations of mechanics, electronics, optics and software programs that are central to the realization of a combined single- and two-photon microscopy-based, optogenetics- and imaging-assisted, stable, simultaneous quadruple-viguple patch-clamp recording system. Setting up the system, from the beginning of instrument assembly and software installation to full operation, can be completed in 3-4 d.

  17. The Physics of Decision Making:. Stochastic Differential Equations as Models for Neural Dynamics and Evidence Accumulation in Cortical Circuits

    Science.gov (United States)

    Holmes, Philip; Eckhoff, Philip; Wong-Lin, K. F.; Bogacz, Rafal; Zacksenhouse, Miriam; Cohen, Jonathan D.

    2010-03-01

    We describe how drift-diffusion (DD) processes - systems familiar in physics - can be used to model evidence accumulation and decision-making in two-alternative, forced choice tasks. We sketch the derivation of these stochastic differential equations from biophysically-detailed models of spiking neurons. DD processes are also continuum limits of the sequential probability ratio test and are therefore optimal in the sense that they deliver decisions of specified accuracy in the shortest possible time. This leaves open the critical balance of accuracy and speed. Using the DD model, we derive a speed-accuracy tradeoff that optimizes reward rate for a simple perceptual decision task, compare human performance with this benchmark, and discuss possible reasons for prevalent sub-optimality, focussing on the question of uncertain estimates of key parameters. We present an alternative theory of robust decisions that allows for uncertainty, and show that its predictions provide better fits to experimental data than a more prevalent account that emphasises a commitment to accuracy. The article illustrates how mathematical models can illuminate the neural basis of cognitive processes.

  18. 超高频化合物数模混合电路研究报告%The Study on Ultra-high Frequency Compound Semiconductor Mixed Analog-digital Circuits

    Institute of Scientific and Technical Information of China (English)

    郝跃; 张玉明; 吕红亮; 武锦; 于伟华

    2016-01-01

    heat transfer mechanism and electromagnetic coupling mechanisms are the key points to break through the bottleneck of ultra-high frequency, high-power circuits and module.In this project, particular emphasis is put on signal integrity analysis of UHF hybrid circuits and thermal effects of electromagnetic field analysis methods. The signal integrity problems and system electromagnetic compatibility problem of compound semiconductor ultra-high speed integrated circuits are investigated. An optimal design theory and methods are developed and a circuit, electromagnetic field, thermal field integrated design platform is established A "top- down" design flow for ultra- high-speed digital-analog hybrid circuit is proposed and the clock distribution circuit critical path is extracted. Based on the physical analysis and implementation methods, high level ultra-high speed integrated circuits are designed and implemented The mechanism of irradiation damage and radiation tolerance of compound semiconductor devices and circuits are studied. Novel device models are established for design and analysis of circuits which will be used in extreme environment. These results indicate the achievement of that high-speed compound semiconductor integrated in this project, which will provide an important theoretical guidance and technical support to the further development of related fields.

  19. Post-training, intrahippocampal HDAC inhibition differentially impacts neural circuits underlying spatial memory in adult and aged mice.

    Science.gov (United States)

    Dagnas, Malorie; Micheau, Jacques; Decorte, Laurence; Beracochea, Daniel; Mons, Nicole

    2015-07-01

    Converging evidence indicates that pharmacologically elevating histone acetylation using post-training, systemic or intrahippocampal, administration of histone deacetylase inhibitor (HDACi) can enhance memory consolidation processes in young rodents but it is not yet clear, whether such treatment is sufficient to prevent memory impairments associated with aging. To address this question, we used a 1-day massed spatial learning task in the water maze to investigate the effects of immediate post-training injection of the HDACi trichostatin A (TSA) into the dorsal hippocampus on long-term memory consolidation in 3-4 and 18-20 month-old mice. We show that TSA improved the 24 h-memory retention for the hidden platform location in young-adults, but failed to rescue memory impairments in older mice. The results further indicate that Young-TSA mice sacrificed 1 h after training had a robust increase in histone H4 acetylation in the dorsal hippocampal CA1 region (dCA1) and the dorsomedial part of the striatum (DMS), a structure important for spatial information processing. Importantly, TSA infusion in aged mice completely rescued altered H4 acetylation in the dCA1 but failed to alleviate age-associated decreased H4 acetylation in the DMS. Moreover, intrahippocampal TSA infusion produced concomitant decreases (in adults) or increases (in older mice) of acetylated histone levels in the ventral hippocampus (vCA1 and vCA3) and the lateral amygdala, two structures critically involved in stress and emotional responses. These data suggest that the failure of post-training, intrahippocampal TSA injection to reverse age-associated memory impairments may be related to an inability to recruit appropriate circuit-specific epigenetic patterns during early consolidation processes.

  20. Identification of neural circuits involved in female genital responses in the rat: A dual virus and anterograde tracing study

    Science.gov (United States)

    Marson, L.; Murphy, A Z

    2010-01-01

    The spinal and peripheral innervation of the clitoris and vagina are fairly well understood. However, little is known regarding supraspinal control of these pelvic structures. The multisynaptic tracer pseudorabies virus (PRV) was used to map the brain neurons that innervate the clitoris and vagina. In order to delineate forebrain input onto PRV labeled cells, the anterograde tracer biotinylated dextran amine (BDA) was injected into the medial preoptic nucleus (MPO), ventromedial nucleus of the hypothalamus (VMN) or the midbrain periaqueductal gray (PAG) 10 days prior to viral injections. These brain regions have been intimately linked to various aspects of female reproductive behavior. Four days after viral injections, into the vagina and clitoris PRV labeled cells were observed in the paraventricular nucleus, Barrington’s nucleus, the A5 region, and the nucleus paragigantocellularis. At 5 days post-viral administration, additional PRV labeled cells were observed within the preoptic region, VMN, PAG and lateral hypothalamus. Anterograde labeling from the MPO terminated among PRV positive cells primarily within the dorsal paraventricular nucleus of the hypothalamus (PVN), ventrolateral VMN (VMNvl), caudal PAG and nucleus paragigantocellularis (nPGi). Anterograde labeling from the VMN terminated among PRV positive cells in the MPO and lateral/ventrolateral PAG. Anterograde labeling from the PAG terminated among PRV positive cells in the PVN, ventral hypothalamus and nPGi. Transynaptically labeled cells in the lateral hypothalamus, Barrington's nucleus and ventromedial medulla received innervation from all three sources. These studies, together, identify several CNS sites participating in the neural control of female sexual responses. They also provide the first data demonstrating a link between the MPO, VMNvl and PAG and CNS regions innervating the clitoris and vagina, providing support that these areas play a major role in female genital responses. PMID:16914428