Single-event transients (SET) in analog circuits

International Nuclear Information System (INIS)

Chen Panxun; Zhou Kaiming

2006-01-01

A new phenomenon of single- event upset is introduced. The transient signal is produced in the output of analog circuits after a heavy ion strikes. The transient upset can influence the circuit connected with the output of analog circuits. For example, the output of operational amplifier can be connected with the input of a digital counter, and the pulse of sufficiently high transient output induced by an ion can increase counts of the counter. On the other hand, the transient voltage signal at the output of analog circuits can change the stage of other circuits. (authors)

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.

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

An analog integrated circuit design laboratory

OpenAIRE

Mondragon-Torres, A.F.; Mayhugh, Jr.; Pineda de Gyvez, J.; Silva-Martinez, J.; Sanchez-Sinencio, E.

2003-01-01

We present the structure of an analog integrated circuit design laboratory to instruct at both, senior undergraduate and entry graduate levels. The teaching material includes: a laboratory manual with analog circuit design theory, pre-laboratory exercises and circuit design specifications; a reference web page with step by step instructions and examples; the use of mathematical tools for automation and analysis; and state of the art CAD design tools in use by industry. Upon completion of the ...

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.

Design of analog integrated circuits and systems

CERN Document Server

Laker, Kenneth R

1994-01-01

This text is designed for senior or graduate level courses in analog integrated circuits or design of analog integrated circuits. This book combines consideration of CMOS and bipolar circuits into a unified treatment. Also included are CMOS-bipolar circuits made possible by BiCMOS technology. The text progresses from MOS and bipolar device modelling to simple one and two transistor building block circuits. The final two chapters present a unified coverage of sample-data and continuous-time signal processing systems.

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.

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...

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.

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.

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.

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

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.

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.

Single-event effects in analog and mixed-signal integrated circuits

International Nuclear Information System (INIS)

Turflinger, T.L.

1996-01-01

Analog and mixed-signal integrated circuits are also susceptible to single-event effects, but they have rarely been tested. Analog circuit single-particle transients require modified test techniques and data analysis. Existing work is reviewed and future concerns are outlined

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.

High-frequency analog integrated circuit design

CERN Document Server

1995-01-01

To learn more about designing analog integrated circuits (ICs) at microwave frequencies using GaAs materials, turn to this text and reference. It addresses GaAs MESFET-based IC processing. Describes the newfound ability to apply silicon analog design techniques to reliable GaAs materials and devices which, until now, was only available through technical papers scattered throughout hundred of articles in dozens of professional journals.

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...

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.

An analog VLSI real time optical character recognition system based on a neural architecture

International Nuclear Information System (INIS)

Bo, G.; Caviglia, D.; Valle, M.

1999-01-01

In this paper a real time Optical Character Recognition system is presented: it is based on a feature extraction module and a neural network classifier which have been designed and fabricated in analog VLSI technology. Experimental results validate the circuit functionality. The results obtained from a validation based on a mixed approach (i.e., an approach based on both experimental and simulation results) confirm the soundness and reliability of the system

An analog VLSI real time optical character recognition system based on a neural architecture

Energy Technology Data Exchange (ETDEWEB)

Bo, G.; Caviglia, D.; Valle, M. [Genoa Univ. (Italy). Dip. of Biophysical and Electronic Engineering

1999-03-01

In this paper a real time Optical Character Recognition system is presented: it is based on a feature extraction module and a neural network classifier which have been designed and fabricated in analog VLSI technology. Experimental results validate the circuit functionality. The results obtained from a validation based on a mixed approach (i.e., an approach based on both experimental and simulation results) confirm the soundness and reliability of the system.

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

A high-speed analog neural processor

NARCIS (Netherlands)

Masa, P.; Masa, Peter; Hoen, Klaas; Hoen, Klaas; Wallinga, Hans

1994-01-01

Targeted at high-energy physics research applications, our special-purpose analog neural processor can classify up to 70 dimensional vectors within 50 nanoseconds. The decision-making process of the implemented feedforward neural network enables this type of computation to tolerate weight

Drosophila olfactory memory: single genes to complex neural circuits.

Science.gov (United States)

Keene, Alex C; Waddell, Scott

2007-05-01

A central goal of neuroscience is to understand how neural circuits encode memory and guide behaviour. Studying simple, genetically tractable organisms, such as Drosophila melanogaster, can illuminate principles of neural circuit organization and function. Early genetic dissection of D. melanogaster olfactory memory focused on individual genes and molecules. These molecular tags subsequently revealed key neural circuits for memory. Recent advances in genetic technology have allowed us to manipulate and observe activity in these circuits, and even individual neurons, in live animals. The studies have transformed D. melanogaster from a useful organism for gene discovery to an ideal model to understand neural circuit function in memory.

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.

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.

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)

Analog circuit design automation for performance

NARCIS (Netherlands)

Ning, Zhen-Qiu; Ning, Zhen-Qiu; Kole, Marq; Kole, M.E.; Mouthaan, A.J.; Wallinga, Hans

1992-01-01

This paper describes an improved version of the program SEAS (a Simulated Evolution approach for Analog circuit Synthesis), in which an approach for selection of alternatives based on the evaluation of mutation values is developed, and design automafion for high performance comparators is covered.

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)

A reconfigurable analog baseband circuit for WLAN, WCDMA, and Bluetooth

International Nuclear Information System (INIS)

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

2010-01-01

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 G m -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 G m 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. (semiconductor integrated circuits)

A reconfigurable analog baseband circuit for WLAN, WCDMA, and Bluetooth

Energy Technology Data Exchange (ETDEWEB)

Tong Tao; Chi Baoyong; Wang Ziqiang; Zhang Ying; Jiang Hanjun; Wang Zhihua, E-mail: tongt05@gmail.co [Institute of Microelectronics, Tsinghua University, Beijing 100084 (China)

2010-05-15

A reconfigurable analog baseband circuit for WLAN, WCDMA, and Bluetooth in 0.35 {mu}m CMOS is presented. The circuit consists of two variable gain amplifiers (VGA) in cascade and a G{sub m}-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 G{sub m} 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. (semiconductor integrated circuits)

SEAS: A simulated evolution approach for analog circuit synthesis

NARCIS (Netherlands)

Ning, Zhen-Qiu; Ning, Zhen-Qiu; Mouthaan, A.J.; 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

Analog front end circuit design of CSNS beam loss monitor system

International Nuclear Information System (INIS)

Xiao Shuai; Guo Xian; Tian Jianmin; Zeng Lei; Xu Taoguang; Fu Shinian

2013-01-01

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. (authors)

MOSFET analog memory circuit achieves long duration signal storage

Science.gov (United States)

1966-01-01

Memory circuit maintains the signal voltage at the output of an analog signal amplifier when the input signal is interrupted or removed. The circuit uses MOSFET /Metal Oxide Semiconductor Field Effect Transistor/ devices as voltage-controlled switches, triggered by an external voltage-sensing device.

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

International Nuclear Information System (INIS)

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. (semiconductor integrated circuits)

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.

Two multichannel integrated circuits for neural recording and signal processing.

Science.gov (United States)

Obeid, Iyad; Morizio, James C; Moxon, Karen A; Nicolelis, Miguel A L; Wolf, Patrick D

2003-02-01

We have developed, manufactured, and tested two analog CMOS integrated circuit "neurochips" for recording from arrays of densely packed neural electrodes. Device A is a 16-channel buffer consisting of parallel noninverting amplifiers with a gain of 2 V/V. Device B is a 16-channel two-stage analog signal processor with differential amplification and high-pass filtering. It features selectable gains of 250 and 500 V/V as well as reference channel selection. The resulting amplifiers on Device A had a mean gain of 1.99 V/V with an equivalent input noise of 10 microV(rms). Those on Device B had mean gains of 53.4 and 47.4 dB with a high-pass filter pole at 211 Hz and an equivalent input noise of 4.4 microV(rms). Both devices were tested in vivo with electrode arrays implanted in the somatosensory cortex.

Development of reconfigurable analog and digital circuits for plasma diagnostics measurement systems

International Nuclear Information System (INIS)

Srivastava, Amit Kumar; Sharma, Atish; Raval, Tushar

2009-01-01

In long pulse discharge tokamak, a large number of diagnostic channels are being used to understand the complex behavior of plasma. Different diagnostics demand different types of analog and digital processing for plasma parameters measurement. This leads to variable requirements of signal processing for diagnostic measurement. For such types of requirements, we have developed hardware with reconfigurable electronic devices, which provide flexible solution for rapid development of measurement system. Here the analog processing is achieved by Field Programmable Analog Array (FPAA) integrated circuit while reconfigurable digital devices (CPLD/FPGA) achieve digital processing. FPAA's provide an ideal integrated platform for implementing low to medium complexity analog signal processing. With dynamic reconfigurability, the functionality of the FPAA can be reconfigured in-system by the designer or on the fly by a microprocessor. This feature is quite useful to manipulate the tuning or the construction of any part of the analog circuit without interrupting operation of the FPAA, thus maintaining system integrity. The hardware operation control logic circuits are configured in the reconfigurable digital devices (CPLD/FPGA) to control proper hardware functioning. These reconfigurable devices provide the design flexibility and save the component space on the board. It also provides the flexibility for various setting through software. The circuit controlling commands are either issued by computer/processor or generated by circuit itself. (author)

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.

An Implantable Mixed Analog/Digital Neural Stimulator Circuit

DEFF Research Database (Denmark)

Gudnason, Gunnar; Bruun, Erik; Haugland, Morten

1999-01-01

This paper describes a chip for a multichannel neural stimulator for functional electrical stimulation. The chip performs all the signal processing required in an implanted neural stimulator. The power and signal transmission to the stimulator is carried out via an inductive link. From the signals...... electrical stimulation is to restore various bodily functions (e.g. motor functions) in patients who have lost them due to injury or disease....

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.

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

OpenAIRE

E. Tlelo-Cuautle; L.A. Sarmiento-Reyes

2003-01-01

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 t...

AnalogRF and mixed-signal circuit systematic design

CERN Document Server

Tlelo-Cuautle, Esteban; Castro-Lopez, Rafael

2013-01-01

Despite the fact that in the digital domain, designers can take full benefits of IPs and design automation tools to synthesize and design very complex systems, the analog designers’ task is still considered as a ‘handcraft’, cumbersome and very time consuming process. Thus, tremendous efforts are being deployed  to develop new design methodologies in the analog/RF and mixed-signal domains. This book collects 16 state-of-the-art contributions devoted to the topic of systematic design of analog, RF and mixed signal circuits. Divided in the two parts Methodologies and Techniques recent theories, synthesis techniques and design methodologies, as well as new sizing approaches in the field of robust analog and mixed signal design automation are presented for researchers and R/D engineers.  

Test signal generation for analog circuits

Directory of Open Access Journals (Sweden)

B. Burdiek

2003-01-01

Full Text Available In this paper a new test signal generation approach for general analog circuits based on the variational calculus and modern control theory methods is presented. The computed transient test signals also called test stimuli are optimal with respect to the detection of a given fault set by means of a predefined merit functional representing a fault detection criterion. The test signal generation problem of finding optimal test stimuli detecting all faults form the fault set is formulated as an optimal control problem. The solution of the optimal control problem representing the test stimuli is computed using an optimization procedure. The optimization procedure is based on the necessary conditions for optimality like the maximum principle of Pontryagin and adjoint circuit equations.

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. Copyright © 2016 Elsevier B.V. All rights reserved.

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

OpenAIRE

Y.C.Wong; Syafeeza A. R; N. A. Hamid

2015-01-01

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 desig...

Designing charge-sensitive preamplifiers based on low-noise analog integrated circuits

International Nuclear Information System (INIS)

Agakhanyan, T.M.

1998-01-01

The methodology for designing charge-sensitive preamplifiers on the low-noise analog integral circuits, including all the stages: the mathematical synthesis with optimization of the intermediate function; the scheme-technical synthesis with parametric optimization of the scheme and analysis of draft projects with the parameter verification is presented. The designing is conducted on the basis of requirements for signal parameters and noise indices of the preamplifier. The system of automated designing of the charge-sensitive preamplifiers on the low-noise analog integral circuits is developed [ru

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

Associative memory in an analog iterated-map neural network

Science.gov (United States)

Marcus, C. M.; Waugh, F. R.; Westervelt, R. M.

1990-03-01

The behavior of an analog neural network with parallel dynamics is studied analytically and numerically for two associative-memory learning algorithms, the Hebb rule and the pseudoinverse rule. Phase diagrams in the parameter space of analog gain β and storage ratio α are presented. For both learning rules, the networks have large ``recall'' phases in which retrieval states exist and convergence to a fixed point is guaranteed by a global stability criterion. We also demonstrate numerically that using a reduced analog gain increases the probability of recall starting from a random initial state. This phenomenon is comparable to thermal annealing used to escape local minima but has the advantage of being deterministic, and therefore easily implemented in electronic hardware. Similarities and differences between analog neural networks and networks with two-state neurons at finite temperature are also discussed.

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…

Neural principles of memory and a neural theory of analogical insight

Science.gov (United States)

Lawson, David I.; Lawson, Anton E.

1993-12-01

Grossberg's principles of neural modeling are reviewed and extended to provide a neural level theory to explain how analogies greatly increase the rate of learning and can, in fact, make learning and retention possible. In terms of memory, the key point is that the mind is able to recognize and recall when it is able to match sensory input from new objects, events, or situations with past memory records of similar objects, events, or situations. When a match occurs, an adaptive resonance is set up in which the synaptic strengths of neurons are increased; thus a long term record of the new input is formed in memory. Systems of neurons called outstars and instars are presumably the underlying units that enable this to occur. Analogies can greatly facilitate learning and retention because they activate the outstars (i.e., the cells that are sampling the to-be-learned pattern) and cause the neural activity to grow exponentially by forming feedback loops. This increased activity insures the boost in synaptic strengths of neurons, thus causing storage and retention in long-term memory (i.e., learning).

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

Science.gov (United States)

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

2013-02-01

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.

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.

An Activity for Demonstrating the Concept of a Neural Circuit

Science.gov (United States)

Kreiner, David S.

2012-01-01

College students in two sections of a general psychology course participated in a demonstration of a simple neural circuit. The activity was based on a neural circuit that Jeffress proposed for localizing sounds. Students in one section responded to a questionnaire prior to participating in the activity, while students in the other section…

Circuits and electronics hands-on learning with analog discovery

CERN Document Server

Okyere Attia, John

2018-01-01

The book provides instructions on building circuits on breadboards, connecting the Analog Discovery wires to the circuit under test, and making electrical measurements. Various measurement techniques are described and used in this book, including: impedance measurements, complex power measurements, frequency response measurements, power spectrum measurements, current versus voltage characteristic measurements of diodes, bipolar junction transistors, and Mosfets. The book includes end-of-chapter problems for additional exercises geared towards hands-on learning, experimentation, comparisons between measured results and those obtained from theoretical calculations.

Wideband continuous-time ΣΔ ADCs, automotive electronics, and power management : advances in analog circuit design 2016

NARCIS (Netherlands)

Baschirotto, A.; Harpe, P.J.A.; Makinwa, K.A.A.

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

Digital circuit for the introduction and later removal of dither from an analog signal

Science.gov (United States)

Borgen, Gary S.

1994-05-01

An electronics circuit is presented for accurately digitizing an analog audio or like data signal into a digital equivalent signal by introducing dither into the analog signal and then subsequently removing the dither from the digitized signal prior to its conversion to an analog signal which is a substantial replica of the incoming analog audio or like data signal. The electronics circuit of the present invention is characterized by a first pseudo-random number generator which generates digital random noise signals or dither for addition to the digital equivalent signal and a second pseudo-random number generator which generates subtractive digital random noise signals for the subsequent removal of dither from the digital equivalent signal prior its conversion to the analog replica signal.

Text Based Analogy in Overcoming Student Misconception on Simple Electricity Circuit Material

Science.gov (United States)

Hesti, R.; Maknun, J.; Feranie, S.

2017-09-01

Some researcher have found that the use of analogy in learning and teaching physics was effective enough in giving comprehension in a complicated physics concept such as electrical circuits. Meanwhile, misconception become main cause that makes students failed when learning physics. To provide teaching physics effectively, the misconception should be resolved. Using Text Based Analogy is one of the way to identifying misconceptions and it is enough to assist teachers in conveying scientific truths in order to overcome misconceptions. The purpose of the study to investigate the use of text based analogy in overcoming students misconception on simple electrical circuit material. The samples of this research were 28 of junior high school students taken purposively from one high school in South Jakarta. The method use in this research is pre-experimental and design in one shot case study. Students who are the participants of sample have been identified misconception on the electrical circuit material by using the Diagnostic Test of Simple Electricity Circuit. The results of this study found that TBA can replace the misconceptions of the concept possessed by students with scientific truths conveyed in the text in a way that is easily understood so that TBA is strongly recommended to use in other physics materials.

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.

Design and implementation of JOM-3 Overhauser magnetometer analog circuit

Science.gov (United States)

Zhang, Xiao; Jiang, Xue; Zhao, Jianchang; Zhang, Shuang; Guo, Xin; Zhou, Tingting

2017-09-01

Overhauser magnetometer, a kind of static-magnetic measurement system based on the Overhauser effect, has been widely used in archaeological exploration, mineral resources exploration, oil and gas basin structure detection, prediction of engineering exploration environment, earthquakes and volcanic eruotions, object magnetic measurement and underground buried booty exploration. Overhauser magnetometer plays an important role in the application of magnetic field measurement for its characteristics of small size, low power consumption and high sensitivity. This paper researches the design and the application of the analog circuit of JOM-3 Overhauser magnetometer. First, the Larmor signal output by the probe is very weak. In order to obtain the signal with high signal to noise rstio(SNR), the design of pre-amplifier circuit is the key to improve the quality of the system signal. Second, in this paper, the effectual step which could improve the frequency characters of bandpass filter amplifier circuit were put forward, and theoretical analysis was made for it. Third, the shaping circuit shapes the amplified sine signal into a square wave signal which is suitable for detecting the rising edge. Fourth, this design elaborated the optimized choice of tuning circuit, so the measurement range of the magnetic field can be covered. Last, integrated analog circuit testing system was formed to detect waveform of each module. By calculating the standard deviation, the sensitivity of the improved Overhauser magnetometer is 0.047nT for Earth's magnetic field observation. Experimental results show that the new magnetometer is sensitive to earth field measurement.

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...

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.

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.

The neural circuits that generate tics in Tourette's syndrome.

Science.gov (United States)

Wang, Zhishun; Maia, Tiago V; Marsh, Rachel; Colibazzi, Tiziano; Gerber, Andrew; Peterson, Bradley S

2011-12-01

The purpose of this study was to examine neural activity and connectivity within cortico-striato-thalamo-cortical circuits and to reveal circuit-based neural mechanisms that govern tic generation in Tourette's syndrome. Functional magnetic resonance imaging data were acquired from 13 individuals with Tourette's syndrome and 21 healthy comparison subjects during spontaneous or simulated tics. Independent component analysis with hierarchical partner matching was used to isolate neural activity within functionally distinct regions of cortico-striato-thalamo-cortical circuits. Granger causality was used to investigate causal interactions among these regions. The Tourette's syndrome group exhibited stronger neural activity and interregional causality than healthy comparison subjects throughout all portions of the motor pathway, including the sensorimotor cortex, putamen, pallidum, and substantia nigra. Activity in these areas correlated positively with the severity of tic symptoms. Activity within the Tourette's syndrome group was stronger during spontaneous tics than during voluntary tics in the somatosensory and posterior parietal cortices, putamen, and amygdala/hippocampus complex, suggesting that activity in these regions may represent features of the premonitory urges that generate spontaneous tic behaviors. In contrast, activity was weaker in the Tourette's syndrome group than in the healthy comparison group within portions of cortico-striato-thalamo-cortical circuits that exert top-down control over motor pathways (the caudate and anterior cingulate cortex), and progressively less activity in these regions accompanied more severe tic symptoms, suggesting that faulty activity in these circuits may result in their failure to control tic behaviors or the premonitory urges that generate them. Our findings, taken together, suggest that tics are caused by the combined effects of excessive activity in motor pathways and reduced activation in control portions of cortico

An improved superconducting neural circuit and its application for a neural network solving a combinatorial optimization problem

International Nuclear Information System (INIS)

Onomi, T; Nakajima, K

2014-01-01

We have proposed a superconducting Hopfield-type neural network for solving the N-Queens problem which is one of combinatorial optimization problems. The sigmoid-shape function of a neuron output is represented by the output of coupled SQUIDs gate consisting of a single-junction and a double-junction SQUIDs. One of the important factors for an improvement of the network performance is an improvement of a threshold characteristic of a neuron circuit. In this paper, we report an improved design of coupled SQUID gates for a superconducting neural network. A step-like function with a steep threshold at a rising edge is desirable for a neuron circuit to solve a combinatorial optimization problem. A neuron circuit is composed of two coupled SQUIDs gates with a cascade connection in order to obtain such characteristics. The designed neuron circuit is fabricated by a 2.5 kA/cm 2 Nb/AlOx/Nb process. The operation of a fabricated neuron circuit is experimentally demonstrated. Moreover, we discuss about the performance of the neural network using the improved neuron circuits and delayed negative self-connections.

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.

A plausible neural circuit for decision making and its formation based on reinforcement learning.

Science.gov (United States)

Wei, Hui; Dai, Dawei; Bu, Yijie

2017-06-01

A human's, or lower insects', behavior is dominated by its nervous system. Each stable behavior has its own inner steps and control rules, and is regulated by a neural circuit. Understanding how the brain influences perception, thought, and behavior is a central mandate of neuroscience. The phototactic flight of insects is a widely observed deterministic behavior. Since its movement is not stochastic, the behavior should be dominated by a neural circuit. Based on the basic firing characteristics of biological neurons and the neural circuit's constitution, we designed a plausible neural circuit for this phototactic behavior from logic perspective. The circuit's output layer, which generates a stable spike firing rate to encode flight commands, controls the insect's angular velocity when flying. The firing pattern and connection type of excitatory and inhibitory neurons are considered in this computational model. We simulated the circuit's information processing using a distributed PC array, and used the real-time average firing rate of output neuron clusters to drive a flying behavior simulation. In this paper, we also explored how a correct neural decision circuit is generated from network flow view through a bee's behavior experiment based on the reward and punishment feedback mechanism. The significance of this study: firstly, we designed a neural circuit to achieve the behavioral logic rules by strictly following the electrophysiological characteristics of biological neurons and anatomical facts. Secondly, our circuit's generality permits the design and implementation of behavioral logic rules based on the most general information processing and activity mode of biological neurons. Thirdly, through computer simulation, we achieved new understanding about the cooperative condition upon which multi-neurons achieve some behavioral control. Fourthly, this study aims in understanding the information encoding mechanism and how neural circuits achieve behavior control

Updating Procedures Can Reorganize the Neural Circuit Supporting a Fear Memory.

Science.gov (United States)

Kwapis, Janine L; Jarome, Timothy J; Ferrara, Nicole C; Helmstetter, Fred J

2017-07-01

Established memories undergo a period of vulnerability following retrieval, a process termed 'reconsolidation.' Recent work has shown that the hypothetical process of reconsolidation is only triggered when new information is presented during retrieval, suggesting that this process may allow existing memories to be modified. Reconsolidation has received increasing attention as a possible therapeutic target for treating disorders that stem from traumatic memories, yet little is known about how this process changes the original memory. In particular, it is unknown whether reconsolidation can reorganize the neural circuit supporting an existing memory after that memory is modified with new information. Here, we show that trace fear memory undergoes a protein synthesis-dependent reconsolidation process following exposure to a single updating trial of delay conditioning. Further, this reconsolidation-dependent updating process appears to reorganize the neural circuit supporting the trace-trained memory, so that it better reflects the circuit supporting delay fear. Specifically, after a trace-to-delay update session, the amygdala is now required for extinction of the updated memory but the retrosplenial cortex is no longer required for retrieval. These results suggest that updating procedures could be used to force a complex, poorly defined memory circuit to rely on a better-defined neural circuit that may be more amenable to behavioral or pharmacological manipulation. This is the first evidence that exposure to new information can fundamentally reorganize the neural circuit supporting an existing memory.

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

Energy Technology Data Exchange (ETDEWEB)

He Xiaofeng; Ye Tianchun [Institute of Microelectronics, Chinese Academy of Science, Beijing 100029 (China); Mo Taishan; Ma Chengyan, E-mail: hexiaofeng@casic.ac.cn [Hangzhou Zhongke Microelectronics Co, Ltd, Hangzhou 310053 (China)

2011-02-15

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 {mu}m 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 x 300 {mu}m{sup 2}. (semiconductor integrated circuits)

Modulation of neural circuits: how stimulus context shapes innate behavior in Drosophila.

Science.gov (United States)

Su, Chih-Ying; Wang, Jing W

2014-12-01

Remarkable advances have been made in recent years in our understanding of innate behavior and the underlying neural circuits. In particular, a wealth of neuromodulatory mechanisms have been uncovered that can alter the input-output relationship of a hereditary neural circuit. It is now clear that this inbuilt flexibility allows animals to modify their behavioral responses according to environmental cues, metabolic demands and physiological states. Here, we discuss recent insights into how modulation of neural circuits impacts innate behavior, with a special focus on how environmental cues and internal physiological states shape different aspects of feeding behavior in Drosophila. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

Real-time emulation of neural images in the outer retinal circuit.

Science.gov (United States)

Hasegawa, Jun; Yagi, Tetsuya

2008-12-01

We describe a novel real-time system that emulates the architecture and functionality of the vertebrate retina. This system reconstructs the neural images formed by the retinal neurons in real time by using a combination of analog and digital systems consisting of a neuromorphic silicon retina chip, a field-programmable gate array, and a digital computer. While the silicon retina carries out the spatial filtering of input images instantaneously, using the embedded resistive networks that emulate the receptive field structure of the outer retinal neurons, the digital computer carries out the temporal filtering of the spatially filtered images to emulate the dynamical properties of the outer retinal circuits. The emulations of the neural image, including 128 x 128 bipolar cells, are carried out at a frame rate of 62.5 Hz. The emulation of the response to the Hermann grid and a spot of light and an annulus of lights has demonstrated that the system responds as expected by previous physiological and psychophysical observations. Furthermore, the emulated dynamics of neural images in response to natural scenes revealed the complex nature of retinal neuron activity. We have concluded that the system reflects the spatiotemporal responses of bipolar cells in the vertebrate retina. The proposed emulation system is expected to aid in understanding the visual computation in the retina and the brain.

Measurement and Analysis of Multiple Output Transient Propagation in BJT Analog Circuits

Science.gov (United States)

Roche, Nicolas J.-H.; Khachatrian, A.; Warner, J. H.; Buchner, S. P.; McMorrow, D.; Clymer, D. A.

2016-08-01

The propagation of Analog Single Event Transients (ASETs) to multiple outputs of Bipolar Junction Transistor (BJTs) Integrated Circuits (ICs) is reported for the first time. The results demonstrate that ASETs can appear at several outputs of a BJT amplifier or comparator as a result of a single ion or single laser pulse strike at a single physical location on the chip of a large-scale integrated BJT analog circuit. This is independent of interconnect cross-talk or charge-sharing effects. Laser experiments, together with SPICE simulations and analysis of the ASET's propagation in the s-domain are used to explain how multiple-output transients (MOTs) are generated and propagate in the device. This study demonstrates that both the charge collection associated with an ASET and the ASET's shape, commonly used to characterize the propagation of SETs in devices and systems, are unable to explain quantitatively how MOTs propagate through an integrated analog circuit. The analysis methodology adopted here involves combining the Fourier transform of the propagating signal and the current-source transfer function in the s-domain. This approach reveals the mechanisms involved in the transient signal propagation from its point of generation to one or more outputs without the signal following a continuous interconnect path.

Electric Circuit Model Analogy for Equilibrium Lattice Relaxation in Semiconductor Heterostructures

Science.gov (United States)

Kujofsa, Tedi; Ayers, John E.

2018-01-01

The design and analysis of semiconductor strained-layer device structures require an understanding of the equilibrium profiles of strain and dislocations associated with mismatched epitaxy. Although it has been shown that the equilibrium configuration for a general semiconductor strained-layer structure may be found numerically by energy minimization using an appropriate partitioning of the structure into sublayers, such an approach is computationally intense and non-intuitive. We have therefore developed a simple electric circuit model approach for the equilibrium analysis of these structures. In it, each sublayer of an epitaxial stack may be represented by an analogous circuit configuration involving an independent current source, a resistor, an independent voltage source, and an ideal diode. A multilayered structure may be built up by the connection of the appropriate number of these building blocks, and the node voltages in the analogous electric circuit correspond to the equilibrium strains in the original epitaxial structure. This enables analysis using widely accessible circuit simulators, and an intuitive understanding of electric circuits can easily be extended to the relaxation of strained-layer structures. Furthermore, the electrical circuit model may be extended to continuously-graded epitaxial layers by considering the limit as the individual sublayer thicknesses are diminished to zero. In this paper, we describe the mathematical foundation of the electrical circuit model, demonstrate its application to several representative structures involving In x Ga1- x As strained layers on GaAs (001) substrates, and develop its extension to continuously-graded layers. This extension allows the development of analytical expressions for the strain, misfit dislocation density, critical layer thickness and widths of misfit dislocation free zones for a continuously-graded layer having an arbitrary compositional profile. It is similar to the transition from circuit

Inter-progenitor pool wiring: An evolutionarily conserved strategy that expands neural circuit diversity.

Science.gov (United States)

Suzuki, Takumi; Sato, Makoto

2017-11-15

Diversification of neuronal types is key to establishing functional variations in neural circuits. The first critical step to generate neuronal diversity is to organize the compartmental domains of developing brains into spatially distinct neural progenitor pools. Neural progenitors in each pool then generate a unique set of diverse neurons through specific spatiotemporal specification processes. In this review article, we focus on an additional mechanism, 'inter-progenitor pool wiring', that further expands the diversity of neural circuits. After diverse types of neurons are generated in one progenitor pool, a fraction of these neurons start migrating toward a remote brain region containing neurons that originate from another progenitor pool. Finally, neurons of different origins are intermingled and eventually form complex but precise neural circuits. The developing cerebral cortex of mammalian brains is one of the best examples of inter-progenitor pool wiring. However, Drosophila visual system development has revealed similar mechanisms in invertebrate brains, suggesting that inter-progenitor pool wiring is an evolutionarily conserved strategy that expands neural circuit diversity. Here, we will discuss how inter-progenitor pool wiring is accomplished in mammalian and fly brain systems. Copyright © 2017 Elsevier Inc. All rights reserved.

Analog circuit design : low voltage low power; short range wireless front-ends; power management and DC-DC

NARCIS (Netherlands)

Steyaert, M.; Roermund, van A.H.M.; Baschirotto, A.

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

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.

Proposal for a fast, zero suppressing circuit for the digitization of analog pulses over long memory times

International Nuclear Information System (INIS)

Bourgeois, F.

1984-01-01

This report describes the design principles of a fast (100 MHz) time and pulse height digitizer that can record up to 15 analog pulses over 10-80 μs memory times. Unlike other triggered circuits prepulse samples are recorded without the help of an analog delay line. The low power requirements of the circuit as well as its fast read-out characteristics make it very attractive for detectors with many digitizing channels. Conventional circuits are described as a reference for the evaluation of this new design. An ECL 10 K implementation of the circuit is presented in the third section. (orig.)

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.

CASTOR a VLSI CMOS mixed analog-digital circuit for low noise multichannel counting applications

International Nuclear Information System (INIS)

Comes, G.; Loddo, F.; Hu, Y.; Kaplon, J.; Ly, F.; Turchetta, R.; Bonvicini, V.; Vacchi, A.

1996-01-01

In this paper we present the design and first experimental results of a VLSI mixed analog-digital 1.2 microns CMOS circuit (CASTOR) for multichannel radiation detectors applications demanding low noise amplification and counting of radiation pulses. This circuit is meant to be connected to pixel-like detectors. Imaging can be obtained by counting the number of hits in each pixel during a user-controlled exposure time. Each channel of the circuit features an analog and a digital part. In the former one, a charge preamplifier is followed by a CR-RC shaper with an output buffer and a threshold discriminator. In the digital part, a 16-bit counter is present together with some control logic. The readout of the counters is done serially on a common tri-state output. Daisy-chaining is possible. A 4-channel prototype has been built. This prototype has been optimised for use in the digital radiography Syrmep experiment at the Elettra synchrotron machine in Trieste (Italy): its main design parameters are: shaping time of about 850 ns, gain of 190 mV/fC and ENC (e - rms)=60+17 C (pF). The counting rate per channel, limited by the analog part, can be as high as about 200 kHz. Characterisation of the circuit and first tests with silicon microstrip detectors are presented. They show the circuit works according to design specification and can be used for imaging applications. (orig.)

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.

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.

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

NARCIS (Netherlands)

Nauta, Bram; Hoogzaad, Gian; Hoogzaad, G.; 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

Fault diagnosis for analog circuits utilizing time-frequency features and improved VVRKFA

Science.gov (United States)

He, Wei; He, Yigang; Luo, Qiwu; Zhang, Chaolong

2018-04-01

This paper proposes a novel scheme for analog circuit fault diagnosis utilizing features extracted from the time-frequency representations of signals and an improved vector-valued regularized kernel function approximation (VVRKFA). First, the cross-wavelet transform is employed to yield the energy-phase distribution of the fault signals over the time and frequency domain. Since the distribution is high-dimensional, a supervised dimensionality reduction technique—the bilateral 2D linear discriminant analysis—is applied to build a concise feature set from the distributions. Finally, VVRKFA is utilized to locate the fault. In order to improve the classification performance, the quantum-behaved particle swarm optimization technique is employed to gradually tune the learning parameter of the VVRKFA classifier. The experimental results for the analog circuit faults classification have demonstrated that the proposed diagnosis scheme has an advantage over other approaches.

Self-Organizing Neural Circuits for Sensory-Guided Motor Control

National Research Council Canada - National Science Library

Grossberg, Stephen

1999-01-01

The reported projects developed mathematical models to explain how self-organizing neural circuits that operate under continuous or intermittent sensory guidance achieve flexible and accurate control of human movement...

Analog neural networks in an upgraded muon trigger for the DZero detector

International Nuclear Information System (INIS)

Fortner, M.R.

1992-04-01

The use of analog neural networks as part of the DZero muon detector is considered. A study was made of tracking through a single muon chamber using neural network techniques. A hardware application based on Intel's ETANN ship was designed and used in a test beam at Fermi National Accelerator Laboratory. Plans to implement a neural network trigger in DZero are also discussed

Beyond excitation/inhibition imbalance in multidimensional models of neural circuit changes in brain disorders.

Science.gov (United States)

O'Donnell, Cian; Gonçalves, J Tiago; Portera-Cailliau, Carlos; Sejnowski, Terrence J

2017-10-11

A leading theory holds that neurodevelopmental brain disorders arise from imbalances in excitatory and inhibitory (E/I) brain circuitry. However, it is unclear whether this one-dimensional model is rich enough to capture the multiple neural circuit alterations underlying brain disorders. Here, we combined computational simulations with analysis of in vivo two-photon Ca 2+ imaging data from somatosensory cortex of Fmr1 knock-out (KO) mice, a model of Fragile-X Syndrome, to test the E/I imbalance theory. We found that: (1) The E/I imbalance model cannot account for joint alterations in the observed neural firing rates and correlations; (2) Neural circuit function is vastly more sensitive to changes in some cellular components over others; (3) The direction of circuit alterations in Fmr1 KO mice changes across development. These findings suggest that the basic E/I imbalance model should be updated to higher dimensional models that can better capture the multidimensional computational functions of neural circuits.

Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks.

Science.gov (United States)

Pu, Yi-Fei; Yi, Zhang; Zhou, Ji-Liu

2017-10-01

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.

Modulation of neural circuits: how stimulus context shapes innate behavior in Drosophila

OpenAIRE

Su, Chih-Ying; Wang, Jing W.

2014-01-01

Remarkable advances have been made in recent years in our understanding of innate behavior and the underlying neural circuits. In particular, a wealth of neuromodulatory mechanisms have been uncovered that can alter the input-output relationship of a hereditary neural circuit. It is now clear that this inbuilt flexibility allows animals to modify their behavioral responses according to environmental cues, metabolic demands and physiological states. Here, we discuss recent insights into how mo...

On the origin of reproducible sequential activity in neural circuits

Science.gov (United States)

Afraimovich, V. S.; Zhigulin, V. P.; Rabinovich, M. I.

2004-12-01

Robustness and reproducibility of sequential spatio-temporal responses is an essential feature of many neural circuits in sensory and motor systems of animals. The most common mathematical images of dynamical regimes in neural systems are fixed points, limit cycles, chaotic attractors, and continuous attractors (attractive manifolds of neutrally stable fixed points). These are not suitable for the description of reproducible transient sequential neural dynamics. In this paper we present the concept of a stable heteroclinic sequence (SHS), which is not an attractor. SHS opens the way for understanding and modeling of transient sequential activity in neural circuits. We show that this new mathematical object can be used to describe robust and reproducible sequential neural dynamics. Using the framework of a generalized high-dimensional Lotka-Volterra model, that describes the dynamics of firing rates in an inhibitory network, we present analytical results on the existence of the SHS in the phase space of the network. With the help of numerical simulations we confirm its robustness in presence of noise in spite of the transient nature of the corresponding trajectories. Finally, by referring to several recent neurobiological experiments, we discuss possible applications of this new concept to several problems in neuroscience.

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.

Molecular annotation of integrative feeding neural circuits.

Science.gov (United States)

Pérez, Cristian A; Stanley, Sarah A; Wysocki, Robert W; Havranova, Jana; Ahrens-Nicklas, Rebecca; Onyimba, Frances; Friedman, Jeffrey M

2011-02-02

The identity of higher-order neurons and circuits playing an associative role to control feeding is unknown. We injected pseudorabies virus, a retrograde tracer, into masseter muscle, salivary gland, and tongue of BAC-transgenic mice expressing GFP in specific neural populations and identified several CNS regions that project multisynaptically to the periphery. MCH and orexin neurons were identified in the lateral hypothalamus, and Nurr1 and Cnr1 in the amygdala and insular/rhinal cortices. Cholera toxin β tracing showed that insular Nurr1(+) and Cnr1(+) neurons project to the amygdala or lateral hypothalamus, respectively. Finally, we show that cortical Cnr1(+) neurons show increased Cnr1 mRNA and c-Fos expression after fasting, consistent with a possible role for Cnr1(+) neurons in feeding. Overall, these studies define a general approach for identifying specific molecular markers for neurons in complex neural circuits. These markers now provide a means for functional studies of specific neuronal populations in feeding or other complex behaviors. Copyright © 2011 Elsevier Inc. All rights reserved.

The Neural Circuits that Generate Tics in Gilles de la Tourette Syndrome

Science.gov (United States)

Wang, Zhishun; Maia, Tiago V.; Marsh, Rachel; Colibazzi, Tiziano; Gerber, Andrew; Peterson, Bradley S.

2014-01-01

Objective To study neural activity and connectivity within cortico-striato-thalamo-cortical circuits and to reveal circuit-based neural mechanisms that govern tic generation in Tourette syndrome. Method We acquired fMRI data from 13 participants with Tourette syndrome and 21 controls during spontaneous or simulated tics. We used independent component analysis with hierarchical partner matching to isolate neural activity within functionally distinct regions of cortico-striato-thalamo-cortical circuits. We used Granger causality to investigate causal interactions among these regions. Results We found that the Tourette group exhibited stronger neural activity and interregional causality than controls throughout all portions of the motor pathway including sensorimotor cortex, putamen, pallidum, and substania nigra. Activity in these areas correlated positively with the severity of tic symptoms. Activity within the Tourette group was stronger during spontaneous tics than during voluntary tics in somatosensory and posterior parietal cortices, putamen, and amygdala/hippocampus complex, suggesting that activity in these regions may represent features of the premonitory urges that generate spontaneous tic behaviors. In contrast, activity was weaker in the Tourette group than in controls within portions of cortico-striato-thalamo-cortical circuits that exert top-down control over motor pathways (caudate and anterior cingulate cortex), and progressively less activity in these regions accompanied more severe tic symptoms, suggesting that faulty activity in these circuits may fail to control tic behaviors or the premonitory urges that generate them. Conclusions Our findings taken together suggest that tics are caused by the combined effects of excessive activity in motor pathways and reduced activation in control portions of cortico-striato-thalamo-cortical circuits. PMID:21955933

δ-Protocadherins: Organizers of neural circuit assembly.

Science.gov (United States)

Light, Sarah E W; Jontes, James D

2017-09-01

The δ-protocadherins comprise a small family of homophilic cell adhesion molecules within the larger cadherin superfamily. They are essential for neural development as mutations in these molecules give rise to human neurodevelopmental disorders, such as schizophrenia and epilepsy, and result in behavioral defects in animal models. Despite their importance to neural development, a detailed understanding of their mechanisms and the ways in which their loss leads to changes in neural function is lacking. However, recent results have begun to reveal roles for the δ-protocadherins in both regulation of neurogenesis and lineage-dependent circuit assembly, as well as in contact-dependent motility and selective axon fasciculation. These evolutionarily conserved mechanisms could have a profound impact on the robust assembly of the vertebrate nervous system. Future work should be focused on unraveling the molecular mechanisms of the δ-protocadherins and understanding how this family functions broadly to regulate neural development. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Optogenetic interrogation of neural circuits: technology for probing mammalian brain structures

Science.gov (United States)

Zhang, Feng; Gradinaru, Viviana; Adamantidis, Antoine R; Durand, Remy; Airan, Raag D; de Lecea, Luis; Deisseroth, Karl

2015-01-01

Elucidation of the neural substrates underlying complex animal behaviors depends on precise activity control tools, as well as compatible readout methods. Recent developments in optogenetics have addressed this need, opening up new possibilities for systems neuroscience. Interrogation of even deep neural circuits can be conducted by directly probing the necessity and sufficiency of defined circuit elements with millisecond-scale, cell type-specific optical perturbations, coupled with suitable readouts such as electrophysiology, optical circuit dynamics measures and freely moving behavior in mammals. Here we collect in detail our strategies for delivering microbial opsin genes to deep mammalian brain structures in vivo, along with protocols for integrating the resulting optical control with compatible readouts (electrophysiological, optical and behavioral). The procedures described here, from initial virus preparation to systems-level functional readout, can be completed within 4–5 weeks. Together, these methods may help in providing circuit-level insight into the dynamics underlying complex mammalian behaviors in health and disease. PMID:20203662

Construction of implantable optical fibers for long-term optogenetic manipulation of neural circuits.

Science.gov (United States)

Sparta, Dennis R; Stamatakis, Alice M; Phillips, Jana L; Hovelsø, Nanna; van Zessen, Ruud; Stuber, Garret D

2011-12-08

In vivo optogenetic strategies have redefined our ability to assay how neural circuits govern behavior. Although acutely implanted optical fibers have previously been used in such studies, long-term control over neuronal activity has been largely unachievable. Here we describe a method to construct implantable optical fibers to readily manipulate neural circuit elements with minimal tissue damage or change in light output over time (weeks to months). Implanted optical fibers readily interface with in vivo electrophysiological arrays or electrochemical detection electrodes. The procedure described here, from implant construction to the start of behavioral experimentation, can be completed in approximately 2-6 weeks. Successful use of implantable optical fibers will allow for long-term control of mammalian neural circuits in vivo, which is integral to the study of the neurobiology of behavior.

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...

Neural Circuit Mechanisms of Social Behavior.

Science.gov (United States)

Chen, Patrick; Hong, Weizhe

2018-04-04

We live in a world that is largely socially constructed, and we are constantly involved in and fundamentally influenced by a broad array of complex social interactions. Social behaviors among conspecifics, either conflictive or cooperative, are exhibited by all sexually reproducing animal species and are essential for the health, survival, and reproduction of animals. Conversely, impairment in social function is a prominent feature of several neuropsychiatric disorders, such as autism spectrum disorders and schizophrenia. Despite the importance of social behaviors, many fundamental questions remain unanswered. How is social sensory information processed and integrated in the nervous system? How are different social behavioral decisions selected and modulated in brain circuits? Here we discuss conceptual issues and recent advances in our understanding of brain regions and neural circuit mechanisms underlying the regulation of social behaviors. Copyright © 2018 Elsevier Inc. All rights reserved.

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.

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

OpenAIRE

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

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 Ne...

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.

Railway track circuit fault diagnosis using recurrent neural networks

NARCIS (Netherlands)

de Bruin, T.D.; Verbert, K.A.J.; Babuska, R.

2017-01-01

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

Hardware implementation of an adaptive resonance theory (ART) neural network using compensated operational amplifiers

Science.gov (United States)

Ho, Ching S.; Liou, Juin J.; Georgiopoulos, Michael; Christodoulou, Christos G.

1994-03-01

This paper presents an analog circuit design and implementation for an adaptive resonance theory neural network architecture called the augmented ART1 neural network (AART1-NN). Practical monolithic operational amplifiers (Op-Amps) LM741 and LM318 are selected to implement the circuit, and a simple compensation scheme is developed to adjust the Op-Amp electrical characteristics to meet the design requirement. A 7-node prototype circuit has been designed and verified using the Pspice circuit simulator run on a Sun workstation. Results simulated from the AART1-NN circuit using the LM741, LM318, and ideal Op-Amps are presented and compared.

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.

Investigation of flip-flop effects in a linear analog comparator-with-hysteresis circuit

International Nuclear Information System (INIS)

Roche, N.J.H.; Buchner, S.P.; Warner, J.H.; McMorrow, D.; Roig, F.; Auriel, G.; Dusseau, L.; Boch, J.; Saigne, F.; Azais, B.

2013-01-01

The impact of the positive feedback loop on analog single event transient (ASET) shapes was investigated for a comparator- with-hysteresis circuit. Simulation based on previous developed ASET simulation tool is used to model the impact of the power supply voltage, the input voltage level and the injected energy. Simulation results show that these kinds of circuits are sensitive to flip-flop effects. This phenomenon occurs if the input voltage is in the hysteresis band range. In this case, simulations show that the ASET can latch the output into a non-desired state by changing the state of the circuit on his transfer characteristic curves. Laser experiments were conducted and show that the simulation outputs are in agreement with the experimental collected data. (authors)

Explicit logic circuits discriminate neural states.

Directory of Open Access Journals (Sweden)

Lane Yoder

Full Text Available The magnitude and apparent complexity of the brain's connectivity have left explicit networks largely unexplored. As a result, the relationship between the organization of synaptic connections and how the brain processes information is poorly understood. A recently proposed retinal network that produces neural correlates of color vision is refined and extended here to a family of general logic circuits. For any combination of high and low activity in any set of neurons, one of the logic circuits can receive input from the neurons and activate a single output neuron whenever the input neurons have the given activity state. The strength of the output neuron's response is a measure of the difference between the smallest of the high inputs and the largest of the low inputs. The networks generate correlates of known psychophysical phenomena. These results follow directly from the most cost-effective architectures for specific logic circuits and the minimal cellular capabilities of excitation and inhibition. The networks function dynamically, making their operation consistent with the speed of most brain functions. The networks show that well-known psychophysical phenomena do not require extraordinarily complex brain structures, and that a single network architecture can produce apparently disparate phenomena in different sensory systems.

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.

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.

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. © 2013 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

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.

Interpretation of correlated neural variability from models of feed-forward and recurrent circuits

Science.gov (United States)

2018-01-01

Neural populations respond to the repeated presentations of a sensory stimulus with correlated variability. These correlations have been studied in detail, with respect to their mechanistic origin, as well as their influence on stimulus discrimination and on the performance of population codes. A number of theoretical studies have endeavored to link network architecture to the nature of the correlations in neural activity. Here, we contribute to this effort: in models of circuits of stochastic neurons, we elucidate the implications of various network architectures—recurrent connections, shared feed-forward projections, and shared gain fluctuations—on the stimulus dependence in correlations. Specifically, we derive mathematical relations that specify the dependence of population-averaged covariances on firing rates, for different network architectures. In turn, these relations can be used to analyze data on population activity. We examine recordings from neural populations in mouse auditory cortex. We find that a recurrent network model with random effective connections captures the observed statistics. Furthermore, using our circuit model, we investigate the relation between network parameters, correlations, and how well different stimuli can be discriminated from one another based on the population activity. As such, our approach allows us to relate properties of the neural circuit to information processing. PMID:29408930

Interpretation of correlated neural variability from models of feed-forward and recurrent circuits.

Directory of Open Access Journals (Sweden)

Volker Pernice

2018-02-01

Full Text Available Neural populations respond to the repeated presentations of a sensory stimulus with correlated variability. These correlations have been studied in detail, with respect to their mechanistic origin, as well as their influence on stimulus discrimination and on the performance of population codes. A number of theoretical studies have endeavored to link network architecture to the nature of the correlations in neural activity. Here, we contribute to this effort: in models of circuits of stochastic neurons, we elucidate the implications of various network architectures-recurrent connections, shared feed-forward projections, and shared gain fluctuations-on the stimulus dependence in correlations. Specifically, we derive mathematical relations that specify the dependence of population-averaged covariances on firing rates, for different network architectures. In turn, these relations can be used to analyze data on population activity. We examine recordings from neural populations in mouse auditory cortex. We find that a recurrent network model with random effective connections captures the observed statistics. Furthermore, using our circuit model, we investigate the relation between network parameters, correlations, and how well different stimuli can be discriminated from one another based on the population activity. As such, our approach allows us to relate properties of the neural circuit to information processing.

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.

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

International Nuclear Information System (INIS)

Vazquez-Medina, R.; Diaz-Mendez, A.; Rio-Correa, J.L. del; Lopez-Hernandez, J.

2009-01-01

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.

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

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…

A Alternative Analog Circuit Design Methodology Employing Integrated Artificial Intelligence Techniques

Science.gov (United States)

Tuttle, Jeffery L.

In consideration of the computer processing power now available to the designer, an alternative analog circuit design methodology is proposed. Computer memory capacities no longer require the reduction of the transistor operational characteristics to an imprecise formulation. Therefore, it is proposed that transistor modelling be abandoned in favor of fully characterized transistor data libraries. Secondly, availability of the transistor libraries would facilitate an automated selection of the most appropriate device(s) for the circuit being designed. More specifically, a preprocessor computer program to a more sophisticated circuit simulator (e.g. SPICE) is developed to assist the designer in developing the basic circuit topology and the selection of the most appropriate transistor. Once this is achieved, the circuit topology and selected transistor data library would be downloaded to the simulator for full circuit operational characterization and subsequent design modifications. It is recognized that the design process is enhanced by the use of heuristics as applied to iterative design results. Accordingly, an artificial intelligence (AI) interface is developed to assist the designer in applying the preprocessor results. To demonstrate the retrofitability of the AI interface to established programs, the interface is specifically designed to be as non-intrusive to the host code as possible. Implementation of the proposed methodology offers the potential to speed the design process, since the preprocessor both minimizes the required number of simulator runs and provides a higher acceptance potential of the initial and subsequent simulator runs. Secondly, part count reductions may be realizable since the circuit topologies are not as strongly driven by transistor limitations. Thirdly, the predicted results should more closely match actual circuit operations since the inadequacies of the transistor models have been virtually eliminated. Finally, the AI interface

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.

High speed VLSI neural network for high energy physics

NARCIS (Netherlands)

Masa, P.; Masa, P.; Hoen, K.; Hoen, Klaas; Wallinga, Hans

1994-01-01

A CMOS neural network IC is discussed which was designed for very high speed applications. The parallel architecture, analog computing and digital weight storage provides unprecedented computing speed combined with ease of use. The circuit classifies up to 70 dimensional vectors within 20

A central neural circuit for itch sensation.

Science.gov (United States)

Mu, Di; Deng, Juan; Liu, Ke-Fei; Wu, Zhen-Yu; Shi, Yu-Feng; Guo, Wei-Min; Mao, Qun-Quan; Liu, Xing-Jun; Li, Hui; Sun, Yan-Gang

2017-08-18

Although itch sensation is an important protective mechanism for animals, chronic itch remains a challenging clinical problem. Itch processing has been studied extensively at the spinal level. However, how itch information is transmitted to the brain and what central circuits underlie the itch-induced scratching behavior remain largely unknown. We found that the spinoparabrachial pathway was activated during itch processing and that optogenetic suppression of this pathway impaired itch-induced scratching behaviors. Itch-mediating spinal neurons, which express the gastrin-releasing peptide receptor, are disynaptically connected to the parabrachial nucleus via glutamatergic spinal projection neurons. Blockade of synaptic output of glutamatergic neurons in the parabrachial nucleus suppressed pruritogen-induced scratching behavior. Thus, our studies reveal a central neural circuit that is critical for itch signal processing. Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

An analog integrated front-end amplifier for neural applications

OpenAIRE

Zhou, Zhijun; Warr, Paul

2017-01-01

The front-end amplifier forms the critical element for signal detection and pre-processing within neural monitoring systems. It determines not only the fidelity of the biosignal, but also impacts power consumption and detector size. In this paper, a combined feedback loop-controlled approach is proposed to neutralize for the input leakage currents generated by low noise amplifiers when in integrated circuit form, alongside signal leakage into the input bias network. Significantly, this loop t...

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.

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

OpenAIRE

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 ...

Analog CMOS peak detect and hold circuits. Part 2. The two-phase offset-free and derandomizing configuration

CERN Document Server

De Geronimo, G; Kandasamy, A

2002-01-01

An analog CMOS peak detect and hold (PDH) circuit, which combines high speed and accuracy, rail-to-rail sensing and driving, low power, and buffering is presented. It is based on a configuration that cancels the major error sources of the classical CMOS PDH, including offset and common mode gain, by re-using the same amplifier for tracking, peak sensing, and output buffering. By virtue of its high absolute accuracy, two or more PDHs can be used in parallel to serve as a data-driven analog memory for derandomization. The first experimental results on the new peak detector and derandomizer (PDD) circuit, fabricated in 0.35 mu m CMOS technology, include a 0.2% absolute accuracy for pulses with 500 ns peaking time, 2.7 V linear input range, 3.3 mW power dissipation, 250 mV/s droop rate, and negligible dead time. The use of such a high performance analog PDD can greatly relax the requirements on the digitization in multi-channel systems.

Analog design of a new neural network for optical character recognition.

Science.gov (United States)

Morns, I P; Dlay, S S

1999-01-01

An electronic circuit is presented for a new type of neural network, which gives a recognition rate of over 100 kHz. The network is used to classify handwritten numerals, presented as Fourier and wavelet descriptors, and has been shown to train far quicker than the popular backpropagation network while maintaining classification accuracy.

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.

Computational aspects of feedback in neural circuits.

Directory of Open Access Journals (Sweden)

Wolfgang Maass

2007-01-01

Full Text Available It has previously been shown that generic cortical microcircuit models can perform complex real-time computations on continuous input streams, provided that these computations can be carried out with a rapidly fading memory. We investigate the computational capability of such circuits in the more realistic case where not only readout neurons, but in addition a few neurons within the circuit, have been trained for specific tasks. This is essentially equivalent to the case where the output of trained readout neurons is fed back into the circuit. We show that this new model overcomes the limitation of a rapidly fading memory. In fact, we prove that in the idealized case without noise it can carry out any conceivable digital or analog computation on time-varying inputs. But even with noise, the resulting computational model can perform a large class of biologically relevant real-time computations that require a nonfading memory. We demonstrate these computational implications of feedback both theoretically, and through computer simulations of detailed cortical microcircuit models that are subject to noise and have complex inherent dynamics. We show that the application of simple learning procedures (such as linear regression or perceptron learning to a few neurons enables such circuits to represent time over behaviorally relevant long time spans, to integrate evidence from incoming spike trains over longer periods of time, and to process new information contained in such spike trains in diverse ways according to the current internal state of the circuit. In particular we show that such generic cortical microcircuits with feedback provide a new model for working memory that is consistent with a large set of biological constraints. Although this article examines primarily the computational role of feedback in circuits of neurons, the mathematical principles on which its analysis is based apply to a variety of dynamical systems. Hence they may also

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...

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...

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.

Olfactory systems and neural circuits that modulate predator odor fear

Directory of Open Access Journals (Sweden)

Lorey K. Takahashi

2014-03-01

Full Text Available 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, paraventricular nucleus of the hypothalamus, and the medial amygdala appear to be broadly involved in predator odor induced autonomic activity and hypothalamic-pituitary-adrenal stress hormone secretion. The medial amygdala 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 appear prominently involve in predator odor fear behavior. The basolateral amygdala, medial hypothalamic nuclei, and medial prefrontal cortex 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 odors activate

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

Analog synthetic biology.

Science.gov (United States)

Sarpeshkar, R

2014-03-28

We analyse the pros and cons of analog versus digital computation in living cells. Our analysis is based on fundamental laws of noise in gene and protein expression, which set limits on the energy, time, space, molecular count and part-count resources needed to compute at a given level of precision. We conclude that analog computation is significantly more efficient in its use of resources than deterministic digital computation even at relatively high levels of precision in the cell. Based on this analysis, we conclude that synthetic biology must use analog, collective analog, probabilistic and hybrid analog-digital computational approaches; otherwise, even relatively simple synthetic computations in cells such as addition will exceed energy and molecular-count budgets. We present schematics for efficiently representing analog DNA-protein computation in cells. Analog electronic flow in subthreshold transistors and analog molecular flux in chemical reactions obey Boltzmann exponential laws of thermodynamics and are described by astoundingly similar logarithmic electrochemical potentials. Therefore, cytomorphic circuits can help to map circuit designs between electronic and biochemical domains. We review recent work that uses positive-feedback linearization circuits to architect wide-dynamic-range logarithmic analog computation in Escherichia coli using three transcription factors, nearly two orders of magnitude more efficient in parts than prior digital implementations.

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.

Neural Circuits via Which Single Prolonged Stress Exposure Leads to Fear Extinction Retention Deficits

Science.gov (United States)

Knox, Dayan; Stanfield, Briana R.; Staib, Jennifer M.; David, Nina P.; Keller, Samantha M.; DePietro, Thomas

2016-01-01

Single prolonged stress (SPS) has been used to examine mechanisms via which stress exposure leads to post-traumatic stress disorder symptoms. SPS induces fear extinction retention deficits, but neural circuits critical for mediating these deficits are unknown. To address this gap, we examined the effect of SPS on neural activity in brain regions…

Realizing a Circuit Analog of an Optomechanical System with Longitudinally Coupled Superconducting Resonators

OpenAIRE

Eichler, C.; Petta, J. R.

2017-01-01

We realize a superconducting circuit analog of the generic cavity-optomechanical Hamiltonian by longitudinally coupling two superconducting resonators, which are an order of magnitude different in frequency. We achieve longitudinal coupling by embedding a superconducting quantum interference device (SQUID) into a high frequency resonator, making its resonance frequency depend on the zero point current fluctuations of a nearby low frequency LC-resonator. By employing sideband drive fields we e...

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.

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

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 ...... chip to solve simple classical conditioning tasks, thus verifying the design methodologies put forward in the paper....

An analog memory integrated circuit for waveform sampling up to 900 MHz

International Nuclear Information System (INIS)

Haller, G.M.; Wooley, B.A.

1994-01-01

The potential of switched-capacitor technology for acquiring analog signals in high-energy physics (HEP) applications has been demonstrated in a number of analog memory designs. The design and implementation of a switched-capacitor memory suitable for capturing high-speed analog waveforms is described. Highlights of the presented circuit are a 900 MHz sampling frequency (generated on chip), input signal independent cell pedestal and sampling instances, and cell gains that are insensitive to component sizes. A two-channel version of the memory with 32 cells for each channel has been integrate in a 2-μm complementary metal oxide semiconductor (CMOS) process with polysilicon-to-polysilicon capacitors. The measured rms cell response variation in a channel after cell pedestal subtraction is less than 0.3 mV across the full input signal range. The cell-to-cell gain matching is better than 0.01% rms, and the nonlinearity is less than 0.03% for a 2.5-V input range. The dynamic range of the memory exceeds 13 bits, and the peak signal-to-(noise + distortion) ratio for a 21.4 MHz sine wave sampled at 900 MHz is 59 dB

The Complexity of Dynamics in Small Neural Circuits.

Directory of Open Access Journals (Sweden)

Diego Fasoli

2016-08-01

Full Text Available Mean-field approximations are a powerful tool for studying large neural networks. However, they do not describe well the behavior of networks composed of a small number of neurons. In this case, major differences between the mean-field approximation and the real behavior of the network can arise. Yet, many interesting problems in neuroscience involve the study of mesoscopic networks composed of a few tens of neurons. Nonetheless, mathematical methods that correctly describe networks of small size are still rare, and this prevents us to make progress in understanding neural dynamics at these intermediate scales. Here we develop a novel systematic analysis of the dynamics of arbitrarily small networks composed of homogeneous populations of excitatory and inhibitory firing-rate neurons. We study the local bifurcations of their neural activity with an approach that is largely analytically tractable, and we numerically determine the global bifurcations. We find that for strong inhibition these networks give rise to very complex dynamics, caused by the formation of multiple branching solutions of the neural dynamics equations that emerge through spontaneous symmetry-breaking. This qualitative change of the neural dynamics is a finite-size effect of the network, that reveals qualitative and previously unexplored differences between mesoscopic cortical circuits and their mean-field approximation. The most important consequence of spontaneous symmetry-breaking is the ability of mesoscopic networks to regulate their degree of functional heterogeneity, which is thought to help reducing the detrimental effect of noise correlations on cortical information processing.

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.

Changed Synaptic Plasticity in Neural Circuits of Depressive-Like and Escitalopram-Treated Rats

Science.gov (United States)

Li, Xiao-Li; Yuan, Yong-Gui; Xu, Hua; Wu, Di; Gong, Wei-Gang; Geng, Lei-Yu; Wu, Fang-Fang; Tang, Hao; Xu, Lin

2015-01-01

Background: Although progress has been made in the detection and characterization of neural plasticity in depression, it has not been fully understood in individual synaptic changes in the neural circuits under chronic stress and antidepressant treatment. Methods: Using electron microscopy and Western-blot analyses, the present study quantitatively examined the changes in the Gray’s Type I synaptic ultrastructures and the expression of synapse-associated proteins in the key brain regions of rats’ depressive-related neural circuit after chronic unpredicted mild stress and/or escitalopram administration. Meanwhile, their depressive behaviors were also determined by several tests. Results: The Type I synapses underwent considerable remodeling after chronic unpredicted mild stress, which resulted in the changed width of the synaptic cleft, length of the active zone, postsynaptic density thickness, and/or synaptic curvature in the subregions of medial prefrontal cortex and hippocampus, as well as the basolateral amygdaloid nucleus of the amygdala, accompanied by changed expression of several synapse-associated proteins. Chronic escitalopram administration significantly changed the above alternations in the chronic unpredicted mild stress rats but had little effect on normal controls. Also, there was a positive correlation between the locomotor activity and the maximal synaptic postsynaptic density thickness in the stratum radiatum of the Cornu Ammonis 1 region and a negative correlation between the sucrose preference and the length of the active zone in the basolateral amygdaloid nucleus region in chronic unpredicted mild stress rats. Conclusion: These findings strongly indicate that chronic stress and escitalopram can alter synaptic plasticity in the neural circuits, and the remodeled synaptic ultrastructure was correlated with the rats’ depressive behaviors, suggesting a therapeutic target for further exploration. PMID:25899067

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

Directory of Open Access Journals (Sweden)

Jacob T. Robinson

2013-03-01

Full Text Available 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 these devices and discuss some of the technical challenges that must be overcome for this technology to become a platform for next-generation closed-loop BMIs.

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.

Emotion and decision making: multiple modulatory neural circuits.

Science.gov (United States)

Phelps, Elizabeth A; Lempert, Karolina M; Sokol-Hessner, Peter

2014-01-01

Although the prevalent view of emotion and decision making is derived from the notion that there are dual systems of emotion and reason, a modulatory relationship more accurately reflects the current research in affective neuroscience and neuroeconomics. Studies show two potential mechanisms for affect's modulation of the computation of subjective value and decisions. Incidental affective states may carry over to the assessment of subjective value and the decision, and emotional reactions to the choice may be incorporated into the value calculation. In addition, this modulatory relationship is reciprocal: Changing emotion can change choices. This research suggests that the neural mechanisms mediating the relation between affect and choice vary depending on which affective component is engaged and which decision variables are assessed. We suggest that a detailed and nuanced understanding of emotion and decision making requires characterizing the multiple modulatory neural circuits underlying the different means by which emotion and affect can influence choices.

Altered topology of neural circuits in congenital prosopagnosia.

Science.gov (United States)

Rosenthal, Gideon; Tanzer, Michal; Simony, Erez; Hasson, Uri; Behrmann, Marlene; Avidan, Galia

2017-08-21

Using a novel, fMRI-based inter-subject functional correlation (ISFC) approach, which isolates stimulus-locked inter-regional correlation patterns, we compared the cortical topology of the neural circuit for face processing in participants with an impairment in face recognition, congenital prosopagnosia (CP), and matched controls. Whereas the anterior temporal lobe served as the major network hub for face processing in controls, this was not the case for the CPs. Instead, this group evinced hyper-connectivity in posterior regions of the visual cortex, mostly associated with the lateral occipital and the inferior temporal cortices. Moreover, the extent of this hyper-connectivity was correlated with the face recognition deficit. These results offer new insights into the perturbed cortical topology in CP, which may serve as the underlying neural basis of the behavioral deficits typical of this disorder. The approach adopted here has the potential to uncover altered topologies in other neurodevelopmental disorders, as well.

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

Science.gov (United States)

Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

2017-01-01

In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

Science.gov (United States)

Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

2017-01-01

In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures. PMID:29066942

Memristor-Based Analog Computation and Neural Network Classification with a Dot Product Engine.

Science.gov (United States)

Hu, Miao; Graves, Catherine E; Li, Can; Li, Yunning; Ge, Ning; Montgomery, Eric; Davila, Noraica; Jiang, Hao; Williams, R Stanley; Yang, J Joshua; Xia, Qiangfei; Strachan, John Paul

2018-03-01

Using memristor crossbar arrays to accelerate computations is a promising approach to efficiently implement algorithms in deep neural networks. Early demonstrations, however, are limited to simulations or small-scale problems primarily due to materials and device challenges that limit the size of the memristor crossbar arrays that can be reliably programmed to stable and analog values, which is the focus of the current work. High-precision analog tuning and control of memristor cells across a 128 × 64 array is demonstrated, and the resulting vector matrix multiplication (VMM) computing precision is evaluated. Single-layer neural network inference is performed in these arrays, and the performance compared to a digital approach is assessed. Memristor computing system used here reaches a VMM accuracy equivalent of 6 bits, and an 89.9% recognition accuracy is achieved for the 10k MNIST handwritten digit test set. Forecasts show that with integrated (on chip) and scaled memristors, a computational efficiency greater than 100 trillion operations per second per Watt is possible. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

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.

Predicting the topology of dynamic neural networks for the simulation of electronic circuits

NARCIS (Netherlands)

Schilders, W.H.A.

2009-01-01

In this paper we discuss the use of the state-space modelling MOESP algorithm to generate precise information about the number of neurons and hidden layers in dynamic neural networks developed for the behavioural modelling of electronic circuits. The Bartels–Stewart algorithm is used to transform

A decision-making model based on a spiking neural circuit and synaptic plasticity.

Science.gov (United States)

Wei, Hui; Bu, Yijie; Dai, Dawei

2017-10-01

To adapt to the environment and survive, most animals can control their behaviors by making decisions. The process of decision-making and responding according to cues in the environment is stable, sustainable, and learnable. Understanding how behaviors are regulated by neural circuits and the encoding and decoding mechanisms from stimuli to responses are important goals in neuroscience. From results observed in Drosophila experiments, the underlying decision-making process is discussed, and a neural circuit that implements a two-choice decision-making model is proposed to explain and reproduce the observations. Compared with previous two-choice decision making models, our model uses synaptic plasticity to explain changes in decision output given the same environment. Moreover, biological meanings of parameters of our decision-making model are discussed. In this paper, we explain at the micro-level (i.e., neurons and synapses) how observable decision-making behavior at the macro-level is acquired and achieved.

Design and Analysis of Compact DNA Strand Displacement Circuits for Analog Computation Using Autocatalytic Amplifiers.

Science.gov (United States)

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

2018-01-19

A main goal in DNA computing is to build DNA circuits to compute designated functions using a minimal number of DNA strands. Here, we propose a novel architecture to build compact DNA strand displacement circuits to compute a broad scope of functions in an analog fashion. A circuit by this architecture is composed of three autocatalytic amplifiers, and the amplifiers interact to perform computation. We show DNA circuits to compute functions sqrt(x), ln(x) and exp(x) for x in tunable ranges with simulation results. A key innovation in our architecture, inspired by Napier's use of logarithm transforms to compute square roots on a slide rule, is to make use of autocatalytic amplifiers to do logarithmic and exponential transforms in concentration and time. In particular, we convert from the input that is encoded by the initial concentration of the input DNA strand, to time, and then back again to the output encoded by the concentration of the output DNA strand at equilibrium. This combined use of strand-concentration and time encoding of computational values may have impact on other forms of molecular computation.

Alteration in neonatal nutrition causes perturbations in hypothalamic neural circuits controlling reproductive function.

Science.gov (United States)

Caron, Emilie; Ciofi, Philippe; Prevot, Vincent; Bouret, Sebastien G

2012-08-15

It is increasingly accepted that alterations of the early life environment may have lasting impacts on physiological functions. In particular, epidemiological and animal studies have indicated that changes in growth and nutrition during childhood and adolescence can impair reproductive function. However, the precise biological mechanisms that underlie these programming effects of neonatal nutrition on reproduction are still poorly understood. Here, we used a mouse model of divergent litter size to investigate the effects of early postnatal overnutrition and undernutrition on the maturation of hypothalamic circuits involved in reproductive function. Neonatally undernourished females display attenuated postnatal growth associated with delayed puberty and defective development of axonal projections from the arcuate nucleus to the preoptic region. These alterations persist into adulthood and specifically affect the organization of neural projections containing kisspeptin, a key neuropeptide involved in pubertal activation and fertility. Neonatal overfeeding also perturbs the development of neural projections from the arcuate nucleus to the preoptic region, but it does not result in alterations in kisspeptin projections. These studies indicate that alterations in the early nutritional environment cause lasting and deleterious effects on the organization of neural circuits involved in the control of reproduction, and that these changes are associated with lifelong functional perturbations.

Pseudo-differential CMOS analog front-end circuit for wide-bandwidth optical probe current sensor

Science.gov (United States)

Uekura, Takaharu; Oyanagi, Kousuke; Sonehara, Makoto; Sato, Toshiro; Miyaji, Kousuke

2018-04-01

In this paper, we present a pseudo-differential analog front-end (AFE) circuit for a novel optical probe current sensor (OPCS) aimed for high-frequency power electronics. It employs a regulated cascode transimpedance amplifier (RGC-TIA) to achieve a high gain and a large bandwidth without using an extremely high performance operational amplifier. The AFE circuit is designed in a 0.18 µm standard CMOS technology achieving a high transimpedance gain of 120 dB Ω and high cut off frequency of 16 MHz. The measured slew rate is 70 V/µs and the input referred current noise is 1.02 pA/\\sqrt{\\text{Hz}} . The magnetic resolution and bandwidth of OPCS are estimated to be 1.29 mTrms and 16 MHz, respectively; the bandwidth is higher than that of the reported Hall effect current sensor.

An analog silicon retina with multichip configuration.

Science.gov (United States)

Kameda, Seiji; Yagi, Tetsuya

2006-01-01

The neuromorphic silicon retina is a novel analog very large scale integrated circuit that emulates the structure and the function of the retinal neuronal circuit. We fabricated a neuromorphic silicon retina, in which sample/hold circuits were embedded to generate fluctuation-suppressed outputs in the previous study [1]. The applications of this silicon retina, however, are limited because of a low spatial resolution and computational variability. In this paper, we have fabricated a multichip silicon retina in which the functional network circuits are divided into two chips: the photoreceptor network chip (P chip) and the horizontal cell network chip (H chip). The output images of the P chip are transferred to the H chip with analog voltages through the line-parallel transfer bus. The sample/hold circuits embedded in the P and H chips compensate for the pattern noise generated on the circuits, including the analog communication pathway. Using the multichip silicon retina together with an off-chip differential amplifier, spatial filtering of the image with an odd- and an even-symmetric orientation selective receptive fields was carried out in real time. The analog data transfer method in the present multichip silicon retina is useful to design analog neuromorphic multichip systems that mimic the hierarchical structure of neuronal networks in the visual system.

Analog Organic Electronics Building Blocks for Organic Smart Sensor Systems on Foil

CERN Document Server

Marien, Hagen; Heremans, Paul

2013-01-01

 This book provides insight into organic electronics technology and in analog circuit techniques that can be used to increase the performance of both analog and digital organic circuits. It explores the domain of organic electronics technology for analog circuit applications, specifically smart sensor systems.  It focuses on all the building blocks in the data path of an organic sensor system between the sensor and the digital processing block. Sensors, amplifiers, analog-to-digital converters and DC-DC converters are discussed in detail. Coverage includes circuit techniques, circuit implementation, design decisions and measurement results of the building blocks described. Offers readers the first book to focus on analog organic circuit design; Discusses organic electronics technology for analog circuit applications in the context of smart sensor systems; Describes all building blocks necessary for an organic sensor system between the sensor and the digital processing block; Includes circuit techniques, cir...

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

Directory of Open Access Journals (Sweden)

Tayfun Gokmen

2017-10-01

Full Text Available In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU devices to convolutional neural networks (CNNs. We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

Circuit Models and Experimental Noise Measurements of Micropipette Amplifiers for Extracellular Neural Recordings from Live Animals

Directory of Open Access Journals (Sweden)

Chang Hao Chen

2014-01-01

Full Text Available Glass micropipettes are widely used to record neural activity from single neurons or clusters of neurons extracellularly in live animals. However, to date, there has been no comprehensive study of noise in extracellular recordings with glass micropipettes. The purpose of this work was to assess various noise sources that affect extracellular recordings and to create model systems in which novel micropipette neural amplifier designs can be tested. An equivalent circuit of the glass micropipette and the noise model of this circuit, which accurately describe the various noise sources involved in extracellular recordings, have been developed. Measurement schemes using dead brain tissue as well as extracellular recordings from neurons in the inferior colliculus, an auditory brain nucleus of an anesthetized gerbil, were used to characterize noise performance and amplification efficacy of the proposed micropipette neural amplifier. According to our model, the major noise sources which influence the signal to noise ratio are the intrinsic noise of the neural amplifier and the thermal noise from distributed pipette resistance. These two types of noise were calculated and measured and were shown to be the dominating sources of background noise for in vivo experiments.

The neural circuits of innate fear: detection, integration, action, and memorization

Science.gov (United States)

Silva, Bianca A.; Gross, Cornelius T.

2016-01-01

How fear is represented in the brain has generated a lot of research attention, not only because fear increases the chances for survival when appropriately expressed but also because it can lead to anxiety and stress-related disorders when inadequately processed. In this review, we summarize recent progress in the understanding of the neural circuits processing innate fear in rodents. We propose that these circuits are contained within three main functional units in the brain: a detection unit, responsible for gathering sensory information signaling the presence of a threat; an integration unit, responsible for incorporating the various sensory information and recruiting downstream effectors; and an output unit, in charge of initiating appropriate bodily and behavioral responses to the threatful stimulus. In parallel, the experience of innate fear also instructs a learning process leading to the memorization of the fearful event. Interestingly, while the detection, integration, and output units processing acute fear responses to different threats tend to be harbored in distinct brain circuits, memory encoding of these threats seems to rely on a shared learning system. PMID:27634145

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 (g_m/g_DS 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.

Why we can talk, debate, and change our minds: neural circuits, basal ganglia operations, and transcriptional factors.

Science.gov (United States)

Lieberman, Philip

2014-12-01

Ackermann et al. disregard attested knowledge concerning aphasia, Parkinson disease, cortical-to-striatal circuits, basal ganglia, laryngeal phonation, and other matters. Their dual-pathway model cannot account for "what is special about the human brain." Their human cortical-to-laryngeal neural circuit does not exist. Basal ganglia operations, enhanced by mutations on FOXP2, confer human motor-control, linguistic, and cognitive capabilities.

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. Copyright © 2016 Elsevier Inc. All rights reserved.

Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems.

Science.gov (United States)

Chang, Sun-Il; Park, Sung-Yun; Yoon, Euisik

2018-01-17

This paper presents a minimally-invasive neural interface for distributed wireless electrocorticogram (ECoG) recording systems. The proposed interface equips all necessary components for ECoG recording, such as the high performance front-end integrated circuits, a fabricated flexible microelectrode array, and wireless communication inside a miniaturized custom-made platform. The multiple units of the interface systems can be deployed to cover a broad range of the target brain region and transmit signals via a built-in intra-skin communication (ISCOM) module. The core integrated circuit (IC) consists of 16-channel, low-power push-pull double-gated preamplifiers, in-channel successive approximation register analog-to-digital converters (SAR ADC) with a single-clocked bootstrapping switch and a time-delayed control unit, an ISCOM module for wireless data transfer through the skin instead of a power-hungry RF wireless transmitter, and a monolithic voltage/current reference generator to support the aforementioned analog and mixed-signal circuit blocks. The IC was fabricated using 250 nm CMOS processes in an area of 3.2 × 0.9 mm² and achieved the low-power operation of 2.5 µW per channel. Input-referred noise was measured as 5.62 µV rms for 10 Hz to 10 kHz and ENOB of 7.21 at 31.25 kS/s. The implemented system successfully recorded multi-channel neural activities in vivo from a primate and demonstrated modular expandability using the ISCOM with power consumption of 160 µW.

Low-power low-noise analog circuits for on-focal-plane signal processing of infrared sensors

Science.gov (United States)

Pain, Bedabrata; Mendis, Sunetra K.; Schober, Robert C.; Nixon, Robert H.; Fossum, Eric R.

1993-10-01

On-focal-plane signal processing circuits for enhancement of IR imager performance are presented. To enable the detection of high background IR images, an in-pixel current-mode background suppression scheme is presented. The background suppression circuit consists of a current memory placed in the feedback loop of a CTIA and is designed for a thousand-fold suppression of the background flux, thereby easing circuit design constraints, and assuring BLIP operation even with detectors having large response non-uniformities. For improving the performance of low-background IR imagers, an on-chip column-parallel analog-to-digital converter (ADC) is presented. The design of a 10-bit ADC with 50 micrometers pitch and based on sigma-delta ((Sigma) -(Delta) ) modulation is presented. A novel IR imager readout technique featuring photoelectron counting in the unit cell is presented for ultra-low background applications. The output of the unit cell is a digital word corresponding to the incident flux density and the readout is noise free. The design of low-power (noise, high-gain (> 100,000), small real estate (60 micrometers pitch) self-biased CMOS amplifiers required for photon counting are presented.

Analog storage integrated circuit

Science.gov (United States)

Walker, J.T.; Larsen, R.S.; Shapiro, S.L.

1989-03-07

A high speed data storage array is defined utilizing a unique cell design for high speed sampling of a rapidly changing signal. Each cell of the array includes two input gates between the signal input and a storage capacitor. The gates are controlled by a high speed row clock and low speed column clock so that the instantaneous analog value of the signal is only sampled and stored by each cell on coincidence of the two clocks. 6 figs.

Time domain analog circuit simulation

NARCIS (Netherlands)

Fijnvandraat, J.G.; Houben, S.H.M.J.; Maten, ter E.J.W.; Peters, J.M.F.

2006-01-01

Recent developments of new methods for simulating electric circuits are described. Emphasis is put on methods that fit existing datastructures for backward differentiation formulae methods. These methods can be modified to apply to hierarchically organized datastructures, which allows for efficient

CMOS circuit design, layout and simulation

CERN Document Server

Baker, R Jacob

2010-01-01

The Third Edition of CMOS Circuit Design, Layout, and Simulation continues to cover the practical design of both analog and digital integrated circuits, offering a vital, contemporary view of a wide range of analog/digital circuit blocks including: phase-locked-loops, delta-sigma sensing circuits, voltage/current references, op-amps, the design of data converters, and much more. Regardless of one's integrated circuit (IC) design skill level, this book allows readers to experience both the theory behind, and the hands-on implementation of, complementary metal oxide semiconductor (CMOS) IC design via detailed derivations, discussions, and hundreds of design, layout, and simulation examples.

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.

Stretchable Transparent Electrode Arrays for Simultaneous Electrical and Optical Interrogation of Neural Circuits in Vivo.

Science.gov (United States)

Zhang, Jing; Liu, Xiaojun; Xu, Wenjing; Luo, Wenhan; Li, Ming; Chu, Fangbing; Xu, Lu; Cao, Anyuan; Guan, Jisong; Tang, Shiming; Duan, Xiaojie

2018-04-09

Recent developments of transparent electrode arrays provide a unique capability for simultaneous optical and electrical interrogation of neural circuits in the brain. However, none of these electrode arrays possess the stretchability highly desired for interfacing with mechanically active neural systems, such as the brain under injury, the spinal cord, and the peripheral nervous system (PNS). Here, we report a stretchable transparent electrode array from carbon nanotube (CNT) web-like thin films that retains excellent electrochemical performance and broad-band optical transparency under stretching and is highly durable under cyclic stretching deformation. We show that the CNT electrodes record well-defined neuronal response signals with negligible light-induced artifacts from cortical surfaces under optogenetic stimulation. Simultaneous two-photon calcium imaging through the transparent CNT electrodes from cortical surfaces of GCaMP-expressing mice with epilepsy shows individual activated neurons in brain regions from which the concurrent electrical recording is taken, thus providing complementary cellular information in addition to the high-temporal-resolution electrical recording. Notably, the studies on rats show that the CNT electrodes remain operational during and after brain contusion that involves the rapid deformation of both the electrode array and brain tissue. This enables real-time, continuous electrophysiological monitoring of cortical activity under traumatic brain injury. These results highlight the potential application of the stretchable transparent CNT electrode arrays in combining electrical and optical modalities to study neural circuits, especially under mechanically active conditions, which could potentially provide important new insights into the local circuit dynamics of the spinal cord and PNS as well as the mechanism underlying traumatic injuries of the nervous system.

Neuromimetic Circuits with Synaptic Devices Based on Strongly Correlated Electron Systems

Science.gov (United States)

Ha, Sieu D.; Shi, Jian; Meroz, Yasmine; Mahadevan, L.; Ramanathan, Shriram

2014-12-01

Strongly correlated electron systems such as the rare-earth nickelates (R NiO3 , R denotes a rare-earth element) can exhibit synapselike continuous long-term potentiation and depression when gated with ionic liquids; exploiting the extreme sensitivity of coupled charge, spin, orbital, and lattice degrees of freedom to stoichiometry. We present experimental real-time, device-level classical conditioning and unlearning using nickelate-based synaptic devices in an electronic circuit compatible with both excitatory and inhibitory neurons. We establish a physical model for the device behavior based on electric-field-driven coupled ionic-electronic diffusion that can be utilized for design of more complex systems. We use the model to simulate a variety of associate and nonassociative learning mechanisms, as well as a feedforward recurrent network for storing memory. Our circuit intuitively parallels biological neural architectures, and it can be readily generalized to other forms of cellular learning and extinction. The simulation of neural function with electronic device analogs may provide insight into biological processes such as decision making, learning, and adaptation, while facilitating advanced parallel information processing in hardware.

Synthetic analog computation in living cells.

Science.gov (United States)

Daniel, Ramiz; Rubens, Jacob R; Sarpeshkar, Rahul; Lu, Timothy K

2013-05-30

A central goal of synthetic biology is to achieve multi-signal integration and processing in living cells for diagnostic, therapeutic and biotechnology applications. Digital logic has been used to build small-scale circuits, but other frameworks may be needed for efficient computation in the resource-limited environments of cells. Here we demonstrate that synthetic analog gene circuits can be engineered to execute sophisticated computational functions in living cells using just three transcription factors. Such synthetic analog gene circuits exploit feedback to implement logarithmically linear sensing, addition, ratiometric and power-law computations. The circuits exhibit Weber's law behaviour as in natural biological systems, operate over a wide dynamic range of up to four orders of magnitude and can be designed to have tunable transfer functions. Our circuits can be composed to implement higher-order functions that are well described by both intricate biochemical models and simple mathematical functions. By exploiting analog building-block functions that are already naturally present in cells, this approach efficiently implements arithmetic operations and complex functions in the logarithmic domain. Such circuits may lead to new applications for synthetic biology and biotechnology that require complex computations with limited parts, need wide-dynamic-range biosensing or would benefit from the fine control of gene expression.

Ultra-low power integrated circuit design circuits, systems, and applications

CERN Document Server

Li, Dongmei; Wang, Zhihua

2014-01-01

This book describes the design of CMOS circuits for ultra-low power consumption including analog, radio frequency (RF), and digital signal processing circuits (DSP). The book addresses issues from circuit and system design to production design, and applies the ultra-low power circuits described to systems for digital hearing aids and capsule endoscope devices. Provides a valuable introduction to ultra-low power circuit design, aimed at practicing design engineers; Describes all key building blocks of ultra-low power circuits, from a systems perspective; Applies circuits and systems described to real product examples such as hearing aids and capsule endoscopes.

CMOS Analog IC Design: Fundamentals

OpenAIRE

Bruun, Erik

2018-01-01

This book is intended for use as the main textbook for an introductory course in CMOS analog integrated circuit design. It is aimed at electronics engineering students who have followed basic courses in mathematics, physics, circuit theory, electronics and signal processing. It takes the students directly from a basic level to a level where they can start working on simple analog IC design projects or continue their studies using more advanced textbooks in the field. A distinct feature of thi...

Neural correlates of creativity in analogical reasoning.

Science.gov (United States)

Green, Adam E; Kraemer, David J M; Fugelsang, Jonathan A; Gray, Jeremy R; Dunbar, Kevin N

2012-03-01

Brain-based evidence has implicated the frontal pole of the brain as important for analogical mapping. Separately, cognitive research has identified semantic distance as a key determinant of the creativity of analogical mapping (i.e., more distant analogies are generally more creative). Here, we used functional magnetic resonance imaging to assess brain activity during an analogy generation task in which we varied the semantic distance of analogical mapping (as derived quantitatively from a latent semantic analysis). Data indicated that activity within an a priori region of interest in left frontopolar cortex covaried parametrically with increasing semantic distance, even after removing effects of task difficulty. Results implicate increased recruitment of frontopolar cortex as a mechanism for integrating semantically distant information to generate solutions in creative analogical reasoning. 2012 APA, all rights reserved

Copper is an endogenous modulator of neural circuit spontaneous activity.

Science.gov (United States)

Dodani, Sheel C; Firl, Alana; Chan, Jefferson; Nam, Christine I; Aron, Allegra T; Onak, Carl S; Ramos-Torres, Karla M; Paek, Jaeho; Webster, Corey M; Feller, Marla B; Chang, Christopher J

2014-11-18

For reasons that remain insufficiently understood, the brain requires among the highest levels of metals in the body for normal function. The traditional paradigm for this organ and others is that fluxes of alkali and alkaline earth metals are required for signaling, but transition metals are maintained in static, tightly bound reservoirs for metabolism and protection against oxidative stress. Here we show that copper is an endogenous modulator of spontaneous activity, a property of functional neural circuitry. Using Copper Fluor-3 (CF3), a new fluorescent Cu(+) sensor for one- and two-photon imaging, we show that neurons and neural tissue maintain basal stores of loosely bound copper that can be attenuated by chelation, which define a labile copper pool. Targeted disruption of these labile copper stores by acute chelation or genetic knockdown of the CTR1 (copper transporter 1) copper channel alters the spatiotemporal properties of spontaneous activity in developing hippocampal and retinal circuits. The data identify an essential role for copper neuronal function and suggest broader contributions of this transition metal to cell signaling.

Low-voltage current-mode CMOS building blocks for field programmable analog arrays and application

International Nuclear Information System (INIS)

Madian, A.H.K.

2007-01-01

The role of analog integrated circuits in modem electronic systems remains important, even though digital circuits dominate the market for VLSI solutions. Analog systems have always played an essential role in interfacing digital electronics to the real world in applications such as analog signal processing and signal conditioning .Industrial process and motion control and biomedical measurements . In addition, analog solutions are becoming increasingly competitive with digital circuits for dense, low-power, high-speed applications in low-precision signal-processing. Because of the wide variety of analog functions required in electronic systems and the complexity of the signals (frequency, time, signal levels, parasitic), analog system design is very specialized and supported by a diverse set of CAD tools that are more difficult to integrate than those required for digital design. The drive towards shorter design cycles for analog integrated circuits has demanded the development of high performance analog circuits that are re configurable and suitable for CAD methodologies. the researcher here try to contribute in this filed

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...

Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems

Directory of Open Access Journals (Sweden)

Sun-Il Chang

2018-01-01

Full Text Available This paper presents a minimally-invasive neural interface for distributed wireless electrocorticogram (ECoG recording systems. The proposed interface equips all necessary components for ECoG recording, such as the high performance front-end integrated circuits, a fabricated flexible microelectrode array, and wireless communication inside a miniaturized custom-made platform. The multiple units of the interface systems can be deployed to cover a broad range of the target brain region and transmit signals via a built-in intra-skin communication (ISCOM module. The core integrated circuit (IC consists of 16-channel, low-power push-pull double-gated preamplifiers, in-channel successive approximation register analog-to-digital converters (SAR ADC with a single-clocked bootstrapping switch and a time-delayed control unit, an ISCOM module for wireless data transfer through the skin instead of a power-hungry RF wireless transmitter, and a monolithic voltage/current reference generator to support the aforementioned analog and mixed-signal circuit blocks. The IC was fabricated using 250 nm CMOS processes in an area of 3.2 × 0.9 mm2 and achieved the low-power operation of 2.5 µW per channel. Input-referred noise was measured as 5.62 µVrms for 10 Hz to 10 kHz and ENOB of 7.21 at 31.25 kS/s. The implemented system successfully recorded multi-channel neural activities in vivo from a primate and demonstrated modular expandability using the ISCOM with power consumption of 160 µW.

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

International Nuclear Information System (INIS)

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 ampersand T's CBIC-U2, 4 GHz f 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 μm pitch double-sided silicon strip detector. The chip measures 6.8 mm x 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 Φ=10 14 protons/cm 2 have been performed on the IC, demonstrating the radiation hardness of the complementary bipolar process

Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits.

Science.gov (United States)

Ujfalussy, Balázs B; Makara, Judit K; Branco, Tiago; Lengyel, Máté

2015-12-24

Cortical neurons integrate thousands of synaptic inputs in their dendrites in highly nonlinear ways. It is unknown how these dendritic nonlinearities in individual cells contribute to computations at the level of neural circuits. Here, we show that dendritic nonlinearities are critical for the efficient integration of synaptic inputs in circuits performing analog computations with spiking neurons. We developed a theory that formalizes how a neuron's dendritic nonlinearity that is optimal for integrating synaptic inputs depends on the statistics of its presynaptic activity patterns. Based on their in vivo preynaptic population statistics (firing rates, membrane potential fluctuations, and correlations due to ensemble dynamics), our theory accurately predicted the responses of two different types of cortical pyramidal cells to patterned stimulation by two-photon glutamate uncaging. These results reveal a new computational principle underlying dendritic integration in cortical neurons by suggesting a functional link between cellular and systems--level properties of cortical circuits.

Circuit with a successive approximation analog to digital converter

NARCIS (Netherlands)

Louwsma, S.M.; Vertregt, Maarten

2011-01-01

During successive approximation analog to digital conversion a series of successive digital reference values is selected that converges towards a digital representation of an analog input signal. An analog reference signal is generated dependent on the successive digital reference values and

Circuit with a successive approximation analog to digital converter

NARCIS (Netherlands)

Louwsma, S.M.; Vertregt, Maarten

2010-01-01

During successive approximation analog to digital conversion a series of successive digital reference values is selected that converges towards a digital representation of an analog input signal. An analog reference signal is generated dependent on the successive digital reference values and

Digital signal processing in power electronics control circuits

CERN Document Server

Sozanski, Krzysztof

2013-01-01

Many digital control circuits in current literature are described using analog transmittance. This may not always be acceptable, especially if the sampling frequency and power transistor switching frequencies are close to the band of interest. Therefore, a digital circuit is considered as a digital controller rather than an analog circuit. This helps to avoid errors and instability in high frequency components. Digital Signal Processing in Power Electronics Control Circuits covers problems concerning the design and realization of digital control algorithms for power electronics circuits using

A 1.4-V 48-μW current-mode front-end circuit for analog hearing aids with frequency compensation

International Nuclear Information System (INIS)

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 (V TON + |V TOP |≈ 1.35 V). The THD is below −45 dB. The fabricated chip only occupies the area of 1 × 0.5 mm 2 and 1 × 1 mm 2 .

Computing with networks of spiking neurons on a biophysically motivated floating-gate based neuromorphic integrated circuit.

Science.gov (United States)

Brink, S; Nease, S; Hasler, P

2013-09-01

Results are presented from several spiking network experiments performed on a novel neuromorphic integrated circuit. The networks are discussed in terms of their computational significance, which includes applications such as arbitrary spatiotemporal pattern generation and recognition, winner-take-all competition, stable generation of rhythmic outputs, and volatile memory. Analogies to the behavior of real biological neural systems are also noted. The alternatives for implementing the same computations are discussed and compared from a computational efficiency standpoint, with the conclusion that implementing neural networks on neuromorphic hardware is significantly more power efficient than numerical integration of model equations on traditional digital hardware. Copyright © 2013 Elsevier Ltd. All rights reserved.

Analog fault diagnosis by inverse problem technique

KAUST Repository

Ahmed, Rania F.

2011-12-01

A novel algorithm for detecting soft faults in linear analog circuits based on the inverse problem concept is proposed. The proposed approach utilizes optimization techniques with the aid of sensitivity analysis. The main contribution of this work is to apply the inverse problem technique to estimate the actual parameter values of the tested circuit and so, to detect and diagnose single fault in analog circuits. The validation of the algorithm is illustrated through applying it to Sallen-Key second order band pass filter and the results show that the detecting percentage efficiency was 100% and also, the maximum error percentage of estimating the parameter values is 0.7%. This technique can be applied to any other linear circuit and it also can be extended to be applied to non-linear circuits. © 2011 IEEE.

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.

Low-Noise CMOS Circuits for On-Chip Signal Processing in Focal-Plane Arrays

Science.gov (United States)

Pain, Bedabrata

The performance of focal-plane arrays can be significantly enhanced through the use of on-chip signal processing. Novel, in-pixel, on-focal-plane, analog signal-processing circuits for high-performance imaging are presented in this thesis. The presence of a high background-radiation is a major impediment for infrared focal-plane array design. An in-pixel, background-suppression scheme, using dynamic analog current memory circuit, is described. The scheme also suppresses spatial noise that results from response non-uniformities of photo-detectors, leading to background limited infrared detector readout performance. Two new, low-power, compact, current memory circuits, optimized for operation at ultra-low current levels required in infrared-detection, are presented. The first one is a self-cascading current memory that increases the output impedance, and the second one is a novel, switch feed-through reducing current memory, implemented using error-current feedback. This circuit can operate with a residual absolute -error of less than 0.1%. The storage-time of the memory is long enough to also find applications in neural network circuits. In addition, a voltage-mode, accurate, low-offset, low-power, high-uniformity, random-access sample-and-hold cell, implemented using a CCD with feedback, is also presented for use in background-suppression and neural network applications. A new, low noise, ultra-low level signal readout technique, implemented by individually counting photo-electrons within the detection pixel, is presented. The output of each unit-cell is a digital word corresponding to the intensity of the photon flux, and the readout is noise free. This technique requires the use of unit-cell amplifiers that feature ultra-high-gain, low-power, self-biasing capability and noise in sub-electron levels. Both single-input and differential-input implementations of such amplifiers are investigated. A noise analysis technique is presented for analyzing sampled

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

Analog and mixed-signal electronics

CERN Document Server

Stephan, Karl

2015-01-01

A practical guide to analog and mixed-signal electronics, with an emphasis on design problems and applications This book provides an in-depth coverage of essential analog and mixed-signal topics such as power amplifiers, active filters, noise and dynamic range, analog-to-digital and digital-to-analog conversion techniques, phase-locked loops, and switching power supplies. Readers will learn the basics of linear systems, types of nonlinearities and their effects, op-amp circuits, the high-gain analog filter-amplifier, and signal generation. The author uses system design examples to motivate

The road to restoring neural circuits for the treatment of Alzheimer's disease.

Science.gov (United States)

Canter, Rebecca G; Penney, Jay; Tsai, Li-Huei

2016-11-10

Alzheimer's disease is a progressive loss of memory and cognition, for which there is no cure. Although genetic studies initially suggested a primary role for amyloid-in Alzheimer's disease, treatment strategies targeted at reducing amyloid-have failed to reverse cognitive symptoms. These clinical findings suggest that cognitive decline is the result of a complex pathophysiology and that targeting amyloid-alone may not be sufficient to treat Alzheimer's disease. Instead, a broad outlook on neural-circuit-damaging processes may yield insights into new therapeutic strategies for curing memory loss in the disease.

Bootstrapped Low-Voltage Analog Switches

DEFF Research Database (Denmark)

Steensgaard-Madsen, Jesper

1999-01-01

Novel low-voltage constant-impedance analog switch circuits are proposed. The switch element is a single MOSFET, and constant-impedance operation is obtained using simple circuits to adjust the gate and bulk voltages relative to the switched signal. Low-voltage (1-volt) operation is made feasible...

Contribution of custom-designed integrated circuits to the electronic equipment of multiwire chambers

International Nuclear Information System (INIS)

Prunier, J.

1977-01-01

The first generations of circuits intended to equip the multiwire proportional chambers provided the user with logical type indications (absence or presence of a signal at a given place). This logical indication was soon associated with a semi-analog data (presence or absence of a signal above an analog threshold, i.e. the discrimination function) as with FILAS, RBA and RBB circuits. The evolution continued with the appearance of analog data capture (time, amplitude, charge) and the corresponding circuits: IFT circuits, analog-to-digital converters [fr

Toward Wireless Health Monitoring via an Analog Signal Compression-Based Biosensing Platform.

Science.gov (United States)

Zhao, Xueyuan; Sadhu, Vidyasagar; Le, Tuan; Pompili, Dario; Javanmard, Mehdi

2018-06-01

Wireless all-analog biosensor design for the concurrent microfluidic and physiological signal monitoring is presented in this paper. The key component is an all-analog circuit capable of compressing two analog sources into one analog signal by the analog joint source-channel coding (AJSCC). Two circuit designs are discussed, including the stacked-voltage-controlled voltage source (VCVS) design with the fixed number of levels, and an improved design, which supports a flexible number of AJSCC levels. Experimental results are presented on the wireless biosensor prototype, composed of printed circuit board realizations of the stacked-VCVS design. Furthermore, circuit simulation and wireless link simulation results are presented on the improved design. Results indicate that the proposed wireless biosensor is well suited for sensing two biological signals simultaneously with high accuracy, and can be applied to a wide variety of low-power and low-cost wireless continuous health monitoring applications.

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).

A note on exponential convergence of neural networks with unbounded distributed delays

Energy Technology Data Exchange (ETDEWEB)

Chu Tianguang [Intelligent Control Laboratory, Center for Systems and Control, Department of Mechanics and Engineering Science, Peking University, Beijing 100871 (China)]. E-mail: chutg@pku.edu.cn; Yang Haifeng [Intelligent Control Laboratory, Center for Systems and Control, Department of Mechanics and Engineering Science, Peking University, Beijing 100871 (China)

2007-12-15

This note examines issues concerning global exponential convergence of neural networks with unbounded distributed delays. Sufficient conditions are derived by exploiting exponentially fading memory property of delay kernel functions. The method is based on comparison principle of delay differential equations and does not need the construction of any Lyapunov functionals. It is simple yet effective in deriving less conservative exponential convergence conditions and more detailed componentwise decay estimates. The results of this note and [Chu T. An exponential convergence estimate for analog neural networks with delay. Phys Lett A 2001;283:113-8] suggest a class of neural networks whose globally exponentially convergent dynamics is completely insensitive to a wide range of time delays from arbitrary bounded discrete type to certain unbounded distributed type. This is of practical interest in designing fast and reliable neural circuits. Finally, an open question is raised on the nature of delay kernels for attaining exponential convergence in an unbounded distributed delayed neural network.

A note on exponential convergence of neural networks with unbounded distributed delays

International Nuclear Information System (INIS)

Chu Tianguang; Yang Haifeng

2007-01-01

This note examines issues concerning global exponential convergence of neural networks with unbounded distributed delays. Sufficient conditions are derived by exploiting exponentially fading memory property of delay kernel functions. The method is based on comparison principle of delay differential equations and does not need the construction of any Lyapunov functionals. It is simple yet effective in deriving less conservative exponential convergence conditions and more detailed componentwise decay estimates. The results of this note and [Chu T. An exponential convergence estimate for analog neural networks with delay. Phys Lett A 2001;283:113-8] suggest a class of neural networks whose globally exponentially convergent dynamics is completely insensitive to a wide range of time delays from arbitrary bounded discrete type to certain unbounded distributed type. This is of practical interest in designing fast and reliable neural circuits. Finally, an open question is raised on the nature of delay kernels for attaining exponential convergence in an unbounded distributed delayed neural network

A 16-Channel CMOS Chopper-Stabilized Analog Front-End ECoG Acquisition Circuit for a Closed-Loop Epileptic Seizure Control System.

Science.gov (United States)

Wu, Chung-Yu; Cheng, Cheng-Hsiang; Chen, Zhi-Xin

2018-06-01

In this paper, a 16-channel analog front-end (AFE) electrocorticography signal acquisition circuit for a closed-loop seizure control system is presented. It is composed of 16 input protection circuits, 16 auto-reset chopper-stabilized capacitive-coupled instrumentation amplifiers (AR-CSCCIA) with bandpass filters, 16 programmable transconductance gain amplifiers, a multiplexer, a transimpedance amplifier, and a 128-kS/s 10-bit delta-modulated successive-approximation-register analog-to-digital converter (SAR ADC). In closed-loop seizure control system applications, the stimulator shares the same electrode with the AFE amplifier for effective suppression of epileptic seizures. To prevent from overstress in MOS devices caused by high stimulation voltage, an input protection circuit with a high-voltage-tolerant switch is proposed for the AFE amplifier. Moreover, low input-referred noise is achieved by using the chopper modulation technique in the AR-CSCCIA. To reduce the undesired effects of chopper modulation, an improved offset reduction loop is proposed to reduce the output offset generated by input chopper mismatches. The digital ripple reduction loop is also used to reduce the chopper ripple. The fabricated AFE amplifier has 49.1-/59.4-/67.9-dB programmable gain and 2.02-μVrms input referred noise in a bandwidth of 0.59-117 Hz. The measured power consumption of the AFE amplifier is 3.26 μW per channel, and the noise efficiency factor is 3.36. The in vivo animal test has been successfully performed to verify the functions. It is shown that the proposed AFE acquisition circuit is suitable for implantable closed-loop seizure control systems.

Reactivating Neural Circuits with Clinically Accessible Stimulation to Restore Hand Function in Persons with Tetraplegia

Science.gov (United States)

2017-09-01

AWARD NUMBER: W81XWH-16-1-0395 TITLE: Reactivating Neural Circuits with Clinically Accessible Stimulation to Restore Hand Function in...estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data...Clinically Accessible Stimulation to Restore Hand Function in Persons with Tetraplegia 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S

An Integrated Circuit for Simultaneous Extracellular Electrophysiology Recording and Optogenetic Neural Manipulation.

Science.gov (United States)

Chen, Chang Hao; McCullagh, Elizabeth A; Pun, Sio Hang; Mak, Peng Un; Vai, Mang I; Mak, Pui In; Klug, Achim; Lei, Tim C

2017-03-01

The ability to record and to control action potential firing in neuronal circuits is critical to understand how the brain functions. The objective of this study is to develop a monolithic integrated circuit (IC) to record action potentials and simultaneously control action potential firing using optogenetics. A low-noise and high input impedance (or low input capacitance) neural recording amplifier is combined with a high current laser/light-emitting diode (LED) driver in a single IC. The low input capacitance of the amplifier (9.7 pF) was achieved by adding a dedicated unity gain stage optimized for high impedance metal electrodes. The input referred noise of the amplifier is [Formula: see text], which is lower than the estimated thermal noise of the metal electrode. Thus, the action potentials originating from a single neuron can be recorded with a signal-to-noise ratio of at least 6.6. The LED/laser current driver delivers a maximum current of 330 mA, which is adequate for optogenetic control. The functionality of the IC was tested with an anesthetized Mongolian gerbil and auditory stimulated action potentials were recorded from the inferior colliculus. Spontaneous firings of fifth (trigeminal) nerve fibers were also inhibited using the optogenetic protein Halorhodopsin. Moreover, a noise model of the system was derived to guide the design. A single IC to measure and control action potentials using optogenetic proteins is realized so that more complicated behavioral neuroscience research and the translational neural disorder treatments become possible in the future.

Advances in analog and RF IC design for wireless communication systems

CERN Document Server

Manganaro, Gabriele

2013-01-01

Advances in Analog and RF IC Design for Wireless Communication Systems gives technical introductions to the latest and most significant topics in the area of circuit design of analog/RF ICs for wireless communication systems, emphasizing wireless infrastructure rather than handsets. The book ranges from very high performance circuits for complex wireless infrastructure systems to selected highly integrated systems for handsets and mobile devices. Coverage includes power amplifiers, low-noise amplifiers, modulators, analog-to-digital converters (ADCs) and digital-to-analog converters

Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

Science.gov (United States)

Broccard, Frédéric D.; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert

2017-08-01

Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Main results. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a

Event-Based Computation of Motion Flow on a Neuromorphic Analog Neural Platform.

Science.gov (United States)

Giulioni, Massimiliano; Lagorce, Xavier; Galluppi, Francesco; Benosman, Ryad B

2016-01-01

Estimating the speed and direction of moving objects is a crucial component of agents behaving in a dynamic world. Biological organisms perform this task by means of the neural connections originating from their retinal ganglion cells. In artificial systems the optic flow is usually extracted by comparing activity of two or more frames captured with a vision sensor. Designing artificial motion flow detectors which are as fast, robust, and efficient as the ones found in biological systems is however a challenging task. Inspired by the architecture proposed by Barlow and Levick in 1965 to explain the spiking activity of the direction-selective ganglion cells in the rabbit's retina, we introduce an architecture for robust optical flow extraction with an analog neuromorphic multi-chip system. The task is performed by a feed-forward network of analog integrate-and-fire neurons whose inputs are provided by contrast-sensitive photoreceptors. Computation is supported by the precise time of spike emission, and the extraction of the optical flow is based on time lag in the activation of nearby retinal neurons. Mimicking ganglion cells our neuromorphic detectors encode the amplitude and the direction of the apparent visual motion in their output spiking pattern. Hereby we describe the architectural aspects, discuss its latency, scalability, and robustness properties and demonstrate that a network of mismatched delicate analog elements can reliably extract the optical flow from a simple visual scene. This work shows how precise time of spike emission used as a computational basis, biological inspiration, and neuromorphic systems can be used together for solving specific tasks.

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

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.

Bio-inspired feedback-circuit implementation of discrete, free energy optimizing, winner-take-all computations.

Science.gov (United States)

Genewein, Tim; Braun, Daniel A

2016-06-01

Bayesian inference and bounded rational decision-making require the accumulation of evidence or utility, respectively, to transform a prior belief or strategy into a posterior probability distribution over hypotheses or actions. Crucially, this process cannot be simply realized by independent integrators, since the different hypotheses and actions also compete with each other. In continuous time, this competitive integration process can be described by a special case of the replicator equation. Here we investigate simple analog electric circuits that implement the underlying differential equation under the constraint that we only permit a limited set of building blocks that we regard as biologically interpretable, such as capacitors, resistors, voltage-dependent conductances and voltage- or current-controlled current and voltage sources. The appeal of these circuits is that they intrinsically perform normalization without requiring an explicit divisive normalization. However, even in idealized simulations, we find that these circuits are very sensitive to internal noise as they accumulate error over time. We discuss in how far neural circuits could implement these operations that might provide a generic competitive principle underlying both perception and action.

Electronic circuit for rapid digital NMR signal imaging

International Nuclear Information System (INIS)

Jurak, P.; Krejci, I.; Belusa, J.

1992-01-01

The circuit is made up of two analog-to-digital converters whose outputs are connected to a process computer and the synchronization inputs to the clock terminal. The one analog-to-digital converter is connected, via the signal input, to the terminal of the nuclear magnetic resonance locking signal. The signal input of the other analog-to-digital converter is connected to the time base generator, which can be switched off, and to the magnetic field sweep circuit. The assets of this citcuit include easy computerized processing of the digitized information independently of the time base generation, and prevention of interfering signals from penetrating into the magnetic field sweep circuits. (Z.S.). 1 fig

A Voltage Mode Memristor Bridge Synaptic Circuit with Memristor Emulators

Directory of Open Access Journals (Sweden)

Leon Chua

2012-03-01

Full Text Available A memristor bridge neural circuit which is able to perform signed synaptic weighting was proposed in our previous study, where the synaptic operation was verified via software simulation of the mathematical model of the HP memristor. This study is an extension of the previous work advancing toward the circuit implementation where the architecture of the memristor bridge synapse is built with memristor emulator circuits. In addition, a simple neural network which performs both synaptic weighting and summation is built by combining memristor emulators-based synapses and differential amplifier circuits. The feasibility of the memristor bridge neural circuit is verified via SPICE simulations.

Transistor analogs of emergent iono-neuronal dynamics.

Science.gov (United States)

Rachmuth, Guy; Poon, Chi-Sang

2008-06-01

Neuromorphic analog metal-oxide-silicon (MOS) transistor circuits promise compact, low-power, and high-speed emulations of iono-neuronal dynamics orders-of-magnitude faster than digital simulation. However, their inherently limited input voltage dynamic range vs power consumption and silicon die area tradeoffs makes them highly sensitive to transistor mismatch due to fabrication inaccuracy, device noise, and other nonidealities. This limitation precludes robust analog very-large-scale-integration (aVLSI) circuits implementation of emergent iono-neuronal dynamics computations beyond simple spiking with limited ion channel dynamics. Here we present versatile neuromorphic analog building-block circuits that afford near-maximum voltage dynamic range operating within the low-power MOS transistor weak-inversion regime which is ideal for aVLSI implementation or implantable biomimetic device applications. The fabricated microchip allowed robust realization of dynamic iono-neuronal computations such as coincidence detection of presynaptic spikes or pre- and postsynaptic activities. As a critical performance benchmark, the high-speed and highly interactive iono-neuronal simulation capability on-chip enabled our prompt discovery of a minimal model of chaotic pacemaker bursting, an emergent iono-neuronal behavior of fundamental biological significance which has hitherto defied experimental testing or computational exploration via conventional digital or analog simulations. These compact and power-efficient transistor analogs of emergent iono-neuronal dynamics open new avenues for next-generation neuromorphic, neuroprosthetic, and brain-machine interface applications.

Automatic analog IC sizing and optimization constrained with PVT corners and layout effects

CERN Document Server

Lourenço, Nuno; Horta, Nuno

2017-01-01

This book introduces readers to a variety of tools for automatic analog integrated circuit (IC) sizing and optimization. The authors provide a historical perspective on the early methods proposed to tackle automatic analog circuit sizing, with emphasis on the methodologies to size and optimize the circuit, and on the methodologies to estimate the circuit’s performance. The discussion also includes robust circuit design and optimization and the most recent advances in layout-aware analog sizing approaches. The authors describe a methodology for an automatic flow for analog IC design, including details of the inputs and interfaces, multi-objective optimization techniques, and the enhancements made in the base implementation by using machine leaning techniques. The Gradient model is discussed in detail, along with the methods to include layout effects in the circuit sizing. The concepts and algorithms of all the modules are thoroughly described, enabling readers to reproduce the methodologies, improve the qual...

Toward a 62.5 MHz analog virtual pipeline integrated data acquisition system

International Nuclear Information System (INIS)

Kleinfelder, S.A.; Levi, M.; Milgrome, O.

1991-01-01

Requirements of analog pipeline memories at the SSC are reviewed and the concept of virtual pipelines is introduced. Design details and test results of several new custom analog and digital integrated circuits implementing sections of the virtual multiple pipeline (VMP) scheme are provied. These include serial, random access and simultaneous read and write random access analog storage and retrieval circuits, a 100 MHz systolic variable depth digital pipeline, and a prototye 32 μs, 12 bit serial analog to digital converter. (orig.)

A Neural Circuit for Auditory Dominance over Visual Perception.

Science.gov (United States)

Song, You-Hyang; Kim, Jae-Hyun; Jeong, Hye-Won; Choi, Ilsong; Jeong, Daun; Kim, Kwansoo; Lee, Seung-Hee

2017-02-22

When conflicts occur during integration of visual and auditory information, one modality often dominates the other, but the underlying neural circuit mechanism remains unclear. Using auditory-visual discrimination tasks for head-fixed mice, we found that audition dominates vision in a process mediated by interaction between inputs from the primary visual (VC) and auditory (AC) cortices in the posterior parietal cortex (PTLp). Co-activation of the VC and AC suppresses VC-induced PTLp responses, leaving AC-induced responses. Furthermore, parvalbumin-positive (PV+) interneurons in the PTLp mainly receive AC inputs, and muscimol inactivation of the PTLp or optogenetic inhibition of its PV+ neurons abolishes auditory dominance in the resolution of cross-modal sensory conflicts without affecting either sensory perception. Conversely, optogenetic activation of PV+ neurons in the PTLp enhances the auditory dominance. Thus, our results demonstrate that AC input-specific feedforward inhibition of VC inputs in the PTLp is responsible for the auditory dominance during cross-modal integration. Copyright © 2017 Elsevier Inc. All rights reserved.

An Analog Computer for Electronic Engineering Education

Science.gov (United States)

Fitch, A. L.; Iu, H. H. C.; Lu, D. D. C.

2011-01-01

This paper describes a compact analog computer and proposes its use in electronic engineering teaching laboratories to develop student understanding of applications in analog electronics, electronic components, engineering mathematics, control engineering, safe laboratory and workshop practices, circuit construction, testing, and maintenance. The…

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.

Precision-analog fiber-optic transmission system

International Nuclear Information System (INIS)

Stover, G.

1981-06-01

This article describes the design, experimental development, and construction of a DC-coupled precision analog fiber optic link. Topics to be covered include overall electrical and mechanical system parameters, basic circuit organization, modulation format, optical system design, optical receiver circuit analysis, and the experimental verification of the major design parameters

Design rules for superconducting analog-digital transducers; Entwurfsregeln fuer Supraleitende Analog-Digital-Wandler

Energy Technology Data Exchange (ETDEWEB)

Haddad, Taghrid

2015-05-29

This Thesis is a contribution for dimensioning aspects of circuits designs in superconductor electronics. Mainly superconductor comparators inclusive Josephson comparators as well as QOJS-Comparators are investigated. Both types were investigated in terms of speed and sensitivity. The influence of the thermal noise on the decision process of the comparators represent in so called gray zone, which is analysed in this thesis. Thereby, different relations between design parameters were derived. A circuit model of the Josephson comparator was verified by experiments. Concepts of superconductor analog-to-digital converters, which are based on above called comparators, were investigated in detail. From the comparator design rules, new rules for AD-converters were derived. Because of the reduced switching energy, the signal to noise ratio (SNR) of the circuits is affected and therefore the reliability of the decision-process is affected. For special applications with very demanding requirements in terms of the speed and accuracy superconductor analog-to-digital converters offer an excellent performance. This thesis provides relations between different design paramenters and shows resulting trade-offs, This method is transparent and easy to transfer to other circuit topologies. As a main result, a highly predictive tool for dimensioning of superconducting ADC's is proved.

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

A low-power current-reuse dual-band analog front-end for multi-channel neural signal recording.

Science.gov (United States)

Sepehrian, H; Gosselin, B

2014-01-01

Thoroughly studying the brain activity of freely moving subjects requires miniature data acquisition systems to measure and wirelessly transmit neural signals in real time. In this application, it is mandatory to simultaneously record the bioelectrical activity of a large number of neurons to gain a better knowledge of brain functions. However, due to limitations in transferring the entire raw data to a remote base station, employing dedicated data reduction techniques to extract the relevant part of neural signals is critical to decrease the amount of data to transfer. In this work, we present a new dual-band neural amplifier to separate the neuronal spike signals (SPK) and the local field potential (LFP) simultaneously in the analog domain, immediately after the pre-amplification stage. By separating these two bands right after the pre-amplification stage, it is possible to process LFP and SPK separately. As a result, the required dynamic range of the entire channel, which is determined by the signal-to-noise ratio of the SPK signal of larger bandwidth, can be relaxed. In this design, a new current-reuse low-power low-noise amplifier and a new dual-band filter that separates SPK and LFP while saving capacitors and pseudo resistors. A four-channel dual-band (SPK, LFP) analog front-end capable of simultaneously separating SPK and LFP is implemented in a TSMC 0.18 μm technology. Simulation results present a total power consumption per channel of 3.1 μw for an input referred noise of 3.28 μV and a NEF for 2.07. The cutoff frequency of the LFP band is fc=280 Hz, and fL=725 Hz and fL=11.2 KHz for SPK, with 36 dB gain for LFP band 46 dB gain for SPK band.

Signal Digitizer and Cross-Correlation Application Specific Integrated Circuit

Science.gov (United States)

Baranauskas, Dalius (Inventor); Baranauskas, Gytis (Inventor); Zelenin, Denis (Inventor); Kangaslahti, Pekka (Inventor); Tanner, Alan B. (Inventor); Lim, Boon H. (Inventor)

2017-01-01

According to one embodiment, a cross-correlator comprises a plurality of analog front ends (AFEs), a cross-correlation circuit and a data serializer. Each of the AFEs comprises a variable gain amplifier (VGA) and a corresponding analog-to-digital converter (ADC) in which the VGA receives and modifies a unique analog signal associates with a measured analog radio frequency (RF) signal and the ADC produces digital data associated with the modified analog signal. Communicatively coupled to the AFEs, the cross-correlation circuit performs a cross-correlation operation on the digital data produced from different measured analog RF signals. The data serializer is communicatively coupled to the summing and cross-correlating matrix and continuously outputs a prescribed amount of the correlated digital data.

A bilateral frontoparietal network underlies visuospatial analogical reasoning.

Science.gov (United States)

Watson, Christine E; Chatterjee, Anjan

2012-02-01

Our ability to reason by analogy facilitates problem solving and allows us to communicate ideas efficiently. In this study, we examined the neural correlates of analogical reasoning and, more specifically, the contribution of rostrolateral prefrontal cortex (RLPFC) to reasoning. This area of the brain has been hypothesized to integrate relational information, as in analogy, or the outcomes of subgoals, as in multi-tasking and complex problem solving. Using fMRI, we compared visuospatial analogical reasoning to a control task that was as complex and difficult as the analogies and required the coordination of subgoals but not the integration of relations. We found that analogical reasoning more strongly activated bilateral RLPFC, suggesting that anterior prefrontal cortex is preferentially recruited by the integration of relational knowledge. Consistent with the need for inhibition during analogy, bilateral, and particularly right, inferior frontal gyri were also more active during analogy. Finally, greater activity in bilateral inferior parietal cortex during the analogy task is consistent with recent evidence for the neural basis of spatial relation knowledge. Together, these findings indicate that a network of frontoparietal areas underlies analogical reasoning; we also suggest that hemispheric differences may emerge depending on the visuospatial or verbal/semantic nature of the analogies. Copyright © 2011 Elsevier Inc. All rights reserved.

Development of analog watch with minute repeater

Science.gov (United States)

Okigami, Tomio; Aoyama, Shigeru; Osa, Takashi; Igarashi, Kiyotaka; Ikegami, Tomomi

A complementary metal oxide semiconductor with large scale integration was developed for an electronic minute repeater. It is equipped with the synthetic struck sound circuit to generate natural struck sound necessary for the minute repeater. This circuit consists of an envelope curve drawing circuit, frequency mixer, polyphonic mixer, and booster circuit made by using analog circuit technology. This large scale integration is a single chip microcomputer with motor drivers and input ports in addition to the synthetic struck sound circuit, and it is possible to make an electronic system of minute repeater at a very low cost in comparison with the conventional type.

An analog front-end bipolar-transistor integrated circuit for the SDC silicon tracker

International Nuclear Information System (INIS)

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

1994-01-01

Since 1989 the Solenoidal Detector Collaboration (SDC) has been developing a general purpose detector to be operated at the Superconducting Super Collider (SSC). 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 SDS silicon tracker. The IC was designed and tested at LBL and was fabricated using AT and T's CBIC-U2, 4 GHz f 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 μm pitch double-sided silicon strip detector. The chip measures 6.8 mm x 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 16nsec time-walk for 1.25 to 10 fC signals is achieved using a time-walk compensation network. Irradiation tests at TRIUMF to a φ = 10 14 protons/cm 2 have been performed on the JC, demonstrating the radiation hardness of the complementary bipolar process

Developing a 300C Analog Tool for EGS

Energy Technology Data Exchange (ETDEWEB)

Normann, Randy

2015-03-23

This paper covers the development of a 300°C geothermal well monitoring tool for supporting future EGS (enhanced geothermal systems) power production. This is the first of 3 tools planed. This is an analog tool designed for monitoring well pressure and temperature. There is discussion on 3 different circuit topologies and the development of the supporting surface electronics and software. There is information on testing electronic circuits and component. One of the major components is the cable used to connect the analog tool to the surface.

System-level techniques for analog performance enhancement

CERN Document Server

Song, Bang-Sup

2016-01-01

This book shows readers to avoid common mistakes in circuit design, and presents classic circuit concepts and design approaches from the transistor to the system levels. The discussion is geared to be accessible and optimized for practical designers who want to learn to create circuits without simulations. Topic by topic, the author guides designers to learn the classic analog design skills by understanding the basic electronics principles correctly, and further prepares them to feel confident in designing high-performance, state-of-the art CMOS analog systems. This book combines and presents all in-depth necessary information to perform various design tasks so that readers can grasp essential material, without reading through the entire book. This top-down approach helps readers to build practical design expertise quickly, starting from their understanding of electronics fundamentals. .

A CMOS four-quadrant analog current multiplier

NARCIS (Netherlands)

Wiegerink, Remco J.

1991-01-01

A CMOS four-quadrant analog current multiplier is described. The circuit is based on the square-law characteristic of an MOS transistor and is insensitive to temperature and process variations. The circuit is insensitive to the body effect so it is not necessary to place transistors in individual

Advanced circuit simulation using Multisim workbench

CERN Document Server

Báez-López, David; Cervantes-Villagómez, Ofelia Delfina

2012-01-01

Multisim is now the de facto standard for circuit simulation. It is a SPICE-based circuit simulator which combines analog, discrete-time, and mixed-mode circuits. In addition, it is the only simulator which incorporates microcontroller simulation in the same environment. It also includes a tool for printed circuit board design.Advanced Circuit Simulation Using Multisim Workbench is a companion book to Circuit Analysis Using Multisim, published by Morgan & Claypool in 2011. This new book covers advanced analyses and the creation of models and subcircuits. It also includes coverage of transmissi

Synthesis of computational structures for analog signal processing

CERN Document Server

Popa, Cosmin Radu

2011-01-01

Presents the most important classes of computational structures for analog signal processing, including differential or multiplier structures, squaring or square-rooting circuits, exponential or Euclidean distance structures and active resistor circuitsIntroduces the original concept of the multifunctional circuit, an active structure that is able to implement, starting from the same circuit core, a multitude of continuous mathematical functionsCovers mathematical analysis, design and implementation of a multitude of function generator structures

Proposal for an All-Spin Artificial Neural Network: Emulating Neural and Synaptic Functionalities Through Domain Wall Motion in Ferromagnets.

Science.gov (United States)

Sengupta, Abhronil; Shim, Yong; Roy, Kaushik

2016-12-01

Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking the neuron, or the synapse functionality. While memristive devices have been proposed to emulate biological synapses, spintronic devices have proved to be efficient at performing the thresholding operation of the neuron at ultra-low currents. In this work, we propose an All-Spin Artificial Neural Network where a single spintronic device acts as the basic building block of the system. The device offers a direct mapping to synapse and neuron functionalities in the brain while inter-layer network communication is accomplished via CMOS transistors. To the best of our knowledge, this is the first demonstration of a neural architecture where a single nanoelectronic device is able to mimic both neurons and synapses. The ultra-low voltage operation of low resistance magneto-metallic neurons enables the low-voltage operation of the array of spintronic synapses, thereby leading to ultra-low power neural architectures. Device-level simulations, calibrated to experimental results, was used to drive the circuit and system level simulations of the neural network for a standard pattern recognition problem. Simulation studies indicate energy savings by  ∼  100× in comparison to a corresponding digital/analog CMOS neuron implementation.

Process and circuiting arrangement for the conversion of analog signals to digital signals and digital signals to analog signals

International Nuclear Information System (INIS)

Wintzer, K.

1977-01-01

Process for analog-to-digital and digital-to-analog conversion in telecommunication systems whose outstations each have an analog transmitter and an analog receiver. The invention illustrates a method of reducing the power demand of the converters at times when no conversion processes take place. (RW) [de

A dual slope charge sampling analog front-end for a wireless neural recording system.

Science.gov (United States)

Lee, Seung Bae; Lee, Byunghun; Gosselin, Benoit; Ghovanloo, Maysam

2014-01-01

This paper presents a novel dual slope charge sampling (DSCS) analog front-end (AFE) architecture, which amplifies neural signals by taking advantage of the charge sampling concept for analog signal conditioning, such as amplification and filtering. The presented DSCS-AFE achieves amplification, filtering, and sampling in a simultaneous fashion, while consuming very small amount of power. The output of the DSCS-AFE produces a pulse width modulated (PWM) signal that is proportional to the input voltage amplitude. A circular shift register (CSR) utilizes time division multiplexing (TDM) of the PWM pulses to create a pseudo-digital TDM-PWM signal that can feed a wireless transmitter. The 8-channel system-on-a-chip was fabricated in a 0.35-μm CMOS process, occupying 2.4 × 2.1 mm(2) and consuming 255 μW from a 1.8V supply. Measured input-referred noise for the entire system, including the FPGA in order to recover PWM signal is 6.50 μV(rms) in the 288 Hz~10 kHz range. For each channel, sampling rate is 31.25 kHz, and power consumption is 31.8 μW.

RF Circuit Design in Nanometer CMOS

NARCIS (Netherlands)

Nauta, Bram

2007-01-01

With CMOS technology entering the nanometer regime, the design of analog and RF circuits is complicated by low supply voltages, very non-linear (and nonquadratic) devices and large 1/f noise. At the same time, circuits are required to operate over increasingly wide bandwidths to implement modern

Neural computation and the computational theory of cognition.

Science.gov (United States)

Piccinini, Gualtiero; Bahar, Sonya

2013-04-01

We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism-neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non-neural notions of computation ought to be replaced or reinterpreted in terms of neural computation. Copyright © 2012 Cognitive Science Society, Inc.

Analog IC Design at the University of Twente

NARCIS (Netherlands)

Nauta, Bram

2007-01-01

This article describes some recent research results from the IC Design group of the University of Twente, located in Enschede, The Netherlands. Our research focuses on analog CMOS circuit design with emphasis on high frequency and broadband circuits. With the trend of system integration in mind, we

HAPS, a Handy Analog Programming System

DEFF Research Database (Denmark)

Højberg, Kristian Søe

1975-01-01

HAPS (Hybrid Analog Programming System) is an analog compiler that can be run on a minicomputer in an interactive mode. Essentially HAPS is written in FORTRAN. The equations to be programmed for an ana log computer are read in by using a FORTRAN-like notation. The input must contain maximum...... and emphasizes the limitations HAPS puts on equation structure, types of computing circuit, scaling, and static testing....

A neural circuit covarying with social hierarchy in macaques.

Science.gov (United States)

Noonan, MaryAnn P; Sallet, Jerome; Mars, Rogier B; Neubert, Franz X; O'Reilly, Jill X; Andersson, Jesper L; Mitchell, Anna S; Bell, Andrew H; Miller, Karla L; Rushworth, Matthew F S

2014-09-01

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.

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.

A Neural Circuit Covarying with Social Hierarchy in Macaques

Science.gov (United States)

Neubert, Franz X.; O'Reilly, Jill X.; Andersson, Jesper L.; Mitchell, Anna S.; Bell, Andrew H.; Miller, Karla L.; Rushworth, Matthew F. S.

2014-01-01

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. PMID:25180883

Tracking cognitive phases in analogical reasoning with event-related potentials.

Science.gov (United States)

Maguire, Mandy J; McClelland, M Michelle; Donovan, Colin M; Tillman, Gail D; Krawczyk, Daniel C

2012-03-01

Analogical reasoning consists of multiple phases. Four-term analogies (A:B::C:D) have an encoding period in which the A:B pair is evaluated prior to a mapping phase. The electrophysiological timing associated with analogical reasoning has remained unclear. We used event-related potentials to identify neural timing related to analogical reasoning relative to perceptual and semantic control conditions. Spatiotemporal principal-components analyses revealed differences primarily in left frontal electrodes during encoding and mapping phases of analogies relative to the other conditions. The timing of the activity differed depending upon the phase of the problem. During the encoding of A:B terms, analogies elicited a positive deflection compared to the control conditions between 400 and 1,200 ms, but for the mapping phase analogical processing elicited a negative deflection that occurred earlier and for a shorter time period, between 350 and 625 ms. These results provide neural and behavioral evidence that 4-term analogy problems involve a highly active evaluation phase of the A:B pair. 2012 APA, all rights reserved

Critical phenomena at a first-order phase transition in a lattice of glow lamps: Experimental findings and analogy to neural activity

Energy Technology Data Exchange (ETDEWEB)

Minati, Ludovico, E-mail: lminati@ieee.org, E-mail: ludovico.minati@unitn.it, E-mail: ludovico.minati@ifj.edu [Center for Mind/Brain Sciences, University of Trento, 38123 Mattarello (Italy); Complex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, Kraków (Poland); Candia, Antonio de [Department of Physics “E. Pancini,” University of Naples “Federico II,” Napoli (Italy); INFN Gr. Coll. Salerno, Unità di Napoli, Napoli (Italy); Scarpetta, Silvia [INFN Gr. Coll. Salerno, Unità di Napoli, Napoli (Italy); Department of Physics “E.R.Caianiello,” University of Salerno, Napoli (Italy)

2016-07-15

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.

Multifractal detrended fluctuation analysis of analog random multiplicative processes

Energy Technology Data Exchange (ETDEWEB)

Silva, L.B.M.; Vermelho, M.V.D. [Instituto de Fisica, Universidade Federal de Alagoas, Maceio - AL, 57072-970 (Brazil); Lyra, M.L. [Instituto de Fisica, Universidade Federal de Alagoas, Maceio - AL, 57072-970 (Brazil)], E-mail: marcelo@if.ufal.br; Viswanathan, G.M. [Instituto de Fisica, Universidade Federal de Alagoas, Maceio - AL, 57072-970 (Brazil)

2009-09-15

We investigate non-Gaussian statistical properties of stationary stochastic signals generated by an analog circuit that simulates a random multiplicative process with weak additive noise. The random noises are originated by thermal shot noise and avalanche processes, while the multiplicative process is generated by a fully analog circuit. The resulting signal describes stochastic time series of current interest in several areas such as turbulence, finance, biology and environment, which exhibit power-law distributions. Specifically, we study the correlation properties of the signal by employing a detrended fluctuation analysis and explore its multifractal nature. The singularity spectrum is obtained and analyzed as a function of the control circuit parameter that tunes the asymptotic power-law form of the probability distribution function.

Evolutionary mechanisms that generate morphology and neural-circuit diversity of the cerebellum.

Science.gov (United States)

Hibi, Masahiko; Matsuda, Koji; Takeuchi, Miki; Shimizu, Takashi; Murakami, Yasunori

2017-05-01

The cerebellum is derived from the dorsal part of the anterior-most hindbrain. The vertebrate cerebellum contains glutamatergic granule cells (GCs) and gamma-aminobutyric acid (GABA)ergic Purkinje cells (PCs). These cerebellar neurons are generated from neuronal progenitors or neural stem cells by mechanisms that are conserved among vertebrates. However, vertebrate cerebella are widely diverse with respect to their gross morphology and neural circuits. The cerebellum of cyclostomes, the basal vertebrates, has a negligible structure. Cartilaginous fishes have a cerebellum containing GCs, PCs, and deep cerebellar nuclei (DCNs), which include projection neurons. Ray-finned fish lack DCNs but have projection neurons termed eurydendroid cells (ECs) in the vicinity of the PCs. Among ray-finned fishes, the cerebellum of teleost zebrafish has a simple lobular structure, whereas that of weakly electric mormyrid fish is large and foliated. Amniotes, which include mammals, independently evolved a large, foliated cerebellum, which contains massive numbers of GCs and has functional connections with the dorsal telencephalon (neocortex). Recent studies of cyclostomes and cartilaginous fish suggest that the genetic program for cerebellum development was already encoded in the genome of ancestral vertebrates. In this review, we discuss how alterations of the genetic and cellular programs generated diversity of the cerebellum during evolution. © 2017 Japanese Society of Developmental Biologists.

Circuit II--A Conversational Graphical Interface.

Science.gov (United States)

Singer, Ronald A.

1993-01-01

Provides an overview of Circuit II, an interactive system that provides users with a graphical representation of an electronic circuit within which questions may be posed and manipulated, and discusses how mouse selections have analogous roles to certain natural language features, such as anaphora, deixis, and ellipsis. (13 references) (EA)

Radio-frequency integrated-circuit engineering

CERN Document Server

Nguyen, Cam

2015-01-01

Radio-Frequency Integrated-Circuit Engineering addresses the theory, analysis and design of passive and active RFIC's using Si-based CMOS and Bi-CMOS technologies, and other non-silicon based technologies. The materials covered are self-contained and presented in such detail that allows readers with only undergraduate electrical engineering knowledge in EM, RF, and circuits to understand and design RFICs. Organized into sixteen chapters, blending analog and microwave engineering, Radio-Frequency Integrated-Circuit Engineering emphasizes the microwave engineering approach for RFICs. Provide

Full Digital Short Circuit Protection for Advanced IGBTs

OpenAIRE

谷村, 拓哉; 湯浅, 一史; 大村, 一郎

2011-01-01

A full digital short circuit protection method for advanced IGBTs has been proposed and experimentally demonstrated for the first time. The method employs combination of digital circuit, the gate charge sense instead of the conventional sense IGBT and analog circuit configuration. Digital protection scheme has significant advantages in thevprotection speed and flexibility.

Cross-talk between the epigenome and neural circuits in drug addiction.

Science.gov (United States)

Mews, Philipp; Calipari, Erin S

2017-01-01

Drug addiction is a behavioral disorder characterized by dysregulated learning about drugs and associated cues that result in compulsive drug seeking and relapse. Learning about drug rewards and predictive cues is a complex process controlled by a computational network of neural connections interacting with transcriptional and molecular mechanisms within each cell to precisely guide behavior. The interplay between rapid, temporally specific neuronal activation, and longer-term changes in transcription is of critical importance in the expression of appropriate, or in the case of drug addiction, inappropriate behaviors. Thus, these factors and their interactions must be considered together, especially in the context of treatment. Understanding the complex interplay between epigenetic gene regulation and circuit connectivity will allow us to formulate novel therapies to normalize maladaptive reward behaviors, with a goal of modulating addictive behaviors, while leaving natural reward-associated behavior unaffected. © 2017 Elsevier B.V. All rights reserved.

Project for a codable central unit for analog data acquisition

International Nuclear Information System (INIS)

Bouras, F.; Da Costa Vieira, D.; Sohier, B.

1974-07-01

The instrumentation for a 256 channel codable central processor intended for an operation in connection with a computer is presented. The computer indicates the adresses of the channels to be measured, orders the conversion, and acquires the results of measurements. The acquisition and computer coupling unit is located in a standard rock CAMAC (6 U 19inch., 25 positions); an example of configuration is given. The measurement velocity depends on the converter speed and dead time of analog circuits; for a ADC 1103 converter the total dead time is 6.5s min. The analog circuits are intended for +-10V range, the accuracy is 1/2n (2n is the number of bits). The result is acquired in words of 12 bits maximum. The information transfer and analog commutation (through integrated analog gates) are discussed [fr

Signal processing: opportunities for superconductive circuits

International Nuclear Information System (INIS)

Ralston, R.W.

1985-01-01

Prime motivators in the evolution of increasingly sophisticated communication and detection systems are the needs for handling ever wider signal bandwidths and higher data processing speeds. These same needs drive the development of electronic device technology. Until recently the superconductive community has been tightly focused on digital devices for high speed computers. The purpose of this paper is to describe opportunities and challenges which exist for both analog and digital devices in a less familiar area, that of wideband signal processing. The function and purpose of analog signal-processing components, including matched filters, correlators and Fourier transformers, will be described and examples of superconductive implementations given. A canonic signal-processing system is then configured using these components in combination with analog/digital converters and digital output circuits to highlight the important issues of dynamic range, accuracy and equivalent computation rate. Superconductive circuits hold promise for processing signals of 10-GHz bandwidth. Signal processing systems, however, can be properly designed and implemented only through a synergistic combination of the talents of device physicists, circuit designers, algorithm architects and system engineers. An immediate challenge to the applied superconductivity community is to begin sharing ideas with these other researchers

Fast parallel-series analog-to-digital converter

International Nuclear Information System (INIS)

Pogosov, A.Yu.

1987-01-01

Fast analog-to-digital converters are used in systems for detection of rapid processes, nuclear spectroscopy. A 12-digit analog-to-digital converter with conversion time of 160 ns and conversion frequency of 8.3 MHz is described; a segmented digital-to-analog converter with differential non-linearity of < 0.01% and a differential amplifier-limiter with setting time of 80 ns at the error of 0.2% are utilized in the converter; a control device is based on the chain of flip-flop circuit

AMIC: an expandable integrated analog front-end for light distribution moments analysis

OpenAIRE

SPAGGIARI, MICHELE; Herrero Bosch, Vicente; Lerche, Christoph Werner; Aliaga Varea, Ramón José; Monzó Ferrer, José María; Gadea Gironés, Rafael

2011-01-01

In this article we introduce AMIC (Analog Moments Integrated Circuit), a novel analog Application Specific Integrated Circuit (ASIC) front-end for Positron Emission Tomography (PET) applications. Its working principle is based on mathematical analysis of light distribution through moments calculation. Each moment provides useful information about light distribution, such as energy, position, depth of interaction, skewness (deformation due to border effect) etc. A current buffer delivers a cop...

Analog implementation of an integral resonant control scheme

International Nuclear Information System (INIS)

Pereira, E; Moheimani, S O R; Aphale, S S

2008-01-01

Integral resonant control (IRC) has been introduced as a high performance controller design methodology for flexible structures with collocated actuator–sensor pairs. IRC has a simple structure and is capable of achieving significant damping, over several modes, while guaranteeing closed-loop stability of the system in the presence of unmodeled out-of-bandwidth dynamics. IRC can be an ideal controller for various industrial damping applications, if packaged in a simple easy-to-implement electronic module. This work proposes an analog implementation of the IRC scheme using a single Op-Amp circuit. The objective is to show that with simple analog realization of the modified IRC scheme, it is possible to damp a large number of vibration modes. A brief discussion about the modeling, circuit considerations, implementation and experimental results is presented in order to validate the usefulness and practicality of the proposed analog IRC implementation. (technical note)

Wavelet-Based Feature Extraction in Fault Diagnosis for Biquad High-Pass Filter Circuit

OpenAIRE

Yuehai Wang; Yongzheng Yan; Qinyong Wang

2016-01-01

Fault diagnosis for analog circuit has become a prominent factor in improving the reliability of integrated circuit due to its irreplaceability in modern integrated circuits. In fact fault diagnosis based on intelligent algorithms has become a popular research topic as efficient feature extraction and selection are a critical and intricate task in analog fault diagnosis. Further, it is extremely important to propose some general guidelines for the optimal feature extraction and selection. In ...

Analog-to-digital conversion using custom CMOS analog memory for the EOS time projection chamber

International Nuclear Information System (INIS)

Lee, K.L.; Arthur, A.A.; Jones, R.W.; Matis, H.S.; Nakamura, M.; Kleinfelder, S.A.; Ritter, H.G.; Wienman, H.H.

1990-01-01

This paper describes the multiplexing scheme of custom CMOS analog memory integrated circuits, 16 channels x 256 cells, into analog to digital converters (ADC's) to handle 15,360 signal channels of a time projection, chamber detector system. Primary requirements of this system are high density, low power and large dynamic range. The analog memory device multiplexing scheme was designed to digitize the information stored in the memory cells. The digitization time of the ADC's and the settling times for the memory unit were carefully interleaved to optimize the performance and timing during the multiplexing operation. This kept the total number of ADC's, a costly and power dissipative component, to an acceptable minimum

A wireless integrated circuit for 100-channel charge-balanced neural stimulation.

Science.gov (United States)

Thurgood, B K; Warren, D J; Ledbetter, N M; Clark, G A; Harrison, R R

2009-12-01

The authors present the design of an integrated circuit for wireless neural stimulation, along with benchtop and in - vivo experimental results. The chip has the ability to drive 100 individual stimulation electrodes with constant-current pulses of varying amplitude, duration, interphasic delay, and repetition rate. The stimulation is performed by using a biphasic (cathodic and anodic) current source, injecting and retracting charge from the nervous system. Wireless communication and power are delivered over a 2.765-MHz inductive link. Only three off-chip components are needed to operate the stimulator: a 10-nF capacitor to aid in power-supply regulation, a small capacitor (power and command reception. The chip was fabricated in a commercially available 0.6- mum 2P3M BiCMOS process. The chip was able to activate motor fibers to produce muscle twitches via a Utah Slanted Electrode Array implanted in cat sciatic nerve, and to activate sensory fibers to recruit evoked potentials in somatosensory cortex.

Structure problems in the analog computation

International Nuclear Information System (INIS)

Braffort, P.L.

1957-01-01

The recent mathematical development showed the importance of elementary structures (algebraic, topological, etc.) in abeyance under the great domains of classical analysis. Such structures in analog computation are put in evidence and possible development of applied mathematics are discussed. It also studied the topological structures of the standard representation of analog schemes such as additional triangles, integrators, phase inverters and functions generators. The analog method gives only the function of the variable: time, as results of its computations. But the course of computation, for systems including reactive circuits, introduces order structures which are called 'chronological'. Finally, it showed that the approximation methods of ordinary numerical and digital computation present the same structure as these analog computation. The structure analysis permits fruitful comparisons between the several domains of applied mathematics and suggests new important domains of application for analog method. (M.P.)

Butyrate reduces appetite and activates brown adipose tissue via the gut-brain neural circuit.

Science.gov (United States)

Li, Zhuang; Yi, Chun-Xia; Katiraei, Saeed; Kooijman, Sander; Zhou, Enchen; Chung, Chih Kit; Gao, Yuanqing; van den Heuvel, José K; Meijer, Onno C; Berbée, Jimmy F P; Heijink, Marieke; Giera, Martin; Willems van Dijk, Ko; Groen, Albert K; Rensen, Patrick C N; Wang, Yanan

2017-11-03

Butyrate exerts metabolic benefits in mice and humans, the underlying mechanisms being still unclear. We aimed to investigate the effect of butyrate on appetite and energy expenditure, and to what extent these two components contribute to the beneficial metabolic effects of butyrate. Acute effects of butyrate on appetite and its method of action were investigated in mice following an intragastric gavage or intravenous injection of butyrate. To study the contribution of satiety to the metabolic benefits of butyrate, mice were fed a high-fat diet with butyrate, and an additional pair-fed group was included. Mechanistic involvement of the gut-brain neural circuit was investigated in vagotomised mice. Acute oral, but not intravenous, butyrate administration decreased food intake, suppressed the activity of orexigenic neurons that express neuropeptide Y in the hypothalamus, and decreased neuronal activity within the nucleus tractus solitarius and dorsal vagal complex in the brainstem. Chronic butyrate supplementation prevented diet-induced obesity, hyperinsulinaemia, hypertriglyceridaemia and hepatic steatosis, largely attributed to a reduction in food intake. Butyrate also modestly promoted fat oxidation and activated brown adipose tissue (BAT), evident from increased utilisation of plasma triglyceride-derived fatty acids. This effect was not due to the reduced food intake, but explained by an increased sympathetic outflow to BAT. Subdiaphragmatic vagotomy abolished the effects of butyrate on food intake as well as the stimulation of metabolic activity in BAT. Butyrate acts on the gut-brain neural circuit to improve energy metabolism via reducing energy intake and enhancing fat oxidation by activating BAT. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

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.

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.

AMPLITUDE AND TIME MEASUREMENT ASIC WITH ANALOG DERANDOMIZATION

International Nuclear Information System (INIS)

O CONNOR, P.; DE GERONIMO, G.; KANDASAMY, A.

2002-01-01

We describe a new ASIC for accurate and efficient processing of high-rate pulse signals from highly segmented detectors. In contrast to conventional approaches, this circuit affords a dramatic reduction in data volume through the use of analog techniques (precision peak detectors and time-to-amplitude converters) together with fast arbitration and sequencing logic to concentrate the data before digitization. In operation the circuit functions like a data-driven analog first-in, first-out (FIFO) memory between the preamplifiers and the ADC. Peak amplitudes of pulses arriving at any one of the 32 inputs are sampled, stored, and queued for readout and digitization through a single output port. Hit timing, pulse risetime, and channel address are also available at the output. Prototype chips have been fabricated in 0.35 micron CMOS and tested. First results indicate proper functionality for pulses down to 30 ns peaking time and input rates up to 1.6 MHz/channel. Amplitude accuracy of the peak detect and hold circuit is 0.3% (absolute). TAC accuracy is within 0.3% of full scale. Power consumption is less than 2 mW/channel. Compared with conventional techniques such as track-and-hold and analog memory, this new ASIC will enable efficient pulse height measurement at 20 to 300 times higher rates

Circuits in the Sun: Solar Panel Physics

Science.gov (United States)

Gfroerer, Tim

2013-01-01

Typical commercial solar panels consist of approximately 60 individual photovoltaic cells connected in series. Since the usual Kirchhoff rules apply, the current is uniform throughout the circuit, while the electric potential of the individual devices is cumulative. Hence, a solar panel is a good analog of a simple resistive series circuit, except…

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.

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.

Design rules for superconducting analog-digital transducers

International Nuclear Information System (INIS)

Haddad, Taghrid

2015-01-01

This Thesis is a contribution for dimensioning aspects of circuits designs in superconductor electronics. Mainly superconductor comparators inclusive Josephson comparators as well as QOJS-Comparators are investigated. Both types were investigated in terms of speed and sensitivity. The influence of the thermal noise on the decision process of the comparators represent in so called gray zone, which is analysed in this thesis. Thereby, different relations between design parameters were derived. A circuit model of the Josephson comparator was verified by experiments. Concepts of superconductor analog-to-digital converters, which are based on above called comparators, were investigated in detail. From the comparator design rules, new rules for AD-converters were derived. Because of the reduced switching energy, the signal to noise ratio (SNR) of the circuits is affected and therefore the reliability of the decision-process is affected. For special applications with very demanding requirements in terms of the speed and accuracy superconductor analog-to-digital converters offer an excellent performance. This thesis provides relations between different design paramenters and shows resulting trade-offs, This method is transparent and easy to transfer to other circuit topologies. As a main result, a highly predictive tool for dimensioning of superconducting ADC's is proved.

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. 2012 APA, all rights reserved

The neural substrate of analogical reasoning: an fMRI study.

Science.gov (United States)

Luo, Qian; Perry, Conrad; Peng, Danling; Jin, Zhen; Xu, Duo; Ding, Guosheng; Xu, Shiyong

2003-10-01

This study investigated the anatomical substrate of analogical reasoning using functional magnetic resonance imaging. In the study, subjects performed a verbal analogy task (e.g., soldier is to army as drummer is to band) and, to control for activation caused by purely semantic access, a semantic judgment task. Significant activation differences between the verbal analogy and the semantic judgment task were found bilaterally in the prefrontal cortex (right BA 11/BA 47 and left BA45), the fusiform gyrus, and the basal ganglia; left lateralized in the postero-superior temporal gyrus (BA 22) and the (para) hippocampal region; and right lateralized in the anterior cingulate. The role of these areas in analogical reasoning is discussed.

Printed circuits and their applications: Which way forward?

Science.gov (United States)

Cantatore, E.

2015-09-01

The continuous advancements in printed electronics make nowadays feasible the design of printed circuits which enable meaningful applications. Examples include ultra-low cost sensors embedded in food packaging, large-area sensing surfaces and biomedical assays. This paper offers an overview of state-of-the-art digital and analog circuit blocks, manufactured with a printed complementary organic TFT technology. An analog to digital converter and an RFID tag implemented exploiting these building blocks are also described. The main remaining drawbacks of the printed technology described are identified, and new approaches to further improve the state of the art, enabling more innovative applications are discussed.

Integrated coherent matter wave circuits

International Nuclear Information System (INIS)

Ryu, C.; Boshier, M. G.

2015-01-01

An integrated coherent matter wave circuit is a single device, analogous to an integrated optical circuit, in which coherent de Broglie waves are created and then launched into waveguides where they can be switched, divided, recombined, and detected as they propagate. Applications of such circuits include guided atom interferometers, atomtronic circuits, and precisely controlled delivery of atoms. We report experiments demonstrating integrated circuits for guided coherent matter waves. The circuit elements are created with the painted potential technique, a form of time-averaged optical dipole potential in which a rapidly moving, tightly focused laser beam exerts forces on atoms through their electric polarizability. Moreover, the source of coherent matter waves is a Bose-Einstein condensate (BEC). Finally, we launch BECs into painted waveguides that guide them around bends and form switches, phase coherent beamsplitters, and closed circuits. These are the basic elements that are needed to engineer arbitrarily complex matter wave circuitry

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

Analogical reasoning: An incremental or insightful process? What cognitive and cortical evidence suggests.

Science.gov (United States)

Antonietti, Alessandro; Balconi, Michela

2010-06-01

Abstract The step-by-step, incremental nature of analogical reasoning can be questioned, since analogy making appears to be an insight-like process. This alternative view of analogical thinking can be integrated in Speed's model, even though the alleged role played by dopaminergic subcortical circuits needs further supporting evidence.

Multichannel analog temperature sensing system

International Nuclear Information System (INIS)

Gribble, R.

1985-08-01

A multichannel system that protects the numerous and costly water-cooled magnet coils on the translation section of the FRX-C/T magnetic fusion experiment is described. The system comprises a thermistor for each coil, a constant current circuit for each thermistor, and a multichannel analog-to-digital converter interfaced to the computer

Grounding and shielding circuits and interference

CERN Document Server

Morrison, Ralph

2016-01-01

Applies basic field behavior in circuit design and demonstrates how it relates to grounding and shielding requirements and techniques in circuit design This book connects the fundamentals of electromagnetic theory to the problems of interference in all types of electronic design. The text covers power distribution in facilities, mixing of analog and digital circuitry, circuit board layout at high clock rates, and meeting radiation and susceptibility standards. The author examines the grounding and shielding requirements and techniques in circuit design and applies basic physics to circuit behavior. The sixth edition of this book has been updated with new material added throughout the chapters where appropriate. The presentation of the book has also been rearranged in order to reflect the current trends in the field.

Parallel consensual neural networks.

Science.gov (United States)

Benediktsson, J A; Sveinsson, J R; Ersoy, O K; Swain, P H

1997-01-01

A new type of a neural-network architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN architecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual decision. In this paper, optimization methods are used in order to weight the outputs from the stage networks. Two approaches are proposed to compute the data transforms for the PCNN, one for binary data and another for analog data. The analog approach uses wavelet packets. The experimental results obtained with the proposed approach show that the PCNN outperforms both a conjugate-gradient backpropagation neural network and conventional statistical methods in terms of overall classification accuracy of test data.

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

Pattern Classification with Memristive Crossbar Circuits

Science.gov (United States)

2016-03-31

Pattern Classification with Memristive Crossbar Circuits Dmitri B. Strukov Department of Electrical and Computer Engineering Department UC Santa...pattern classification ; deep learning; convolutional neural network networks. Introduction Deep-learning convolutional neural networks (DLCNN), which...the best classification performances on a variety of benchmark tasks [1]. The major challenge in building fast and energy- efficient networks of this

Memristor-based nanoelectronic computing circuits and architectures

CERN Document Server

Vourkas, Ioannis

2016-01-01

This book considers the design and development of nanoelectronic computing circuits, systems and architectures focusing particularly on memristors, which represent one of today’s latest technology breakthroughs in nanoelectronics. The book studies, explores, and addresses the related challenges and proposes solutions for the smooth transition from conventional circuit technologies to emerging computing memristive nanotechnologies. Its content spans from fundamental device modeling to emerging storage system architectures and novel circuit design methodologies, targeting advanced non-conventional analog/digital massively parallel computational structures. Several new results on memristor modeling, memristive interconnections, logic circuit design, memory circuit architectures, computer arithmetic systems, simulation software tools, and applications of memristors in computing are presented. High-density memristive data storage combined with memristive circuit-design paradigms and computational tools applied t...

Analog/RF Circuit Design Techniques for Nanometerscale IC Technologies

NARCIS (Netherlands)

Nauta, Bram; Annema, Anne J.

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

Modeling a verification test system for mixed-signal circuits

NARCIS (Netherlands)

San Segundo Bello, D.; Tangelder, R.J.W.T.; Kerkhoff, Hans G.

In contrast to the large number of logic gates and storage circuits encountered in digital networks, purely analog networks usually have relatively few circuit primitives (operational amplifiers and so on). The complexity lies not in the number of building blocks but in the complexity of each block

A Quantized Analog Delay for an ir-UWB Quadrature Downconversion Autocorrelation Receiver

NARCIS (Netherlands)

Bagga, S.; Zhang, L.; Serdijn, W.A.; Long, J.R.; Busking, E.B.

2005-01-01

A quantized analog delay is designed as a requirement for the autocorrelation function in the quadrature downconversion autocorrelation receiver (QDAR). The quantized analog delay is comprised of a quantizer, multiple binary delay lines and an adder circuit. Being the foremost element, the quantizer

Frank Beach Award Winner: Steroids as Neuromodulators of Brain Circuits and Behavior

Science.gov (United States)

Remage-Healey, Luke

2014-01-01

Neurons communicate primarily via action potentials that transmit information on the timescale of milliseconds. Neurons also integrate information via alterations in gene transcription and protein translation that are sustained for hours to days after initiation. Positioned between these two signaling timescales are the minute-by-minute actions of neuromodulators. Over the course of minutes, the classical neuromodulators (such as serotonin, dopamine, octopamine, and norepinephrine) can alter and/or stabilize neural circuit patterning as well as behavioral states. Neuromodulators allow many flexible outputs from neural circuits and can encode information content into the firing state of neural networks. The idea that steroid molecules can operate as genuine behavioral neuromodulators - synthesized by and acting within brain circuits on a minute-by-minute timescale - has gained traction in recent years. Evidence for brain steroid synthesis at synaptic terminals has converged with evidence for the rapid actions of brain-derived steroids on neural circuits and behavior. The general principle emerging from this work is that the production of steroid hormones within brain circuits can alter their functional connectivity and shift sensory representations by enhancing their information coding. Steroids produced in the brain can therefore change the information content of neuronal networks to rapidly modulate sensory experience and sensorimotor functions. PMID:25110187

Introduction to engineering a starter's guide with hands-on analog multimedia explorations

CERN Document Server

Karam, Lina

2008-01-01

This lecture provides a hands-on glimpse of the field of electrical engineering. The introduced applications utilize the NI ELVIS hardware and software platform to explore concepts such as circuits, power, analog sensing, and introductory analog signal processing such as signal generation, analog filtering, and audio and music processing. These principals and technologies are introduced in a very practical way and are fundamental to many of the electronic devices we use today. Some examples include photodetection, analog signal (audio, light, temperature) level meter, and analog music equalize

The Pleiotropic MET Receptor Network: Circuit Development and the Neural-Medical Interface of Autism.

Science.gov (United States)

Eagleson, Kathie L; Xie, Zhihui; Levitt, Pat

2017-03-01

People with autism spectrum disorder and other neurodevelopmental disorders (NDDs) are behaviorally and medically heterogeneous. The combination of polygenicity and gene pleiotropy-the influence of one gene on distinct phenotypes-raises questions of how specific genes and their protein products interact to contribute to NDDs. A preponderance of evidence supports developmental and pathophysiological roles for the MET receptor tyrosine kinase, a multifunctional receptor that mediates distinct biological responses depending upon cell context. MET influences neuron architecture and synapse maturation in the forebrain and regulates homeostasis in gastrointestinal and immune systems, both commonly disrupted in NDDs. Peak expression of synapse-enriched MET is conserved across rodent and primate forebrain, yet regional differences in primate neocortex are pronounced, with enrichment in circuits that participate in social information processing. A functional risk allele in the MET promoter, enriched in subgroups of children with autism spectrum disorder, reduces transcription and disrupts socially relevant neural circuits structurally and functionally. In mice, circuit-specific deletion of Met causes distinct atypical behaviors. MET activation increases dendritic complexity and nascent synapse number, but synapse maturation requires reductions in MET. MET mediates its specific biological effects through different intracellular signaling pathways and has a complex protein interactome that is enriched in autism spectrum disorder and other NDD candidates. The interactome is coregulated in developing human neocortex. We suggest that a gene as pleiotropic and highly regulated as MET, together with its interactome, is biologically relevant in normal and pathophysiological contexts, affecting central and peripheral phenotypes that contribute to NDD risk and clinical symptoms. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

The role of zebrafish (Danio rerio in dissecting the genetics and neural circuits of executive function

Directory of Open Access Journals (Sweden)

Matthew O Parker

2013-04-01

Full Text Available Zebrafish have great potential to contribute to our understanding of behavioural genetics and thus to contribute to our understanding of the aetiology of psychiatric disease. However, progress is dependent upon the rate at which behavioural assays addressing complex behavioural phenotypes are designed, reported and validated. Here we critically review existing behavioural assays with particular focus on the use of adult zebrafish to explore executive processes and phenotypes associated with human psychiatric disease. We outline the case for using zebrafish as models to study impulse control and attention, discussing the validity of applying extant rodent assays to zebrafish and evidence for the conservation of relevant neural circuits.

Synaptic plasticity, neural circuits, and the emerging role of altered short-term information processing in schizophrenia

Science.gov (United States)

Crabtree, Gregg W.; Gogos, Joseph A.

2014-01-01

Synaptic plasticity alters the strength of information flow between presynaptic and postsynaptic neurons and thus modifies the likelihood that action potentials in a presynaptic neuron will lead to an action potential in a postsynaptic neuron. As such, synaptic plasticity and pathological changes in synaptic plasticity impact the synaptic computation which controls the information flow through the neural microcircuits responsible for the complex information processing necessary to drive adaptive behaviors. As current theories of neuropsychiatric disease suggest that distinct dysfunctions in neural circuit performance may critically underlie the unique symptoms of these diseases, pathological alterations in synaptic plasticity mechanisms may be fundamental to the disease process. Here we consider mechanisms of both short-term and long-term plasticity of synaptic transmission and their possible roles in information processing by neural microcircuits in both health and disease. As paradigms of neuropsychiatric diseases with strongly implicated risk genes, we discuss the findings in schizophrenia and autism and consider the alterations in synaptic plasticity and network function observed in both human studies and genetic mouse models of these diseases. Together these studies have begun to point toward a likely dominant role of short-term synaptic plasticity alterations in schizophrenia while dysfunction in autism spectrum disorders (ASDs) may be due to a combination of both short-term and long-term synaptic plasticity alterations. PMID:25505409

Doubling-resolution analog-to-digital conversion based on PIC18F45K80

Directory of Open Access Journals (Sweden)

Yueyang Yuan

2014-08-01

Full Text Available Aiming at the analog signal being converted into the digital with a higher precision, a method to improve the analog-to-digital converter (ADC resolution is proposed and described. Based on the microcomputer PIC18F45K80 in which the internal ADC modules are embedded, a circuit is designed for doubling the resolution of ADC. According to the circuit diagram, the mathematical formula for calculating this resolution is derived. The corresponding software and print circuit board assembly is also prepared. With the experiment, a 13 bit ADC is achieved based on the 12 bit ADC module predesigned in the PIC18F45K80.

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 (pmen. Crown Copyright © 2010. Published by Elsevier Ireland Ltd. All rights reserved.

Dissociable attentional and affective circuits in medication-naïve children with attention-deficit/hyperactivity disorder.

Science.gov (United States)

Posner, Jonathan; Rauh, Virginia; Gruber, Allison; Gat, Inbal; Wang, Zhishun; Peterson, Bradley S

2013-07-30

Current neurocognitive models of attention-deficit/hyperactivity disorder (ADHD) suggest that neural circuits involving both attentional and affective processing make independent contributions to the phenomenology of the disorder. However, a clear dissociation of attentional and affective circuits and their behavioral correlates has yet to be shown in medication-naïve children with ADHD. Using resting-state functional connectivity MRI (rs-fcMRI) in a cohort of medication naïve children with (N=22) and without (N=20) ADHD, we demonstrate that children with ADHD have reduced connectivity in two neural circuits: one underlying executive attention (EA) and the other emotional regulation (ER). We also demonstrate a double dissociation between these two neural circuits and their behavioral correlates such that reduced connectivity in the EA circuit correlates with executive attention deficits but not with emotional lability, while on the other hand, reduced connectivity in the ER circuit correlates with emotional lability but not with executive attention deficits. These findings suggest potential avenues for future research such as examining treatment effects on these two neural circuits as well as the potential prognostic and developmental significance of disturbances in one circuit vs the other. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Simulation of a spiking neuron circuit using carbon nanotube transistors

Energy Technology Data Exchange (ETDEWEB)

Najari, Montassar, E-mail: malnjar@jazanu.edu.sa [Departement of Physics, Faculty of Sciences, University of Gabes, Gabes (Tunisia); IKCE unit, Jazan University, Jazan (Saudi Arabia); El-Grour, Tarek, E-mail: grour-tarek@hotmail.fr [Departement of Physics, Faculty of Sciences, University of Gabes, Gabes (Tunisia); Jelliti, Sami, E-mail: sjelliti@jazanu.edu.sa [IKCE unit, Jazan University, Jazan (Saudi Arabia); Hakami, Othman Mousa, E-mail: omhakami@jazanu.edu.sa [IKCE unit, Jazan University, Jazan (Saudi Arabia); Faculty of Sciences, Jazan University, Jazan (Saudi Arabia)

2016-06-10

Neuromorphic engineering is related to the existing analogies between the physical semiconductor VLSI (Very Large Scale Integration) and biophysics. Neuromorphic systems propose to reproduce the structure and function of biological neural systems for transferring their calculation capacity on silicon. Since the innovative research of Carver Mead, the neuromorphic engineering continues to emerge remarkable implementation of biological system. This work presents a simulation of an elementary neuron cell with a carbon nanotube transistor (CNTFET) based technology. The model of the cell neuron which was simulated is called integrate and fire (I&F) model firstly introduced by G. Indiveri in 2009. This circuit has been simulated with CNTFET technology using ADS environment to verify the neuromorphic activities in terms of membrane potential. This work has demonstrated the efficiency of this emergent device; i.e CNTFET on the design of such architecture in terms of power consumption and technology integration density.

Simulation of a spiking neuron circuit using carbon nanotube transistors

International Nuclear Information System (INIS)

Najari, Montassar; El-Grour, Tarek; Jelliti, Sami; Hakami, Othman Mousa

2016-01-01

Neuromorphic engineering is related to the existing analogies between the physical semiconductor VLSI (Very Large Scale Integration) and biophysics. Neuromorphic systems propose to reproduce the structure and function of biological neural systems for transferring their calculation capacity on silicon. Since the innovative research of Carver Mead, the neuromorphic engineering continues to emerge remarkable implementation of biological system. This work presents a simulation of an elementary neuron cell with a carbon nanotube transistor (CNTFET) based technology. The model of the cell neuron which was simulated is called integrate and fire (I&F) model firstly introduced by G. Indiveri in 2009. This circuit has been simulated with CNTFET technology using ADS environment to verify the neuromorphic activities in terms of membrane potential. This work has demonstrated the efficiency of this emergent device; i.e CNTFET on the design of such architecture in terms of power consumption and technology integration density.

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

Design of a Closed-Loop, Bidirectional Brain Machine Interface System With Energy Efficient Neural Feature Extraction and PID Control.

Science.gov (United States)

Liu, Xilin; Zhang, Milin; Richardson, Andrew G; Lucas, Timothy H; Van der Spiegel, Jan

2017-08-01

This paper presents a bidirectional brain machine interface (BMI) microsystem designed for closed-loop neuroscience research, especially experiments in freely behaving animals. The system-on-chip (SoC) consists of 16-channel neural recording front-ends, neural feature extraction units, 16-channel programmable neural stimulator back-ends, in-channel programmable closed-loop controllers, global analog-digital converters (ADC), and peripheral circuits. The proposed neural feature extraction units includes 1) an ultra low-power neural energy extraction unit enabling a 64-step natural logarithmic domain frequency tuning, and 2) a current-mode action potential (AP) detection unit with time-amplitude window discriminator. A programmable proportional-integral-derivative (PID) controller has been integrated in each channel enabling a various of closed-loop operations. The implemented ADCs include a 10-bit voltage-mode successive approximation register (SAR) ADC for the digitization of the neural feature outputs and/or local field potential (LFP) outputs, and an 8-bit current-mode SAR ADC for the digitization of the action potential outputs. The multi-mode stimulator can be programmed to perform monopolar or bipolar, symmetrical or asymmetrical charge balanced stimulation with a maximum current of 4 mA in an arbitrary channel configuration. The chip has been fabricated in 0.18 μ m CMOS technology, occupying a silicon area of 3.7 mm 2 . The chip dissipates 56 μW/ch on average. General purpose low-power microcontroller with Bluetooth module are integrated in the system to provide wireless link and SoC configuration. Methods, circuit techniques and system topology proposed in this work can be used in a wide range of relevant neurophysiology research, especially closed-loop BMI experiments.

Modern analog filter analysis and design a practical approach

CERN Document Server

Raut, R

2011-01-01

Starting from the fundamentals, the present book describes methods of designing analog electronic filters and illustrates these methods by providing numerical and circuit simulation programs. The subject matters comprise many concepts and techniques that are not available in other text books on the market. To name a few - principle of transposition and its application in directly realizing current mode filters from well known voltage mode filters; an insight into the technological aspect of integrated circuit components used to implement an integrated circuit filter; a careful blending of basi

SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.

Science.gov (United States)

Jimenez-Romero, Cristian; Johnson, Jeffrey

2017-01-01

The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work.

Spherical Harmonics Reveal Standing EEG Waves and Long-Range Neural Synchronization during Non-REM Sleep.

Science.gov (United States)

Sivakumar, Siddharth S; Namath, Amalia G; Galán, Roberto F

2016-01-01

Previous work from our lab has demonstrated how the connectivity of brain circuits constrains the repertoire of activity patterns that those circuits can display. Specifically, we have shown that the principal components of spontaneous neural activity are uniquely determined by the underlying circuit connections, and that although the principal components do not uniquely resolve the circuit structure, they do reveal important features about it. Expanding upon this framework on a larger scale of neural dynamics, we have analyzed EEG data recorded with the standard 10-20 electrode system from 41 neurologically normal children and adolescents during stage 2, non-REM sleep. We show that the principal components of EEG spindles, or sigma waves (10-16 Hz), reveal non-propagating, standing waves in the form of spherical harmonics. We mathematically demonstrate that standing EEG waves exist when the spatial covariance and the Laplacian operator on the head's surface commute. This in turn implies that the covariance between two EEG channels decreases as the inverse of their relative distance; a relationship that we corroborate with empirical data. Using volume conduction theory, we then demonstrate that superficial current sources are more synchronized at larger distances, and determine the characteristic length of large-scale neural synchronization as 1.31 times the head radius, on average. Moreover, consistent with the hypothesis that EEG spindles are driven by thalamo-cortical rather than cortico-cortical loops, we also show that 8 additional patients with hypoplasia or complete agenesis of the corpus callosum, i.e., with deficient or no connectivity between cortical hemispheres, similarly exhibit standing EEG waves in the form of spherical harmonics. We conclude that spherical harmonics are a hallmark of spontaneous, large-scale synchronization of neural activity in the brain, which are associated with unconscious, light sleep. The analogy with spherical harmonics in

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.

Integrating Neural Circuits Controlling Female Sexual Behavior

Directory of Open Access Journals (Sweden)

Paul E. Micevych

2017-06-01

Full Text Available 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.

Bridging the Gap: Towards a Cell-Type Specific Understanding of Neural Circuits Underlying Fear Behaviors

Science.gov (United States)

McCullough, KM; Morrison, FG; Ressler, KJ

2016-01-01

Fear and anxiety-related disorders are remarkably common and debilitating, and are often characterized by dysregulated fear responses. Rodent models of fear learning and memory have taken great strides towards elucidating the specific neuronal circuitries underlying the learning of fear responses. The present review addresses recent research utilizing optogenetic approaches to parse circuitries underlying fear behaviors. It also highlights the powerful advances made when optogenetic techniques are utilized in a genetically defined, cell-type specific, manner. The application of next-generation genetic and sequencing approaches in a cell-type specific context will be essential for a mechanistic understanding of the neural circuitry underlying fear behavior and for the rational design of targeted, circuit specific, pharmacologic interventions for the treatment and prevention of fear-related disorders. PMID:27470092

Emergence of task-dependent representations in working memory circuits

Directory of Open Access Journals (Sweden)

Cristina eSavin

2014-05-01

Full Text Available A wealth of experimental evidence suggests that working memory circuits preferentially represent information that is behaviorally relevant. Still, we are missing a mechanistic account of how these representations come about. Here we provide a simple explanation for a range of experimental findings, in light of prefrontal circuits adapting to task constraints by reward-dependent learning. In particular, we model a neural network shaped by reward-modulated spike-timing dependent plasticity (r-STDP and homeostatic plasticity (intrinsic excitability and synaptic scaling. We show that the experimentally-observed neural representations naturally emerge in an initially unstructured circuit as it learns to solve several working memory tasks. These results point to a critical, and previously unappreciated, role for reward-dependent learning in shaping prefrontal cortex activity.

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.

Synthesizing genetic sequential logic circuit with clock pulse generator.

Science.gov (United States)

Chuang, Chia-Hua; Lin, Chun-Liang

2014-05-28

Rhythmic clock widely occurs in biological systems which controls several aspects of cell physiology. For the different cell types, it is supplied with various rhythmic frequencies. How to synthesize a specific clock signal is a preliminary but a necessary step to further development of a biological computer in the future. This paper presents a genetic sequential logic circuit with a clock pulse generator based on a synthesized genetic oscillator, which generates a consecutive clock signal whose frequency is an inverse integer multiple to that of the genetic oscillator. An analogous electronic waveform-shaping circuit is constructed by a series of genetic buffers to shape logic high/low levels of an oscillation input in a basic sinusoidal cycle and generate a pulse-width-modulated (PWM) output with various duty cycles. By controlling the threshold level of the genetic buffer, a genetic clock pulse signal with its frequency consistent to the genetic oscillator is synthesized. A synchronous genetic counter circuit based on the topology of the digital sequential logic circuit is triggered by the clock pulse to synthesize the clock signal with an inverse multiple frequency to the genetic oscillator. The function acts like a frequency divider in electronic circuits which plays a key role in the sequential logic circuit with specific operational frequency. A cascaded genetic logic circuit generating clock pulse signals is proposed. Based on analogous implement of digital sequential logic circuits, genetic sequential logic circuits can be constructed by the proposed approach to generate various clock signals from an oscillation signal.

Monolitic integrated circuit for the strobed charge-to-time converter

International Nuclear Information System (INIS)

Bel'skij, V.I.; Bushnin, Yu.B.; Zimin, S.A.; Punzhin, Yu.N.; Sen'ko, V.A.; Soldatov, M.M.; Tokarchuk, V.P.

1985-01-01

The developed and comercially produced semiconducting circuit - gating charge-to-time converter KR1101PD1 is described. The considered integrated circuit is a short pulse charge-to-time converter with integration of input current. The circuit is designed for construction of time-to-pulse analog-to-digital converters utilized in multichannel detection systems when studying complex topology processes. Input resistance of the circuit is 0.1 Ω permissible input current is 50 mA, maximum measured charge is 300-1000 pC

Artificial immune system algorithm in VLSI circuit configuration

Science.gov (United States)

Mansor, Mohd. Asyraf; Sathasivam, Saratha; Kasihmuddin, Mohd Shareduwan Mohd

2017-08-01

In artificial intelligence, the artificial immune system is a robust bio-inspired heuristic method, extensively used in solving many constraint optimization problems, anomaly detection, and pattern recognition. This paper discusses the implementation and performance of artificial immune system (AIS) algorithm integrated with Hopfield neural networks for VLSI circuit configuration based on 3-Satisfiability problems. Specifically, we emphasized on the clonal selection technique in our binary artificial immune system algorithm. We restrict our logic construction to 3-Satisfiability (3-SAT) clauses in order to outfit with the transistor configuration in VLSI circuit. The core impetus of this research is to find an ideal hybrid model to assist in the VLSI circuit configuration. In this paper, we compared the artificial immune system (AIS) algorithm (HNN-3SATAIS) with the brute force algorithm incorporated with Hopfield neural network (HNN-3SATBF). Microsoft Visual C++ 2013 was used as a platform for training, simulating and validating the performances of the proposed network. The results depict that the HNN-3SATAIS outperformed HNN-3SATBF in terms of circuit accuracy and CPU time. Thus, HNN-3SATAIS can be used to detect an early error in the VLSI circuit design.

Role of motoneuron-derived neurotrophin 3 in survival and axonal projection of sensory neurons during neural circuit formation.

Science.gov (United States)

Usui, Noriyoshi; Watanabe, Keisuke; Ono, Katsuhiko; Tomita, Koichi; Tamamaki, Nobuaki; Ikenaka, Kazuhiro; Takebayashi, Hirohide

2012-03-01

Sensory neurons possess the central and peripheral branches and they form unique spinal neural circuits with motoneurons during development. Peripheral branches of sensory axons fasciculate with the motor axons that extend toward the peripheral muscles from the central nervous system (CNS), whereas the central branches of proprioceptive sensory neurons directly innervate motoneurons. Although anatomically well documented, the molecular mechanism underlying sensory-motor interaction during neural circuit formation is not fully understood. To investigate the role of motoneuron on sensory neuron development, we analyzed sensory neuron phenotypes in the dorsal root ganglia (DRG) of Olig2 knockout (KO) mouse embryos, which lack motoneurons. We found an increased number of apoptotic cells in the DRG of Olig2 KO embryos at embryonic day (E) 10.5. Furthermore, abnormal axonal projections of sensory neurons were observed in both the peripheral branches at E10.5 and central branches at E15.5. To understand the motoneuron-derived factor that regulates sensory neuron development, we focused on neurotrophin 3 (Ntf3; NT-3), because Ntf3 and its receptors (Trk) are strongly expressed in motoneurons and sensory neurons, respectively. The significance of motoneuron-derived Ntf3 was analyzed using Ntf3 conditional knockout (cKO) embryos, in which we observed increased apoptosis and abnormal projection of the central branch innervating motoneuron, the phenotypes being apparently comparable with that of Olig2 KO embryos. Taken together, we show that the motoneuron is a functional source of Ntf3 and motoneuron-derived Ntf3 is an essential pre-target neurotrophin for survival and axonal projection of sensory neurons.

Shaping vulnerability to addiction - the contribution of behavior, neural circuits and molecular mechanisms.

Science.gov (United States)

Egervari, Gabor; Ciccocioppo, Roberto; Jentsch, J David; Hurd, Yasmin L

2018-02-01

Substance use disorders continue to impose increasing medical, financial and emotional burdens on society in the form of morbidity and overdose, family disintegration, loss of employment and crime, while advances in prevention and treatment options remain limited. Importantly, not all individuals exposed to abused substances effectively develop the disease. Genetic factors play a significant role in determining addiction vulnerability and interactions between innate predisposition, environmental factors and personal experiences are also critical. Thus, understanding individual differences that contribute to the initiation of substance use as well as on long-term maladaptations driving compulsive drug use and relapse propensity is of critical importance to reduce this devastating disorder. In this paper, we discuss current topics in the field of addiction regarding individual vulnerability related to behavioral endophenotypes, neural circuits, as well as genetics and epigenetic mechanisms. Expanded knowledge of these factors is of importance to improve and personalize prevention and treatment interventions in the future. Copyright © 2017 Elsevier Ltd. All rights reserved.

Inherently stochastic spiking neurons for probabilistic neural computation

KAUST Repository

Al-Shedivat, Maruan

2015-04-01

Neuromorphic engineering aims to design hardware that efficiently mimics neural circuitry and provides the means for emulating and studying neural systems. In this paper, we propose a new memristor-based neuron circuit that uniquely complements the scope of neuron implementations and follows the stochastic spike response model (SRM), which plays a cornerstone role in spike-based probabilistic algorithms. We demonstrate that the switching of the memristor is akin to the stochastic firing of the SRM. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards memristive, scalable and efficient stochastic neuromorphic platforms. © 2015 IEEE.

Neural circuits of disgust induced by sexual stimuli in homosexual and heterosexual men: An fMRI study

International Nuclear Information System (INIS)

Zhang Minming; Hu Shaohua; Xu Lijuan; Wang Qidong; Xu Xiaojun; Wei Erqing; Yan Leqin; Hu Jianbo; Wei Ning; Zhou Weihua; Huang Manli; Xu Yi

2011-01-01

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 (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.

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.

Delineating the Diversity of Spinal Interneurons in Locomotor Circuits.

Science.gov (United States)

Gosgnach, Simon; Bikoff, Jay B; Dougherty, Kimberly J; El Manira, Abdeljabbar; Lanuza, Guillermo M; Zhang, Ying

2017-11-08

Locomotion is common to all animals and is essential for survival. Neural circuits located in the spinal cord have been shown to be necessary and sufficient for the generation and control of the basic locomotor rhythm by activating muscles on either side of the body in a specific sequence. Activity in these neural circuits determines the speed, gait pattern, and direction of movement, so the specific locomotor pattern generated relies on the diversity of the neurons within spinal locomotor circuits. Here, we review findings demonstrating that developmental genetics can be used to identify populations of neurons that comprise these circuits and focus on recent work indicating that many of these populations can be further subdivided into distinct subtypes, with each likely to play complementary functions during locomotion. Finally, we discuss data describing the manner in which these populations interact with each other to produce efficient, task-dependent locomotion. Copyright © 2017 the authors 0270-6474/17/3710835-07$15.00/0.

Efficient computation in adaptive artificial spiking neural networks

NARCIS (Netherlands)

D. Zambrano (Davide); R.B.P. Nusselder (Roeland); H.S. Scholte; S.M. Bohte (Sander)

2017-01-01

textabstractArtificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven highly effective. Still, ANNs lack a natural notion of time, and neural units in ANNs exchange analog values in a frame-based manner, a computationally and energetically inefficient form of

Computer-integrated environments for electronics problem by means of the analog simulator PSPICE; Komp`yuterno-integrirovannye sredy dlya problemnogo obucheniya po ehlektronike na osnove analogovogo simulyatora PSPICE

Energy Technology Data Exchange (ETDEWEB)

Mileva, I; Petrov, A; Pavlov, I [Plovdivskij Univ., Plovdiv (Bulgaria)

1996-12-31

For the problem teaching purpose the computer-integrated environments are developed for simulation of electronic circuits. CIE for study of typical analog electronic circuits called STUDENT`s MODULE are described. Simulation of electronic circuits carried out by means of the analog simulator PSPICE. 9 refs.; 3 figs.

Full-Circle Resolver-to-Linear-Analog Converter

Science.gov (United States)

Alhorn, Dean C.; Smith, Dennis A.; Howard, David E.

2005-01-01

A circuit generates sinusoidal excitation signals for a shaft-angle resolver and, like the arctangent circuit described in the preceding article, generates an analog voltage proportional to the shaft angle. The disadvantages of the circuit described in the preceding article arise from the fact that it must be made from precise analog subcircuits, including a functional block capable of implementing some trigonometric identities; this circuitry tends to be expensive, sensitive to noise, and susceptible to errors caused by temperature-induced drifts and imprecise matching of gains and phases. These disadvantages are overcome by the design of the present circuit. The present circuit (see figure) includes an excitation circuit, which generates signals Ksin(Omega(t)) and Kcos(Omega(t)) [where K is an amplitude, Omega denotes 2(pi)x a carrier frequency (the design value of which is 10 kHz), and t denotes time]. These signals are applied to the excitation terminals of a shaft-angle resolver, causing the resolver to put out signals C sin(Omega(t)-Theta) and C cos(Omega(t)-Theta). The cosine excitation signal and the cosine resolver output signal are processed through inverting comparator circuits, which are configured to function as inverting squarers, to obtain logic-level or square-wave signals .-LL[cos(Omega(t)] and -LL[cos(Omega(t)-Theta)], respectively. These signals are fed as inputs to a block containing digital logic circuits that effectively measure the phase difference (which equals Theta between the two logic-level signals). The output of this block is a pulse-width-modulated signal, PWM(Theta), the time-averaged value of which ranges from 0 to 5 VDC as Theta ranges from .180 to +180deg. PWM(Theta) is fed to a block of amplifying and level-shifting circuitry, which converts the input PWM waveform to an output waveform that switches between precise reference voltage levels of +10 and -10 V. This waveform is processed by a two-pole, low-pass filter, which removes

Design and implementation of a hybrid circuit system for micro sensor signal processing

International Nuclear Information System (INIS)

Wang Zhuping; Chen Jing; Liu Ruqing

2011-01-01

This paper covers a micro sensor analog signal processing circuit system (MASPS) chip with low power and a digital signal processing circuit board implementation including hardware connection and software design. Attention has been paid to incorporate the MASPS chip into the digital circuit board. The ultimate aim is to form a hybrid circuit used for mixed-signal processing, which can be applied to a micro sensor flow monitoring system. (semiconductor integrated circuits)

Large-scale digitizer system, analog converters

International Nuclear Information System (INIS)

Althaus, R.F.; Lee, K.L.; Kirsten, F.A.; Wagner, L.J.

1976-10-01

Analog to digital converter circuits that are based on the sharing of common resources, including those which are critical to the linearity and stability of the individual channels, are described. Simplicity of circuit composition is valued over other more costly approaches. These are intended to be applied in a large-scale processing and digitizing system for use with high-energy physics detectors such as drift-chambers or phototube-scintillator arrays. Signal distribution techniques are of paramount importance in maintaining adequate signal-to-noise ratio. Noise in both amplitude and time-jitter senses is held sufficiently low so that conversions with 10-bit charge resolution and 12-bit time resolution are achieved

Sample-hold and analog multiplexer for multidetector systems

Energy Technology Data Exchange (ETDEWEB)

Goswami, G C; Ghoshdostidar, M R; Ghosh, B; Chaudhuri, N [North Bengal Univ., Darjeeling (India). Dept. of Physics

1982-08-15

A new sample-hold circuit with an analog multiplexer system is described. Designed for multichannel acquistion of data from an air shower array, the system is being used for accurate measurements of pulse heights from 16 channels by the use of a single ADC.

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.

Swarm intelligence-based approach for optimal design of CMOS differential amplifier and comparator circuit using a hybrid salp swarm algorithm

Science.gov (United States)

Asaithambi, Sasikumar; Rajappa, Muthaiah

2018-05-01

In this paper, an automatic design method based on a swarm intelligence approach for CMOS analog integrated circuit (IC) design is presented. The hybrid meta-heuristics optimization technique, namely, the salp swarm algorithm (SSA), is applied to the optimal sizing of a CMOS differential amplifier and the comparator circuit. SSA is a nature-inspired optimization algorithm which mimics the navigating and hunting behavior of salp. The hybrid SSA is applied to optimize the circuit design parameters and to minimize the MOS transistor sizes. The proposed swarm intelligence approach was successfully implemented for an automatic design and optimization of CMOS analog ICs using Generic Process Design Kit (GPDK) 180 nm technology. The circuit design parameters and design specifications are validated through a simulation program for integrated circuit emphasis simulator. To investigate the efficiency of the proposed approach, comparisons have been carried out with other simulation-based circuit design methods. The performances of hybrid SSA based CMOS analog IC designs are better than the previously reported studies.

Test and verification of a reactor protection system application-specific integrated circuit

International Nuclear Information System (INIS)

Battle, R.E.; Turner, G.W.; Vandermolen, R.I.; Vitalbo, C.

1997-01-01

Application-specific integrated circuits (ASICs) were utilized in the design of nuclear plant safety systems because they have certain advantages over software-based systems and analog-based systems. An advantage they have over software-based systems is that an ASIC design can be simple enough to not include branch statements and also can be thoroughly tested. A circuit card on which an ASIC is mounted can be configured to replace various versions of older analog equipment with fewer design types required. The approach to design and testing of ASICs for safety system applications is discussed in this paper. Included are discussions of the ASIC architecture, how it is structured to assist testing, and of the functional and enhanced circuit testing

Two Kinds of Self-Oscillating Circuits Mechanically Demonstrated

OpenAIRE

Shiang-Hwua Yu; Po-Hsun Wu

2015-01-01

This study introduces two types of self-oscillating circuits that are frequently found in power electronics applications. Special effort is made to relate the circuits to the analogous mechanical systems of some important scientific inventions: Galileo’s pendulum clock and Coulomb’s friction model. A little touch of related history and philosophy of science will hopefully encourage curiosity, advance the understanding of self-oscillating systems and satisfy the aspiration ...

Activity-regulated genes as mediators of neural circuit plasticity.

Science.gov (United States)

Leslie, Jennifer H; Nedivi, Elly

2011-08-01

Modifications of neuronal circuits allow the brain to adapt and change with experience. This plasticity manifests during development and throughout life, and can be remarkably long lasting. Evidence has linked activity-regulated gene expression to the long-term structural and electrophysiological adaptations that take place during developmental critical periods, learning and memory, and alterations to sensory map representations in the adult. In all these cases, the cellular response to neuronal activity integrates multiple tightly coordinated mechanisms to precisely orchestrate long-lasting, functional and structural changes in brain circuits. Experience-dependent plasticity is triggered when neuronal excitation activates cellular signaling pathways from the synapse to the nucleus that initiate new programs of gene expression. The protein products of activity-regulated genes then work via a diverse array of cellular mechanisms to modify neuronal functional properties. Synaptic strengthening or weakening can reweight existing circuit connections, while structural changes including synapse addition and elimination create new connections. Posttranscriptional regulatory mechanisms, often also dependent on activity, further modulate activity-regulated gene transcript and protein function. Thus, activity-regulated genes implement varied forms of structural and functional plasticity to fine-tune brain circuit wiring. Copyright © 2011 Elsevier Ltd. All rights reserved.

Designed cell consortia as fragrance-programmable analog-to-digital converters.

Science.gov (United States)

Müller, Marius; Ausländer, Simon; Spinnler, Andrea; Ausländer, David; Sikorski, Julian; Folcher, Marc; Fussenegger, Martin

2017-03-01

Synthetic biology advances the rational engineering of mammalian cells to achieve cell-based therapy goals. Synthetic gene networks have nearly reached the complexity of digital electronic circuits and enable single cells to perform programmable arithmetic calculations or to provide dynamic remote control of transgenes through electromagnetic waves. We designed a synthetic multilayered gaseous-fragrance-programmable analog-to-digital converter (ADC) allowing for remote control of digital gene expression with 2-bit AND-, OR- and NOR-gate logic in synchronized cell consortia. The ADC consists of multiple sampling-and-quantization modules sensing analog gaseous fragrance inputs; a gas-to-liquid transducer converting fragrance intensity into diffusible cell-to-cell signaling compounds; a digitization unit with a genetic amplifier circuit to improve the signal-to-noise ratio; and recombinase-based digital expression switches enabling 2-bit processing of logic gates. Synthetic ADCs that can remotely control cellular activities with digital precision may enable the development of novel biosensors and may provide bioelectronic interfaces synchronizing analog metabolic pathways with digital electronics.

[Lower urinary tract dysfunction and neuropathological findings of the neural circuits controlling micturition in familial amyotrophic lateral sclerosis with L106V mutation in the SOD1 gene].

Science.gov (United States)

Hineno, Akiyo; Oyanagi, Kiyomitsu; Nakamura, Akinori; Shimojima, Yoshio; Yoshida, Kunihiro; Ikeda, Shu-Ichi

2016-01-01

We report lower urinary tract dysfunction and neuropathological findings of the neural circuits controlling micturition in the patients with familial amyotrophic lateral sclerosis having L106V mutation in the SOD1 gene. Ten of 20 patients showed lower urinary tract dysfunction and 5 patients developed within 1 year after the onset of weakness. In 8 patients with an artificial respirator, 6 patients showed lower urinary tract dysfunction. Lower urinary tract dysfunction and respiratory failure requiring an artificial respirator occurred simultaneously in 3 patients. Neuronal loss and gliosis were observed in the neural circuits controlling micturition, such as frontal lobe, thalamus, hypothalamus, striatum, periaqueductal gray, ascending spinal tract, lateral corticospinal tract, intermediolateral nucleus and Onufrowicz' nucleus. Lower urinary tract dysfunction, especially storage symptoms, developed about 1 year after the onset of weakness, and the dysfunction occurred simultaneously with artificial respirator use in the patients.

A Genetic Toolkit for Dissecting Dopamine Circuit Function in Drosophila

Directory of Open Access Journals (Sweden)

Tingting Xie

2018-04-01

Full Text Available Summary: The neuromodulator dopamine (DA plays a key role in motor control, motivated behaviors, and higher-order cognitive processes. Dissecting how these DA neural networks tune the activity of local neural circuits to regulate behavior requires tools for manipulating small groups of DA neurons. To address this need, we assembled a genetic toolkit that allows for an exquisite level of control over the DA neural network in Drosophila. To further refine targeting of specific DA neurons, we also created reagents that allow for the conversion of any existing GAL4 line into Split GAL4 or GAL80 lines. We demonstrated how this toolkit can be used with recently developed computational methods to rapidly generate additional reagents for manipulating small subsets or individual DA neurons. Finally, we used the toolkit to reveal a dynamic interaction between a small subset of DA neurons and rearing conditions in a social space behavioral assay. : The rapid analysis of how dopaminergic circuits regulate behavior is limited by the genetic tools available to target and manipulate small numbers of these neurons. Xie et al. present genetic tools in Drosophila that allow rational targeting of sparse dopaminergic neuronal subsets and selective knockdown of dopamine signaling. Keywords: dopamine, genetics, behavior, neural circuits, neuromodulation, Drosophila

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.

Sex-specific neural circuits of emotion regulation in the centromedial amygdala.

Science.gov (United States)

Wu, Yan; Li, Huandong; Zhou, Yuan; Yu, Jian; Zhang, Yuanchao; Song, Ming; Qin, Wen; Yu, Chunshui; Jiang, Tianzi

2016-03-23

Sex-related differences in emotion regulation (ER) in the frequency power distribution within the human amygdala, a brain region involved in emotion processing, have been reported. However, how sex differences in ER are manifested in the brain networks which are seeded on the amygdala subregions is unclear. The goal of this study was to investigate this issue from a brain network perspective. Utilizing resting-state functional connectivity (RSFC) analysis, we found that the sex-specific functional connectivity patterns associated with ER trait level were only seeded in the centromedial amygdala (CM). Women with a higher trait-level ER had a stronger negative RSFC between the right CM and the medial superior frontal gyrus (mSFG), and stronger positive RSFC between the right CM and the anterior insula (AI) and the superior temporal gyrus (STG). But men with a higher trait-level ER was associated with weaker negative RSFC of the right CM-mSFG and positive RSFCs of the right CM-left AI, right CM-right AI/STG, and right CM-left STG. These results provide evidence for the sex-related effects in ER based on CM and indicate that men and women may differ in the neural circuits associated with emotion representation and integration.

High speed digital interfacing for a neural data acquisition system

Directory of Open Access Journals (Sweden)

Bahr Andreas

2016-09-01

Full Text Available Diseases like schizophrenia and genetic epilepsy are supposed to be caused by disorders in the early development of the brain. For the further investigation of these relationships a custom designed application specific integrated circuit (ASIC was developed that is optimized for the recording from neonatal mice [Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. 16 Channel Neural Recording Integrated Circuit with SPI Interface and Error Correction Coding. Proc. 9th BIOSTEC 2016. Biodevices: Rome, Italy, 2016; 1: 263; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. Development of a neural recording mixed signal integrated circuit for biomedical signal acquisition. Biomed Eng Biomed Tech Abstracts 2015; 60(S1: 298–299; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider WH. 16 Channel Neural Recording Mixed Signal ASIC. CDNLive EMEA 2015 Conference Proceedings, 2015.]. To enable the live display of the neural signals a multichannel neural data acquisition system with live display functionality is presented. It implements a high speed data transmission from the ASIC to a computer with a live display functionality. The system has been successfully implemented and was used in a neural recording of a head-fixed mouse.

An Analog Correlator for Ultra-Wideband Receivers

Directory of Open Access Journals (Sweden)

Tu Chunjiang

2005-01-01

Full Text Available We present a new analog circuit exhibiting high bandwidth and low distortion, specially designed for signal correlation in an ultra-wideband receiver front end. The ultra-wideband short impulse signals are correlated with a local pulse template by the correlator. A comparator then samples the output for signal detection. A typical Gilbert mixer core is adopted for multiplication of broadband signals up to . As a result of synchronization of the received signal and the local template, the output voltage level after integration and sampling can reach up to , which is sufficient for detection by the comparator. The circuit dissipates about from double voltage supplies of and using SiGe BiCMOS technology. Simulation results are presented to show the feasibility of this circuit design for use in ultra-wideband receivers.

A CMOS integrated timing discriminator circuit for fast scintillation counters

International Nuclear Information System (INIS)

Jochmann, M.W.

1998-01-01

Based on a zero-crossing discriminator using a CR differentiation network for pulse shaping, a new CMOS integrated timing discriminator circuit is proposed for fast (t r ≥ 2 ns) scintillation counters at the cooler synchrotron COSY-Juelich. By eliminating the input signal's amplitude information by means of an analog continuous-time divider, a normalized pulse shape at the zero-crossing point is gained over a wide dynamic input amplitude range. In combination with an arming comparator and a monostable multivibrator this yields in a highly precise timing discriminator circuit, that is expected to be useful in different time measurement applications. First measurement results of a CMOS integrated logarithmic amplifier, which is part of the analog continuous-time divider, agree well with the corresponding simulations. Moreover, SPICE simulations of the integrated discriminator circuit promise a time walk well below 200 ps (FWHM) over a 40 dB input amplitude dynamic range

A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning.

Science.gov (United States)

Kappel, David; Legenstein, Robert; Habenschuss, Stefan; Hsieh, Michael; Maass, Wolfgang

2018-01-01

Synaptic connections between neurons in the brain are dynamic because of continuously ongoing spine dynamics, axonal sprouting, and other processes. In fact, it was recently shown that the spontaneous synapse-autonomous component of spine dynamics is at least as large as the component that depends on the history of pre- and postsynaptic neural activity. These data are inconsistent with common models for network plasticity and raise the following questions: how can neural circuits maintain a stable computational function in spite of these continuously ongoing processes, and what could be functional uses of these ongoing processes? Here, we present a rigorous theoretical framework for these seemingly stochastic spine dynamics and rewiring processes in the context of reward-based learning tasks. We show that spontaneous synapse-autonomous processes, in combination with reward signals such as dopamine, can explain the capability of networks of neurons in the brain to configure themselves for specific computational tasks, and to compensate automatically for later changes in the network or task. Furthermore, we show theoretically and through computer simulations that stable computational performance is compatible with continuously ongoing synapse-autonomous changes. After reaching good computational performance it causes primarily a slow drift of network architecture and dynamics in task-irrelevant dimensions, as observed for neural activity in motor cortex and other areas. On the more abstract level of reinforcement learning the resulting model gives rise to an understanding of reward-driven network plasticity as continuous sampling of network configurations.

VHDL-based programming environment for Floating-Gate analog memory cell

Directory of Open Access Journals (Sweden)

Carlos Alberto dos Reis Filho

2005-02-01

Full Text Available An implementation in CMOS technology of a Floating-Gate Analog Memory Cell and Programming Environment is presented. A digital closed-loop control compares a reference value set by user and the memory output and after cycling, the memory output is updated and the new value stored. The circuit can be used as analog trimming for VLSI applications where mechanical trimming associated with postprocessing chip is prohibitive due to high costs.

The 128-channel fully differential digital integrated neural recording and stimulation interface.

Science.gov (United States)

Shahrokhi, Farzaneh; Abdelhalim, Karim; Serletis, Demitre; Carlen, Peter L; Genov, Roman

2010-06-01

We present a fully differential 128-channel integrated neural interface. It consists of an array of 8 X 16 low-power low-noise signal-recording and generation circuits for electrical neural activity monitoring and stimulation, respectively. The recording channel has two stages of signal amplification and conditioning with and a fully differential 8-b column-parallel successive approximation (SAR) analog-to-digital converter (ADC). The total measured power consumption of each recording channel, including the SAR ADC, is 15.5 ¿W. The measured input-referred noise is 6.08 ¿ Vrms over a 5-kHz bandwidth, resulting in a noise efficiency factor of 5.6. The stimulation channel performs monophasic or biphasic voltage-mode stimulation, with a maximum stimulation current of 5 mA and a quiescent power dissipation of 51.5 ¿W. The design is implemented in 0.35-¿m complementary metal-oxide semiconductor technology with the channel pitch of 200 ¿m for a total die size of 3.4 mm × 2.5 mm and a total power consumption of 9.33 mW. The neural interface was validated in in vitro recording of a low-Mg(2+)/high-K(+) epileptic seizure model in an intact hippocampus of a mouse.

A Global Electric Circuit on Mars

Science.gov (United States)

Delory, G. T.; Farrell, W. M.; Desch, M. D.

2001-01-01

We describe conditions on the surface of Mars conducive to the formation of a martian global electric circuit, in a direct analogy to the terrestrial case where atmospheric currents and electric fields are generated worldwide through the charging in thunderstorms. Additional information is contained in the original extended abstract.

"E pluribus unum" or How to Derive Single-equation Descriptions for Output-quantities in Nonlinear Circuits using Differential Algebra

OpenAIRE

Gerbracht, Eberhard H. -A.

2008-01-01

In this paper we describe by a number of examples how to deduce one single characterizing higher order differential equation for output quantities of an analog circuit. In the linear case, we apply basic "symbolic" methods from linear algebra to the system of differential equations which is used to model the analog circuit. For nonlinear circuits and their corresponding nonlinear differential equations, we show how to employ computer algebra tools implemented in Maple, which are based on diff...

Inherently stochastic spiking neurons for probabilistic neural computation

KAUST Repository

Al-Shedivat, Maruan; Naous, Rawan; Neftci, Emre; Cauwenberghs, Gert; Salama, Khaled N.

2015-01-01

. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards

Deep Modeling: Circuit Characterization Using Theory Based Models in a Data Driven Framework

Energy Technology Data Exchange (ETDEWEB)

Bolme, David S [ORNL; Mikkilineni, Aravind K [ORNL; Rose, Derek C [ORNL; Yoginath, Srikanth B [ORNL; Holleman, Jeremy [University of Tennessee, Knoxville (UTK); Judy, Mohsen [University of Tennessee, Knoxville (UTK), Department of Electrical Engineering and Computer Science

2017-01-01

Analog computational circuits have been demonstrated to provide substantial improvements in power and speed relative to digital circuits, especially for applications requiring extreme parallelism but only modest precision. Deep machine learning is one such area and stands to benefit greatly from analog and mixed-signal implementations. However, even at modest precisions, offsets and non-linearity can degrade system performance. Furthermore, in all but the simplest systems, it is impossible to directly measure the intermediate outputs of all sub-circuits. The result is that circuit designers are unable to accurately evaluate the non-idealities of computational circuits in-situ and are therefore unable to fully utilize measurement results to improve future designs. In this paper we present a technique to use deep learning frameworks to model physical systems. Recently developed libraries like TensorFlow make it possible to use back propagation to learn parameters in the context of modeling circuit behavior. Offsets and scaling errors can be discovered even for sub-circuits that are deeply embedded in a computational system and not directly observable. The learned parameters can be used to refine simulation methods or to identify appropriate compensation strategies. We demonstrate the framework using a mixed-signal convolution operator as an example circuit.

Hybrid integrated circuit for charge-to-time interval conversion

Energy Technology Data Exchange (ETDEWEB)

Basiladze, S.G.; Dotsenko, Yu.Yu.; Man' yakov, P.K.; Fedorchenko, S.N. (Joint Inst. for Nuclear Research, Dubna (USSR))

The hybrid integrated circuit for charge-to time interval conversion with nanosecond input fast response is described. The circuit can be used in energy measuring channels, time-to-digital converters and in the modified variant in amplitude-to-digital converters. The converter described consists of a buffer amplifier, a linear transmission circuit, a direct current source and a unit of time interval separation. The buffer amplifier represents a current follower providing low input and high output resistances by the current feedback. It is concluded that the described converter excelled the QT100B circuit analogous to it in a number of parameters especially, in thermostability.

Single-flux-quantum circuit technology for superconducting radiation detectors

International Nuclear Information System (INIS)

Fujimaki, Akira; Onogi, Masashi; Matsumoto, Tomohiro; Tanaka, Masamitsu; Sekiya, Akito; Hayakawa, Hisao; Yorozu, Shinichi; Terai, Hirotaka; Yoshikawa, Nobuyuki

2003-01-01

We discuss the application of the single-flux-quantum (SFQ) logic circuits to multi superconducting radiation detectors system. The SFQ-based analog-to-digital converters (ADCs) have the advantage in current sensitivity, which can reach less than 10 nA in a well-tuned ADC. We have also developed the design technology of the SFQ circuits. We demonstrate high-speed operation of large-scale integrated circuits such as a 2x2 cross/bar switch, arithmetic logic unit, indicating that our present SFQ technology is applicable to the multi radiation detectors system. (author)

Imaging the neural circuitry and chemical control of aggressive motivation

Directory of Open Access Journals (Sweden)

Blanchard D Caroline

2008-11-01

Full Text Available Abstract Background With the advent of functional magnetic resonance imaging (fMRI in awake animals it is possible to resolve patterns of neuronal activity across the entire brain with high spatial and temporal resolution. Synchronized changes in neuronal activity across multiple brain areas can be viewed as functional neuroanatomical circuits coordinating the thoughts, memories and emotions for particular behaviors. To this end, fMRI in conscious rats combined with 3D computational analysis was used to identifying the putative distributed neural circuit involved in aggressive motivation and how this circuit is affected by drugs that block aggressive behavior. Results To trigger aggressive motivation, male rats were presented with their female cage mate plus a novel male intruder in the bore of the magnet during image acquisition. As expected, brain areas previously identified as critical in the organization and expression of aggressive behavior were activated, e.g., lateral hypothalamus, medial basal amygdala. Unexpected was the intense activation of the forebrain cortex and anterior thalamic nuclei. Oral administration of a selective vasopressin V1a receptor antagonist SRX251 or the selective serotonin reuptake inhibitor fluoxetine, drugs that block aggressive behavior, both caused a general suppression of the distributed neural circuit involved in aggressive motivation. However, the effect of SRX251, but not fluoxetine, was specific to aggression as brain activation in response to a novel sexually receptive female was unaffected. Conclusion The putative neural circuit of aggressive motivation identified with fMRI includes neural substrates contributing to emotional expression (i.e. cortical and medial amygdala, BNST, lateral hypothalamus, emotional experience (i.e. hippocampus, forebrain cortex, anterior cingulate, retrosplenial cortex and the anterior thalamic nuclei that bridge the motor and cognitive components of aggressive responding

Monolithic circuit development for RHIC at Oak Ridge National Laboratory

Energy Technology Data Exchange (ETDEWEB)

Alley, G.T.; Britton, C.L. Jr.; Kennedy, E.J.; Newport, D.F.; Wintenberg, A.L.; Young, G.R. [Oak Ridge National Laboratory, TN (United States)

1991-12-31

The work performed for RHIC at Oak Ridge National Laboratory during FY 91 is presented in this paper. The work includes preamplifier, analog memory, and analog-digital converter development for Dimuon Pad Readout, and evaluation and development of preamplifier-shapers for silicon strip readout. The approaches for implementation are considered as well as measured data for the various circuits that have been developed.

QCD-Aware Neural Networks for Jet Physics

CERN Multimedia

CERN. Geneva

2017-01-01

Recent progress in applying machine learning for jet physics has been built upon an analogy between calorimeters and images. In this work, we present a novel class of recursive neural networks built instead upon an analogy between QCD and natural languages. In the analogy, four-momenta are like words and the clustering history of sequential recombination jet algorithms is like the parsing of a sentence. Our approach works directly with the four-momenta of a variable-length set of particles, and the jet-based neural network topology varies on an event-by-event basis. Our experiments highlight the flexibility of our method for building task-specific jet embeddings and show that recursive architectures are significantly more accurate and data efficient than previous image-based networks. We extend the analogy from individual jets (sentences) to full events (paragraphs), and show for the first time an event-level classifier operating...

The Electron Runaround: Understanding Electric Circuit Basics through a Classroom Activity

Science.gov (United States)

Singh, Vandana

2010-01-01

Several misconceptions abound among college students taking their first general physics course, and to some extent pre-engineering physics students, regarding the physics and applications of electric circuits. Analogies used in textbooks, such as those that liken an electric circuit to a piped closed loop of water driven by a water pump, do not…

Model, analysis, and evaluation of the effects of analog VLSI arithmetic on linear subspace-based image recognition.

Science.gov (United States)

Carvajal, Gonzalo; Figueroa, Miguel

2014-07-01

Typical image recognition systems operate in two stages: feature extraction to reduce the dimensionality of the input space, and classification based on the extracted features. Analog Very Large Scale Integration (VLSI) is an attractive technology to achieve compact and low-power implementations of these computationally intensive tasks for portable embedded devices. However, device mismatch limits the resolution of the circuits fabricated with this technology. Traditional layout techniques to reduce the mismatch aim to increase the resolution at the transistor level, without considering the intended application. Relating mismatch parameters to specific effects in the application level would allow designers to apply focalized mismatch compensation techniques according to predefined performance/cost tradeoffs. This paper models, analyzes, and evaluates the effects of mismatched analog arithmetic in both feature extraction and classification circuits. For the feature extraction, we propose analog adaptive linear combiners with on-chip learning for both Least Mean Square (LMS) and Generalized Hebbian Algorithm (GHA). Using mathematical abstractions of analog circuits, we identify mismatch parameters that are naturally compensated during the learning process, and propose cost-effective guidelines to reduce the effect of the rest. For the classification, we derive analog models for the circuits necessary to implement Nearest Neighbor (NN) approach and Radial Basis Function (RBF) networks, and use them to emulate analog classifiers with standard databases of face and hand-writing digits. Formal analysis and experiments show how we can exploit adaptive structures and properties of the input space to compensate the effects of device mismatch at the application level, thus reducing the design overhead of traditional layout techniques. Results are also directly extensible to multiple application domains using linear subspace methods. Copyright © 2014 Elsevier Ltd. All rights

Three Pillars for the Neural Control of Appetite.

Science.gov (United States)

Sternson, Scott M; Eiselt, Anne-Kathrin

2017-02-10

The neural control of appetite is important for understanding motivated behavior as well as the present rising prevalence of obesity. Over the past several years, new tools for cell type-specific neuron activity monitoring and perturbation have enabled increasingly detailed analyses of the mechanisms underlying appetite-control systems. Three major neural circuits strongly and acutely influence appetite but with notably different characteristics. Although these circuits interact, they have distinct properties and thus appear to contribute to separate but interlinked processes influencing appetite, thereby forming three pillars of appetite control. Here, we summarize some of the key characteristics of appetite circuits that are emerging from recent work and synthesize the findings into a provisional framework that can guide future studies.

Research of Measurement Circuits for High Voltage Current Transformer Based on Rogowski Coils

Directory of Open Access Journals (Sweden)

Yan Bing

2014-02-01

Full Text Available The electronic current transformer plays an irreplaceable position in the field of relay protection and current measurement of the power system. Rogowski coils are used as sensor parts, and in order to improve the measurement accuracy and reliability, the circuits at the high voltage system are introduced and improved in this paper, including the analog integral element, the filtering circuit and the phase shift circuit. Simulations results proved the reliability and accuracy of the improved circuits.

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

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

2016-09-01

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.

High speed, wide dynamic range analog signal processing for avalanche photodiode

CERN Document Server

Walder, J P; Pangaud, P

2000-01-01

A wide dynamic range multi-gain analog transimpedance amplifier integrated circuit has been developed for avalanche photodiode signal processing. The 96 dB input dynamic range is divided into four ranges of 12-bits each in order to provide 40 MHz analog sampled data to a 12-bits ADC. This concept which has been integrated in both BiCMOS and full complementary bipolar technology along with fitted design techniques will be presented.

High speed, wide dynamic range analog signal processing for avalanche photodiode

International Nuclear Information System (INIS)

Walder, J.P.; El Mamouni, Houmani; Pangaud, Patrick

2000-01-01

A wide dynamic range multi-gain analog transimpedance amplifier integrated circuit has been developed for avalanche photodiode signal processing. The 96 dB input dynamic range is divided into four ranges of 12-bits each in order to provide 40 MHz analog sampled data to a 12-bits ADC. This concept which has been integrated in both BiCMOS and full complementary bipolar technology along with fitted design techniques will be presented

High speed, wide dynamic range analog signal processing for avalanche photodiode

Energy Technology Data Exchange (ETDEWEB)

Walder, J.P. E-mail: walder@in2p3.fr; El Mamouni, Houmani; Pangaud, Patrick

2000-03-11

A wide dynamic range multi-gain analog transimpedance amplifier integrated circuit has been developed for avalanche photodiode signal processing. The 96 dB input dynamic range is divided into four ranges of 12-bits each in order to provide 40 MHz analog sampled data to a 12-bits ADC. This concept which has been integrated in both BiCMOS and full complementary bipolar technology along with fitted design techniques will be presented.

Nanophotonic integrated circuits from nanoresonators grown on silicon.

Science.gov (United States)

Chen, Roger; Ng, Kar Wei; Ko, Wai Son; Parekh, Devang; Lu, Fanglu; Tran, Thai-Truong D; Li, Kun; Chang-Hasnain, Connie

2014-07-07

Harnessing light with photonic circuits promises to catalyse powerful new technologies much like electronic circuits have in the past. Analogous to Moore's law, complexity and functionality of photonic integrated circuits depend on device size and performance scale. Semiconductor nanostructures offer an attractive approach to miniaturize photonics. However, shrinking photonics has come at great cost to performance, and assembling such devices into functional photonic circuits has remained an unfulfilled feat. Here we demonstrate an on-chip optical link constructed from InGaAs nanoresonators grown directly on a silicon substrate. Using nanoresonators, we show a complete toolkit of circuit elements including light emitters, photodetectors and a photovoltaic power supply. Devices operate with gigahertz bandwidths while consuming subpicojoule energy per bit, vastly eclipsing performance of prior nanostructure-based optoelectronics. Additionally, electrically driven stimulated emission from an as-grown nanostructure is presented for the first time. These results reveal a roadmap towards future ultradense nanophotonic integrated circuits.

Deciphering the Cognitive and Neural Mechanisms Underlying ...

International Development Research Centre (IDRC) Digital Library (Canada)

Deciphering the Cognitive and Neural Mechanisms Underlying Auditory Learning. This project seeks to understand the brain mechanisms necessary for people to learn to perceive sounds. Neural circuits and learning. The research team will test people with and without musical training to evaluate their capacity to learn ...

Common and dissociable prefrontal loci associated with component mechanisms of analogical reasoning.

Science.gov (United States)

Cho, Soohyun; Moody, Teena D; Fernandino, Leonardo; Mumford, Jeanette A; Poldrack, Russell A; Cannon, Tyrone D; Knowlton, Barbara J; Holyoak, Keith J

2010-03-01

The ability to draw analogies requires 2 key cognitive processes, relational integration and resolution of interference. The present study aimed to identify the neural correlates of both component processes of analogical reasoning within a single, nonverbal analogy task using event-related functional magnetic resonance imaging. Participants verified whether a visual analogy was true by considering either 1 or 3 relational dimensions. On half of the trials, there was an additional need to resolve interference in order to make a correct judgment. Increase in the number of dimensions to integrate was associated with increased activation in the lateral prefrontal cortex as well as lateral frontal pole in both hemispheres. When there was a need to resolve interference during reasoning, activation increased in the lateral prefrontal cortex but not in the frontal pole. We identified regions in the middle and inferior frontal gyri which were exclusively sensitive to demands on each component process, in addition to a partial overlap between these neural correlates of each component process. These results indicate that analogical reasoning is mediated by the coordination of multiple regions of the prefrontal cortex, of which some are sensitive to demands on only one of these 2 component processes, whereas others are sensitive to both.

Logic analysis and verification of n-input genetic logic circuits

DEFF Research Database (Denmark)

Baig, Hasan; Madsen, Jan

2017-01-01

. In this paper, we present an approach to analyze and verify the Boolean logic of a genetic circuit from the data obtained through stochastic analog circuit simulations. The usefulness of this analysis is demonstrated through different case studies illustrating how our approach can be used to verify the expected......Nature is using genetic logic circuits to regulate the fundamental processes of life. These genetic logic circuits are triggered by a combination of external signals, such as chemicals, proteins, light and temperature, to emit signals to control other gene expressions or metabolic pathways...... accordingly. As compared to electronic circuits, genetic circuits exhibit stochastic behavior and do not always behave as intended. Therefore, there is a growing interest in being able to analyze and verify the logical behavior of a genetic circuit model, prior to its physical implementation in a laboratory...

Design and Verification of Application Specific Integrated Circuits in a Network of Online Labs

Directory of Open Access Journals (Sweden)

A.Y. Al-Zoubi

2009-08-01

Full Text Available A solution to implement a remote laboratory for testing and designing analog Application-Specific Integrated Circuits of the type (ispPAC10 is presented. The application allows electrical engineering students to access and perform measurements and conduct analog electronics experiments over the internet. PAC-Designer software, running on a Citrix server, is used in the circuit design in which the signals are generated and the responses are acquired by a data acquisition board controlled by LabVIEW. Three interconnected remote labs located in three different continents will be implementing the proposed system.

Rodent Zic Genes in Neural Network Wiring.

Science.gov (United States)

Herrera, Eloísa

2018-01-01

The formation of the nervous system is a multistep process that yields a mature brain. Failure in any of the steps of this process may cause brain malfunction. In the early stages of embryonic development, neural progenitors quickly proliferate and then, at a specific moment, differentiate into neurons or glia. Once they become postmitotic neurons, they migrate to their final destinations and begin to extend their axons to connect with other neurons, sometimes located in quite distant regions, to establish different neural circuits. During the last decade, it has become evident that Zic genes, in addition to playing important roles in early development (e.g., gastrulation and neural tube closure), are involved in different processes of late brain development, such as neuronal migration, axon guidance, and refinement of axon terminals. ZIC proteins are therefore essential for the proper wiring and connectivity of the brain. In this chapter, we review our current knowledge of the role of Zic genes in the late stages of neural circuit formation.

Flexible, High-Speed CdSe Nanocrystal Integrated Circuits.

Science.gov (United States)

Stinner, F Scott; Lai, Yuming; Straus, Daniel B; Diroll, Benjamin T; Kim, David K; Murray, Christopher B; Kagan, Cherie R

2015-10-14

We report large-area, flexible, high-speed analog and digital colloidal CdSe nanocrystal integrated circuits operating at low voltages. Using photolithography and a newly developed process to fabricate vertical interconnect access holes, we scale down device dimensions, reducing parasitic capacitances and increasing the frequency of circuit operation, and scale up device fabrication over 4 in. flexible substrates. We demonstrate amplifiers with ∼7 kHz bandwidth, ring oscillators with <10 μs stage delays, and NAND and NOR logic gates.

Negative Resistance Circuit for Damping an Array of Coupled FitzHugh-Nagumo Oscillators

DEFF Research Database (Denmark)

Tamaševičius, Arūnas; Adomaitienė, Elena; Bumelienė, Skaidra

2015-01-01

An analog circuit, based on a negative impedance converter and a capacitor, for damping oscillations in an array of mean-field coupled neuronal FitzHugh–Nagumo (FHN) type oscillators is described. The circuit is essentially a two-terminal feedback controller. When coupled to an array of the FHN...

Advanced Breakdown Modeling for Solid-State Circuit Design

NARCIS (Netherlands)

Milovanovi?, V.

2010-01-01

Modeling of the effects occurring outside the usual region of application of semiconductor devices is becoming more important with increasing demands set upon electronic systems for simultaneous speed and output power. Analog integrated circuit designers are forced to enter regimes of transistor

Refining the Role of 5-HT in Postnatal Development of Brain Circuits

Directory of Open Access Journals (Sweden)

Anne Teissier

2017-05-01

Full Text Available Changing serotonin (5-hydroxytryptamine, 5-HT brain levels during critical periods in development has long-lasting effects on brain function, particularly on later anxiety/depression-related behaviors in adulthood. A large part of the known developmental effects of 5-HT occur during critical periods of postnatal life, when activity-dependent mechanisms remodel neural circuits. This was first demonstrated for the maturation of sensory brain maps in the barrel cortex and the visual system. More recently this has been extended to the 5-HT raphe circuits themselves and to limbic circuits. Recent studies overviewed here used new genetic models in mice and rats and combined physiological and structural approaches to provide new insights on the cellular and molecular mechanisms controlled by 5-HT during late stages of neural circuit maturation in the raphe projections, the somatosensory cortex and the visual system. Similar mechanisms appear to be also involved in the maturation of limbic circuits such as prefrontal circuits. The latter are of particular relevance to understand the impact of transient 5-HT dysfunction during postnatal life on psychiatric illnesses and emotional disorders in adult life.

From circuits to behaviour in the amygdala

Science.gov (United States)

Janak, Patricia H.; Tye, Kay M.

2015-01-01

The amygdala has long been associated with emotion and motivation, playing an essential part in processing both fearful and rewarding environmental stimuli. How can a single structure be crucial for such different functions? With recent technological advances that allow for causal investigations of specific neural circuit elements, we can now begin to map the complex anatomical connections of the amygdala onto behavioural function. Understanding how the amygdala contributes to a wide array of behaviours requires the study of distinct amygdala circuits. PMID:25592533

A 13-Bits wilkinson analog-digital converter for NIM acquisition system

International Nuclear Information System (INIS)

Acosta Toledo, R.; Osorio Deliz, J.; Arista Romeu, E.; Fernandez, J.

1994-01-01

A new 13-bits Wilkinson analog-digital converter is described. The aim of this work is to describe the circuits of sample and hold, memory condensator loading and releasing PROM based control memory logic, zero level detection and correction. The converter is designed for the digital measurement of the peak amplitudes of pulses with statistical or periodical time distribution. The analog-digital converter may be used in spectrometric systems, multi-channel analysers or any similar PC based system

Unbalanced Neuronal Circuits in Addiction

OpenAIRE

Volkow, Nora D.; Wang, Gen-Jack; Tomasi, Dardo; Baler, Ruben D.

2013-01-01

Through sequential waves of drug-induced neurochemical stimulation, addiction co-opts the brain's neuronal circuits that mediate reward, motivation, , to behavioral inflexibility and a severe disruption of self-control and compulsive drug intake. Brain imaging technologies have allowed neuroscientists to map out the neural landscape of addiction in the human brain and to understand how drugs modify it.

Large-scale multielectrode recording and stimulation of neural activity

International Nuclear Information System (INIS)

Sher, A.; Chichilnisky, E.J.; Dabrowski, W.; Grillo, A.A.; Grivich, M.; Gunning, D.; Hottowy, P.; Kachiguine, S.; Litke, A.M.; Mathieson, K.; Petrusca, D.

2007-01-01

Large circuits of neurons are employed by the brain to encode and process information. How this encoding and processing is carried out is one of the central questions in neuroscience. Since individual neurons communicate with each other through electrical signals (action potentials), the recording of neural activity with arrays of extracellular electrodes is uniquely suited for the investigation of this question. Such recordings provide the combination of the best spatial (individual neurons) and temporal (individual action-potentials) resolutions compared to other large-scale imaging methods. Electrical stimulation of neural activity in turn has two very important applications: it enhances our understanding of neural circuits by allowing active interactions with them, and it is a basis for a large variety of neural prosthetic devices. Until recently, the state-of-the-art in neural activity recording systems consisted of several dozen electrodes with inter-electrode spacing ranging from tens to hundreds of microns. Using silicon microstrip detector expertise acquired in the field of high-energy physics, we created a unique neural activity readout and stimulation framework that consists of high-density electrode arrays, multi-channel custom-designed integrated circuits, a data acquisition system, and data-processing software. Using this framework we developed a number of neural readout and stimulation systems: (1) a 512-electrode system for recording the simultaneous activity of as many as hundreds of neurons, (2) a 61-electrode system for electrical stimulation and readout of neural activity in retinas and brain-tissue slices, and (3) a system with telemetry capabilities for recording neural activity in the intact brain of awake, naturally behaving animals. We will report on these systems, their various applications to the field of neurobiology, and novel scientific results obtained with some of them. We will also outline future directions

Calcium Imaging of Neuronal Circuits In Vivo Using a Circuit-Tracing Pseudorabies Virus

OpenAIRE

sprotocols

2014-01-01

Authors: Andrea E. Granstedt, Bernd Kuhn, Samuel S.-H. Wang and Lynn W. Enquist Corresponding author ([]()). ### INTRODUCTION Pseudorabies virus (PRV) is a neuroinvasive virus of the herpes family that has a broad host range but does not infect higher-order primates. PRV characteristically travels along chains of synaptically connected neurons and has been used extensively for elucidating neural circuits in the peripheral and central ner...

Digital-Analog Hybrid Scheme and Its Application to Chaotic Random Number Generators

Science.gov (United States)

Yuan, Zeshi; Li, Hongtao; Miao, Yunchi; Hu, Wen; Zhu, Xiaohua

2017-12-01

Practical random number generation (RNG) circuits are typically achieved with analog devices or digital approaches. Digital-based techniques, which use field programmable gate array (FPGA) and graphics processing units (GPU) etc. usually have better performances than analog methods as they are programmable, efficient and robust. However, digital realizations suffer from the effect of finite precision. Accordingly, the generated random numbers (RNs) are actually periodic instead of being real random. To tackle this limitation, in this paper we propose a novel digital-analog hybrid scheme that employs the digital unit as the main body, and minimum analog devices to generate physical RNs. Moreover, the possibility of realizing the proposed scheme with only one memory element is discussed. Without loss of generality, we use the capacitor and the memristor along with FPGA to construct the proposed hybrid system, and a chaotic true random number generator (TRNG) circuit is realized, producing physical RNs at a throughput of Gbit/s scale. These RNs successfully pass all the tests in the NIST SP800-22 package, confirming the significance of the scheme in practical applications. In addition, the use of this new scheme is not restricted to RNGs, and it also provides a strategy to solve the effect of finite precision in other digital systems.

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.

Impact of adolescent social experiences on behavior and neural circuits implicated in mental illnesses.

Science.gov (United States)

Burke, Andrew R; McCormick, Cheryl M; Pellis, Sergio M; Lukkes, Jodi L

2017-05-01

Negative social experiences during adolescence are central features for several stress-related mental illnesses. Social play fighting behavior in rats peaks during early adolescence and is essential for the final maturation of brain and behavior. Manipulation of the rat adolescent social experience alters many neurobehavioral measurements implicated in anxiety, depression, and substance abuse. In this review, we will highlight the importance of social play and the use of three separate social stress models (isolation-rearing, social defeat, and social instability stress) to disrupt the acquisition of this adaptive behavior. Social stress during adolescence leads to the development of anxiety and depressive behavior as well as escalated drug use in adulthood. Furthermore, sex- and age-dependent effects on the hormonal stress response following adolescent social stress are also observed. Finally, manipulation of the social experience during adolescence alters stress-related neural circuits and monoaminergic systems. Overall, positive social experiences among age-matched conspecifics during rat adolescence are critical for healthy neurobehavioral maturation. Copyright © 2017 Elsevier Ltd. All rights reserved.

The neural basis of financial risk taking.

Science.gov (United States)

Kuhnen, Camelia M; Knutson, Brian

2005-09-01

Investors systematically deviate from rationality when making financial decisions, yet the mechanisms responsible for these deviations have not been identified. Using event-related fMRI, we examined whether anticipatory neural activity would predict optimal and suboptimal choices in a financial decision-making task. We characterized two types of deviations from the optimal investment strategy of a rational risk-neutral agent as risk-seeking mistakes and risk-aversion mistakes. Nucleus accumbens activation preceded risky choices as well as risk-seeking mistakes, while anterior insula activation preceded riskless choices as well as risk-aversion mistakes. These findings suggest that distinct neural circuits linked to anticipatory affect promote different types of financial choices and indicate that excessive activation of these circuits may lead to investing mistakes. Thus, consideration of anticipatory neural mechanisms may add predictive power to the rational actor model of economic decision making.

A Novel Analog-to-digital conversion Technique using nonlinear duty-cycle modulation

OpenAIRE

Jean Mbihi; François Ndjali Beng; Martin Kom; Léandre Nneme Nneme

2012-01-01

A new type of analog-to-digital conversion technique is presented in this paper. The interfacing hardware is a very simple nonlinear circuit with 1-bit modulated output. As a implication, behind the hardware simplicity retained is hidden a dreadful nonlinear duty-cycle modulation ratio. However, the overall nonlinear behavior embeds a sufficiently wide linear range, for a rigorous digital reconstitution of the analog input signal using a standard linear filter. Simulation and experimental r...

Modulation of anxiety circuits by serotonergic systems

DEFF Research Database (Denmark)

Lowry, Christopher A; Johnson, Philip L; Hay-Schmidt, Anders

2005-01-01

of emotionally salient events, often when both rewarding and aversive outcomes are possible. In this review, we highlight recent advances in our understanding of the neural circuits regulating anxiety states and anxiety-related behavior with an emphasis on the role of brainstem serotonergic systems in modulating...... anxiety-related circuits. In particular, we explore the possibility that the regulation of anxiety states and anxiety-related behavior by serotonergic systems is dependent on a specific, topographically organized mesolimbocortical serotonergic system that originates in the mid-rostrocaudal and caudal...

READ - Remote Analog ASIC Design System

Directory of Open Access Journals (Sweden)

Michael E. Auer

2006-11-01

Full Text Available The scope of this work is to present a solution to implement a remote electronic laboratory for testing and designing analog ASICs (ispPAC10. The application allows users to create circuit schematics, upload the design to the device and perform measurements. The software used for designing circuits is the PAC-Designer and it runs on a Citrix server. The signals are generated and the responses are acquired by a data acquisition board controlled by LabView. The virtual instruments interact with some ActiveX controls specially designed to look like real oscilloscope and function generator devices and represent the user interface of the lab. These ActiveX give users the control over the LabView VIs and the access to its facilities in order to perform electronic exercises.

Quantum-circuit model of Hamiltonian search algorithms

International Nuclear Information System (INIS)

Roland, Jeremie; Cerf, Nicolas J.

2003-01-01

We analyze three different quantum search algorithms, namely, the traditional circuit-based Grover's algorithm, its continuous-time analog by Hamiltonian evolution, and the quantum search by local adiabatic evolution. We show that these algorithms are closely related in the sense that they all perform a rotation, at a constant angular velocity, from a uniform superposition of all states to the solution state. This makes it possible to implement the two Hamiltonian-evolution algorithms on a conventional quantum circuit, while keeping the quadratic speedup of Grover's original algorithm. It also clarifies the link between the adiabatic search algorithm and Grover's algorithm

MIMS circuit scrapbook V.I.

CERN Document Server

Mims, Forrest

2000-01-01

Here it is--a collection of Forrest Mims's classic work from the original Popular Electronics magazine! Using commonly available components and remarkable ingenuity, Forrest shows you how to build and experiment with circuits like these:analog computers color organs digital phase-locked loops frequency-to-voltage and voltage-to-frequency converters interval timers LED oscilloscopes light wave communicators magnetic field sensors optoelectronics pseudorandom number generators tone sequencers and much, much, more!

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

Deep learning with coherent nanophotonic circuits

Science.gov (United States)

Shen, Yichen; Harris, Nicholas C.; Skirlo, Scott; Prabhu, Mihika; Baehr-Jones, Tom; Hochberg, Michael; Sun, Xin; Zhao, Shijie; Larochelle, Hugo; Englund, Dirk; Soljačić, Marin

2017-07-01

Artificial neural networks are computational network models inspired by signal processing in the brain. These models have dramatically improved performance for many machine-learning tasks, including speech and image recognition. However, today's computing hardware is inefficient at implementing neural networks, in large part because much of it was designed for von Neumann computing schemes. Significant effort has been made towards developing electronic architectures tuned to implement artificial neural networks that exhibit improved computational speed and accuracy. Here, we propose a new architecture for a fully optical neural network that, in principle, could offer an enhancement in computational speed and power efficiency over state-of-the-art electronics for conventional inference tasks. We experimentally demonstrate the essential part of the concept using a programmable nanophotonic processor featuring a cascaded array of 56 programmable Mach-Zehnder interferometers in a silicon photonic integrated circuit and show its utility for vowel recognition.

Central neural pathways for thermoregulation

Science.gov (United States)

Morrison, Shaun F.; Nakamura, Kazuhiro

2010-01-01

Central neural circuits orchestrate a homeostatic repertoire to maintain body temperature during environmental temperature challenges and to alter body temperature during the inflammatory response. This review summarizes the functional organization of the neural pathways through which cutaneous thermal receptors alter thermoregulatory effectors: the cutaneous circulation for heat loss, the brown adipose tissue, skeletal muscle and heart for thermogenesis and species-dependent mechanisms (sweating, panting and saliva spreading) for evaporative heat loss. These effectors are regulated by parallel but distinct, effector-specific neural pathways that share a common peripheral thermal sensory input. The thermal afferent circuits include cutaneous thermal receptors, spinal dorsal horn neurons and lateral parabrachial nucleus neurons projecting to the preoptic area to influence warm-sensitive, inhibitory output neurons which control thermogenesis-promoting neurons in the dorsomedial hypothalamus that project to premotor neurons in the rostral ventromedial medulla, including the raphe pallidus, that descend to provide the excitation necessary to drive thermogenic thermal effectors. A distinct population of warm-sensitive preoptic neurons controls heat loss through an inhibitory input to raphe pallidus neurons controlling cutaneous vasoconstriction. PMID:21196160

Unbalanced neuronal circuits in addiction.

Science.gov (United States)

Volkow, Nora D; Wang, Gen-Jack; Tomasi, Dardo; Baler, Ruben D

2013-08-01

Through sequential waves of drug-induced neurochemical stimulation, addiction co-opts the brain's neuronal circuits that mediate reward, motivation to behavioral inflexibility and a severe disruption of self-control and compulsive drug intake. Brain imaging technologies have allowed neuroscientists to map out the neural landscape of addiction in the human brain and to understand how drugs modify it. Published by Elsevier Ltd.

Neural electrical activity and neural network growth.

Science.gov (United States)

Gafarov, F M

2018-05-01

The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

Pulsed neural networks consisting of single-flux-quantum spiking neurons

International Nuclear Information System (INIS)

Hirose, T.; Asai, T.; Amemiya, Y.

2007-01-01

An inhibitory pulsed neural network was developed for brain-like information processing, by using single-flux-quantum (SFQ) circuits. It consists of spiking neuron devices that are coupled to each other through all-to-all inhibitory connections. The network selects neural activity. The operation of the neural network was confirmed by computer simulation. SFQ neuron devices can imitate the operation of the inhibition phenomenon of neural networks

Neural correlates and neural computations in posterior parietal cortex during perceptual decision-making

Directory of Open Access Journals (Sweden)

Alexander eHuk

2012-10-01

Full Text Available A recent line of work has found remarkable success in relating perceptual decision-making and the spiking activity in the macaque lateral intraparietal area (LIP. In this review, we focus on questions about the neural computations in LIP that are not answered by demonstrations of neural correlates of psychological processes. We highlight three areas of limitations in our current understanding of the precise neural computations that might underlie neural correlates of decisions: (1 empirical questions not yet answered by existing data; (2 implementation issues related to how neural circuits could actually implement the mechanisms suggested by both physiology and psychology; and (3 ecological constraints related to the use of well-controlled laboratory tasks and whether they provide an accurate window on sensorimotor computation. These issues motivate the adoption of a more general encoding-decoding framework that will be fruitful for more detailed contemplation of how neural computations in LIP relate to the formation of perceptual decisions.

A Computational Account of Children's Analogical Reasoning: Balancing Inhibitory Control in Working Memory and Relational Representation

Science.gov (United States)

Morrison, Robert G.; Doumas, Leonidas A. A.; Richland, Lindsey E.

2011-01-01

Theories accounting for the development of analogical reasoning tend to emphasize either the centrality of relational knowledge accretion or changes in information processing capability. Simulations in LISA (Hummel & Holyoak, 1997, 2003), a neurally inspired computer model of analogical reasoning, allow us to explore how these factors may…

Functional neural networks underlying response inhibition in adolescents and adults.

Science.gov (United States)

Stevens, Michael C; Kiehl, Kent A; Pearlson, Godfrey D; Calhoun, Vince D

2007-07-19

This study provides the first description of neural network dynamics associated with response inhibition in healthy adolescents and adults. Functional and effective connectivity analyses of whole brain hemodynamic activity elicited during performance of a Go/No-Go task were used to identify functionally integrated neural networks and characterize their causal interactions. Three response inhibition circuits formed a hierarchical, inter-dependent system wherein thalamic modulation of input to premotor cortex by fronto-striatal regions led to response suppression. Adolescents differed from adults in the degree of network engagement, regional fronto-striatal-thalamic connectivity, and network dynamics. We identify and characterize several age-related differences in the function of neural circuits that are associated with behavioral performance changes across adolescent development.

Development switch in neural circuitry underlying odor-malaise learning.

Science.gov (United States)

Shionoya, Kiseko; Moriceau, Stephanie; Lunday, Lauren; Miner, Cathrine; Roth, Tania L; Sullivan, Regina M

2006-01-01

Fetal and infant rats can learn to avoid odors paired with illness before development of brain areas supporting this learning in adults, suggesting an alternate learning circuit. Here we begin to document the transition from the infant to adult neural circuit underlying odor-malaise avoidance learning using LiCl (0.3 M; 1% of body weight, ip) and a 30-min peppermint-odor exposure. Conditioning groups included: Paired odor-LiCl, Paired odor-LiCl-Nursing, LiCl, and odor-saline. Results showed that Paired LiCl-odor conditioning induced a learned odor aversion in postnatal day (PN) 7, 12, and 23 pups. Odor-LiCl Paired Nursing induced a learned odor preference in PN7 and PN12 pups but blocked learning in PN23 pups. 14C 2-deoxyglucose (2-DG) autoradiography indicated enhanced olfactory bulb activity in PN7 and PN12 pups with odor preference and avoidance learning. The odor aversion in weanling aged (PN23) pups resulted in enhanced amygdala activity in Paired odor-LiCl pups, but not if they were nursing. Thus, the neural circuit supporting malaise-induced aversions changes over development, indicating that similar infant and adult-learned behaviors may have distinct neural circuits.

Neural Architectures for Control

Science.gov (United States)

Peterson, James K.

1991-01-01

The cerebellar model articulated controller (CMAC) neural architectures are shown to be viable for the purposes of real-time learning and control. Software tools for the exploration of CMAC performance are developed for three hardware platforms, the MacIntosh, the IBM PC, and the SUN workstation. All algorithm development was done using the C programming language. These software tools were then used to implement an adaptive critic neuro-control design that learns in real-time how to back up a trailer truck. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. CMAC neural architectures are also used in conjunction with a hierarchical planning approach to find collision-free paths over 2-D analog valued obstacle fields. The method constructs a coarse resolution version of the original problem and then finds the corresponding coarse optimal path using multipass dynamic programming. CMAC artificial neural architectures are used to estimate the analog transition costs that dynamic programming requires. The CMAC architectures are trained in real-time for each obstacle field presented. The coarse optimal path is then used as a baseline for the construction of a fine scale optimal path through the original obstacle array. These results are a very good indication of the potential power of the neural architectures in control design. In order to reach as wide an audience as possible, we have run a seminar on neuro-control that has met once per week since 20 May 1991. This seminar has thoroughly discussed the CMAC architecture, relevant portions of classical control, back propagation through time, and adaptive critic designs.

Otanps synapse linear relation multiplier circuit

International Nuclear Information System (INIS)

Chible, H.

2008-01-01

In this paper, a four quadrant VLSI analog multiplier will be proposed, in order to be used in the implementation of the neurons and synapses modules of the artificial neural networks. The main characteristics of this multiplier are the small silicon area and the low power consumption and the high value of the weight input voltage. (author)

Circuit motifs for contrast-adaptive differentiation in early sensory systems: the role of presynaptic inhibition and short-term plasticity.

Science.gov (United States)

Zhang, Danke; Wu, Si; Rasch, Malte J

2015-01-01

In natural signals, such as the luminance value across of a visual scene, abrupt changes in intensity value are often more relevant to an organism than intensity values at other positions and times. Thus to reduce redundancy, sensory systems are specialized to detect the times and amplitudes of informative abrupt changes in the input stream rather than coding the intensity values at all times. In theory, a system that responds transiently to fast changes is called a differentiator. In principle, several different neural circuit mechanisms exist that are capable of responding transiently to abrupt input changes. However, it is unclear which circuit would be best suited for early sensory systems, where the dynamic range of the natural input signals can be very wide. We here compare the properties of different simple neural circuit motifs for implementing signal differentiation. We found that a circuit motif based on presynaptic inhibition (PI) is unique in a sense that the vesicle resources in the presynaptic site can be stably maintained over a wide range of stimulus intensities, making PI a biophysically plausible mechanism to implement a differentiator with a very wide dynamical range. Moreover, by additionally considering short-term plasticity (STP), differentiation becomes contrast adaptive in the PI-circuit but not in other potential neural circuit motifs. Numerical simulations show that the behavior of the adaptive PI-circuit is consistent with experimental observations suggesting that adaptive presynaptic inhibition might be a good candidate neural mechanism to achieve differentiation in early sensory systems.

Orientation-Selective Retinal Circuits in Vertebrates.

Science.gov (United States)

Antinucci, Paride; Hindges, Robert

2018-01-01

Visual information is already processed in the retina before it is transmitted to higher visual centers in the brain. This includes the extraction of salient features from visual scenes, such as motion directionality or contrast, through neurons belonging to distinct neural circuits. Some retinal neurons are tuned to the orientation of elongated visual stimuli. Such 'orientation-selective' neurons are present in the retinae of most, if not all, vertebrate species analyzed to date, with species-specific differences in frequency and degree of tuning. In some cases, orientation-selective neurons have very stereotyped functional and morphological properties suggesting that they represent distinct cell types. In this review, we describe the retinal cell types underlying orientation selectivity found in various vertebrate species, and highlight their commonalities and differences. In addition, we discuss recent studies that revealed the cellular, synaptic and circuit mechanisms at the basis of retinal orientation selectivity. Finally, we outline the significance of these findings in shaping our current understanding of how this fundamental neural computation is implemented in the visual systems of vertebrates.

Recent Advances in Neural Recording Microsystems

Directory of Open Access Journals (Sweden)

Benoit Gosselin

2011-04-01

Full Text Available The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field.

Model-based evaluation of the short-circuited tripolar cuff configuration.

Science.gov (United States)

Andreasen, Lotte N S; Struijk, Johannes J

2006-05-01

Recordings of neural information for use as feedback in functional electrical stimulation are often contaminated with interfering signals from muscles and from stimulus pulses. The cuff electrode used for the neural recording can be optimized to improve the S/I ratio. In this work, we evaluate a model of both the nerve signal and the interfering signals recorded by a cuff, and subsequently use this model to study the signal to interference ratio of different cuff designs and to evaluate a recently introduced short-circuited tripolar cuff configuration. The results of the model showed good agreement with results from measurements in rabbits and confirmed the superior performance of the short-circuited tripolar configuration as compared with the traditionally used tripolar configuration.

SP-100 Position Multiplexer and Analog Input Processor

International Nuclear Information System (INIS)

Syed, A.; Gilliland, K.; Shukla, J.N.

1992-01-01

This paper describes the design, implementation, and performance test results of an engineering model of the Position Multiplexer (MUX)-Analog Input Processor (AIP) System for the transmission and continuous measurements of Reflector Control Drive position in SP-100. This paper describes the work performed to determine the practical circuit limitations, investigate the circuit/component degradation of the multiplexer due to radiation, develop an interference cancellation technique, and evaluate the measurement accuracy as a function of resolver angle, temperature, radiation, and interference. The system developed performs a complex cross-correlation between the resolver excitation and the resolver sine cosine outputs, from which the precise resolver amplitude and phase can be determined while simultaneously eliminating virtually all uncorrelated interference

A neurocomputational model of analogical reasoning and its breakdown in frontotemporal lobar degeneration.

Science.gov (United States)

Morrison, Robert G; Krawczyk, Daniel C; Holyoak, Keith J; Hummel, John E; Chow, Tiffany W; Miller, Bruce L; Knowlton, Barbara J

2004-03-01

Analogy is important for learning and discovery and is considered a core component of intelligence. We present a computational account of analogical reasoning that is compatible with data we have collected from patients with cortical degeneration of either their frontal or anterior temporal cortices due to frontotemporal lobar degeneration (FTLD). These two patient groups showed different deficits in picture and verbal analogies: frontal lobe FTLD patients tended to make errors due to impairments in working memory and inhibitory abilities, whereas temporal lobe FTLD patients tended to make errors due to semantic memory loss. Using the "Learning and Inference with Schemas and Analogies" model, we provide a specific account of how such deficits may arise within neural networks supporting analogical problem solving.

An eight channel low-noise CMOS readout circuit for silicon detectors with on-chip front-end FET

International Nuclear Information System (INIS)

Fiorini, C.; Porro, M.

2006-01-01

We propose a CMOS readout circuit for the processing of signals from multi-channel silicon detectors to be used in X-ray spectroscopy and γ-ray imaging applications. The circuit is composed by eight channels, each one featuring a low-noise preamplifier, a 6th-order semigaussian shaping amplifier with four selectable peaking times, from 1.8 up to 6 μs, a peak stretcher and a discriminator. The circuit is conceived to be used with silicon detectors with a front-end FET integrated on the detector chips itself, like silicon drift detectors with JFET and pixel detectors with DEPMOS. The integrated time constants used for the shaping are implemented by means of an RC-cell, based on the technique of demagnification of the current flowing in a resistor R by means of the use of current mirrors. The eight analog channels of the chip are multiplexed to a single analog output. A suitable digital section provides self-resetting of each channel and trigger output and is able to set independent thresholds on the analog channels by means of a programmable serial register and 3-bit DACs. The circuit has been realized in the 0.35 μm CMOS AMS technology. In this work, the main features of the circuit are presented along with the experimental results of its characterization

Designing of analog computer prototype for linear differential equation. Pt. 2

International Nuclear Information System (INIS)

Tiyono Wijoyo.

1978-01-01

In this second report, the circuits of the system in the analog computer prototype have been modified and the system of the electromagnetic switches is used to replace the system of the manual switches which was used previously, so that the higher reliability could be achieved. (author)

Neuromorphic Silicon Neuron Circuits

Science.gov (United States)

Indiveri, Giacomo; Linares-Barranco, Bernabé; Hamilton, Tara Julia; van Schaik, André; Etienne-Cummings, Ralph; Delbruck, Tobi; Liu, Shih-Chii; Dudek, Piotr; Häfliger, Philipp; Renaud, Sylvie; Schemmel, Johannes; Cauwenberghs, Gert; Arthur, John; Hynna, Kai; Folowosele, Fopefolu; Saighi, Sylvain; Serrano-Gotarredona, Teresa; Wijekoon, Jayawan; Wang, Yingxue; Boahen, Kwabena

2011-01-01

Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain–machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips. PMID:21747754

Neuromorphic silicon neuron circuits

Directory of Open Access Journals (Sweden)

Giacomo eIndiveri

2011-05-01

Full Text Available Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance based Hodgkin-Huxley models to bi-dimensional generalized adaptive Integrate and Fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.

An ADC-free adaptive interface circuit of resistive sensor for electronic nose system.

Science.gov (United States)

Chang, Chia-Lin; Chiu, Shih-Wen; Tang, Kea-Tiong

2013-01-01

The initial resistance of chemiresistive gas sensors could be affected by temperature, humidity, and background odors. In a sensing system, the traditional interface circuit always requires an ADC to convert analog signal to digital signal. In this paper, we propose an ADC-free adaptive interface circuit for a resistive gas sensor to read sensor signal and cancel the baseline drift. Furthermore, methanol was used to test the proposed interface circuit, which was connected with a FIGARO® gas sensor. This circuit was fabricated by TSMC 0.18 µm CMOS process, and consumed 86.41 µW under 1 V supply voltage.

Virtual Analog Models of the Lockhart and Serge Wavefolders

Directory of Open Access Journals (Sweden)

Fabián Esqueda

2017-12-01

Full Text Available Wavefolders are a particular class of nonlinear waveshaping circuits, and a staple of the “West Coast” tradition of analog sound synthesis. In this paper, we present analyses of two popular wavefolding circuits—the Lockhart and Serge wavefolders—and show that they achieve a very similar audio effect. We digitally model the input–output relationship of both circuits using the Lambert-W function, and examine their time- and frequency-domain behavior. To ameliorate the issue of aliasing distortion introduced by the nonlinear nature of wavefolding, we propose the use of the first-order antiderivative method. This method allows us to implement the proposed digital models in real-time without having to resort to high oversampling factors. The practical synthesis usage of both circuits is discussed by considering the case of multiple wavefolder stages arranged in series.

Vertically integrated circuit development at Fermilab for detectors

International Nuclear Information System (INIS)

Yarema, R; Deptuch, G; Hoff, J; Khalid, F; Lipton, R; Shenai, A; Trimpl, M; Zimmerman, T

2013-01-01

Today vertically integrated circuits, (a.k.a. 3D integrated circuits) is a popular topic in many trade journals. The many advantages of these circuits have been described such as higher speed due to shorter trace lenghts, the ability to reduce cross talk by placing analog and digital circuits on different levels, higher circuit density without the going to smaller feature sizes, lower interconnect capacitance leading to lower power, reduced chip size, and different processing for the various layers to optimize performance. There are some added advantages specifically for MAPS (Monolithic Active Pixel Sensors) in High Energy Physics: four side buttable pixel arrays, 100% diode fill factor, the ability to move PMOS transistors out of the diode sensing layer, and a increase in channel density. Fermilab began investigating 3D circuits in 2006. Many different bonding processes have been described for fabricating 3D circuits [1]. Fermilab has used three different processes to fabricate several circuits for specific applications in High Energy Physics and X-ray imaging. This paper covers some of the early 3D work at Fermilab and then moves to more recent activities. The major processes we have used are discussed and some of the problems encountered are described. An overview of pertinent 3D circuit designs is presented along with test results thus far.

Combining BMI stimulation and mathematical modeling for acute stroke recovery and neural repair

Directory of Open Access Journals (Sweden)

Sara L Gonzalez Andino

2011-07-01

Full Text Available Rehabilitation is a neural plasticity-exploiting approach that forces undamaged neural circuits to undertake the functionality of other circuits damaged by stroke. It aims to partial restoration of the neural functions by circuit remodeling rather than by the regeneration of damaged circuits. The core hypothesis of the present paper is that - in stroke - Brain Machine Interfaces can be designed to target neural repair instead of rehabilitation. To support this hypothesis we first review existing evidence on the role of endogenous or externally applied electric fields on all processes involved in CNS repair. We then describe our own results to illustrate the neuroprotective and neuroregenerative effects of BMI- electrical stimulation on sensory deprivation-related degenerative processes of the CNS. Finally, we discuss three of the crucial issues involved in the design of neural repair-oriented BMIs: when to stimulate, where to stimulate and - the particularly important but unsolved issue of - how to stimulate. We argue that optimal parameters for the electrical stimulation can be determined from studying and modeling the dynamics of the electric fields that naturally emerge at the central and peripheral nervous system during spontaneous healing in both, experimental animals and human patients. We conclude that a closed-loop BMI that defines the optimal stimulation parameters from a priori developed experimental models of the dynamics of spontaneous repair and the on-line monitoring of neural activity might place BMIs as an alternative or complement to stem-cell transplantation or pharmacological approaches, intensively pursued nowadays.

Radio frequency integrated circuit design for cognitive radio systems

CERN Document Server

Fahim, Amr

2015-01-01

This book fills a disconnect in the literature between Cognitive Radio systems and a detailed account of the circuit implementation and architectures required to implement such systems.  Throughout the book, requirements and constraints imposed by cognitive radio systems are emphasized when discussing the circuit implementation details.  In addition, this book details several novel concepts that advance state-of-the-art cognitive radio systems.  This is a valuable reference for anybody with background in analog and radio frequency (RF) integrated circuit design, needing to learn more about integrated circuits requirements and implementation for cognitive radio systems. ·         Describes in detail cognitive radio systems, as well as the circuit implementation and architectures required to implement them; ·         Serves as an excellent reference to state-of-the-art wideband transceiver design; ·         Emphasizes practical requirements and constraints imposed by cognitive radi...

Ultra low-power biomedical signal processing : An analog wavelet filter approach for pacemakers

NARCIS (Netherlands)

Pavlík Haddad, S.A.

2006-01-01

The purpose of this thesis is to describe novel signal processing methodologies and analog integrated circuit techniques for low-power biomedical systems. Physiological signals, such as the electrocardiogram (ECG), the electroencephalogram (EEG) and the electromyogram (EMG) are mostly

Structure problems in the analog computation; Problemes de structure dans le calcul analogique

Energy Technology Data Exchange (ETDEWEB)

Braffort, P.L. [Commissariat a l' Energie Atomique, Saclay (France). Centre d' Etudes Nucleaires

1957-07-01

The recent mathematical development showed the importance of elementary structures (algebraic, topological, etc.) in abeyance under the great domains of classical analysis. Such structures in analog computation are put in evidence and possible development of applied mathematics are discussed. It also studied the topological structures of the standard representation of analog schemes such as additional triangles, integrators, phase inverters and functions generators. The analog method gives only the function of the variable: time, as results of its computations. But the course of computation, for systems including reactive circuits, introduces order structures which are called 'chronological'. Finally, it showed that the approximation methods of ordinary numerical and digital computation present the same structure as these analog computation. The structure analysis permits fruitful comparisons between the several domains of applied mathematics and suggests new important domains of application for analog method. (M.P.)

Structure problems in the analog computation; Problemes de structure dans le calcul analogique

Energy Technology Data Exchange (ETDEWEB)

Braffort, P L [Commissariat a l' Energie Atomique, Saclay (France). Centre d' Etudes Nucleaires

1957-07-01

The recent mathematical development showed the importance of elementary structures (algebraic, topological, etc.) in abeyance under the great domains of classical analysis. Such structures in analog computation are put in evidence and possible development of applied mathematics are discussed. It also studied the topological structures of the standard representation of analog schemes such as additional triangles, integrators, phase inverters and functions generators. The analog method gives only the function of the variable: time, as results of its computations. But the course of computation, for systems including reactive circuits, introduces order structures which are called 'chronological'. Finally, it showed that the approximation methods of ordinary numerical and digital computation present the same structure as these analog computation. The structure analysis permits fruitful comparisons between the several domains of applied mathematics and suggests new important domains of application for analog method. (M.P.)

The malleable brain: plasticity of neural circuits and behavior - a review from students to students.

Science.gov (United States)

Schaefer, Natascha; Rotermund, Carola; Blumrich, Eva-Maria; Lourenco, Mychael V; Joshi, Pooja; Hegemann, Regina U; Jamwal, Sumit; Ali, Nilufar; García Romero, Ezra Michelet; Sharma, Sorabh; Ghosh, Shampa; Sinha, Jitendra K; Loke, Hannah; Jain, Vishal; Lepeta, Katarzyna; Salamian, Ahmad; Sharma, Mahima; Golpich, Mojtaba; Nawrotek, Katarzyna; Paidi, Ramesh K; Shahidzadeh, Sheila M; Piermartiri, Tetsade; Amini, Elham; Pastor, Veronica; Wilson, Yvette; Adeniyi, Philip A; Datusalia, Ashok K; Vafadari, Benham; Saini, Vedangana; Suárez-Pozos, Edna; Kushwah, Neetu; Fontanet, Paula; Turner, Anthony J

2017-06-20

One of the most intriguing features of the brain is its ability to be malleable, allowing it to adapt continually to changes in the environment. Specific neuronal activity patterns drive long-lasting increases or decreases in the strength of synaptic connections, referred to as long-term potentiation and long-term depression, respectively. Such phenomena have been described in a variety of model organisms, which are used to study molecular, structural, and functional aspects of synaptic plasticity. This review originated from the first International Society for Neurochemistry (ISN) and Journal of Neurochemistry (JNC) Flagship School held in Alpbach, Austria (Sep 2016), and will use its curriculum and discussions as a framework to review some of the current knowledge in the field of synaptic plasticity. First, we describe the role of plasticity during development and the persistent changes of neural circuitry occurring when sensory input is altered during critical developmental stages. We then outline the signaling cascades resulting in the synthesis of new plasticity-related proteins, which ultimately enable sustained changes in synaptic strength. Going beyond the traditional understanding of synaptic plasticity conceptualized by long-term potentiation and long-term depression, we discuss system-wide modifications and recently unveiled homeostatic mechanisms, such as synaptic scaling. Finally, we describe the neural circuits and synaptic plasticity mechanisms driving associative memory and motor learning. Evidence summarized in this review provides a current view of synaptic plasticity in its various forms, offers new insights into the underlying mechanisms and behavioral relevance, and provides directions for future research in the field of synaptic plasticity. Read the Editorial Highlight for this article on doi: 10.1111/jnc.14102. © 2017 International Society for Neurochemistry.

Commentary: Elucidating the Neural Correlates of Early Childhood Memory

Science.gov (United States)

Mullally, Sinead L.

2015-01-01

Both episodic memory and the key neural structure believed to support it, namely the hippocampus, are believed to undergo protracted periods of postnatal developmental. Critically however, the hippocampus is comprised of distinct subfields and circuits, and these circuits appear to mature at different rates (Lavenex and Banta Lavenex, 2013).…

4-bit digital to analog converter using R-2R ladder and binary weighted resistors

Science.gov (United States)

Diosanto, J.; Batac, M. L.; Pereda, K. J.; Caldo, R.

2017-06-01

The use of a 4-bit digital-to-analog converter using two methods; Binary Weighted Resistors and R-2R Ladder is designed and presented in this paper. The main components that were used in constructing both circuits were different resistor values, operational amplifier (LM741) and single pole double throw switches. Both circuits were designed using MULTISIM software to be able to test the circuit for its ideal application and FRITZING software for the layout designing and fabrication to the printed circuit board. The implementation of both systems in an actual circuit benefits in determining and comparing the advantages and disadvantages of each. It was realized that the binary weighted circuit is more efficient DAC, having lower percentage error of 0.267% compared to R-2R ladder circuit which has a minimum of percentage error of 4.16%.

Track Circuit Fault Diagnosis Method based on Least Squares Support Vector

Science.gov (United States)

Cao, Yan; Sun, Fengru

2018-01-01

In order to improve the troubleshooting efficiency and accuracy of the track circuit, track circuit fault diagnosis method was researched. Firstly, the least squares support vector machine was applied to design the multi-fault classifier of the track circuit, and then the measured track data as training samples was used to verify the feasibility of the methods. Finally, the results based on BP neural network fault diagnosis methods and the methods used in this paper were compared. Results shows that the track fault classifier based on least squares support vector machine can effectively achieve the five track circuit fault diagnosis with less computing time.

Advanced models of neural networks nonlinear dynamics and stochasticity in biological neurons

CERN Document Server

Rigatos, Gerasimos G

2015-01-01

This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical systems theory. It is suitable for researchers and postgraduate students engaged with neural networks and dynamical systems theory.

Neural correlates underlying micrographia in Parkinson’s disease

Science.gov (United States)

Zhang, Jiarong; Hallett, Mark; Feng, Tao; Hou, Yanan; Chan, Piu

2016-01-01

Micrographia is a common symptom in Parkinson’s disease, which manifests as either a consistent or progressive reduction in the size of handwriting or both. Neural correlates underlying micrographia remain unclear. We used functional magnetic resonance imaging to investigate micrographia-related neural activity and connectivity modulations. In addition, the effect of attention and dopaminergic administration on micrographia was examined. We found that consistent micrographia was associated with decreased activity and connectivity in the basal ganglia motor circuit; while progressive micrographia was related to the dysfunction of basal ganglia motor circuit together with disconnections between the rostral supplementary motor area, rostral cingulate motor area and cerebellum. Attention significantly improved both consistent and progressive micrographia, accompanied by recruitment of anterior putamen and dorsolateral prefrontal cortex. Levodopa improved consistent micrographia accompanied by increased activity and connectivity in the basal ganglia motor circuit, but had no effect on progressive micrographia. Our findings suggest that consistent micrographia is related to dysfunction of the basal ganglia motor circuit; while dysfunction of the basal ganglia motor circuit and disconnection between the rostral supplementary motor area, rostral cingulate motor area and cerebellum likely contributes to progressive micrographia. Attention improves both types of micrographia by recruiting additional brain networks. Levodopa improves consistent micrographia by restoring the function of the basal ganglia motor circuit, but does not improve progressive micrographia, probably because of failure to repair the disconnected networks. PMID:26525918

A main amplifier circuit and data acquisition system for charged particle detector array

International Nuclear Information System (INIS)

Hao Rui; Ge Yucheng

2011-01-01

The charged particle detector array has huge amounts of signal and needs high counting rate. To meet the requirements, a main amplifier and analog-to-digital conversion circuit based on high-speed op-amp chips and ADC chip was designed. A 51-MCU was used to control the circuit of ADC and the USB communication chip. The signals were digitized and uploaded by the MCU-ADC-USB circuit. The whole system has a compact hardware structure and a reasonable controlling software, which meet the design requirements. (authors)

Activation in mesolimbic and visuospatial neural circuits elicited by smoking cues: evidence from functional magnetic resonance imaging.

Science.gov (United States)

Due, Deborah L; Huettel, Scott A; Hall, Warren G; Rubin, David C

2002-06-01

The authors sought to increase understanding of the brain mechanisms involved in cigarette addiction by identifying neural substrates modulated by visual smoking cues in nicotine-deprived smokers. Event-related functional magnetic resonance imaging (fMRI) was used to detect brain activation after exposure to smoking-related images in a group of nicotine-deprived smokers and a nonsmoking comparison group. Subjects viewed a pseudo-random sequence of smoking images, neutral nonsmoking images, and rare targets (photographs of animals). Subjects pressed a button whenever a rare target appeared. In smokers, the fMRI signal was greater after exposure to smoking-related images than after exposure to neutral images in mesolimbic dopamine reward circuits known to be activated by addictive drugs (right posterior amygdala, posterior hippocampus, ventral tegmental area, and medial thalamus) as well as in areas related to visuospatial attention (bilateral prefrontal and parietal cortex and right fusiform gyrus). In nonsmokers, no significant differences in fMRI signal following exposure to smoking-related and neutral images were detected. In most regions studied, both subject groups showed greater activation following presentation of rare target images than after exposure to neutral images. In nicotine-deprived smokers, both reward and attention circuits were activated by exposure to smoking-related images. Smoking cues are processed like rare targets in that they activate attentional regions. These cues are also processed like addictive drugs in that they activate mesolimbic reward regions.

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.

Clock generators for SOC processors circuits and architectures

CERN Document Server

Fahim, Amr

2004-01-01

This book explores the design of fully-integrated frequency synthesizers suitable for system-on-a-chip (SOC) processors. The text takes a more global design perspective in jointly examining the design space at the circuit level as well as at the architectural level. The comprehensive coverage includes summary chapters on circuit theory as well as feedback control theory relevant to the operation of phase locked loops (PLLs). On the circuit level, the discussion includes low-voltage analog design in deep submicron digital CMOS processes, effects of supply noise, substrate noise, as well device noise. On the architectural level, the discussion includes PLL analysis using continuous-time as well as discrete-time models, linear and nonlinear effects of PLL performance, and detailed analysis of locking behavior. The book provides numerous real world applications, as well as practical rules-of-thumb for modern designers to use at the system, architectural, as well as the circuit level.

Non-linear feedback neural networks VLSI implementations and applications

CERN Document Server

Ansari, Mohd Samar

2014-01-01

This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known that the standard HNN suffers from problems of convergence to local minima, and requirement of a large number of neurons and synaptic weights. Therefore, improved solutions are needed. The non-linear synapse neural network (NoSyNN) is one such possibility and is discussed in detail in this book. This book also discusses the applications in computationally intensive tasks like graph coloring, ranking, and linear as well as quadratic programming. The material in the book is useful to students, researchers and academician working in the area of analog computation.

Neural Control of the Lower Urinary Tract

Science.gov (United States)

de Groat, William C.; Griffiths, Derek; Yoshimura, Naoki

2015-01-01

This article summarizes anatomical, neurophysiological, pharmacological, and brain imaging studies in humans and animals that have provided insights into the neural circuitry and neurotransmitter mechanisms controlling the lower urinary tract. The functions of the lower urinary tract to store and periodically eliminate urine are regulated by a complex neural control system in the brain, spinal cord, and peripheral autonomic ganglia that coordinates the activity of smooth and striated muscles of the bladder and urethral outlet. The neural control of micturition is organized as a hierarchical system in which spinal storage mechanisms are in turn regulated by circuitry in the rostral brain stem that initiates reflex voiding. Input from the forebrain triggers voluntary voiding by modulating the brain stem circuitry. Many neural circuits controlling the lower urinary tract exhibit switch-like patterns of activity that turn on and off in an all-or-none manner. The major component of the micturition switching circuit is a spinobulbospinal parasympathetic reflex pathway that has essential connections in the periaqueductal gray and pontine micturition center. A computer model of this circuit that mimics the switching functions of the bladder and urethra at the onset of micturition is described. Micturition occurs involuntarily in infants and young children until the age of 3 to 5 years, after which it is regulated voluntarily. Diseases or injuries of the nervous system in adults can cause the re-emergence of involuntary micturition, leading to urinary incontinence. Neuroplasticity underlying these developmental and pathological changes in voiding function is discussed. PMID:25589273

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.

Integrated neuron circuit for implementing neuromorphic system with synaptic device

Science.gov (United States)

Lee, Jeong-Jun; Park, Jungjin; Kwon, Min-Woo; Hwang, Sungmin; Kim, Hyungjin; Park, Byung-Gook

2018-02-01

In this paper, we propose and fabricate Integrate & Fire neuron circuit for implementing neuromorphic system. Overall operation of the circuit is verified by measuring discrete devices and the output characteristics of the circuit. Since the neuron circuit shows asymmetric output characteristic that can drive synaptic device with Spike-Timing-Dependent-Plasticity (STDP) characteristic, the autonomous weight update process is also verified by connecting the synaptic device and the neuron circuit. The timing difference of the pre-neuron and the post-neuron induce autonomous weight change of the synaptic device. Unlike 2-terminal devices, which is frequently used to implement neuromorphic system, proposed scheme of the system enables autonomous weight update and simple configuration by using 4-terminal synapse device and appropriate neuron circuit. Weight update process in the multi-layer neuron-synapse connection ensures implementation of the hardware-based artificial intelligence, based on Spiking-Neural- Network (SNN).

Bayesian analogy with relational transformations.

Science.gov (United States)

Lu, Hongjing; Chen, Dawn; Holyoak, Keith J

2012-07-01

How can humans acquire relational representations that enable analogical inference and other forms of high-level reasoning? Using comparative relations as a model domain, we explore the possibility that bottom-up learning mechanisms applied to objects coded as feature vectors can yield representations of relations sufficient to solve analogy problems. We introduce Bayesian analogy with relational transformations (BART) and apply the model to the task of learning first-order comparative relations (e.g., larger, smaller, fiercer, meeker) from a set of animal pairs. Inputs are coded by vectors of continuous-valued features, based either on human magnitude ratings, normed feature ratings (De Deyne et al., 2008), or outputs of the topics model (Griffiths, Steyvers, & Tenenbaum, 2007). Bootstrapping from empirical priors, the model is able to induce first-order relations represented as probabilistic weight distributions, even when given positive examples only. These learned representations allow classification of novel instantiations of the relations and yield a symbolic distance effect of the sort obtained with both humans and other primates. BART then transforms its learned weight distributions by importance-guided mapping, thereby placing distinct dimensions into correspondence. These transformed representations allow BART to reliably solve 4-term analogies (e.g., larger:smaller::fiercer:meeker), a type of reasoning that is arguably specific to humans. Our results provide a proof-of-concept that structured analogies can be solved with representations induced from unstructured feature vectors by mechanisms that operate in a largely bottom-up fashion. We discuss potential implications for algorithmic and neural models of relational thinking, as well as for the evolution of abstract thought. Copyright 2012 APA, all rights reserved.

Analog Amplitude Modulation of a High Voltage, Solid State Inductive Adder, Pulse Generator Using MOSFETS

International Nuclear Information System (INIS)

Gower, E J; Sullivan, J S

2002-01-01

High voltage, solid state, inductive adder, pulse generators have found increasing application as fast kicker pulse modulators for charged particle beams. The solid state, inductive adder, pulse generator is similar in operation to the linear induction accelerator. The main difference is that the solid state, adder couples energy by transformer action from multiple primaries to a voltage summing stalk, instead of an electron beam. Ideally, the inductive adder produces a rectangular voltage pulse at the load. In reality, there is usually some voltage variation at the load due to droop on primary circuit storage capacitors, or, temporal variations in the load impedance. Power MOSFET circuits have been developed to provide analog modulation of the output voltage amplitude of a solid state, inductive adder, pulse generator. The modulation is achieved by including MOSFET based, variable subtraction circuits in the multiple primary stack. The subtraction circuits can be used to compensate for voltage droop, or, to tailor the output pulse amplitude to provide a desired effect in the load. Power MOSFET subtraction circuits have been developed to modulate short, temporal (60-400 ns), voltage and current pulses. MOSFET devices have been tested up to 20 amps and 800 Volts with a band pass of 50 MHz. An analog modulation cell has been tested in a five cell high, voltage adder stack

Analog Integrated Circuit Design for Spike Time Dependent Encoder and Reservoir in Reservoir Computing Processors

Science.gov (United States)

2018-01-01

HAS BEEN REVIEWED AND IS APPROVED FOR PUBLICATION IN ACCORDANCE WITH ASSIGNED DISTRIBUTION STATEMENT. FOR THE CHIEF ENGINEER : / S / / S...bridged high-performance computing, nanotechnology , and integrated circuits & systems. 15. SUBJECT TERMS neuromorphic computing, neuron design, spike...multidisciplinary effort encompassed high-performance computing, nanotechnology , integrated circuits, and integrated systems. The project’s architecture was

Noise Expands the Response Range of the Bacillus subtilis Competence Circuit.

Directory of Open Access Journals (Sweden)

Andrew Mugler

2016-03-01

Full Text Available Gene regulatory circuits must contend with intrinsic noise that arises due to finite numbers of proteins. While some circuits act to reduce this noise, others appear to exploit it. A striking example is the competence circuit in Bacillus subtilis, which exhibits much larger noise in the duration of its competence events than a synthetically constructed analog that performs the same function. Here, using stochastic modeling and fluorescence microscopy, we show that this larger noise allows cells to exit terminal phenotypic states, which expands the range of stress levels to which cells are responsive and leads to phenotypic heterogeneity at the population level. This is an important example of how noise confers a functional benefit in a genetic decision-making circuit.

Complex-Valued Neural Networks

CERN Document Server

Hirose, Akira

2012-01-01

This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published in 2006, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields. In the second edition the recent trends in CVNNs research are included, resulting in e.g. almost a doubled number of references. The parametron invented in 1954 is also referred to with discussion on analogy and disparity. Also various additional arguments on the advantages of the complex-valued neural networks enhancing the difference to real-valued neural networks are given in various sections. The book is useful for those beginning their studies, for instance, in adaptive signal processing for highly functional sensing and imaging, control in unknown and changing environment, robotics inspired by human neural systems, and brain-like information processing, as well as interdisciplina...

Neural correlates of consciousness

African Journals Online (AJOL)

neural cells.1 Under this approach, consciousness is believed to be a product of the ... possible only when the 40 Hz electrical hum is sustained among the brain circuits, ... expect the brain stem ascending reticular activating system. (ARAS) and the ... related synchrony of cortical neurons.11 Indeed, stimulation of brainstem ...

Modeling selective attention using a neuromorphic analog VLSI device.

Science.gov (United States)

Indiveri, G

2000-12-01

Attentional mechanisms are required to overcome the problem of flooding a limited processing capacity system with information. They are present in biological sensory systems and can be a useful engineering tool for artificial visual systems. In this article we present a hardware model of a selective attention mechanism implemented on a very large-scale integration (VLSI) chip, using analog neuromorphic circuits. The chip exploits a spike-based representation to receive, process, and transmit signals. It can be used as a transceiver module for building multichip neuromorphic vision systems. We describe the circuits that carry out the main processing stages of the selective attention mechanism and provide experimental data for each circuit. We demonstrate the expected behavior of the model at the system level by stimulating the chip with both artificially generated control signals and signals obtained from a saliency map, computed from an image containing several salient features.

A Wireless and Batteryless Microsystem with Implantable Grid Electrode/3-Dimensional Probe Array for ECoG and Extracellular Neural Recording in Rats