Livermore Big Artificial Neural Network Toolkit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Essen, Brian Van; Jacobs, Sam; Kim, Hyojin

2016-07-01

LBANN is a toolkit that is designed to train artificial neural networks efficiently on high performance computing architectures. It is optimized to take advantages of key High Performance Computing features to accelerate neural network training. Specifically it is optimized for low-latency, high bandwidth interconnects, node-local NVRAM, node-local GPU accelerators, and high bandwidth parallel file systems. It is built on top of the open source Elemental distributed-memory dense and spars-direct linear algebra and optimization library that is released under the BSD license. The algorithms contained within LBANN are drawn from the academic literature and implemented to work within a distributed-memory framework.

Efficient implementation of neural network deinterlacing

NASA Astrophysics Data System (ADS)

Seo, Guiwon; Choi, Hyunsoo; Lee, Chulhee

2009-02-01

Interlaced scanning has been widely used in most broadcasting systems. However, there are some undesirable artifacts such as jagged patterns, flickering, and line twitters. Moreover, most recent TV monitors utilize flat panel display technologies such as LCD or PDP monitors and these monitors require progressive formats. Consequently, the conversion of interlaced video into progressive video is required in many applications and a number of deinterlacing methods have been proposed. Recently deinterlacing methods based on neural network have been proposed with good results. On the other hand, with high resolution video contents such as HDTV, the amount of video data to be processed is very large. As a result, the processing time and hardware complexity become an important issue. In this paper, we propose an efficient implementation of neural network deinterlacing using polynomial approximation of the sigmoid function. Experimental results show that these approximations provide equivalent performance with a considerable reduction of complexity. This implementation of neural network deinterlacing can be efficiently incorporated in HW implementation.

Efficiently passing messages in distributed spiking neural network simulation.

PubMed

Thibeault, Corey M; Minkovich, Kirill; O'Brien, Michael J; Harris, Frederick C; Srinivasa, Narayan

2013-01-01

Efficiently passing spiking messages in a neural model is an important aspect of high-performance simulation. As the scale of networks has increased so has the size of the computing systems required to simulate them. In addition, the information exchange of these resources has become more of an impediment to performance. In this paper we explore spike message passing using different mechanisms provided by the Message Passing Interface (MPI). A specific implementation, MVAPICH, designed for high-performance clusters with Infiniband hardware is employed. The focus is on providing information about these mechanisms for users of commodity high-performance spiking simulators. In addition, a novel hybrid method for spike exchange was implemented and benchmarked.

A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors

PubMed Central

Sriraam, N.

2012-01-01

Developments of new classes of efficient compression algorithms, software systems, and hardware for data intensive applications in today's digital health care systems provide timely and meaningful solutions in response to exponentially growing patient information data complexity and associated analysis requirements. Of the different 1D medical signals, electroencephalography (EEG) data is of great importance to the neurologist for detecting brain-related disorders. The volume of digitized EEG data generated and preserved for future reference exceeds the capacity of recent developments in digital storage and communication media and hence there is a need for an efficient compression system. This paper presents a new and efficient high performance lossless EEG compression using wavelet transform and neural network predictors. The coefficients generated from the EEG signal by integer wavelet transform are used to train the neural network predictors. The error residues are further encoded using a combinational entropy encoder, Lempel-Ziv-arithmetic encoder. Also a new context-based error modeling is also investigated to improve the compression efficiency. A compression ratio of 2.99 (with compression efficiency of 67%) is achieved with the proposed scheme with less encoding time thereby providing diagnostic reliability for lossless transmission as well as recovery of EEG signals for telemedicine applications. PMID:22489238

A high-performance lossless compression scheme for EEG signals using wavelet transform and neural network predictors.

PubMed

Sriraam, N

2012-01-01

Developments of new classes of efficient compression algorithms, software systems, and hardware for data intensive applications in today's digital health care systems provide timely and meaningful solutions in response to exponentially growing patient information data complexity and associated analysis requirements. Of the different 1D medical signals, electroencephalography (EEG) data is of great importance to the neurologist for detecting brain-related disorders. The volume of digitized EEG data generated and preserved for future reference exceeds the capacity of recent developments in digital storage and communication media and hence there is a need for an efficient compression system. This paper presents a new and efficient high performance lossless EEG compression using wavelet transform and neural network predictors. The coefficients generated from the EEG signal by integer wavelet transform are used to train the neural network predictors. The error residues are further encoded using a combinational entropy encoder, Lempel-Ziv-arithmetic encoder. Also a new context-based error modeling is also investigated to improve the compression efficiency. A compression ratio of 2.99 (with compression efficiency of 67%) is achieved with the proposed scheme with less encoding time thereby providing diagnostic reliability for lossless transmission as well as recovery of EEG signals for telemedicine applications.

An Efficient Neural-Network-Based Microseismic Monitoring Platform for Hydraulic Fracture on an Edge Computing Architecture.

PubMed

Zhang, Xiaopu; Lin, Jun; Chen, Zubin; Sun, Feng; Zhu, Xi; Fang, Gengfa

2018-06-05

Microseismic monitoring is one of the most critical technologies for hydraulic fracturing in oil and gas production. To detect events in an accurate and efficient way, there are two major challenges. One challenge is how to achieve high accuracy due to a poor signal-to-noise ratio (SNR). The other one is concerned with real-time data transmission. Taking these challenges into consideration, an edge-computing-based platform, namely Edge-to-Center LearnReduce, is presented in this work. The platform consists of a data center with many edge components. At the data center, a neural network model combined with convolutional neural network (CNN) and long short-term memory (LSTM) is designed and this model is trained by using previously obtained data. Once the model is fully trained, it is sent to edge components for events detection and data reduction. At each edge component, a probabilistic inference is added to the neural network model to improve its accuracy. Finally, the reduced data is delivered to the data center. Based on experiment results, a high detection accuracy (over 96%) with less transmitted data (about 90%) was achieved by using the proposed approach on a microseismic monitoring system. These results show that the platform can simultaneously improve the accuracy and efficiency of microseismic monitoring.

Representation of the Characteristics of Piezoelectric Fiber Composites with Neural Networks

NASA Astrophysics Data System (ADS)

Yapici, A.; Bickraj, K.; Yenilmez, A.; Li, M.; Tansel, I. N.; Martin, S. A.; Pereira, C. M.; Roth, L. E.

2007-03-01

Ideal sensors for the future should be economical, efficient, highly intelligent, and capable of obtaining their operation power from the environment. The use of piezoelectric fiber composites coupled with a low power microprocessor and backpropagation type neural networks is proposed for the development of a simple sensor to estimate the characteristics of harmonic forces. Three neural networks were used for the estimation of amplitude, gain and variation of the load in the time domain. The average estimation errors of the neural networks were less than 8% in all of the studied cases.

Efficient and rapid derivation of primitive neural stem cells and generation of brain subtype neurons from human pluripotent stem cells.

PubMed

Yan, Yiping; Shin, Soojung; Jha, Balendu Shekhar; Liu, Qiuyue; Sheng, Jianting; Li, Fuhai; Zhan, Ming; Davis, Janine; Bharti, Kapil; Zeng, Xianmin; Rao, Mahendra; Malik, Nasir; Vemuri, Mohan C

2013-11-01

Human pluripotent stem cells (hPSCs), including human embryonic stem cells and human induced pluripotent stem cells, are unique cell sources for disease modeling, drug discovery screens, and cell therapy applications. The first step in producing neural lineages from hPSCs is the generation of neural stem cells (NSCs). Current methods of NSC derivation involve the time-consuming, labor-intensive steps of an embryoid body generation or coculture with stromal cell lines that result in low-efficiency derivation of NSCs. In this study, we report a highly efficient serum-free pluripotent stem cell neural induction medium that can induce hPSCs into primitive NSCs (pNSCs) in 7 days, obviating the need for time-consuming, laborious embryoid body generation or rosette picking. The pNSCs expressed the neural stem cell markers Pax6, Sox1, Sox2, and Nestin; were negative for Oct4; could be expanded for multiple passages; and could be differentiated into neurons, astrocytes, and oligodendrocytes, in addition to the brain region-specific neuronal subtypes GABAergic, dopaminergic, and motor neurons. Global gene expression of the transcripts of pNSCs was comparable to that of rosette-derived and human fetal-derived NSCs. This work demonstrates an efficient method to generate expandable pNSCs, which can be further differentiated into central nervous system neurons and glia with temporal, spatial, and positional cues of brain regional heterogeneity. This method of pNSC derivation sets the stage for the scalable production of clinically relevant neural cells for cell therapy applications in good manufacturing practice conditions.

A subthreshold aVLSI implementation of the Izhikevich simple neuron model.

PubMed

Rangan, Venkat; Ghosh, Abhishek; Aparin, Vladimir; Cauwenberghs, Gert

2010-01-01

We present a circuit architecture for compact analog VLSI implementation of the Izhikevich neuron model, which efficiently describes a wide variety of neuron spiking and bursting dynamics using two state variables and four adjustable parameters. Log-domain circuit design utilizing MOS transistors in subthreshold results in high energy efficiency, with less than 1pJ of energy consumed per spike. We also discuss the effects of parameter variations on the dynamics of the equations, and present simulation results that replicate several types of neural dynamics. The low power operation and compact analog VLSI realization make the architecture suitable for human-machine interface applications in neural prostheses and implantable bioelectronics, as well as large-scale neural emulation tools for computational neuroscience.

Decision-making conflict and the neural efficiency hypothesis of intelligence: a functional near-infrared spectroscopy investigation.

PubMed

Di Domenico, Stefano I; Rodrigo, Achala H; Ayaz, Hasan; Fournier, Marc A; Ruocco, Anthony C

2015-04-01

Research on the neural efficiency hypothesis of intelligence (NEH) has revealed that the brains of more intelligent individuals consume less energy when performing easy cognitive tasks but more energy when engaged in difficult mental operations. However, previous studies testing the NEH have relied on cognitive tasks that closely resemble psychometric tests of intelligence, potentially confounding efficiency during intelligence-test performance with neural efficiency per se. The present study sought to provide a novel test of the NEH by examining patterns of prefrontal activity while participants completed an experimental paradigm that is qualitatively distinct from the contents of psychometric tests of intelligence. Specifically, participants completed a personal decision-making task (e.g., which occupation would you prefer, dancer or chemist?) in which they made a series of forced choices according to their subjective preferences. The degree of decisional conflict (i.e., choice difficulty) between the available response options was manipulated on the basis of participants' unique preference ratings for the target stimuli, which were obtained prior to scanning. Evoked oxygenation of the prefrontal cortex was measured using 16-channel continuous-wave functional near-infrared spectroscopy. Consistent with the NEH, intelligence predicted decreased activation of the right inferior frontal gyrus (IFG) during low-conflict situations and increased activation of the right-IFG during high-conflict situations. This pattern of right-IFG activity among more intelligent individuals was complemented by faster reaction times in high-conflict situations. These results provide new support for the NEH and suggest that the neural efficiency of more intelligent individuals generalizes to the performance of cognitive tasks that are distinct from intelligence tests. Copyright © 2015 Elsevier Inc. All rights reserved.

An improved protocol that induces human embryonic stem cells to differentiate into neural cells in vitro.

PubMed

Zhou, Jun-Mei; Chu, Jian-Xin; Chen, Xue-Jin

2008-01-01

Human embryonic stem (ES) cells have the capacity for self-renewal and are able to differentiate into any cell type. However, obtaining high-efficient neural differentiation from human ES cells remains a challenge. This study describes an improved 4-stage protocol to induce a human ES cell line derived from a Chinese population to differentiate into neural cells. At the first stage, embryonic bodies (EBs) were formed in a chemically-defined neural inducing medium rather than in traditional serum or serum-replacement medium. At the second stage, rosette-like structures were formed. At the third stage, the rosette-like structures were manually selected rather than enzymatically digested to form floating neurospheres. At the fourth stage, the neurospheres were further differentiated into neurons. The results show that, at the second stage, the rate of the formation of rosette-like structures from EBs induced by noggin was 88+/-6.32%, higher than that of retinoic acid 55+/-5.27%. Immunocytochemistry staining was used to confirm the neural identity of the cells. These results show a major improvement in obtaining efficient neural differentiation of human ES cells.

Highly efficient methods to obtain homogeneous dorsal neural progenitor cells from human and mouse embryonic stem cells and induced pluripotent stem cells.

PubMed

Zhang, Meixiang; Ngo, Justine; Pirozzi, Filomena; Sun, Ying-Pu; Wynshaw-Boris, Anthony

2018-03-15

Embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) have been widely used to generate cellular models harboring specific disease-related genotypes. Of particular importance are ESC and iPSC applications capable of producing dorsal telencephalic neural progenitor cells (NPCs) that are representative of the cerebral cortex and overcome the challenges of maintaining a homogeneous population of cortical progenitors over several passages in vitro. While previous studies were able to derive NPCs from pluripotent cell types, the fraction of dorsal NPCs in this population is small and decreases over several passages. Here, we present three protocols that are highly efficient in differentiating mouse and human ESCs, as well as human iPSCs, into a homogeneous and stable population of dorsal NPCs. These protocols will be useful for modeling cerebral cortical neurological and neurodegenerative disorders in both mouse and human as well as for high-throughput drug screening for therapeutic development. We optimized three different strategies for generating dorsal telencephalic NPCs from mouse and human pluripotent cell types through single or double inhibition of bone morphogenetic protein (BMP) and/or SMAD pathways. Mouse and human pluripotent cells were aggregated to form embryoid bodies in suspension and were treated with dorsomorphin alone (BMP inhibition) or combined with SB431542 (double BMP/SMAD inhibition) during neural induction. Neural rosettes were then selected from plated embryoid bodies to purify the population of dorsal NPCs. We tested the expression of key dorsal NPC markers as well as nonectodermal markers to confirm the efficiency of our three methods in comparison to published and commercial protocols. Single and double inhibition of BMP and/or SMAD during neural induction led to the efficient differentiation of dorsal NPCs, based on the high percentage of PAX6-positive cells and the NPC gene expression profile. There were no statistically significant differences in the variation of PAX6 and SOX1-positive NPCs between the two human pluripotent cell-derived methods; therefore, both methods are suitable for producing stable dorsal NPCs. When further differentiated into mature neurons, NPCs gave rise to a population of almost exclusively forebrain cortical neurons, confirming the dorsal fate commitment of the progenitors. The methods described in this study show improvements over previously published studies and are highly efficient at differentiating human and mouse pluripotent cell types into dorsal PAX6-positive NPCs and eventually into forebrain cortical neurons.

An automated high throughput screening-compatible assay to identify regulators of stem cell neural differentiation.

PubMed

Casalino, Laura; Magnani, Dario; De Falco, Sandro; Filosa, Stefania; Minchiotti, Gabriella; Patriarca, Eduardo J; De Cesare, Dario

2012-03-01

The use of Embryonic Stem Cells (ESCs) holds considerable promise both for drug discovery programs and the treatment of degenerative disorders in regenerative medicine approaches. Nevertheless, the successful use of ESCs is still limited by the lack of efficient control of ESC self-renewal and differentiation capabilities. In this context, the possibility to modulate ESC biological properties and to obtain homogenous populations of correctly specified cells will help developing physiologically relevant screens, designed for the identification of stem cell modulators. Here, we developed a high throughput screening-suitable ESC neural differentiation assay by exploiting the Cell(maker) robotic platform and demonstrated that neural progenies can be generated from ESCs in complete automation, with high standards of accuracy and reliability. Moreover, we performed a pilot screening providing proof of concept that this assay allows the identification of regulators of ESC neural differentiation in full automation.

A Spiking Neural Simulator Integrating Event-Driven and Time-Driven Computation Schemes Using Parallel CPU-GPU Co-Processing: A Case Study.

PubMed

Naveros, Francisco; Luque, Niceto R; Garrido, Jesús A; Carrillo, Richard R; Anguita, Mancia; Ros, Eduardo

2015-07-01

Time-driven simulation methods in traditional CPU architectures perform well and precisely when simulating small-scale spiking neural networks. Nevertheless, they still have drawbacks when simulating large-scale systems. Conversely, event-driven simulation methods in CPUs and time-driven simulation methods in graphic processing units (GPUs) can outperform CPU time-driven methods under certain conditions. With this performance improvement in mind, we have developed an event-and-time-driven spiking neural network simulator suitable for a hybrid CPU-GPU platform. Our neural simulator is able to efficiently simulate bio-inspired spiking neural networks consisting of different neural models, which can be distributed heterogeneously in both small layers and large layers or subsystems. For the sake of efficiency, the low-activity parts of the neural network can be simulated in CPU using event-driven methods while the high-activity subsystems can be simulated in either CPU (a few neurons) or GPU (thousands or millions of neurons) using time-driven methods. In this brief, we have undertaken a comparative study of these different simulation methods. For benchmarking the different simulation methods and platforms, we have used a cerebellar-inspired neural-network model consisting of a very dense granular layer and a Purkinje layer with a smaller number of cells (according to biological ratios). Thus, this cerebellar-like network includes a dense diverging neural layer (increasing the dimensionality of its internal representation and sparse coding) and a converging neural layer (integration) similar to many other biologically inspired and also artificial neural networks.

Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule.

PubMed

Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin

2015-11-01

In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Hui; Song, Yongduan; Xue, Fangzheng

In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than themore » SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.« less

TECHNICAL NOTE: The development of a PZT-based microdrive for neural signal recording

NASA Astrophysics Data System (ADS)

Park, Sangkyu; Yoon, Euisung; Lee, Sukchan; Shin, Hee-sup; Park, Hyunjun; Kim, Byungkyu; Kim, Daesoo; Park, Jongoh; Park, Sukho

2008-04-01

A hand-controlled microdrive has been used to obtain neural signals from rodents such as rats and mice. However, it places severe physical stress on the rodents during its manipulation, and this stress leads to alertness in the mice and low efficiency in obtaining neural signals from the mice. To overcome this issue, we developed a novel microdrive, which allows one to adjust the electrodes by a piezoelectric device (PZT) with high precision. Its mass is light enough to install on the mouse's head. The proposed microdrive has three H-type PZT actuators and their guiding structure. The operation principle of the microdrive is based on the well known inchworm mechanism. When the three PZT actuators are synchronized, linear motion of the electrode is produced along the guiding structure. The electrodes used for the recording of the neural signals from neuron cells were fixed at one of the PZT actuators. Our proposed microdrive has an accuracy of about 400 nm and a long stroke of about 5 mm. In response to formalin-induced pain, single unit activities are robustly measured at the thalamus with electrodes whose vertical depth is adjusted by the microdrive under urethane anesthesia. In addition, the microdrive was efficient in detecting neural signals from mice that were moving freely. Thus, the present study suggests that the PZT-based microdrive could be an alternative for the efficient detection of neural signals from mice during behavioral states without any stress to the mice.

In vitro generation of three-dimensional substrate-adherent embryonic stem cell-derived neural aggregates for application in animal models of neurological disorders.

PubMed

Hargus, Gunnar; Cui, Yi-Fang; Dihné, Marcel; Bernreuther, Christian; Schachner, Melitta

2012-05-01

In vitro-differentiated embryonic stem (ES) cells comprise a useful source for cell replacement therapy, but the efficiency and safety of a translational approach are highly dependent on optimized protocols for directed differentiation of ES cells into the desired cell types in vitro. Furthermore, the transplantation of three-dimensional ES cell-derived structures instead of a single-cell suspension may improve graft survival and function by providing a beneficial microenvironment for implanted cells. To this end, we have developed a new method to efficiently differentiate mouse ES cells into neural aggregates that consist predominantly (>90%) of postmitotic neurons, neural progenitor cells, and radial glia-like cells. When transplanted into the excitotoxically lesioned striatum of adult mice, these substrate-adherent embryonic stem cell-derived neural aggregates (SENAs) showed significant advantages over transplanted single-cell suspensions of ES cell-derived neural cells, including improved survival of GABAergic neurons, increased cell migration, and significantly decreased risk of teratoma formation. Furthermore, SENAs mediated functional improvement after transplantation into animal models of Parkinson's disease and spinal cord injury. This unit describes in detail how SENAs are efficiently derived from mouse ES cells in vitro and how SENAs are isolated for transplantation. Furthermore, methods are presented for successful implantation of SENAs into animal models of Huntington's disease, Parkinson's disease, and spinal cord injury to study the effects of stem cell-derived neural aggregates in a disease context in vivo.

Convolutional Neural Network for Histopathological Analysis of Osteosarcoma.

PubMed

Mishra, Rashika; Daescu, Ovidiu; Leavey, Patrick; Rakheja, Dinesh; Sengupta, Anita

2018-03-01

Pathologists often deal with high complexity and sometimes disagreement over osteosarcoma tumor classification due to cellular heterogeneity in the dataset. Segmentation and classification of histology tissue in H&E stained tumor image datasets is a challenging task because of intra-class variations, inter-class similarity, crowded context, and noisy data. In recent years, deep learning approaches have led to encouraging results in breast cancer and prostate cancer analysis. In this article, we propose convolutional neural network (CNN) as a tool to improve efficiency and accuracy of osteosarcoma tumor classification into tumor classes (viable tumor, necrosis) versus nontumor. The proposed CNN architecture contains eight learned layers: three sets of stacked two convolutional layers interspersed with max pooling layers for feature extraction and two fully connected layers with data augmentation strategies to boost performance. The use of a neural network results in higher accuracy of average 92% for the classification. We compare the proposed architecture with three existing and proven CNN architectures for image classification: AlexNet, LeNet, and VGGNet. We also provide a pipeline to calculate percentage necrosis in a given whole slide image. We conclude that the use of neural networks can assure both high accuracy and efficiency in osteosarcoma classification.

Configurable analog-digital conversion using the neural engineering framework

PubMed Central

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

2014-01-01

Efficient Analog-Digital Converters (ADC) are one of the mainstays of mixed-signal integrated circuit design. Besides the conventional ADCs used in mainstream ICs, there have been various attempts in the past to utilize neuromorphic networks to accomplish an efficient crossing between analog and digital domains, i.e., to build neurally inspired ADCs. Generally, these have suffered from the same problems as conventional ADCs, that is they require high-precision, handcrafted analog circuits and are thus not technology portable. In this paper, we present an ADC based on the Neural Engineering Framework (NEF). It carries out a large fraction of the overall ADC process in the digital domain, i.e., it is easily portable across technologies. The analog-digital conversion takes full advantage of the high degree of parallelism inherent in neuromorphic networks, making for a very scalable ADC. In addition, it has a number of features not commonly found in conventional ADCs, such as a runtime reconfigurability of the ADC sampling rate, resolution and transfer characteristic. PMID:25100933

Increasing magnetite contents of polymeric magnetic particles dramatically improves labeling of neural stem cell transplant populations.

PubMed

Adams, Christopher F; Rai, Ahmad; Sneddon, Gregor; Yiu, Humphrey H P; Polyak, Boris; Chari, Divya M

2015-01-01

Safe and efficient delivery of therapeutic cells to sites of injury/disease in the central nervous system is a key goal for the translation of clinical cell transplantation therapies. Recently, 'magnetic cell localization strategies' have emerged as a promising and safe approach for targeted delivery of magnetic particle (MP) labeled stem cells to pathology sites. For neuroregenerative applications, this approach is limited by the lack of available neurocompatible MPs, and low cell labeling achieved in neural stem/precursor populations. We demonstrate that high magnetite content, self-sedimenting polymeric MPs [unfunctionalized poly(lactic acid) coated, without a transfecting component] achieve efficient labeling (≥90%) of primary neural stem cells (NSCs)-a 'hard-to-label' transplant population of major clinical relevance. Our protocols showed high safety with respect to key stem cell regenerative parameters. Critically, labeled cells were effectively localized in an in vitro flow system by magnetic force highlighting the translational potential of the methods used. Copyright © 2015 Elsevier Inc. All rights reserved.

Image and Video Compression with VLSI Neural Networks

NASA Technical Reports Server (NTRS)

Fang, W.; Sheu, B.

1993-01-01

An advanced motion-compensated predictive video compression system based on artificial neural networks has been developed to effectively eliminate the temporal and spatial redundancy of video image sequences and thus reduce the bandwidth and storage required for the transmission and recording of the video signal. The VLSI neuroprocessor for high-speed high-ratio image compression based upon a self-organization network and the conventional algorithm for vector quantization are compared. The proposed method is quite efficient and can achieve near-optimal results.

A Streaming PCA VLSI Chip for Neural Data Compression.

PubMed

Wu, Tong; Zhao, Wenfeng; Guo, Hongsun; Lim, Hubert H; Yang, Zhi

2017-12-01

Neural recording system miniaturization and integration with low-power wireless technologies require compressing neural data before transmission. Feature extraction is a procedure to represent data in a low-dimensional space; its integration into a recording chip can be an efficient approach to compress neural data. In this paper, we propose a streaming principal component analysis algorithm and its microchip implementation to compress multichannel local field potential (LFP) and spike data. The circuits have been designed in a 65-nm CMOS technology and occupy a silicon area of 0.06 mm. Throughout the experiments, the chip compresses LFPs by 10 at the expense of as low as 1% reconstruction errors and 144-nW/channel power consumption; for spikes, the achieved compression ratio is 25 with 8% reconstruction errors and 3.05-W/channel power consumption. In addition, the algorithm and its hardware architecture can swiftly adapt to nonstationary spiking activities, which enables efficient hardware sharing among multiple channels to support a high-channel count recorder.

An Application Development Platform for Neuromorphic Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dean, Mark; Chan, Jason; Daffron, Christopher

2016-01-01

Dynamic Adaptive Neural Network Arrays (DANNAs) are neuromorphic computing systems developed as a hardware based approach to the implementation of neural networks. They feature highly adaptive and programmable structural elements, which model arti cial neural networks with spiking behavior. We design them to solve problems using evolutionary optimization. In this paper, we highlight the current hardware and software implementations of DANNA, including their features, functionalities and performance. We then describe the development of an Application Development Platform (ADP) to support efficient application implementation and testing of DANNA based solutions. We conclude with future directions.

A Case Study on Neural Inspired Dynamic Memory Management Strategies for High Performance Computing.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vineyard, Craig Michael; Verzi, Stephen Joseph

As high performance computing architectures pursue more computational power there is a need for increased memory capacity and bandwidth as well. A multi-level memory (MLM) architecture addresses this need by combining multiple memory types with different characteristics as varying levels of the same architecture. How to efficiently utilize this memory infrastructure is an unknown challenge, and in this research we sought to investigate whether neural inspired approaches can meaningfully help with memory management. In particular we explored neurogenesis inspired re- source allocation, and were able to show a neural inspired mixed controller policy can beneficially impact how MLM architectures utilizemore » memory.« less

Geometry correction Algorithm for UAV Remote Sensing Image Based on Improved Neural Network

NASA Astrophysics Data System (ADS)

Liu, Ruian; Liu, Nan; Zeng, Beibei; Chen, Tingting; Yin, Ninghao

2018-03-01

Aiming at the disadvantage of current geometry correction algorithm for UAV remote sensing image, a new algorithm is proposed. Adaptive genetic algorithm (AGA) and RBF neural network are introduced into this algorithm. And combined with the geometry correction principle for UAV remote sensing image, the algorithm and solving steps of AGA-RBF are presented in order to realize geometry correction for UAV remote sensing. The correction accuracy and operational efficiency is improved through optimizing the structure and connection weight of RBF neural network separately with AGA and LMS algorithm. Finally, experiments show that AGA-RBF algorithm has the advantages of high correction accuracy, high running rate and strong generalization ability.

Individual Differences in Sentence Comprehension: A Functional Magnetic Resonance Imaging Investigation of Syntactic and Lexical Processing Demands

PubMed Central

Prat, Chantel S.; Keller, Timothy A.; Just, Marcel Adam

2008-01-01

Language comprehension is neurally underpinned by a network of collaborating cortical processing centers; individual differences in comprehension must be related to some set of this network’s properties. This study investigated the neural bases of individual differences during sentence comprehension by examining the network’s response to two variations in processing demands: reading sentences containing words of high versus low lexical frequency and having simpler versus more complex syntax. In a functional magnetic resonance imaging study, readers who were independently identified as having high or low working memory capacity for language exhibited three differentiating properties of their language network, namely, neural efficiency, adaptability, and synchronization. First, greater efficiency (defined as a reduction in activation associated with improved performance) was manifested as less activation in the bilateral middle frontal and right lingual gyri in high-capacity readers. Second, increased adaptability was indexed by larger lexical frequency effects in high-capacity readers across bilateral middle frontal, bilateral inferior occipital, and right temporal regions. Third, greater synchronization was observed in high-capacity readers between left temporal and left inferior frontal, left parietal, and right occipital regions. Synchronization interacted with adaptability, such that functional connectivity remained constant or increased with increasing lexical and syntactic demands in high-capacity readers, whereas low-capacity readers either showed no reliable differentiation or a decrease in functional connectivity with increasing demands. These results are among the first to relate multiple cortical network properties to individual differences in reading capacity and suggest a more general framework for understanding the relation between neural function and individual differences in cognitive performance. PMID:17892384

Individual differences in sentence comprehension: a functional magnetic resonance imaging investigation of syntactic and lexical processing demands.

PubMed

Prat, Chantel S; Keller, Timothy A; Just, Marcel Adam

2007-12-01

Language comprehension is neurally underpinned by a network of collaborating cortical processing centers; individual differences in comprehension must be related to some set of this network's properties. This study investigated the neural bases of individual differences during sentence comprehension by examining the network's response to two variations in processing demands: reading sentences containing words of high versus low lexical frequency and having simpler versus more complex syntax. In a functional magnetic resonance imaging study, readers who were independently identified as having high or low working memory capacity for language exhibited three differentiating properties of their language network, namely, neural efficiency, adaptability, and synchronization. First, greater efficiency (defined as a reduction in activation associated with improved performance) was manifested as less activation in the bilateral middle frontal and right lingual gyri in high-capacity readers. Second, increased adaptability was indexed by larger lexical frequency effects in high-capacity readers across bilateral middle frontal, bilateral inferior occipital, and right temporal regions. Third, greater synchronization was observed in high-capacity readers between left temporal and left inferior frontal, left parietal, and right occipital regions. Synchronization interacted with adaptability, such that functional connectivity remained constant or increased with increasing lexical and syntactic demands in high-capacity readers, whereas low-capacity readers either showed no reliable differentiation or a decrease in functional connectivity with increasing demands. These results are among the first to relate multiple cortical network properties to individual differences in reading capacity and suggest a more general framework for understanding the relation between neural function and individual differences in cognitive performance.

(abstract) A High Throughput 3-D Inner Product Processor

NASA Technical Reports Server (NTRS)

Daud, Tuan

1996-01-01

A particularily challenging image processing application is the real time scene acquisition and object discrimination. It requires spatio-temporal recognition of point and resolved objects at high speeds with parallel processing algorithms. Neural network paradigms provide fine grain parallism and, when implemented in hardware, offer orders of magnitude speed up. However, neural networks implemented on a VLSI chip are planer architectures capable of efficient processing of linear vector signals rather than 2-D images. Therefore, for processing of images, a 3-D stack of neural-net ICs receiving planar inputs and consuming minimal power are required. Details of the circuits with chip architectures will be described with need to develop ultralow-power electronics. Further, use of the architecture in a system for high-speed processing will be illustrated.

Simplified Design Equations for Class-E Neural Prosthesis Transmitters

PubMed Central

Troyk, Philip; Hu, Zhe

2013-01-01

Extreme miniaturization of implantable electronic devices is recognized as essential for the next generation of neural prostheses, owing to the need for minimizing the damage and disruption of the surrounding neural tissue. Transcutaneous power and data transmission via a magnetic link remains the most effective means of powering and controlling implanted neural prostheses. Reduction in the size of the coil, within the neural prosthesis, demands the generation of a high-intensity radio frequency magnetic field from the extracoporeal transmitter. The Class-E power amplifier circuit topology has been recognized as a highly effective means of producing large radio frequency currents within the transmitter coil. Unfortunately, design of a Class-E circuit is most often fraught by the need to solve a complex set of equations so as to implement both the zero-voltage-switching and zero-voltage-derivative-switching conditions that are required for efficient operation. This paper presents simple explicit design equations for designing the Class-E circuit topology. Numerical design examples are presented to illustrate the design procedure. PMID:23292784

Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

NASA Technical Reports Server (NTRS)

Yao, Weigang; Liou, Meng-Sing

2012-01-01

The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis

Efficient Coding and Energy Efficiency Are Promoted by Balanced Excitatory and Inhibitory Synaptic Currents in Neuronal Network

PubMed Central

Yu, Lianchun; Shen, Zhou; Wang, Chen; Yu, Yuguo

2018-01-01

Selective pressure may drive neural systems to process as much information as possible with the lowest energy cost. Recent experiment evidence revealed that the ratio between synaptic excitation and inhibition (E/I) in local cortex is generally maintained at a certain value which may influence the efficiency of energy consumption and information transmission of neural networks. To understand this issue deeply, we constructed a typical recurrent Hodgkin-Huxley network model and studied the general principles that governs the relationship among the E/I synaptic current ratio, the energy cost and total amount of information transmission. We observed in such a network that there exists an optimal E/I synaptic current ratio in the network by which the information transmission achieves the maximum with relatively low energy cost. The coding energy efficiency which is defined as the mutual information divided by the energy cost, achieved the maximum with the balanced synaptic current. Although background noise degrades information transmission and imposes an additional energy cost, we find an optimal noise intensity that yields the largest information transmission and energy efficiency at this optimal E/I synaptic transmission ratio. The maximization of energy efficiency also requires a certain part of energy cost associated with spontaneous spiking and synaptic activities. We further proved this finding with analytical solution based on the response function of bistable neurons, and demonstrated that optimal net synaptic currents are capable of maximizing both the mutual information and energy efficiency. These results revealed that the development of E/I synaptic current balance could lead a cortical network to operate at a highly efficient information transmission rate at a relatively low energy cost. The generality of neuronal models and the recurrent network configuration used here suggest that the existence of an optimal E/I cell ratio for highly efficient energy costs and information maximization is a potential principle for cortical circuit networks. Summary We conducted numerical simulations and mathematical analysis to examine the energy efficiency of neural information transmission in a recurrent network as a function of the ratio of excitatory and inhibitory synaptic connections. We obtained a general solution showing that there exists an optimal E/I synaptic ratio in a recurrent network at which the information transmission as well as the energy efficiency of this network achieves a global maximum. These results reflect general mechanisms for sensory coding processes, which may give insight into the energy efficiency of neural communication and coding. PMID:29773979

Efficient Coding and Energy Efficiency Are Promoted by Balanced Excitatory and Inhibitory Synaptic Currents in Neuronal Network.

PubMed

Yu, Lianchun; Shen, Zhou; Wang, Chen; Yu, Yuguo

2018-01-01

Selective pressure may drive neural systems to process as much information as possible with the lowest energy cost. Recent experiment evidence revealed that the ratio between synaptic excitation and inhibition (E/I) in local cortex is generally maintained at a certain value which may influence the efficiency of energy consumption and information transmission of neural networks. To understand this issue deeply, we constructed a typical recurrent Hodgkin-Huxley network model and studied the general principles that governs the relationship among the E/I synaptic current ratio, the energy cost and total amount of information transmission. We observed in such a network that there exists an optimal E/I synaptic current ratio in the network by which the information transmission achieves the maximum with relatively low energy cost. The coding energy efficiency which is defined as the mutual information divided by the energy cost, achieved the maximum with the balanced synaptic current. Although background noise degrades information transmission and imposes an additional energy cost, we find an optimal noise intensity that yields the largest information transmission and energy efficiency at this optimal E/I synaptic transmission ratio. The maximization of energy efficiency also requires a certain part of energy cost associated with spontaneous spiking and synaptic activities. We further proved this finding with analytical solution based on the response function of bistable neurons, and demonstrated that optimal net synaptic currents are capable of maximizing both the mutual information and energy efficiency. These results revealed that the development of E/I synaptic current balance could lead a cortical network to operate at a highly efficient information transmission rate at a relatively low energy cost. The generality of neuronal models and the recurrent network configuration used here suggest that the existence of an optimal E/I cell ratio for highly efficient energy costs and information maximization is a potential principle for cortical circuit networks. We conducted numerical simulations and mathematical analysis to examine the energy efficiency of neural information transmission in a recurrent network as a function of the ratio of excitatory and inhibitory synaptic connections. We obtained a general solution showing that there exists an optimal E/I synaptic ratio in a recurrent network at which the information transmission as well as the energy efficiency of this network achieves a global maximum. These results reflect general mechanisms for sensory coding processes, which may give insight into the energy efficiency of neural communication and coding.

On-board fault diagnostics for fly-by-light flight control systems using neural network flight processors

NASA Astrophysics Data System (ADS)

Urnes, James M., Sr.; Cushing, John; Bond, William E.; Nunes, Steve

1996-10-01

Fly-by-Light control systems offer higher performance for fighter and transport aircraft, with efficient fiber optic data transmission, electric control surface actuation, and multi-channel high capacity centralized processing combining to provide maximum aircraft flight control system handling qualities and safety. The key to efficient support for these vehicles is timely and accurate fault diagnostics of all control system components. These diagnostic tests are best conducted during flight when all facts relating to the failure are present. The resulting data can be used by the ground crew for efficient repair and turnaround of the aircraft, saving time and money in support costs. These difficult to diagnose (Cannot Duplicate) fault indications average 40 - 50% of maintenance activities on today's fighter and transport aircraft, adding significantly to fleet support cost. Fiber optic data transmission can support a wealth of data for fault monitoring; the most efficient method of fault diagnostics is accurate modeling of the component response under normal and failed conditions for use in comparison with the actual component flight data. Neural Network hardware processors offer an efficient and cost-effective method to install fault diagnostics in flight systems, permitting on-board diagnostic modeling of very complex subsystems. Task 2C of the ARPA FLASH program is a design demonstration of this diagnostics approach, using the very high speed computation of the Adaptive Solutions Neural Network processor to monitor an advanced Electrohydrostatic control surface actuator linked through a AS-1773A fiber optic bus. This paper describes the design approach and projected performance of this on-line diagnostics system.

Large-scale two-photon imaging revealed super-sparse population codes in the V1 superficial layer of awake monkeys.

PubMed

Tang, Shiming; Zhang, Yimeng; Li, Zhihao; Li, Ming; Liu, Fang; Jiang, Hongfei; Lee, Tai Sing

2018-04-26

One general principle of sensory information processing is that the brain must optimize efficiency by reducing the number of neurons that process the same information. The sparseness of the sensory representations in a population of neurons reflects the efficiency of the neural code. Here, we employ large-scale two-photon calcium imaging to examine the responses of a large population of neurons within the superficial layers of area V1 with single-cell resolution, while simultaneously presenting a large set of natural visual stimuli, to provide the first direct measure of the population sparseness in awake primates. The results show that only 0.5% of neurons respond strongly to any given natural image - indicating a ten-fold increase in the inferred sparseness over previous measurements. These population activities are nevertheless necessary and sufficient to discriminate visual stimuli with high accuracy, suggesting that the neural code in the primary visual cortex is both super-sparse and highly efficient. © 2018, Tang et al.

A sub-1-volt analog metal oxide memristive-based synaptic device with large conductance change for energy-efficient spike-based computing systems

NASA Astrophysics Data System (ADS)

Hsieh, Cheng-Chih; Roy, Anupam; Chang, Yao-Feng; Shahrjerdi, Davood; Banerjee, Sanjay K.

2016-11-01

Nanoscale metal oxide memristors have potential in the development of brain-inspired computing systems that are scalable and efficient. In such systems, memristors represent the native electronic analogues of the biological synapses. In this work, we show cerium oxide based bilayer memristors that are forming-free, low-voltage (˜|0.8 V|), energy-efficient (full on/off switching at ˜8 pJ with 20 ns pulses, intermediate states switching at ˜fJ), and reliable. Furthermore, pulse measurements reveal the analog nature of the memristive device; that is, it can directly be programmed to intermediate resistance states. Leveraging this finding, we demonstrate spike-timing-dependent plasticity, a spike-based Hebbian learning rule. In those experiments, the memristor exhibits a marked change in the normalized synaptic strength (>30 times), when the pre- and post-synaptic neural spikes overlap. This demonstration is an important step towards the physical construction of high density and high connectivity neural networks.

Shy herbivores forage more efficiently than bold ones regardless of information-processing overload.

PubMed

Tan, Ming Kai; Chang, Chia-Chen; Tan, Hugh T W

2018-04-01

The neural constraint hypothesis is central to understanding decision-making by foraging herbivorous insects which make decisions less efficiently when they face multiple choices for numerous resource types and/or at high densities instead of a fewer choices. Previous studies have also shown the relationship between personality type and decision-making style. How personality types correlate with foraging efficiency among herbivores is however, largely untested. To answer this question, we used a widespread, polyphagous, floriphilic katydid, Phaneroptera brevis (Orthoptera: Tettigoniidae) and two naturalised, Asteraceae, food plants, Bidens pilosa and Sphagneticola trilobata, as model systems. After we determined each katydid's exploration and boldness levels, we examined its foraging efficiency across different combinations of floral resource choice and density. We showed: (1) For the first time within the Tettigonioidea lineage that this katydid exhibits different personality types in exploration and boldness. (2) Contrary to our prediction, we did not find any support for the neural constraint hypothesis because more floral resource choice at a high density did not reduce foraging efficiency. (3) Surprisingly, bold katydids tend to be less efficient foragers than shy ones. Our findings have enhanced understanding of herbivore behavioural ecology and knowledge to better deal with potential pest herbivores. Copyright © 2018 Elsevier B.V. All rights reserved.

Limited receptive area neural classifier for recognition of swallowing sounds using continuous wavelet transform.

PubMed

Makeyev, Oleksandr; Sazonov, Edward; Schuckers, Stephanie; Lopez-Meyer, Paulo; Melanson, Ed; Neuman, Michael

2007-01-01

In this paper we propose a sound recognition technique based on the limited receptive area (LIRA) neural classifier and continuous wavelet transform (CWT). LIRA neural classifier was developed as a multipurpose image recognition system. Previous tests of LIRA demonstrated good results in different image recognition tasks including: handwritten digit recognition, face recognition, metal surface texture recognition, and micro work piece shape recognition. We propose a sound recognition technique where scalograms of sound instances serve as inputs of the LIRA neural classifier. The methodology was tested in recognition of swallowing sounds. Swallowing sound recognition may be employed in systems for automated swallowing assessment and diagnosis of swallowing disorders. The experimental results suggest high efficiency and reliability of the proposed approach.

Is there "neural efficiency" during the processing of visuo-spatial information in male humans? An EEG study.

PubMed

Capotosto, Paolo; Perrucci, M Gianni; Brunetti, Marcella; Del Gratta, Cosimo; Doppelmayr, Michael; Grabner, Roland H; Klimesch, Wolfgang; Neubauer, Aljoscha; Neuper, Christa; Pfurtscheller, Gert; Romani, Gian Luca; Babiloni, Claudio

2009-12-28

More intelligent persons (high IQ) typically present a higher cortical activity during tasks requiring the encoding of visuo-spatial information, namely higher alpha (about 10 Hz) event-related desynchronization (ERD; Doppelmayr et al., 2005). The opposite is true ("neural efficiency") during the retrieval of the encoded information, as revealed by both lower alpha ERD and/or lower theta (about 5 Hz) event-related synchronization (ERS; Grabner et al., 2004). To reconcile these contrasting results, here we evaluated the working hypothesis that more intelligent male subjects are characterized by a high cortical activity during the encoding phase. This deep encoding would explain the relatively low cortical activity for the retrieval of the encoded information. To test this hypothesis, electroencephalographic (EEG) data were recorded in 22 healthy young male volunteers during visuo-spatial information processing (encoding) and short-term retrieval of the encoded information. Cortical activity was indexed by theta ERS and alpha ERD. It was found that the higher the subjects' total IQ, the stronger the frontal theta ERS during the encoding task. Furthermore, the higher the subjects' total IQ, the lower the frontal high-frequency alpha ERD (about 10-12 Hz) during the retrieval task. This was not true for parietal counterpart of these EEG rhythms. These results reconcile previous contrasting evidence confirming that more intelligent persons do not ever show event-related cortical responses compatible with "neural efficiency" hypothesis. Rather, their cortical activity would depend on flexible and task-adapting features of frontal activation.

Model validation of untethered, ultrasonic neural dust motes for cortical recording.

PubMed

Seo, Dongjin; Carmena, Jose M; Rabaey, Jan M; Maharbiz, Michel M; Alon, Elad

2015-04-15

A major hurdle in brain-machine interfaces (BMI) is the lack of an implantable neural interface system that remains viable for a substantial fraction of the user's lifetime. Recently, sub-mm implantable, wireless electromagnetic (EM) neural interfaces have been demonstrated in an effort to extend system longevity. However, EM systems do not scale down in size well due to the severe inefficiency of coupling radio-waves at those scales within tissue. This paper explores fundamental system design trade-offs as well as size, power, and bandwidth scaling limits of neural recording systems built from low-power electronics coupled with ultrasonic power delivery and backscatter communication. Such systems will require two fundamental technology innovations: (1) 10-100 μm scale, free-floating, independent sensor nodes, or neural dust, that detect and report local extracellular electrophysiological data via ultrasonic backscattering and (2) a sub-cranial ultrasonic interrogator that establishes power and communication links with the neural dust. We provide experimental verification that the predicted scaling effects follow theory; (127 μm)(3) neural dust motes immersed in water 3 cm from the interrogator couple with 0.002064% power transfer efficiency and 0.04246 ppm backscatter, resulting in a maximum received power of ∼0.5 μW with ∼1 nW of change in backscatter power with neural activity. The high efficiency of ultrasonic transmission can enable the scaling of the sensing nodes down to 10s of micrometer. We conclude with a brief discussion of the application of neural dust for both central and peripheral nervous system recordings, and perspectives on future research directions. Copyright © 2014 Elsevier B.V. All rights reserved.

Studying the Relationship between High-Latitude Geomagnetic Activity and Parameters of Interplanetary Magnetic Clouds with the Use of Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Barkhatov, N. A.; Revunov, S. E.; Vorobjev, V. G.; Yagodkina, O. I.

2018-03-01

The cause-and-effect relations of the dynamics of high-latitude geomagnetic activity (in terms of the AL index) and the type of the magnetic cloud of the solar wind are studied with the use of artificial neural networks. A recurrent neural network model has been created based on the search for the optimal physically coupled input and output parameters characterizing the action of a plasma flux belonging to a certain magnetic cloud type on the magnetosphere. It has been shown that, with IMF components as input parameters of neural networks with allowance for a 90-min prehistory, it is possible to retrieve the AL sequence with an accuracy to 80%. The successful retrieval of the AL dynamics by the used data indicates the presence of a close nonlinear connection of the AL index with cloud parameters. The created neural network models can be applied with high efficiency to retrieve the AL index, both in periods of isolated magnetospheric substorms and in periods of the interaction between the Earth's magnetosphere and magnetic clouds of different types. The developed model of AL index retrieval can be used to detect magnetic clouds.

ChemTS: an efficient python library for de novo molecular generation.

PubMed

Yang, Xiufeng; Zhang, Jinzhe; Yoshizoe, Kazuki; Terayama, Kei; Tsuda, Koji

2017-01-01

Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel Python library ChemTS that explores the chemical space by combining Monte Carlo tree search and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github.com/tsudalab/ChemTS.

ChemTS: an efficient python library for de novo molecular generation

NASA Astrophysics Data System (ADS)

Yang, Xiufeng; Zhang, Jinzhe; Yoshizoe, Kazuki; Terayama, Kei; Tsuda, Koji

2017-12-01

Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel Python library ChemTS that explores the chemical space by combining Monte Carlo tree search and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github.com/tsudalab/ChemTS.

Sex differences in neural efficiency: Are they due to the stereotype threat effect?☆

PubMed Central

Dunst, Beate; Benedek, Mathias; Bergner, Sabine; Athenstaedt, Ursula; Neubauer, Aljoscha C.

2013-01-01

The neural efficiency hypothesis postulates a more efficient use of brain resources in more intelligent people as compared to less intelligent ones. However, this relationship was found to be moderated by sex and task content. While the phenomenon of neural efficiency was previously supported for men when performing visuo-spatial tasks it occurred for women only when performing verbal tasks. One possible explanation for this finding could be provided by the well-studied phenomenon called stereotype threat. Stereotype threat arises when a negative stereotype of one’s own group is made salient and can result in behavior that confirms the stereotype. Overall, 32 boys and 31 girls of varying intellectual ability were tested with a mental rotation task, either under a stereotype exposure or a no-stereotype exposure condition while measuring their EEG. The behavioral results show that an activated negative stereotype not necessarily hampers the performance of girls. Physiologically, a confirmation of the neural efficiency phenomenon was only obtained for boys working under a no-stereotype exposure condition. This result pattern replicates previous findings without threat and thus suggests that sex differences in neural efficiency during visuo-spatial tasks may not be due to the stereotype threat effect. PMID:24092950

Efficiency turns the table on neural encoding, decoding and noise.

PubMed

Deneve, Sophie; Chalk, Matthew

2016-04-01

Sensory neurons are usually described with an encoding model, for example, a function that predicts their response from the sensory stimulus using a receptive field (RF) or a tuning curve. However, central to theories of sensory processing is the notion of 'efficient coding'. We argue here that efficient coding implies a completely different neural coding strategy. Instead of a fixed encoding model, neural populations would be described by a fixed decoding model (i.e. a model reconstructing the stimulus from the neural responses). Because the population solves a global optimization problem, individual neurons are variable, but not noisy, and have no truly invariant tuning curve or receptive field. We review recent experimental evidence and implications for neural noise correlations, robustness and adaptation. Copyright © 2016. Published by Elsevier Ltd.

Devices and circuits for nanoelectronic implementation of artificial neural networks

NASA Astrophysics Data System (ADS)

Turel, Ozgur

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

Multipotent Caudal Neural Progenitors Derived from Human Pluripotent Stem Cells That Give Rise to Lineages of the Central and Peripheral Nervous System

PubMed Central

Hasegawa, Kouichi; Menheniott, Trevelyan; Rollo, Ben; Zhang, Dongcheng; Hough, Shelley; Alshawaf, Abdullah; Febbraro, Fabia; Ighaniyan, Samiramis; Leung, Jessie; Elliott, David A.; Newgreen, Donald F.; Pera, Martin F.

2015-01-01

Abstract The caudal neural plate is a distinct region of the embryo that gives rise to major progenitor lineages of the developing central and peripheral nervous system, including neural crest and floor plate cells. We show that dual inhibition of the glycogen synthase kinase 3β and activin/nodal pathways by small molecules differentiate human pluripotent stem cells (hPSCs) directly into a preneuroepithelial progenitor population we named “caudal neural progenitors” (CNPs). CNPs coexpress caudal neural plate and mesoderm markers, and, share high similarities to embryonic caudal neural plate cells in their lineage differentiation potential. Exposure of CNPs to BMP2/4, sonic hedgehog, or FGF2 signaling efficiently directs their fate to neural crest/roof plate cells, floor plate cells, and caudally specified neuroepithelial cells, respectively. Neural crest derived from CNPs differentiated to neural crest derivatives and demonstrated extensive migratory properties in vivo. Importantly, we also determined the key extrinsic factors specifying CNPs from human embryonic stem cell include FGF8, canonical WNT, and IGF1. Our studies are the first to identify a multipotent neural progenitor derived from hPSCs, that is the precursor for major neural lineages of the embryonic caudal neural tube. Stem Cells 2015;33:1759–1770 PMID:25753817

Neural network configuration and efficiency underlies individual differences in spatial orientation ability.

PubMed

Arnold, Aiden E G F; Protzner, Andrea B; Bray, Signe; Levy, Richard M; Iaria, Giuseppe

2014-02-01

Spatial orientation is a complex cognitive process requiring the integration of information processed in a distributed system of brain regions. Current models on the neural basis of spatial orientation are based primarily on the functional role of single brain regions, with limited understanding of how interaction among these brain regions relates to behavior. In this study, we investigated two sources of variability in the neural networks that support spatial orientation--network configuration and efficiency--and assessed whether variability in these topological properties relates to individual differences in orientation accuracy. Participants with higher accuracy were shown to express greater activity in the right supramarginal gyrus, the right precentral cortex, and the left hippocampus, over and above a core network engaged by the whole group. Additionally, high-performing individuals had increased levels of global efficiency within a resting-state network composed of brain regions engaged during orientation and increased levels of node centrality in the right supramarginal gyrus, the right primary motor cortex, and the left hippocampus. These results indicate that individual differences in the configuration of task-related networks and their efficiency measured at rest relate to the ability to spatially orient. Our findings advance systems neuroscience models of orientation and navigation by providing insight into the role of functional integration in shaping orientation behavior.

Neuromorphic Hardware Architecture Using the Neural Engineering Framework for Pattern Recognition.

PubMed

Wang, Runchun; Thakur, Chetan Singh; Cohen, Gregory; Hamilton, Tara Julia; Tapson, Jonathan; van Schaik, Andre

2017-06-01

We present a hardware architecture that uses the neural engineering framework (NEF) to implement large-scale neural networks on field programmable gate arrays (FPGAs) for performing massively parallel real-time pattern recognition. NEF is a framework that is capable of synthesising large-scale cognitive systems from subnetworks and we have previously presented an FPGA implementation of the NEF that successfully performs nonlinear mathematical computations. That work was developed based on a compact digital neural core, which consists of 64 neurons that are instantiated by a single physical neuron using a time-multiplexing approach. We have now scaled this approach up to build a pattern recognition system by combining identical neural cores together. As a proof of concept, we have developed a handwritten digit recognition system using the MNIST database and achieved a recognition rate of 96.55%. The system is implemented on a state-of-the-art FPGA and can process 5.12 million digits per second. The architecture and hardware optimisations presented offer high-speed and resource-efficient means for performing high-speed, neuromorphic, and massively parallel pattern recognition and classification tasks.

Generation of H1 PAX6WT/EGFP reporter cells to purify PAX6 positive neural stem/progenitor cells.

PubMed

Wu, Wei; Liu, Juli; Su, Zhenghui; Li, Zhonghao; Ma, Ning; Huang, Ke; Zhou, Tiancheng; Wang, Linli

2018-08-25

Neural conversion from human pluripotent cells (hPSCs) is a potential therapy to neurological disease in the future. However, this is still limited by efficiency and stability of existed protocols used for neural induction from hPSCs. To overcome this obstacle, we developed a reporter system to screen PAX6 + neural progenitor/stem cells using transcription activator like effector nuclease (TALEN). We found that knock-in 2 A-EGFP cassette into PAX6 exon of human embryonic stem cells H1 with TALEN-based homology recombination could establish PAX6 WT/EGFP H1 reporter cell line fast and efficiently. This reporter cell line could differentiate into PAX6 and EGFP double positive neural progenitor/stem cells (NPCs/NSCs) after neural induction. Those PAX6 WT/EGFP NPCs could be purified, expanded and specified to post-mitotic neurons in vitro efficiently. With this reporter cell line, we also screened out 1 NPC-specific microRNA, hsa-miR-99a-5p, and 3 ESCs-enriched miRNAs, hsa-miR-302c-5p, hsa-miR-512-3p and hsa-miR-518 b. In conclusion, the TALEN-based neural stem cell screening system is safe and efficient and could help researcher to acquire adequate and pure neural progenitor cells for further application. Copyright © 2018 Elsevier Inc. All rights reserved.

Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data.

PubMed

Ching, Travers; Zhu, Xun; Garmire, Lana X

2018-04-01

Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN framework called Cox-nnet to predict patient prognosis from high throughput transcriptomics data. In 10 TCGA RNA-Seq data sets, Cox-nnet achieves the same or better predictive accuracy compared to other methods, including Cox-proportional hazards regression (with LASSO, ridge, and mimimax concave penalty), Random Forests Survival and CoxBoost. Cox-nnet also reveals richer biological information, at both the pathway and gene levels. The outputs from the hidden layer node provide an alternative approach for survival-sensitive dimension reduction. In summary, we have developed a new method for accurate and efficient prognosis prediction on high throughput data, with functional biological insights. The source code is freely available at https://github.com/lanagarmire/cox-nnet.

A computational model of pattern separation efficiency in the dentate gyrus with implications in schizophrenia

PubMed Central

Faghihi, Faramarz; Moustafa, Ahmed A.

2015-01-01

Information processing in the hippocampus begins by transferring spiking activity of the entorhinal cortex (EC) into the dentate gyrus (DG). Activity pattern in the EC is separated by the DG such that it plays an important role in hippocampal functions including memory. The structural and physiological parameters of these neural networks enable the hippocampus to be efficient in encoding a large number of inputs that animals receive and process in their life time. The neural encoding capacity of the DG depends on its single neurons encoding and pattern separation efficiency. In this study, encoding by the DG is modeled such that single neurons and pattern separation efficiency are measured using simulations of different parameter values. For this purpose, a probabilistic model of single neurons efficiency is presented to study the role of structural and physiological parameters. Known neurons number of the EC and the DG is used to construct a neural network by electrophysiological features of granule cells of the DG. Separated inputs as activated neurons in the EC with different firing probabilities are presented into the DG. For different connectivity rates between the EC and DG, pattern separation efficiency of the DG is measured. The results show that in the absence of feedback inhibition on the DG neurons, the DG demonstrates low separation efficiency and high firing frequency. Feedback inhibition can increase separation efficiency while resulting in very low single neuron’s encoding efficiency in the DG and very low firing frequency of neurons in the DG (sparse spiking). This work presents a mechanistic explanation for experimental observations in the hippocampus, in combination with theoretical measures. Moreover, the model predicts a critical role for impaired inhibitory neurons in schizophrenia where deficiency in pattern separation of the DG has been observed. PMID:25859189

The Synapse Project: Engagement in mentally challenging activities enhances neural efficiency.

PubMed

McDonough, Ian M; Haber, Sara; Bischof, Gérard N; Park, Denise C

2015-01-01

Correlational and limited experimental evidence suggests that an engaged lifestyle is associated with the maintenance of cognitive vitality in old age. However, the mechanisms underlying these engagement effects are poorly understood. We hypothesized that mental effort underlies engagement effects and used fMRI to examine the impact of high-challenge activities (digital photography and quilting) compared with low-challenge activities (socializing or performing low-challenge cognitive tasks) on neural function at pretest, posttest, and one year after the engagement program. In the scanner, participants performed a semantic-classification task with two levels of difficulty to assess the modulation of brain activity in response to task demands. The High-Challenge group, but not the Low-Challenge group, showed increased modulation of brain activity in medial frontal, lateral temporal, and parietal cortex-regions associated with attention and semantic processing-some of which were maintained a year later. This increased modulation stemmed from decreases in brain activity during the easy condition for the High-Challenge group and was associated with time committed to the program, age, and cognition. Sustained engagement in cognitively demanding activities facilitated cognition by increasing neural efficiency. Mentally-challenging activities may be neuroprotective and an important element to maintaining a healthy brain into late adulthood.

The Synapse Project: Engagement in mentally challenging activities enhances neural efficiency

PubMed Central

McDonough, Ian M.; Haber, Sara; Bischof, Gérard N.; Park, Denise C.

2015-01-01

Purpose: Correlational and limited experimental evidence suggests that an engaged lifestyle is associated with the maintenance of cognitive vitality in old age. However, the mechanisms underlying these engagement effects are poorly understood. We hypothesized that mental effort underlies engagement effects and used fMRI to examine the impact of high-challenge activities (digital photography and quilting) compared with low-challenge activities (socializing or performing low-challenge cognitive tasks) on neural function at pretest, posttest, and one year after the engagement program. Methods: In the scanner, participants performed a semantic-classification task with two levels of difficulty to assess the modulation of brain activity in response to task demands. Results: The High-Challenge group, but not the Low-Challenge group, showed increased modulation of brain activity in medial frontal, lateral temporal, and parietal cortex—regions associated with attention and semantic processing—some of which were maintained a year later. This increased modulation stemmed from decreases in brain activity during the easy condition for the High-Challenge group and was associated with time committed to the program, age, and cognition. Conclusions: Sustained engagement in cognitively demanding activities facilitated cognition by increasing neural efficiency. Mentally-challenging activities may be neuroprotective and an important element to maintaining a healthy brain into late adulthood. PMID:26484698

Egg production forecasting: Determining efficient modeling approaches.

PubMed

Ahmad, H A

2011-12-01

Several mathematical or statistical and artificial intelligence models were developed to compare egg production forecasts in commercial layers. Initial data for these models were collected from a comparative layer trial on commercial strains conducted at the Poultry Research Farms, Auburn University. Simulated data were produced to represent new scenarios by using means and SD of egg production of the 22 commercial strains. From the simulated data, random examples were generated for neural network training and testing for the weekly egg production prediction from wk 22 to 36. Three neural network architectures-back-propagation-3, Ward-5, and the general regression neural network-were compared for their efficiency to forecast egg production, along with other traditional models. The general regression neural network gave the best-fitting line, which almost overlapped with the commercial egg production data, with an R(2) of 0.71. The general regression neural network-predicted curve was compared with original egg production data, the average curves of white-shelled and brown-shelled strains, linear regression predictions, and the Gompertz nonlinear model. The general regression neural network was superior in all these comparisons and may be the model of choice if the initial overprediction is managed efficiently. In general, neural network models are efficient, are easy to use, require fewer data, and are practical under farm management conditions to forecast egg production.

Quantitative Analysis of Human Pluripotency and Neural Specification by In-Depth (Phospho)Proteomic Profiling.

PubMed

Singec, Ilyas; Crain, Andrew M; Hou, Junjie; Tobe, Brian T D; Talantova, Maria; Winquist, Alicia A; Doctor, Kutbuddin S; Choy, Jennifer; Huang, Xiayu; La Monaca, Esther; Horn, David M; Wolf, Dieter A; Lipton, Stuart A; Gutierrez, Gustavo J; Brill, Laurence M; Snyder, Evan Y

2016-09-13

Controlled differentiation of human embryonic stem cells (hESCs) can be utilized for precise analysis of cell type identities during early development. We established a highly efficient neural induction strategy and an improved analytical platform, and determined proteomic and phosphoproteomic profiles of hESCs and their specified multipotent neural stem cell derivatives (hNSCs). This quantitative dataset (nearly 13,000 proteins and 60,000 phosphorylation sites) provides unique molecular insights into pluripotency and neural lineage entry. Systems-level comparative analysis of proteins (e.g., transcription factors, epigenetic regulators, kinase families), phosphorylation sites, and numerous biological pathways allowed the identification of distinct signatures in pluripotent and multipotent cells. Furthermore, as predicted by the dataset, we functionally validated an autocrine/paracrine mechanism by demonstrating that the secreted protein midkine is a regulator of neural specification. This resource is freely available to the scientific community, including a searchable website, PluriProt. Published by Elsevier Inc.

Multiprocessor Neural Network in Healthcare.

PubMed

Godó, Zoltán Attila; Kiss, Gábor; Kocsis, Dénes

2015-01-01

A possible way of creating a multiprocessor artificial neural network is by the use of microcontrollers. The RISC processors' high performance and the large number of I/O ports mean they are greatly suitable for creating such a system. During our research, we wanted to see if it is possible to efficiently create interaction between the artifical neural network and the natural nervous system. To achieve as much analogy to the living nervous system as possible, we created a frequency-modulated analog connection between the units. Our system is connected to the living nervous system through 128 microelectrodes. Two-way communication is provided through A/D transformation, which is even capable of testing psychopharmacons. The microcontroller-based analog artificial neural network can play a great role in medical singal processing, such as ECG, EEG etc.

Method of gear fault diagnosis based on EEMD and improved Elman neural network

NASA Astrophysics Data System (ADS)

Zhang, Qi; Zhao, Wei; Xiao, Shungen; Song, Mengmeng

2017-05-01

Aiming at crack and wear and so on of gears Fault information is difficult to diagnose usually due to its weak, a gear fault diagnosis method that is based on EEMD and improved Elman neural network fusion is proposed. A number of IMF components are obtained by decomposing denoised all kinds of fault signals with EEMD, and the pseudo IMF components is eliminated by using the correlation coefficient method to obtain the effective IMF component. The energy characteristic value of each effective component is calculated as the input feature quantity of Elman neural network, and the improved Elman neural network is based on standard network by adding a feedback factor. The fault data of normal gear, broken teeth, cracked gear and attrited gear were collected by field collecting. The results were analyzed by the diagnostic method proposed in this paper. The results show that compared with the standard Elman neural network, Improved Elman neural network has the advantages of high diagnostic efficiency.

Digital micromirror based near-infrared illumination system for plasmonic photothermal neuromodulation.

PubMed

Jung, Hyunjun; Kang, Hongki; Nam, Yoonkey

2017-06-01

Light-mediated neuromodulation techniques provide great advantages to investigate neuroscience due to its high spatial and temporal resolution. To generate a spatial pattern of neural activity, it is necessary to develop a system for patterned-light illumination to a specific area. Digital micromirror device (DMD) based patterned illumination system have been used for neuromodulation due to its simple configuration and design flexibility. In this paper, we developed a patterned near-infrared (NIR) illumination system for region specific photothermal manipulation of neural activity using NIR-sensitive plasmonic gold nanorods (GNRs). The proposed system had high power transmission efficiency for delivering power density up to 19 W/mm 2 . We used a GNR-coated microelectrode array (MEA) to perform biological experiments using E18 rat hippocampal neurons and showed that it was possible to inhibit neural spiking activity of specific area in neural circuits with the patterned NIR illumination. This patterned NIR illumination system can serve as a promising neuromodulation tool to investigate neuroscience in a wide range of physiological and clinical applications.

Competition in high dimensional spaces using a sparse approximation of neural fields.

PubMed

Quinton, Jean-Charles; Girau, Bernard; Lefort, Mathieu

2011-01-01

The Continuum Neural Field Theory implements competition within topologically organized neural networks with lateral inhibitory connections. However, due to the polynomial complexity of matrix-based implementations, updating dense representations of the activity becomes computationally intractable when an adaptive resolution or an arbitrary number of input dimensions is required. This paper proposes an alternative to self-organizing maps with a sparse implementation based on Gaussian mixture models, promoting a trade-off in redundancy for higher computational efficiency and alleviating constraints on the underlying substrate.This version reproduces the emergent attentional properties of the original equations, by directly applying them within a continuous approximation of a high dimensional neural field. The model is compatible with preprocessed sensory flows but can also be interfaced with artificial systems. This is particularly important for sensorimotor systems, where decisions and motor actions must be taken and updated in real-time. Preliminary tests are performed on a reactive color tracking application, using spatially distributed color features.

Using Neural Net Technology To Enhance the Efficiency of a Computer Adaptive Testing Application.

ERIC Educational Resources Information Center

Van Nelson, C.; Henriksen, Larry W.

The potential for computer adaptive testing (CAT) has been well documented. In order to improve the efficiency of this process, it may be possible to utilize a neural network, or more specifically, a back propagation neural network. The paper asserts that in order to accomplish this end, it must be shown that grouping examinees by ability as…

Using high-throughput barcode sequencing to efficiently map connectomes

PubMed Central

Peikon, Ian D.; Kebschull, Justus M.; Vagin, Vasily V.; Ravens, Diana I.; Sun, Yu-Chi; Brouzes, Eric; Corrêa, Ivan R.; Bressan, Dario

2017-01-01

Abstract The function of a neural circuit is determined by the details of its synaptic connections. At present, the only available method for determining a neural wiring diagram with single synapse precision—a ‘connectome’—is based on imaging methods that are slow, labor-intensive and expensive. Here, we present SYNseq, a method for converting the connectome into a form that can exploit the speed and low cost of modern high-throughput DNA sequencing. In SYNseq, each neuron is labeled with a unique random nucleotide sequence—an RNA ‘barcode’—which is targeted to the synapse using engineered proteins. Barcodes in pre- and postsynaptic neurons are then associated through protein-protein crosslinking across the synapse, extracted from the tissue, and joined into a form suitable for sequencing. Although our failure to develop an efficient barcode joining scheme precludes the widespread application of this approach, we expect that with further development SYNseq will enable tracing of complex circuits at high speed and low cost. PMID:28449067

The neural correlates of impaired inhibitory control in anxiety.

PubMed

Ansari, Tahereh L; Derakshan, Nazanin

2011-04-01

According to Attentional Control Theory (Eysenck et al., 2007) anxiety impairs the inhibition function of working memory by increasing the influence of stimulus-driven processes over efficient top-down control. We investigated the neural correlates of impaired inhibitory control in anxiety using an antisaccade task. Low- and high-anxious participants performed anti- and prosaccade tasks and electrophysiological activity was recorded. Consistent with previous research high-anxious individuals had longer antisaccade latencies in response to the to-be-inhibited target, compared with low-anxious individuals. Central to our predictions, high-anxious individuals showed lower ERP activity, at frontocentral and central recording sites, than low anxious individuals, in the period immediately prior to onset of the to-be-inhibited target on correct antisaccade trials. Our findings indicate that anxiety interferes with the efficient recruitment of top-down mechanisms required for the suppression of prepotent responses. Implications are discussed within current models of attentional control in anxiety (Bishop, 2009; Eysenck et al., 2007). Copyright © 2011 Elsevier Ltd. All rights reserved.

Characterizing Deep Brain Stimulation effects in computationally efficient neural network models.

PubMed

Latteri, Alberta; Arena, Paolo; Mazzone, Paolo

2011-04-15

Recent studies on the medical treatment of Parkinson's disease (PD) led to the introduction of the so called Deep Brain Stimulation (DBS) technique. This particular therapy allows to contrast actively the pathological activity of various Deep Brain structures, responsible for the well known PD symptoms. This technique, frequently joined to dopaminergic drugs administration, replaces the surgical interventions implemented to contrast the activity of specific brain nuclei, called Basal Ganglia (BG). This clinical protocol gave the possibility to analyse and inspect signals measured from the electrodes implanted into the deep brain regions. The analysis of these signals led to the possibility to study the PD as a specific case of dynamical synchronization in biological neural networks, with the advantage to apply the theoretical analysis developed in such scientific field to find efficient treatments to face with this important disease. Experimental results in fact show that the PD neurological diseases are characterized by a pathological signal synchronization in BG. Parkinsonian tremor, for example, is ascribed to be caused by neuron populations of the Thalamic and Striatal structures that undergo an abnormal synchronization. On the contrary, in normal conditions, the activity of the same neuron populations do not appear to be correlated and synchronized. To study in details the effect of the stimulation signal on a pathological neural medium, efficient models of these neural structures were built, which are able to show, without any external input, the intrinsic properties of a pathological neural tissue, mimicking the BG synchronized dynamics.We start considering a model already introduced in the literature to investigate the effects of electrical stimulation on pathologically synchronized clusters of neurons. This model used Morris Lecar type neurons. This neuron model, although having a high level of biological plausibility, requires a large computational effort to simulate large scale networks. For this reason we considered a reduced order model, the Izhikevich one, which is computationally much lighter. The comparison between neural lattices built using both neuron models provided comparable results, both without traditional stimulation and in presence of all the stimulation protocols. This was a first result toward the study and simulation of the large scale neural networks involved in pathological dynamics.Using the reduced order model an inspection on the activity of two neural lattices was also carried out at the aim to analyze how the stimulation in one area could affect the dynamics in another area, like the usual medical treatment protocols require.The study of population dynamics that was carried out allowed us to investigate, through simulations, the positive effects of the stimulation signals in terms of desynchronization of the neural dynamics. The results obtained constitute a significant added value to the analysis of synchronization and desynchronization effects due to neural stimulation. This work gives the opportunity to more efficiently study the effect of stimulation in large scale yet computationally efficient neural networks. Results were compared both with the other mathematical models, using Morris Lecar and Izhikevich neurons, and with simulated Local Field Potentials (LFP).

A robust and scalable neuromorphic communication system by combining synaptic time multiplexing and MIMO-OFDM.

PubMed

Srinivasa, Narayan; Zhang, Deying; Grigorian, Beayna

2014-03-01

This paper describes a novel architecture for enabling robust and efficient neuromorphic communication. The architecture combines two concepts: 1) synaptic time multiplexing (STM) that trades space for speed of processing to create an intragroup communication approach that is firing rate independent and offers more flexibility in connectivity than cross-bar architectures and 2) a wired multiple input multiple output (MIMO) communication with orthogonal frequency division multiplexing (OFDM) techniques to enable a robust and efficient intergroup communication for neuromorphic systems. The MIMO-OFDM concept for the proposed architecture was analyzed by simulating large-scale spiking neural network architecture. Analysis shows that the neuromorphic system with MIMO-OFDM exhibits robust and efficient communication while operating in real time with a high bit rate. Through combining STM with MIMO-OFDM techniques, the resulting system offers a flexible and scalable connectivity as well as a power and area efficient solution for the implementation of very large-scale spiking neural architectures in hardware.

A Stochastic Spiking Neural Network for Virtual Screening.

PubMed

Morro, A; Canals, V; Oliver, A; Alomar, M L; Galan-Prado, F; Ballester, P J; Rossello, J L

2018-04-01

Virtual screening (VS) has become a key computational tool in early drug design and screening performance is of high relevance due to the large volume of data that must be processed to identify molecules with the sought activity-related pattern. At the same time, the hardware implementations of spiking neural networks (SNNs) arise as an emerging computing technique that can be applied to parallelize processes that normally present a high cost in terms of computing time and power. Consequently, SNN represents an attractive alternative to perform time-consuming processing tasks, such as VS. In this brief, we present a smart stochastic spiking neural architecture that implements the ultrafast shape recognition (USR) algorithm achieving two order of magnitude of speed improvement with respect to USR software implementations. The neural system is implemented in hardware using field-programmable gate arrays allowing a highly parallelized USR implementation. The results show that, due to the high parallelization of the system, millions of compounds can be checked in reasonable times. From these results, we can state that the proposed architecture arises as a feasible methodology to efficiently enhance time-consuming data-mining processes such as 3-D molecular similarity search.

Artificial intelligence-based computer modeling tools for controlling slag foaming in electric arc furnaces

NASA Astrophysics Data System (ADS)

Wilson, Eric Lee

Due to increased competition in a world economy, steel companies are currently interested in developing techniques that will allow for the improvement of the steelmaking process, either by increasing output efficiency or by improving the quality of their product, or both. Slag foaming is one practice that has been shown to contribute to both these goals. However, slag foaming is highly dynamic and difficult to model or control. This dissertation describes an effort to use artificial intelligence-based tools (genetic algorithms, fuzzy logic, and neural networks) to both model and control the slag foaming process. Specifically, a neural network is trained and tested on slag foaming data provided by a steel plant. This neural network model is then controlled by a fuzzy logic controller, which in turn is optimized by a genetic algorithm. This tuned controller is then installed at a steel plant and given control be a more efficient slag foaming controller than what was previously used by the steel plant.

Statistical process control using optimized neural networks: a case study.

PubMed

Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid

2014-09-01

The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

The neural correlates of cognitive effort in anxiety: effects on processing efficiency.

PubMed

Ansari, Tahereh L; Derakshan, Nazanin

2011-03-01

We investigated the neural correlates of cognitive effort/pre-target preparation (Contingent Negative Variation activity; CNV) in anxiety using a mixed antisaccade task that manipulated the interval between offset of instructional cue and onset of target (CTI). According to attentional control theory (Eysenck et al., 2007) we predicted that anxiety should result in increased levels of compensatory effort, as indicated by greater frontal CNV, to maintain comparable levels of performance under competing task demands. Our results showed that anxiety resulted in faster antisaccade latencies during medium compared with short and long CTIs. Accordingly, high-anxious individuals compared with low-anxious individuals showed greater levels of CNV activity at frontal sites during medium CTI suggesting that they exerted greater cognitive effort and invested more attentional resources in preparation for the task goal. Our results are the first to demonstrate the neural correlates of processing efficiency and compensatory effort in anxiety and are discussed within the framework of attentional control theory. Copyright © 2011 Elsevier B.V. All rights reserved.

Neural Signatures of Phonetic Learning in Adulthood: A Magnetoencephalography Study

PubMed Central

Zhang, Yang; Kuhl, Patricia K.; Imada, Toshiaki; Iverson, Paul; Pruitt, John; Stevens, Erica B.; Kawakatsu, Masaki; Tohkura, Yoh'ichi; Nemoto, Iku

2010-01-01

The present study used magnetoencephalography (MEG) to examine perceptual learning of American English /r/ and /l/ categories by Japanese adults who had limited English exposure. A training software program was developed based on the principles of infant phonetic learning, featuring systematic acoustic exaggeration, multi-talker variability, visible articulation, and adaptive listening. The program was designed to help Japanese listeners utilize an acoustic dimension relevant for phonemic categorization of /r-l/ in English. Although training did not produce native-like phonetic boundary along the /r-l/ synthetic continuum in the second language learners, success was seen in highly significant identification improvement over twelve training sessions and transfer of learning to novel stimuli. Consistent with behavioral results, pre-post MEG measures showed not only enhanced neural sensitivity to the /r-l/ distinction in the left-hemisphere mismatch field (MMF) response but also bilateral decreases in equivalent current dipole (ECD) cluster and duration measures for stimulus coding in the inferior parietal region. The learning-induced increases in neural sensitivity and efficiency were also found in distributed source analysis using Minimum Current Estimates (MCE). Furthermore, the pre-post changes exhibited significant brain-behavior correlations between speech discrimination scores and MMF amplitudes as well as between the behavioral scores and ECD measures of neural efficiency. Together, the data provide corroborating evidence that substantial neural plasticity for second-language learning in adulthood can be induced with adaptive and enriched linguistic exposure. Like the MMF, the ECD cluster and duration measures are sensitive neural markers of phonetic learning. PMID:19457395

Generation of Oligodendrogenic Spinal Neural Progenitor Cells From Human Induced Pluripotent Stem Cells.

PubMed

Khazaei, Mohamad; Ahuja, Christopher S; Fehlings, Michael G

2017-08-14

This unit describes protocols for the efficient generation of oligodendrogenic neural progenitor cells (o-NPCs) from human induced pluripotent stem cells (hiPSCs). Specifically, detailed methods are provided for the maintenance and differentiation of hiPSCs, human induced pluripotent stem cell-derived neural progenitor cells (hiPS-NPCs), and human induced pluripotent stem cell-oligodendrogenic neural progenitor cells (hiPSC-o-NPCs) with the final products being suitable for in vitro experimentation or in vivo transplantation. Throughout, cell exposure to growth factors and patterning morphogens has been optimized for both concentration and timing, based on the literature and empirical experience, resulting in a robust and highly efficient protocol. Using this derivation procedure, it is possible to obtain millions of oligodendrogenic-NPCs within 40 days of initial cell plating which is substantially shorter than other protocols for similar cell types. This protocol has also been optimized to use translationally relevant human iPSCs as the parent cell line. The resultant cells have been extensively characterized both in vitro and in vivo and express key markers of an oligodendrogenic lineage. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley and Sons, Inc.

ChemTS: an efficient python library for de novo molecular generation

PubMed Central

Yang, Xiufeng; Zhang, Jinzhe; Yoshizoe, Kazuki; Terayama, Kei; Tsuda, Koji

2017-01-01

Abstract Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel Python library ChemTS that explores the chemical space by combining Monte Carlo tree search and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github.com/tsudalab/ChemTS. PMID:29435094

Machine learning action parameters in lattice quantum chromodynamics

NASA Astrophysics Data System (ADS)

Shanahan, Phiala E.; Trewartha, Daniel; Detmold, William

2018-05-01

Numerical lattice quantum chromodynamics studies of the strong interaction are important in many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods promise improved efficiency of lattice calculations, and access to regions of parameter space that are currently computationally intractable, via multi-scale action-matching approaches that necessitate parametric regression of generated lattice datasets. The applicability of machine learning to this regression task is investigated, with deep neural networks found to provide an efficient solution even in cases where approaches such as principal component analysis fail. The high information content and complex symmetries inherent in lattice QCD datasets require custom neural network layers to be introduced and present opportunities for further development.

An Implantable Wireless Neural Interface System for Simultaneous Recording and Stimulation of Peripheral Nerve with a Single Cuff Electrode.

PubMed

Shon, Ahnsei; Chu, Jun-Uk; Jung, Jiuk; Kim, Hyungmin; Youn, Inchan

2017-12-21

Recently, implantable devices have become widely used in neural prostheses because they eliminate endemic drawbacks of conventional percutaneous neural interface systems. However, there are still several issues to be considered: low-efficiency wireless power transmission; wireless data communication over restricted operating distance with high power consumption; and limited functionality, working either as a neural signal recorder or as a stimulator. To overcome these issues, we suggest a novel implantable wireless neural interface system for simultaneous neural signal recording and stimulation using a single cuff electrode. By using widely available commercial off-the-shelf (COTS) components, an easily reconfigurable implantable wireless neural interface system was implemented into one compact module. The implantable device includes a wireless power consortium (WPC)-compliant power transmission circuit, a medical implant communication service (MICS)-band-based radio link and a cuff-electrode path controller for simultaneous neural signal recording and stimulation. During in vivo experiments with rabbit models, the implantable device successfully recorded and stimulated the tibial and peroneal nerves while communicating with the external device. The proposed system can be modified for various implantable medical devices, especially such as closed-loop control based implantable neural prostheses requiring neural signal recording and stimulation at the same time.

An Implantable Wireless Neural Interface System for Simultaneous Recording and Stimulation of Peripheral Nerve with a Single Cuff Electrode

PubMed Central

Shon, Ahnsei; Chu, Jun-Uk; Jung, Jiuk; Youn, Inchan

2017-01-01

Recently, implantable devices have become widely used in neural prostheses because they eliminate endemic drawbacks of conventional percutaneous neural interface systems. However, there are still several issues to be considered: low-efficiency wireless power transmission; wireless data communication over restricted operating distance with high power consumption; and limited functionality, working either as a neural signal recorder or as a stimulator. To overcome these issues, we suggest a novel implantable wireless neural interface system for simultaneous neural signal recording and stimulation using a single cuff electrode. By using widely available commercial off-the-shelf (COTS) components, an easily reconfigurable implantable wireless neural interface system was implemented into one compact module. The implantable device includes a wireless power consortium (WPC)-compliant power transmission circuit, a medical implant communication service (MICS)-band-based radio link and a cuff-electrode path controller for simultaneous neural signal recording and stimulation. During in vivo experiments with rabbit models, the implantable device successfully recorded and stimulated the tibial and peroneal nerves while communicating with the external device. The proposed system can be modified for various implantable medical devices, especially such as closed-loop control based implantable neural prostheses requiring neural signal recording and stimulation at the same time. PMID:29267230

Three-dimensional neural differentiation of embryonic stem cells with ACM induction in microfibrous matrices in bioreactors.

PubMed

Liu, Ning; Ouyang, Anli; Li, Yan; Yang, Shang-Tian

2013-01-01

The clinical use of pluripotent stem cell (PSC)-derived neural cells requires an efficient differentiation process for mass production in a bioreactor. Toward this goal, neural differentiation of murine embryonic stem cells (ESCs) in three-dimensional (3D) polyethylene terephthalate microfibrous matrices was investigated in this study. To streamline the process and provide a platform for process integration, the neural differentiation of ESCs was induced with astrocyte-conditioned medium without the formation of embryoid bodies, starting from undifferentiated ESC aggregates expanded in a suspension bioreactor. The 3D neural differentiation was able to generate a complex neural network in the matrices. When compared to 2D differentiation, 3D differentiation in microfibrous matrices resulted in a higher percentage of nestin-positive cells (68% vs. 54%) and upregulated gene expressions of nestin, Nurr1, and tyrosine hydroxylase. High purity of neural differentiation in 3D microfibrous matrix was also demonstrated in a spinner bioreactor with 74% nestin + cells. This study demonstrated the feasibility of a scalable process based on 3D differentiation in microfibrous matrices for the production of ESC-derived neural cells. © 2013 American Institute of Chemical Engineers.

Synaptic E-I Balance Underlies Efficient Neural Coding.

PubMed

Zhou, Shanglin; Yu, Yuguo

2018-01-01

Both theoretical and experimental evidence indicate that synaptic excitation and inhibition in the cerebral cortex are well-balanced during the resting state and sensory processing. Here, we briefly summarize the evidence for how neural circuits are adjusted to achieve this balance. Then, we discuss how such excitatory and inhibitory balance shapes stimulus representation and information propagation, two basic functions of neural coding. We also point out the benefit of adopting such a balance during neural coding. We conclude that excitatory and inhibitory balance may be a fundamental mechanism underlying efficient coding.

Synaptic E-I Balance Underlies Efficient Neural Coding

PubMed Central

Zhou, Shanglin; Yu, Yuguo

2018-01-01

Both theoretical and experimental evidence indicate that synaptic excitation and inhibition in the cerebral cortex are well-balanced during the resting state and sensory processing. Here, we briefly summarize the evidence for how neural circuits are adjusted to achieve this balance. Then, we discuss how such excitatory and inhibitory balance shapes stimulus representation and information propagation, two basic functions of neural coding. We also point out the benefit of adopting such a balance during neural coding. We conclude that excitatory and inhibitory balance may be a fundamental mechanism underlying efficient coding. PMID:29456491

Online particle detection with Neural Networks based on topological calorimetry information

NASA Astrophysics Data System (ADS)

Ciodaro, T.; Deva, D.; de Seixas, J. M.; Damazio, D.

2012-06-01

This paper presents the latest results from the Ringer algorithm, which is based on artificial neural networks for the electron identification at the online filtering system of the ATLAS particle detector, in the context of the LHC experiment at CERN. The algorithm performs topological feature extraction using the ATLAS calorimetry information (energy measurements). The extracted information is presented to a neural network classifier. Studies showed that the Ringer algorithm achieves high detection efficiency, while keeping the false alarm rate low. Optimizations, guided by detailed analysis, reduced the algorithm execution time by 59%. Also, the total memory necessary to store the Ringer algorithm information represents less than 6.2 percent of the total filtering system amount.

Rock images classification by using deep convolution neural network

NASA Astrophysics Data System (ADS)

Cheng, Guojian; Guo, Wenhui

2017-08-01

Granularity analysis is one of the most essential issues in authenticate under microscope. To improve the efficiency and accuracy of traditional manual work, an convolutional neural network based method is proposed for granularity analysis from thin section image, which chooses and extracts features from image samples while build classifier to recognize granularity of input image samples. 4800 samples from Ordos basin are used for experiments under colour spaces of HSV, YCbCr and RGB respectively. On the test dataset, the correct rate in RGB colour space is 98.5%, and it is believable in HSV and YCbCr colour space. The results show that the convolution neural network can classify the rock images with high reliability.

A preferential design approach for energy-efficient and robust implantable neural signal processing hardware.

PubMed

Narasimhan, Seetharam; Chiel, Hillel J; Bhunia, Swarup

2009-01-01

For implantable neural interface applications, it is important to compress data and analyze spike patterns across multiple channels in real time. Such a computational task for online neural data processing requires an innovative circuit-architecture level design approach for low-power, robust and area-efficient hardware implementation. Conventional microprocessor or Digital Signal Processing (DSP) chips would dissipate too much power and are too large in size for an implantable system. In this paper, we propose a novel hardware design approach, referred to as "Preferential Design" that exploits the nature of the neural signal processing algorithm to achieve a low-voltage, robust and area-efficient implementation using nanoscale process technology. The basic idea is to isolate the critical components with respect to system performance and design them more conservatively compared to the noncritical ones. This allows aggressive voltage scaling for low power operation while ensuring robustness and area efficiency. We have applied the proposed approach to a neural signal processing algorithm using the Discrete Wavelet Transform (DWT) and observed significant improvement in power and robustness over conventional design.

Multiscale Quantum Mechanics/Molecular Mechanics Simulations with Neural Networks.

PubMed

Shen, Lin; Wu, Jingheng; Yang, Weitao

2016-10-11

Molecular dynamics simulation with multiscale quantum mechanics/molecular mechanics (QM/MM) methods is a very powerful tool for understanding the mechanism of chemical and biological processes in solution or enzymes. However, its computational cost can be too high for many biochemical systems because of the large number of ab initio QM calculations. Semiempirical QM/MM simulations have much higher efficiency. Its accuracy can be improved with a correction to reach the ab initio QM/MM level. The computational cost on the ab initio calculation for the correction determines the efficiency. In this paper we developed a neural network method for QM/MM calculation as an extension of the neural-network representation reported by Behler and Parrinello. With this approach, the potential energy of any configuration along the reaction path for a given QM/MM system can be predicted at the ab initio QM/MM level based on the semiempirical QM/MM simulations. We further applied this method to three reactions in water to calculate the free energy changes. The free-energy profile obtained from the semiempirical QM/MM simulation is corrected to the ab initio QM/MM level with the potential energies predicted with the constructed neural network. The results are in excellent accordance with the reference data that are obtained from the ab initio QM/MM molecular dynamics simulation or corrected with direct ab initio QM/MM potential energies. Compared with the correction using direct ab initio QM/MM potential energies, our method shows a speed-up of 1 or 2 orders of magnitude. It demonstrates that the neural network method combined with the semiempirical QM/MM calculation can be an efficient and reliable strategy for chemical reaction simulations.

Blur identification by multilayer neural network based on multivalued neurons.

PubMed

Aizenberg, Igor; Paliy, Dmitriy V; Zurada, Jacek M; Astola, Jaakko T

2008-05-01

A multilayer neural network based on multivalued neurons (MLMVN) is a neural network with a traditional feedforward architecture. At the same time, this network has a number of specific different features. Its backpropagation learning algorithm is derivative-free. The functionality of MLMVN is superior to that of the traditional feedforward neural networks and of a variety kernel-based networks. Its higher flexibility and faster adaptation to the target mapping enables to model complex problems using simpler networks. In this paper, the MLMVN is used to identify both type and parameters of the point spread function, whose precise identification is of crucial importance for the image deblurring. The simulation results show the high efficiency of the proposed approach. It is confirmed that the MLMVN is a powerful tool for solving classification problems, especially multiclass ones.

A Wireless Fully Passive Neural Recording Device for Unobtrusive Neuropotential Monitoring.

PubMed

Kiourti, Asimina; Lee, Cedric W L; Chae, Junseok; Volakis, John L

2016-01-01

We propose a novel wireless fully passive neural recording device for unobtrusive neuropotential monitoring. Previous work demonstrated the feasibility of monitoring emulated brain signals in a wireless fully passive manner. In this paper, we propose a novel realistic recorder that is significantly smaller and much more sensitive. The proposed recorder utilizes a highly efficient microwave backscattering method and operates without any formal power supply or regulating elements. Also, no intracranial wires or cables are required. In-vitro testing is performed inside a four-layer head phantom (skin, bone, gray matter, and white matter). Compared to our former implementation, the neural recorder proposed in this study has the following improved features: 1) 59% smaller footprint, 2) up to 20-dB improvement in neuropotential detection sensitivity, and 3) encapsulation in biocompatible polymer. For the first time, temporal emulated neuropotentials as low as 63 μVpp can be detected in a wireless fully passive manner. Remarkably, the high-sensitivity achieved in this study implies reading of most neural signals generated by the human brain. The proposed recorder brings forward transformational possibilities in wireless fully passive neural detection for a very wide range of applications (e.g., epilepsy, Alzheimer's, mental disorders, etc.).

The Brain as a Distributed Intelligent Processing System: An EEG Study

PubMed Central

da Rocha, Armando Freitas; Rocha, Fábio Theoto; Massad, Eduardo

2011-01-01

Background Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS), first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS. Methodology and Principal Findings In our study, a new EEG brain mapping technique, based on the neural efficiency hypothesis and the notion of the brain as a Distributed Intelligence Processing System, was used to investigate the correlations between IQ evaluated with WAIS (Whechsler Adult Intelligence Scale) and WISC (Wechsler Intelligence Scale for Children), and the brain activity associated with visual and verbal processing, in order to test the validity of a distributed neural basis for intelligence. Conclusion The present results support these claims and the neural efficiency hypothesis. PMID:21423657

Detecting atrial fibrillation by deep convolutional neural networks.

PubMed

Xia, Yong; Wulan, Naren; Wang, Kuanquan; Zhang, Henggui

2018-02-01

Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of AF increases with age, causing high risks of stroke and increased morbidity and mortality. Efficient and accurate diagnosis of AF based on the ECG is valuable in clinical settings and remains challenging. In this paper, we proposed a novel method with high reliability and accuracy for AF detection via deep learning. The short-term Fourier transform (STFT) and stationary wavelet transform (SWT) were used to analyze ECG segments to obtain two-dimensional (2-D) matrix input suitable for deep convolutional neural networks. Then, two different deep convolutional neural network models corresponding to STFT output and SWT output were developed. Our new method did not require detection of P or R peaks, nor feature designs for classification, in contrast to existing algorithms. Finally, the performances of the two models were evaluated and compared with those of existing algorithms. Our proposed method demonstrated favorable performances on ECG segments as short as 5 s. The deep convolutional neural network using input generated by STFT, presented a sensitivity of 98.34%, specificity of 98.24% and accuracy of 98.29%. For the deep convolutional neural network using input generated by SWT, a sensitivity of 98.79%, specificity of 97.87% and accuracy of 98.63% was achieved. The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data

PubMed Central

Ching, Travers; Zhu, Xun

2018-01-01

Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN framework called Cox-nnet to predict patient prognosis from high throughput transcriptomics data. In 10 TCGA RNA-Seq data sets, Cox-nnet achieves the same or better predictive accuracy compared to other methods, including Cox-proportional hazards regression (with LASSO, ridge, and mimimax concave penalty), Random Forests Survival and CoxBoost. Cox-nnet also reveals richer biological information, at both the pathway and gene levels. The outputs from the hidden layer node provide an alternative approach for survival-sensitive dimension reduction. In summary, we have developed a new method for accurate and efficient prognosis prediction on high throughput data, with functional biological insights. The source code is freely available at https://github.com/lanagarmire/cox-nnet. PMID:29634719

Machine learning action parameters in lattice quantum chromodynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shanahan, Phiala; Trewartha, Daneil; Detmold, William

Numerical lattice quantum chromodynamics studies of the strong interaction underpin theoretical understanding of many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods promise improved efficiency of lattice calculations, and access to regions of parameter space that are currently computationally intractable, via multi-scale action-matching approaches that necessitate parametric regression of generated lattice datasets. The applicability of machine learning to this regression task is investigated, with deep neural networks found to provide an efficient solution even in cases where approaches such as principal component analysis fail. Finally, the high information contentmore » and complex symmetries inherent in lattice QCD datasets require custom neural network layers to be introduced and present opportunities for further development.« less

Machine learning action parameters in lattice quantum chromodynamics

DOE PAGES

Shanahan, Phiala; Trewartha, Daneil; Detmold, William

2018-05-16

Numerical lattice quantum chromodynamics studies of the strong interaction underpin theoretical understanding of many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods promise improved efficiency of lattice calculations, and access to regions of parameter space that are currently computationally intractable, via multi-scale action-matching approaches that necessitate parametric regression of generated lattice datasets. The applicability of machine learning to this regression task is investigated, with deep neural networks found to provide an efficient solution even in cases where approaches such as principal component analysis fail. Finally, the high information contentmore » and complex symmetries inherent in lattice QCD datasets require custom neural network layers to be introduced and present opportunities for further development.« less

Functional Comparison of Neuronal Cells Differentiated from Human Induced Pluripotent Stem Cell-Derived Neural Stem Cells under Different Oxygen and Medium Conditions.

PubMed

Yamazaki, Kazuto; Fukushima, Kazuyuki; Sugawara, Michiko; Tabata, Yoshikuni; Imaizumi, Yoichi; Ishihara, Yasuharu; Ito, Masashi; Tsukahara, Kappei; Kohyama, Jun; Okano, Hideyuki

2016-12-01

Because neurons are difficult to obtain from humans, generating functional neurons from human induced pluripotent stem cells (hiPSCs) is important for establishing physiological or disease-relevant screening systems for drug discovery. To examine the culture conditions leading to efficient differentiation of functional neural cells, we investigated the effects of oxygen stress (2% or 20% O 2 ) and differentiation medium (DMEM/F12:Neurobasal-based [DN] or commercial [PhoenixSongs Biologicals; PS]) on the expression of genes related to neural differentiation, glutamate receptor function, and the formation of networks of neurons differentiated from hiPSCs (201B7) via long-term self-renewing neuroepithelial-like stem (lt-NES) cells. Expression of genes related to neural differentiation occurred more quickly in PS and/or 2% O 2 than in DN and/or 20% O 2 , resulting in high responsiveness of neural cells to glutamate, N-methyl-d-aspartate (NMDA), α-amino-3-hydroxy-5-methyl-4-isoxazolepropionate (AMPA), and ( S)-3,5-dihydroxyphenylglycine (an agonist for mGluR 1/5 ), as revealed by calcium imaging assays. NMDA receptors, AMPA receptors, mGluR 1 , and mGluR 5 were functionally validated by using the specific antagonists MK-801, NBQX, JNJ16259685, and 2-methyl-6-(phenylethynyl)-pyridine, respectively. Multielectrode array analysis showed that spontaneous firing occurred earlier in cells cultured in 2% O 2 than in 20% O 2 . Optimization of O 2 tension and culture medium for neural differentiation of hiPSCs can efficiently generate physiologically relevant cells for screening systems.

High-Density Liquid-State Machine Circuitry for Time-Series Forecasting.

PubMed

Rosselló, Josep L; Alomar, Miquel L; Morro, Antoni; Oliver, Antoni; Canals, Vincent

2016-08-01

Spiking neural networks (SNN) are the last neural network generation that try to mimic the real behavior of biological neurons. Although most research in this area is done through software applications, it is in hardware implementations in which the intrinsic parallelism of these computing systems are more efficiently exploited. Liquid state machines (LSM) have arisen as a strategic technique to implement recurrent designs of SNN with a simple learning methodology. In this work, we show a new low-cost methodology to implement high-density LSM by using Boolean gates. The proposed method is based on the use of probabilistic computing concepts to reduce hardware requirements, thus considerably increasing the neuron count per chip. The result is a highly functional system that is applied to high-speed time series forecasting.

Is less really more: Does a prefrontal efficiency genotype actually confer better performance when working memory becomes difficult?

PubMed

Ihne, Jessica L; Gallagher, Natalie M; Sullivan, Marie; Callicott, Joseph H; Green, Adam E

2016-01-01

Perhaps the most widely studied effect to emerge from the combination of neuroimaging and human genetics is the association of the COMT-Val(108/158)Met polymorphism with prefrontal activity during working memory. COMT-Val is a putative risk factor in schizophrenia, which is characterized by disordered prefrontal function. Work in healthy populations has sought to characterize mechanisms by which the valine (Val) allele may lead to disadvantaged prefrontal cognition. Lower activity in methionine (Met) carriers has been interpreted as advantageous neural efficiency. Notably, however, studies reporting COMT effects on neural efficiency have generally not reported working memory performance effects. Those studies have employed relatively low/easy working memory loads. Higher loads are known to elicit individual differences in working memory performance that are not visible at lower loads. If COMT-Met confers greater neural efficiency when working memory is easy, a reasonable prediction is that Met carriers will be better able to cope with increasing demand for neural resources when working memory becomes difficult. To our knowledge, this prediction has thus far gone untested. Here, we tested performance on three working memory tasks. Performance on each task was measured at multiple levels of load/difficulty, including loads more demanding than those used in prior studies. We found no genotype-by-load interactions or main effects of COMT genotype on accuracy or reaction time. Indeed, even testing for performance differences at each load of each task failed to find a single significant effect of COMT genotype. Thus, even if COMT genotype has the effects on prefrontal efficiency that prior work has suggested, such effects may not directly impact high-load working memory ability. The present findings accord with previous evidence that behavioral effects of COMT are small or nonexistent and, more broadly, with a growing consensus that substantial effects on phenotype will not emerge from candidate gene studies. Copyright © 2015 Elsevier Ltd. All rights reserved.

Qualitative and quantitative comparison of geostatistical techniques of porosity prediction from the seismic and logging data: a case study from the Blackfoot Field, Alberta, Canada

NASA Astrophysics Data System (ADS)

Maurya, S. P.; Singh, K. H.; Singh, N. P.

2018-05-01

In present study, three recently developed geostatistical methods, single attribute analysis, multi-attribute analysis and probabilistic neural network algorithm have been used to predict porosity in inter well region for Blackfoot field, Alberta, Canada, an offshore oil field. These techniques make use of seismic attributes, generated by model based inversion and colored inversion techniques. The principle objective of the study is to find the suitable combination of seismic inversion and geostatistical techniques to predict porosity and identification of prospective zones in 3D seismic volume. The porosity estimated from these geostatistical approaches is corroborated with the well log porosity. The results suggest that all the three implemented geostatistical methods are efficient and reliable to predict the porosity but the multi-attribute and probabilistic neural network analysis provide more accurate and high resolution porosity sections. A low impedance (6000-8000 m/s g/cc) and high porosity (> 15%) zone is interpreted from inverted impedance and porosity sections respectively between 1060 and 1075 ms time interval and is characterized as reservoir. The qualitative and quantitative results demonstrate that of all the employed geostatistical methods, the probabilistic neural network along with model based inversion is the most efficient method for predicting porosity in inter well region.

Comparing feed-forward versus neural gas as estimators: application to coke wastewater treatment.

PubMed

Machón-González, Iván; López-García, Hilario; Rodríguez-Iglesias, Jesús; Marañón-Maison, Elena; Castrillón-Peláez, Leonor; Fernández-Nava, Yolanda

2013-01-01

Numerous papers related to the estimation of wastewater parameters have used artificial neural networks. Although successful results have been reported, different problems have arisen such as overtraining, local minima and model instability. In this paper, two types of neural networks, feed-forward and neural gas, are trained to obtain a model that estimates the values of nitrates in the effluent stream of a three-step activated sludge system (two oxic and one anoxic). Placing the denitrification (anoxic) step at the head of the process can force denitrifying bacteria to use internal organic carbon. However, methanol has to be added to achieve high denitrification efficiencies in some industrial wastewaters. The aim of this paper is to compare the two networks in addition to suggesting a methodology to validate the models. The influence of the neighbourhood radius is important in the neural gas approach and must be selected correctly. Neural gas performs well due to its cooperation--competition procedure, with no problems of stability or overfitting arising in the experimental results. The neural gas model is also interesting for use as a direct plant model because of its robustness and deterministic behaviour.

Enhancement of digital radiography image quality using a convolutional neural network.

PubMed

Sun, Yuewen; Li, Litao; Cong, Peng; Wang, Zhentao; Guo, Xiaojing

2017-01-01

Digital radiography system is widely used for noninvasive security check and medical imaging examination. However, the system has a limitation of lower image quality in spatial resolution and signal to noise ratio. In this study, we explored whether the image quality acquired by the digital radiography system can be improved with a modified convolutional neural network to generate high-resolution images with reduced noise from the original low-quality images. The experiment evaluated on a test dataset, which contains 5 X-ray images, showed that the proposed method outperformed the traditional methods (i.e., bicubic interpolation and 3D block-matching approach) as measured by peak signal to noise ratio (PSNR) about 1.3 dB while kept highly efficient processing time within one second. Experimental results demonstrated that a residual to residual (RTR) convolutional neural network remarkably improved the image quality of object structural details by increasing the image resolution and reducing image noise. Thus, this study indicated that applying this RTR convolutional neural network system was useful to improve image quality acquired by the digital radiography system.

Digital micromirror based near-infrared illumination system for plasmonic photothermal neuromodulation

PubMed Central

Jung, Hyunjun; Kang, Hongki; Nam, Yoonkey

2017-01-01

Light-mediated neuromodulation techniques provide great advantages to investigate neuroscience due to its high spatial and temporal resolution. To generate a spatial pattern of neural activity, it is necessary to develop a system for patterned-light illumination to a specific area. Digital micromirror device (DMD) based patterned illumination system have been used for neuromodulation due to its simple configuration and design flexibility. In this paper, we developed a patterned near-infrared (NIR) illumination system for region specific photothermal manipulation of neural activity using NIR-sensitive plasmonic gold nanorods (GNRs). The proposed system had high power transmission efficiency for delivering power density up to 19 W/mm2. We used a GNR-coated microelectrode array (MEA) to perform biological experiments using E18 rat hippocampal neurons and showed that it was possible to inhibit neural spiking activity of specific area in neural circuits with the patterned NIR illumination. This patterned NIR illumination system can serve as a promising neuromodulation tool to investigate neuroscience in a wide range of physiological and clinical applications. PMID:28663912

ELIPS: Toward a Sensor Fusion Processor on a Chip

NASA Technical Reports Server (NTRS)

Daud, Taher; Stoica, Adrian; Tyson, Thomas; Li, Wei-te; Fabunmi, James

1998-01-01

The paper presents the concept and initial tests from the hardware implementation of a low-power, high-speed reconfigurable sensor fusion processor. The Extended Logic Intelligent Processing System (ELIPS) processor is developed to seamlessly combine rule-based systems, fuzzy logic, and neural networks to achieve parallel fusion of sensor in compact low power VLSI. The first demonstration of the ELIPS concept targets interceptor functionality; other applications, mainly in robotics and autonomous systems are considered for the future. The main assumption behind ELIPS is that fuzzy, rule-based and neural forms of computation can serve as the main primitives of an "intelligent" processor. Thus, in the same way classic processors are designed to optimize the hardware implementation of a set of fundamental operations, ELIPS is developed as an efficient implementation of computational intelligence primitives, and relies on a set of fuzzy set, fuzzy inference and neural modules, built in programmable analog hardware. The hardware programmability allows the processor to reconfigure into different machines, taking the most efficient hardware implementation during each phase of information processing. Following software demonstrations on several interceptor data, three important ELIPS building blocks (a fuzzy set preprocessor, a rule-based fuzzy system and a neural network) have been fabricated in analog VLSI hardware and demonstrated microsecond-processing times.

Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce.

PubMed

Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan

2016-01-01

A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network's initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data.

Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system

NASA Astrophysics Data System (ADS)

Kim, Hyungjin; Hwang, Sungmin; Park, Jungjin; Park, Byung-Gook

2017-10-01

Brain-inspired neuromorphic systems have attracted much attention as new computing paradigms for power-efficient computation. Here, we report a silicon synaptic transistor with two electrically independent gates to realize a hardware-based neural network system without any switching components. The spike-timing dependent plasticity characteristics of the synaptic devices are measured and analyzed. With the help of the device model based on the measured data, the pattern recognition capability of the hardware-based spiking neural network systems is demonstrated using the modified national institute of standards and technology handwritten dataset. By comparing systems with and without inhibitory synapse part, it is confirmed that the inhibitory synapse part is an essential element in obtaining effective and high pattern classification capability.

Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system.

PubMed

Kim, Hyungjin; Hwang, Sungmin; Park, Jungjin; Park, Byung-Gook

2017-10-06

Brain-inspired neuromorphic systems have attracted much attention as new computing paradigms for power-efficient computation. Here, we report a silicon synaptic transistor with two electrically independent gates to realize a hardware-based neural network system without any switching components. The spike-timing dependent plasticity characteristics of the synaptic devices are measured and analyzed. With the help of the device model based on the measured data, the pattern recognition capability of the hardware-based spiking neural network systems is demonstrated using the modified national institute of standards and technology handwritten dataset. By comparing systems with and without inhibitory synapse part, it is confirmed that the inhibitory synapse part is an essential element in obtaining effective and high pattern classification capability.

The evolution of cost-efficiency in neural networks during recovery from traumatic brain injury.

PubMed

Roy, Arnab; Bernier, Rachel A; Wang, Jianli; Benson, Monica; French, Jerry J; Good, David C; Hillary, Frank G

2017-01-01

A somewhat perplexing finding in the systems neuroscience has been the observation that physical injury to neural systems may result in enhanced functional connectivity (i.e., hyperconnectivity) relative to the typical network response. The consequences of local or global enhancement of functional connectivity remain uncertain and this is particularly true for the overall metabolic cost of the network. We examine the hyperconnectivity hypothesis in a sample of 14 individuals with TBI with data collected at approximately 3, 6, and 12 months following moderate and severe TBI. As anticipated, individuals with TBI showed increased network strength and cost early after injury, but by one-year post injury hyperconnectivity was more circumscribed to frontal DMN and temporal-parietal attentional control regions. Cost in these subregions was a significant predictor of cognitive performance. Cost-efficiency analysis in the Power 264 data parcellation suggested that at 6 months post injury the network requires higher cost connections to achieve high efficiency as compared to the network 12 months post injury. These results demonstrate that networks self-organize to re-establish connectivity while balancing cost-efficiency trade-offs.

The evolution of cost-efficiency in neural networks during recovery from traumatic brain injury

PubMed Central

Roy, Arnab; Bernier, Rachel A.; Wang, Jianli; Benson, Monica; French, Jerry J.; Good, David C.; Hillary, Frank G.

2017-01-01

A somewhat perplexing finding in the systems neuroscience has been the observation that physical injury to neural systems may result in enhanced functional connectivity (i.e., hyperconnectivity) relative to the typical network response. The consequences of local or global enhancement of functional connectivity remain uncertain and this is particularly true for the overall metabolic cost of the network. We examine the hyperconnectivity hypothesis in a sample of 14 individuals with TBI with data collected at approximately 3, 6, and 12 months following moderate and severe TBI. As anticipated, individuals with TBI showed increased network strength and cost early after injury, but by one-year post injury hyperconnectivity was more circumscribed to frontal DMN and temporal-parietal attentional control regions. Cost in these subregions was a significant predictor of cognitive performance. Cost-efficiency analysis in the Power 264 data parcellation suggested that at 6 months post injury the network requires higher cost connections to achieve high efficiency as compared to the network 12 months post injury. These results demonstrate that networks self-organize to re-establish connectivity while balancing cost-efficiency trade-offs. PMID:28422992

Experimental evaluation of heat transfer efficiency of nanofluid in a double pipe heat exchanger and prediction of experimental results using artificial neural networks

NASA Astrophysics Data System (ADS)

Maddah, Heydar; Ghasemi, Nahid

2017-12-01

In this study, heat transfer efficiency of water and iron oxide nanofluid in a double pipe heat exchanger equipped with a typical twisted tape is experimentally investigated and impacts of the concentration of nanofluid and twisted tape on the heat transfer efficiency are also studied. Experiments were conducted under the laminar and turbulent flow for Reynolds numbers in the range of 1000 to 6000 and the concentration of nanofluid was 0.01, 0.02 and 0.03 wt%. In order to model and predict the heat transfer efficiency, an artificial neural network was used. The temperature of the hot fluid (nanofluid), the temperature of the cold fluid (water), mass flow rate of hot fluid (nanofluid), mass flow rate of cold fluid (water), the concentration of nanofluid and twist ratio are input data in artificial neural network and heat transfer is output or target. Heat transfer efficiency in the presence of 0.03 wt% nanofluid increases by 30% while using both the 0.03 wt% nanofluid and twisted tape with twist ratio 2 increases the heat transfer efficiency by 60%. Implementation of various structures of neural network with different number of neurons in the middle layer showed that 1-10-6 arrangement with the correlation coefficient 0.99181 and normal root mean square error 0.001621 is suggested as a desirable arrangement. The above structure has been successful in predicting 72% to 97%of variation in heat transfer efficiency characteristics based on the independent variables changes. In total, comparing the predicted results in this study with other studies and also the statistical measures shows the efficiency of artificial neural network.

Prediction of Aerodynamic Coefficient using Genetic Algorithm Optimized Neural Network for Sparse Data

NASA Technical Reports Server (NTRS)

Rajkumar, T.; Bardina, Jorge; Clancy, Daniel (Technical Monitor)

2002-01-01

Wind tunnels use scale models to characterize aerodynamic coefficients, Wind tunnel testing can be slow and costly due to high personnel overhead and intensive power utilization. Although manual curve fitting can be done, it is highly efficient to use a neural network to define the complex relationship between variables. Numerical simulation of complex vehicles on the wide range of conditions required for flight simulation requires static and dynamic data. Static data at low Mach numbers and angles of attack may be obtained with simpler Euler codes. Static data of stalled vehicles where zones of flow separation are usually present at higher angles of attack require Navier-Stokes simulations which are costly due to the large processing time required to attain convergence. Preliminary dynamic data may be obtained with simpler methods based on correlations and vortex methods; however, accurate prediction of the dynamic coefficients requires complex and costly numerical simulations. A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation I'S presented using a neural network. The training data for the neural network are derived from numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as functions of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation. Genetic Algorithms (GA) are used to optimize a previously built Artificial Neural Network (ANN) that reliably predicts aerodynamic coefficients. Results indicate that the GA provided an efficient method of optimizing the ANN model to predict aerodynamic coefficients. The reliability of the ANN using the GA includes prediction of aerodynamic coefficients to an accuracy of 110% . In our problem, we would like to get an optimized neural network architecture and minimum data set. This has been accomplished within 500 training cycles of a neural network. After removing training pairs (outliers), the GA has produced much better results. The neural network constructed is a feed forward neural network with a back propagation learning mechanism. The main goal has been to free the network design process from constraints of human biases, and to discover better forms of neural network architectures. The automation of the network architecture search by genetic algorithms seems to have been the best way to achieve this goal.

Using high-throughput barcode sequencing to efficiently map connectomes.

PubMed

Peikon, Ian D; Kebschull, Justus M; Vagin, Vasily V; Ravens, Diana I; Sun, Yu-Chi; Brouzes, Eric; Corrêa, Ivan R; Bressan, Dario; Zador, Anthony M

2017-07-07

The function of a neural circuit is determined by the details of its synaptic connections. At present, the only available method for determining a neural wiring diagram with single synapse precision-a 'connectome'-is based on imaging methods that are slow, labor-intensive and expensive. Here, we present SYNseq, a method for converting the connectome into a form that can exploit the speed and low cost of modern high-throughput DNA sequencing. In SYNseq, each neuron is labeled with a unique random nucleotide sequence-an RNA 'barcode'-which is targeted to the synapse using engineered proteins. Barcodes in pre- and postsynaptic neurons are then associated through protein-protein crosslinking across the synapse, extracted from the tissue, and joined into a form suitable for sequencing. Although our failure to develop an efficient barcode joining scheme precludes the widespread application of this approach, we expect that with further development SYNseq will enable tracing of complex circuits at high speed and low cost. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Efficient Decoding With Steady-State Kalman Filter in Neural Interface Systems

PubMed Central

Malik, Wasim Q.; Truccolo, Wilson; Brown, Emery N.; Hochberg, Leigh R.

2011-01-01

The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics. We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5 ± 0.5 s (mean ± s.d.). The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25 ± 3 single units by a factor of 7.0 ± 0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems. PMID:21078582

Precise Spatiotemporal Control of Optogenetic Activation Using an Acousto-Optic Device

PubMed Central

Guo, Yanmeng; Song, Peipei; Zhang, Xiaohui; Zeng, Shaoqun; Wang, Zuoren

2011-01-01

Light activation and inactivation of neurons by optogenetic techniques has emerged as an important tool for studying neural circuit function. To achieve a high resolution, new methods are being developed to selectively manipulate the activity of individual neurons. Here, we report that the combination of an acousto-optic device (AOD) and single-photon laser was used to achieve rapid and precise spatiotemporal control of light stimulation at multiple points in a neural circuit with millisecond time resolution. The performance of this system in activating ChIEF expressed on HEK 293 cells as well as cultured neurons was first evaluated, and the laser stimulation patterns were optimized. Next, the spatiotemporally selective manipulation of multiple neurons was achieved in a precise manner. Finally, we demonstrated the versatility of this high-resolution method in dissecting neural circuits both in the mouse cortical slice and the Drosophila brain in vivo. Taken together, our results show that the combination of AOD-assisted laser stimulation and optogenetic tools provides a flexible solution for manipulating neuronal activity at high efficiency and with high temporal precision. PMID:22174813

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

PubMed

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.

Modulation Depth Estimation and Variable Selection in State-Space Models for Neural Interfaces

PubMed Central

Hochberg, Leigh R.; Donoghue, John P.; Brown, Emery N.

2015-01-01

Rapid developments in neural interface technology are making it possible to record increasingly large signal sets of neural activity. Various factors such as asymmetrical information distribution and across-channel redundancy may, however, limit the benefit of high-dimensional signal sets, and the increased computational complexity may not yield corresponding improvement in system performance. High-dimensional system models may also lead to overfitting and lack of generalizability. To address these issues, we present a generalized modulation depth measure using the state-space framework that quantifies the tuning of a neural signal channel to relevant behavioral covariates. For a dynamical system, we develop computationally efficient procedures for estimating modulation depth from multivariate data. We show that this measure can be used to rank neural signals and select an optimal channel subset for inclusion in the neural decoding algorithm. We present a scheme for choosing the optimal subset based on model order selection criteria. We apply this method to neuronal ensemble spike-rate decoding in neural interfaces, using our framework to relate motor cortical activity with intended movement kinematics. With offline analysis of intracortical motor imagery data obtained from individuals with tetraplegia using the BrainGate neural interface, we demonstrate that our variable selection scheme is useful for identifying and ranking the most information-rich neural signals. We demonstrate that our approach offers several orders of magnitude lower complexity but virtually identical decoding performance compared to greedy search and other selection schemes. Our statistical analysis shows that the modulation depth of human motor cortical single-unit signals is well characterized by the generalized Pareto distribution. Our variable selection scheme has wide applicability in problems involving multisensor signal modeling and estimation in biomedical engineering systems. PMID:25265627

Adult Palatum as a Novel Source of Neural Crest-Related Stem Cells

PubMed Central

Widera, Darius; Zander, Christin; Heidbreder, Meike; Kasperek, Yvonne; Noll, Thomas; Seitz, Oliver; Saldamli, Belma; Sudhoff, Holger; Sader, Robert; Kaltschmidt, Christian; Kaltschmidt, Barbara

2009-01-01

Somatic neural and neural crest stem cells are promising sources for cellular therapy of several neurodegenerative diseases. However, because of practical considerations such as inadequate accessibility of the source material, the application of neural crest stem cells is strictly limited. The secondary palate is a highly regenerative and heavily innervated tissue, which develops embryonically under direct contribution of neural crest cells. Here, we describe for the first time the presence of nestin-positive neural crest-related stem cells within Meissner corpuscles and Merkel cell-neurite complexes located in the hard palate of adult Wistar rats. After isolation, palatal neural crest-related stem cells (pNC-SCs) were cultivated in the presence of epidermal growth factor and fibroblast growth factor under serum-free conditions, resulting in large amounts of neurospheres. We used immunocytochemical techniques and reverse transcriptase-polymerase chain reaction to assess the expression profile of pNC-SCs. In addition to the expression of neural crest stem cell markers such as Nestin, Sox2, and p75, we detected the expression of Klf4, Oct4, and c-Myc. pNC-SCs differentiated efficiently into neuronal and glial cells. Finally, we investigated the potential expression of stemness markers within the human palate. We identified expression of stem cell markers nestin and CD133 and the transcription factors needed for reprogramming of somatic cells into pluripotent cells: Sox2, Oct4, Klf4, and c-Myc. These data show that cells isolated from palatal rugae form neurospheres, are highly plastic, and express neural crest stem cell markers. In addition, pNC-SCs may have the ability to differentiate into functional neurons and glial cells, serving as a starting point for therapeutic studies. Stem Cells 2009;27:1899–1910 PMID:19544446

WNT/β-catenin signaling mediates human neural crest induction via a pre-neural border intermediate.

PubMed

Leung, Alan W; Murdoch, Barbara; Salem, Ahmed F; Prasad, Maneeshi S; Gomez, Gustavo A; García-Castro, Martín I

2016-02-01

Neural crest (NC) cells arise early in vertebrate development, migrate extensively and contribute to a diverse array of ectodermal and mesenchymal derivatives. Previous models of NC formation suggested derivation from neuralized ectoderm, via meso-ectodermal, or neural-non-neural ectoderm interactions. Recent studies using bird and amphibian embryos suggest an earlier origin of NC, independent of neural and mesodermal tissues. Here, we set out to generate a model in which to decipher signaling and tissue interactions involved in human NC induction. Our novel human embryonic stem cell (ESC)-based model yields high proportions of multipotent NC cells (expressing SOX10, PAX7 and TFAP2A) in 5 days. We demonstrate a crucial role for WNT/β-catenin signaling in launching NC development, while blocking placodal and surface ectoderm fates. We provide evidence of the delicate temporal effects of BMP and FGF signaling, and find that NC development is separable from neural and/or mesodermal contributions. We further substantiate the notion of a neural-independent origin of NC through PAX6 expression and knockdown studies. Finally, we identify a novel pre-neural border state characterized by early WNT/β-catenin signaling targets that displays distinct responses to BMP and FGF signaling from the traditional neural border genes. In summary, our work provides a fast and efficient protocol for human NC differentiation under signaling constraints similar to those identified in vivo in model organisms, and strengthens a framework for neural crest ontogeny that is separable from neural and mesodermal fates. © 2016. Published by The Company of Biologists Ltd.

1/f neural noise and electrophysiological indices of contextual prediction in aging.

PubMed

Dave, S; Brothers, T A; Swaab, T Y

2018-07-15

Prediction of upcoming words during reading has been suggested to enhance the efficiency of discourse processing. Emerging models have postulated that predictive mechanisms require synchronous firing of neural networks, but to date, this relationship has been investigated primarily through oscillatory activity in narrow frequency bands. A recently-developed measure proposed to reflect broadband neural activity - and thereby synchronous neuronal firing - is 1/f neural noise extracted from EEG spectral power. Previous research has indicated that this measure of 1/f neural noise changes across the lifespan, and these neural changes predict age-related behavioral impairments in visual working memory. Using a cross-sectional sample of young and older adults, we examined age-related changes in 1/f neural noise and whether this measure predicted ERP correlates of successful lexical prediction during discourse comprehension. 1/f neural noise across two different language tasks revealed high within-subject correlations, indicating that this measure can provide a reliable index of individualized patterns of neural activation. In addition to age, 1/f noise was a significant predictor of N400 effects of successful lexical prediction; however, noise did not mediate age-related declines in other ERP effects. We discuss broader implications of these findings for theories of predictive processing, as well as potential applications of 1/f noise across research populations. Copyright © 2018 Elsevier B.V. All rights reserved.

Design of artificial neural networks using a genetic algorithm to predict collection efficiency in venturi scrubbers.

PubMed

Taheri, Mahboobeh; Mohebbi, Ali

2008-08-30

In this study, a new approach for the auto-design of neural networks, based on a genetic algorithm (GA), has been used to predict collection efficiency in venturi scrubbers. The experimental input data, including particle diameter, throat gas velocity, liquid to gas flow rate ratio, throat hydraulic diameter, pressure drop across the venturi scrubber and collection efficiency as an output, have been used to create a GA-artificial neural network (ANN) model. The testing results from the model are in good agreement with the experimental data. Comparison of the results of the GA optimized ANN model with the results from the trial-and-error calibrated ANN model indicates that the GA-ANN model is more efficient. Finally, the effects of operating parameters such as liquid to gas flow rate ratio, throat gas velocity, and particle diameter on collection efficiency were determined.

Can adult neural stem cells create new brains? Plasticity in the adult mammalian neurogenic niches: realities and expectations in the era of regenerative biology.

PubMed

Kazanis, Ilias

2012-02-01

Since the first experimental reports showing the persistence of neurogenic activity in the adult mammalian brain, this field of neurosciences has expanded significantly. It is now widely accepted that neural stem and precursor cells survive during adulthood and are able to respond to various endogenous and exogenous cues by altering their proliferation and differentiation activity. Nevertheless, the pathway to therapeutic applications still seems to be long. This review attempts to summarize and revisit the available data regarding the plasticity potential of adult neural stem cells and of their normal microenvironment, the neurogenic niche. Recent data have demonstrated that adult neural stem cells retain a high level of pluripotency and that adult neurogenic systems can switch the balance between neurogenesis and gliogenesis and can generate a range of cell types with an efficiency that was not initially expected. Moreover, adult neural stem and precursor cells seem to be able to self-regulate their interaction with the microenvironment and even to contribute to its synthesis, altogether revealing a high level of plasticity potential. The next important step will be to elucidate the factors that limit this plasticity in vivo, and such a restrictive role for the microenvironment is discussed in more details.

Force Field for Water Based on Neural Network.

PubMed

Wang, Hao; Yang, Weitao

2018-05-18

We developed a novel neural network based force field for water based on training with high level ab initio theory. The force field was built based on electrostatically embedded many-body expansion method truncated at binary interactions. Many-body expansion method is a common strategy to partition the total Hamiltonian of large systems into a hierarchy of few-body terms. Neural networks were trained to represent electrostatically embedded one-body and two-body interactions, which require as input only one and two water molecule calculations at the level of ab initio electronic structure method CCSD/aug-cc-pVDZ embedded in the molecular mechanics water environment, making it efficient as a general force field construction approach. Structural and dynamic properties of liquid water calculated with our force field show good agreement with experimental results. We constructed two sets of neural network based force fields: non-polarizable and polarizable force fields. Simulation results show that the non-polarizable force field using fixed TIP3P charges has already behaved well, since polarization effects and many-body effects are implicitly included due to the electrostatic embedding scheme. Our results demonstrate that the electrostatically embedded many-body expansion combined with neural network provides a promising and systematic way to build the next generation force fields at high accuracy and low computational costs, especially for large systems.

Discrete-time neural network for fast solving large linear L1 estimation problems and its application to image restoration.

PubMed

Xia, Youshen; Sun, Changyin; Zheng, Wei Xing

2012-05-01

There is growing interest in solving linear L1 estimation problems for sparsity of the solution and robustness against non-Gaussian noise. This paper proposes a discrete-time neural network which can calculate large linear L1 estimation problems fast. The proposed neural network has a fixed computational step length and is proved to be globally convergent to an optimal solution. Then, the proposed neural network is efficiently applied to image restoration. Numerical results show that the proposed neural network is not only efficient in solving degenerate problems resulting from the nonunique solutions of the linear L1 estimation problems but also needs much less computational time than the related algorithms in solving both linear L1 estimation and image restoration problems.

Character recognition from trajectory by recurrent spiking neural networks.

PubMed

Jiangrong Shen; Kang Lin; Yueming Wang; Gang Pan

2017-07-01

Spiking neural networks are biologically plausible and power-efficient on neuromorphic hardware, while recurrent neural networks have been proven to be efficient on time series data. However, how to use the recurrent property to improve the performance of spiking neural networks is still a problem. This paper proposes a recurrent spiking neural network for character recognition using trajectories. In the network, a new encoding method is designed, in which varying time ranges of input streams are used in different recurrent layers. This is able to improve the generalization ability of our model compared with general encoding methods. The experiments are conducted on four groups of the character data set from University of Edinburgh. The results show that our method can achieve a higher average recognition accuracy than existing methods.

Memory and neural networks on the basis of color centers in solids.

PubMed

Winnacker, Albrecht; Osvet, Andres

2009-11-01

Optical data recording is one of the most widely used and efficient systems of memory in the non-living world. The application of color centers in this context offers not only systems of high speed in writing and read-out due to a high degree of parallelism in data handling but also a possibility to set up models of neural networks. In this way, systems with a high potential for image processing, pattern recognition and logical operations can be constructed. A limitation to storage density is given by the diffraction limit of optical data recording. It is shown that this limitation can at least in principle be overcome by the principle of spectral hole burning, which results in systems of storage capacities close to the human brain system.

Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce

PubMed Central

Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan

2016-01-01

A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network’s initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data. PMID:27304987

An efficient optical architecture for sparsely connected neural networks

NASA Technical Reports Server (NTRS)

Hine, Butler P., III; Downie, John D.; Reid, Max B.

1990-01-01

An architecture for general-purpose optical neural network processor is presented in which the interconnections and weights are formed by directing coherent beams holographically, thereby making use of the space-bandwidth products of the recording medium for sparsely interconnected networks more efficiently that the commonly used vector-matrix multiplier, since all of the hologram area is in use. An investigation is made of the use of computer-generated holograms recorded on such updatable media as thermoplastic materials, in order to define the interconnections and weights of a neural network processor; attention is given to limits on interconnection densities, diffraction efficiencies, and weighing accuracies possible with such an updatable thin film holographic device.

Highly Expandable Human iPS Cell-Derived Neural Progenitor Cells (NPC) and Neurons for Central Nervous System Disease Modeling and High-Throughput Screening.

PubMed

Cheng, Chialin; Fass, Daniel M; Folz-Donahue, Kat; MacDonald, Marcy E; Haggarty, Stephen J

2017-01-11

Reprogramming of human somatic cells into induced pluripotent stem (iPS) cells has greatly expanded the set of research tools available to investigate the molecular and cellular mechanisms underlying central nervous system (CNS) disorders. Realizing the promise of iPS cell technology for the identification of novel therapeutic targets and for high-throughput drug screening requires implementation of methods for the large-scale production of defined CNS cell types. Here we describe a protocol for generating stable, highly expandable, iPS cell-derived CNS neural progenitor cells (NPC) using multi-dimensional fluorescence activated cell sorting (FACS) to purify NPC defined by cell surface markers. In addition, we describe a rapid, efficient, and reproducible method for generating excitatory cortical-like neurons from these NPC through inducible expression of the pro-neural transcription factor Neurogenin 2 (iNgn2-NPC). Finally, we describe methodology for the use of iNgn2-NPC for probing human neuroplasticity and mechanisms underlying CNS disorders using high-content, single-cell-level automated microscopy assays. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

PubMed Central

Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process. PMID:26977450

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network.

PubMed

Falat, Lukas; Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

New baseline correction algorithm for text-line recognition with bidirectional recurrent neural networks

NASA Astrophysics Data System (ADS)

Morillot, Olivier; Likforman-Sulem, Laurence; Grosicki, Emmanuèle

2013-04-01

Many preprocessing techniques have been proposed for isolated word recognition. However, recently, recognition systems have dealt with text blocks and their compound text lines. In this paper, we propose a new preprocessing approach to efficiently correct baseline skew and fluctuations. Our approach is based on a sliding window within which the vertical position of the baseline is estimated. Segmentation of text lines into subparts is, thus, avoided. Experiments conducted on a large publicly available database (Rimes), with a BLSTM (bidirectional long short-term memory) recurrent neural network recognition system, show that our baseline correction approach highly improves performance.

Implantable liquid metal-based flexible neural microelectrode array and its application in recovering animal locomotion functions

NASA Astrophysics Data System (ADS)

Guo, Rui; Liu, Jing

2017-10-01

With significant advantages in rapidly restoring the nerve function, electrical stimulation of nervous tissue is a crucial treatment of peripheral nerve injuries leading to common movement disorder. However, the currently available stimulating electrodes generally based on rigid conductive materials would cause a potential mechanical mismatch with soft neural tissues which thus reduces long-term effects of electrical stimulation. Here, we proposed and fabricated a flexible neural microelectrode array system based on the liquid metal GaIn alloy (75.5% Ga and 24.5% In by weight) and via printing approach. Such an alloy with a unique low melting point (10.35 °C) owns excellent electrical conductivity and high compliance, which are beneficial to serve as implantable flexible neural electrodes. The flexible neural microelectrode array embeds four liquid metal electrodes and stretchable interconnects in a PDMS membrane (500 µm in thickness) that possess a lower elastic modulus (1.055 MPa), which is similar to neural tissues with elastic moduli in the 0.1-1.5 MPa range. The electrical experiments indicate that the liquid metal interconnects could sustain over 7000 mechanical stretch cycles with resistance approximately staying at 4 Ω. Over the conceptual experiments on animal sciatic nerve electrical stimulation, the dead bullfrog implanted with flexible neural microelectrode array could even rhythmically contract and move its lower limbs under the electrical stimulations from the implant. This demonstrates a highly efficient way for quickly recovering biological nerve functions. Further, the good biocompatibility of the liquid metal material was justified via a series of biological experiments. This liquid metal modality for neural stimulation is expected to play important roles as biologic electrodes to overcome the fundamental mismatch in mechanics between biological tissues and electronic devices in the coming time.

Neural nets on the MPP

NASA Technical Reports Server (NTRS)

Hastings, Harold M.; Waner, Stefan

1987-01-01

The Massively Parallel Processor (MPP) is an ideal machine for computer experiments with simulated neural nets as well as more general cellular automata. Experiments using the MPP with a formal model neural network are described. The results on problem mapping and computational efficiency apply equally well to the neural nets of Hopfield, Hinton et al., and Geman and Geman.

Human adenovirus serotypes 4p and 11p are efficiently expressed in cell lines of neural tumour origin.

PubMed

Skog, Johan; Mei, Ya-Fang; Wadell, Göran

2002-06-01

Most currently used adenovirus vectors are based upon adenovirus serotypes 2 and 5 (Ad2 and Ad5), which have limited efficiencies for gene transfer to human neural cells. Both serotypes bind to the known adenovirus receptor, CAR (coxsackievirus and adenovirus receptor), and have restricted cell tropism. The purpose of this study was to find vector candidates that are superior to Ad5 in infecting human neural tumours. Using flow cytometry, the vector candidates Ad4p, Ad11p and Ad17p were compared to the commonly used adenovirus vector Ad5v for their binding capacity to neural cell lines derived from glioblastoma, medulloblastoma and neuroblastoma cell lines. The production of viral structural proteins and the CAR-binding properties of the different serotypes were also assessed in these cells. Computer-based models of the fibre knobs of Ad4p and Ad17 were created based upon the crystallized fibre knob structure of adenoviruses and analysed for putative receptor-interacting regions that differed from the fibre knob of Ad5. The non CAR-binding vector candidate Ad11p showed clearly the best binding capacity to all of the neural cell lines, binding more than 90% of cells of all of the neural cell lines tested, in contrast to 20% or less for the commonly used vector Ad5v. Ad4p and Ad11p were also internalized and produced viral proteins more successfully than Ad5. Ad4p showed a low binding ability but a very efficient capacity for infection in cell culture. Ad17p virions neither bound or efficiently infected any of the neural cell lines studied.

A High-Performance Neural Prosthesis Incorporating Discrete State Selection With Hidden Markov Models.

PubMed

Kao, Jonathan C; Nuyujukian, Paul; Ryu, Stephen I; Shenoy, Krishna V

2017-04-01

Communication neural prostheses aim to restore efficient communication to people with motor neurological injury or disease by decoding neural activity into control signals. These control signals are both analog (e.g., the velocity of a computer mouse) and discrete (e.g., clicking an icon with a computer mouse) in nature. Effective, high-performing, and intuitive-to-use communication prostheses should be capable of decoding both analog and discrete state variables seamlessly. However, to date, the highest-performing autonomous communication prostheses rely on precise analog decoding and typically do not incorporate high-performance discrete decoding. In this report, we incorporated a hidden Markov model (HMM) into an intracortical communication prosthesis to enable accurate and fast discrete state decoding in parallel with analog decoding. In closed-loop experiments with nonhuman primates implanted with multielectrode arrays, we demonstrate that incorporating an HMM into a neural prosthesis can increase state-of-the-art achieved bitrate by 13.9% and 4.2% in two monkeys ( ). We found that the transition model of the HMM is critical to achieving this performance increase. Further, we found that using an HMM resulted in the highest achieved peak performance we have ever observed for these monkeys, achieving peak bitrates of 6.5, 5.7, and 4.7 bps in Monkeys J, R, and L, respectively. Finally, we found that this neural prosthesis was robustly controllable for the duration of entire experimental sessions. These results demonstrate that high-performance discrete decoding can be beneficially combined with analog decoding to achieve new state-of-the-art levels of performance.

Neural network fusion capabilities for efficient implementation of tracking algorithms

NASA Astrophysics Data System (ADS)

Sundareshan, Malur K.; Amoozegar, Farid

1997-03-01

The ability to efficiently fuse information of different forms to facilitate intelligent decision making is one of the major capabilities of trained multilayer neural networks that is now being recognized. While development of innovative adaptive control algorithms for nonlinear dynamical plants that attempt to exploit these capabilities seems to be more popular, a corresponding development of nonlinear estimation algorithms using these approaches, particularly for application in target surveillance and guidance operations, has not received similar attention. We describe the capabilities and functionality of neural network algorithms for data fusion and implementation of tracking filters. To discuss details and to serve as a vehicle for quantitative performance evaluations, the illustrative case of estimating the position and velocity of surveillance targets is considered. Efficient target- tracking algorithms that can utilize data from a host of sensing modalities and are capable of reliably tracking even uncooperative targets executing fast and complex maneuvers are of interest in a number of applications. The primary motivation for employing neural networks in these applications comes from the efficiency with which more features extracted from different sensor measurements can be utilized as inputs for estimating target maneuvers. A system architecture that efficiently integrates the fusion capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described. The innovation lies in the way the fusion of multisensor data is accomplished to facilitate improved estimation without increasing the computational complexity of the dynamical state estimator itself.

Localizing Tortoise Nests by Neural Networks.

PubMed

Barbuti, Roberto; Chessa, Stefano; Micheli, Alessio; Pucci, Rita

2016-01-01

The goal of this research is to recognize the nest digging activity of tortoises using a device mounted atop the tortoise carapace. The device classifies tortoise movements in order to discriminate between nest digging, and non-digging activity (specifically walking and eating). Accelerometer data was collected from devices attached to the carapace of a number of tortoises during their two-month nesting period. Our system uses an accelerometer and an activity recognition system (ARS) which is modularly structured using an artificial neural network and an output filter. For the purpose of experiment and comparison, and with the aim of minimizing the computational cost, the artificial neural network has been modelled according to three different architectures based on the input delay neural network (IDNN). We show that the ARS can achieve very high accuracy on segments of data sequences, with an extremely small neural network that can be embedded in programmable low power devices. Given that digging is typically a long activity (up to two hours), the application of ARS on data segments can be repeated over time to set up a reliable and efficient system, called Tortoise@, for digging activity recognition.

The application of artificial neural networks and support vector regression for simultaneous spectrophotometric determination of commercial eye drop contents

NASA Astrophysics Data System (ADS)

Valizadeh, Maryam; Sohrabi, Mahmoud Reza

2018-03-01

In the present study, artificial neural networks (ANNs) and support vector regression (SVR) as intelligent methods coupled with UV spectroscopy for simultaneous quantitative determination of Dorzolamide (DOR) and Timolol (TIM) in eye drop. Several synthetic mixtures were analyzed for validating the proposed methods. At first, neural network time series, which one type of network from the artificial neural network was employed and its efficiency was evaluated. Afterwards, the radial basis network was applied as another neural network. Results showed that the performance of this method is suitable for predicting. Finally, support vector regression was proposed to construct the Zilomole prediction model. Also, root mean square error (RMSE) and mean recovery (%) were calculated for SVR method. Moreover, the proposed methods were compared to the high-performance liquid chromatography (HPLC) as a reference method. One way analysis of variance (ANOVA) test at the 95% confidence level applied to the comparison results of suggested and reference methods that there were no significant differences between them. Also, the effect of interferences was investigated in spike solutions.

Towards multifocal ultrasonic neural stimulation: pattern generation algorithms

NASA Astrophysics Data System (ADS)

Hertzberg, Yoni; Naor, Omer; Volovick, Alexander; Shoham, Shy

2010-10-01

Focused ultrasound (FUS) waves directed onto neural structures have been shown to dynamically modulate neural activity and excitability, opening up a range of possible systems and applications where the non-invasiveness, safety, mm-range resolution and other characteristics of FUS are advantageous. As in other neuro-stimulation and modulation modalities, the highly distributed and parallel nature of neural systems and neural information processing call for the development of appropriately patterned stimulation strategies which could simultaneously address multiple sites in flexible patterns. Here, we study the generation of sparse multi-focal ultrasonic distributions using phase-only modulation in ultrasonic phased arrays. We analyse the relative performance of an existing algorithm for generating multifocal ultrasonic distributions and new algorithms that we adapt from the field of optical digital holography, and find that generally the weighted Gerchberg-Saxton algorithm leads to overall superior efficiency and uniformity in the focal spots, without significantly increasing the computational burden. By combining phased-array FUS and magnetic-resonance thermometry we experimentally demonstrate the simultaneous generation of tightly focused multifocal distributions in a tissue phantom, a first step towards patterned FUS neuro-modulation systems and devices.

Trade-off between Multiple Constraints Enables Simultaneous Formation of Modules and Hubs in Neural Systems

PubMed Central

Chen, Yuhan; Wang, Shengjun; Hilgetag, Claus C.; Zhou, Changsong

2013-01-01

The formation of the complex network architecture of neural systems is subject to multiple structural and functional constraints. Two obvious but apparently contradictory constraints are low wiring cost and high processing efficiency, characterized by short overall wiring length and a small average number of processing steps, respectively. Growing evidence shows that neural networks are results from a trade-off between physical cost and functional value of the topology. However, the relationship between these competing constraints and complex topology is not well understood quantitatively. We explored this relationship systematically by reconstructing two known neural networks, Macaque cortical connectivity and C. elegans neuronal connections, from combinatory optimization of wiring cost and processing efficiency constraints, using a control parameter , and comparing the reconstructed networks to the real networks. We found that in both neural systems, the reconstructed networks derived from the two constraints can reveal some important relations between the spatial layout of nodes and the topological connectivity, and match several properties of the real networks. The reconstructed and real networks had a similar modular organization in a broad range of , resulting from spatial clustering of network nodes. Hubs emerged due to the competition of the two constraints, and their positions were close to, and partly coincided, with the real hubs in a range of values. The degree of nodes was correlated with the density of nodes in their spatial neighborhood in both reconstructed and real networks. Generally, the rebuilt network matched a significant portion of real links, especially short-distant ones. These findings provide clear evidence to support the hypothesis of trade-off between multiple constraints on brain networks. The two constraints of wiring cost and processing efficiency, however, cannot explain all salient features in the real networks. The discrepancy suggests that there are further relevant factors that are not yet captured here. PMID:23505352

A novel neural-wavelet approach for process diagnostics and complex system modeling

NASA Astrophysics Data System (ADS)

Gao, Rong

Neural networks have been effective in several engineering applications because of their learning abilities and robustness. However certain shortcomings, such as slow convergence and local minima, are always associated with neural networks, especially neural networks applied to highly nonlinear and non-stationary problems. These problems can be effectively alleviated by integrating a new powerful tool, wavelets, into conventional neural networks. The multi-resolution analysis and feature localization capabilities of the wavelet transform offer neural networks new possibilities for learning. A neural wavelet network approach developed in this thesis enjoys fast convergence rate with little possibility to be caught at a local minimum. It combines the localization properties of wavelets with the learning abilities of neural networks. Two different testbeds are used for testing the efficiency of the new approach. The first is magnetic flowmeter-based process diagnostics: here we extend previous work, which has demonstrated that wavelet groups contain process information, to more general process diagnostics. A loop at Applied Intelligent Systems Lab (AISL) is used for collecting and analyzing data through the neural-wavelet approach. The research is important for thermal-hydraulic processes in nuclear and other engineering fields. The neural-wavelet approach developed is also tested with data from the electric power grid. More specifically, the neural-wavelet approach is used for performing short-term and mid-term prediction of power load demand. In addition, the feasibility of determining the type of load using the proposed neural wavelet approach is also examined. The notion of cross scale product has been developed as an expedient yet reliable discriminator of loads. Theoretical issues involved in the integration of wavelets and neural networks are discussed and future work outlined.

Dynamic neural networks based on-line identification and control of high performance motor drives

NASA Technical Reports Server (NTRS)

Rubaai, Ahmed; Kotaru, Raj

1995-01-01

In the automated and high-tech industries of the future, there wil be a need for high performance motor drives both in the low-power range and in the high-power range. To meet very straight demands of tracking and regulation in the two quadrants of operation, advanced control technologies are of a considerable interest and need to be developed. In response a dynamics learning control architecture is developed with simultaneous on-line identification and control. the feature of the proposed approach, to efficiently combine the dual task of system identification (learning) and adaptive control of nonlinear motor drives into a single operation is presented. This approach, therefore, not only adapts to uncertainties of the dynamic parameters of the motor drives but also learns about their inherent nonlinearities. In fact, most of the neural networks based adaptive control approaches in use have an identification phase entirely separate from the control phase. Because these approaches separate the identification and control modes, it is not possible to cope with dynamic changes in a controlled process. Extensive simulation studies have been conducted and good performance was observed. The robustness characteristics of neuro-controllers to perform efficiently in a noisy environment is also demonstrated. With this initial success, the principal investigator believes that the proposed approach with the suggested neural structure can be used successfully for the control of high performance motor drives. Two identification and control topologies based on the model reference adaptive control technique are used in this present analysis. No prior knowledge of load dynamics is assumed in either topology while the second topology also assumes no knowledge of the motor parameters.

Linear matrix inequality approach to exponential synchronization of a class of chaotic neural networks with time-varying delays

NASA Astrophysics Data System (ADS)

Wu, Wei; Cui, Bao-Tong

2007-07-01

In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented. This class of chaotic neural networks covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks. The obtained criteria are expressed in terms of linear matrix inequalities, thus they can be efficiently verified. A comparison between our results and the previous results shows that our results are less restrictive.

Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks.

PubMed

Khan, Taimoor; De, Asok

2014-01-01

In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results.

Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks

PubMed Central

De, Asok

2014-01-01

In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results. PMID:27382616

USC orthogonal multiprocessor for image processing with neural networks

NASA Astrophysics Data System (ADS)

Hwang, Kai; Panda, Dhabaleswar K.; Haddadi, Navid

1990-07-01

This paper presents the architectural features and imaging applications of the Orthogonal MultiProcessor (OMP) system, which is under construction at the University of Southern California with research funding from NSF and assistance from several industrial partners. The prototype OMP is being built with 16 Intel i860 RISC microprocessors and 256 parallel memory modules using custom-designed spanning buses, which are 2-D interleaved and orthogonally accessed without conflicts. The 16-processor OMP prototype is targeted to achieve 430 MIPS and 600 Mflops, which have been verified by simulation experiments based on the design parameters used. The prototype OMP machine will be initially applied for image processing, computer vision, and neural network simulation applications. We summarize important vision and imaging algorithms that can be restructured with neural network models. These algorithms can efficiently run on the OMP hardware with linear speedup. The ultimate goal is to develop a high-performance Visual Computer (Viscom) for integrated low- and high-level image processing and vision tasks.

An integrated modelling framework for neural circuits with multiple neuromodulators.

PubMed

Joshi, Alok; Youssofzadeh, Vahab; Vemana, Vinith; McGinnity, T M; Prasad, Girijesh; Wong-Lin, KongFatt

2017-01-01

Neuromodulators are endogenous neurochemicals that regulate biophysical and biochemical processes, which control brain function and behaviour, and are often the targets of neuropharmacological drugs. Neuromodulator effects are generally complex partly owing to the involvement of broad innervation, co-release of neuromodulators, complex intra- and extrasynaptic mechanism, existence of multiple receptor subtypes and high interconnectivity within the brain. In this work, we propose an efficient yet sufficiently realistic computational neural modelling framework to study some of these complex behaviours. Specifically, we propose a novel dynamical neural circuit model that integrates the effective neuromodulator-induced currents based on various experimental data (e.g. electrophysiology, neuropharmacology and voltammetry). The model can incorporate multiple interacting brain regions, including neuromodulator sources, simulate efficiently and easily extendable to large-scale brain models, e.g. for neuroimaging purposes. As an example, we model a network of mutually interacting neural populations in the lateral hypothalamus, dorsal raphe nucleus and locus coeruleus, which are major sources of neuromodulator orexin/hypocretin, serotonin and norepinephrine/noradrenaline, respectively, and which play significant roles in regulating many physiological functions. We demonstrate that such a model can provide predictions of systemic drug effects of the popular antidepressants (e.g. reuptake inhibitors), neuromodulator antagonists or their combinations. Finally, we developed user-friendly graphical user interface software for model simulation and visualization for both fundamental sciences and pharmacological studies. © 2017 The Authors.

An integrated modelling framework for neural circuits with multiple neuromodulators

PubMed Central

Vemana, Vinith

2017-01-01

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

A 2.4 GHz ULP reconfigurable asymmetric transceiver for single-chip wireless neural recording IC.

PubMed

Tan, Jun; Liew, Wen-Sin; Heng, Chun-Huat; Lian, Yong

2014-08-01

This paper presents a 2.4 GHz ultra-low-power (ULP) reconfigurable asymmetric transceiver and demonstrates its application in wireless neural recording. Fabricated in 0.13 μm CMOS technology, the transceiver is optimized for sensor-gateway communications within a star-shaped network, and supports both the sensor and gateway operation modes. Binary phase-shift keying (BPSK) modulation with high data rate (DR) of 1 to 8 Mbps is used in the uplink from sensor to gateway, while on-off keying (OOK) modulation with low DR of 100 kbps is adopted in the downlink. A fully integrated Class-E PA with moderate output power has also been proposed and achieves power efficiency of 53%. To minimize area usage, inductor reuse is adopted between PA and LNA, and eliminates the need of lossy T/R switch in the RF signal path. When used as sensor, the transceiver with frequency locked phase-locked loop (PLL) achieves TX (BPSK) power efficiency of 28% @ 0 dBm output power, and RX (OOK) sensitivity of -80 dBm @ 100 kbps while consuming only 780 μW . When configured as gateway, the transceiver achieves sensitivity levels of -92, -84.5, and -77 dBm for 1, 5, and 8 Mbps BPSK, respectively. The transceiver is integrated with an 8-channel neural recording front-end, and neural signals from a rat are captured to verify the system functionality.

Higher Language Ability is Related to Angular Gyrus Activation Increase During Semantic Processing, Independent of Sentence Incongruency.

PubMed

Van Ettinger-Veenstra, Helene; McAllister, Anita; Lundberg, Peter; Karlsson, Thomas; Engström, Maria

2016-01-01

This study investigates the relation between individual language ability and neural semantic processing abilities. Our aim was to explore whether high-level language ability would correlate to decreased activation in language-specific regions or rather increased activation in supporting language regions during processing of sentences. Moreover, we were interested if observed neural activation patterns are modulated by semantic incongruency similarly to previously observed changes upon syntactic congruency modulation. We investigated 27 healthy adults with a sentence reading task-which tapped language comprehension and inference, and modulated sentence congruency-employing functional magnetic resonance imaging (fMRI). We assessed the relation between neural activation, congruency modulation, and test performance on a high-level language ability assessment with multiple regression analysis. Our results showed increased activation in the left-hemispheric angular gyrus extending to the temporal lobe related to high language ability. This effect was independent of semantic congruency, and no significant relation between language ability and incongruency modulation was observed. Furthermore, there was a significant increase of activation in the inferior frontal gyrus (IFG) bilaterally when the sentences were incongruent, indicating that processing incongruent sentences was more demanding than processing congruent sentences and required increased activation in language regions. The correlation of high-level language ability with increased rather than decreased activation in the left angular gyrus, a region specific for language processing, is opposed to what the neural efficiency hypothesis would predict. We can conclude that no evidence is found for an interaction between semantic congruency related brain activation and high-level language performance, even though the semantic incongruent condition shows to be more demanding and evoking more neural activation.

Higher Language Ability is Related to Angular Gyrus Activation Increase During Semantic Processing, Independent of Sentence Incongruency

PubMed Central

Van Ettinger-Veenstra, Helene; McAllister, Anita; Lundberg, Peter; Karlsson, Thomas; Engström, Maria

2016-01-01

This study investigates the relation between individual language ability and neural semantic processing abilities. Our aim was to explore whether high-level language ability would correlate to decreased activation in language-specific regions or rather increased activation in supporting language regions during processing of sentences. Moreover, we were interested if observed neural activation patterns are modulated by semantic incongruency similarly to previously observed changes upon syntactic congruency modulation. We investigated 27 healthy adults with a sentence reading task—which tapped language comprehension and inference, and modulated sentence congruency—employing functional magnetic resonance imaging (fMRI). We assessed the relation between neural activation, congruency modulation, and test performance on a high-level language ability assessment with multiple regression analysis. Our results showed increased activation in the left-hemispheric angular gyrus extending to the temporal lobe related to high language ability. This effect was independent of semantic congruency, and no significant relation between language ability and incongruency modulation was observed. Furthermore, there was a significant increase of activation in the inferior frontal gyrus (IFG) bilaterally when the sentences were incongruent, indicating that processing incongruent sentences was more demanding than processing congruent sentences and required increased activation in language regions. The correlation of high-level language ability with increased rather than decreased activation in the left angular gyrus, a region specific for language processing, is opposed to what the neural efficiency hypothesis would predict. We can conclude that no evidence is found for an interaction between semantic congruency related brain activation and high-level language performance, even though the semantic incongruent condition shows to be more demanding and evoking more neural activation. PMID:27014040

Effect of Heterogeneity on Decorrelation Mechanisms in Spiking Neural Networks: A Neuromorphic-Hardware Study

NASA Astrophysics Data System (ADS)

Pfeil, Thomas; Jordan, Jakob; Tetzlaff, Tom; Grübl, Andreas; Schemmel, Johannes; Diesmann, Markus; Meier, Karlheinz

2016-04-01

High-level brain function, such as memory, classification, or reasoning, can be realized by means of recurrent networks of simplified model neurons. Analog neuromorphic hardware constitutes a fast and energy-efficient substrate for the implementation of such neural computing architectures in technical applications and neuroscientific research. The functional performance of neural networks is often critically dependent on the level of correlations in the neural activity. In finite networks, correlations are typically inevitable due to shared presynaptic input. Recent theoretical studies have shown that inhibitory feedback, abundant in biological neural networks, can actively suppress these shared-input correlations and thereby enable neurons to fire nearly independently. For networks of spiking neurons, the decorrelating effect of inhibitory feedback has so far been explicitly demonstrated only for homogeneous networks of neurons with linear subthreshold dynamics. Theory, however, suggests that the effect is a general phenomenon, present in any system with sufficient inhibitory feedback, irrespective of the details of the network structure or the neuronal and synaptic properties. Here, we investigate the effect of network heterogeneity on correlations in sparse, random networks of inhibitory neurons with nonlinear, conductance-based synapses. Emulations of these networks on the analog neuromorphic-hardware system Spikey allow us to test the efficiency of decorrelation by inhibitory feedback in the presence of hardware-specific heterogeneities. The configurability of the hardware substrate enables us to modulate the extent of heterogeneity in a systematic manner. We selectively study the effects of shared input and recurrent connections on correlations in membrane potentials and spike trains. Our results confirm that shared-input correlations are actively suppressed by inhibitory feedback also in highly heterogeneous networks exhibiting broad, heavy-tailed firing-rate distributions. In line with former studies, cell heterogeneities reduce shared-input correlations. Overall, however, correlations in the recurrent system can increase with the level of heterogeneity as a consequence of diminished effective negative feedback.

Fish and chips: implementation of a neural network model into computer chips to maximize swimming efficiency in autonomous underwater vehicles.

PubMed

Blake, R W; Ng, H; Chan, K H S; Li, J

2008-09-01

Recent developments in the design and propulsion of biomimetic autonomous underwater vehicles (AUVs) have focused on boxfish as models (e.g. Deng and Avadhanula 2005 Biomimetic micro underwater vehicle with oscillating fin propulsion: system design and force measurement Proc. 2005 IEEE Int. Conf. Robot. Auto. (Barcelona, Spain) pp 3312-7). Whilst such vehicles have many potential advantages in operating in complex environments (e.g. high manoeuvrability and stability), limited battery life and payload capacity are likely functional disadvantages. Boxfish employ undulatory median and paired fins during routine swimming which are characterized by high hydromechanical Froude efficiencies (approximately 0.9) at low forward speeds. Current boxfish-inspired vehicles are propelled by a low aspect ratio, 'plate-like' caudal fin (ostraciiform tail) which can be shown to operate at a relatively low maximum Froude efficiency (approximately 0.5) and is mainly employed as a rudder for steering and in rapid swimming bouts (e.g. escape responses). Given this and the fact that bioinspired engineering designs are not obligated to wholly duplicate a biological model, computer chips were developed using a multilayer perception neural network model of undulatory fin propulsion in the knifefish Xenomystus nigri that would potentially allow an AUV to achieve high optimum values of propulsive efficiency at any given forward velocity, giving a minimum energy drain on the battery. We envisage that externally monitored information on flow velocity (sensory system) would be conveyed to the chips residing in the vehicle's control unit, which in turn would signal the locomotor unit to adopt kinematics (e.g. fin frequency, amplitude) associated with optimal propulsion efficiency. Power savings could protract vehicle operational life and/or provide more power to other functions (e.g. communications).

Modeling and optimization by particle swarm embedded neural network for adsorption of zinc (II) by palm kernel shell based activated carbon from aqueous environment.

PubMed

Karri, Rama Rao; Sahu, J N

2018-01-15

Zn (II) is one the common pollutant among heavy metals found in industrial effluents. Removal of pollutant from industrial effluents can be accomplished by various techniques, out of which adsorption was found to be an efficient method. Applications of adsorption limits itself due to high cost of adsorbent. In this regard, a low cost adsorbent produced from palm oil kernel shell based agricultural waste is examined for its efficiency to remove Zn (II) from waste water and aqueous solution. The influence of independent process variables like initial concentration, pH, residence time, activated carbon (AC) dosage and process temperature on the removal of Zn (II) by palm kernel shell based AC from batch adsorption process are studied systematically. Based on the design of experimental matrix, 50 experimental runs are performed with each process variable in the experimental range. The optimal values of process variables to achieve maximum removal efficiency is studied using response surface methodology (RSM) and artificial neural network (ANN) approaches. A quadratic model, which consists of first order and second order degree regressive model is developed using the analysis of variance and RSM - CCD framework. The particle swarm optimization which is a meta-heuristic optimization is embedded on the ANN architecture to optimize the search space of neural network. The optimized trained neural network well depicts the testing data and validation data with R 2 equal to 0.9106 and 0.9279 respectively. The outcomes indicates that the superiority of ANN-PSO based model predictions over the quadratic model predictions provided by RSM. Copyright © 2017 Elsevier Ltd. All rights reserved.

An adaptable neural-network model for recursive nonlinear traffic prediction and modeling of MPEG video sources.

PubMed

Doulamis, A D; Doulamis, N D; Kollias, S D

2003-01-01

Multimedia services and especially digital video is expected to be the major traffic component transmitted over communication networks [such as internet protocol (IP)-based networks]. For this reason, traffic characterization and modeling of such services are required for an efficient network operation. The generated models can be used as traffic rate predictors, during the network operation phase (online traffic modeling), or as video generators for estimating the network resources, during the network design phase (offline traffic modeling). In this paper, an adaptable neural-network architecture is proposed covering both cases. The scheme is based on an efficient recursive weight estimation algorithm, which adapts the network response to current conditions. In particular, the algorithm updates the network weights so that 1) the network output, after the adaptation, is approximately equal to current bit rates (current traffic statistics) and 2) a minimal degradation over the obtained network knowledge is provided. It can be shown that the proposed adaptable neural-network architecture simulates a recursive nonlinear autoregressive model (RNAR) similar to the notation used in the linear case. The algorithm presents low computational complexity and high efficiency in tracking traffic rates in contrast to conventional retraining schemes. Furthermore, for the problem of offline traffic modeling, a novel correlation mechanism is proposed for capturing the burstness of the actual MPEG video traffic. The performance of the model is evaluated using several real-life MPEG coded video sources of long duration and compared with other linear/nonlinear techniques used for both cases. The results indicate that the proposed adaptable neural-network architecture presents better performance than other examined techniques.

Ship detection in optical remote sensing images based on deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Yao, Yuan; Jiang, Zhiguo; Zhang, Haopeng; Zhao, Danpei; Cai, Bowen

2017-10-01

Automatic ship detection in optical remote sensing images has attracted wide attention for its broad applications. Major challenges for this task include the interference of cloud, wave, wake, and the high computational expenses. We propose a fast and robust ship detection algorithm to solve these issues. The framework for ship detection is designed based on deep convolutional neural networks (CNNs), which provide the accurate locations of ship targets in an efficient way. First, the deep CNN is designed to extract features. Then, a region proposal network (RPN) is applied to discriminate ship targets and regress the detection bounding boxes, in which the anchors are designed by intrinsic shape of ship targets. Experimental results on numerous panchromatic images demonstrate that, in comparison with other state-of-the-art ship detection methods, our method is more efficient and achieves higher detection accuracy and more precise bounding boxes in different complex backgrounds.

An Information Theoretic Characterisation of Auditory Encoding

PubMed Central

Overath, Tobias; Cusack, Rhodri; Kumar, Sukhbinder; von Kriegstein, Katharina; Warren, Jason D; Grube, Manon; Carlyon, Robert P; Griffiths, Timothy D

2007-01-01

The entropy metric derived from information theory provides a means to quantify the amount of information transmitted in acoustic streams like speech or music. By systematically varying the entropy of pitch sequences, we sought brain areas where neural activity and energetic demands increase as a function of entropy. Such a relationship is predicted to occur in an efficient encoding mechanism that uses less computational resource when less information is present in the signal: we specifically tested the hypothesis that such a relationship is present in the planum temporale (PT). In two convergent functional MRI studies, we demonstrated this relationship in PT for encoding, while furthermore showing that a distributed fronto-parietal network for retrieval of acoustic information is independent of entropy. The results establish PT as an efficient neural engine that demands less computational resource to encode redundant signals than those with high information content. PMID:17958472

Seismic signal auto-detecing from different features by using Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Huang, Y.; Zhou, Y.; Yue, H.; Zhou, S.

2017-12-01

We try Convolutional Neural Network to detect some features of seismic data and compare their efficience. The features include whether a signal is seismic signal or noise and the arrival time of P and S phase and each feature correspond to a Convolutional Neural Network. We first use traditional STA/LTA to recongnize some events and then use templete matching to find more events as training set for the Neural Network. To make the training set more various, we add some noise to the seismic data and make some synthetic seismic data and noise. The 3-component raw signal and time-frequancy ananlyze are used as the input data for our neural network. Our Training is performed on GPUs to achieve efficient convergence. Our method improved the precision in comparison with STA/LTA and template matching. We will move to recurrent neural network to see if this kind network is better in detect P and S phase.

Training Data Requirement for a Neural Network to Predict Aerodynamic Coefficients

NASA Technical Reports Server (NTRS)

Korsmeyer, David (Technical Monitor); Rajkumar, T.; Bardina, Jorge

2003-01-01

Basic aerodynamic coefficients are modeled as functions of angle of attack, speed brake deflection angle, Mach number, and side slip angle. Most of the aerodynamic parameters can be well-fitted using polynomial functions. We previously demonstrated that a neural network is a fast, reliable way of predicting aerodynamic coefficients. We encountered few under fitted and/or over fitted results during prediction. The training data for the neural network are derived from wind tunnel test measurements and numerical simulations. The basic questions that arise are: how many training data points are required to produce an efficient neural network prediction, and which type of transfer functions should be used between the input-hidden layer and hidden-output layer. In this paper, a comparative study of the efficiency of neural network prediction based on different transfer functions and training dataset sizes is presented. The results of the neural network prediction reflect the sensitivity of the architecture, transfer functions, and training dataset size.

ANNarchy: a code generation approach to neural simulations on parallel hardware

PubMed Central

Vitay, Julien; Dinkelbach, Helge Ü.; Hamker, Fred H.

2015-01-01

Many modern neural simulators focus on the simulation of networks of spiking neurons on parallel hardware. Another important framework in computational neuroscience, rate-coded neural networks, is mostly difficult or impossible to implement using these simulators. We present here the ANNarchy (Artificial Neural Networks architect) neural simulator, which allows to easily define and simulate rate-coded and spiking networks, as well as combinations of both. The interface in Python has been designed to be close to the PyNN interface, while the definition of neuron and synapse models can be specified using an equation-oriented mathematical description similar to the Brian neural simulator. This information is used to generate C++ code that will efficiently perform the simulation on the chosen parallel hardware (multi-core system or graphical processing unit). Several numerical methods are available to transform ordinary differential equations into an efficient C++code. We compare the parallel performance of the simulator to existing solutions. PMID:26283957

Neural circuits. Labeling of active neural circuits in vivo with designed calcium integrators.

PubMed

Fosque, Benjamin F; Sun, Yi; Dana, Hod; Yang, Chao-Tsung; Ohyama, Tomoko; Tadross, Michael R; Patel, Ronak; Zlatic, Marta; Kim, Douglas S; Ahrens, Misha B; Jayaraman, Vivek; Looger, Loren L; Schreiter, Eric R

2015-02-13

The identification of active neurons and circuits in vivo is a fundamental challenge in understanding the neural basis of behavior. Genetically encoded calcium (Ca(2+)) indicators (GECIs) enable quantitative monitoring of cellular-resolution activity during behavior. However, such indicators require online monitoring within a limited field of view. Alternatively, post hoc staining of immediate early genes (IEGs) indicates highly active cells within the entire brain, albeit with poor temporal resolution. We designed a fluorescent sensor, CaMPARI, that combines the genetic targetability and quantitative link to neural activity of GECIs with the permanent, large-scale labeling of IEGs, allowing a temporally precise "activity snapshot" of a large tissue volume. CaMPARI undergoes efficient and irreversible green-to-red conversion only when elevated intracellular Ca(2+) and experimenter-controlled illumination coincide. We demonstrate the utility of CaMPARI in freely moving larvae of zebrafish and flies, and in head-fixed mice and adult flies. Copyright © 2015, American Association for the Advancement of Science.

Feature Extraction of Event-Related Potentials Using Wavelets: An Application to Human Performance Monitoring

NASA Technical Reports Server (NTRS)

Trejo, Leonard J.; Shensa, Mark J.; Remington, Roger W. (Technical Monitor)

1998-01-01

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many f ree parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation,-, algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance.

Feature extraction of event-related potentials using wavelets: an application to human performance monitoring

NASA Technical Reports Server (NTRS)

Trejo, L. J.; Shensa, M. J.

1999-01-01

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance. Copyright 1999 Academic Press.

Embedded Streaming Deep Neural Networks Accelerator With Applications.

PubMed

Dundar, Aysegul; Jin, Jonghoon; Martini, Berin; Culurciello, Eugenio

2017-07-01

Deep convolutional neural networks (DCNNs) have become a very powerful tool in visual perception. DCNNs have applications in autonomous robots, security systems, mobile phones, and automobiles, where high throughput of the feedforward evaluation phase and power efficiency are important. Because of this increased usage, many field-programmable gate array (FPGA)-based accelerators have been proposed. In this paper, we present an optimized streaming method for DCNNs' hardware accelerator on an embedded platform. The streaming method acts as a compiler, transforming a high-level representation of DCNNs into operation codes to execute applications in a hardware accelerator. The proposed method utilizes maximum computational resources available based on a novel-scheduled routing topology that combines data reuse and data concatenation. It is tested with a hardware accelerator implemented on the Xilinx Kintex-7 XC7K325T FPGA. The system fully explores weight-level and node-level parallelizations of DCNNs and achieves a peak performance of 247 G-ops while consuming less than 4 W of power. We test our system with applications on object classification and object detection in real-world scenarios. Our results indicate high-performance efficiency, outperforming all other presented platforms while running these applications.

Dynamic frame resizing with convolutional neural network for efficient video compression

NASA Astrophysics Data System (ADS)

Kim, Jaehwan; Park, Youngo; Choi, Kwang Pyo; Lee, JongSeok; Jeon, Sunyoung; Park, JeongHoon

2017-09-01

In the past, video codecs such as vc-1 and H.263 used a technique to encode reduced-resolution video and restore original resolution from the decoder for improvement of coding efficiency. The techniques of vc-1 and H.263 Annex Q are called dynamic frame resizing and reduced-resolution update mode, respectively. However, these techniques have not been widely used due to limited performance improvements that operate well only under specific conditions. In this paper, video frame resizing (reduced/restore) technique based on machine learning is proposed for improvement of coding efficiency. The proposed method features video of low resolution made by convolutional neural network (CNN) in encoder and reconstruction of original resolution using CNN in decoder. The proposed method shows improved subjective performance over all the high resolution videos which are dominantly consumed recently. In order to assess subjective quality of the proposed method, Video Multi-method Assessment Fusion (VMAF) which showed high reliability among many subjective measurement tools was used as subjective metric. Moreover, to assess general performance, diverse bitrates are tested. Experimental results showed that BD-rate based on VMAF was improved by about 51% compare to conventional HEVC. Especially, VMAF values were significantly improved in low bitrate. Also, when the method is subjectively tested, it had better subjective visual quality in similar bit rate.

Efficient Digital Implementation of The Sigmoidal Function For Artificial Neural Network

NASA Astrophysics Data System (ADS)

Pratap, Rana; Subadra, M.

2011-10-01

An efficient piecewise linear approximation of a nonlinear function (PLAN) is proposed. This uses simulink environment design to perform a direct transformation from X to Y, where X is the input and Y is the approximated sigmoidal output. This PLAN is then used within the outputs of an artificial neural network to perform the nonlinear approximation. In This paper, is proposed a method to implement in FPGA (Field Programmable Gate Array) circuits different approximation of the sigmoid function.. The major benefit of the proposed method resides in the possibility to design neural networks by means of predefined block systems created in System Generator environment and the possibility to create a higher level design tools used to implement neural networks in logical circuits.

DMH1, a Highly Selective Small Molecule BMP Inhibitor Promotes Neurogenesis of hiPSCs: Comparison of PAX6 and SOX1 Expression during Neural Induction

PubMed Central

2012-01-01

Recent successes in deriving human-induced pluripotent stem cells (hiPSCs) allow for the possibility of studying human neurons derived from patients with neurological diseases. Concomitant inhibition of the BMP and TGF-β1 branches of the TGF-β signaling pathways by the endogenous antagonist, Noggin, and the small molecule SB431542, respectively, induces efficient neuralization of hiPSCs, a method known as dual-SMAD inhibition. The use of small molecule inhibitors instead of their endogenous counterparts has several advantages including lower cost, consistent activity, and the maintenance of xeno-free culture conditions. We tested the efficacy of DMH1, a highly selective small molecule BMP-inhibitor for its potential to replace Noggin in the neuralization of hiPSCs. We compare Noggin and DMH1-induced neuralization of hiPSCs by measuring protein and mRNA levels of pluripotency and neural precursor markers over a period of seven days. The regulation of five of the six markers assessed was indistinguishable in the presence of concentrations of Noggin or DMH1 that have been shown to effectively inhibit BMP signaling in other systems. We observed that by varying the DMH1 or Noggin concentration, we could selectively modulate the number of SOX1 expressing cells, whereas PAX6, another neural precursor marker, remained the same. The level and timing of SOX1 expression have been shown to affect neural induction as well as neural lineage. Our observations, therefore, suggest that BMP-inhibitor concentrations need to be carefully monitored to ensure appropriate expression levels of all transcription factors necessary for the induction of a particular neuronal lineage. We further demonstrate that DMH1-induced neural progenitors can be differentiated into β3-tubulin expressing neurons, a subset of which also express tyrosine hydroxylase. Thus, the combined use of DMH1, a highly specific BMP-pathway inhibitor, and SB431542, a TGF-β1-pathway specific inhibitor, provides us with the tools to independently regulate these two pathways through the exclusive use of small molecule inhibitors. PMID:22860217

Neural Correlates of Performance Monitoring during the Transition to Young Adulthood

ERIC Educational Resources Information Center

Kneževic, Martina; Veroude, Kim; Jolles, Jelle; Krabbendam, Lydia

2016-01-01

Cognitive challenges during transition to adulthood are generally high and require particular skills, such as self-control, performance evaluation, and behavioral adjustment for success in everyday living. However, age and sex differences in timing and efficiency of brain maturational processes in the early twenties are not well known. We used a…

A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing

DOE PAGES

van de Burgt, Yoeri; Lubberman, Ewout; Fuller, Elliot J.; ...

2017-02-20

The brain is capable of massively parallel information processing while consuming only ~1- 100 fJ per synaptic event. Inspired by the efficiency of the brain, CMOS-based neural architectures and memristors are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach. Here we describe an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors. ENODe switches at low energymore » (<10 pJ for 10 3 μm 2 devices) and voltage, displays >500 distinct, non-volatile conductance states within a ~1 V range, and achieves high classification accuracy when implemented in neural network simulations. Plastic ENODEs are also fabricated on flexible substrates enabling the integration of neuromorphic functionality in stretchable electronic systems. Mechanical flexibility makes ENODes compatible with 3D architectures, opening a path towards extreme interconnectivity comparable to the human brain.« less

A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing

NASA Astrophysics Data System (ADS)

van de Burgt, Yoeri; Lubberman, Ewout; Fuller, Elliot J.; Keene, Scott T.; Faria, Grégorio C.; Agarwal, Sapan; Marinella, Matthew J.; Alec Talin, A.; Salleo, Alberto

2017-04-01

The brain is capable of massively parallel information processing while consuming only ~1-100 fJ per synaptic event. Inspired by the efficiency of the brain, CMOS-based neural architectures and memristors are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach. Here we describe an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors. ENODe switches at low voltage and energy (<10 pJ for 103 μm2 devices), displays >500 distinct, non-volatile conductance states within a ~1 V range, and achieves high classification accuracy when implemented in neural network simulations. Plastic ENODes are also fabricated on flexible substrates enabling the integration of neuromorphic functionality in stretchable electronic systems. Mechanical flexibility makes ENODes compatible with three-dimensional architectures, opening a path towards extreme interconnectivity comparable to the human brain.

A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing.

PubMed

van de Burgt, Yoeri; Lubberman, Ewout; Fuller, Elliot J; Keene, Scott T; Faria, Grégorio C; Agarwal, Sapan; Marinella, Matthew J; Alec Talin, A; Salleo, Alberto

2017-04-01

The brain is capable of massively parallel information processing while consuming only ∼1-100 fJ per synaptic event. Inspired by the efficiency of the brain, CMOS-based neural architectures and memristors are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach. Here we describe an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors. ENODe switches at low voltage and energy (<10 pJ for 10 3  μm 2 devices), displays >500 distinct, non-volatile conductance states within a ∼1 V range, and achieves high classification accuracy when implemented in neural network simulations. Plastic ENODes are also fabricated on flexible substrates enabling the integration of neuromorphic functionality in stretchable electronic systems. Mechanical flexibility makes ENODes compatible with three-dimensional architectures, opening a path towards extreme interconnectivity comparable to the human brain.

Hadoop neural network for parallel and distributed feature selection.

PubMed

Hodge, Victoria J; O'Keefe, Simon; Austin, Jim

2016-06-01

In this paper, we introduce a theoretical basis for a Hadoop-based neural network for parallel and distributed feature selection in Big Data sets. It is underpinned by an associative memory (binary) neural network which is highly amenable to parallel and distributed processing and fits with the Hadoop paradigm. There are many feature selectors described in the literature which all have various strengths and weaknesses. We present the implementation details of five feature selection algorithms constructed using our artificial neural network framework embedded in Hadoop YARN. Hadoop allows parallel and distributed processing. Each feature selector can be divided into subtasks and the subtasks can then be processed in parallel. Multiple feature selectors can also be processed simultaneously (in parallel) allowing multiple feature selectors to be compared. We identify commonalities among the five features selectors. All can be processed in the framework using a single representation and the overall processing can also be greatly reduced by only processing the common aspects of the feature selectors once and propagating these aspects across all five feature selectors as necessary. This allows the best feature selector and the actual features to select to be identified for large and high dimensional data sets through exploiting the efficiency and flexibility of embedding the binary associative-memory neural network in Hadoop. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Permeability Estimation of Rock Reservoir Based on PCA and Elman Neural Networks

NASA Astrophysics Data System (ADS)

Shi, Ying; Jian, Shaoyong

2018-03-01

an intelligent method which based on fuzzy neural networks with PCA algorithm, is proposed to estimate the permeability of rock reservoir. First, the dimensionality reduction process is utilized for these parameters by principal component analysis method. Further, the mapping relationship between rock slice characteristic parameters and permeability had been found through fuzzy neural networks. The estimation validity and reliability for this method were tested with practical data from Yan’an region in Ordos Basin. The result showed that the average relative errors of permeability estimation for this method is 6.25%, and this method had the better convergence speed and more accuracy than other. Therefore, by using the cheap rock slice related information, the permeability of rock reservoir can be estimated efficiently and accurately, and it is of high reliability, practicability and application prospect.

An analog neural hardware implementation using charge-injection multipliers and neutron-specific gain control.

PubMed

Massengill, L W; Mundie, D B

1992-01-01

A neural network IC based on a dynamic charge injection is described. The hardware design is space and power efficient, and achieves massive parallelism of analog inner products via charge-based multipliers and spatially distributed summing buses. Basic synaptic cells are constructed of exponential pulse-decay modulation (EPDM) dynamic injection multipliers operating sequentially on propagating signal vectors and locally stored analog weights. Individually adjustable gain controls on each neutron reduce the effects of limited weight dynamic range. A hardware simulator/trainer has been developed which incorporates the physical (nonideal) characteristics of actual circuit components into the training process, thus absorbing nonlinearities and parametric deviations into the macroscopic performance of the network. Results show that charge-based techniques may achieve a high degree of neural density and throughput using standard CMOS processes.

High probability neurotransmitter release sites represent an energy efficient design

PubMed Central

Lu, Zhongmin; Chouhan, Amit K.; Borycz, Jolanta A.; Lu, Zhiyuan; Rossano, Adam J; Brain, Keith L.; Zhou, You; Meinertzhagen, Ian A.; Macleod, Gregory T.

2016-01-01

Nerve terminals contain multiple sites specialized for the release of neurotransmitters. Release usually occurs with low probability, a design thought to confer many advantages. High probability release sites are not uncommon but their advantages are not well understood. Here we test the hypothesis that high probability release sites represent an energy efficient design. We examined release site probabilities and energy efficiency at the terminals of two glutamatergic motor neurons synapsing on the same muscle fiber in Drosophila larvae. Through electrophysiological and ultrastructural measurements we calculated release site probabilities to differ considerably between terminals (0.33 vs. 0.11). We estimated the energy required to release and recycle glutamate from the same measurements. The energy required to remove calcium and sodium ions subsequent to nerve excitation was estimated through microfluorimetric and morphological measurements. We calculated energy efficiency as the number of glutamate molecules released per ATP molecule hydrolyzed, and high probability release site terminals were found to be more efficient (0.13 vs. 0.06). Our analytical model indicates that energy efficiency is optimal (~0.15) at high release site probabilities (~0.76). As limitations in energy supply constrain neural function, high probability release sites might ameliorate such constraints by demanding less energy. Energy efficiency can be viewed as one aspect of nerve terminal function, in balance with others, because high efficiency terminals depress significantly during episodic bursts of activity. PMID:27593375

Polydopamine-mediated surface modification of scaffold materials for human neural stem cell engineering.

PubMed

Yang, Kisuk; Lee, Jung Seung; Kim, Jin; Lee, Yu Bin; Shin, Heungsoo; Um, Soong Ho; Kim, Jeong Beom; Park, Kook In; Lee, Haeshin; Cho, Seung-Woo

2012-10-01

Surface modification of tissue engineering scaffolds and substrates is required for improving the efficacy of stem cell therapy by generating physicochemical stimulation promoting proliferation and differentiation of stem cells. However, typical surface modification methods including chemical conjugation or physical absorption have several limitations such as multistep, complicated procedures, surface denaturation, batch-to-batch inconsistencies, and low surface conjugation efficiency. In this study, we report a mussel-inspired, biomimetic approach to surface modification for efficient and reliable manipulation of human neural stem cell (NSC) differentiation and proliferation. Our study demonstrates that polydopamine coating facilitates highly efficient, simple immobilization of neurotrophic growth factors and adhesion peptides onto polymer substrates. The growth factor or peptide-immobilized substrates greatly enhance differentiation and proliferation of human NSCs (human fetal brain-derived NSCs and human induced pluripotent stem cell-derived NSCs) at a level comparable or greater than currently available animal-derived coating materials (Matrigel) with safety issues. Therefore, polydopamine-mediated surface modification can provide a versatile platform technology for developing chemically defined, safe, functional substrates and scaffolds for therapeutic applications of human NSCs. Copyright © 2012 Elsevier Ltd. All rights reserved.

Neural network fusion capabilities for efficient implementation of tracking algorithms

NASA Astrophysics Data System (ADS)

Sundareshan, Malur K.; Amoozegar, Farid

1996-05-01

The ability to efficiently fuse information of different forms for facilitating intelligent decision-making is one of the major capabilities of trained multilayer neural networks that is being recognized int eh recent times. While development of innovative adaptive control algorithms for nonlinear dynamical plants which attempt to exploit these capabilities seems to be more popular, a corresponding development of nonlinear estimation algorithms using these approaches, particularly for application in target surveillance and guidance operations, has not received similar attention. In this paper we describe the capabilities and functionality of neural network algorithms for data fusion and implementation of nonlinear tracking filters. For a discussion of details and for serving as a vehicle for quantitative performance evaluations, the illustrative case of estimating the position and velocity of surveillance targets is considered. Efficient target tracking algorithms that can utilize data from a host of sensing modalities and are capable of reliably tracking even uncooperative targets executing fast and complex maneuvers are of interest in a number of applications. The primary motivation for employing neural networks in these applications comes form the efficiency with which more features extracted from different sensor measurements can be utilized as inputs for estimating target maneuvers. Such an approach results in an overall nonlinear tracking filter which has several advantages over the popular efforts at designing nonlinear estimation algorithms for tracking applications, the principle one being the reduction of mathematical and computational complexities. A system architecture that efficiently integrates the processing capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described in this paper.

High MRI performance fluorescent mesoporous silica-coated magnetic nanoparticles for tracking neural progenitor cells in an ischemic mouse model.

PubMed

Zhang, Lu; Wang, Yao; Tang, Yaohui; Jiao, Zheng; Xie, Chengying; Zhang, Haijiao; Gu, Ping; Wei, Xunbin; Yang, Guo-Yuan; Gu, Hongchen; Zhang, Chunfu

2013-05-21

Multifunctional probes with high MRI sensitivity and high efficiency for cell labeling are desirable for MR cell imaging. Herein, we have fabricated fluorescent mesoporous silica-coated superparamagnetic iron oxide nanoparticles (fmSiO4@SPIONs) for neural progenitor cell (C17.2) MR imaging. FmSiO4@SPIONs were discrete and uniform in size, and had a clear core-shell structure. The magnetic core size was about 10 nm and the fluorescent mesoporous silica coating layer was around 20 nm. Compared with fluorescent dense silica-coated SPIONs (fdSiO4@SPIONs) with a similar size, fmSiO4@SPIONs demonstrated higher MR sensitivity and cell labeling efficiency. When implanted into the right hemisphere of stroke mice, contralateral to the ischemic territory, a small amount of labeled cells were able to be tracked migrating to the lesion sites using a clinical MRI scanner (3 T). More impressively, even when administered intravenously, the labeled cells could also be monitored homing to the ischemic area. MRI observations were corroborated by histological studies of the brain tissues. Our study demonstrated that fmSiO4@SPIONs are highly effective for cell imaging and hold great promise for MRI cell tracking in future.

Three-Dimensional Terahertz Coded-Aperture Imaging Based on Matched Filtering and Convolutional Neural Network.

PubMed

Chen, Shuo; Luo, Chenggao; Wang, Hongqiang; Deng, Bin; Cheng, Yongqiang; Zhuang, Zhaowen

2018-04-26

As a promising radar imaging technique, terahertz coded-aperture imaging (TCAI) can achieve high-resolution, forward-looking, and staring imaging by producing spatiotemporal independent signals with coded apertures. However, there are still two problems in three-dimensional (3D) TCAI. Firstly, the large-scale reference-signal matrix based on meshing the 3D imaging area creates a heavy computational burden, thus leading to unsatisfactory efficiency. Secondly, it is difficult to resolve the target under low signal-to-noise ratio (SNR). In this paper, we propose a 3D imaging method based on matched filtering (MF) and convolutional neural network (CNN), which can reduce the computational burden and achieve high-resolution imaging for low SNR targets. In terms of the frequency-hopping (FH) signal, the original echo is processed with MF. By extracting the processed echo in different spike pulses separately, targets in different imaging planes are reconstructed simultaneously to decompose the global computational complexity, and then are synthesized together to reconstruct the 3D target. Based on the conventional TCAI model, we deduce and build a new TCAI model based on MF. Furthermore, the convolutional neural network (CNN) is designed to teach the MF-TCAI how to reconstruct the low SNR target better. The experimental results demonstrate that the MF-TCAI achieves impressive performance on imaging ability and efficiency under low SNR. Moreover, the MF-TCAI has learned to better resolve the low-SNR 3D target with the help of CNN. In summary, the proposed 3D TCAI can achieve: (1) low-SNR high-resolution imaging by using MF; (2) efficient 3D imaging by downsizing the large-scale reference-signal matrix; and (3) intelligent imaging with CNN. Therefore, the TCAI based on MF and CNN has great potential in applications such as security screening, nondestructive detection, medical diagnosis, etc.

Coordinated within-trial dynamics of low-frequency neural rhythms controls evidence accumulation.

PubMed

Werkle-Bergner, Markus; Grandy, Thomas H; Chicherio, Christian; Schmiedek, Florian; Lövdén, Martin; Lindenberger, Ulman

2014-06-18

Higher cognitive functions, such as human perceptual decision making, require information processing and transmission across wide-spread cortical networks. Temporally synchronized neural firing patterns are advantageous for efficiently representing and transmitting information within and between assemblies. Computational, empirical, and conceptual considerations all lead to the expectation that the informational redundancy of neural firing rates is positively related to their synchronization. Recent theorizing and initial evidence also suggest that the coding of stimulus characteristics and their integration with behavioral goal states require neural interactions across a hierarchy of timescales. However, most studies thus have focused on neural activity in a single frequency range or on a restricted set of brain regions. Here we provide evidence for cooperative spatiotemporal dynamics of slow and fast EEG signals during perceptual decision making at the single-trial level. Participants performed three masked two-choice decision tasks, one each with numerical, verbal, or figural content. Decrements in posterior α power (8-14 Hz) were paralleled by increments in high-frequency (>30 Hz) signal entropy in trials demanding active sensory processing. Simultaneously, frontocentral θ power (4-7 Hz) increased, indicating evidence integration. The coordinated α/θ dynamics were tightly linked to decision speed and remarkably similar across tasks, suggesting a domain-general mechanism. In sum, we demonstrate an inverse association between decision-related changes in widespread low-frequency power and local high-frequency entropy. The cooperation among mechanisms captured by these changes enhances the informational density of neural response patterns and qualifies as a neural coding system in the service of perceptual decision making. Copyright © 2014 the authors 0270-6474/14/348519-10$15.00/0.

Research of PV Power Generation MPPT based on GABP Neural Network

NASA Astrophysics Data System (ADS)

Su, Yu; Lin, Xianfu

2018-05-01

Photovoltaic power generation has become the main research direction of new energy power generation. But high investment and low efficiency of photovoltaic industry arouse concern in some extent. So maximum power point tracking of photovoltaic power generation has been a popular study point. Due to slow response, oscillation at maximum power point and low precision, the algorithm based on genetic algorithm combined with BP neural network are designed detailedly in this paper. And the modeling and simulation are completed by use of MATLAB/SIMULINK. The results show that the algorithm is effective and the maximum power point can be tracked accurately and quickly.

Hardware Prototyping of Neural Network based Fetal Electrocardiogram Extraction

NASA Astrophysics Data System (ADS)

Hasan, M. A.; Reaz, M. B. I.

2012-01-01

The aim of this paper is to model the algorithm for Fetal ECG (FECG) extraction from composite abdominal ECG (AECG) using VHDL (Very High Speed Integrated Circuit Hardware Description Language) for FPGA (Field Programmable Gate Array) implementation. Artificial Neural Network that provides efficient and effective ways of separating FECG signal from composite AECG signal has been designed. The proposed method gives an accuracy of 93.7% for R-peak detection in FHR monitoring. The designed VHDL model is synthesized and fitted into Altera's Stratix II EP2S15F484C3 using the Quartus II version 8.0 Web Edition for FPGA implementation.

Evaluation of Deep Learning Models for Predicting CO2 Flux

NASA Astrophysics Data System (ADS)

Halem, M.; Nguyen, P.; Frankel, D.

2017-12-01

Artificial neural networks have been employed to calculate surface flux measurements from station data because they are able to fit highly nonlinear relations between input and output variables without knowing the detail relationships between the variables. However, the accuracy in performing neural net estimates of CO2 flux from observations of CO2 and other atmospheric variables is influenced by the architecture of the neural model, the availability, and complexity of interactions between physical variables such as wind, temperature, and indirect variables like latent heat, and sensible heat, etc. We evaluate two deep learning models, feed forward and recurrent neural network models to learn how they each respond to the physical measurements, time dependency of the measurements of CO2 concentration, humidity, pressure, temperature, wind speed etc. for predicting the CO2 flux. In this paper, we focus on a) building neural network models for estimating CO2 flux based on DOE data from tower Atmospheric Radiation Measurement data; b) evaluating the impact of choosing the surface variables and model hyper-parameters on the accuracy and predictions of surface flux; c) assessing the applicability of the neural network models on estimate CO2 flux by using OCO-2 satellite data; d) studying the efficiency of using GPU-acceleration for neural network performance using IBM Power AI deep learning software and packages on IBM Minsky system.

Neural-Network Quantum States, String-Bond States, and Chiral Topological States

NASA Astrophysics Data System (ADS)

Glasser, Ivan; Pancotti, Nicola; August, Moritz; Rodriguez, Ivan D.; Cirac, J. Ignacio

2018-01-01

Neural-network quantum states have recently been introduced as an Ansatz for describing the wave function of quantum many-body systems. We show that there are strong connections between neural-network quantum states in the form of restricted Boltzmann machines and some classes of tensor-network states in arbitrary dimensions. In particular, we demonstrate that short-range restricted Boltzmann machines are entangled plaquette states, while fully connected restricted Boltzmann machines are string-bond states with a nonlocal geometry and low bond dimension. These results shed light on the underlying architecture of restricted Boltzmann machines and their efficiency at representing many-body quantum states. String-bond states also provide a generic way of enhancing the power of neural-network quantum states and a natural generalization to systems with larger local Hilbert space. We compare the advantages and drawbacks of these different classes of states and present a method to combine them together. This allows us to benefit from both the entanglement structure of tensor networks and the efficiency of neural-network quantum states into a single Ansatz capable of targeting the wave function of strongly correlated systems. While it remains a challenge to describe states with chiral topological order using traditional tensor networks, we show that, because of their nonlocal geometry, neural-network quantum states and their string-bond-state extension can describe a lattice fractional quantum Hall state exactly. In addition, we provide numerical evidence that neural-network quantum states can approximate a chiral spin liquid with better accuracy than entangled plaquette states and local string-bond states. Our results demonstrate the efficiency of neural networks to describe complex quantum wave functions and pave the way towards the use of string-bond states as a tool in more traditional machine-learning applications.

Efficient airport detection using region-based fully convolutional neural networks

NASA Astrophysics Data System (ADS)

Xin, Peng; Xu, Yuelei; Zhang, Xulei; Ma, Shiping; Li, Shuai; Lv, Chao

2018-04-01

This paper presents a model for airport detection using region-based fully convolutional neural networks. To achieve fast detection with high accuracy, we shared the conv layers between the region proposal procedure and the airport detection procedure and used graphics processing units (GPUs) to speed up the training and testing time. For lack of labeled data, we transferred the convolutional layers of ZF net pretrained by ImageNet to initialize the shared convolutional layers, then we retrained the model using the alternating optimization training strategy. The proposed model has been tested on an airport dataset consisting of 600 images. Experiments show that the proposed method can distinguish airports in our dataset from similar background scenes almost real-time with high accuracy, which is much better than traditional methods.

Online graphic symbol recognition using neural network and ARG matching

NASA Astrophysics Data System (ADS)

Yang, Bing; Li, Changhua; Xie, Weixing

2001-09-01

This paper proposes a novel method for on-line recognition of line-based graphic symbol. The input strokes are usually warped into a cursive form due to the sundry drawing style, and classifying them is very difficult. To deal with this, an ART-2 neural network is used to classify the input strokes. It has the advantages of high recognition rate, less recognition time and forming classes in a self-organized manner. The symbol recognition is achieved by an Attribute Relational Graph (ARG) matching algorithm. The ARG is very efficient for representing complex objects, but computation cost is very high. To over come this, we suggest a fast graph matching algorithm using symbol structure information. The experimental results show that the proposed method is effective for recognition of symbols with hierarchical structure.

Neural Control of a Tracking Task via Attention-Gated Reinforcement Learning for Brain-Machine Interfaces.

PubMed

Wang, Yiwen; Wang, Fang; Xu, Kai; Zhang, Qiaosheng; Zhang, Shaomin; Zheng, Xiaoxiang

2015-05-01

Reinforcement learning (RL)-based brain machine interfaces (BMIs) enable the user to learn from the environment through interactions to complete the task without desired signals, which is promising for clinical applications. Previous studies exploited Q-learning techniques to discriminate neural states into simple directional actions providing the trial initial timing. However, the movements in BMI applications can be quite complicated, and the action timing explicitly shows the intention when to move. The rich actions and the corresponding neural states form a large state-action space, imposing generalization difficulty on Q-learning. In this paper, we propose to adopt attention-gated reinforcement learning (AGREL) as a new learning scheme for BMIs to adaptively decode high-dimensional neural activities into seven distinct movements (directional moves, holdings and resting) due to the efficient weight-updating. We apply AGREL on neural data recorded from M1 of a monkey to directly predict a seven-action set in a time sequence to reconstruct the trajectory of a center-out task. Compared to Q-learning techniques, AGREL could improve the target acquisition rate to 90.16% in average with faster convergence and more stability to follow neural activity over multiple days, indicating the potential to achieve better online decoding performance for more complicated BMI tasks.

Efficient Training of Supervised Spiking Neural Network via Accurate Synaptic-Efficiency Adjustment Method.

PubMed

Xie, Xiurui; Qu, Hong; Yi, Zhang; Kurths, Jurgen

2017-06-01

The spiking neural network (SNN) is the third generation of neural networks and performs remarkably well in cognitive tasks, such as pattern recognition. The temporal neural encode mechanism found in biological hippocampus enables SNN to possess more powerful computation capability than networks with other encoding schemes. However, this temporal encoding approach requires neurons to process information serially on time, which reduces learning efficiency significantly. To keep the powerful computation capability of the temporal encoding mechanism and to overcome its low efficiency in the training of SNNs, a new training algorithm, the accurate synaptic-efficiency adjustment method is proposed in this paper. Inspired by the selective attention mechanism of the primate visual system, our algorithm selects only the target spike time as attention areas, and ignores voltage states of the untarget ones, resulting in a significant reduction of training time. Besides, our algorithm employs a cost function based on the voltage difference between the potential of the output neuron and the firing threshold of the SNN, instead of the traditional precise firing time distance. A normalized spike-timing-dependent-plasticity learning window is applied to assigning this error to different synapses for instructing their training. Comprehensive simulations are conducted to investigate the learning properties of our algorithm, with input neurons emitting both single spike and multiple spikes. Simulation results indicate that our algorithm possesses higher learning performance than the existing other methods and achieves the state-of-the-art efficiency in the training of SNN.

Efficient differentiation of mouse embryonic stem cells into motor neurons.

PubMed

Wu, Chia-Yen; Whye, Dosh; Mason, Robert W; Wang, Wenlan

2012-06-09

Direct differentiation of embryonic stem (ES) cells into functional motor neurons represents a promising resource to study disease mechanisms, to screen new drug compounds, and to develop new therapies for motor neuron diseases such as spinal muscular atrophy (SMA) and amyotrophic lateral sclerosis (ALS). Many current protocols use a combination of retinoic acid (RA) and sonic hedgehog (Shh) to differentiate mouse embryonic stem (mES) cells into motor neurons. However, the differentiation efficiency of mES cells into motor neurons has only met with moderate success. We have developed a two-step differentiation protocol that significantly improves the differentiation efficiency compared with currently established protocols. The first step is to enhance the neuralization process by adding Noggin and fibroblast growth factors (FGFs). Noggin is a bone morphogenetic protein (BMP) antagonist and is implicated in neural induction according to the default model of neurogenesis and results in the formation of anterior neural patterning. FGF signaling acts synergistically with Noggin in inducing neural tissue formation by promoting a posterior neural identity. In this step, mES cells were primed with Noggin, bFGF, and FGF-8 for two days to promote differentiation towards neural lineages. The second step is to induce motor neuron specification. Noggin/FGFs exposed mES cells were incubated with RA and a Shh agonist, Smoothened agonist (SAG), for another 5 days to facilitate motor neuron generation. To monitor the differentiation of mESs into motor neurons, we used an ES cell line derived from a transgenic mouse expressing eGFP under the control of the motor neuron specific promoter Hb9. Using this robust protocol, we achieved 51 ± 0.8% of differentiation efficiency (n = 3; p < 0.01, Student's t-test). Results from immunofluorescent staining showed that GFP+ cells express the motor neuron specific markers, Islet-1 and choline acetyltransferase (ChAT). Our two-step differentiation protocol provides an efficient way to differentiate mES cells into spinal motor neurons.

Functional neural networks of honesty and dishonesty in children: Evidence from graph theory analysis.

PubMed

Ding, Xiao Pan; Wu, Si Jia; Liu, Jiangang; Fu, Genyue; Lee, Kang

2017-09-21

The present study examined how different brain regions interact with each other during spontaneous honest vs. dishonest communication. More specifically, we took a complex network approach based on the graph-theory to analyze neural response data when children are spontaneously engaged in honest or dishonest acts. Fifty-nine right-handed children between 7 and 12 years of age participated in the study. They lied or told the truth out of their own volition. We found that lying decreased both the global and local efficiencies of children's functional neural network. This finding, for the first time, suggests that lying disrupts the efficiency of children's cortical network functioning. Further, it suggests that the graph theory based network analysis is a viable approach to study the neural development of deception.

Bio-inspired spiking neural network for nonlinear systems control.

PubMed

Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

2018-08-01

Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

An Implantable Neural Sensing Microsystem with Fiber-Optic Data Transmission and Power Delivery

PubMed Central

Park, Sunmee; Borton, David A.; Kang, Mingyu; Nurmikko, Arto V.; Song, Yoon-Kyu

2013-01-01

We have developed a prototype cortical neural sensing microsystem for brain implantable neuroengineering applications. Its key feature is that both the transmission of broadband, multichannel neural data and power required for the embedded microelectronics are provided by optical fiber access. The fiber-optic system is aimed at enabling neural recording from rodents and primates by converting cortical signals to a digital stream of infrared light pulses. In the full microsystem whose performance is summarized in this paper, an analog-to-digital converter and a low power digital controller IC have been integrated with a low threshold, semiconductor laser to extract the digitized neural signals optically from the implantable unit. The microsystem also acquires electrical power and synchronization clocks via optical fibers from an external laser by using a highly efficient photovoltaic cell on board. The implantable unit employs a flexible polymer substrate to integrate analog and digital microelectronics and on-chip optoelectronic components, while adapting to the anatomical and physiological constraints of the environment. A low power analog CMOS chip, which includes preamplifier and multiplexing circuitry, is directly flip-chip bonded to the microelectrode array to form the cortical neurosensor device. PMID:23666130

Discrimination of crop and weeds on visible and visible/near-infrared spectrums using support vector machines, artificial neural network and decision tree

USDA-ARS?s Scientific Manuscript database

Weeds are regarded as farmers' natural enemy. In order to avoid excessive pesticide residues, the destruction of ecological environment, and to guarantee the quality and safety of agricultural products, it is urgent to develop highly-efficient weed management methods. Amongst, weed discrimination is...

Dysfunction of sensory oscillations in Autism Spectrum Disorder

PubMed Central

Simon, David M.; Wallace, Mark T.

2016-01-01

Autism Spectrum Disorder (ASD) is a highly prevalent developmental disability characterized by deficits in social communication and interaction, restricted interests, and repetitive behaviors. Recently, anomalous sensory and perceptual function has gained an increased level of recognition as an important feature of ASD. A specific impairment in the ability to integrate information across brain networks has been proposed to contribute to these disruptions. A crucial mechanism for these integrative processes is the rhythmic synchronization of neuronal excitability across neural populations; collectively known as oscillations. In ASD there is believed to be a deficit in the ability to efficiently couple functional neural networks using these oscillations. This review discusses evidence for disruptions in oscillatory synchronization in ASD, and how disturbance of this neural mechanism contributes to alterations in sensory and perceptual function. The review also frames oscillatory data from the perspective of prevailing neurobiologically-inspired theories of ASD. PMID:27451342

Dynamic gesture recognition using neural networks: a fundament for advanced interaction construction

NASA Astrophysics Data System (ADS)

Boehm, Klaus; Broll, Wolfgang; Sokolewicz, Michael A.

1994-04-01

Interaction in virtual reality environments is still a challenging task. Static hand posture recognition is currently the most common and widely used method for interaction using glove input devices. In order to improve the naturalness of interaction, and thereby decrease the user-interface learning time, there is a need to be able to recognize dynamic gestures. In this paper we describe our approach to overcoming the difficulties of dynamic gesture recognition (DGR) using neural networks. Backpropagation neural networks have already proven themselves to be appropriate and efficient for posture recognition. However, the extensive amount of data involved in DGR requires a different approach. Because of features such as topology preservation and automatic-learning, Kohonen Feature Maps are particularly suitable for the reduction of the high dimensional data space that is the result of a dynamic gesture, and are thus implemented for this task.

Thermodynamic efficiency of learning a rule in neural networks

NASA Astrophysics Data System (ADS)

Goldt, Sebastian; Seifert, Udo

2017-11-01

Biological systems have to build models from their sensory input data that allow them to efficiently process previously unseen inputs. Here, we study a neural network learning a binary classification rule for these inputs from examples provided by a teacher. We analyse the ability of the network to apply the rule to new inputs, that is to generalise from past experience. Using stochastic thermodynamics, we show that the thermodynamic costs of the learning process provide an upper bound on the amount of information that the network is able to learn from its teacher for both batch and online learning. This allows us to introduce a thermodynamic efficiency of learning. We analytically compute the dynamics and the efficiency of a noisy neural network performing online learning in the thermodynamic limit. In particular, we analyse three popular learning algorithms, namely Hebbian, Perceptron and AdaTron learning. Our work extends the methods of stochastic thermodynamics to a new type of learning problem and might form a suitable basis for investigating the thermodynamics of decision-making.

Large Deviations for Nonlocal Stochastic Neural Fields

PubMed Central

2014-01-01

We study the effect of additive noise on integro-differential neural field equations. In particular, we analyze an Amari-type model driven by a Q-Wiener process, and focus on noise-induced transitions and escape. We argue that proving a sharp Kramers’ law for neural fields poses substantial difficulties, but that one may transfer techniques from stochastic partial differential equations to establish a large deviation principle (LDP). Then we demonstrate that an efficient finite-dimensional approximation of the stochastic neural field equation can be achieved using a Galerkin method and that the resulting finite-dimensional rate function for the LDP can have a multiscale structure in certain cases. These results form the starting point for an efficient practical computation of the LDP. Our approach also provides the technical basis for further rigorous study of noise-induced transitions in neural fields based on Galerkin approximations. Mathematics Subject Classification (2000): 60F10, 60H15, 65M60, 92C20. PMID:24742297

Input-output relation and energy efficiency in the neuron with different spike threshold dynamics.

PubMed

Yi, Guo-Sheng; Wang, Jiang; Tsang, Kai-Ming; Wei, Xi-Le; Deng, Bin

2015-01-01

Neuron encodes and transmits information through generating sequences of output spikes, which is a high energy-consuming process. The spike is initiated when membrane depolarization reaches a threshold voltage. In many neurons, threshold is dynamic and depends on the rate of membrane depolarization (dV/dt) preceding a spike. Identifying the metabolic energy involved in neural coding and their relationship to threshold dynamic is critical to understanding neuronal function and evolution. Here, we use a modified Morris-Lecar model to investigate neuronal input-output property and energy efficiency associated with different spike threshold dynamics. We find that the neurons with dynamic threshold sensitive to dV/dt generate discontinuous frequency-current curve and type II phase response curve (PRC) through Hopf bifurcation, and weak noise could prohibit spiking when bifurcation just occurs. The threshold that is insensitive to dV/dt, instead, results in a continuous frequency-current curve, a type I PRC and a saddle-node on invariant circle bifurcation, and simultaneously weak noise cannot inhibit spiking. It is also shown that the bifurcation, frequency-current curve and PRC type associated with different threshold dynamics arise from the distinct subthreshold interactions of membrane currents. Further, we observe that the energy consumption of the neuron is related to its firing characteristics. The depolarization of spike threshold improves neuronal energy efficiency by reducing the overlap of Na(+) and K(+) currents during an action potential. The high energy efficiency is achieved at more depolarized spike threshold and high stimulus current. These results provide a fundamental biophysical connection that links spike threshold dynamics, input-output relation, energetics and spike initiation, which could contribute to uncover neural encoding mechanism.

Input-output relation and energy efficiency in the neuron with different spike threshold dynamics

PubMed Central

Yi, Guo-Sheng; Wang, Jiang; Tsang, Kai-Ming; Wei, Xi-Le; Deng, Bin

2015-01-01

Neuron encodes and transmits information through generating sequences of output spikes, which is a high energy-consuming process. The spike is initiated when membrane depolarization reaches a threshold voltage. In many neurons, threshold is dynamic and depends on the rate of membrane depolarization (dV/dt) preceding a spike. Identifying the metabolic energy involved in neural coding and their relationship to threshold dynamic is critical to understanding neuronal function and evolution. Here, we use a modified Morris-Lecar model to investigate neuronal input-output property and energy efficiency associated with different spike threshold dynamics. We find that the neurons with dynamic threshold sensitive to dV/dt generate discontinuous frequency-current curve and type II phase response curve (PRC) through Hopf bifurcation, and weak noise could prohibit spiking when bifurcation just occurs. The threshold that is insensitive to dV/dt, instead, results in a continuous frequency-current curve, a type I PRC and a saddle-node on invariant circle bifurcation, and simultaneously weak noise cannot inhibit spiking. It is also shown that the bifurcation, frequency-current curve and PRC type associated with different threshold dynamics arise from the distinct subthreshold interactions of membrane currents. Further, we observe that the energy consumption of the neuron is related to its firing characteristics. The depolarization of spike threshold improves neuronal energy efficiency by reducing the overlap of Na+ and K+ currents during an action potential. The high energy efficiency is achieved at more depolarized spike threshold and high stimulus current. These results provide a fundamental biophysical connection that links spike threshold dynamics, input-output relation, energetics and spike initiation, which could contribute to uncover neural encoding mechanism. PMID:26074810

Event-driven processing for hardware-efficient neural spike sorting

NASA Astrophysics Data System (ADS)

Liu, Yan; Pereira, João L.; Constandinou, Timothy G.

2018-02-01

Objective. The prospect of real-time and on-node spike sorting provides a genuine opportunity to push the envelope of large-scale integrated neural recording systems. In such systems the hardware resources, power requirements and data bandwidth increase linearly with channel count. Event-based (or data-driven) processing can provide here a new efficient means for hardware implementation that is completely activity dependant. In this work, we investigate using continuous-time level-crossing sampling for efficient data representation and subsequent spike processing. Approach. (1) We first compare signals (synthetic neural datasets) encoded with this technique against conventional sampling. (2) We then show how such a representation can be directly exploited by extracting simple time domain features from the bitstream to perform neural spike sorting. (3) The proposed method is implemented in a low power FPGA platform to demonstrate its hardware viability. Main results. It is observed that considerably lower data rates are achievable when using 7 bits or less to represent the signals, whilst maintaining the signal fidelity. Results obtained using both MATLAB and reconfigurable logic hardware (FPGA) indicate that feature extraction and spike sorting accuracies can be achieved with comparable or better accuracy than reference methods whilst also requiring relatively low hardware resources. Significance. By effectively exploiting continuous-time data representation, neural signal processing can be achieved in a completely event-driven manner, reducing both the required resources (memory, complexity) and computations (operations). This will see future large-scale neural systems integrating on-node processing in real-time hardware.

High efficiency versus maximal performance--the cause of oxidative stress in eukaryotes: a hypothesis.

PubMed

Kadenbach, Bernhard; Ramzan, Rabia; Vogt, Sebastian

2013-01-01

Degenerative diseases are in part based on elevated production of ROS (reactive oxygen species) in mitochondria, mainly during stress and excessive work under stress (strenuous exercise). The production of ROS increases with increasing mitochondrial membrane potential (ΔΨ(m)). A mechanism is described which is suggested to keep ΔΨ(m) at low values under normal conditions thus preventing ROS formation, but is switched off under stress and excessive work to maximize the rate of ATP synthesis, accompanied by decreased efficiency. Low ΔΨ(m) and low ROS production are suggested to occur by inhibition of respiration at high [ATP]/[ADP] ratios. The nucleotides interact with phosphorylated cytochrome c oxidase (COX), representing the step with the highest flux-control coefficient of mitochondrial respiration. At stress and excessive work neural signals are suggested to dephosphorylate the enzyme and abolish the control of COX activity (respiration) by the [ATP]/[ADP] ratio with consequent increase of ΔΨ(m) and ROS production. The control of COX by the [ATP]/[ADP] ratio, in addition, is proposed to increase the efficiency of ATP production via a third proton pumping pathway, identified in eukaryotic but not in prokaryotic COX. We conclude that 'oxidative stress' occurs when the control of COX activity by the [ATP]/[ADP] ratio is switched off via neural signals. 2012 Elsevier B.V. All rights reserved

A fuzzy integral method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification across multiple subjects.

PubMed

Cacha, L A; Parida, S; Dehuri, S; Cho, S-B; Poznanski, R R

2016-12-01

The huge number of voxels in fMRI over time poses a major challenge to for effective analysis. Fast, accurate, and reliable classifiers are required for estimating the decoding accuracy of brain activities. Although machine-learning classifiers seem promising, individual classifiers have their own limitations. To address this limitation, the present paper proposes a method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification for application across multiple subjects. Similarly, the fuzzy integral (FI) approach has been employed as an efficient tool for combining different classifiers. The FI approach led to the development of a classifiers ensemble technique that performs better than any of the single classifier by reducing the misclassification, the bias, and the variance. The proposed method successfully classified the different cognitive states for multiple subjects with high accuracy of classification. Comparison of the performance improvement, while applying ensemble neural networks method, vs. that of the individual neural network strongly points toward the usefulness of the proposed method.

The dynamical analysis of modified two-compartment neuron model and FPGA implementation

NASA Astrophysics Data System (ADS)

Lin, Qianjin; Wang, Jiang; Yang, Shuangming; Yi, Guosheng; Deng, Bin; Wei, Xile; Yu, Haitao

2017-10-01

The complexity of neural models is increasing with the investigation of larger biological neural network, more various ionic channels and more detailed morphologies, and the implementation of biological neural network is a task with huge computational complexity and power consumption. This paper presents an efficient digital design using piecewise linearization on field programmable gate array (FPGA), to succinctly implement the reduced two-compartment model which retains essential features of more complicated models. The design proposes an approximate neuron model which is composed of a set of piecewise linear equations, and it can reproduce different dynamical behaviors to depict the mechanisms of a single neuron model. The consistency of hardware implementation is verified in terms of dynamical behaviors and bifurcation analysis, and the simulation results including varied ion channel characteristics coincide with the biological neuron model with a high accuracy. Hardware synthesis on FPGA demonstrates that the proposed model has reliable performance and lower hardware resource compared with the original two-compartment model. These investigations are conducive to scalability of biological neural network in reconfigurable large-scale neuromorphic system.

Inverse simulation system for manual-controlled rendezvous and docking based on artificial neural network

NASA Astrophysics Data System (ADS)

Zhou, Wanmeng; Wang, Hua; Tang, Guojin; Guo, Shuai

2016-09-01

The time-consuming experimental method for handling qualities assessment cannot meet the increasing fast design requirements for the manned space flight. As a tool for the aircraft handling qualities research, the model-predictive-control structured inverse simulation (MPC-IS) has potential applications in the aerospace field to guide the astronauts' operations and evaluate the handling qualities more effectively. Therefore, this paper establishes MPC-IS for the manual-controlled rendezvous and docking (RVD) and proposes a novel artificial neural network inverse simulation system (ANN-IS) to further decrease the computational cost. The novel system was obtained by replacing the inverse model of MPC-IS with the artificial neural network. The optimal neural network was trained by the genetic Levenberg-Marquardt algorithm, and finally determined by the Levenberg-Marquardt algorithm. In order to validate MPC-IS and ANN-IS, the manual-controlled RVD experiments on the simulator were carried out. The comparisons between simulation results and experimental data demonstrated the validity of two systems and the high computational efficiency of ANN-IS.

Puzzle Imaging: Using Large-Scale Dimensionality Reduction Algorithms for Localization.

PubMed

Glaser, Joshua I; Zamft, Bradley M; Church, George M; Kording, Konrad P

2015-01-01

Current high-resolution imaging techniques require an intact sample that preserves spatial relationships. We here present a novel approach, "puzzle imaging," that allows imaging a spatially scrambled sample. This technique takes many spatially disordered samples, and then pieces them back together using local properties embedded within the sample. We show that puzzle imaging can efficiently produce high-resolution images using dimensionality reduction algorithms. We demonstrate the theoretical capabilities of puzzle imaging in three biological scenarios, showing that (1) relatively precise 3-dimensional brain imaging is possible; (2) the physical structure of a neural network can often be recovered based only on the neural connectivity matrix; and (3) a chemical map could be reproduced using bacteria with chemosensitive DNA and conjugative transfer. The ability to reconstruct scrambled images promises to enable imaging based on DNA sequencing of homogenized tissue samples.

Structural and functional correlates for language efficiency in auditory word processing.

PubMed

Jung, JeYoung; Kim, Sunmi; Cho, Hyesuk; Nam, Kichun

2017-01-01

This study aims to provide convergent understanding of the neural basis of auditory word processing efficiency using a multimodal imaging. We investigated the structural and functional correlates of word processing efficiency in healthy individuals. We acquired two structural imaging (T1-weighted imaging and diffusion tensor imaging) and functional magnetic resonance imaging (fMRI) during auditory word processing (phonological and semantic tasks). Our results showed that better phonological performance was predicted by the greater thalamus activity. In contrary, better semantic performance was associated with the less activation in the left posterior middle temporal gyrus (pMTG), supporting the neural efficiency hypothesis that better task performance requires less brain activation. Furthermore, our network analysis revealed the semantic network including the left anterior temporal lobe (ATL), dorsolateral prefrontal cortex (DLPFC) and pMTG was correlated with the semantic efficiency. Especially, this network acted as a neural efficient manner during auditory word processing. Structurally, DLPFC and cingulum contributed to the word processing efficiency. Also, the parietal cortex showed a significate association with the word processing efficiency. Our results demonstrated that two features of word processing efficiency, phonology and semantics, can be supported in different brain regions and, importantly, the way serving it in each region was different according to the feature of word processing. Our findings suggest that word processing efficiency can be achieved by in collaboration of multiple brain regions involved in language and general cognitive function structurally and functionally.

Structural and functional correlates for language efficiency in auditory word processing

PubMed Central

Kim, Sunmi; Cho, Hyesuk; Nam, Kichun

2017-01-01

This study aims to provide convergent understanding of the neural basis of auditory word processing efficiency using a multimodal imaging. We investigated the structural and functional correlates of word processing efficiency in healthy individuals. We acquired two structural imaging (T1-weighted imaging and diffusion tensor imaging) and functional magnetic resonance imaging (fMRI) during auditory word processing (phonological and semantic tasks). Our results showed that better phonological performance was predicted by the greater thalamus activity. In contrary, better semantic performance was associated with the less activation in the left posterior middle temporal gyrus (pMTG), supporting the neural efficiency hypothesis that better task performance requires less brain activation. Furthermore, our network analysis revealed the semantic network including the left anterior temporal lobe (ATL), dorsolateral prefrontal cortex (DLPFC) and pMTG was correlated with the semantic efficiency. Especially, this network acted as a neural efficient manner during auditory word processing. Structurally, DLPFC and cingulum contributed to the word processing efficiency. Also, the parietal cortex showed a significate association with the word processing efficiency. Our results demonstrated that two features of word processing efficiency, phonology and semantics, can be supported in different brain regions and, importantly, the way serving it in each region was different according to the feature of word processing. Our findings suggest that word processing efficiency can be achieved by in collaboration of multiple brain regions involved in language and general cognitive function structurally and functionally. PMID:28892503

Optical information-processing systems and architectures II; Proceedings of the Meeting, San Diego, CA, July 9-13, 1990

NASA Astrophysics Data System (ADS)

Javidi, Bahram

The present conference discusses topics in the fields of neural networks, acoustooptic signal processing, pattern recognition, phase-only processing, nonlinear signal processing, image processing, optical computing, and optical information processing. Attention is given to the optical implementation of an inner-product neural associative memory, optoelectronic associative recall via motionless-head/parallel-readout optical disk, a compact real-time acoustooptic image correlator, a multidimensional synthetic estimation filter, and a light-efficient joint transform optical correlator. Also discussed are a high-resolution spatial light modulator, compact real-time interferometric Fourier-transform processors, a fast decorrelation algorithm for permutation arrays, the optical interconnection of optical modules, and carry-free optical binary adders.

ConvNetQuake: Convolutional Neural Network for Earthquake Detection and Location

NASA Astrophysics Data System (ADS)

Denolle, M.; Perol, T.; Gharbi, M.

2017-12-01

Over the last decades, the volume of seismic data has increased exponentially, creating a need for efficient algorithms to reliably detect and locate earthquakes. Today's most elaborate methods scan through the plethora of continuous seismic records, searching for repeating seismic signals. In this work, we leverage the recent advances in artificial intelligence and present ConvNetQuake, a highly scalable convolutional neural network for probabilistic earthquake detection and location from single stations. We apply our technique to study two years of induced seismicity in Oklahoma (USA). We detect 20 times more earthquakes than previously cataloged by the Oklahoma Geological Survey. Our algorithm detection performances are at least one order of magnitude faster than other established methods.

Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network, particle swarm optimization and expectation maximization

NASA Astrophysics Data System (ADS)

Qiu, Sihang; Chen, Bin; Wang, Rongxiao; Zhu, Zhengqiu; Wang, Yuan; Qiu, Xiaogang

2018-04-01

Hazardous gas leak accident has posed a potential threat to human beings. Predicting atmospheric dispersion and estimating its source become increasingly important in emergency management. Current dispersion prediction and source estimation models cannot satisfy the requirement of emergency management because they are not equipped with high efficiency and accuracy at the same time. In this paper, we develop a fast and accurate dispersion prediction and source estimation method based on artificial neural network (ANN), particle swarm optimization (PSO) and expectation maximization (EM). The novel method uses a large amount of pre-determined scenarios to train the ANN for dispersion prediction, so that the ANN can predict concentration distribution accurately and efficiently. PSO and EM are applied for estimating the source parameters, which can effectively accelerate the process of convergence. The method is verified by the Indianapolis field study with a SF6 release source. The results demonstrate the effectiveness of the method.

Optimized pulsed write schemes improve linearity and write speed for low-power organic neuromorphic devices

NASA Astrophysics Data System (ADS)

Keene, Scott T.; Melianas, Armantas; Fuller, Elliot J.; van de Burgt, Yoeri; Talin, A. Alec; Salleo, Alberto

2018-06-01

Neuromorphic devices are becoming increasingly appealing as efficient emulators of neural networks used to model real world problems. However, no hardware to date has demonstrated the necessary high accuracy and energy efficiency gain over CMOS in both (1) training via backpropagation and (2) in read via vector matrix multiplication. Such shortcomings are due to device non-idealities, particularly asymmetric conductance tuning in response to uniform voltage pulse inputs. Here, by formulating a general circuit model for capacitive ion-exchange neuromorphic devices, we show that asymmetric nonlinearity in organic electrochemical neuromorphic devices (ENODes) can be suppressed by an appropriately chosen write scheme. Simulations based upon our model suggest that a nonlinear write-selector could reduce the switching voltage and energy, enabling analog tuning via a continuous set of resistance states (100 states) with extremely low switching energy (~170 fJ · µm‑2). This work clarifies the pathway to neural algorithm accelerators capable of parallelism during both read and write operations.

Neural processing of race during imitation: self-similarity versus social status

PubMed Central

Reynolds Losin, Elizabeth A.; Cross, Katy A.; Iacoboni, Marco; Dapretto, Mirella

2017-01-01

People preferentially imitate others who are similar to them or have high social status. Such imitative biases are thought to have evolved because they increase the efficiency of cultural acquisition. Here we focused on distinguishing between self-similarity and social status as two candidate mechanisms underlying neural responses to a person’s race during imitation. We used fMRI to measure neural responses when 20 African American (AA) and 20 European American (EA) young adults imitated AA, EA and Chinese American (CA) models and also passively observed their gestures and faces. We found that both AA and EA participants exhibited more activity in lateral fronto-parietal and visual regions when imitating AAs compared to EAs or CAs. These results suggest that racial self-similarity is not likely to modulate neural responses to race during imitation, in contrast with findings from previous neuroimaging studies of face perception and action observation. Furthermore, AA and EA participants associated AAs with lower social status than EAs or CAs, suggesting that the social status associated with different racial groups may instead modulate neural activity during imitation of individuals from those groups. Taken together, these findings suggest that neural responses to race during imitation are driven by socially-learned associations rather than self-similarity. This may reflect the adaptive role of imitation in social learning, where learning from higher-status models can be more beneficial. This study provides neural evidence consistent with evolutionary theories of cultural acquisition. PMID:23813738

Human induced pluripotent stem cell-derived glial cells and neural progenitors display divergent responses to Zika and dengue infections.

PubMed

Muffat, Julien; Li, Yun; Omer, Attya; Durbin, Ann; Bosch, Irene; Bakiasi, Grisilda; Richards, Edward; Meyer, Aaron; Gehrke, Lee; Jaenisch, Rudolf

2018-06-18

Maternal Zika virus (ZIKV) infection during pregnancy is recognized as the cause of an epidemic of microcephaly and other neurological anomalies in human fetuses. It remains unclear how ZIKV accesses the highly vulnerable population of neural progenitors of the fetal central nervous system (CNS), and which cell types of the CNS may be viral reservoirs. In contrast, the related dengue virus (DENV) does not elicit teratogenicity. To model viral interaction with cells of the fetal CNS in vitro, we investigated the tropism of ZIKV and DENV for different induced pluripotent stem cell-derived human cells, with a particular focus on microglia-like cells. We show that ZIKV infected isogenic neural progenitors, astrocytes, and microglia-like cells (pMGLs), but was only cytotoxic to neural progenitors. Infected glial cells propagated ZIKV and maintained ZIKV load over time, leading to viral spread to susceptible cells. DENV triggered stronger immune responses and could be cleared by neural and glial cells more efficiently. pMGLs, when cocultured with neural spheroids, invaded the tissue and, when infected with ZIKV, initiated neural infection. Since microglia derive from primitive macrophages originating in proximity to the maternal vasculature, they may act as a viral reservoir for ZIKV and establish infection of the fetal brain. Infection of immature neural stem cells by invading microglia may occur in the early stages of pregnancy, before angiogenesis in the brain rudiments. Our data are also consistent with ZIKV and DENV affecting the integrity of the blood-brain barrier, thus allowing infection of the brain later in life.

Analysis of the dynamic behavior of structures using the high-rate GNSS-PPP method combined with a wavelet-neural model: Numerical simulation and experimental tests

NASA Astrophysics Data System (ADS)

Kaloop, Mosbeh R.; Yigit, Cemal O.; Hu, Jong W.

2018-03-01

Recently, the high rate global navigation satellite system-precise point positioning (GNSS-PPP) technique has been used to detect the dynamic behavior of structures. This study aimed to increase the accuracy of the extraction oscillation properties of structural movements based on the high-rate (10 Hz) GNSS-PPP monitoring technique. A developmental model based on the combination of wavelet package transformation (WPT) de-noising and neural network prediction (NN) was proposed to improve the dynamic behavior of structures for GNSS-PPP method. A complicated numerical simulation involving highly noisy data and 13 experimental cases with different loads were utilized to confirm the efficiency of the proposed model design and the monitoring technique in detecting the dynamic behavior of structures. The results revealed that, when combined with the proposed model, GNSS-PPP method can be used to accurately detect the dynamic behavior of engineering structures as an alternative to relative GNSS method.

Convolutional Neural Network-Based Robot Navigation Using Uncalibrated Spherical Images †

PubMed Central

Ran, Lingyan; Zhang, Yanning; Zhang, Qilin; Yang, Tao

2017-01-01

Vision-based mobile robot navigation is a vibrant area of research with numerous algorithms having been developed, the vast majority of which either belong to the scene-oriented simultaneous localization and mapping (SLAM) or fall into the category of robot-oriented lane-detection/trajectory tracking. These methods suffer from high computational cost and require stringent labelling and calibration efforts. To address these challenges, this paper proposes a lightweight robot navigation framework based purely on uncalibrated spherical images. To simplify the orientation estimation, path prediction and improve computational efficiency, the navigation problem is decomposed into a series of classification tasks. To mitigate the adverse effects of insufficient negative samples in the “navigation via classification” task, we introduce the spherical camera for scene capturing, which enables 360° fisheye panorama as training samples and generation of sufficient positive and negative heading directions. The classification is implemented as an end-to-end Convolutional Neural Network (CNN), trained on our proposed Spherical-Navi image dataset, whose category labels can be efficiently collected. This CNN is capable of predicting potential path directions with high confidence levels based on a single, uncalibrated spherical image. Experimental results demonstrate that the proposed framework outperforms competing ones in realistic applications. PMID:28604624

Convolutional Neural Network-Based Robot Navigation Using Uncalibrated Spherical Images.

PubMed

Ran, Lingyan; Zhang, Yanning; Zhang, Qilin; Yang, Tao

2017-06-12

Vision-based mobile robot navigation is a vibrant area of research with numerous algorithms having been developed, the vast majority of which either belong to the scene-oriented simultaneous localization and mapping (SLAM) or fall into the category of robot-oriented lane-detection/trajectory tracking. These methods suffer from high computational cost and require stringent labelling and calibration efforts. To address these challenges, this paper proposes a lightweight robot navigation framework based purely on uncalibrated spherical images. To simplify the orientation estimation, path prediction and improve computational efficiency, the navigation problem is decomposed into a series of classification tasks. To mitigate the adverse effects of insufficient negative samples in the "navigation via classification" task, we introduce the spherical camera for scene capturing, which enables 360° fisheye panorama as training samples and generation of sufficient positive and negative heading directions. The classification is implemented as an end-to-end Convolutional Neural Network (CNN), trained on our proposed Spherical-Navi image dataset, whose category labels can be efficiently collected. This CNN is capable of predicting potential path directions with high confidence levels based on a single, uncalibrated spherical image. Experimental results demonstrate that the proposed framework outperforms competing ones in realistic applications.

Lidar detection of underwater objects using a neuro-SVM-based architecture.

PubMed

Mitra, Vikramjit; Wang, Chia-Jiu; Banerjee, Satarupa

2006-05-01

This paper presents a neural network architecture using a support vector machine (SVM) as an inference engine (IE) for classification of light detection and ranging (Lidar) data. Lidar data gives a sequence of laser backscatter intensities obtained from laser shots generated from an airborne object at various altitudes above the earth surface. Lidar data is pre-filtered to remove high frequency noise. As the Lidar shots are taken from above the earth surface, it has some air backscatter information, which is of no importance for detecting underwater objects. Because of these, the air backscatter information is eliminated from the data and a segment of this data is subsequently selected to extract features for classification. This is then encoded using linear predictive coding (LPC) and polynomial approximation. The coefficients thus generated are used as inputs to the two branches of a parallel neural architecture. The decisions obtained from the two branches are vector multiplied and the result is fed to an SVM-based IE that presents the final inference. Two parallel neural architectures using multilayer perception (MLP) and hybrid radial basis function (HRBF) are considered in this paper. The proposed structure fits the Lidar data classification task well due to the inherent classification efficiency of neural networks and accurate decision-making capability of SVM. A Bayesian classifier and a quadratic classifier were considered for the Lidar data classification task but they failed to offer high prediction accuracy. Furthermore, a single-layered artificial neural network (ANN) classifier was also considered and it failed to offer good accuracy. The parallel ANN architecture proposed in this paper offers high prediction accuracy (98.9%) and is found to be the most suitable architecture for the proposed task of Lidar data classification.

Neural Network and Regression Soft Model Extended for PAX-300 Aircraft Engine

NASA Technical Reports Server (NTRS)

Patnaik, Surya N.; Hopkins, Dale A.

2002-01-01

In fiscal year 2001, the neural network and regression capabilities of NASA Glenn Research Center's COMETBOARDS design optimization testbed were extended to generate approximate models for the PAX-300 aircraft engine. The analytical model of the engine is defined through nine variables: the fan efficiency factor, the low pressure of the compressor, the high pressure of the compressor, the high pressure of the turbine, the low pressure of the turbine, the operating pressure, and three critical temperatures (T(sub 4), T(sub vane), and T(sub metal)). Numerical Propulsion System Simulation (NPSS) calculations of the specific fuel consumption (TSFC), as a function of the variables can become time consuming, and numerical instabilities can occur during these design calculations. "Soft" models can alleviate both deficiencies. These approximate models are generated from a set of high-fidelity input-output pairs obtained from the NPSS code and a design of the experiment strategy. A neural network and a regression model with 45 weight factors were trained for the input/output pairs. Then, the trained models were validated through a comparison with the original NPSS code. Comparisons of TSFC versus the operating pressure and of TSFC versus the three temperatures (T(sub 4), T(sub vane), and T(sub metal)) are depicted in the figures. The overall performance was satisfactory for both the regression and the neural network model. The regression model required fewer calculations than the neural network model, and it produced marginally superior results. Training the approximate methods is time consuming. Once trained, the approximate methods generated the solution with only a trivial computational effort, reducing the solution time from hours to less than a minute.

Efficient Cancer Detection Using Multiple Neural Networks.

PubMed

Shell, John; Gregory, William D

2017-01-01

The inspection of live excised tissue specimens to ascertain malignancy is a challenging task in dermatopathology and generally in histopathology. We introduce a portable desktop prototype device that provides highly accurate neural network classification of malignant and benign tissue. The handheld device collects 47 impedance data samples from 1 Hz to 32 MHz via tetrapolar blackened platinum electrodes. The data analysis was implemented with six different backpropagation neural networks (BNN). A data set consisting of 180 malignant and 180 benign breast tissue data files in an approved IRB study at the Aurora Medical Center, Milwaukee, WI, USA, were utilized as a neural network input. The BNN structure consisted of a multi-tiered consensus approach autonomously selecting four of six neural networks to determine a malignant or benign classification. The BNN analysis was then compared with the histology results with consistent sensitivity of 100% and a specificity of 100%. This implementation successfully relied solely on statistical variation between the benign and malignant impedance data and intricate neural network configuration. This device and BNN implementation provides a novel approach that could be a valuable tool to augment current medical practice assessment of the health of breast, squamous, and basal cell carcinoma and other excised tissue without requisite tissue specimen expertise. It has the potential to provide clinical management personnel with a fast non-invasive accurate assessment of biopsied or sectioned excised tissue in various clinical settings.

Efficient Cancer Detection Using Multiple Neural Networks

PubMed Central

Gregory, William D.

2017-01-01

The inspection of live excised tissue specimens to ascertain malignancy is a challenging task in dermatopathology and generally in histopathology. We introduce a portable desktop prototype device that provides highly accurate neural network classification of malignant and benign tissue. The handheld device collects 47 impedance data samples from 1 Hz to 32 MHz via tetrapolar blackened platinum electrodes. The data analysis was implemented with six different backpropagation neural networks (BNN). A data set consisting of 180 malignant and 180 benign breast tissue data files in an approved IRB study at the Aurora Medical Center, Milwaukee, WI, USA, were utilized as a neural network input. The BNN structure consisted of a multi-tiered consensus approach autonomously selecting four of six neural networks to determine a malignant or benign classification. The BNN analysis was then compared with the histology results with consistent sensitivity of 100% and a specificity of 100%. This implementation successfully relied solely on statistical variation between the benign and malignant impedance data and intricate neural network configuration. This device and BNN implementation provides a novel approach that could be a valuable tool to augment current medical practice assessment of the health of breast, squamous, and basal cell carcinoma and other excised tissue without requisite tissue specimen expertise. It has the potential to provide clinical management personnel with a fast non-invasive accurate assessment of biopsied or sectioned excised tissue in various clinical settings. PMID:29282435

Neural reactivity to reward in school-age offspring of depressed mothers.

PubMed

Wiggins, Jillian Lee; Schwartz, Karen T G; Kryza-Lacombe, Maria; Spechler, Philip A; Blankenship, Sarah L; Dougherty, Lea R

2017-05-01

Identifying neural profiles predictive of future psychopathology in at-risk individuals is important to efficiently direct preventive care. Alterations in reward processing may be a risk factor for depression. The current study characterized neural substrates of reward processing in children at low- and high-risk for psychopathology due to maternal depression status. Children with (n=27) and without (n=19) maternal depression (ages 5.9-9.6 years) performed a monetary incentive delay task in which they received rewards, if they successfully hit a target, or no reward regardless of performance, during fMRI acquisition. Multiple dorsal prefrontal, temporal, and striatal regions showed significant Group (high- vs. low-risk)×Performance (hit vs. miss)×Condition (no reward vs. reward) interactions in a whole-brain analysis. All regions exhibited similar patterns, whereby the high-risk group showed blunted activation differences between trials with vs. without rewards when participants hit the target. Moreover, high-risk children showed activation differences between trials with vs. without rewards in the opposite direction, compared to the low-risk group, when they missed the target. This study had a modest sample size, though larger than existing studies. Children with maternal depression are at elevated risk for future psychopathology, yet not all experience clinically significant symptoms; longitudinal research is necessary to fully track the pathway from risk to disorder. Children of depressed mothers exhibited attenuated neural activation differences and activation patterns opposite to children without depressed mothers. Our findings may provide targets for hypothesis-driven preventive interventions and lead to earlier identification of individuals at risk. Copyright © 2017 Elsevier B.V. All rights reserved.

Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks.

PubMed

Navlakha, Saket; Barth, Alison L; Bar-Joseph, Ziv

2015-07-01

Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains.

Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks

PubMed Central

Navlakha, Saket; Barth, Alison L.; Bar-Joseph, Ziv

2015-01-01

Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains. PMID:26217933

Building on prior knowledge without building it in.

PubMed

Hansen, Steven S; Lampinen, Andrew K; Suri, Gaurav; McClelland, James L

2017-01-01

Lake et al. propose that people rely on "start-up software," "causal models," and "intuitive theories" built using compositional representations to learn new tasks more efficiently than some deep neural network models. We highlight the many drawbacks of a commitment to compositional representations and describe our continuing effort to explore how the ability to build on prior knowledge and to learn new tasks efficiently could arise through learning in deep neural networks.

Learning and diagnosing faults using neural networks

NASA Technical Reports Server (NTRS)

Whitehead, Bruce A.; Kiech, Earl L.; Ali, Moonis

1990-01-01

Neural networks have been employed for learning fault behavior from rocket engine simulator parameters and for diagnosing faults on the basis of the learned behavior. Two problems in applying neural networks to learning and diagnosing faults are (1) the complexity of the sensor data to fault mapping to be modeled by the neural network, which implies difficult and lengthy training procedures; and (2) the lack of sufficient training data to adequately represent the very large number of different types of faults which might occur. Methods are derived and tested in an architecture which addresses these two problems. First, the sensor data to fault mapping is decomposed into three simpler mappings which perform sensor data compression, hypothesis generation, and sensor fusion. Efficient training is performed for each mapping separately. Secondly, the neural network which performs sensor fusion is structured to detect new unknown faults for which training examples were not presented during training. These methods were tested on a task of fault diagnosis by employing rocket engine simulator data. Results indicate that the decomposed neural network architecture can be trained efficiently, can identify faults for which it has been trained, and can detect the occurrence of faults for which it has not been trained.

Fuzzy wavelet plus a quantum neural network as a design base for power system stability enhancement.

PubMed

Ganjefar, Soheil; Tofighi, Morteza; Karami, Hamidreza

2015-11-01

In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the proposed scheme. All of the controller parameters are tuned online based on the Lyapunov stability theory to guarantee the closed-loop stability. A two-machine, two-area power system equipped with a static synchronous series compensator as a series flexible ac transmission system was used to demonstrate the effectiveness of the proposed controller. The simulation and experimental results demonstrated that the proposed IAFWNC scheme can achieve favorable control performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

Fault Analysis of Space Station DC Power Systems-Using Neural Network Adaptive Wavelets to Detect Faults

NASA Technical Reports Server (NTRS)

Momoh, James A.; Wang, Yanchun; Dolce, James L.

1997-01-01

This paper describes the application of neural network adaptive wavelets for fault diagnosis of space station power system. The method combines wavelet transform with neural network by incorporating daughter wavelets into weights. Therefore, the wavelet transform and neural network training procedure become one stage, which avoids the complex computation of wavelet parameters and makes the procedure more straightforward. The simulation results show that the proposed method is very efficient for the identification of fault locations.

High MRI performance fluorescent mesoporous silica-coated magnetic nanoparticles for tracking neural progenitor cells in an ischemic mouse model

NASA Astrophysics Data System (ADS)

Zhang, Lu; Wang, Yao; Tang, Yaohui; Jiao, Zheng; Xie, Chengying; Zhang, Haijiao; Gu, Ping; Wei, Xunbin; Yang, Guo-Yuan; Gu, Hongchen; Zhang, Chunfu

2013-05-01

Multifunctional probes with high MRI sensitivity and high efficiency for cell labeling are desirable for MR cell imaging. Herein, we have fabricated fluorescent mesoporous silica-coated superparamagnetic iron oxide nanoparticles (fmSiO4@SPIONs) for neural progenitor cell (C17.2) MR imaging. FmSiO4@SPIONs were discrete and uniform in size, and had a clear core-shell structure. The magnetic core size was about 10 nm and the fluorescent mesoporous silica coating layer was around 20 nm. Compared with fluorescent dense silica-coated SPIONs (fdSiO4@SPIONs) with a similar size, fmSiO4@SPIONs demonstrated higher MR sensitivity and cell labeling efficiency. When implanted into the right hemisphere of stroke mice, contralateral to the ischemic territory, a small amount of labeled cells were able to be tracked migrating to the lesion sites using a clinical MRI scanner (3 T). More impressively, even when administered intravenously, the labeled cells could also be monitored homing to the ischemic area. MRI observations were corroborated by histological studies of the brain tissues. Our study demonstrated that fmSiO4@SPIONs are highly effective for cell imaging and hold great promise for MRI cell tracking in future.Multifunctional probes with high MRI sensitivity and high efficiency for cell labeling are desirable for MR cell imaging. Herein, we have fabricated fluorescent mesoporous silica-coated superparamagnetic iron oxide nanoparticles (fmSiO4@SPIONs) for neural progenitor cell (C17.2) MR imaging. FmSiO4@SPIONs were discrete and uniform in size, and had a clear core-shell structure. The magnetic core size was about 10 nm and the fluorescent mesoporous silica coating layer was around 20 nm. Compared with fluorescent dense silica-coated SPIONs (fdSiO4@SPIONs) with a similar size, fmSiO4@SPIONs demonstrated higher MR sensitivity and cell labeling efficiency. When implanted into the right hemisphere of stroke mice, contralateral to the ischemic territory, a small amount of labeled cells were able to be tracked migrating to the lesion sites using a clinical MRI scanner (3 T). More impressively, even when administered intravenously, the labeled cells could also be monitored homing to the ischemic area. MRI observations were corroborated by histological studies of the brain tissues. Our study demonstrated that fmSiO4@SPIONs are highly effective for cell imaging and hold great promise for MRI cell tracking in future. Electronic supplementary information (ESI) available: Details of cell internalization of fmSiO4@SPIONs compared with SHU555A, immunofluorescence image of the immature phenotype of labeled C17.2. See DOI: 10.1039/c3nr00119a

Anger in brain and body: the neural and physiological perturbation of decision-making by emotion

PubMed Central

Zorab, Emma; Navaratnam, Nakulan; Engels, Miriam; Mallorquí-Bagué, Núria; Minati, Ludovico; Dowell, Nicholas G.; Brosschot, Jos F.; Thayer, Julian F.; Critchley, Hugo D.

2016-01-01

Emotion and cognition are dynamically coupled to bodily arousal: the induction of anger, even unconsciously, can reprioritise neural and physiological resources toward action states that bias cognitive processes. Here we examine behavioural, neural and bodily effects of covert anger processing and its influence on cognition, indexed by lexical decision-making. While recording beat-to-beat blood pressure, the words ANGER or RELAX were presented subliminally just prior to rapid word/non-word reaction-time judgements of letter-strings. Subliminal ANGER primes delayed the time taken to reach rapid lexical decisions, relative to RELAX primes. However, individuals with high trait anger were speeded up by subliminal anger primes. ANGER primes increased systolic blood pressure and the magnitude of this increase predicted reaction time prolongation. Within the brain, ANGER trials evoked an enhancement of activity within dorsal pons and an attenuation of activity within visual occipitotemporal and attentional parietal cortices. Activity within periaqueductal grey matter, occipital and parietal regions increased linearly with evoked blood pressure changes, indicating neural substrates through which covert anger impairs semantic decisions, putatively through its expression as visceral arousal. The behavioural and physiological impact of anger states compromises the efficiency of cognitive processing through action-ready changes in autonomic response that skew regional neural activity. PMID:26253525

Skin-derived neural precursors competitively generate functional myelin in adult demyelinated mice

PubMed Central

Mozafari, Sabah; Laterza, Cecilia; Roussel, Delphine; Bachelin, Corinne; Marteyn, Antoine; Deboux, Cyrille; Martino, Gianvito; Evercooren, Anne Baron-Van

2015-01-01

Induced pluripotent stem cell–derived (iPS-derived) neural precursor cells may represent the ideal autologous cell source for cell-based therapy to promote remyelination and neuroprotection in myelin diseases. So far, the therapeutic potential of reprogrammed cells has been evaluated in neonatal demyelinating models. However, the repair efficacy and safety of these cells has not been well addressed in the demyelinated adult CNS, which has decreased cell plasticity and scarring. Moreover, it is not clear if these induced pluripotent–derived cells have the same reparative capacity as physiologically committed CNS-derived precursors. Here, we performed a side-by-side comparison of CNS-derived and skin-derived neural precursors in culture and following engraftment in murine models of adult spinal cord demyelination. Grafted induced neural precursors exhibited a high capacity for survival, safe integration, migration, and timely differentiation into mature bona fide oligodendrocytes. Moreover, grafted skin–derived neural precursors generated compact myelin around host axons and restored nodes of Ranvier and conduction velocity as efficiently as CNS-derived precursors while outcompeting endogenous cells. Together, these results provide important insights into the biology of reprogrammed cells in adult demyelinating conditions and support use of these cells for regenerative biomedicine of myelin diseases that affect the adult CNS. PMID:26301815

Intelligence related upper alpha desynchronization in a semantic memory task.

PubMed

Doppelmayr, M; Klimesch, W; Hödlmoser, K; Sauseng, P; Gruber, W

2005-07-30

Recent evidence shows that event-related (upper) alpha desynchronization (ERD) is related to cognitive performance. Several studies observed a positive, some a negative relationship. The latter finding, interpreted in terms of the neural efficiency hypothesis, suggests that good performance is associated with a more 'efficient', smaller extent of cortical activation. Other studies found that ERD increases with semantic processing demands and that this increase is larger for good performers. Studies supporting the neural efficiency hypothesis used tasks that do not specifically require semantic processing. Thus, we assume that the lack of semantic processing demands may at least in part be responsible for the reduced ERD. In the present study we measured ERD during a difficult verbal-semantic task. The findings demonstrate that during semantic processing, more intelligent (as compared to less intelligent) subjects exhibited a significantly larger upper alpha ERD over the left hemisphere. We conclude that more intelligent subjects exhibit a more extensive activation in a semantic processing system and suggest that divergent findings regarding the neural efficiency hypotheses are due to task specific differences in semantic processing demands.

The Prediction of the Risk Level of Pulmonary Embolism and Deep Vein Thrombosis through Artificial Neural Network

PubMed Central

Agharezaei, Laleh; Agharezaei, Zhila; Nemati, Ali; Bahaadinbeigy, Kambiz; Keynia, Farshid; Baneshi, Mohammad Reza; Iranpour, Abedin; Agharezaei, Moslem

2016-01-01

Background: Venous thromboembolism is a common cause of mortality among hospitalized patients and yet it is preventable through detecting the precipitating factors and a prompt diagnosis by specialists. The present study has been carried out in order to assist specialists in the diagnosis and prediction of the risk level of pulmonary embolism in patients, by means of artificial neural network. Method: A number of 31 risk factors have been used in this study in order to evaluate the conditions of 294 patients hospitalized in 3 educational hospitals affiliated with Kerman University of Medical Sciences. Two types of artificial neural networks, namely Feed-Forward Back Propagation and Elman Back Propagation, were compared in this study. Results: Through an optimized artificial neural network model, an accuracy and risk level index of 93.23 percent was achieved and, subsequently, the results have been compared with those obtained from the perfusion scan of the patients. 86.61 percent of high risk patients diagnosed through perfusion scan diagnostic method were also diagnosed correctly through the method proposed in the present study. Conclusions: The results of this study can be a good resource for physicians, medical assistants, and healthcare staff to diagnose high risk patients more precisely and prevent the mortalities. Additionally, expenses and other unnecessary diagnostic methods such as perfusion scans can be efficiently reduced. PMID:28077893

The Prediction of the Risk Level of Pulmonary Embolism and Deep Vein Thrombosis through Artificial Neural Network.

PubMed

Agharezaei, Laleh; Agharezaei, Zhila; Nemati, Ali; Bahaadinbeigy, Kambiz; Keynia, Farshid; Baneshi, Mohammad Reza; Iranpour, Abedin; Agharezaei, Moslem

2016-10-01

Venous thromboembolism is a common cause of mortality among hospitalized patients and yet it is preventable through detecting the precipitating factors and a prompt diagnosis by specialists. The present study has been carried out in order to assist specialists in the diagnosis and prediction of the risk level of pulmonary embolism in patients, by means of artificial neural network. A number of 31 risk factors have been used in this study in order to evaluate the conditions of 294 patients hospitalized in 3 educational hospitals affiliated with Kerman University of Medical Sciences. Two types of artificial neural networks, namely Feed-Forward Back Propagation and Elman Back Propagation, were compared in this study. Through an optimized artificial neural network model, an accuracy and risk level index of 93.23 percent was achieved and, subsequently, the results have been compared with those obtained from the perfusion scan of the patients. 86.61 percent of high risk patients diagnosed through perfusion scan diagnostic method were also diagnosed correctly through the method proposed in the present study. The results of this study can be a good resource for physicians, medical assistants, and healthcare staff to diagnose high risk patients more precisely and prevent the mortalities. Additionally, expenses and other unnecessary diagnostic methods such as perfusion scans can be efficiently reduced.

Toward a distributed free-floating wireless implantable neural recording system.

PubMed

Pyungwoo Yeon; Xingyuan Tong; Byunghun Lee; Mirbozorgi, Abdollah; Ash, Bruce; Eckhardt, Helmut; Ghovanloo, Maysam

2016-08-01

To understand the complex correlations between neural networks across different regions in the brain and their functions at high spatiotemporal resolution, a tool is needed for obtaining long-term single unit activity (SUA) across the entire brain area. The concept and preliminary design of a distributed free-floating wireless implantable neural recording (FF-WINeR) system are presented, which can enabling SUA acquisition by dispersedly implanting tens to hundreds of untethered 1 mm3 neural recording probes, floating with the brain and operating wirelessly across the cortical surface. For powering FF-WINeR probes, a 3-coil link with an intermediate high-Q resonator provides a minimum S21 of -22.22 dB (in the body medium) and -21.23 dB (in air) at 2.8 cm coil separation, which translates to 0.76%/759 μW and 0.6%/604 μW of power transfer efficiency (PTE) / power delivered to a 9 kΩ load (PDL), in body and air, respectively. A mock-up FF-WINeR is implemented to explore microassembly method of the 1×1 mm2 micromachined silicon die with a bonding wire-wound coil and a tungsten micro-wire electrode. Circuit design methods to fit the active circuitry in only 0.96 mm2 of die area in a 130 nm standard CMOS process, and satisfy the strict power and performance requirements (in simulations) are discussed.

Complex Environmental Data Modelling Using Adaptive General Regression Neural Networks

NASA Astrophysics Data System (ADS)

Kanevski, Mikhail

2015-04-01

The research deals with an adaptation and application of Adaptive General Regression Neural Networks (GRNN) to high dimensional environmental data. GRNN [1,2,3] are efficient modelling tools both for spatial and temporal data and are based on nonparametric kernel methods closely related to classical Nadaraya-Watson estimator. Adaptive GRNN, using anisotropic kernels, can be also applied for features selection tasks when working with high dimensional data [1,3]. In the present research Adaptive GRNN are used to study geospatial data predictability and relevant feature selection using both simulated and real data case studies. The original raw data were either three dimensional monthly precipitation data or monthly wind speeds embedded into 13 dimensional space constructed by geographical coordinates and geo-features calculated from digital elevation model. GRNN were applied in two different ways: 1) adaptive GRNN with the resulting list of features ordered according to their relevancy; and 2) adaptive GRNN applied to evaluate all possible models N [in case of wind fields N=(2^13 -1)=8191] and rank them according to the cross-validation error. In both cases training were carried out applying leave-one-out procedure. An important result of the study is that the set of the most relevant features depends on the month (strong seasonal effect) and year. The predictabilities of precipitation and wind field patterns, estimated using the cross-validation and testing errors of raw and shuffled data, were studied in detail. The results of both approaches were qualitatively and quantitatively compared. In conclusion, Adaptive GRNN with their ability to select features and efficient modelling of complex high dimensional data can be widely used in automatic/on-line mapping and as an integrated part of environmental decision support systems. 1. Kanevski M., Pozdnoukhov A., Timonin V. Machine Learning for Spatial Environmental Data. Theory, applications and software. EPFL Press. With a CD: data, software, guides. (2009). 2. Kanevski M. Spatial Predictions of Soil Contamination Using General Regression Neural Networks. Systems Research and Information Systems, Volume 8, number 4, 1999. 3. Robert S., Foresti L., Kanevski M. Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks. International Journal of Climatology, 33 pp. 1793-1804, 2013.

Emotional System for Military Target Identification

DTIC Science & Technology

2009-10-01

algorithm [23], and used it to solve a facial recognition problem. In other works [24,25], we explored the potential of using emotional neural...other application areas, such as security ( facial recognition ) and medical (blood cell identification), can be also efficiently used in military...Application of an emotional neural network to facial recognition . Neural Computing and Applications, 18(4), 309-320. [25] Khashman, A. (2009). Blood cell

Functional screening assays with neurons generated from pluripotent stem cell-derived neural stem cells.

PubMed

Efthymiou, Anastasia; Shaltouki, Atossa; Steiner, Joseph P; Jha, Balendu; Heman-Ackah, Sabrina M; Swistowski, Andrzej; Zeng, Xianmin; Rao, Mahendra S; Malik, Nasir

2014-01-01

Rapid and effective drug discovery for neurodegenerative disease is currently impeded by an inability to source primary neural cells for high-throughput and phenotypic screens. This limitation can be addressed through the use of pluripotent stem cells (PSCs), which can be derived from patient-specific samples and differentiated to neural cells for use in identifying novel compounds for the treatment of neurodegenerative diseases. We have developed an efficient protocol to culture pure populations of neurons, as confirmed by gene expression analysis, in the 96-well format necessary for screens. These differentiated neurons were subjected to viability assays to illustrate their potential in future high-throughput screens. We have also shown that organelles such as nuclei and mitochondria could be live-labeled and visualized through fluorescence, suggesting that we should be able to monitor subcellular phenotypic changes. Neurons derived from a green fluorescent protein-expressing reporter line of PSCs were live-imaged to assess markers of neuronal maturation such as neurite length and co-cultured with astrocytes to demonstrate further maturation. These studies confirm that PSC-derived neurons can be used effectively in viability and functional assays and pave the way for high-throughput screens on neurons derived from patients with neurodegenerative disorders.

Puzzle Imaging: Using Large-Scale Dimensionality Reduction Algorithms for Localization

PubMed Central

Glaser, Joshua I.; Zamft, Bradley M.; Church, George M.; Kording, Konrad P.

2015-01-01

Current high-resolution imaging techniques require an intact sample that preserves spatial relationships. We here present a novel approach, “puzzle imaging,” that allows imaging a spatially scrambled sample. This technique takes many spatially disordered samples, and then pieces them back together using local properties embedded within the sample. We show that puzzle imaging can efficiently produce high-resolution images using dimensionality reduction algorithms. We demonstrate the theoretical capabilities of puzzle imaging in three biological scenarios, showing that (1) relatively precise 3-dimensional brain imaging is possible; (2) the physical structure of a neural network can often be recovered based only on the neural connectivity matrix; and (3) a chemical map could be reproduced using bacteria with chemosensitive DNA and conjugative transfer. The ability to reconstruct scrambled images promises to enable imaging based on DNA sequencing of homogenized tissue samples. PMID:26192446

Visual search for object categories is predicted by the representational architecture of high-level visual cortex

PubMed Central

Alvarez, George A.; Nakayama, Ken; Konkle, Talia

2016-01-01

Visual search is a ubiquitous visual behavior, and efficient search is essential for survival. Different cognitive models have explained the speed and accuracy of search based either on the dynamics of attention or on similarity of item representations. Here, we examined the extent to which performance on a visual search task can be predicted from the stable representational architecture of the visual system, independent of attentional dynamics. Participants performed a visual search task with 28 conditions reflecting different pairs of categories (e.g., searching for a face among cars, body among hammers, etc.). The time it took participants to find the target item varied as a function of category combination. In a separate group of participants, we measured the neural responses to these object categories when items were presented in isolation. Using representational similarity analysis, we then examined whether the similarity of neural responses across different subdivisions of the visual system had the requisite structure needed to predict visual search performance. Overall, we found strong brain/behavior correlations across most of the higher-level visual system, including both the ventral and dorsal pathways when considering both macroscale sectors as well as smaller mesoscale regions. These results suggest that visual search for real-world object categories is well predicted by the stable, task-independent architecture of the visual system. NEW & NOTEWORTHY Here, we ask which neural regions have neural response patterns that correlate with behavioral performance in a visual processing task. We found that the representational structure across all of high-level visual cortex has the requisite structure to predict behavior. Furthermore, when directly comparing different neural regions, we found that they all had highly similar category-level representational structures. These results point to a ubiquitous and uniform representational structure in high-level visual cortex underlying visual object processing. PMID:27832600

Differential theory of learning for efficient neural network pattern recognition

NASA Astrophysics Data System (ADS)

Hampshire, John B., II; Vijaya Kumar, Bhagavatula

1993-09-01

We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generate well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts.

Differential theory of learning for efficient neural network pattern recognition

NASA Astrophysics Data System (ADS)

Hampshire, John B., II; Vijaya Kumar, Bhagavatula

1993-08-01

We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generalize well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts.

Algorithmic design of a noise-resistant and efficient closed-loop deep brain stimulation system: A computational approach.

PubMed

Karamintziou, Sofia D; Custódio, Ana Luísa; Piallat, Brigitte; Polosan, Mircea; Chabardès, Stéphan; Stathis, Pantelis G; Tagaris, George A; Sakas, Damianos E; Polychronaki, Georgia E; Tsirogiannis, George L; David, Olivier; Nikita, Konstantina S

2017-01-01

Advances in the field of closed-loop neuromodulation call for analysis and modeling approaches capable of confronting challenges related to the complex neuronal response to stimulation and the presence of strong internal and measurement noise in neural recordings. Here we elaborate on the algorithmic aspects of a noise-resistant closed-loop subthalamic nucleus deep brain stimulation system for advanced Parkinson's disease and treatment-refractory obsessive-compulsive disorder, ensuring remarkable performance in terms of both efficiency and selectivity of stimulation, as well as in terms of computational speed. First, we propose an efficient method drawn from dynamical systems theory, for the reliable assessment of significant nonlinear coupling between beta and high-frequency subthalamic neuronal activity, as a biomarker for feedback control. Further, we present a model-based strategy through which optimal parameters of stimulation for minimum energy desynchronizing control of neuronal activity are being identified. The strategy integrates stochastic modeling and derivative-free optimization of neural dynamics based on quadratic modeling. On the basis of numerical simulations, we demonstrate the potential of the presented modeling approach to identify, at a relatively low computational cost, stimulation settings potentially associated with a significantly higher degree of efficiency and selectivity compared with stimulation settings determined post-operatively. Our data reinforce the hypothesis that model-based control strategies are crucial for the design of novel stimulation protocols at the backstage of clinical applications.

Investigating the Potential of Deep Neural Networks for Large-Scale Classification of Very High Resolution Satellite Images

NASA Astrophysics Data System (ADS)

Postadjian, T.; Le Bris, A.; Sahbi, H.; Mallet, C.

2017-05-01

Semantic classification is a core remote sensing task as it provides the fundamental input for land-cover map generation. The very recent literature has shown the superior performance of deep convolutional neural networks (DCNN) for many classification tasks including the automatic analysis of Very High Spatial Resolution (VHR) geospatial images. Most of the recent initiatives have focused on very high discrimination capacity combined with accurate object boundary retrieval. Therefore, current architectures are perfectly tailored for urban areas over restricted areas but not designed for large-scale purposes. This paper presents an end-to-end automatic processing chain, based on DCNNs, that aims at performing large-scale classification of VHR satellite images (here SPOT 6/7). Since this work assesses, through various experiments, the potential of DCNNs for country-scale VHR land-cover map generation, a simple yet effective architecture is proposed, efficiently discriminating the main classes of interest (namely buildings, roads, water, crops, vegetated areas) by exploiting existing VHR land-cover maps for training.

Automated information-analytical system for thunderstorm monitoring and early warning alarms using modern physical sensors and information technologies with elements of artificial intelligence

NASA Astrophysics Data System (ADS)

Boldyreff, Anton S.; Bespalov, Dmitry A.; Adzhiev, Anatoly Kh.

2017-05-01

Methods of artificial intelligence are a good solution for weather phenomena forecasting. They allow to process a large amount of diverse data. Recirculation Neural Networks is implemented in the paper for the system of thunderstorm events prediction. Large amounts of experimental data from lightning sensors and electric field mills networks are received and analyzed. The average recognition accuracy of sensor signals is calculated. It is shown that Recirculation Neural Networks is a promising solution in the forecasting of thunderstorms and weather phenomena, characterized by the high efficiency of the recognition elements of the sensor signals, allows to compress images and highlight their characteristic features for subsequent recognition.

Automatic temporal segment detection via bilateral long short-term memory recurrent neural networks

NASA Astrophysics Data System (ADS)

Sun, Bo; Cao, Siming; He, Jun; Yu, Lejun; Li, Liandong

2017-03-01

Constrained by the physiology, the temporal factors associated with human behavior, irrespective of facial movement or body gesture, are described by four phases: neutral, onset, apex, and offset. Although they may benefit related recognition tasks, it is not easy to accurately detect such temporal segments. An automatic temporal segment detection framework using bilateral long short-term memory recurrent neural networks (BLSTM-RNN) to learn high-level temporal-spatial features, which synthesizes the local and global temporal-spatial information more efficiently, is presented. The framework is evaluated in detail over the face and body database (FABO). The comparison shows that the proposed framework outperforms state-of-the-art methods for solving the problem of temporal segment detection.

Background rejection in NEXT using deep neural networks

DOE PAGES

Renner, J.; Farbin, A.; Vidal, J. Muñoz; ...

2017-01-16

Here, we investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the usemore » of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.« less

Natural language acquisition in large scale neural semantic networks

NASA Astrophysics Data System (ADS)

Ealey, Douglas

This thesis puts forward the view that a purely signal- based approach to natural language processing is both plausible and desirable. By questioning the veracity of symbolic representations of meaning, it argues for a unified, non-symbolic model of knowledge representation that is both biologically plausible and, potentially, highly efficient. Processes to generate a grounded, neural form of this model-dubbed the semantic filter-are discussed. The combined effects of local neural organisation, coincident with perceptual maturation, are used to hypothesise its nature. This theoretical model is then validated in light of a number of fundamental neurological constraints and milestones. The mechanisms of semantic and episodic development that the model predicts are then used to explain linguistic properties, such as propositions and verbs, syntax and scripting. To mimic the growth of locally densely connected structures upon an unbounded neural substrate, a system is developed that can grow arbitrarily large, data- dependant structures composed of individual self- organising neural networks. The maturational nature of the data used results in a structure in which the perception of concepts is refined by the networks, but demarcated by subsequent structure. As a consequence, the overall structure shows significant memory and computational benefits, as predicted by the cognitive and neural models. Furthermore, the localised nature of the neural architecture also avoids the increasing error sensitivity and redundancy of traditional systems as the training domain grows. The semantic and episodic filters have been demonstrated to perform as well, or better, than more specialist networks, whilst using significantly larger vocabularies, more complex sentence forms and more natural corpora.

Application of artificial neural networks to composite ply micromechanics

NASA Technical Reports Server (NTRS)

Brown, D. A.; Murthy, P. L. N.; Berke, L.

1991-01-01

Artificial neural networks can provide improved computational efficiency relative to existing methods when an algorithmic description of functional relationships is either totally unavailable or is complex in nature. For complex calculations, significant reductions in elapsed computation time are possible. The primary goal is to demonstrate the applicability of artificial neural networks to composite material characterization. As a test case, a neural network was trained to accurately predict composite hygral, thermal, and mechanical properties when provided with basic information concerning the environment, constituent materials, and component ratios used in the creation of the composite. A brief introduction on neural networks is provided along with a description of the project itself.

Laser Fluence Recognition Using Computationally Intelligent Pulsed Photoacoustics Within the Trace Gases Analysis

NASA Astrophysics Data System (ADS)

Lukić, M.; Ćojbašić, Ž.; Rabasović, M. D.; Markushev, D. D.; Todorović, D. M.

2017-11-01

In this paper, the possibilities of computational intelligence applications for trace gas monitoring are discussed. For this, pulsed infrared photoacoustics is used to investigate SF6-Ar mixtures in a multiphoton regime, assisted by artificial neural networks. Feedforward multilayer perceptron networks are applied in order to recognize both the spatial characteristics of the laser beam and the values of laser fluence Φ from the given photoacoustic signal and prevent changes. Neural networks are trained in an offline batch training regime to simultaneously estimate four parameters from theoretical or experimental photoacoustic signals: the laser beam spatial profile R(r), vibrational-to-translational relaxation time τ _{V-T} , distance from the laser beam to the absorption molecules in the photoacoustic cell r* and laser fluence Φ . The results presented in this paper show that neural networks can estimate an unknown laser beam spatial profile and the parameters of photoacoustic signals in real time and with high precision. Real-time operation, high accuracy and the possibility of application for higher intensities of radiation for a wide range of laser fluencies are factors that classify the computational intelligence approach as efficient and powerful for the in situ measurement of atmospheric pollutants.

Neural network for control of rearrangeable Clos networks.

PubMed

Park, Y K; Cherkassky, V

1994-09-01

Rapid evolution in the field of communication networks requires high speed switching technologies. This involves a high degree of parallelism in switching control and routing performed at the hardware level. The multistage crossbar networks have always been attractive to switch designers. In this paper a neural network approach to controlling a three-stage Clos network in real time is proposed. This controller provides optimal routing of communication traffic requests on a call-by-call basis by rearranging existing connections, with a minimum length of rearrangement sequence so that a new blocked call request can be accommodated. The proposed neural network controller uses Paull's rearrangement algorithm, along with the special (least used) switch selection rule in order to minimize the length of rearrangement sequences. The functional behavior of our model is verified by simulations and it is shown that the convergence time required for finding an optimal solution is constant, regardless of the switching network size. The performance is evaluated for random traffic with various traffic loads. Simulation results show that applying the least used switch selection rule increases the efficiency in switch rearrangements, reducing the network convergence time. The implementation aspects are also discussed to show the feasibility of the proposed approach.

On the of neural modeling of some dynamic parameters of earthquakes and fire safety in high-rise construction

NASA Astrophysics Data System (ADS)

Haritonova, Larisa

2018-03-01

The recent change in the correlation of the number of man-made and natural catastrophes is presented in the paper. Some recommendations are proposed to increase the firefighting efficiency in the high-rise buildings. The article analyzes the methodology of modeling seismic effects. The prospectivity of applying the neural modeling and artificial neural networks to analyze a such dynamic parameters of the earthquake foci as the value of dislocation (or the average rupture slip) is shown. The following two input signals were used: the power class and the number of earthquakes. The regression analysis has been carried out for the predicted results and the target outputs. The equations of the regression for the outputs and target are presented in the work as well as the correlation coefficients in training, validation, testing, and the total (All) for the network structure 2-5-5-1for the average rupture slip. The application of the results obtained in the article for the seismic design for the newly constructed buildings and structures and the given recommendations will provide the additional protection from fire and earthquake risks, reduction of their negative economic and environmental consequences.

Calibration of a shock wave position sensor using artificial neural networks

NASA Technical Reports Server (NTRS)

Decker, Arthur J.; Weiland, Kenneth E.

1993-01-01

This report discusses the calibration of a shock wave position sensor. The position sensor works by using artificial neural networks to map cropped CCD frames of the shadows of the shock wave into the value of the shock wave position. This project was done as a tutorial demonstration of method and feasibility. It used a laboratory shadowgraph, nozzle, and commercial neural network package. The results were quite good, indicating that artificial neural networks can be used efficiently to automate the semi-quantitative applications of flow visualization.

Robust model predictive control of nonlinear systems with unmodeled dynamics and bounded uncertainties based on neural networks.

PubMed

Yan, Zheng; Wang, Jun

2014-03-01

This paper presents a neural network approach to robust model predictive control (MPC) for constrained discrete-time nonlinear systems with unmodeled dynamics affected by bounded uncertainties. The exact nonlinear model of underlying process is not precisely known, but a partially known nominal model is available. This partially known nonlinear model is first decomposed to an affine term plus an unknown high-order term via Jacobian linearization. The linearization residue combined with unmodeled dynamics is then modeled using an extreme learning machine via supervised learning. The minimax methodology is exploited to deal with bounded uncertainties. The minimax optimization problem is reformulated as a convex minimization problem and is iteratively solved by a two-layer recurrent neural network. The proposed neurodynamic approach to nonlinear MPC improves the computational efficiency and sheds a light for real-time implementability of MPC technology. Simulation results are provided to substantiate the effectiveness and characteristics of the proposed approach.

Detection of broken rotor bar faults in induction motor at low load using neural network.

PubMed

Bessam, B; Menacer, A; Boumehraz, M; Cherif, H

2016-09-01

The knowledge of the broken rotor bars characteristic frequencies and amplitudes has a great importance for all related diagnostic methods. The monitoring of motor faults requires a high resolution spectrum to separate different frequency components. The Discrete Fourier Transform (DFT) has been widely used to achieve these requirements. However, at low slip this technique cannot give good results. As a solution for these problems, this paper proposes an efficient technique based on a neural network approach and Hilbert transform (HT) for broken rotor bar diagnosis in induction machines at low load. The Hilbert transform is used to extract the stator current envelope (SCE). Two features are selected from the (SCE) spectrum (the amplitude and frequency of the harmonic). These features will be used as input for neural network. The results obtained are astonishing and it is capable to detect the correct number of broken rotor bars under different load conditions. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Neuronal avalanches of a self-organized neural network with active-neuron-dominant structure.

PubMed

Li, Xiumin; Small, Michael

2012-06-01

Neuronal avalanche is a spontaneous neuronal activity which obeys a power-law distribution of population event sizes with an exponent of -3/2. It has been observed in the superficial layers of cortex both in vivo and in vitro. In this paper, we analyze the information transmission of a novel self-organized neural network with active-neuron-dominant structure. Neuronal avalanches can be observed in this network with appropriate input intensity. We find that the process of network learning via spike-timing dependent plasticity dramatically increases the complexity of network structure, which is finally self-organized to be active-neuron-dominant connectivity. Both the entropy of activity patterns and the complexity of their resulting post-synaptic inputs are maximized when the network dynamics are propagated as neuronal avalanches. This emergent topology is beneficial for information transmission with high efficiency and also could be responsible for the large information capacity of this network compared with alternative archetypal networks with different neural connectivity.

Evaluation and prediction of solar radiation for energy management based on neural networks

NASA Astrophysics Data System (ADS)

Aldoshina, O. V.; Van Tai, Dinh

2017-08-01

Currently, there is a high rate of distribution of renewable energy sources and distributed power generation based on intelligent networks; therefore, meteorological forecasts are particularly useful for planning and managing the energy system in order to increase its overall efficiency and productivity. The application of artificial neural networks (ANN) in the field of photovoltaic energy is presented in this article. Implemented in this study, two periodically repeating dynamic ANS, that are the concentration of the time delay of a neural network (CTDNN) and the non-linear autoregression of a network with exogenous inputs of the NAEI, are used in the development of a model for estimating and daily forecasting of solar radiation. ANN show good productivity, as reliable and accurate models of daily solar radiation are obtained. This allows to successfully predict the photovoltaic output power for this installation. The potential of the proposed method for controlling the energy of the electrical network is shown using the example of the application of the NAEI network for predicting the electric load.

A more secure parallel keyed hash function based on chaotic neural network

NASA Astrophysics Data System (ADS)

Huang, Zhongquan

2011-08-01

Although various hash functions based on chaos or chaotic neural network were proposed, most of them can not work efficiently in parallel computing environment. Recently, an algorithm for parallel keyed hash function construction based on chaotic neural network was proposed [13]. However, there is a strict limitation in this scheme that its secret keys must be nonce numbers. In other words, if the keys are used more than once in this scheme, there will be some potential security flaw. In this paper, we analyze the cause of vulnerability of the original one in detail, and then propose the corresponding enhancement measures, which can remove the limitation on the secret keys. Theoretical analysis and computer simulation indicate that the modified hash function is more secure and practical than the original one. At the same time, it can keep the parallel merit and satisfy the other performance requirements of hash function, such as good statistical properties, high message and key sensitivity, and strong collision resistance, etc.

Repair kinetics of DNA double-strand breaks and incidence of apoptosis in mouse neural stem/progenitor cells and their differentiated neurons exposed to ionizing radiation.

PubMed

Kashiwagi, Hiroki; Shiraishi, Kazunori; Sakaguchi, Kenta; Nakahama, Tomoya; Kodama, Seiji

2018-05-01

Neuronal loss leads to neurodegenerative disorders, including Alzheimer's disease, Parkinson's disease and Huntington's disease. Because of their long lifespans, neurons are assumed to possess highly efficient DNA repair ability and to be able to protect themselves from deleterious DNA damage such as DNA double-strand breaks (DSBs) produced by intrinsic and extrinsic sources. However, it remains largely unknown whether the DSB repair ability of neurons is more efficient compared with that of other cells. Here, we investigated the repair kinetics of X-ray-induced DSBs in mouse neural cells by scoring the number of phosphorylated 53BP1 foci post irradiation. We found that p53-independent apoptosis was induced time dependently during differentiation from neural stem/progenitor cells (NSPCs) into neurons in culture for 48 h. DSB repair in neurons differentiated from NSPCs in culture was faster than that in mouse embryonic fibroblasts (MEFs), possibly due to the higher DNA-dependent protein kinase activity, but it was similar to that in NSPCs. Further, the incidence of p53-dependent apoptosis induced by X-irradiation in neurons was significantly higher than that in NSPCs. This difference in response of X-ray-induced apoptosis between neurons and NSPCs may reflect a difference in the fidelity of non-homologous end joining or a differential sensitivity to DNA damage other than DSBs.

From neuro-pigments to neural efficiency: The relationship between retinal carotenoids and behavioral and neuroelectric indices of cognitive control in childhood.

PubMed

Walk, Anne M; Khan, Naiman A; Barnett, Sasha M; Raine, Lauren B; Kramer, Arthur F; Cohen, Neal J; Moulton, Christopher J; Renzi-Hammond, Lisa M; Hammond, Billy R; Hillman, Charles H

2017-08-01

Lutein and zeaxanthin are plant pigments known to preferentially accumulate in neural tissue. Macular Pigment Optical Density (MPOD), a non-invasive measure of retinal carotenoids and surrogate measure of brain carotenoid concentration, has been associated with disease prevention and cognitive health. Superior MPOD status in later adulthood has been shown to provide neuroprotective effects on cognition. Given that childhood signifies a critical period for carotenoid accumulation in brain, it is likely that the beneficial impact would be evident during development, though this relationship has not been directly investigated. The present study investigated the relationship between MPOD and the behavioral and neuroelectric indices elicited during a cognitive control task in preadolescent children. 49 participants completed a modified flanker task while event-related potentials (ERPs) were recorded to assess the P3 component of the ERP waveform. MPOD was associated with both behavioral performance and P3 amplitude such that children with higher MPOD had more accurate performance and lower P3 amplitudes. These relationships were more pronounced for trials requiring greater amounts of cognitive control. These results indicate that children with higher MPOD may respond to cognitive tasks more efficiently, maintaining high performance while displaying neural indices indicative of lower cognitive load. These findings provide novel support for the neuroprotective influence of retinal carotenoids during preadolescence. Copyright © 2017 Elsevier B.V. All rights reserved.

Neural joint control for Space Shuttle Remote Manipulator System

NASA Technical Reports Server (NTRS)

Atkins, Mark A.; Cox, Chadwick J.; Lothers, Michael D.; Pap, Robert M.; Thomas, Charles R.

1992-01-01

Neural networks are being used to control a robot arm in a telerobotic operation. The concept uses neural networks for both joint and inverse kinematics in a robotic control application. An upper level neural network is trained to learn inverse kinematic mappings. The output, a trajectory, is then fed to the Decentralized Adaptive Joint Controllers. This neural network implementation has shown that the controlled arm recovers from unexpected payload changes while following the reference trajectory. The neural network-based decentralized joint controller is faster, more robust and efficient than conventional approaches. Implementations of this architecture are discussed that would relax assumptions about dynamics, obstacles, and heavy loads. This system is being developed to use with the Space Shuttle Remote Manipulator System.

High Glucose Inhibits Neural Stem Cell Differentiation Through Oxidative Stress and Endoplasmic Reticulum Stress.

PubMed

Chen, Xi; Shen, Wei-Bin; Yang, Penghua; Dong, Daoyin; Sun, Winny; Yang, Peixin

2018-06-01

Maternal diabetes induces neural tube defects by suppressing neurogenesis in the developing neuroepithelium. Our recent study further revealed that high glucose inhibited embryonic stem cell differentiation into neural lineage cells. However, the mechanism whereby high glucose suppresses neural differentiation is unclear. To investigate whether high glucose-induced oxidative stress and endoplasmic reticulum (ER) stress lead to the inhibition of neural differentiation, the effect of high glucose on neural stem cell (the C17.2 cell line) differentiation was examined. Neural stem cells were cultured in normal glucose (5 mM) or high glucose (25 mM) differentiation medium for 3, 5, and 7 days. High glucose suppressed neural stem cell differentiation by significantly decreasing the expression of the neuron marker Tuj1 and the glial cell marker GFAP and the numbers of Tuj1 + and GFAP + cells. The antioxidant enzyme superoxide dismutase mimetic Tempol reversed high glucose-decreased Tuj1 and GFAP expression and restored the numbers of neurons and glial cells differentiated from neural stem cells. Hydrogen peroxide treatment imitated the inhibitory effect of high glucose on neural stem cell differentiation. Both high glucose and hydrogen peroxide triggered ER stress, whereas Tempol blocked high glucose-induced ER stress. The ER stress inhibitor, 4-phenylbutyrate, abolished the inhibition of high glucose or hydrogen peroxide on neural stem cell differentiation. Thus, oxidative stress and its resultant ER stress mediate the inhibitory effect of high glucose on neural stem cell differentiation.

Design of a Thermoacoustic Sensor for Low Intensity Ultrasound Measurements Based on an Artificial Neural Network.

PubMed

Xing, Jida; Chen, Jie

2015-06-23

In therapeutic ultrasound applications, accurate ultrasound output intensities are crucial because the physiological effects of therapeutic ultrasound are very sensitive to the intensity and duration of these applications. Although radiation force balance is a benchmark technique for measuring ultrasound intensity and power, it is costly, difficult to operate, and compromised by noise vibration. To overcome these limitations, the development of a low-cost, easy to operate, and vibration-resistant alternative device is necessary for rapid ultrasound intensity measurement. Therefore, we proposed and validated a novel two-layer thermoacoustic sensor using an artificial neural network technique to accurately measure low ultrasound intensities between 30 and 120 mW/cm2. The first layer of the sensor design is a cylindrical absorber made of plexiglass, followed by a second layer composed of polyurethane rubber with a high attenuation coefficient to absorb extra ultrasound energy. The sensor determined ultrasound intensities according to a temperature elevation induced by heat converted from incident acoustic energy. Compared with our previous one-layer sensor design, the new two-layer sensor enhanced the ultrasound absorption efficiency to provide more rapid and reliable measurements. Using a three-dimensional model in the K-wave toolbox, our simulation of the ultrasound propagation process demonstrated that the two-layer design is more efficient than the single layer design. We also integrated an artificial neural network algorithm to compensate for the large measurement offset. After obtaining multiple parameters of the sensor characteristics through calibration, the artificial neural network is built to correct temperature drifts and increase the reliability of our thermoacoustic measurements through iterative training about ten seconds. The performance of the artificial neural network method was validated through a series of experiments. Compared to our previous design, the new design reduced sensing time from 20 s to 12 s, and the sensor's average error from 3.97 mW/cm2 to 1.31 mW/cm2 respectively.

Design of a Thermoacoustic Sensor for Low Intensity Ultrasound Measurements Based on an Artificial Neural Network

PubMed Central

Xing, Jida; Chen, Jie

2015-01-01

In therapeutic ultrasound applications, accurate ultrasound output intensities are crucial because the physiological effects of therapeutic ultrasound are very sensitive to the intensity and duration of these applications. Although radiation force balance is a benchmark technique for measuring ultrasound intensity and power, it is costly, difficult to operate, and compromised by noise vibration. To overcome these limitations, the development of a low-cost, easy to operate, and vibration-resistant alternative device is necessary for rapid ultrasound intensity measurement. Therefore, we proposed and validated a novel two-layer thermoacoustic sensor using an artificial neural network technique to accurately measure low ultrasound intensities between 30 and 120 mW/cm2. The first layer of the sensor design is a cylindrical absorber made of plexiglass, followed by a second layer composed of polyurethane rubber with a high attenuation coefficient to absorb extra ultrasound energy. The sensor determined ultrasound intensities according to a temperature elevation induced by heat converted from incident acoustic energy. Compared with our previous one-layer sensor design, the new two-layer sensor enhanced the ultrasound absorption efficiency to provide more rapid and reliable measurements. Using a three-dimensional model in the K-wave toolbox, our simulation of the ultrasound propagation process demonstrated that the two-layer design is more efficient than the single layer design. We also integrated an artificial neural network algorithm to compensate for the large measurement offset. After obtaining multiple parameters of the sensor characteristics through calibration, the artificial neural network is built to correct temperature drifts and increase the reliability of our thermoacoustic measurements through iterative training about ten seconds. The performance of the artificial neural network method was validated through a series of experiments. Compared to our previous design, the new design reduced sensing time from 20 s to 12 s, and the sensor’s average error from 3.97 mW/cm2 to 1.31 mW/cm2 respectively. PMID:26110412

From neural-based object recognition toward microelectronic eyes

NASA Technical Reports Server (NTRS)

Sheu, Bing J.; Bang, Sa Hyun

1994-01-01

Engineering neural network systems are best known for their abilities to adapt to the changing characteristics of the surrounding environment by adjusting system parameter values during the learning process. Rapid advances in analog current-mode design techniques have made possible the implementation of major neural network functions in custom VLSI chips. An electrically programmable analog synapse cell with large dynamic range can be realized in a compact silicon area. New designs of the synapse cells, neurons, and analog processor are presented. A synapse cell based on Gilbert multiplier structure can perform the linear multiplication for back-propagation networks. A double differential-pair synapse cell can perform the Gaussian function for radial-basis network. The synapse cells can be biased in the strong inversion region for high-speed operation or biased in the subthreshold region for low-power operation. The voltage gain of the sigmoid-function neurons is externally adjustable which greatly facilitates the search of optimal solutions in certain networks. Various building blocks can be intelligently connected to form useful industrial applications. Efficient data communication is a key system-level design issue for large-scale networks. We also present analog neural processors based on perceptron architecture and Hopfield network for communication applications. Biologically inspired neural networks have played an important role towards the creation of powerful intelligent machines. Accuracy, limitations, and prospects of analog current-mode design of the biologically inspired vision processing chips and cellular neural network chips are key design issues.

Detection, location, and quantification of structural damage by neural-net-processed moiré profilometry

NASA Astrophysics Data System (ADS)

Grossman, Barry G.; Gonzalez, Frank S.; Blatt, Joel H.; Hooker, Jeffery A.

1992-03-01

The development of efficient high speed techniques to recognize, locate, and quantify damage is vitally important for successful automated inspection systems such as ones used for the inspection of undersea pipelines. Two critical problems must be solved to achieve these goals: the reduction of nonuseful information present in the video image and automatic recognition and quantification of extent and location of damage. Artificial neural network processed moire profilometry appears to be a promising technique to accomplish this. Real time video moire techniques have been developed which clearly distinguish damaged and undamaged areas on structures, thus reducing the amount of extraneous information input into an inspection system. Artificial neural networks have demonstrated advantages for image processing, since they can learn the desired response to a given input and are inherently fast when implemented in hardware due to their parallel computing architecture. Video moire images of pipes with dents of different depths were used to train a neural network, with the desired output being the location and severity of the damage. The system was then successfully tested with a second series of moire images. The techniques employed and the results obtained are discussed.

UAV Trajectory Modeling Using Neural Networks

NASA Technical Reports Server (NTRS)

Xue, Min

2017-01-01

Massive small unmanned aerial vehicles are envisioned to operate in the near future. While there are lots of research problems need to be addressed before dense operations can happen, trajectory modeling remains as one of the keys to understand and develop policies, regulations, and requirements for safe and efficient unmanned aerial vehicle operations. The fidelity requirement of a small unmanned vehicle trajectory model is high because these vehicles are sensitive to winds due to their small size and low operational altitude. Both vehicle control systems and dynamic models are needed for trajectory modeling, which makes the modeling a great challenge, especially considering the fact that manufactures are not willing to share their control systems. This work proposed to use a neural network approach for modelling small unmanned vehicle's trajectory without knowing its control system and bypassing exhaustive efforts for aerodynamic parameter identification. As a proof of concept, instead of collecting data from flight tests, this work used the trajectory data generated by a mathematical vehicle model for training and testing the neural network. The results showed great promise because the trained neural network can predict 4D trajectories accurately, and prediction errors were less than 2:0 meters in both temporal and spatial dimensions.

Design of a universal two-layered neural network derived from the PLI theory

NASA Astrophysics Data System (ADS)

Hu, Chia-Lun J.

2004-05-01

The if-and-only-if (IFF) condition that a set of M analog-to-digital vector-mapping relations can be learned by a one-layered-feed-forward neural network (OLNN) is that all the input analog vectors dichotomized by the i-th output bit must be positively, linearly independent, or PLI. If they are not PLI, then the OLNN just cannot learn no matter what learning rules is employed because the solution of the connection matrix does not exist mathematically. However, in this case, one can still design a parallel-cascaded, two-layered, perceptron (PCTLP) to acheive this general mapping goal. The design principle of this "universal" neural network is derived from the major mathematical properties of the PLI theory - changing the output bits of the dependent relations existing among the dichotomized input vectors to make the PLD relations PLI. Then with a vector concatenation technique, the required mapping can still be learned by this PCTLP system with very high efficiency. This paper will report in detail the mathematical derivation of the general design principle and the design procedures of the PCTLP neural network system. It then will be verified in general by a practical numerical example.

Neural processing of musical meter in musicians and non-musicians.

PubMed

Zhao, T Christina; Lam, H T Gloria; Sohi, Harkirat; Kuhl, Patricia K

2017-11-01

Musical sounds, along with speech, are the most prominent sounds in our daily lives. They are highly dynamic, yet well structured in the temporal domain in a hierarchical manner. The temporal structures enhance the predictability of musical sounds. Western music provides an excellent example: while time intervals between musical notes are highly variable, underlying beats can be realized. The beat-level temporal structure provides a sense of regular pulses. Beats can be further organized into units, giving the percept of alternating strong and weak beats (i.e. metrical structure or meter). Examining neural processing at the meter level offers a unique opportunity to understand how the human brain extracts temporal patterns, predicts future stimuli and optimizes neural resources for processing. The present study addresses two important questions regarding meter processing, using the mismatch negativity (MMN) obtained with electroencephalography (EEG): 1) how tempo (fast vs. slow) and type of metrical structure (duple: two beats per unit vs. triple: three beats per unit) affect the neural processing of metrical structure in non-musically trained individuals, and 2) how early music training modulates the neural processing of metrical structure. Metrical structures were established by patterns of consecutive strong and weak tones (Standard) with occasional violations that disrupted and reset the structure (Deviant). Twenty non-musicians listened passively to these tones while their neural activities were recorded. MMN indexed the neural sensitivity to the meter violations. Results suggested that MMNs were larger for fast tempo and for triple meter conditions. Further, 20 musically trained individuals were tested using the same methods and the results were compared to the non-musicians. While tempo and meter type similarly influenced MMNs in both groups, musicians overall exhibited significantly reduced MMNs, compared to their non-musician counterparts. Further analyses indicated that the reduction was driven by responses to sounds that defined the structure (Standard), not by responses to Deviants. We argue that musicians maintain a more accurate and efficient mental model for metrical structures, which incorporates occasional disruptions using significantly fewer neural resources. Copyright © 2017 Elsevier Ltd. All rights reserved.

Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines

PubMed Central

Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

2013-01-01

Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines. PMID:23408775

Integrating the behavioral and neural dynamics of response selection in a dual-task paradigm: a dynamic neural field model of Dux et al. (2009).

PubMed

Buss, Aaron T; Wifall, Tim; Hazeltine, Eliot; Spencer, John P

2014-02-01

People are typically slower when executing two tasks than when only performing a single task. These dual-task costs are initially robust but are reduced with practice. Dux et al. (2009) explored the neural basis of dual-task costs and learning using fMRI. Inferior frontal junction (IFJ) showed a larger hemodynamic response on dual-task trials compared with single-task trial early in learning. As dual-task costs were eliminated, dual-task hemodynamics in IFJ reduced to single-task levels. Dux and colleagues concluded that the reduction of dual-task costs is accomplished through increased efficiency of information processing in IFJ. We present a dynamic field theory of response selection that addresses two questions regarding these results. First, what mechanism leads to the reduction of dual-task costs and associated changes in hemodynamics? We show that a simple Hebbian learning mechanism is able to capture the quantitative details of learning at both the behavioral and neural levels. Second, is efficiency isolated to cognitive control areas such as IFJ, or is it also evident in sensory motor areas? To investigate this, we restrict Hebbian learning to different parts of the neural model. None of the restricted learning models showed the same reductions in dual-task costs as the unrestricted learning model, suggesting that efficiency is distributed across cognitive control and sensory motor processing systems.

Deficits in context-dependent adaptive coding of reward in schizophrenia

PubMed Central

Kirschner, Matthias; Hager, Oliver M; Bischof, Martin; Hartmann-Riemer, Matthias N; Kluge, Agne; Seifritz, Erich; Tobler, Philippe N; Kaiser, Stefan

2016-01-01

Theoretical principles of information processing and empirical findings suggest that to efficiently represent all possible rewards in the natural environment, reward-sensitive neurons have to adapt their coding range dynamically to the current reward context. Adaptation ensures that the reward system is most sensitive for the most likely rewards, enabling the system to efficiently represent a potentially infinite range of reward information. A deficit in neural adaptation would prevent precise representation of rewards and could have detrimental effects for an organism’s ability to optimally engage with its environment. In schizophrenia, reward processing is known to be impaired and has been linked to different symptom dimensions. However, despite the fundamental significance of coding reward adaptively, no study has elucidated whether adaptive reward processing is impaired in schizophrenia. We therefore studied patients with schizophrenia (n=27) and healthy controls (n=25), using functional magnetic resonance imaging in combination with a variant of the monetary incentive delay task. Compared with healthy controls, patients with schizophrenia showed less efficient neural adaptation to the current reward context, which leads to imprecise neural representation of reward. Importantly, the deficit correlated with total symptom severity. Our results suggest that some of the deficits in reward processing in schizophrenia might be due to inefficient neural adaptation to the current reward context. Furthermore, because adaptive coding is a ubiquitous feature of the brain, we believe that our findings provide an avenue in defining a general impairment in neural information processing underlying this debilitating disorder. PMID:27430009

Prediction of moving bed biofilm reactor (MBBR) performance for the treatment of aniline using artificial neural networks (ANN).

PubMed

Delnavaz, M; Ayati, B; Ganjidoust, H

2010-07-15

In this study, the results of 1-year efficiency forecasting using artificial neural networks (ANN) models of a moving bed biofilm reactor (MBBR) for a toxic and hard biodegradable aniline removal were investigated. The reactor was operated in an aerobic batch and continuous condition with 50% by volume which was filled with light expanded clay aggregate (LECA) as carrier. Efficiency evaluation of the reactors was obtained at different retention time (RT) of 8, 24, 48 and 72 h with an influent COD from 100 to 4000 mg/L. Exploratory data analysis was used to detect relationships between the data and dependent evaluated one. The appropriate architecture of the neural network models was determined using several steps of training and testing of the models. The ANN-based models were found to provide an efficient and a robust tool in predicting MBBR performance for treating aromatic amine compounds. 2010 Elsevier B.V. All rights reserved.

Neural Correlates of Intersensory Processing in Five-Month-Old Infants

PubMed Central

Reynolds, Greg D.; Bahrick, Lorraine E.; Lickliter, Robert; Guy, Maggie W.

2014-01-01

Two experiments assessing event-related potentials in 5-month-old infants were conducted to examine neural correlates of attentional salience and efficiency of processing of a visual event (woman speaking) paired with redundant (synchronous) speech, nonredundant (asynchronous) speech, or no speech. In Experiment 1, the Nc component associated with attentional salience was greater in amplitude following synchronous audiovisual as compared with asynchronous audiovisual and unimodal visual presentations. A block design was utilized in Experiment 2 to examine efficiency of processing of a visual event. Only infants exposed to synchronous audiovisual speech demonstrated a significant reduction in amplitude of the late slow wave associated with successful stimulus processing and recognition memory from early to late blocks of trials. These findings indicate that events that provide intersensory redundancy are associated with enhanced neural responsiveness indicative of greater attentional salience and more efficient stimulus processing as compared with the same events when they provide no intersensory redundancy in 5-month-old infants. PMID:23423948

Understanding the Implications of Neural Population Activity on Behavior

NASA Astrophysics Data System (ADS)

Briguglio, John

Learning how neural activity in the brain leads to the behavior we exhibit is one of the fundamental questions in Neuroscience. In this dissertation, several lines of work are presented to that use principles of neural coding to understand behavior. In one line of work, we formulate the efficient coding hypothesis in a non-traditional manner in order to test human perceptual sensitivity to complex visual textures. We find a striking agreement between how variable a particular texture signal is and how sensitive humans are to its presence. This reveals that the efficient coding hypothesis is still a guiding principle for neural organization beyond the sensory periphery, and that the nature of cortical constraints differs from the peripheral counterpart. In another line of work, we relate frequency discrimination acuity to neural responses from auditory cortex in mice. It has been previously observed that optogenetic manipulation of auditory cortex, in addition to changing neural responses, evokes changes in behavioral frequency discrimination. We are able to account for changes in frequency discrimination acuity on an individual basis by examining the Fisher information from the neural population with and without optogenetic manipulation. In the third line of work, we address the question of what a neural population should encode given that its inputs are responses from another group of neurons. Drawing inspiration from techniques in machine learning, we train Deep Belief Networks on fake retinal data and show the emergence of Garbor-like filters, reminiscent of responses in primary visual cortex. In the last line of work, we model the state of a cortical excitatory-inhibitory network during complex adaptive stimuli. Using a rate model with Wilson-Cowan dynamics, we demonstrate that simple non-linearities in the signal transferred from inhibitory to excitatory neurons can account for real neural recordings taken from auditory cortex. This work establishes and tests a variety of hypotheses that will be useful in helping to understand the relationship between neural activity and behavior as recorded neural populations continue to grow.

A method for compression of intra-cortically-recorded neural signals dedicated to implantable brain-machine interfaces.

PubMed

Shaeri, Mohammad Ali; Sodagar, Amir M

2015-05-01

This paper proposes an efficient data compression technique dedicated to implantable intra-cortical neural recording devices. The proposed technique benefits from processing neural signals in the Discrete Haar Wavelet Transform space, a new spike extraction approach, and a novel data framing scheme to telemeter the recorded neural information to the outside world. Based on the proposed technique, a 64-channel neural signal processor was designed and prototyped as a part of a wireless implantable extra-cellular neural recording microsystem. Designed in a 0.13- μ m standard CMOS process, the 64-channel neural signal processor reported in this paper occupies ∼ 0.206 mm(2) of silicon area, and consumes 94.18 μW when operating under a 1.2-V supply voltage at a master clock frequency of 1.28 MHz.

The Variability of Neural Responses to Naturalistic Videos Change with Age and Sex.

PubMed

Petroni, Agustin; Cohen, Samantha S; Ai, Lei; Langer, Nicolas; Henin, Simon; Vanderwal, Tamara; Milham, Michael P; Parra, Lucas C

2018-01-01

Neural development is generally marked by an increase in the efficiency and diversity of neural processes. In a large sample ( n = 114) of human children and adults with ages ranging from 5 to 44 yr, we investigated the neural responses to naturalistic video stimuli. Videos from both real-life classroom settings and Hollywood feature films were used to probe different aspects of attention and engagement. For all stimuli, older ages were marked by more variable neural responses. Variability was assessed by the intersubject correlation of evoked electroencephalographic responses. Young males also had less-variable responses than young females. These results were replicated in an independent cohort ( n = 303). When interpreted in the context of neural maturation, we conclude that neural function becomes more variable with maturity, at least during the passive viewing of real-world stimuli.

Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling.

PubMed

Yang, S; Wang, D

2000-01-01

This paper presents a constraint satisfaction adaptive neural network, together with several heuristics, to solve the generalized job-shop scheduling problem, one of NP-complete constraint satisfaction problems. The proposed neural network can be easily constructed and can adaptively adjust its weights of connections and biases of units based on the sequence and resource constraints of the job-shop scheduling problem during its processing. Several heuristics that can be combined with the neural network are also presented. In the combined approaches, the neural network is used to obtain feasible solutions, the heuristic algorithms are used to improve the performance of the neural network and the quality of the obtained solutions. Simulations have shown that the proposed neural network and its combined approaches are efficient with respect to the quality of solutions and the solving speed.

Contextual Modulation is Related to Efficiency in a Spiking Network Model of Visual Cortex.

PubMed

Sharifian, Fariba; Heikkinen, Hanna; Vigário, Ricardo; Vanni, Simo

2015-01-01

In the visual cortex, stimuli outside the classical receptive field (CRF) modulate the neural firing rate, without driving the neuron by themselves. In the primary visual cortex (V1), such contextual modulation can be parametrized with an area summation function (ASF): increasing stimulus size causes first an increase and then a decrease of firing rate before reaching an asymptote. Earlier work has reported increase of sparseness when CRF stimulation is extended to its surroundings. However, there has been no clear connection between the ASF and network efficiency. Here we aimed to investigate possible link between ASF and network efficiency. In this study, we simulated the responses of a biomimetic spiking neural network model of the visual cortex to a set of natural images. We varied the network parameters, and compared the V1 excitatory neuron spike responses to the corresponding responses predicted from earlier single neuron data from primate visual cortex. The network efficiency was quantified with firing rate (which has direct association to neural energy consumption), entropy per spike and population sparseness. All three measures together provided a clear association between the network efficiency and the ASF. The association was clear when varying the horizontal connectivity within V1, which influenced both the efficiency and the distance to ASF, DAS. Given the limitations of our biophysical model, this association is qualitative, but nevertheless suggests that an ASF-like receptive field structure can cause efficient population response.

Neural network based load and price forecasting and confidence interval estimation in deregulated power markets

NASA Astrophysics Data System (ADS)

Zhang, Li

With the deregulation of the electric power market in New England, an independent system operator (ISO) has been separated from the New England Power Pool (NEPOOL). The ISO provides a regional spot market, with bids on various electricity-related products and services submitted by utilities and independent power producers. A utility can bid on the spot market and buy or sell electricity via bilateral transactions. Good estimation of market clearing prices (MCP) will help utilities and independent power producers determine bidding and transaction strategies with low risks, and this is crucial for utilities to compete in the deregulated environment. MCP prediction, however, is difficult since bidding strategies used by participants are complicated and MCP is a non-stationary process. The main objective of this research is to provide efficient short-term load and MCP forecasting and corresponding confidence interval estimation methodologies. In this research, the complexity of load and MCP with other factors is investigated, and neural networks are used to model the complex relationship between input and output. With improved learning algorithm and on-line update features for load forecasting, a neural network based load forecaster was developed, and has been in daily industry use since summer 1998 with good performance. MCP is volatile because of the complexity of market behaviors. In practice, neural network based MCP predictors usually have a cascaded structure, as several key input factors need to be estimated first. In this research, the uncertainties involved in a cascaded neural network structure for MCP prediction are analyzed, and prediction distribution under the Bayesian framework is developed. A fast algorithm to evaluate the confidence intervals by using the memoryless Quasi-Newton method is also developed. The traditional back-propagation algorithm for neural network learning needs to be improved since MCP is a non-stationary process. The extended Kalman filter (EKF) can be used as an integrated adaptive learning and confidence interval estimation algorithm for neural networks, with fast convergence and small confidence intervals. However, EKF learning is computationally expensive because it involves high dimensional matrix manipulations. A modified U-D factorization within the decoupled EKF (DEKF-UD) framework is developed in this research. The computational efficiency and numerical stability are significantly improved.

On supertaskers and the neural basis of efficient multitasking.

PubMed

Medeiros-Ward, Nathan; Watson, Jason M; Strayer, David L

2015-06-01

The present study used brain imaging to determine the neural basis of individual differences in multitasking, the ability to successfully perform at least two attention-demanding tasks at once. Multitasking is mentally taxing and, therefore, should recruit the prefrontal cortex to maintain task goals when coordinating attentional control and managing the cognitive load. To investigate this possibility, we used functional neuroimaging to assess neural activity in both extraordinary multitaskers (Supertaskers) and control subjects who were matched on working memory capacity. Participants performed a challenging dual N-back task in which auditory and visual stimuli were presented simultaneously, requiring independent and continuous maintenance, updating, and verification of the contents of verbal and spatial working memory. With the task requirements and considerable cognitive load that accompanied increasing N-back, relative to the controls, the multitasking of Supertaskers was characterized by more efficient recruitment of anterior cingulate and posterior frontopolar prefrontal cortices. Results are interpreted using neuropsychological and evolutionary perspectives on individual differences in multitasking ability and the neural correlates of attentional control.

Eliciting Naturalistic Cortical Responses with a Sensory Prosthesis via Optimized Microstimulation

DTIC Science & Technology

2016-08-12

error and correlation as metrics amenable to highly efficient convex optimization. This study concentrates on characterizing the neural responses to both...spiking signal. For LFP, distance measures such as the traditional mean-squared error and cross- correlation can be used, whereas distances between spike...with parameters that describe their associated temporal dynamics and relations to the observed output. A description of the model follows, but we

Advancing interconnect density for spiking neural network hardware implementations using traffic-aware adaptive network-on-chip routers.

PubMed

Carrillo, Snaider; Harkin, Jim; McDaid, Liam; Pande, Sandeep; Cawley, Seamus; McGinley, Brian; Morgan, Fearghal

2012-09-01

The brain is highly efficient in how it processes information and tolerates faults. Arguably, the basic processing units are neurons and synapses that are interconnected in a complex pattern. Computer scientists and engineers aim to harness this efficiency and build artificial neural systems that can emulate the key information processing principles of the brain. However, existing approaches cannot provide the dense interconnect for the billions of neurons and synapses that are required. Recently a reconfigurable and biologically inspired paradigm based on network-on-chip (NoC) and spiking neural networks (SNNs) has been proposed as a new method of realising an efficient, robust computing platform. However, the use of the NoC as an interconnection fabric for large-scale SNNs demands a good trade-off between scalability, throughput, neuron/synapse ratio and power consumption. This paper presents a novel traffic-aware, adaptive NoC router, which forms part of a proposed embedded mixed-signal SNN architecture called EMBRACE (EMulating Biologically-inspiRed ArChitectures in hardwarE). The proposed adaptive NoC router provides the inter-neuron connectivity for EMBRACE, maintaining router communication and avoiding dropped router packets by adapting to router traffic congestion. Results are presented on throughput, power and area performance analysis of the adaptive router using a 90 nm CMOS technology which outperforms existing NoCs in this domain. The adaptive behaviour of the router is also verified on a Stratix II FPGA implementation of a 4 × 2 router array with real-time traffic congestion. The presented results demonstrate the feasibility of using the proposed adaptive NoC router within the EMBRACE architecture to realise large-scale SNNs on embedded hardware. Copyright © 2012 Elsevier Ltd. All rights reserved.

Equivalent Skin Analysis of Wing Structures Using Neural Networks

NASA Technical Reports Server (NTRS)

Liu, Youhua; Kapania, Rakesh K.

2000-01-01

An efficient method of modeling trapezoidal built-up wing structures is developed by coupling. in an indirect way, an Equivalent Plate Analysis (EPA) with Neural Networks (NN). Being assumed to behave like a Mindlin-plate, the wing is solved using the Ritz method with Legendre polynomials employed as the trial functions. This analysis method can be made more efficient by avoiding most of the computational effort spent on calculating contributions to the stiffness and mass matrices from each spar and rib. This is accomplished by replacing the wing inner-structure with an "equivalent" material that combines to the skin and whose properties are simulated by neural networks. The constitutive matrix, which relates the stress vector to the strain vector, and the density of the equivalent material are obtained by enforcing mass and stiffness matrix equities with rec,ard to the EPA in a least-square sense. Neural networks for the material properties are trained in terms of the design variables of the wing structure. Examples show that the present method, which can be called an Equivalent Skin Analysis (ESA) of the wing structure, is more efficient than the EPA and still fairly good results can be obtained. The present ESA is very promising to be used at the early stages of wing structure design.

Microfabrication, characterization and in vivo MRI compatibility of diamond microelectrodes array for neural interfacing.

PubMed

Hébert, Clément; Warnking, Jan; Depaulis, Antoine; Garçon, Laurie Amandine; Mermoux, Michel; Eon, David; Mailley, Pascal; Omnès, Franck

2015-01-01

Neural interfacing still requires highly stable and biocompatible materials, in particular for in vivo applications. Indeed, most of the currently used materials are degraded and/or encapsulated by the proximal tissue leading to a loss of efficiency. Here, we considered boron doped diamond microelectrodes to address this issue and we evaluated the performances of a diamond microelectrode array. We described the microfabrication process of the device and discuss its functionalities. We characterized its electrochemical performances by cyclic voltammetry and impedance spectroscopy in saline buffer and observed the typical diamond electrode electrochemical properties, wide potential window and low background current, allowing efficient electrochemical detection. The charge storage capacitance and the modulus of the electrochemical impedance were found to remain in the same range as platinum electrodes used for standard commercial devices. Finally we observed a reduced Magnetic Resonance Imaging artifact when the device was implanted on a rat cortex, suggesting that boron doped-diamond is a very promising electrode material allowing functional imaging. Copyright © 2014 Elsevier B.V. All rights reserved.

A decentralized training algorithm for Echo State Networks in distributed big data applications.

PubMed

Scardapane, Simone; Wang, Dianhui; Panella, Massimo

2016-06-01

The current big data deluge requires innovative solutions for performing efficient inference on large, heterogeneous amounts of information. Apart from the known challenges deriving from high volume and velocity, real-world big data applications may impose additional technological constraints, including the need for a fully decentralized training architecture. While several alternatives exist for training feed-forward neural networks in such a distributed setting, less attention has been devoted to the case of decentralized training of recurrent neural networks (RNNs). In this paper, we propose such an algorithm for a class of RNNs known as Echo State Networks. The algorithm is based on the well-known Alternating Direction Method of Multipliers optimization procedure. It is formulated only in terms of local exchanges between neighboring agents, without reliance on a coordinating node. Additionally, it does not require the communication of training patterns, which is a crucial component in realistic big data implementations. Experimental results on large scale artificial datasets show that it compares favorably with a fully centralized implementation, in terms of speed, efficiency and generalization accuracy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Design and Validation of a Ten-Port Waveguide Reflectometer Sensor: Application to Efficiency Measurement and Optimization of Microwave-Heating Ovens

PubMed Central

Pedreño-Molina, Juan L.; Monzó-Cabrera, Juan; Lozano-Guerrero, Antonio; Toledo-Moreo, Ana

2008-01-01

This work presents the design, manufacturing process, calibration and validation of a new microwave ten-port waveguide reflectometer based on the use of neural networks. This low-cost novel device solves some of the shortcomings of previous reflectometers such as non-linear behavior of power sensors, noise presence and the complexity of the calibration procedure, which is often based on complex mathematical equations. These problems, which imply the reduction of the reflection coefficient measurement accuracy, have been overcome by using a higher number of probes than usual six-port configurations and by means of the use of Radial Basis Function (RBF) neural networks in order to reduce the influence of noise and non-linear processes over the measurements. Additionally, this sensor can be reconfigured whenever some of the eight coaxial power detectors fail, still providing accurate values in real time. The ten-port performance has been compared against a high-cost measurement instrument such as a vector network analyzer and applied to the measurement and optimization of energy efficiency of microwave ovens, with good results. PMID:27873961

The Neural Basis of Contextual Influences on Face Categorization

PubMed Central

Freeman, Jonathan B.; Ma, Yina; Barth, Maria; Young, Steven G.; Han, Shihui; Ambady, Nalini

2015-01-01

From only brief exposure to a face, individuals spontaneously categorize another's race. Recent behavioral evidence suggests that visual context may affect such categorizations. We used fMRI to examine the neural basis of contextual influences on the race categorization of faces. Participants categorized the race of faces that varied along a White-Asian morph continuum and were surrounded by American, neutral, or Chinese scene contexts. As expected, the context systematically influenced categorization responses and their efficiency (response times). Neuroimaging results indicated that the retrosplenial cortex (RSC) and orbitofrontal cortex (OFC) exhibited highly sensitive, graded responses to the compatibility of facial and contextual cues. These regions showed linearly increasing responses as a face became more White when in an American context, and linearly increasing responses as a face became more Asian when in a Chinese context. Further, RSC activity partially mediated the effect of this face-context compatibility on the efficiency of categorization responses. Together, the findings suggest a critical role of the RSC and OFC in driving contextual influences on face categorization, and highlight the impact of extraneous cues beyond the face in categorizing other people. PMID:24006403

Limits to high-speed simulations of spiking neural networks using general-purpose computers.

PubMed

Zenke, Friedemann; Gerstner, Wulfram

2014-01-01

To understand how the central nervous system performs computations using recurrent neuronal circuitry, simulations have become an indispensable tool for theoretical neuroscience. To study neuronal circuits and their ability to self-organize, increasing attention has been directed toward synaptic plasticity. In particular spike-timing-dependent plasticity (STDP) creates specific demands for simulations of spiking neural networks. On the one hand a high temporal resolution is required to capture the millisecond timescale of typical STDP windows. On the other hand network simulations have to evolve over hours up to days, to capture the timescale of long-term plasticity. To do this efficiently, fast simulation speed is the crucial ingredient rather than large neuron numbers. Using different medium-sized network models consisting of several thousands of neurons and off-the-shelf hardware, we compare the simulation speed of the simulators: Brian, NEST and Neuron as well as our own simulator Auryn. Our results show that real-time simulations of different plastic network models are possible in parallel simulations in which numerical precision is not a primary concern. Even so, the speed-up margin of parallelism is limited and boosting simulation speeds beyond one tenth of real-time is difficult. By profiling simulation code we show that the run times of typical plastic network simulations encounter a hard boundary. This limit is partly due to latencies in the inter-process communications and thus cannot be overcome by increased parallelism. Overall, these results show that to study plasticity in medium-sized spiking neural networks, adequate simulation tools are readily available which run efficiently on small clusters. However, to run simulations substantially faster than real-time, special hardware is a prerequisite.

Anger in brain and body: the neural and physiological perturbation of decision-making by emotion.

PubMed

Garfinkel, Sarah N; Zorab, Emma; Navaratnam, Nakulan; Engels, Miriam; Mallorquí-Bagué, Núria; Minati, Ludovico; Dowell, Nicholas G; Brosschot, Jos F; Thayer, Julian F; Critchley, Hugo D

2016-01-01

Emotion and cognition are dynamically coupled to bodily arousal: the induction of anger, even unconsciously, can reprioritise neural and physiological resources toward action states that bias cognitive processes. Here we examine behavioural, neural and bodily effects of covert anger processing and its influence on cognition, indexed by lexical decision-making. While recording beat-to-beat blood pressure, the words ANGER or RELAX were presented subliminally just prior to rapid word/non-word reaction-time judgements of letter-strings. Subliminal ANGER primes delayed the time taken to reach rapid lexical decisions, relative to RELAX primes. However, individuals with high trait anger were speeded up by subliminal anger primes. ANGER primes increased systolic blood pressure and the magnitude of this increase predicted reaction time prolongation. Within the brain, ANGER trials evoked an enhancement of activity within dorsal pons and an attenuation of activity within visual occipitotemporal and attentional parietal cortices. Activity within periaqueductal grey matter, occipital and parietal regions increased linearly with evoked blood pressure changes, indicating neural substrates through which covert anger impairs semantic decisions, putatively through its expression as visceral arousal. The behavioural and physiological impact of anger states compromises the efficiency of cognitive processing through action-ready changes in autonomic response that skew regional neural activity. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Application of artificial neural networks to chemostratigraphy

NASA Astrophysics Data System (ADS)

Malmgren, BjöRn A.; Nordlund, Ulf

1996-08-01

Artificial neural networks, a branch of artificial intelligence, are computer systems formed by a number of simple, highly interconnected processing units that have the ability to learn a set of target vectors from a set of associated input signals. Neural networks learn by self-adjusting a set of parameters, using some pertinent algorithm to minimize the error between the desired output and network output. We explore the potential of this approach in solving a problem involving classification of geochemical data. The data, taken from the literature, are derived from four late Quaternary zones of volcanic ash of basaltic and rhyolithic origin from the Norwegian Sea. These ash layers span the oxygen isotope zones 1, 5, 7, and 11, respectively (last 420,000 years). The data consist of nine geochemical variables (oxides) determined in each of 183 samples. We employed a three-layer back propagation neural network to assess its efficiency to optimally differentiate samples from the four ash zones on the basis of their geochemical composition. For comparison, three statistical pattern recognition techniques, linear discriminant analysis, the k-nearest neighbor (k-NN) technique, and SIMCA (soft independent modeling of class analogy), were applied to the same data. All of these showed considerably higher error rates than the artificial neural network, indicating that the back propagation network was indeed more powerful in correctly classifying the ash particles to the appropriate zone on the basis of their geochemical composition.

Use of neural image analysis methods in the process to determine the dry matter content in the compost

NASA Astrophysics Data System (ADS)

Wojcieszak, D.; Przybył, J.; Lewicki, A.; Ludwiczak, A.; Przybylak, A.; Boniecki, P.; Koszela, K.; Zaborowicz, M.; Przybył, K.; Witaszek, K.

2015-07-01

The aim of this research was investigate the possibility of using methods of computer image analysis and artificial neural networks for to assess the amount of dry matter in the tested compost samples. The research lead to the conclusion that the neural image analysis may be a useful tool in determining the quantity of dry matter in the compost. Generated neural model may be the beginning of research into the use of neural image analysis assess the content of dry matter and other constituents of compost. The presented model RBF 19:19-2-1:1 characterized by test error 0.092189 may be more efficient.

The Brain as an Efficient and Robust Adaptive Learner.

PubMed

Denève, Sophie; Alemi, Alireza; Bourdoukan, Ralph

2017-06-07

Understanding how the brain learns to compute functions reliably, efficiently, and robustly with noisy spiking activity is a fundamental challenge in neuroscience. Most sensory and motor tasks can be described as dynamical systems and could presumably be learned by adjusting connection weights in a recurrent biological neural network. However, this is greatly complicated by the credit assignment problem for learning in recurrent networks, e.g., the contribution of each connection to the global output error cannot be determined based only on locally accessible quantities to the synapse. Combining tools from adaptive control theory and efficient coding theories, we propose that neural circuits can indeed learn complex dynamic tasks with local synaptic plasticity rules as long as they associate two experimentally established neural mechanisms. First, they should receive top-down feedbacks driving both their activity and their synaptic plasticity. Second, inhibitory interneurons should maintain a tight balance between excitation and inhibition in the circuit. The resulting networks could learn arbitrary dynamical systems and produce irregular spike trains as variable as those observed experimentally. Yet, this variability in single neurons may hide an extremely efficient and robust computation at the population level. Copyright © 2017 Elsevier Inc. All rights reserved.

Intelligence and working memory systems: evidence of neural efficiency in alpha band ERD.

PubMed

Grabner, R H; Fink, A; Stipacek, A; Neuper, C; Neubauer, A C

2004-07-01

Starting from the well-established finding that brighter individuals display a more efficient brain function when performing cognitive tasks (i.e., neural efficiency), we investigated the relationship between intelligence and cortical activation in the context of working memory (WM) tasks. Fifty-five male (n=28) and female (n=27) participants worked on (1) a classical forward digit span task demanding only short-term memory (STM), (2) an attention-switching task drawing on the central executive (CE) of WM and (3) a WM task involving both STM storage and CE processes. During performance of these three types of tasks, cortical activation was quantified by the extent of Event-Related Desynchronization (ERD) in the alpha band of the human EEG. Correlational analyses revealed associations between the amount of ERD in the upper alpha band and intelligence in several brain regions. In all tasks, the males were more likely to display the negative intelligence-cortical activation relationship. Furthermore, stronger associations between ERD and intelligence were found for fluid rather than crystallized intelligence. Analyses also point to topographical differences in neural efficiency depending on sex, task type and the associated cognitive subsystems engaged during task performance.

Hybrid response surface methodology-artificial neural network optimization of drying process of banana slices in a forced convective dryer.

PubMed

Taheri-Garavand, Amin; Karimi, Fatemeh; Karimi, Mahmoud; Lotfi, Valiullah; Khoobbakht, Golmohammad

2018-06-01

The aim of the study is to fit models for predicting surfaces using the response surface methodology and the artificial neural network to optimize for obtaining the maximum acceptability using desirability functions methodology in a hot air drying process of banana slices. The drying air temperature, air velocity, and drying time were chosen as independent factors and moisture content, drying rate, energy efficiency, and exergy efficiency were dependent variables or responses in the mentioned drying process. A rotatable central composite design as an adequate method was used to develop models for the responses in the response surface methodology. Moreover, isoresponse contour plots were useful to predict the results by performing only a limited set of experiments. The optimum operating conditions obtained from the artificial neural network models were moisture content 0.14 g/g, drying rate 1.03 g water/g h, energy efficiency 0.61, and exergy efficiency 0.91, when the air temperature, air velocity, and drying time values were equal to -0.42 (74.2 ℃), 1.00 (1.50 m/s), and -0.17 (2.50 h) in the coded units, respectively.

Individual differences in brain activity during visuo-spatial processing assessed by slow cortical potentials and LORETA.

PubMed

Lamm, Claus; Fischmeister, Florian Ph S; Bauer, Herbert

2005-12-01

Using slow-cortical potentials (SCPs), Vitouch et al. demonstrated that subjects with low ability to solve a complex visuo-spatial imagery task show higher activity in occipital, parietal and frontal cortex during task processing than subjects with high ability. This finding has been interpreted in the sense of the so-called "neural efficiency" hypothesis, which assumes that the central nervous system of individuals with higher intellectual abilities is functioning in a more efficient way than the one of individuals with lower abilities. Using a higher spatial resolution of SCP recordings, and by employing the source localization method of LORETA (low-resolution electromagnetic tomography), we investigated this hypothesis by performing an extended replication of Vitouch et al.'s study. SCPs during processing of a visuo-spatial imagery task were recorded in pre-selected subjects with either high or low abilities in solving the imagery task. Topographic and LORETA analyses of SCPs revealed that a distributed network of extrastriate occipital, superior parietal, temporal, medial frontal and prefrontal areas was active during task solving. This network is well in line with former studies of the functional neuroanatomy of visuo-spatial imagery. Contrary to our expectations, however, the results of Vitouch et al. as well as of other studies supporting the neural efficiency hypothesis could not be confirmed since no difference in brain activity between groups was observed. This inconsistency between studies might be due to differing task processing strategies. While subjects with high abilities in the Vitouch et al. study seemed to use a visuo-perceptual task solving approach, all other subjects relied upon a visuo-motor task processing strategy.

Memristive Mixed-Signal Neuromorphic Systems: Energy-Efficient Learning at the Circuit-Level

DOE PAGES

Chakma, Gangotree; Adnan, Md Musabbir; Wyer, Austin R.; ...

2017-11-23

Neuromorphic computing is non-von Neumann computer architecture for the post Moore’s law era of computing. Since a main focus of the post Moore’s law era is energy-efficient computing with fewer resources and less area, neuromorphic computing contributes effectively in this research. Here in this paper, we present a memristive neuromorphic system for improved power and area efficiency. Our particular mixed-signal approach implements neural networks with spiking events in a synchronous way. Moreover, the use of nano-scale memristive devices saves both area and power in the system. We also provide device-level considerations that make the system more energy-efficient. The proposed systemmore » additionally includes synchronous digital long term plasticity, an online learning methodology that helps the system train the neural networks during the operation phase and improves the efficiency in learning considering the power consumption and area overhead.« less

Memristive Mixed-Signal Neuromorphic Systems: Energy-Efficient Learning at the Circuit-Level

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chakma, Gangotree; Adnan, Md Musabbir; Wyer, Austin R.

Neuromorphic computing is non-von Neumann computer architecture for the post Moore’s law era of computing. Since a main focus of the post Moore’s law era is energy-efficient computing with fewer resources and less area, neuromorphic computing contributes effectively in this research. Here in this paper, we present a memristive neuromorphic system for improved power and area efficiency. Our particular mixed-signal approach implements neural networks with spiking events in a synchronous way. Moreover, the use of nano-scale memristive devices saves both area and power in the system. We also provide device-level considerations that make the system more energy-efficient. The proposed systemmore » additionally includes synchronous digital long term plasticity, an online learning methodology that helps the system train the neural networks during the operation phase and improves the efficiency in learning considering the power consumption and area overhead.« less

The Neural Dynamics of Attentional Selection in Natural Scenes.

PubMed

Kaiser, Daniel; Oosterhof, Nikolaas N; Peelen, Marius V

2016-10-12

The human visual system can only represent a small subset of the many objects present in cluttered scenes at any given time, such that objects compete for representation. Despite these processing limitations, the detection of object categories in cluttered natural scenes is remarkably rapid. How does the brain efficiently select goal-relevant objects from cluttered scenes? In the present study, we used multivariate decoding of magneto-encephalography (MEG) data to track the neural representation of within-scene objects as a function of top-down attentional set. Participants detected categorical targets (cars or people) in natural scenes. The presence of these categories within a scene was decoded from MEG sensor patterns by training linear classifiers on differentiating cars and people in isolation and testing these classifiers on scenes containing one of the two categories. The presence of a specific category in a scene could be reliably decoded from MEG response patterns as early as 160 ms, despite substantial scene clutter and variation in the visual appearance of each category. Strikingly, we find that these early categorical representations fully depend on the match between visual input and top-down attentional set: only objects that matched the current attentional set were processed to the category level within the first 200 ms after scene onset. A sensor-space searchlight analysis revealed that this early attention bias was localized to lateral occipitotemporal cortex, reflecting top-down modulation of visual processing. These results show that attention quickly resolves competition between objects in cluttered natural scenes, allowing for the rapid neural representation of goal-relevant objects. Efficient attentional selection is crucial in many everyday situations. For example, when driving a car, we need to quickly detect obstacles, such as pedestrians crossing the street, while ignoring irrelevant objects. How can humans efficiently perform such tasks, given the multitude of objects contained in real-world scenes? Here we used multivariate decoding of magnetoencephalogaphy data to characterize the neural underpinnings of attentional selection in natural scenes with high temporal precision. We show that brain activity quickly tracks the presence of objects in scenes, but crucially only for those objects that were immediately relevant for the participant. These results provide evidence for fast and efficient attentional selection that mediates the rapid detection of goal-relevant objects in real-world environments. Copyright © 2016 the authors 0270-6474/16/3610522-07$15.00/0.

Implementation of pulse-coupled neural networks in a CNAPS environment.

PubMed

Kinser, J M; Lindblad, T

1999-01-01

Pulse coupled neural networks (PCNN's) are biologically inspired algorithms very well suited for image/signal preprocessing. While several analog implementations are proposed we suggest a digital implementation in an existing environment, the connected network of adapted processors system (CNAPS). The reason for this is two fold. First, CNAPS is a commercially available chip which has been used for several neural-network implementations. Second, the PCNN is, in almost all applications, a very efficient component of a system requiring subsequent and additional processing. This may include gating, Fourier transforms, neural classifiers, data mining, etc, with or without feedback to the PCNN.

A neural network approach to burst detection.

PubMed

Mounce, S R; Day, A J; Wood, A S; Khan, A; Widdop, P D; Machell, J

2002-01-01

This paper describes how hydraulic and water quality data from a distribution network may be used to provide a more efficient leakage management capability for the water industry. The research presented concerns the application of artificial neural networks to the issue of detection and location of leakage in treated water distribution systems. An architecture for an Artificial Neural Network (ANN) based system is outlined. The neural network uses time series data produced by sensors to directly construct an empirical model for predication and classification of leaks. Results are presented using data from an experimental site in Yorkshire Water's Keighley distribution system.

Neural network decoder for quantum error correcting codes

NASA Astrophysics Data System (ADS)

Krastanov, Stefan; Jiang, Liang

Artificial neural networks form a family of extremely powerful - albeit still poorly understood - tools used in anything from image and sound recognition through text generation to, in our case, decoding. We present a straightforward Recurrent Neural Network architecture capable of deducing the correcting procedure for a quantum error-correcting code from a set of repeated stabilizer measurements. We discuss the fault-tolerance of our scheme and the cost of training the neural network for a system of a realistic size. Such decoders are especially interesting when applied to codes, like the quantum LDPC codes, that lack known efficient decoding schemes.

Neural-Net Processing of Characteristic Patterns From Electronic Holograms of Vibrating Blades

NASA Technical Reports Server (NTRS)

Decker, Arthur J.

1999-01-01

Finite-element-model-trained artificial neural networks can be used to process efficiently the characteristic patterns or mode shapes from electronic holograms of vibrating blades. The models used for routine design may not yet be sufficiently accurate for this application. This document discusses the creation of characteristic patterns; compares model generated and experimental characteristic patterns; and discusses the neural networks that transform the characteristic patterns into strain or damage information. The current potential to adapt electronic holography to spin rigs, wind tunnels and engines provides an incentive to have accurate finite element models lor training neural networks.

Design of a MIMD neural network processor

NASA Astrophysics Data System (ADS)

Saeks, Richard E.; Priddy, Kevin L.; Pap, Robert M.; Stowell, S.

1994-03-01

The Accurate Automation Corporation (AAC) neural network processor (NNP) module is a fully programmable multiple instruction multiple data (MIMD) parallel processor optimized for the implementation of neural networks. The AAC NNP design fully exploits the intrinsic sparseness of neural network topologies. Moreover, by using a MIMD parallel processing architecture one can update multiple neurons in parallel with efficiency approaching 100 percent as the size of the network increases. Each AAC NNP module has 8 K neurons and 32 K interconnections and is capable of 140,000,000 connections per second with an eight processor array capable of over one billion connections per second.

The involvement of heparan sulfate proteoglycans in stem cell differentiation and in malignant glioma

NASA Astrophysics Data System (ADS)

Kundu, Soumi; Xiong, Anqi; Forsberg-Nilsson, Karin

2016-04-01

Heparan sulfate (HS) proteoglycans (HSPG) are major components of the extracellular matrix. They interact with a plethora of macromolecules that are of physiological importance. The pattern of sulfation of the HS chain determines the specificity of these interactions. The enzymes that synthesize and degrade HS are thus key regulators of processes ranging from embryonic development to tissue homeostasis and tumor development. Formation of the nervous system is also critically dependent on appropriate HSPGs as shown by several studies on the role of HS in neural induction from embryonic stem cells. High-grade glioma is the most common primary malignant brain tumor among adults, and the prognosis is poor. Neural and glioma stem cells share several traits, including sustained proliferation and highly efficient migration in the brain. There are also similarities between the neurogenic niche where adult neural stem cells reside and the tumorigenic niche, including their interactions with components of the extracellular matrix (ECM). The levels of many of these components, for example HSPGs and enzymes involved in the biosynthesis and modification of HS are attenuated in gliomas. In this paper, HS regulation of pathways involved in neural differentiation and how these may be of importance for brain development are discussed. The literature suggesting that modifications of HS could regulate glioma growth and invasion is reviewed. Targeting the invasiveness of glioma cells by modulating HS may improve upon present therapeutic options, which only marginally enhance the survival of glioma patients.

Bioinspired Nanocomplex for Spatiotemporal Imaging of Sequential mRNA Expression in Differentiating Neural Stem Cells

PubMed Central

2015-01-01

Messenger RNA plays a pivotal role in regulating cellular activities. The expression dynamics of specific mRNA contains substantial information on the intracellular milieu. Unlike the imaging of stationary mRNAs, real-time intracellular imaging of the dynamics of mRNA expression is of great value for investigating mRNA biology and exploring specific cellular cascades. In addition to advanced imaging methods, timely extracellular stimulation is another key factor in regulating the mRNA expression repertoire. The integration of effective stimulation and imaging into a single robust system would significantly improve stimulation efficiency and imaging accuracy, producing fewer unwanted artifacts. In this study, we developed a multifunctional nanocomplex to enable self-activating and spatiotemporal imaging of the dynamics of mRNA sequential expression during the neural stem cell differentiation process. This nanocomplex showed improved enzymatic stability, fast recognition kinetics, and high specificity. With a mechanism regulated by endogenous cell machinery, this nanocomplex realized the successive stimulating motif release and the dynamic imaging of chronological mRNA expression during neural stem cell differentiation without the use of transgenetic manipulation. The dynamic imaging montage of mRNA expression ultimately facilitated genetic heterogeneity analysis. In vivo lateral ventricle injection of this nanocomplex enabled endogenous neural stem cell activation and labeling at their specific differentiation stages. This nanocomplex is highly amenable as an alternative tool to explore the dynamics of intricate mRNA activities in various physiological and pathological conditions. PMID:25494492

Bioinspired nanocomplex for spatiotemporal imaging of sequential mRNA expression in differentiating neural stem cells.

PubMed

Wang, Zhe; Zhang, Ruili; Wang, Zhongliang; Wang, He-Fang; Wang, Yu; Zhao, Jun; Wang, Fu; Li, Weitao; Niu, Gang; Kiesewetter, Dale O; Chen, Xiaoyuan

2014-12-23

Messenger RNA plays a pivotal role in regulating cellular activities. The expression dynamics of specific mRNA contains substantial information on the intracellular milieu. Unlike the imaging of stationary mRNAs, real-time intracellular imaging of the dynamics of mRNA expression is of great value for investigating mRNA biology and exploring specific cellular cascades. In addition to advanced imaging methods, timely extracellular stimulation is another key factor in regulating the mRNA expression repertoire. The integration of effective stimulation and imaging into a single robust system would significantly improve stimulation efficiency and imaging accuracy, producing fewer unwanted artifacts. In this study, we developed a multifunctional nanocomplex to enable self-activating and spatiotemporal imaging of the dynamics of mRNA sequential expression during the neural stem cell differentiation process. This nanocomplex showed improved enzymatic stability, fast recognition kinetics, and high specificity. With a mechanism regulated by endogenous cell machinery, this nanocomplex realized the successive stimulating motif release and the dynamic imaging of chronological mRNA expression during neural stem cell differentiation without the use of transgenetic manipulation. The dynamic imaging montage of mRNA expression ultimately facilitated genetic heterogeneity analysis. In vivo lateral ventricle injection of this nanocomplex enabled endogenous neural stem cell activation and labeling at their specific differentiation stages. This nanocomplex is highly amenable as an alternative tool to explore the dynamics of intricate mRNA activities in various physiological and pathological conditions.

The Variability of Neural Responses to Naturalistic Videos Change with Age and Sex

PubMed Central

Petroni, Agustin; Langer, Nicolas; Milham, Michael P.

2018-01-01

Abstract Neural development is generally marked by an increase in the efficiency and diversity of neural processes. In a large sample (n = 114) of human children and adults with ages ranging from 5 to 44 yr, we investigated the neural responses to naturalistic video stimuli. Videos from both real-life classroom settings and Hollywood feature films were used to probe different aspects of attention and engagement. For all stimuli, older ages were marked by more variable neural responses. Variability was assessed by the intersubject correlation of evoked electroencephalographic responses. Young males also had less-variable responses than young females. These results were replicated in an independent cohort (n = 303). When interpreted in the context of neural maturation, we conclude that neural function becomes more variable with maturity, at least during the passive viewing of real-world stimuli. PMID:29379880

Neural Differentiation of Embryonic Stem Cells In Vitro: A Road Map to Neurogenesis in the Embryo

PubMed Central

Abranches, Elsa; Silva, Margarida; Pradier, Laurent; Schulz, Herbert; Hummel, Oliver; Henrique, Domingos; Bekman, Evguenia

2009-01-01

Background The in vitro generation of neurons from embryonic stem (ES) cells is a promising approach to produce cells suitable for neural tissue repair and cell-based replacement therapies of the nervous system. Available methods to promote ES cell differentiation towards neural lineages attempt to replicate, in different ways, the multistep process of embryonic neural development. However, to achieve this aim in an efficient and reproducible way, a better knowledge of the cellular and molecular events that are involved in the process, from the initial specification of neuroepithelial progenitors to their terminal differentiation into neurons and glial cells, is required. Methodology/Principal Findings In this work, we characterize the main stages and transitions that occur when ES cells are driven into a neural fate, using an adherent monolayer culture system. We established improved conditions to routinely produce highly homogeneous cultures of neuroepithelial progenitors, which organize into neural tube-like rosettes when they acquire competence for neuronal production. Within rosettes, neuroepithelial progenitors display morphological and functional characteristics of their embryonic counterparts, namely, apico-basal polarity, active Notch signalling, and proper timing of production of neurons and glia. In order to characterize the global gene activity correlated with each particular stage of neural development, the full transcriptome of different cell populations that arise during the in vitro differentiation protocol was determined by microarray analysis. By using embryo-oriented criteria to cluster the differentially expressed genes, we define five gene expression signatures that correlate with successive stages in the path from ES cells to neurons. These include a gene signature for a primitive ectoderm-like stage that appears after ES cells enter differentiation, and three gene signatures for subsequent stages of neural progenitor development, from an early stage that follows neural induction to a final stage preceding terminal differentiation. Conclusions/Significance Overall, our work confirms and extends the cellular and molecular parallels between monolayer ES cell neural differentiation and embryonic neural development, revealing in addition novel aspects of the genetic network underlying the multistep process that leads from uncommitted cells to differentiated neurons. PMID:19621087

Brian: a simulator for spiking neural networks in python.

PubMed

Goodman, Dan; Brette, Romain

2008-01-01

"Brian" is a new simulator for spiking neural networks, written in Python (http://brian. di.ens.fr). It is an intuitive and highly flexible tool for rapidly developing new models, especially networks of single-compartment neurons. In addition to using standard types of neuron models, users can define models by writing arbitrary differential equations in ordinary mathematical notation. Python scientific libraries can also be used for defining models and analysing data. Vectorisation techniques allow efficient simulations despite the overheads of an interpreted language. Brian will be especially valuable for working on non-standard neuron models not easily covered by existing software, and as an alternative to using Matlab or C for simulations. With its easy and intuitive syntax, Brian is also very well suited for teaching computational neuroscience.

Convolutional neural network for earthquake detection and location

PubMed Central

Perol, Thibaut; Gharbi, Michaël; Denolle, Marine

2018-01-01

The recent evolution of induced seismicity in Central United States calls for exhaustive catalogs to improve seismic hazard assessment. Over the last decades, the volume of seismic data has increased exponentially, creating a need for efficient algorithms to reliably detect and locate earthquakes. Today’s most elaborate methods scan through the plethora of continuous seismic records, searching for repeating seismic signals. We leverage the recent advances in artificial intelligence and present ConvNetQuake, a highly scalable convolutional neural network for earthquake detection and location from a single waveform. We apply our technique to study the induced seismicity in Oklahoma, USA. We detect more than 17 times more earthquakes than previously cataloged by the Oklahoma Geological Survey. Our algorithm is orders of magnitude faster than established methods. PMID:29487899

Random Bits Forest: a Strong Classifier/Regressor for Big Data

NASA Astrophysics Data System (ADS)

Wang, Yi; Li, Yi; Pu, Weilin; Wen, Kathryn; Shugart, Yin Yao; Xiong, Momiao; Jin, Li

2016-07-01

Efficiency, memory consumption, and robustness are common problems with many popular methods for data analysis. As a solution, we present Random Bits Forest (RBF), a classification and regression algorithm that integrates neural networks (for depth), boosting (for width), and random forests (for prediction accuracy). Through a gradient boosting scheme, it first generates and selects ~10,000 small, 3-layer random neural networks. These networks are then fed into a modified random forest algorithm to obtain predictions. Testing with datasets from the UCI (University of California, Irvine) Machine Learning Repository shows that RBF outperforms other popular methods in both accuracy and robustness, especially with large datasets (N > 1000). The algorithm also performed highly in testing with an independent data set, a real psoriasis genome-wide association study (GWAS).

The Prediction of Length-of-day Variations Based on Gaussian Processes

NASA Astrophysics Data System (ADS)

Lei, Y.; Zhao, D. N.; Gao, Y. P.; Cai, H. B.

2015-01-01

Due to the complicated time-varying characteristics of the length-of-day (LOD) variations, the accuracies of traditional strategies for the prediction of the LOD variations such as the least squares extrapolation model, the time-series analysis model, and so on, have not met the requirements for real-time and high-precision applications. In this paper, a new machine learning algorithm --- the Gaussian process (GP) model is employed to forecast the LOD variations. Its prediction precisions are analyzed and compared with those of the back propagation neural networks (BPNN), general regression neural networks (GRNN) models, and the Earth Orientation Parameters Prediction Comparison Campaign (EOP PCC). The results demonstrate that the application of the GP model to the prediction of the LOD variations is efficient and feasible.

Neural architecture design based on extreme learning machine.

PubMed

Bueno-Crespo, Andrés; García-Laencina, Pedro J; Sancho-Gómez, José-Luis

2013-12-01

Selection of the optimal neural architecture to solve a pattern classification problem entails to choose the relevant input units, the number of hidden neurons and its corresponding interconnection weights. This problem has been widely studied in many research works but their solutions usually involve excessive computational cost in most of the problems and they do not provide a unique solution. This paper proposes a new technique to efficiently design the MultiLayer Perceptron (MLP) architecture for classification using the Extreme Learning Machine (ELM) algorithm. The proposed method provides a high generalization capability and a unique solution for the architecture design. Moreover, the selected final network only retains those input connections that are relevant for the classification task. Experimental results show these advantages. Copyright © 2013 Elsevier Ltd. All rights reserved.

Development of drug-loaded polymer microcapsules for treatment of epilepsy.

PubMed

Chen, Yu; Gu, Qi; Yue, Zhilian; Crook, Jeremy M; Moulton, Simon E; Cook, Mark J; Wallace, Gordon G

2017-09-26

Despite significant progress in developing new drugs for seizure control, epilepsy still affects 1% of the global population and is drug-resistant in more than 30% of cases. To improve the therapeutic efficacy of epilepsy medication, a promising approach is to deliver anti-epilepsy drugs directly to affected brain areas using local drug delivery systems. The drug delivery systems must meet a number of criteria, including high drug loading efficiency, biodegradability, neuro-cytocompatibility and predictable drug release profiles. Here we report the development of fibre- and sphere-based microcapsules that exhibit controllable uniform morphologies and drug release profiles as predicted by mathematical modelling. Importantly, both forms of fabricated microcapsules are compatible with human brain derived neural stem cells and differentiated neurons and neuroglia, indicating clinical compliance for neural implantation and therapeutic drug delivery.

Forecasting sea cliff retreat in Southern California using process-based models and artificial neural networks

NASA Astrophysics Data System (ADS)

Limber, P. W.; Barnard, P.; Erikson, L. H.

2016-02-01

Modeling coastal geomorphic change over multi-decadal time and regional spatial scales (i.e. >20 km alongshore) is in high demand due to rising global sea levels and heavily populated coastal zones, but is challenging for several reasons: adequate geomorphic and oceanographic data often does not exist over the entire study area or time period; models can be too computationally expensive; and model uncertainty is high. In the absence of rich datasets and unlimited computer processing power, researchers are forced to leverage existing data, however sparse, and find analytical methods that minimize computation time without sacrificing (too much) model reliability. Machine learning techniques, such as artificial neural networks, can assimilate and efficiently extrapolate geomorphic model behavior over large areas. They can also facilitate ensemble model forecasts over a broad range of parameter space, which is useful when a paucity of observational data inhibits the constraint of model parameters. Here, we assimilate the behavior of two established process-based sea cliff erosion and retreat models into a neural network to forecast the impacts of sea level rise on sea cliff retreat in Southern California ( 400 km) through the 21st century. Using inputs such as historical cliff retreat rates, mean wave power, and whether or not a beach is present, the neural network independently reproduces modeled sea cliff retreat as a function of sea level rise with a high degree of confidence (R2 > 0.9, mean squared error < 0.1 m yr-1). Results will continuously improve as more model scenarios are assimilated into the neural network, and more field data (i.e., cliff composition and rock hardness) becomes available to tune the cliff retreat models. Preliminary results suggest that sea level rise rates of 2 to 20 mm yr-1 during the next century could accelerate historical cliff retreat rates in Southern California by an average of 0.10 - 0.56 m yr-1.

A stimulus-dependent spike threshold is an optimal neural coder

PubMed Central

Jones, Douglas L.; Johnson, Erik C.; Ratnam, Rama

2015-01-01

A neural code based on sequences of spikes can consume a significant portion of the brain's energy budget. Thus, energy considerations would dictate that spiking activity be kept as low as possible. However, a high spike-rate improves the coding and representation of signals in spike trains, particularly in sensory systems. These are competing demands, and selective pressure has presumably worked to optimize coding by apportioning a minimum number of spikes so as to maximize coding fidelity. The mechanisms by which a neuron generates spikes while maintaining a fidelity criterion are not known. Here, we show that a signal-dependent neural threshold, similar to a dynamic or adapting threshold, optimizes the trade-off between spike generation (encoding) and fidelity (decoding). The threshold mimics a post-synaptic membrane (a low-pass filter) and serves as an internal decoder. Further, it sets the average firing rate (the energy constraint). The decoding process provides an internal copy of the coding error to the spike-generator which emits a spike when the error equals or exceeds a spike threshold. When optimized, the trade-off leads to a deterministic spike firing-rule that generates optimally timed spikes so as to maximize fidelity. The optimal coder is derived in closed-form in the limit of high spike-rates, when the signal can be approximated as a piece-wise constant signal. The predicted spike-times are close to those obtained experimentally in the primary electrosensory afferent neurons of weakly electric fish (Apteronotus leptorhynchus) and pyramidal neurons from the somatosensory cortex of the rat. We suggest that KCNQ/Kv7 channels (underlying the M-current) are good candidates for the decoder. They are widely coupled to metabolic processes and do not inactivate. We conclude that the neural threshold is optimized to generate an energy-efficient and high-fidelity neural code. PMID:26082710

Using Upper Layer Weights to Efficiently Construct and Train Feedforward Neural Networks Executing Backpropagation

DTIC Science & Technology

2011-03-01

algorithm is utilized by Belue, Steppe, & Bauer and Kocur , et al. (Belue, Steppe, & Bauer, April 1996) ( Kocur , et al., 1996). Bacauskiene and...Society. Cardiff, UK. Kocur , C., Roger, S., Myers, L., Burns, T., Hoffmeister, J., Bauer, K., et al. (1996). Using neural networks to select

Using input feature information to improve ultraviolet retrieval in neural networks

NASA Astrophysics Data System (ADS)

Sun, Zhibin; Chang, Ni-Bin; Gao, Wei; Chen, Maosi; Zempila, Melina

2017-09-01

In neural networks, the training/predicting accuracy and algorithm efficiency can be improved significantly via accurate input feature extraction. In this study, some spatial features of several important factors in retrieving surface ultraviolet (UV) are extracted. An extreme learning machine (ELM) is used to retrieve the surface UV of 2014 in the continental United States, using the extracted features. The results conclude that more input weights can improve the learning capacities of neural networks.

An efficient annealing in Boltzmann machine in Hopfield neural network

NASA Astrophysics Data System (ADS)

Kin, Teoh Yeong; Hasan, Suzanawati Abu; Bulot, Norhisam; Ismail, Mohammad Hafiz

2012-09-01

This paper proposes and implements Boltzmann machine in Hopfield neural network doing logic programming based on the energy minimization system. The temperature scheduling in Boltzmann machine enhancing the performance of doing logic programming in Hopfield neural network. The finest temperature is determined by observing the ratio of global solution and final hamming distance using computer simulations. The study shows that Boltzmann Machine model is more stable and competent in term of representing and solving difficult combinatory problems.

Implicit motor learning promotes neural efficiency during laparoscopy.

PubMed

Zhu, Frank F; Poolton, Jamie M; Wilson, Mark R; Hu, Yong; Maxwell, Jon P; Masters, Rich S W

2011-09-01

An understanding of differences in expert and novice neural behavior can inform surgical skills training. Outside the surgical domain, electroencephalographic (EEG) coherence analyses have shown that during motor performance, experts display less coactivation between the verbal-analytic and motor planning regions than their less skilled counterparts. Reduced involvement of verbal-analytic processes suggests greater neural efficiency. The authors tested the utility of an implicit motor learning intervention specifically devised to promote neural efficiency by reducing verbal-analytic involvement in laparoscopic performance. In this study, 18 novices practiced a movement pattern on a laparoscopic trainer with either conscious awareness of the movement pattern (explicit motor learning) or suppressed awareness of the movement pattern (implicit motor learning). In a retention test, movement accuracy was compared between the conditions, and coactivation (EEG coherence) was assessed between the motor planning (Fz) region and both the verbal-analytic (T3) and the visuospatial (T4) cortical regions (T3-Fz and T4-Fz, respectively). Movement accuracy in the conditions was not different in a retention test (P = 0.231). Findings showed that the EEG coherence scores for the T3-Fz regions were lower for the implicit learners than for the explicit learners (P = 0.027), but no differences were apparent for the T4-Fz regions (P = 0.882). Implicit motor learning reduced EEG coactivation between verbal-analytic and motor planning regions, suggesting that verbal-analytic processes were less involved in laparoscopic performance. The findings imply that training techniques that discourage nonessential coactivation during motor performance may provide surgeons with more neural resources with which to manage other aspects of surgery.

Algorithmic design of a noise-resistant and efficient closed-loop deep brain stimulation system: A computational approach

PubMed Central

Karamintziou, Sofia D.; Custódio, Ana Luísa; Piallat, Brigitte; Polosan, Mircea; Chabardès, Stéphan; Stathis, Pantelis G.; Tagaris, George A.; Sakas, Damianos E.; Polychronaki, Georgia E.; Tsirogiannis, George L.; David, Olivier; Nikita, Konstantina S.

2017-01-01

Advances in the field of closed-loop neuromodulation call for analysis and modeling approaches capable of confronting challenges related to the complex neuronal response to stimulation and the presence of strong internal and measurement noise in neural recordings. Here we elaborate on the algorithmic aspects of a noise-resistant closed-loop subthalamic nucleus deep brain stimulation system for advanced Parkinson’s disease and treatment-refractory obsessive-compulsive disorder, ensuring remarkable performance in terms of both efficiency and selectivity of stimulation, as well as in terms of computational speed. First, we propose an efficient method drawn from dynamical systems theory, for the reliable assessment of significant nonlinear coupling between beta and high-frequency subthalamic neuronal activity, as a biomarker for feedback control. Further, we present a model-based strategy through which optimal parameters of stimulation for minimum energy desynchronizing control of neuronal activity are being identified. The strategy integrates stochastic modeling and derivative-free optimization of neural dynamics based on quadratic modeling. On the basis of numerical simulations, we demonstrate the potential of the presented modeling approach to identify, at a relatively low computational cost, stimulation settings potentially associated with a significantly higher degree of efficiency and selectivity compared with stimulation settings determined post-operatively. Our data reinforce the hypothesis that model-based control strategies are crucial for the design of novel stimulation protocols at the backstage of clinical applications. PMID:28222198

A Dynamic Neural Network Approach to CBM

DTIC Science & Technology

2011-03-15

high efficiency water cooled heat exchanger positioned on the side of the engine. The air temperature was controlled at the desired set-point by...regulating the inlet water flow in the heat exchanger. The temperature of the cooling water was not regulated. The typical set-point for the air charge...temperature was 127 degF, as used in other durability tests carried out in these facilities. Because the heat exchanger controller was optimized for

Artificial neural network modeling and optimization of ultrahigh pressure extraction of green tea polyphenols.

PubMed

Xi, Jun; Xue, Yujing; Xu, Yinxiang; Shen, Yuhong

2013-11-01

In this study, the ultrahigh pressure extraction of green tea polyphenols was modeled and optimized by a three-layer artificial neural network. A feed-forward neural network trained with an error back-propagation algorithm was used to evaluate the effects of pressure, liquid/solid ratio and ethanol concentration on the total phenolic content of green tea extracts. The neural network coupled with genetic algorithms was also used to optimize the conditions needed to obtain the highest yield of tea polyphenols. The obtained optimal architecture of artificial neural network model involved a feed-forward neural network with three input neurons, one hidden layer with eight neurons and one output layer including single neuron. The trained network gave the minimum value in the MSE of 0.03 and the maximum value in the R(2) of 0.9571, which implied a good agreement between the predicted value and the actual value, and confirmed a good generalization of the network. Based on the combination of neural network and genetic algorithms, the optimum extraction conditions for the highest yield of green tea polyphenols were determined as follows: 498.8 MPa for pressure, 20.8 mL/g for liquid/solid ratio and 53.6% for ethanol concentration. The total phenolic content of the actual measurement under the optimum predicated extraction conditions was 582.4 ± 0.63 mg/g DW, which was well matched with the predicted value (597.2mg/g DW). This suggests that the artificial neural network model described in this work is an efficient quantitative tool to predict the extraction efficiency of green tea polyphenols. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Is "efficiency" a useful concept in cognitive neuroscience?

PubMed

Poldrack, Russell A

2015-02-01

It is common in the cognitive neuroscience literature to explain differences in activation in terms of differences in the "efficiency" of neural function. I argue here that this usage of the concept of efficiency is empty and simply redescribes activation differences rather than providing a useful explanation of them. I examine a number of possible explanations for differential activation in terms of task performance, neuronal computation, neuronal energetics, and network organization. While the concept of "efficiency" is vacuous as it is commonly employed in the neuroimaging literature, an examination of brain development in the context of neural coding, neuroenergetics, and network structure provides a roadmap for future investigation, which is fundamental to an improved understanding of developmental effects and group differences in neuroimaging signals. Copyright © 2014 The Author. Published by Elsevier Ltd.. All rights reserved.

Neural and hybrid modeling: an alternative route to efficiently predict the behavior of biotechnological processes aimed at biofuels obtainment.

PubMed

Curcio, Stefano; Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele

2014-01-01

The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved.

Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels Obtainment

PubMed Central

Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele

2014-01-01

The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved. PMID:24516363

Research on optimization of combustion efficiency of thermal power unit based on genetic algorithm

NASA Astrophysics Data System (ADS)

Zhou, Qiongyang

2018-04-01

In order to improve the economic performance and reduce pollutant emissions of thermal power units, the characteristics of neural network in establishing boiler combustion model are analyzed based on the analysis of the main factors affecting boiler efficiency by using orthogonal method. In addition, on the basis of this model, the genetic algorithm is used to find the best control amount of the furnace combustion in a certain working condition. Through the genetic algorithm based on real number encoding and roulette selection is concluded: the best control quantity at a condition of furnace combustion can be combined with the boiler combustion system model for neural network training. The precision of the neural network model is further improved, and the basic work is laid for the research of the whole boiler combustion optimization system.

Neural computation of arithmetic functions

NASA Technical Reports Server (NTRS)

Siu, Kai-Yeung; Bruck, Jehoshua

1990-01-01

An area of application of neural networks is considered. A neuron is modeled as a linear threshold gate, and the network architecture considered is the layered feedforward network. It is shown how common arithmetic functions such as multiplication and sorting can be efficiently computed in a shallow neural network. Some known results are improved by showing that the product of two n-bit numbers and sorting of n n-bit numbers can be computed by a polynomial-size neural network using only four and five unit delays, respectively. Moreover, the weights of each threshold element in the neural networks require O(log n)-bit (instead of n-bit) accuracy. These results can be extended to more complicated functions such as multiple products, division, rational functions, and approximation of analytic functions.

Neural Networks for Rapid Design and Analysis

NASA Technical Reports Server (NTRS)

Sparks, Dean W., Jr.; Maghami, Peiman G.

1998-01-01

Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems. Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic components with artificial neural networks, recurrent networks, which use state feedback with the appropriate number of time delays, as inputs to the networks, are employed. Once properly trained, neural networks can give very good approximations to nonlinear dynamic components, and by their judicious use in simulations, allow the analyst the potential to speed up the analysis process considerably. To illustrate this potential speed up, an existing simulation model of a spacecraft reaction wheel system is executed, first conventionally, and then with an artificial neural network in place.

Global exponential stability of inertial memristor-based neural networks with time-varying delays and impulses.

PubMed

Zhang, Wei; Huang, Tingwen; He, Xing; Li, Chuandong

2017-11-01

In this study, we investigate the global exponential stability of inertial memristor-based neural networks with impulses and time-varying delays. We construct inertial memristor-based neural networks based on the characteristics of the inertial neural networks and memristor. Impulses with and without delays are considered when modeling the inertial neural networks simultaneously, which are of great practical significance in the current study. Some sufficient conditions are derived under the framework of the Lyapunov stability method, as well as an extended Halanay differential inequality and a new delay impulsive differential inequality, which depend on impulses with and without delays, in order to guarantee the global exponential stability of the inertial memristor-based neural networks. Finally, two numerical examples are provided to illustrate the efficiency of the proposed methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Designing an artificial neural network using radial basis function to model exergetic efficiency of nanofluids in mini double pipe heat exchanger

NASA Astrophysics Data System (ADS)

Ghasemi, Nahid; Aghayari, Reza; Maddah, Heydar

2018-06-01

The present study aims at predicting and optimizing exergetic efficiency of TiO2-Al2O3/water nanofluid at different Reynolds numbers, volume fractions and twisted ratios using Artificial Neural Networks (ANN) and experimental data. Central Composite Design (CCD) and cascade Radial Basis Function (RBF) were used to display the significant levels of the analyzed factors on the exergetic efficiency. The size of TiO2-Al2O3/water nanocomposite was 20-70 nm. The parameters of ANN model were adapted by a training algorithm of radial basis function (RBF) with a wide range of experimental data set. Total mean square error and correlation coefficient were used to evaluate the results which the best result was obtained from double layer perceptron neural network with 30 neurons in which total Mean Square Error(MSE) and correlation coefficient (R2) were equal to 0.002 and 0.999, respectively. This indicated successful prediction of the network. Moreover, the proposed equation for predicting exergetic efficiency was extremely successful. According to the optimal curves, the optimum designing parameters of double pipe heat exchanger with inner twisted tape and nanofluid under the constrains of exergetic efficiency 0.937 are found to be Reynolds number 2500, twisted ratio 2.5 and volume fraction( v/v%) 0.05.

A FPGA-Based, Granularity-Variable Neuromorphic Processor and Its Application in a MIMO Real-Time Control System.

PubMed

Zhang, Zhen; Ma, Cheng; Zhu, Rong

2017-08-23

Artificial Neural Networks (ANNs), including Deep Neural Networks (DNNs), have become the state-of-the-art methods in machine learning and achieved amazing success in speech recognition, visual object recognition, and many other domains. There are several hardware platforms for developing accelerated implementation of ANN models. Since Field Programmable Gate Array (FPGA) architectures are flexible and can provide high performance per watt of power consumption, they have drawn a number of applications from scientists. In this paper, we propose a FPGA-based, granularity-variable neuromorphic processor (FBGVNP). The traits of FBGVNP can be summarized as granularity variability, scalability, integrated computing, and addressing ability: first, the number of neurons is variable rather than constant in one core; second, the multi-core network scale can be extended in various forms; third, the neuron addressing and computing processes are executed simultaneously. These make the processor more flexible and better suited for different applications. Moreover, a neural network-based controller is mapped to FBGVNP and applied in a multi-input, multi-output, (MIMO) real-time, temperature-sensing and control system. Experiments validate the effectiveness of the neuromorphic processor. The FBGVNP provides a new scheme for building ANNs, which is flexible, highly energy-efficient, and can be applied in many areas.

A FPGA-Based, Granularity-Variable Neuromorphic Processor and Its Application in a MIMO Real-Time Control System

PubMed Central

Zhang, Zhen; Zhu, Rong

2017-01-01

Artificial Neural Networks (ANNs), including Deep Neural Networks (DNNs), have become the state-of-the-art methods in machine learning and achieved amazing success in speech recognition, visual object recognition, and many other domains. There are several hardware platforms for developing accelerated implementation of ANN models. Since Field Programmable Gate Array (FPGA) architectures are flexible and can provide high performance per watt of power consumption, they have drawn a number of applications from scientists. In this paper, we propose a FPGA-based, granularity-variable neuromorphic processor (FBGVNP). The traits of FBGVNP can be summarized as granularity variability, scalability, integrated computing, and addressing ability: first, the number of neurons is variable rather than constant in one core; second, the multi-core network scale can be extended in various forms; third, the neuron addressing and computing processes are executed simultaneously. These make the processor more flexible and better suited for different applications. Moreover, a neural network-based controller is mapped to FBGVNP and applied in a multi-input, multi-output, (MIMO) real-time, temperature-sensing and control system. Experiments validate the effectiveness of the neuromorphic processor. The FBGVNP provides a new scheme for building ANNs, which is flexible, highly energy-efficient, and can be applied in many areas. PMID:28832522

Partial Adaptation of Obtained and Observed Value Signals Preserves Information about Gains and Losses

PubMed Central

Baddeley, Michelle; Tobler, Philippe N.; Schultz, Wolfram

2016-01-01

Given that the range of rewarding and punishing outcomes of actions is large but neural coding capacity is limited, efficient processing of outcomes by the brain is necessary. One mechanism to increase efficiency is to rescale neural output to the range of outcomes expected in the current context, and process only experienced deviations from this expectation. However, this mechanism comes at the cost of not being able to discriminate between unexpectedly low losses when times are bad versus unexpectedly high gains when times are good. Thus, too much adaptation would result in disregarding information about the nature and absolute magnitude of outcomes, preventing learning about the longer-term value structure of the environment. Here we investigate the degree of adaptation in outcome coding brain regions in humans, for directly experienced outcomes and observed outcomes. We scanned participants while they performed a social learning task in gain and loss blocks. Multivariate pattern analysis showed two distinct networks of brain regions adapt to the most likely outcomes within a block. Frontostriatal areas adapted to directly experienced outcomes, whereas lateral frontal and temporoparietal regions adapted to observed social outcomes. Critically, in both cases, adaptation was incomplete and information about whether the outcomes arose in a gain block or a loss block was retained. Univariate analysis confirmed incomplete adaptive coding in these regions but also detected nonadapting outcome signals. Thus, although neural areas rescale their responses to outcomes for efficient coding, they adapt incompletely and keep track of the longer-term incentives available in the environment. SIGNIFICANCE STATEMENT Optimal value-based choice requires that the brain precisely and efficiently represents positive and negative outcomes. One way to increase efficiency is to adapt responding to the most likely outcomes in a given context. However, too strong adaptation would result in loss of precise representation (e.g., when the avoidance of a loss in a loss-context is coded the same as receipt of a gain in a gain-context). We investigated an intermediate form of adaptation that is efficient while maintaining information about received gains and avoided losses. We found that frontostriatal areas adapted to directly experienced outcomes, whereas lateral frontal and temporoparietal regions adapted to observed social outcomes. Importantly, adaptation was intermediate, in line with influential models of reference dependence in behavioral economics. PMID:27683899

Impact parameter determination in experimental analysis using a neural network

NASA Astrophysics Data System (ADS)

Haddad, F.; Hagel, K.; Li, J.; Mdeiwayeh, N.; Natowitz, J. B.; Wada, R.; Xiao, B.; David, C.; Freslier, M.; Aichelin, J.

1997-03-01

A neural network is used to determine the impact parameter in 40Ca+40Ca reactions. The effect of the detection efficiency as well as the model dependence of the training procedure has been studied carefully. An overall improvement of the impact parameter determination of 25% is obtained using this technique. The analysis of Amphora 40Ca+40Ca data at 35 MeV per nucleon using a neural network shows two well-separated classes of events among the selected ``complete'' events.

Inversion of quasi-3D DC resistivity imaging data using artificial neural networks

NASA Astrophysics Data System (ADS)

Neyamadpour, Ahmad; Wan Abdullah, W. A. T.; Taib, Samsudin

2010-02-01

The objective of this paper is to investigate the applicability of artificial neural networks in inverting quasi-3D DC resistivity imaging data. An electrical resistivity imaging survey was carried out along seven parallel lines using a dipole-dipole array to confirm the validation of the results of an inversion using an artificial neural network technique. The model used to produce synthetic data to train the artificial neural network was a homogeneous medium of 100Ωm resistivity with an embedded anomalous body of 1000Ωm resistivity. The network was trained using 21 datasets (comprising 12159 data points) and tested on another 11 synthetic datasets (comprising 6369 data points) and on real field data. Another 24 test datasets (comprising 13896 data points) consisting of different resistivities for the background and the anomalous bodies were used in order to test the interpolation and extrapolation of network properties. Different learning paradigms were tried in the training process of the neural network, with the resilient propagation paradigm being the most efficient. The number of nodes, hidden layers, and efficient values for learning rate and momentum coefficient have been studied. Although a significant correlation between results of the neural network and the conventional robust inversion technique was found, the ANN results show more details of the subsurface structure, and the RMS misfits for the results of the neural network are less than seen with conventional methods. The interpreted results show that the trained network was able to invert quasi-3D electrical resistivity imaging data obtained by dipole-dipole configuration both rapidly and accurately.

Selective and Efficient Neural Coding of Communication Signals Depends on Early Acoustic and Social Environment

PubMed Central

Amin, Noopur; Gastpar, Michael; Theunissen, Frédéric E.

2013-01-01

Previous research has shown that postnatal exposure to simple, synthetic sounds can affect the sound representation in the auditory cortex as reflected by changes in the tonotopic map or other relatively simple tuning properties, such as AM tuning. However, their functional implications for neural processing in the generation of ethologically-based perception remain unexplored. Here we examined the effects of noise-rearing and social isolation on the neural processing of communication sounds such as species-specific song, in the primary auditory cortex analog of adult zebra finches. Our electrophysiological recordings reveal that neural tuning to simple frequency-based synthetic sounds is initially established in all the laminae independent of patterned acoustic experience; however, we provide the first evidence that early exposure to patterned sound statistics, such as those found in native sounds, is required for the subsequent emergence of neural selectivity for complex vocalizations and for shaping neural spiking precision in superficial and deep cortical laminae, and for creating efficient neural representations of song and a less redundant ensemble code in all the laminae. Our study also provides the first causal evidence for ‘sparse coding’, such that when the statistics of the stimuli were changed during rearing, as in noise-rearing, that the sparse or optimal representation for species-specific vocalizations disappeared. Taken together, these results imply that a layer-specific differential development of the auditory cortex requires patterned acoustic input, and a specialized and robust sensory representation of complex communication sounds in the auditory cortex requires a rich acoustic and social environment. PMID:23630587

Model of brain activation predicts the neural collective influence map of the brain

PubMed Central

Morone, Flaviano; Roth, Kevin; Min, Byungjoon; Makse, Hernán A.

2017-01-01

Efficient complex systems have a modular structure, but modularity does not guarantee robustness, because efficiency also requires an ingenious interplay of the interacting modular components. The human brain is the elemental paradigm of an efficient robust modular system interconnected as a network of networks (NoN). Understanding the emergence of robustness in such modular architectures from the interconnections of its parts is a longstanding challenge that has concerned many scientists. Current models of dependencies in NoN inspired by the power grid express interactions among modules with fragile couplings that amplify even small shocks, thus preventing functionality. Therefore, we introduce a model of NoN to shape the pattern of brain activations to form a modular environment that is robust. The model predicts the map of neural collective influencers (NCIs) in the brain, through the optimization of the influence of the minimal set of essential nodes responsible for broadcasting information to the whole-brain NoN. Our results suggest intervention protocols to control brain activity by targeting influential neural nodes predicted by network theory. PMID:28351973

On-Chip Neural Data Compression Based On Compressed Sensing With Sparse Sensing Matrices.

PubMed

Zhao, Wenfeng; Sun, Biao; Wu, Tong; Yang, Zhi

2018-02-01

On-chip neural data compression is an enabling technique for wireless neural interfaces that suffer from insufficient bandwidth and power budgets to transmit the raw data. The data compression algorithm and its implementation should be power and area efficient and functionally reliable over different datasets. Compressed sensing is an emerging technique that has been applied to compress various neurophysiological data. However, the state-of-the-art compressed sensing (CS) encoders leverage random but dense binary measurement matrices, which incur substantial implementation costs on both power and area that could offset the benefits from the reduced wireless data rate. In this paper, we propose two CS encoder designs based on sparse measurement matrices that could lead to efficient hardware implementation. Specifically, two different approaches for the construction of sparse measurement matrices, i.e., the deterministic quasi-cyclic array code (QCAC) matrix and -sparse random binary matrix [-SRBM] are exploited. We demonstrate that the proposed CS encoders lead to comparable recovery performance. And efficient VLSI architecture designs are proposed for QCAC-CS and -SRBM encoders with reduced area and total power consumption.

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

PubMed

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 driven active mass damper by using output of a neural oscillator (effects of position control system changes on vibration mitigation performance)

NASA Astrophysics Data System (ADS)

Hongu, J.; Iba, D.; Sasaki, T.; Nakamura, M.; Moriwaki, I.

2015-03-01

In this paper, a design method for a PD controller, which is a part of a new active mass damper system using a neural oscillator for high-rise buildings, is proposed. The new system mimicking the motion of bipedal mammals is a quite simple system, which has the neural oscillator synchronizing with the acceleration response of the structure. The travel distance and direction of the auxiliary mass of the active mass damper is decided by the output of the neural oscillator, and then, the auxiliary mass is transferred to the decided location by using the PD controller. Therefore, the performance of the PD controller must be evaluated by the vibration energy absorbing efficiency by the system. In order to bring the actual path driven by the PD controller in closer alignment with the ideal path, which is assumed to be a sinusoidal wave under resonance, firstly, the path of the auxiliary mass driven by the PD controller is analytically derived, and the inner product between the vector of ideal and analytical path is evaluated. And then, the PD gain is decided by the maximum value of the inner product. Finally, numerical simulations confirm the validity of the proposed design method of the PD controller.

Efficiency Analysis of Waveform Shape for Electrical Excitation of Nerve Fibers

PubMed Central

Wongsarnpigoon, Amorn; Woock, John P.; Grill, Warren M.

2011-01-01

Stimulation efficiency is an important consideration in the stimulation parameters of implantable neural stimulators. The objective of this study was to analyze the effects of waveform shape and duration on the charge, power, and energy efficiency of neural stimulation. Using a population model of mammalian axons and in vivo experiments on cat sciatic nerve, we analyzed the stimulation efficiency of four waveform shapes: square, rising exponential, decaying exponential, and rising ramp. No waveform was simultaneously energy-, charge-, and power-optimal, and differences in efficiency among waveform shapes varied with pulse width (PW) For short PWs (≤ 0.1 ms), square waveforms were no less energy-efficient than exponential waveforms, and the most charge-efficient shape was the ramp. For long PWs (≥0.5 ms), the square was the least energy-efficient and charge-efficient shape, but across most PWs, the square was the most power-efficient shape. Rising exponentials provided no practical gains in efficiency over the other shapes, and our results refute previous claims that the rising exponential is the energy-optimal shape. An improved understanding of how stimulation parameters affect stimulation efficiency will help improve the design and programming of implantable stimulators to minimize tissue damage and extend battery life. PMID:20388602

Effective prediction of biodiversity in tidal flat habitats using an artificial neural network.

PubMed

Yoo, Jae-Won; Lee, Yong-Woo; Lee, Chang-Gun; Kim, Chang-Soo

2013-02-01

Accurate predictions of benthic macrofaunal biodiversity greatly benefit the efficient planning and management of habitat restoration efforts in tidal flat habitats. Artificial neural network (ANN) prediction models for such biodiversity were developed and tested based on 13 biophysical variables, collected from 50 sites of tidal flats along the coast of Korea during 1991-2006. The developed model showed high predictions during training, cross-validation and testing. Besides the training and testing procedures, an independent dataset from a different time period (2007-2010) was used to test the robustness and practical usage of the model. High prediction on the independent dataset (r = 0.84) validated the networks proper learning of predictive relationship and its generality. Key influential variables identified by follow-up sensitivity analyses were related with topographic dimension, environmental heterogeneity, and water column properties. Study demonstrates the successful application of ANN for the accurate prediction of benthic macrofaunal biodiversity and understanding of dynamics of candidate variables. Copyright © 2012 Elsevier Ltd. All rights reserved.

A Novel Stimulus Artifact Removal Technique for High-Rate Electrical Stimulation

PubMed Central

Heffer, Leon F; Fallon, James B

2008-01-01

Electrical stimulus artifact corrupting electrophysiological recordings often make the subsequent analysis of the underlying neural response difficult. This is particularly evident when investigating short-latency neural activity in response to high-rate electrical stimulation. We developed and evaluated an off-line technique for the removal of stimulus artifact from electrophysiological recordings. Pulsatile electrical stimulation was presented at rates of up to 5000 pulses/s during extracellular recordings of guinea pig auditory nerve fibers. Stimulus artifact was removed by replacing the sample points at each stimulus artifact event with values interpolated along a straight line, computed from neighbouring sample points. This technique required only that artifact events be identifiable and that the artifact duration remained less than both the inter-stimulus interval and the time course of the action potential. We have demonstrated that this computationally efficient sample-and-interpolate technique removes the stimulus artifact with minimal distortion of the action potential waveform. We suggest that this technique may have potential applications in a range of electrophysiological recording systems. PMID:18339428

A new method for 3D thinning of hybrid shaped porous media using artificial intelligence. Application to trabecular bone.

PubMed

Jennane, Rachid; Aufort, Gabriel; Benhamou, Claude Laurent; Ceylan, Murat; Ozbay, Yüksel; Ucan, Osman Nuri

2012-04-01

Curve and surface thinning are widely-used skeletonization techniques for modeling objects in three dimensions. In the case of disordered porous media analysis, however, neither is really efficient since the internal geometry of the object is usually composed of both rod and plate shapes. This paper presents an alternative to compute a hybrid shape-dependent skeleton and its application to porous media. The resulting skeleton combines 2D surfaces and 1D curves to represent respectively the plate-shaped and rod-shaped parts of the object. For this purpose, a new technique based on neural networks is proposed: cascade combinations of complex wavelet transform (CWT) and complex-valued artificial neural network (CVANN). The ability of the skeleton to characterize hybrid shaped porous media is demonstrated on a trabecular bone sample. Results show that the proposed method achieves high accuracy rates about 99.78%-99.97%. Especially, CWT (2nd level)-CVANN structure converges to optimum results as high accuracy rate-minimum time consumption.

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

PubMed

Foutz, Thomas J; Ackermann, D Michael; Kilgore, Kevin L; McIntyre, Cameron C

2012-01-01

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

Reconstruction of magnetic configurations in W7-X using artificial neural networks

NASA Astrophysics Data System (ADS)

Böckenhoff, Daniel; Blatzheim, Marko; Hölbe, Hauke; Niemann, Holger; Pisano, Fabio; Labahn, Roger; Pedersen, Thomas Sunn; The W7-X Team

2018-05-01

It is demonstrated that artificial neural networks can be used to accurately and efficiently predict details of the magnetic topology at the plasma edge of the Wendelstein 7-X stellarator, based on simulated as well as measured heat load patterns onto plasma-facing components observed with infrared cameras. The connection between heat load patterns and the magnetic topology is a challenging regression problem, but one that suits artificial neural networks well. The use of a neural network makes it feasible to analyze and control the plasma exhaust in real-time, an important goal for Wendelstein 7-X, and for magnetic confinement fusion research in general.

The optimization of force inputs for active structural acoustic control using a neural network

NASA Technical Reports Server (NTRS)

Cabell, R. H.; Lester, H. C.; Silcox, R. J.

1992-01-01

This paper investigates the use of a neural network to determine which force actuators, of a multi-actuator array, are best activated in order to achieve structural-acoustic control. The concept is demonstrated using a cylinder/cavity model on which the control forces, produced by piezoelectric actuators, are applied with the objective of reducing the interior noise. A two-layer neural network is employed and the back propagation solution is compared with the results calculated by a conventional, least-squares optimization analysis. The ability of the neural network to accurately and efficiently control actuator activation for interior noise reduction is demonstrated.

TernaryNet: faster deep model inference without GPUs for medical 3D segmentation using sparse and binary convolutions.

PubMed

Heinrich, Mattias P; Blendowski, Max; Oktay, Ozan

2018-05-30

Deep convolutional neural networks (DCNN) are currently ubiquitous in medical imaging. While their versatility and high-quality results for common image analysis tasks including segmentation, localisation and prediction is astonishing, the large representational power comes at the cost of highly demanding computational effort. This limits their practical applications for image-guided interventions and diagnostic (point-of-care) support using mobile devices without graphics processing units (GPU). We propose a new scheme that approximates both trainable weights and neural activations in deep networks by ternary values and tackles the open question of backpropagation when dealing with non-differentiable functions. Our solution enables the removal of the expensive floating-point matrix multiplications throughout any convolutional neural network and replaces them by energy- and time-preserving binary operators and population counts. We evaluate our approach for the segmentation of the pancreas in CT. Here, our ternary approximation within a fully convolutional network leads to more than 90% memory reductions and high accuracy (without any post-processing) with a Dice overlap of 71.0% that comes close to the one obtained when using networks with high-precision weights and activations. We further provide a concept for sub-second inference without GPUs and demonstrate significant improvements in comparison with binary quantisation and without our proposed ternary hyperbolic tangent continuation. We present a key enabling technique for highly efficient DCNN inference without GPUs that will help to bring the advances of deep learning to practical clinical applications. It has also great promise for improving accuracies in large-scale medical data retrieval.

Toyota Prius HEV neurocontrol and diagnostics.

PubMed

Prokhorov, Danil V

2008-01-01

A neural network controller for improved fuel efficiency of the Toyota Prius hybrid electric vehicle is proposed. A new method to detect and mitigate a battery fault is also presented. The approach is based on recurrent neural networks and includes the extended Kalman filter. The proposed approach is quite general and applicable to other control systems.

Comparison of Intelligent Systems in Detecting a Child's Mathematical Gift

ERIC Educational Resources Information Center

Pavlekovic, Margita; Zekic-Susac, Marijana; Djurdjevic, Ivana

2009-01-01

This paper compares the efficiency of two intelligent methods: expert systems and neural networks, in detecting children's mathematical gift at the fourth grade of elementary school. The input space for the expert system and the neural network model consisted of 60 variables describing five basic components of a child's mathematical gift…

Pragmatic Bootstrapping: A Neural Network Model of Vocabulary Acquisition

ERIC Educational Resources Information Center

Caza, Gregory A.; Knott, Alistair

2012-01-01

The social-pragmatic theory of language acquisition proposes that children only become efficient at learning the meanings of words once they acquire the ability to understand the intentions of other agents, in particular the intention to communicate (Akhtar & Tomasello, 2000). In this paper we present a neural network model of word learning which…

A hybrid architecture for the implementation of the Athena neural net model

NASA Technical Reports Server (NTRS)

Koutsougeras, C.; Papachristou, C.

1989-01-01

The implementation of an earlier introduced neural net model for pattern classification is considered. Data flow principles are employed in the development of a machine that efficiently implements the model and can be useful for real time classification tasks. Further enhancement with optical computing structures is also considered.

Compressor and Turbine Multidisciplinary Design for Highly Efficient Micro-gas Turbine

NASA Astrophysics Data System (ADS)

Barsi, Dario; Perrone, Andrea; Qu, Yonglei; Ratto, Luca; Ricci, Gianluca; Sergeev, Vitaliy; Zunino, Pietro

2018-06-01

Multidisciplinary design optimization (MDO) is widely employed to enhance turbomachinery components efficiency. The aim of this work is to describe a complete tool for the aero-mechanical design of a radial inflow turbine and a centrifugal compressor. The high rotational speed of such machines and the high exhaust gas temperature (only for the turbine) expose blades to really high stresses and therefore the aerodynamics design has to be coupled with the mechanical one through an integrated procedure. The described approach employs a fully 3D Reynolds Averaged Navier-Stokes (RANS) solver for the aerodynamics and an open source Finite Element Analysis (FEA) solver for the mechanical integrity assessment. Due to the high computational cost of both these two solvers, a meta model, such as an artificial neural network (ANN), is used to speed up the optimization design process. The interaction between two codes, the mesh generation and the post processing of the results are achieved via in-house developed scripting modules. The obtained results are widely presented and discussed.

Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks.

PubMed

Längkvist, Martin; Jendeberg, Johan; Thunberg, Per; Loutfi, Amy; Lidén, Mats

2018-06-01

Computed tomography (CT) is the method of choice for diagnosing ureteral stones - kidney stones that obstruct the ureter. The purpose of this study is to develop a computer aided detection (CAD) algorithm for identifying a ureteral stone in thin slice CT volumes. The challenge in CAD for urinary stones lies in the similarity in shape and intensity of stones with non-stone structures and how to efficiently deal with large high-resolution CT volumes. We address these challenges by using a Convolutional Neural Network (CNN) that works directly on the high resolution CT volumes. The method is evaluated on a large data base of 465 clinically acquired high-resolution CT volumes of the urinary tract with labeling of ureteral stones performed by a radiologist. The best model using 2.5D input data and anatomical information achieved a sensitivity of 100% and an average of 2.68 false-positives per patient on a test set of 88 scans. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback.

PubMed

Orhan, A Emin; Ma, Wei Ji

2017-07-26

Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties associated with task variables and subsequent use of this representation. Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations. We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks. In a probabilistic categorization task, error-based learning in a generic network simultaneously explains a monkey's learning curve and the evolution of qualitative aspects of its choice behavior. In all tasks, the number of neurons required for a given level of performance grows sublinearly with the input population size, a substantial improvement on previous implementations of probabilistic inference. The trained networks develop a novel sparsity-based probabilistic population code. Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules.Behavioural tasks often require probability distributions to be inferred about task specific variables. Here, the authors demonstrate that generic neural networks can be trained using a simple error-based learning rule to perform such probabilistic computations efficiently without any need for task specific operations.

Bioimpedance Harmonic Analysis as a Diagnostic Tool to Assess Regional Circulation and Neural Activity

NASA Astrophysics Data System (ADS)

Mudraya, I. S.; Revenko, S. V.; Khodyreva, L. A.; Markosyan, T. G.; Dudareva, A. A.; Ibragimov, A. R.; Romich, V. V.; Kirpatovsky, V. I.

2013-04-01

The novel technique based on harmonic analysis of bioimpedance microvariations with original hard- and software complex incorporating a high-resolution impedance converter was used to assess the neural activity and circulation in human urinary bladder and penis in patients with pelvic pain, erectile dysfunction, and overactive bladder. The therapeutic effects of shock wave therapy and Botulinum toxin detrusor injections were evaluated quantitatively according to the spectral peaks at low 0.1 Hz frequency (M for Mayer wave), respiratory (R) and cardiac (C) rhythms with their harmonics. Enhanced baseline regional neural activity identified according to M and R peaks was found to be presumably sympathetic in pelvic pain patients, and parasympathetic - in patients with overactive bladder. Total pulsatile activity and pulsatile resonances found in the bladder as well as in the penile spectrum characterised regional circulation and vascular tone. The abnormal spectral parameters characteristic of the patients with genitourinary diseases shifted to the norm in the cases of efficient therapy. Bioimpedance harmonic analysis seems to be a potent tool to assess regional peculiarities of circulatory and autonomic nervous activity in the course of patient treatment.

Application of induced pluripotent stem cells to understand neurobiological basis of bipolar disorder and schizophrenia.

PubMed

Liu, Yao-Nan; Lu, Si-Yao; Yao, Jun

2017-09-01

The etiology of neuropsychiatric disorders, such as schizophrenia and bipolar disorder, usually involves complex combinations of genetic defects/variations and environmental impacts, which hindered, for a long time, research efforts based on animal models and patients' non-neuronal cells or post-mortem tissues. However, the development of human induced pluripotent stem cell (iPSC) technology by the Yamanaka group was immediately applied to establish cell research models for neuronal disorders. Since then, techniques to achieve highly efficient differentiation of different types of neural cells following iPSC modeling have made much progress. The fast-growing iPSC and neural differentiation techniques have brought valuable insights into the pathology and neurobiology of neuropsychiatric disorders. In this article, we first review the application of iPSC technology in modeling neuronal disorders and discuss the progress in the accompanying neural differentiation. Then, we summarize the progress in iPSC-based research that has been accomplished so far regarding schizophrenia and bipolar disorder. © 2017 The Authors. Psychiatry and Clinical Neurosciences © 2017 Japanese Society of Psychiatry and Neurology.

Triangular Quantum Loop Topography for Machine Learning

NASA Astrophysics Data System (ADS)

Zhang, Yi; Kim, Eun-Ah

Despite rapidly growing interest in harnessing machine learning in the study of quantum many-body systems there has been little success in training neural networks to identify topological phases. The key challenge is in efficiently extracting essential information from the many-body Hamiltonian or wave function and turning the information into an image that can be fed into a neural network. When targeting topological phases, this task becomes particularly challenging as topological phases are defined in terms of non-local properties. Here we introduce triangular quantum loop (TQL) topography: a procedure of constructing a multi-dimensional image from the ''sample'' Hamiltonian or wave function using two-point functions that form triangles. Feeding the TQL topography to a fully-connected neural network with a single hidden layer, we demonstrate that the architecture can be effectively trained to distinguish Chern insulator and fractional Chern insulator from trivial insulators with high fidelity. Given the versatility of the TQL topography procedure that can handle different lattice geometries, disorder, interaction and even degeneracy our work paves the route towards powerful applications of machine learning in the study of topological quantum matters.

Neural Network Model For Fast Learning And Retrieval

NASA Astrophysics Data System (ADS)

Arsenault, Henri H.; Macukow, Bohdan

1989-05-01

An approach to learning in a multilayer neural network is presented. The proposed network learns by creating interconnections between the input layer and the intermediate layer. In one of the new storage prescriptions proposed, interconnections are excitatory (positive) only and the weights depend on the stored patterns. In the intermediate layer each mother cell is responsible for one stored pattern. Mutually interconnected neurons in the intermediate layer perform a winner-take-all operation, taking into account correlations between stored vectors. The performance of networks using this interconnection prescription is compared with two previously proposed schemes, one using inhibitory connections at the output and one using all-or-nothing interconnections. The network can be used as a content-addressable memory or as a symbolic substitution system that yields an arbitrarily defined output for any input. The training of a model to perform Boolean logical operations is also described. Computer simulations using the network as an autoassociative content-addressable memory show the model to be efficient. Content-addressable associative memories and neural logic modules can be combined to perform logic operations on highly corrupted data.

Self-learning Monte Carlo with deep neural networks

NASA Astrophysics Data System (ADS)

Shen, Huitao; Liu, Junwei; Fu, Liang

2018-05-01

The self-learning Monte Carlo (SLMC) method is a general algorithm to speedup MC simulations. Its efficiency has been demonstrated in various systems by introducing an effective model to propose global moves in the configuration space. In this paper, we show that deep neural networks can be naturally incorporated into SLMC, and without any prior knowledge can learn the original model accurately and efficiently. Demonstrated in quantum impurity models, we reduce the complexity for a local update from O (β2) in Hirsch-Fye algorithm to O (β lnβ ) , which is a significant speedup especially for systems at low temperatures.

Derivation and Expansion Using Only Small Molecules of Human Neural Progenitors for Neurodegenerative Disease Modeling

PubMed Central

Reinhardt, Peter; Glatza, Michael; Hemmer, Kathrin; Tsytsyura, Yaroslav; Thiel, Cora S.; Höing, Susanne; Moritz, Sören; Parga, Juan A.; Wagner, Lydia; Bruder, Jan M.; Wu, Guangming; Schmid, Benjamin; Röpke, Albrecht; Klingauf, Jürgen; Schwamborn, Jens C.; Gasser, Thomas; Schöler, Hans R.; Sterneckert, Jared

2013-01-01

Phenotypic drug discovery requires billions of cells for high-throughput screening (HTS) campaigns. Because up to several million different small molecules will be tested in a single HTS campaign, even small variability within the cell populations for screening could easily invalidate an entire campaign. Neurodegenerative assays are particularly challenging because neurons are post-mitotic and cannot be expanded for implementation in HTS. Therefore, HTS for neuroprotective compounds requires a cell type that is robustly expandable and able to differentiate into all of the neuronal subtypes involved in disease pathogenesis. Here, we report the derivation and propagation using only small molecules of human neural progenitor cells (small molecule neural precursor cells; smNPCs). smNPCs are robust, exhibit immortal expansion, and do not require cumbersome manual culture and selection steps. We demonstrate that smNPCs have the potential to clonally and efficiently differentiate into neural tube lineages, including motor neurons (MNs) and midbrain dopaminergic neurons (mDANs) as well as neural crest lineages, including peripheral neurons and mesenchymal cells. These properties are so far only matched by pluripotent stem cells. Finally, to demonstrate the usefulness of smNPCs we show that mDANs differentiated from smNPCs with LRRK2 G2019S are more susceptible to apoptosis in the presence of oxidative stress compared to wild-type. Therefore, smNPCs are a powerful biological tool with properties that are optimal for large-scale disease modeling, phenotypic screening, and studies of early human development. PMID:23533608

Applying cybernetic technology to diagnose human pulmonary sounds.

PubMed

Chen, Mei-Yung; Chou, Cheng-Han

2014-06-01

Chest auscultation is a crucial and efficient method for diagnosing lung disease; however, it is a subjective process that relies on physician experience and the ability to differentiate between various sound patterns. Because the physiological signals composed of heart sounds and pulmonary sounds (PSs) are greater than 120 Hz and the human ear is not sensitive to low frequencies, successfully making diagnostic classifications is difficult. To solve this problem, we constructed various PS recognition systems for classifying six PS classes: vesicular breath sounds, bronchial breath sounds, tracheal breath sounds, crackles, wheezes, and stridor sounds. First, we used a piezoelectric microphone and data acquisition card to acquire PS signals and perform signal preprocessing. A wavelet transform was used for feature extraction, and the PS signals were decomposed into frequency subbands. Using a statistical method, we extracted 17 features that were used as the input vectors of a neural network. We proposed a 2-stage classifier combined with a back-propagation (BP) neural network and learning vector quantization (LVQ) neural network, which improves classification accuracy by using a haploid neural network. The receiver operating characteristic (ROC) curve verifies the high performance level of the neural network. To expand traditional auscultation methods, we constructed various PS diagnostic systems that can correctly classify the six common PSs. The proposed device overcomes the lack of human sensitivity to low-frequency sounds and various PS waves, characteristic values, and a spectral analysis charts are provided to elucidate the design of the human-machine interface.

Reconstruction of three-dimensional porous media using generative adversarial neural networks

NASA Astrophysics Data System (ADS)

Mosser, Lukas; Dubrule, Olivier; Blunt, Martin J.

2017-10-01

To evaluate the variability of multiphase flow properties of porous media at the pore scale, it is necessary to acquire a number of representative samples of the void-solid structure. While modern x-ray computer tomography has made it possible to extract three-dimensional images of the pore space, assessment of the variability in the inherent material properties is often experimentally not feasible. We present a method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image data sets. We show, by using an adversarial learning approach for neural networks, that this method of unsupervised learning is able to generate representative samples of porous media that honor their statistics. We successfully compare measures of pore morphology, such as the Euler characteristic, two-point statistics, and directional single-phase permeability of synthetic realizations with the calculated properties of a bead pack, Berea sandstone, and Ketton limestone. Results show that generative adversarial networks can be used to reconstruct high-resolution three-dimensional images of porous media at different scales that are representative of the morphology of the images used to train the neural network. The fully convolutional nature of the trained neural network allows the generation of large samples while maintaining computational efficiency. Compared to classical stochastic methods of image reconstruction, the implicit representation of the learned data distribution can be stored and reused to generate multiple realizations of the pore structure very rapidly.

Efficient induction of dopaminergic neuron differentiation from induced pluripotent stem cells reveals impaired mitophagy in PARK2 neurons.

PubMed

Suzuki, Sadafumi; Akamatsu, Wado; Kisa, Fumihiko; Sone, Takefumi; Ishikawa, Kei-Ichi; Kuzumaki, Naoko; Katayama, Hiroyuki; Miyawaki, Atsushi; Hattori, Nobutaka; Okano, Hideyuki

2017-01-29

Patient-specific induced pluripotent stem cells (iPSCs) show promise for use as tools for in vitro modeling of Parkinson's disease. We sought to improve the efficiency of dopaminergic (DA) neuron induction from iPSCs by the using surface markers expressed in DA progenitors to increase the significance of the phenotypic analysis. By sorting for a CD184 high /CD44 - fraction during neural differentiation, we obtained a population of cells that were enriched in DA neuron precursor cells and achieved higher differentiation efficiencies than those obtained through the same protocol without sorting. This high efficiency method of DA neuronal induction enabled reliable detection of reactive oxygen species (ROS) accumulation and vulnerable phenotypes in PARK2 iPSCs-derived DA neurons. We additionally established a quantitative system using the mt-mKeima reporter system to monitor mitophagy in which mitochondria fuse with lysosomes and, by combining this system with the method of DA neuronal induction described above, determined that mitophagy is impaired in PARK2 neurons. These findings suggest that the efficiency of DA neuron induction is important for the precise detection of cellular phenotypes in modeling Parkinson's disease. Copyright © 2016. Published by Elsevier Inc.

Recurrent neural network based virtual detection line

NASA Astrophysics Data System (ADS)

Kadikis, Roberts

2018-04-01

The paper proposes an efficient method for detection of moving objects in the video. The objects are detected when they cross a virtual detection line. Only the pixels of the detection line are processed, which makes the method computationally efficient. A Recurrent Neural Network processes these pixels. The machine learning approach allows one to train a model that works in different and changing outdoor conditions. Also, the same network can be trained for various detection tasks, which is demonstrated by the tests on vehicle and people counting. In addition, the paper proposes a method for semi-automatic acquisition of labeled training data. The labeling method is used to create training and testing datasets, which in turn are used to train and evaluate the accuracy and efficiency of the detection method. The method shows similar accuracy as the alternative efficient methods but provides greater adaptability and usability for different tasks.

Efficient modeling of vector hysteresis using a novel Hopfield neural network implementation of Stoner–Wohlfarth-like operators

PubMed Central

Adly, Amr A.; Abd-El-Hafiz, Salwa K.

2012-01-01

Incorporation of hysteresis models in electromagnetic analysis approaches is indispensable to accurate field computation in complex magnetic media. Throughout those computations, vector nature and computational efficiency of such models become especially crucial when sophisticated geometries requiring massive sub-region discretization are involved. Recently, an efficient vector Preisach-type hysteresis model constructed from only two scalar models having orthogonally coupled elementary operators has been proposed. This paper presents a novel Hopfield neural network approach for the implementation of Stoner–Wohlfarth-like operators that could lead to a significant enhancement in the computational efficiency of the aforementioned model. Advantages of this approach stem from the non-rectangular nature of these operators that substantially minimizes the number of operators needed to achieve an accurate vector hysteresis model. Details of the proposed approach, its identification and experimental testing are presented in the paper. PMID:25685446

Artificial Neuron Based on Integrated Semiconductor Quantum Dot Mode-Locked Lasers

NASA Astrophysics Data System (ADS)

Mesaritakis, Charis; Kapsalis, Alexandros; Bogris, Adonis; Syvridis, Dimitris

2016-12-01

Neuro-inspired implementations have attracted strong interest as a power efficient and robust alternative to the digital model of computation with a broad range of applications. Especially, neuro-mimetic systems able to produce and process spike-encoding schemes can offer merits like high noise-resiliency and increased computational efficiency. Towards this direction, integrated photonics can be an auspicious platform due to its multi-GHz bandwidth, its high wall-plug efficiency and the strong similarity of its dynamics under excitation with biological spiking neurons. Here, we propose an integrated all-optical neuron based on an InAs/InGaAs semiconductor quantum-dot passively mode-locked laser. The multi-band emission capabilities of these lasers allows, through waveband switching, the emulation of the excitation and inhibition modes of operation. Frequency-response effects, similar to biological neural circuits, are observed just as in a typical two-section excitable laser. The demonstrated optical building block can pave the way for high-speed photonic integrated systems able to address tasks ranging from pattern recognition to cognitive spectrum management and multi-sensory data processing.

Artificial Neuron Based on Integrated Semiconductor Quantum Dot Mode-Locked Lasers

PubMed Central

Mesaritakis, Charis; Kapsalis, Alexandros; Bogris, Adonis; Syvridis, Dimitris

2016-01-01

Neuro-inspired implementations have attracted strong interest as a power efficient and robust alternative to the digital model of computation with a broad range of applications. Especially, neuro-mimetic systems able to produce and process spike-encoding schemes can offer merits like high noise-resiliency and increased computational efficiency. Towards this direction, integrated photonics can be an auspicious platform due to its multi-GHz bandwidth, its high wall-plug efficiency and the strong similarity of its dynamics under excitation with biological spiking neurons. Here, we propose an integrated all-optical neuron based on an InAs/InGaAs semiconductor quantum-dot passively mode-locked laser. The multi-band emission capabilities of these lasers allows, through waveband switching, the emulation of the excitation and inhibition modes of operation. Frequency-response effects, similar to biological neural circuits, are observed just as in a typical two-section excitable laser. The demonstrated optical building block can pave the way for high-speed photonic integrated systems able to address tasks ranging from pattern recognition to cognitive spectrum management and multi-sensory data processing. PMID:27991574

Memory Efficient Evaluations of Nonlinear Stochastic Equations and C3 Applications.

DTIC Science & Technology

1987-12-01

time. In the 1960’s, when Massachusetts Institute of Technology’s Marvin Minsky [16] and others criticized the concept on the grounds of .nsufficient...recognize letters of the alphabet [231. Minsky and Papert [16] criticized the perceptron, asserting that too little is known of the human brain to...1987). [15] Kinoshita, J. and Palevsky, N. G., "Computing with neural networks," High Technology (May 1987). . [16] Minsky , M. and Papert, S

Model-based decoding, information estimation, and change-point detection techniques for multineuron spike trains.

PubMed

Pillow, Jonathan W; Ahmadian, Yashar; Paninski, Liam

2011-01-01

One of the central problems in systems neuroscience is to understand how neural spike trains convey sensory information. Decoding methods, which provide an explicit means for reading out the information contained in neural spike responses, offer a powerful set of tools for studying the neural coding problem. Here we develop several decoding methods based on point-process neural encoding models, or forward models that predict spike responses to stimuli. These models have concave log-likelihood functions, which allow efficient maximum-likelihood model fitting and stimulus decoding. We present several applications of the encoding model framework to the problem of decoding stimulus information from population spike responses: (1) a tractable algorithm for computing the maximum a posteriori (MAP) estimate of the stimulus, the most probable stimulus to have generated an observed single- or multiple-neuron spike train response, given some prior distribution over the stimulus; (2) a gaussian approximation to the posterior stimulus distribution that can be used to quantify the fidelity with which various stimulus features are encoded; (3) an efficient method for estimating the mutual information between the stimulus and the spike trains emitted by a neural population; and (4) a framework for the detection of change-point times (the time at which the stimulus undergoes a change in mean or variance) by marginalizing over the posterior stimulus distribution. We provide several examples illustrating the performance of these estimators with simulated and real neural data.

Magnetically enhanced adeno-associated viral vector delivery for human neural stem cell infection.

PubMed

Kim, Eunmi; Oh, Ji-Seon; Ahn, Ik-Sung; Park, Kook In; Jang, Jae-Hyung

2011-11-01

Gene therapy technology is a powerful tool to elucidate the molecular cues that precisely regulate stem cell fates, but developing safe vehicles or mechanisms that are capable of delivering genes to stem cells with high efficiency remains a challenge. In this study, we developed a magnetically guided adeno-associated virus (AAV) delivery system for gene delivery to human neural stem cells (hNSCs). Magnetically guided AAV delivery resulted in rapid accumulation of vectors on target cells followed by forced penetration of the vectors across the plasma membrane, ultimately leading to fast and efficient cellular transduction. To combine AAV vectors with the magnetically guided delivery, AAV was genetically modified to display hexa-histidine (6xHis) on the physically exposed loop of the AAV2 capsid (6xHis AAV), which interacted with nickel ions chelated on NTA-biotin conjugated to streptavidin-coated superparamagnetic iron oxide nanoparticles (NiStNPs). NiStNP-mediated 6xHis AAV delivery under magnetic fields led to significantly enhanced cellular transduction in a non-permissive cell type (i.e., hNSCs). In addition, this delivery method reduced the viral exposure times required to induce a high level of transduction by as much as to 2-10 min of hNSC infection, thus demonstrating the great potential of magnetically guided AAV delivery for numerous gene therapy and stem cell applications. Copyright © 2011 Elsevier Ltd. All rights reserved.

Machine learning on-a-chip: a high-performance low-power reusable neuron architecture for artificial neural networks in ECG classifications.

PubMed

Sun, Yuwen; Cheng, Allen C

2012-07-01

Artificial neural networks (ANNs) are a promising machine learning technique in classifying non-linear electrocardiogram (ECG) signals and recognizing abnormal patterns suggesting risks of cardiovascular diseases (CVDs). In this paper, we propose a new reusable neuron architecture (RNA) enabling a performance-efficient and cost-effective silicon implementation for ANN. The RNA architecture consists of a single layer of physical RNA neurons, each of which is designed to use minimal hardware resource (e.g., a single 2-input multiplier-accumulator is used to compute the dot product of two vectors). By carefully applying the principal of time sharing, RNA can multiplexs this single layer of physical neurons to efficiently execute both feed-forward and back-propagation computations of an ANN while conserving the area and reducing the power dissipation of the silicon. A three-layer 51-30-12 ANN is implemented in RNA to perform the ECG classification for CVD detection. This RNA hardware also allows on-chip automatic training update. A quantitative design space exploration in area, power dissipation, and execution speed between RNA and three other implementations representative of different reusable hardware strategies is presented and discussed. Compared with an equivalent software implementation in C executed on an embedded microprocessor, the RNA ASIC achieves three orders of magnitude improvements in both the execution speed and the energy efficiency. Copyright © 2012 Elsevier Ltd. All rights reserved.

Optimization of controlled release nanoparticle formulation of verapamil hydrochloride using artificial neural networks with genetic algorithm and response surface methodology.

PubMed

Li, Yongqiang; Abbaspour, Mohammadreza R; Grootendorst, Paul V; Rauth, Andrew M; Wu, Xiao Yu

2015-08-01

This study was performed to optimize the formulation of polymer-lipid hybrid nanoparticles (PLN) for the delivery of an ionic water-soluble drug, verapamil hydrochloride (VRP) and to investigate the roles of formulation factors. Modeling and optimization were conducted based on a spherical central composite design. Three formulation factors, i.e., weight ratio of drug to lipid (X1), and concentrations of Tween 80 (X2) and Pluronic F68 (X3), were chosen as independent variables. Drug loading efficiency (Y1) and mean particle size (Y2) of PLN were selected as dependent variables. The predictive performance of artificial neural networks (ANN) and the response surface methodology (RSM) were compared. As ANN was found to exhibit better recognition and generalization capability over RSM, multi-objective optimization of PLN was then conducted based upon the validated ANN models and continuous genetic algorithms (GA). The optimal PLN possess a high drug loading efficiency (92.4%, w/w) and a small mean particle size (∼100nm). The predicted response variables matched well with the observed results. The three formulation factors exhibited different effects on the properties of PLN. ANN in coordination with continuous GA represent an effective and efficient approach to optimize the PLN formulation of VRP with desired properties. Copyright © 2015 Elsevier B.V. All rights reserved.

An integrated data envelopment analysis-artificial neural network approach for benchmarking of bank branches

NASA Astrophysics Data System (ADS)

Shokrollahpour, Elsa; Hosseinzadeh Lotfi, Farhad; Zandieh, Mostafa

2016-06-01

Efficiency and quality of services are crucial to today's banking industries. The competition in this section has become increasingly intense, as a result of fast improvements in Technology. Therefore, performance analysis of the banking sectors attracts more attention these days. Even though data envelopment analysis (DEA) is a pioneer approach in the literature as of an efficiency measurement tool and finding benchmarks, it is on the other hand unable to demonstrate the possible future benchmarks. The drawback to it could be that the benchmarks it provides us with, may still be less efficient compared to the more advanced future benchmarks. To cover for this weakness, artificial neural network is integrated with DEA in this paper to calculate the relative efficiency and more reliable benchmarks of one of the Iranian commercial bank branches. Therefore, each branch could have a strategy to improve the efficiency and eliminate the cause of inefficiencies based on a 5-year time forecast.

Neural correlates of working memory training in HIV patients: study protocol for a randomized controlled trial.

PubMed

Chang, L; Løhaugen, G C; Douet, V; Miller, E N; Skranes, J; Ernst, T

2016-02-02

Potent combined antiretroviral therapy decreased the incidence and severity of HIV-associated neurocognitive disorders (HAND); however, no specific effective pharmacotherapy exists for HAND. Patients with HIV commonly have deficits in working memory and attention, which may negatively impact many other cognitive domains, leading to HAND. Since HAND may lead to loss of independence in activities of daily living and negative emotional well-being, and incur a high economic burden, effective treatments for HAND are urgently needed. This study aims to determine whether adaptive working memory training might improve cognitive functions and neural network efficiency and possibly decrease neuroinflammation. This study also aims to assess whether subjects with the LMX1A-rs4657412 TT(AA) genotype show greater training effects from working memory training than TC(AG) or CC(GG)-carriers. 60 HIV-infected and 60 seronegative control participants will be randomized to a double-blind active-controlled study, using adaptive versus non-adaptive Cogmed Working Memory Training® (CWMT), 20-25 sessions over 5-8 weeks. Each subject will be assessed with near- and far-transfer cognitive tasks, self-reported mood and executive function questionnaires, and blood-oxygenation level-dependent functional MRI during working memory (n-back) and visual attention (ball tracking) tasks, at baseline, 1-month, and 6-months after CWMT. Furthermore, genotyping for LMX1A-rs4657412 will be performed to identify whether subjects with the TT(AA)-genotype show greater gain or neural efficiency after CWMT than those with other genotypes. Lastly, cerebrospinal fluid will be obtained before and after CWMT to explore changes in levels of inflammatory proteins (cytokines and chemokines) and monoamines. Improving working memory in HIV patients, using CWMT, might slow the progression or delay the onset of HAND. Observation of decreased brain activation or normalized neural networks, using fMRI, after CWMT would lead to a better understanding of how neural networks are modulated by CWMT. Moreover, validating the greater training gain in subjects with the LMX1A-TT(AA) genotype could lead to a personalized approach for future working memory training studies. Demonstrating and understanding the neural correlates of the efficacy of CWMT in HIV patients could lead to a safe adjunctive therapy for HAND, and possibly other brain disorders. ClinicalTrial.gov, NCT02602418.

The Roles and Regulation of Polycomb Complexes in Neural Development

PubMed Central

Corley, Matthew; Kroll, Kristen L.

2014-01-01

In the developing mammalian nervous system, common progenitors integrate both cell extrinsic and intrinsic regulatory programs to produce distinct neuronal and glial cell types as development proceeds. This spatiotemporal restriction of neural progenitor differentiation is enforced, in part, by the dynamic reorganization of chromatin into repressive domains by Polycomb Repressive Complexes, effectively limiting the expression of fate-determining genes. Here, we review distinct roles that the Polycomb Repressive Complexes play during neurogenesis and gliogenesis, while also highlighting recent work describing the molecular mechanisms that govern their dynamic activity in neural development. Further investigation of how Polycomb complexes are regulated in neural development will enable more precise manipulation of neural progenitor differentiation, facilitating the efficient generation of specific neuronal and glial cell types for many biological applications. PMID:25367430

Attractor neural networks with resource-efficient synaptic connectivity

NASA Astrophysics Data System (ADS)

Pehlevan, Cengiz; Sengupta, Anirvan

Memories are thought to be stored in the attractor states of recurrent neural networks. Here we explore how resource constraints interplay with memory storage function to shape synaptic connectivity of attractor networks. We propose that given a set of memories, in the form of population activity patterns, the neural circuit choses a synaptic connectivity configuration that minimizes a resource usage cost. We argue that the total synaptic weight (l1-norm) in the network measures the resource cost because synaptic weight is correlated with synaptic volume, which is a limited resource, and is proportional to neurotransmitter release and post-synaptic current, both of which cost energy. Using numerical simulations and replica theory, we characterize optimal connectivity profiles in resource-efficient attractor networks. Our theory explains several experimental observations on cortical connectivity profiles, 1) connectivity is sparse, because synapses are costly, 2) bidirectional connections are overrepresented and 3) are stronger, because attractor states need strong recurrence.

Fitting neuron models to spike trains.

PubMed

Rossant, Cyrille; Goodman, Dan F M; Fontaine, Bertrand; Platkiewicz, Jonathan; Magnusson, Anna K; Brette, Romain

2011-01-01

Computational modeling is increasingly used to understand the function of neural circuits in systems neuroscience. These studies require models of individual neurons with realistic input-output properties. Recently, it was found that spiking models can accurately predict the precisely timed spike trains produced by cortical neurons in response to somatically injected currents, if properly fitted. This requires fitting techniques that are efficient and flexible enough to easily test different candidate models. We present a generic solution, based on the Brian simulator (a neural network simulator in Python), which allows the user to define and fit arbitrary neuron models to electrophysiological recordings. It relies on vectorization and parallel computing techniques to achieve efficiency. We demonstrate its use on neural recordings in the barrel cortex and in the auditory brainstem, and confirm that simple adaptive spiking models can accurately predict the response of cortical neurons. Finally, we show how a complex multicompartmental model can be reduced to a simple effective spiking model.

Binary Associative Memories as a Benchmark for Spiking Neuromorphic Hardware

PubMed Central

Stöckel, Andreas; Jenzen, Christoph; Thies, Michael; Rückert, Ulrich

2017-01-01

Large-scale neuromorphic hardware platforms, specialized computer systems for energy efficient simulation of spiking neural networks, are being developed around the world, for example as part of the European Human Brain Project (HBP). Due to conceptual differences, a universal performance analysis of these systems in terms of runtime, accuracy and energy efficiency is non-trivial, yet indispensable for further hard- and software development. In this paper we describe a scalable benchmark based on a spiking neural network implementation of the binary neural associative memory. We treat neuromorphic hardware and software simulators as black-boxes and execute exactly the same network description across all devices. Experiments on the HBP platforms under varying configurations of the associative memory show that the presented method allows to test the quality of the neuron model implementation, and to explain significant deviations from the expected reference output. PMID:28878642

[Measurement and performance analysis of functional neural network].

PubMed

Li, Shan; Liu, Xinyu; Chen, Yan; Wan, Hong

2018-04-01

The measurement of network is one of the important researches in resolving neuronal population information processing mechanism using complex network theory. For the quantitative measurement problem of functional neural network, the relation between the measure indexes, i.e. the clustering coefficient, the global efficiency, the characteristic path length and the transitivity, and the network topology was analyzed. Then, the spike-based functional neural network was established and the simulation results showed that the measured network could represent the original neural connections among neurons. On the basis of the former work, the coding of functional neural network in nidopallium caudolaterale (NCL) about pigeon's motion behaviors was studied. We found that the NCL functional neural network effectively encoded the motion behaviors of the pigeon, and there were significant differences in four indexes among the left-turning, the forward and the right-turning. Overall, the establishment method of spike-based functional neural network is available and it is an effective tool to parse the brain information processing mechanism.

Real-time simulation of large-scale neural architectures for visual features computation based on GPU.

PubMed

Chessa, Manuela; Bianchi, Valentina; Zampetti, Massimo; Sabatini, Silvio P; Solari, Fabio

2012-01-01

The intrinsic parallelism of visual neural architectures based on distributed hierarchical layers is well suited to be implemented on the multi-core architectures of modern graphics cards. The design strategies that allow us to optimally take advantage of such parallelism, in order to efficiently map on GPU the hierarchy of layers and the canonical neural computations, are proposed. Specifically, the advantages of a cortical map-like representation of the data are exploited. Moreover, a GPU implementation of a novel neural architecture for the computation of binocular disparity from stereo image pairs, based on populations of binocular energy neurons, is presented. The implemented neural model achieves good performances in terms of reliability of the disparity estimates and a near real-time execution speed, thus demonstrating the effectiveness of the devised design strategies. The proposed approach is valid in general, since the neural building blocks we implemented are a common basis for the modeling of visual neural functionalities.

A closed-loop compressive-sensing-based neural recording system.

PubMed

Zhang, Jie; Mitra, Srinjoy; Suo, Yuanming; Cheng, Andrew; Xiong, Tao; Michon, Frederic; Welkenhuysen, Marleen; Kloosterman, Fabian; Chin, Peter S; Hsiao, Steven; Tran, Trac D; Yazicioglu, Firat; Etienne-Cummings, Ralph

2015-06-01

This paper describes a low power closed-loop compressive sensing (CS) based neural recording system. This system provides an efficient method to reduce data transmission bandwidth for implantable neural recording devices. By doing so, this technique reduces a majority of system power consumption which is dissipated at data readout interface. The design of the system is scalable and is a viable option for large scale integration of electrodes or recording sites onto a single device. The entire system consists of an application-specific integrated circuit (ASIC) with 4 recording readout channels with CS circuits, a real time off-chip CS recovery block and a recovery quality evaluation block that provides a closed feedback to adaptively adjust compression rate. Since CS performance is strongly signal dependent, the ASIC has been tested in vivo and with standard public neural databases. Implemented using efficient digital circuit, this system is able to achieve >10 times data compression on the entire neural spike band (500-6KHz) while consuming only 0.83uW (0.53 V voltage supply) additional digital power per electrode. When only the spikes are desired, the system is able to further compress the detected spikes by around 16 times. Unlike other similar systems, the characteristic spikes and inter-spike data can both be recovered which guarantes a >95% spike classification success rate. The compression circuit occupied 0.11mm(2)/electrode in a 180nm CMOS process. The complete signal processing circuit consumes <16uW/electrode. Power and area efficiency demonstrated by the system make it an ideal candidate for integration into large recording arrays containing thousands of electrode. Closed-loop recording and reconstruction performance evaluation further improves the robustness of the compression method, thus making the system more practical for long term recording.

Ictal high frequency oscillations distinguish two types of seizure territories in humans

PubMed Central

Weiss, Shennan A.; Banks, Garrett P.; McKhann, Guy M.; Goodman, Robert R.; Emerson, Ronald G.; Trevelyan, Andrew J.

2013-01-01

High frequency oscillations have been proposed as a clinically useful biomarker of seizure generating sites. We used a unique set of human microelectrode array recordings (four patients, 10 seizures), in which propagating seizure wavefronts could be readily identified, to investigate the basis of ictal high frequency activity at the cortical (subdural) surface. Sustained, repetitive transient increases in high gamma (80–150 Hz) amplitude, phase-locked to the low-frequency (1–25 Hz) ictal rhythm, correlated with strong multi-unit firing bursts synchronized across the core territory of the seizure. These repetitive high frequency oscillations were seen in recordings from subdural electrodes adjacent to the microelectrode array several seconds after seizure onset, following ictal wavefront passage. Conversely, microelectrode recordings demonstrating only low-level, heterogeneous neural firing correlated with a lack of high frequency oscillations in adjacent subdural recording sites, despite the presence of a strong low-frequency signature. Previously, we reported that this pattern indicates a failure of the seizure to invade the area, because of a feedforward inhibitory veto mechanism. Because multi-unit firing rate and high gamma amplitude are closely related, high frequency oscillations can be used as a surrogate marker to distinguish the core seizure territory from the surrounding penumbra. We developed an efficient measure to detect delayed-onset, sustained ictal high frequency oscillations based on cross-frequency coupling between high gamma amplitude and the low-frequency (1–25 Hz) ictal rhythm. When applied to the broader subdural recording, this measure consistently predicted the timing or failure of ictal invasion, and revealed a surprisingly small and slowly spreading seizure core surrounded by a far larger penumbral territory. Our findings thus establish an underlying neural mechanism for delayed-onset, sustained ictal high frequency oscillations, and provide a practical, efficient method for using them to identify the small ictal core regions. Our observations suggest that it may be possible to reduce substantially the extent of cortical resections in epilepsy surgery procedures without compromising seizure control. PMID:24176977

Dendritic Properties Control Energy Efficiency of Action Potentials in Cortical Pyramidal Cells

PubMed Central

Yi, Guosheng; Wang, Jiang; Wei, Xile; Deng, Bin

2017-01-01

Neural computation is performed by transforming input signals into sequences of action potentials (APs), which is metabolically expensive and limited by the energy available to the brain. The metabolic efficiency of single AP has important consequences for the computational power of the cell, which is determined by its biophysical properties and morphologies. Here we adopt biophysically-based two-compartment models to investigate how dendrites affect energy efficiency of APs in cortical pyramidal neurons. We measure the Na+ entry during the spike and examine how it is efficiently used for generating AP depolarization. We show that increasing the proportion of dendritic area or coupling conductance between two chambers decreases Na+ entry efficiency of somatic AP. Activating inward Ca2+ current in dendrites results in dendritic spike, which increases AP efficiency. Activating Ca2+-activated outward K+ current in dendrites, however, decreases Na+ entry efficiency. We demonstrate that the active and passive dendrites take effects by altering the overlap between Na+ influx and internal current flowing from soma to dendrite. We explain a fundamental link between dendritic properties and AP efficiency, which is essential to interpret how neural computation consumes metabolic energy and how biophysics and morphologies contribute to such consumption. PMID:28919852

Dendritic Properties Control Energy Efficiency of Action Potentials in Cortical Pyramidal Cells.

PubMed

Yi, Guosheng; Wang, Jiang; Wei, Xile; Deng, Bin

2017-01-01

Neural computation is performed by transforming input signals into sequences of action potentials (APs), which is metabolically expensive and limited by the energy available to the brain. The metabolic efficiency of single AP has important consequences for the computational power of the cell, which is determined by its biophysical properties and morphologies. Here we adopt biophysically-based two-compartment models to investigate how dendrites affect energy efficiency of APs in cortical pyramidal neurons. We measure the Na + entry during the spike and examine how it is efficiently used for generating AP depolarization. We show that increasing the proportion of dendritic area or coupling conductance between two chambers decreases Na + entry efficiency of somatic AP. Activating inward Ca 2+ current in dendrites results in dendritic spike, which increases AP efficiency. Activating Ca 2+ -activated outward K + current in dendrites, however, decreases Na + entry efficiency. We demonstrate that the active and passive dendrites take effects by altering the overlap between Na + influx and internal current flowing from soma to dendrite. We explain a fundamental link between dendritic properties and AP efficiency, which is essential to interpret how neural computation consumes metabolic energy and how biophysics and morphologies contribute to such consumption.

Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks.

PubMed

Walter, Florian; Röhrbein, Florian; Knoll, Alois

2015-12-01

The application of biologically inspired methods in design and control has a long tradition in robotics. Unlike previous approaches in this direction, the emerging field of neurorobotics not only mimics biological mechanisms at a relatively high level of abstraction but employs highly realistic simulations of actual biological nervous systems. Even today, carrying out these simulations efficiently at appropriate timescales is challenging. Neuromorphic chip designs specially tailored to this task therefore offer an interesting perspective for neurorobotics. Unlike Von Neumann CPUs, these chips cannot be simply programmed with a standard programming language. Like real brains, their functionality is determined by the structure of neural connectivity and synaptic efficacies. Enabling higher cognitive functions for neurorobotics consequently requires the application of neurobiological learning algorithms to adjust synaptic weights in a biologically plausible way. In this paper, we therefore investigate how to program neuromorphic chips by means of learning. First, we provide an overview over selected neuromorphic chip designs and analyze them in terms of neural computation, communication systems and software infrastructure. On the theoretical side, we review neurobiological learning techniques. Based on this overview, we then examine on-die implementations of these learning algorithms on the considered neuromorphic chips. A final discussion puts the findings of this work into context and highlights how neuromorphic hardware can potentially advance the field of autonomous robot systems. The paper thus gives an in-depth overview of neuromorphic implementations of basic mechanisms of synaptic plasticity which are required to realize advanced cognitive capabilities with spiking neural networks. Copyright © 2015 Elsevier Ltd. All rights reserved.

MicroRNA let-7b regulates neural stem cell proliferation and differentiation by targeting nuclear receptor TLX signaling

PubMed Central

Zhao, Chunnian; Sun, GuoQiang; Li, Shengxiu; Lang, Ming-Fei; Yang, Su; Li, Wendong; Shi, Yanhong

2010-01-01

Neural stem cell self-renewal and differentiation is orchestrated by precise control of gene expression involving nuclear receptor TLX. Let-7b, a member of the let-7 microRNA family, is expressed in mammalian brains and exhibits increased expression during neural differentiation. However, the role of let-7b in neural stem cell proliferation and differentiation remains unknown. Here we show that let-7b regulates neural stem cell proliferation and differentiation by targeting the stem cell regulator TLX and the cell cycle regulator cyclin D1. Overexpression of let-7b led to reduced neural stem cell proliferation and increased neural differentiation, whereas antisense knockdown of let-7b resulted in enhanced proliferation of neural stem cells. Moreover, in utero electroporation of let-7b to embryonic mouse brains led to reduced cell cycle progression in neural stem cells. Introducing an expression vector of Tlx or cyclin D1 that lacks the let-7b recognition site rescued let-7b-induced proliferation deficiency, suggesting that both TLX and cyclin D1 are important targets for let-7b-mediated regulation of neural stem cell proliferation. Let-7b, by targeting TLX and cyclin D1, establishes an efficient strategy to control neural stem cell proliferation and differentiation. PMID:20133835

MicroRNA let-7b regulates neural stem cell proliferation and differentiation by targeting nuclear receptor TLX signaling.

PubMed

Zhao, Chunnian; Sun, GuoQiang; Li, Shengxiu; Lang, Ming-Fei; Yang, Su; Li, Wendong; Shi, Yanhong

2010-02-02

Neural stem cell self-renewal and differentiation is orchestrated by precise control of gene expression involving nuclear receptor TLX. Let-7b, a member of the let-7 microRNA family, is expressed in mammalian brains and exhibits increased expression during neural differentiation. However, the role of let-7b in neural stem cell proliferation and differentiation remains unknown. Here we show that let-7b regulates neural stem cell proliferation and differentiation by targeting the stem cell regulator TLX and the cell cycle regulator cyclin D1. Overexpression of let-7b led to reduced neural stem cell proliferation and increased neural differentiation, whereas antisense knockdown of let-7b resulted in enhanced proliferation of neural stem cells. Moreover, in utero electroporation of let-7b to embryonic mouse brains led to reduced cell cycle progression in neural stem cells. Introducing an expression vector of Tlx or cyclin D1 that lacks the let-7b recognition site rescued let-7b-induced proliferation deficiency, suggesting that both TLX and cyclin D1 are important targets for let-7b-mediated regulation of neural stem cell proliferation. Let-7b, by targeting TLX and cyclin D1, establishes an efficient strategy to control neural stem cell proliferation and differentiation.

GANEing on emotion and emotion regulation.

PubMed

Hull, Thomas D

2016-01-01

The function of emotion and its underlying neural mechanisms are often left underspecified. I extend the GANE (glutamate amplifies noradrenergic effects) model by examining its success in accounting for findings in emotion regulation. I also identify points of alignment with construction models of emotion and with the hypothesis that emotion states function to push neural activity toward rapid and efficient action.

Streamflow Forecasting Using Nuero-Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Nanduri, U. V.; Swain, P. C.

2005-12-01

The prediction of flow into a reservoir is fundamental in water resources planning and management. The need for timely and accurate streamflow forecasting is widely recognized and emphasized by many in water resources fraternity. Real-time forecasts of natural inflows to reservoirs are of particular interest for operation and scheduling. The physical system of the river basin that takes the rainfall as an input and produces the runoff is highly nonlinear, complicated and very difficult to fully comprehend. The system is influenced by large number of factors and variables. The large spatial extent of the systems forces the uncertainty into the hydrologic information. A variety of methods have been proposed for forecasting reservoir inflows including conceptual (physical) and empirical (statistical) models (WMO 1994), but none of them can be considered as unique superior model (Shamseldin 1997). Owing to difficulties of formulating reasonable non-linear watershed models, recent attempts have resorted to Neural Network (NN) approach for complex hydrologic modeling. In recent years the use of soft computing in the field of hydrological forecasting is gaining ground. The relatively new soft computing technique of Adaptive Neuro-Fuzzy Inference System (ANFIS), developed by Jang (1993) is able to take care of the non-linearity, uncertainty, and vagueness embedded in the system. It is a judicious combination of the Neural Networks and fuzzy systems. It can learn and generalize highly nonlinear and uncertain phenomena due to the embedded neural network (NN). NN is efficient in learning and generalization, and the fuzzy system mimics the cognitive capability of human brain. Hence, ANFIS can learn the complicated processes involved in the basin and correlate the precipitation to the corresponding discharge. In the present study, one step ahead forecasts are made for ten-daily flows, which are mostly required for short term operational planning of multipurpose reservoirs. A Neuro-Fuzzy model is developed to forecast ten-daily flows into the Hirakud reservoir on River Mahanadi in the state of Orissa in India. Correlation analysis is carried out to find out the most influential variables on the ten daily flow at Hirakud. Based on this analysis, four variables, namely, flow during the previous time period, ql1, rainfall during the previous two time periods, rl1 and rl2, and flow during the same period in previous year, qpy, are identified as the most influential variables to forecast the ten daily flow. Performance measures such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and coefficient of efficiency R2 are computed for training and testing phases of the model to evaluate its performance. The results indicate that the ten-daily forecasting model is efficient in predicting the high and medium flows with reasonable accuracy. The forecast of low flows is associated with less efficiency. REFERENCES Jang, J.S.R. (1993). "ANFIS: Adaptive - network- based fuzzy inference system." IEEE Trans. on Systems, Man and Cybernetics, 23 (3), 665-685. Shamseldin, A.Y. (1997). "Application of a neural network technique to rainfall-runoff modeling." Journal of Hydrology, 199, 272-294. World Meteorological Organization (1975). Intercomparison of conceptual models used in operational hydrological forecasting. World Meteorological Organization, Technical Report No.429, Geneva, Switzerland.

An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks

PubMed Central

Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen

2016-01-01

The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper. PMID:27044001

Dispersion compensation of fiber optic communication system with direct detection using artificial neural networks (ANNs)

NASA Astrophysics Data System (ADS)

Maghrabi, Mahmoud M. T.; Kumar, Shiva; Bakr, Mohamed H.

2018-02-01

This work introduces a powerful digital nonlinear feed-forward equalizer (NFFE), exploiting multilayer artificial neural network (ANN). It mitigates impairments of optical communication systems arising due to the nonlinearity introduced by direct photo-detection. In a direct detection system, the detection process is nonlinear due to the fact that the photo-current is proportional to the absolute square of the electric field intensity. The proposed equalizer provides the most efficient computational cost with high equalization performance. Its performance is comparable to the benchmark compensation performance achieved by maximum-likelihood sequence estimator. The equalizer trains an ANN to act as a nonlinear filter whose impulse response removes the intersymbol interference (ISI) distortions of the optical channel. Owing to the proposed extensive training of the equalizer, it achieves the ultimate performance limit of any feed-forward equalizer (FFE). The performance and efficiency of the equalizer is investigated by applying it to various practical short-reach fiber optic communication system scenarios. These scenarios are extracted from practical metro/media access networks and data center applications. The obtained results show that the ANN-NFFE compensates for the received BER degradation and significantly increases the tolerance to the chromatic dispersion distortion.

A four-dimensional virtual hand brain-machine interface using active dimension selection.

PubMed

Rouse, Adam G

2016-06-01

Brain-machine interfaces (BMI) traditionally rely on a fixed, linear transformation from neural signals to an output state-space. In this study, the assumption that a BMI must control a fixed, orthogonal basis set was challenged and a novel active dimension selection (ADS) decoder was explored. ADS utilizes a two stage decoder by using neural signals to both (i) select an active dimension being controlled and (ii) control the velocity along the selected dimension. ADS decoding was tested in a monkey using 16 single units from premotor and primary motor cortex to successfully control a virtual hand avatar to move to eight different postures. Following training with the ADS decoder to control 2, 3, and then 4 dimensions, each emulating a grasp shape of the hand, performance reached 93% correct with a bit rate of 2.4 bits s(-1) for eight targets. Selection of eight targets using ADS control was more efficient, as measured by bit rate, than either full four-dimensional control or computer assisted one-dimensional control. ADS decoding allows a user to quickly and efficiently select different hand postures. This novel decoding scheme represents a potential method to reduce the complexity of high-dimension BMI control of the hand.

Arrested neural and advanced mesenchymal differentiation of glioblastoma cells-comparative study with neural progenitors

PubMed Central

2009-01-01

Background Although features of variable differentiation in glioblastoma cell cultures have been reported, a comparative analysis of differentiation properties of normal neural GFAP positive progenitors, and those shown by glioblastoma cells, has not been performed. Methods Following methods were used to compare glioblastoma cells and GFAP+NNP (NHA): exposure to neural differentiation medium, exposure to adipogenic and osteogenic medium, western blot analysis, immunocytochemistry, single cell assay, BrdU incorporation assay. To characterize glioblastoma cells EGFR amplification analysis, LOH/MSI analysis, and P53 nucleotide sequence analysis were performed. Results In vitro differentiation of cancer cells derived from eight glioblastomas was compared with GFAP-positive normal neural progenitors (GFAP+NNP). Prior to exposure to differentiation medium, both types of cells showed similar multilineage phenotype (CD44+/MAP2+/GFAP+/Vimentin+/Beta III-tubulin+/Fibronectin+) and were positive for SOX-2 and Nestin. In contrast to GFAP+NNP, an efficient differentiation arrest was observed in all cell lines isolated from glioblastomas. Nevertheless, a subpopulation of cells isolated from four glioblastomas differentiated after serum-starvation with varying efficiency into derivatives indistinguishable from the neural derivatives of GFAP+NNP. Moreover, the cells derived from a majority of glioblastomas (7 out of 8), as well as GFAP+NNP, showed features of mesenchymal differentiation when exposed to medium with serum. Conclusion Our results showed that stable co-expression of multilineage markers by glioblastoma cells resulted from differentiation arrest. According to our data up to 95% of glioblastoma cells can present in vitro multilineage phenotype. The mesenchymal differentiation of glioblastoma cells is advanced and similar to mesenchymal differentiation of normal neural progenitors GFAP+NNP. PMID:19216795

Aberrant transcriptional networks in step-wise neurogenesis of paroxysmal kinesigenic dyskinesia-induced pluripotent stem cells.

PubMed

Li, Chun; Ma, Yu; Zhang, Kunshan; Gu, Junjie; Tang, Fan; Chen, Shengdi; Cao, Li; Li, Siguang; Jin, Ying

2016-08-16

Paroxysmal kinesigenic dyskinesia (PKD) is an episodic movement disorder with autosomal-dominant inheritance and marked variability in clinical manifestations.Proline-rich transmembrane protein 2 (PRRT2) has been identified as a causative gene of PKD, but the molecular mechanism underlying the pathogenesis of PKD still remains a mystery. The phenotypes and transcriptional patterns of the PKD disease need further clarification. Here, we report the generation and neural differentiation of iPSC lines from two familial PKD patients with c.487C>T (p. Gln163X) and c.573dupT (p. Gly192Trpfs*8) PRRT2 mutations, respectively. Notably, an extremely lower efficiency in neural conversion from PKD-iPSCs than control-iPSCs is observed by a step-wise neural differentiation method of dual inhibition of SMAD signaling. Moreover, we show the high expression level of PRRT2 throughout the human brain and the expression pattern of PRRT2 in other human tissues for the first time. To gain molecular insight into the development of the disease, we conduct global gene expression profiling of PKD cells at four different stages of neural induction and identify altered gene expression patterns, which peculiarly reflect dysregulated neural transcriptome signatures and a differentiation tendency to mesodermal development, in comparison to control-iPSCs. Additionally, functional and signaling pathway analyses indicate significantly different cell fate determination between PKD-iPSCs and control-iPSCs. Together, the establishment of PKD-specific in vitro models and the illustration of transcriptome features in PKD cells would certainly help us with better understanding of the defects in neural conversion as well as further investigations in the pathogenesis of the PKD disease.

Training-specific functional, neural, and hypertrophic adaptations to explosive- vs. sustained-contraction strength training.

PubMed

Balshaw, Thomas G; Massey, Garry J; Maden-Wilkinson, Thomas M; Tillin, Neale A; Folland, Jonathan P

2016-06-01

Training specificity is considered important for strength training, although the functional and underpinning physiological adaptations to different types of training, including brief explosive contractions, are poorly understood. This study compared the effects of 12 wk of explosive-contraction (ECT, n = 13) vs. sustained-contraction (SCT, n = 16) strength training vs. control (n = 14) on the functional, neural, hypertrophic, and intrinsic contractile characteristics of healthy young men. Training involved 40 isometric knee extension repetitions (3 times/wk): contracting as fast and hard as possible for ∼1 s (ECT) or gradually increasing to 75% of maximum voluntary torque (MVT) before holding for 3 s (SCT). Torque and electromyography during maximum and explosive contractions, torque during evoked octet contractions, and total quadriceps muscle volume (QUADSVOL) were quantified pre and post training. MVT increased more after SCT than ECT [23 vs. 17%; effect size (ES) = 0.69], with similar increases in neural drive, but greater QUADSVOL changes after SCT (8.1 vs. 2.6%; ES = 0.74). ECT improved explosive torque at all time points (17-34%; 0.54 ≤ ES ≤ 0.76) because of increased neural drive (17-28%), whereas only late-phase explosive torque (150 ms, 12%; ES = 1.48) and corresponding neural drive (18%) increased after SCT. Changes in evoked torque indicated slowing of the contractile properties of the muscle-tendon unit after both training interventions. These results showed training-specific functional changes that appeared to be due to distinct neural and hypertrophic adaptations. ECT produced a wider range of functional adaptations than SCT, and given the lesser demands of ECT, this type of training provides a highly efficient means of increasing function. Copyright © 2016 the American Physiological Society.

Cosmic-ray discrimination capabilities of /ΔE-/E silicon nuclear telescopes using neural networks

NASA Astrophysics Data System (ADS)

Ambriola, M.; Bellotti, R.; Cafagna, F.; Castellano, M.; Ciacio, F.; Circella, M.; Marzo, C. N. D.; Montaruli, T.

2000-02-01

An isotope classifier of cosmic-ray events collected by space detectors has been implemented using a multi-layer perceptron neural architecture. In order to handle a great number of different isotopes a modular architecture of the ``mixture of experts'' type is proposed. The performance of this classifier has been tested on simulated data and has been compared with a ``classical'' classifying procedure. The quantitative comparison with traditional techniques shows that the neural approach has classification performances comparable - within /1% - with that of the classical one, with efficiency of the order of /98%. A possible hardware implementation of such a kind of neural architecture in future space missions is considered.

An Application to the Prediction of LOD Change Based on General Regression Neural Network

NASA Astrophysics Data System (ADS)

Zhang, X. H.; Wang, Q. J.; Zhu, J. J.; Zhang, H.

2011-07-01

Traditional prediction of the LOD (length of day) change was based on linear models, such as the least square model and the autoregressive technique, etc. Due to the complex non-linear features of the LOD variation, the performances of the linear model predictors are not fully satisfactory. This paper applies a non-linear neural network - general regression neural network (GRNN) model to forecast the LOD change, and the results are analyzed and compared with those obtained with the back propagation neural network and other models. The comparison shows that the performance of the GRNN model in the prediction of the LOD change is efficient and feasible.

Diagnosing Parkinson's Diseases Using Fuzzy Neural System

PubMed Central

Abiyev, Rahib H.; Abizade, Sanan

2016-01-01

This study presents the design of the recognition system that will discriminate between healthy people and people with Parkinson's disease. A diagnosing of Parkinson's diseases is performed using fusion of the fuzzy system and neural networks. The structure and learning algorithms of the proposed fuzzy neural system (FNS) are presented. The approach described in this paper allows enhancing the capability of the designed system and efficiently distinguishing healthy individuals. It was proved through simulation of the system that has been performed using data obtained from UCI machine learning repository. A comparative study was carried out and the simulation results demonstrated that the proposed fuzzy neural system improves the recognition rate of the designed system. PMID:26881009

Delay dynamics of neuromorphic optoelectronic nanoscale resonators: Perspectives and applications

NASA Astrophysics Data System (ADS)

Romeira, Bruno; Figueiredo, José M. L.; Javaloyes, Julien

2017-11-01

With the recent exponential growth of applications using artificial intelligence (AI), the development of efficient and ultrafast brain-like (neuromorphic) systems is crucial for future information and communication technologies. While the implementation of AI systems using computer algorithms of neural networks is emerging rapidly, scientists are just taking the very first steps in the development of the hardware elements of an artificial brain, specifically neuromorphic microchips. In this review article, we present the current state of the art of neuromorphic photonic circuits based on solid-state optoelectronic oscillators formed by nanoscale double barrier quantum well resonant tunneling diodes. We address, both experimentally and theoretically, the key dynamic properties of recently developed artificial solid-state neuron microchips with delayed perturbations and describe their role in the study of neural activity and regenerative memory. This review covers our recent research work on excitable and delay dynamic characteristics of both single and autaptic (delayed) artificial neurons including all-or-none response, spike-based data encoding, storage, signal regeneration and signal healing. Furthermore, the neural responses of these neuromorphic microchips display all the signatures of extended spatio-temporal localized structures (LSs) of light, which are reviewed here in detail. By taking advantage of the dissipative nature of LSs, we demonstrate potential applications in optical data reconfiguration and clock and timing at high-speeds and with short transients. The results reviewed in this article are a key enabler for the development of high-performance optoelectronic devices in future high-speed brain-inspired optical memories and neuromorphic computing.

Delay dynamics of neuromorphic optoelectronic nanoscale resonators: Perspectives and applications.

PubMed

Romeira, Bruno; Figueiredo, José M L; Javaloyes, Julien

2017-11-01

With the recent exponential growth of applications using artificial intelligence (AI), the development of efficient and ultrafast brain-like (neuromorphic) systems is crucial for future information and communication technologies. While the implementation of AI systems using computer algorithms of neural networks is emerging rapidly, scientists are just taking the very first steps in the development of the hardware elements of an artificial brain, specifically neuromorphic microchips. In this review article, we present the current state of the art of neuromorphic photonic circuits based on solid-state optoelectronic oscillators formed by nanoscale double barrier quantum well resonant tunneling diodes. We address, both experimentally and theoretically, the key dynamic properties of recently developed artificial solid-state neuron microchips with delayed perturbations and describe their role in the study of neural activity and regenerative memory. This review covers our recent research work on excitable and delay dynamic characteristics of both single and autaptic (delayed) artificial neurons including all-or-none response, spike-based data encoding, storage, signal regeneration and signal healing. Furthermore, the neural responses of these neuromorphic microchips display all the signatures of extended spatio-temporal localized structures (LSs) of light, which are reviewed here in detail. By taking advantage of the dissipative nature of LSs, we demonstrate potential applications in optical data reconfiguration and clock and timing at high-speeds and with short transients. The results reviewed in this article are a key enabler for the development of high-performance optoelectronic devices in future high-speed brain-inspired optical memories and neuromorphic computing.

Neural control and transient analysis of the LCL-type resonant converter

NASA Astrophysics Data System (ADS)

Zouggar, S.; Nait Charif, H.; Azizi, M.

2000-07-01

This paper proposes a generalised inverse learning structure to control the LCL converter. A feedforward neural network is trained to act as an inverse model of the LCL converter then both are cascaded such that the composed system results in an identity mapping between desired response and the LCL output voltage. Using the large signal model, we analyse the transient output response of the controlled LCL converter in the case of large variation of the load. The simulation results show the efficiency of using neural networks to regulate the LCL converter.

Engagement with the auditory processing system during targeted auditory cognitive training mediates changes in cognitive outcomes in individuals with schizophrenia

PubMed Central

Biagianti, Bruno; Fisher, Melissa; Neilands, Torsten B.; Loewy, Rachel; Vinogradov, Sophia

2016-01-01

BACKGROUND Individuals with schizophrenia who engage in targeted cognitive training (TCT) of the auditory system show generalized cognitive improvements. The high degree of variability in cognitive gains maybe due to individual differences in the level of engagement of the underlying neural system target. METHODS 131 individuals with schizophrenia underwent 40 hours of TCT. We identified target engagement of auditory system processing efficiency by modeling subject-specific trajectories of auditory processing speed (APS) over time. Lowess analysis, mixed models repeated measures analysis, and latent growth curve modeling were used to examine whether APS trajectories were moderated by age and illness duration, and mediated improvements in cognitive outcome measures. RESULTS We observed signifcant improvements in APS from baseline to 20 hours of training (initial change), followed by a flat APS trajectory (plateau) at subsequent time-points. Participants showed inter-individual variability in the steepness of the initial APS change and in the APS plateau achieved and sustained between 20–40 hours. We found that participants who achieved the fastest APS plateau, showed the greatest transfer effects to untrained cognitive domains. CONCLUSIONS There is a significant association between an individual's ability to generate and sustain auditory processing efficiency and their degree of cognitive improvement after TCT, independent of baseline neurocognition. APS plateau may therefore represent a behavioral measure of target engagement mediating treatment response. Future studies should examine the optimal plateau of auditory processing efficiency required to induce significant cognitive improvements, in the context of inter-individual differences in neural plasticity and sensory system efficiency that characterize schizophrenia. PMID:27617637

The fidelity of Kepler eclipsing binary parameters inferred by the neural network

NASA Astrophysics Data System (ADS)

Holanda, N.; da Silva, J. R. P.

2018-04-01

This work aims to test the fidelity and efficiency of obtaining automatic orbital elements of eclipsing binary systems, from light curves using neural network models. We selected a random sample with 78 systems, from over 1400 eclipsing binary detached obtained from the Kepler Eclipsing Binaries Catalog, processed using the neural network approach. The orbital parameters of the sample systems were measured applying the traditional method of light curve adjustment with uncertainties calculated by the bootstrap method, employing the JKTEBOP code. These estimated parameters were compared with those obtained by the neural network approach for the same systems. The results reveal a good agreement between techniques for the sum of the fractional radii and moderate agreement for e cos ω and e sin ω, but orbital inclination is clearly underestimated in neural network tests.

The fidelity of Kepler eclipsing binary parameters inferred by the neural network

NASA Astrophysics Data System (ADS)

Holanda, N.; da Silva, J. R. P.

2018-07-01

This work aims to test the fidelity and efficiency of obtaining automatic orbital elements of eclipsing binary systems, from light curves using neural network models. We selected a random sample with 78 systems, from over 1400 detached eclipsing binaries obtained from the Kepler Eclipsing Binaries Catalog, processed using the neural network approach. The orbital parameters of the sample systems were measured applying the traditional method of light-curve adjustment with uncertainties calculated by the bootstrap method, employing the JKTEBOP code. These estimated parameters were compared with those obtained by the neural network approach for the same systems. The results reveal a good agreement between techniques for the sum of the fractional radii and moderate agreement for e cosω and e sinω, but orbital inclination is clearly underestimated in neural network tests.

Development of efficiency module of organization of Arctic sea cargo transportation with application of neural network technologies

NASA Astrophysics Data System (ADS)

Sobolevskaya, E. Yu; Glushkov, S. V.; Levchenko, N. G.; Orlov, A. P.

2018-05-01

The analysis of software intended for organizing and managing the processes of sea cargo transportation has been carried out. The shortcomings of information resources are presented, for the organization of work in the Arctic and Subarctic regions of the Far East: the lack of decision support systems, the lack of factor analysis to calculate the time and cost of delivery. The architecture of the module for calculating the effectiveness of the organization of sea cargo transportation has been developed. The simulation process has been considered, which is based on the neural network. The main classification factors with their weighting coefficients have been identified. The architecture of the neural network has been developed to calculate the efficiency of the organization of sea cargo transportation in Arctic conditions. The architecture of the intellectual system of organization of sea cargo transportation has been developed, taking into account the difficult navigation conditions in the Arctic. Its implementation will allow one to provide the management of the shipping company with predictive analytics; to support decision-making; to calculate the most efficient delivery route; to provide on demand online transportation forecast, to minimize the shipping cost, delays in transit, and risks to cargo safety.

Neural efficiency as a function of task demands☆

PubMed Central

Dunst, Beate; Benedek, Mathias; Jauk, Emanuel; Bergner, Sabine; Koschutnig, Karl; Sommer, Markus; Ischebeck, Anja; Spinath, Birgit; Arendasy, Martin; Bühner, Markus; Freudenthaler, Heribert; Neubauer, Aljoscha C.

2014-01-01

The neural efficiency hypothesis describes the phenomenon that brighter individuals show lower brain activation than less bright individuals when working on the same cognitive tasks. The present study investigated whether the brain activation–intelligence relationship still applies when more versus less intelligent individuals perform tasks with a comparable person-specific task difficulty. In an fMRI-study, 58 persons with lower (n = 28) or respectively higher (n = 30) intelligence worked on simple and difficult inductive reasoning tasks having the same person-specific task difficulty. Consequently, less bright individuals received sample-based easy and medium tasks, whereas bright subjects received sample-based medium and difficult tasks. This design also allowed a comparison of lower versus higher intelligent individuals when working on the same tasks (i.e. sample-based medium task difficulty). In line with expectations, differences in task performance and in brain activation were only found for the subset of tasks with the same sample-based task difficulty, but not when comparing tasks with the same person-specific task difficulty. These results suggest that neural efficiency reflects an (ability-dependent) adaption of brain activation to the respective task demands. PMID:24489416

The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption

NASA Astrophysics Data System (ADS)

Efremenko, Vladimir; Belyaevsky, Roman; Skrebneva, Evgeniya

2017-11-01

In article the analysis of electric power consumption and problems of power saving on coal mines are considered. Nowadays the share of conditionally constant costs of electric power for providing safe working conditions underground on coal mines is big. Therefore, the power efficiency of underground coal mining depends on electric power expense of the main technological processes and size of conditionally constant costs. The important direction of increase of power efficiency of coal mining is forecasting of a power consumption and monitoring of electric power expense. One of the main approaches to reducing of electric power costs is increase in accuracy of the enterprise demand in the wholesale electric power market. It is offered to use artificial neural networks to forecasting of day-ahead power consumption with hourly breakdown. At the same time use of neural and indistinct (hybrid) systems on the principles of fuzzy logic, neural networks and genetic algorithms is more preferable. This model allows to do exact short-term forecasts at a small array of input data. A set of the input parameters characterizing mining-and-geological and technological features of the enterprise is offered.

Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.

PubMed

Schwalger, Tilo; Deger, Moritz; Gerstner, Wulfram

2017-04-01

Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50-2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations.

The quadriceps muscle of knee joint modelling Using Hybrid Particle Swarm Optimization-Neural Network (PSO-NN)

NASA Astrophysics Data System (ADS)

Kamaruddin, Saadi Bin Ahmad; Marponga Tolos, Siti; Hee, Pah Chin; Ghani, Nor Azura Md; Ramli, Norazan Mohamed; Nasir, Noorhamizah Binti Mohamed; Ksm Kader, Babul Salam Bin; Saiful Huq, Mohammad

2017-03-01

Neural framework has for quite a while been known for its ability to handle a complex nonlinear system without a logical model and can learn refined nonlinear associations gives. Theoretically, the most surely understood computation to set up the framework is the backpropagation (BP) count which relies on upon the minimization of the mean square error (MSE). However, this algorithm is not totally efficient in the presence of outliers which usually exist in dynamic data. This paper exhibits the modelling of quadriceps muscle model by utilizing counterfeit smart procedures named consolidated backpropagation neural network nonlinear autoregressive (BPNN-NAR) and backpropagation neural network nonlinear autoregressive moving average (BPNN-NARMA) models in view of utilitarian electrical incitement (FES). We adapted particle swarm optimization (PSO) approach to enhance the performance of backpropagation algorithm. In this research, a progression of tests utilizing FES was led. The information that is gotten is utilized to build up the quadriceps muscle model. 934 preparing information, 200 testing and 200 approval information set are utilized as a part of the improvement of muscle model. It was found that both BPNN-NAR and BPNN-NARMA performed well in modelling this type of data. As a conclusion, the neural network time series models performed reasonably efficient for non-linear modelling such as active properties of the quadriceps muscle with one input, namely output namely muscle force.

Developmental dissociation in the neural responses to simple multiplication and subtraction problems

PubMed Central

Prado, Jérôme; Mutreja, Rachna; Booth, James R.

2014-01-01

Mastering single-digit arithmetic during school years is commonly thought to depend upon an increasing reliance on verbally memorized facts. An alternative model, however, posits that fluency in single-digit arithmetic might also be achieved via the increasing use of efficient calculation procedures. To test between these hypotheses, we used a cross-sectional design to measure the neural activity associated with single-digit subtraction and multiplication in 34 children from 2nd to 7th grade. The neural correlates of language and numerical processing were also identified in each child via localizer scans. Although multiplication and subtraction were undistinguishable in terms of behavior, we found a striking developmental dissociation in their neural correlates. First, we observed grade-related increases of activity for multiplication, but not for subtraction, in a language-related region of the left temporal cortex. Second, we found grade-related increases of activity for subtraction, but not for multiplication, in a region of the right parietal cortex involved in the procedural manipulation of numerical quantities. The present results suggest that fluency in simple arithmetic in children may be achieved by both increasing reliance on verbal retrieval and by greater use of efficient quantity-based procedures, depending on the operation. PMID:25089323

Computing Generalized Matrix Inverse on Spiking Neural Substrate.

PubMed

Shukla, Rohit; Khoram, Soroosh; Jorgensen, Erik; Li, Jing; Lipasti, Mikko; Wright, Stephen

2018-01-01

Emerging neural hardware substrates, such as IBM's TrueNorth Neurosynaptic System, can provide an appealing platform for deploying numerical algorithms. For example, a recurrent Hopfield neural network can be used to find the Moore-Penrose generalized inverse of a matrix, thus enabling a broad class of linear optimizations to be solved efficiently, at low energy cost. However, deploying numerical algorithms on hardware platforms that severely limit the range and precision of representation for numeric quantities can be quite challenging. This paper discusses these challenges and proposes a rigorous mathematical framework for reasoning about range and precision on such substrates. The paper derives techniques for normalizing inputs and properly quantizing synaptic weights originating from arbitrary systems of linear equations, so that solvers for those systems can be implemented in a provably correct manner on hardware-constrained neural substrates. The analytical model is empirically validated on the IBM TrueNorth platform, and results show that the guarantees provided by the framework for range and precision hold under experimental conditions. Experiments with optical flow demonstrate the energy benefits of deploying a reduced-precision and energy-efficient generalized matrix inverse engine on the IBM TrueNorth platform, reflecting 10× to 100× improvement over FPGA and ARM core baselines.

Artificial neural network detects human uncertainty

NASA Astrophysics Data System (ADS)

Hramov, Alexander E.; Frolov, Nikita S.; Maksimenko, Vladimir A.; Makarov, Vladimir V.; Koronovskii, Alexey A.; Garcia-Prieto, Juan; Antón-Toro, Luis Fernando; Maestú, Fernando; Pisarchik, Alexander N.

2018-03-01

Artificial neural networks (ANNs) are known to be a powerful tool for data analysis. They are used in social science, robotics, and neurophysiology for solving tasks of classification, forecasting, pattern recognition, etc. In neuroscience, ANNs allow the recognition of specific forms of brain activity from multichannel EEG or MEG data. This makes the ANN an efficient computational core for brain-machine systems. However, despite significant achievements of artificial intelligence in recognition and classification of well-reproducible patterns of neural activity, the use of ANNs for recognition and classification of patterns in neural networks still requires additional attention, especially in ambiguous situations. According to this, in this research, we demonstrate the efficiency of application of the ANN for classification of human MEG trials corresponding to the perception of bistable visual stimuli with different degrees of ambiguity. We show that along with classification of brain states associated with multistable image interpretations, in the case of significant ambiguity, the ANN can detect an uncertain state when the observer doubts about the image interpretation. With the obtained results, we describe the possible application of ANNs for detection of bistable brain activity associated with difficulties in the decision-making process.

In vitro assessment of TAT — Ciliary Neurotrophic Factor therapeutic potential for peripheral nerve regeneration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barbon, Silvia, E-mail: silvia.barbon@yahoo.it

In regenerative neurobiology, Ciliary Neurotrophic Factor (CNTF) is raising high interest as a multifunctional neurocytokine, playing a key role in the regeneration of injured peripheral nerves. Despite its promising trophic and regulatory activity, its clinical application is limited by the onset of severe side effects, due to the lack of efficient intracellular trafficking after administration. In this study, recombinant CNTF linked to the transactivator transduction domain (TAT) was investigated in vitro and found to be an optimized fusion protein which preserves neurotrophic activity, besides enhancing cellular uptake for therapeutic advantage. Moreover, a compelling protein delivery method was defined, in themore » future perspective of improving nerve regeneration strategies. Following determination of TAT-CNTF molecular weight and concentration, its specific effect on neural SH-SY5Y and PC12 cultures was assessed. Cell proliferation assay demonstrated that the fusion protein triggers PC12 cell growth within 6 h of stimulation. At the same time, the activation of signal transduction pathway and enhancement of cellular trafficking were found to be accomplished in both neural cell lines after specific treatment with TAT-CNTF. Finally, the recombinant growth factor was successfully loaded on oxidized polyvinyl alcohol (PVA) scaffolds, and more efficiently released when polymer oxidation rate increased. Taken together, our results highlight that the TAT domain addiction to the protein sequence preserves CNTF specific neurotrophic activity in vitro, besides improving cellular uptake. Moreover, oxidized PVA could represent an ideal biomaterial for the development of nerve conduits loaded with the fusion protein to be delivered to the site of nerve injury. - Highlights: • TAT-CNTF is an optimized fusion protein that preserves neurotrophic activity. • In neural cell lines, TAT-CNTF triggers the activation of signal transduction. • Fast cellular uptake of TAT-CNTF was accomplished after cell treatment. • TAT-CNTF can be efficiently loaded on oxidized PVA cylinders for local delivery. • TAT-CNTF features make it ideal for peripheral nerve regeneration therapies.« less

A neuro-data envelopment analysis approach for optimization of uncorrelated multiple response problems with smaller the better type controllable factors

NASA Astrophysics Data System (ADS)

Bashiri, Mahdi; Farshbaf-Geranmayeh, Amir; Mogouie, Hamed

2013-11-01

In this paper, a new method is proposed to optimize a multi-response optimization problem based on the Taguchi method for the processes where controllable factors are the smaller-the-better (STB)-type variables and the analyzer desires to find an optimal solution with smaller amount of controllable factors. In such processes, the overall output quality of the product should be maximized while the usage of the process inputs, the controllable factors, should be minimized. Since all possible combinations of factors' levels, are not considered in the Taguchi method, the response values of the possible unpracticed treatments are estimated using the artificial neural network (ANN). The neural network is tuned by the central composite design (CCD) and the genetic algorithm (GA). Then data envelopment analysis (DEA) is applied for determining the efficiency of each treatment. Although the important issue for implementation of DEA is its philosophy, which is maximization of outputs versus minimization of inputs, this important issue has been neglected in previous similar studies in multi-response problems. Finally, the most efficient treatment is determined using the maximin weight model approach. The performance of the proposed method is verified in a plastic molding process. Moreover a sensitivity analysis has been done by an efficiency estimator neural network. The results show efficiency of the proposed approach.

Spiking Neurons for Analysis of Patterns

NASA Technical Reports Server (NTRS)

Huntsberger, Terrance

2008-01-01

Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological neurons). These features enable the neurons to adapt their responses to high-rate inputs from sensors, and to adapt their firing thresholds to mitigate noise or effects of potential sensor failure. The mathematical derivation of the SVM starts from a prior model, known in the art as the point soma model, which captures all of the salient properties of neuronal response while keeping the computational cost low. The point-soma latency time is modified to be an exponentially decaying function of the strength of the applied potential. Choosing computational efficiency over biological fidelity, the dendrites surrounding a neuron are represented by simplified compartmental submodels and there are no dendritic spines. Updates to the dendritic potential, calcium-ion concentrations and conductances, and potassium-ion conductances are done by use of equations similar to those of the point soma. Diffusion processes in dendrites are modeled by averaging among nearest-neighbor compartments. Inputs to each of the dendritic compartments come from sensors. Alternatively or in addition, when an affected neuron is part of a pool, inputs can come from other spiking neurons. At present, SVM neural networks are implemented by computational simulation, using algorithms that encode the SVM and its submodels. However, it should be possible to implement these neural networks in hardware: The differential equations for the dendritic and cellular processes in the SVM model of spiking neurons map to equivalent circuits that can be implemented directly in analog very-large-scale integrated (VLSI) circuits.

Synergistic Gating of Electro-Iono-Photoactive 2D Chalcogenide Neuristors: Coexistence of Hebbian and Homeostatic Synaptic Metaplasticity.

PubMed

John, Rohit Abraham; Liu, Fucai; Chien, Nguyen Anh; Kulkarni, Mohit R; Zhu, Chao; Fu, Qundong; Basu, Arindam; Liu, Zheng; Mathews, Nripan

2018-06-01

Emulation of brain-like signal processing with thin-film devices can lay the foundation for building artificially intelligent learning circuitry in future. Encompassing higher functionalities into single artificial neural elements will allow the development of robust neuromorphic circuitry emulating biological adaptation mechanisms with drastically lesser neural elements, mitigating strict process challenges and high circuit density requirements necessary to match the computational complexity of the human brain. Here, 2D transition metal di-chalcogenide (MoS 2 ) neuristors are designed to mimic intracellular ion endocytosis-exocytosis dynamics/neurotransmitter-release in chemical synapses using three approaches: (i) electronic-mode: a defect modulation approach where the traps at the semiconductor-dielectric interface are perturbed; (ii) ionotronic-mode: where electronic responses are modulated via ionic gating; and (iii) photoactive-mode: harnessing persistent photoconductivity or trap-assisted slow recombination mechanisms. Exploiting a novel multigated architecture incorporating electrical and optical biases, this incarnation not only addresses different charge-trapping probabilities to finely modulate the synaptic weights, but also amalgamates neuromodulation schemes to achieve "plasticity of plasticity-metaplasticity" via dynamic control of Hebbian spike-time dependent plasticity and homeostatic regulation. Coexistence of such multiple forms of synaptic plasticity increases the efficacy of memory storage and processing capacity of artificial neuristors, enabling design of highly efficient novel neural architectures. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Combination of uniform design with artificial neural network coupling genetic algorithm: an effective way to obtain high yield of biomass and algicidal compound of a novel HABs control actinomycete

PubMed Central

2014-01-01

Controlling harmful algae blooms (HABs) using microbial algicides is cheap, efficient and environmental-friendly. However, obtaining high yield of algicidal microbes to meet the need of field test is still a big challenge since qualitative and quantitative analysis of algicidal compounds is difficult. In this study, we developed a protocol to increase the yield of both biomass and algicidal compound present in a novel algicidal actinomycete Streptomyces alboflavus RPS, which kills Phaeocystis globosa. To overcome the problem in algicidal compound quantification, we chose algicidal ratio as the index and used artificial neural network to fit the data, which was appropriate for this nonlinear situation. In this protocol, we firstly determined five main influencing factors through single factor experiments and generated the multifactorial experimental groups with a U15(155) uniform-design-table. Then, we used the traditional quadratic polynomial stepwise regression model and an accurate, fully optimized BP-neural network to simulate the fermentation. Optimized with genetic algorithm and verified using experiments, we successfully increased the algicidal ratio of the fermentation broth by 16.90% and the dry mycelial weight by 69.27%. These results suggested that this newly developed approach is a viable and easy way to optimize the fermentation conditions for algicidal microorganisms. PMID:24886410

Combination of uniform design with artificial neural network coupling genetic algorithm: an effective way to obtain high yield of biomass and algicidal compound of a novel HABs control actinomycete.

PubMed

Cai, Guanjing; Zheng, Wei; Yang, Xujun; Zhang, Bangzhou; Zheng, Tianling

2014-05-24

Controlling harmful algae blooms (HABs) using microbial algicides is cheap, efficient and environmental-friendly. However, obtaining high yield of algicidal microbes to meet the need of field test is still a big challenge since qualitative and quantitative analysis of algicidal compounds is difficult. In this study, we developed a protocol to increase the yield of both biomass and algicidal compound present in a novel algicidal actinomycete Streptomyces alboflavus RPS, which kills Phaeocystis globosa. To overcome the problem in algicidal compound quantification, we chose algicidal ratio as the index and used artificial neural network to fit the data, which was appropriate for this nonlinear situation. In this protocol, we firstly determined five main influencing factors through single factor experiments and generated the multifactorial experimental groups with a U15(155) uniform-design-table. Then, we used the traditional quadratic polynomial stepwise regression model and an accurate, fully optimized BP-neural network to simulate the fermentation. Optimized with genetic algorithm and verified using experiments, we successfully increased the algicidal ratio of the fermentation broth by 16.90% and the dry mycelial weight by 69.27%. These results suggested that this newly developed approach is a viable and easy way to optimize the fermentation conditions for algicidal microorganisms.

Multiplicative mixing of object identity and image attributes in single inferior temporal neurons.

PubMed

Ratan Murty, N Apurva; Arun, S P

2018-04-03

Object recognition is challenging because the same object can produce vastly different images, mixing signals related to its identity with signals due to its image attributes, such as size, position, rotation, etc. Previous studies have shown that both signals are present in high-level visual areas, but precisely how they are combined has remained unclear. One possibility is that neurons might encode identity and attribute signals multiplicatively so that each can be efficiently decoded without interference from the other. Here, we show that, in high-level visual cortex, responses of single neurons can be explained better as a product rather than a sum of tuning for object identity and tuning for image attributes. This subtle effect in single neurons produced substantially better population decoding of object identity and image attributes in the neural population as a whole. This property was absent both in low-level vision models and in deep neural networks. It was also unique to invariances: when tested with two-part objects, neural responses were explained better as a sum than as a product of part tuning. Taken together, our results indicate that signals requiring separate decoding, such as object identity and image attributes, are combined multiplicatively in IT neurons, whereas signals that require integration (such as parts in an object) are combined additively. Copyright © 2018 the Author(s). Published by PNAS.

ERPs and oscillations during encoding predict retrieval of digit memory in superior mnemonists.

PubMed

Pan, Yafeng; Li, Xianchun; Chen, Xi; Ku, Yixuan; Dong, Yujie; Dou, Zheng; He, Lin; Hu, Yi; Li, Weidong; Zhou, Xiaolin

2017-10-01

Previous studies have consistently demonstrated that superior mnemonists (SMs) outperform normal individuals in domain-specific memory tasks. However, the neural correlates of memory-related processes remain unclear. In the current EEG study, SMs and control participants performed a digit memory task during which their brain activity was recorded. Chinese SMs used a digit-image mnemonic for encoding digits, in which they associated 2-digit groups with images immediately after the presentation of each even-position digit in sequences. Behaviorally, SMs' memory of digit sequences was better than the controls'. During encoding in the study phase, SMs showed an increased right central P2 (150-250ms post onset) and a larger right posterior high-alpha (10-14Hz, 500-1720ms) oscillation on digits at even-positions compared with digits at odd-positions. Both P2 and high-alpha oscillations in the study phase co-varied with performance in the recall phase, but only in SMs, indicating that neural dynamics during encoding could predict successful retrieval of digit memory in SMs. Our findings suggest that representation of a digit sequence in SMs using mnemonics may recruit both the early-stage attention allocation process and the sustained information preservation process. This study provides evidence for the role of dynamic and efficient neural encoding processes in mnemonists. Copyright © 2017. Published by Elsevier Inc.

A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy.

PubMed

Zhu, Yanan; Ouyang, Qi; Mao, Youdong

2017-07-21

Single-particle cryo-electron microscopy (cryo-EM) has become a mainstream tool for the structural determination of biological macromolecular complexes. However, high-resolution cryo-EM reconstruction often requires hundreds of thousands of single-particle images. Particle extraction from experimental micrographs thus can be laborious and presents a major practical bottleneck in cryo-EM structural determination. Existing computational methods for particle picking often use low-resolution templates for particle matching, making them susceptible to reference-dependent bias. It is critical to develop a highly efficient template-free method for the automatic recognition of particle images from cryo-EM micrographs. We developed a deep learning-based algorithmic framework, DeepEM, for single-particle recognition from noisy cryo-EM micrographs, enabling automated particle picking, selection and verification in an integrated fashion. The kernel of DeepEM is built upon a convolutional neural network (CNN) composed of eight layers, which can be recursively trained to be highly "knowledgeable". Our approach exhibits an improved performance and accuracy when tested on the standard KLH dataset. Application of DeepEM to several challenging experimental cryo-EM datasets demonstrated its ability to avoid the selection of un-wanted particles and non-particles even when true particles contain fewer features. The DeepEM methodology, derived from a deep CNN, allows automated particle extraction from raw cryo-EM micrographs in the absence of a template. It demonstrates an improved performance, objectivity and accuracy. Application of this novel method is expected to free the labor involved in single-particle verification, significantly improving the efficiency of cryo-EM data processing.

Performance sustaining intracortical neural prostheses

NASA Astrophysics Data System (ADS)

Nuyujukian, Paul; Kao, Jonathan C.; Fan, Joline M.; Stavisky, Sergey D.; Ryu, Stephen I.; Shenoy, Krishna V.

2014-12-01

Objective. Neural prostheses, or brain-machine interfaces, aim to restore efficient communication and movement ability to those suffering from paralysis. A major challenge these systems face is robust performance, particularly with aging signal sources. The aim in this study was to develop a neural prosthesis that could sustain high performance in spite of signal instability while still minimizing retraining time. Approach. We trained two rhesus macaques implanted with intracortical microelectrode arrays 1-4 years prior to this study to acquire targets with a neurally-controlled cursor. We measured their performance via achieved bitrate (bits per second, bps). This task was repeated over contiguous days to evaluate the sustained performance across time. Main results. We found that in the monkey with a younger (i.e., two year old) implant and better signal quality, a fixed decoder could sustain performance for a month at a rate of 4 bps, the highest achieved communication rate reported to date. This fixed decoder was evaluated across 22 months and experienced a performance decline at a rate of 0.24 bps yr-1. In the monkey with the older (i.e., 3.5 year old) implant and poorer signal quality, a fixed decoder could not sustain performance for more than a few days. Nevertheless, performance in this monkey was maintained for two weeks without requiring additional online retraining time by utilizing prior days’ experimental data. Upon analysis of the changes in channel tuning, we found that this stability appeared partially attributable to the cancelling-out of neural tuning fluctuations when projected to two-dimensional cursor movements. Significance. The findings in this study (1) document the highest-performing communication neural prosthesis in monkeys, (2) confirm and extend prior reports of the stability of fixed decoders, and (3) demonstrate a protocol for system stability under conditions where fixed decoders would otherwise fail. These improvements to decoder stability are important for minimizing training time and should make neural prostheses more practical to use.

FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model.

PubMed

Yaghini Bonabi, Safa; Asgharian, Hassan; Safari, Saeed; Nili Ahmadabadi, Majid

2014-01-01

A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the network behavior is the subject of study. To solve the problem, we used computational techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and step-by-step integration in the implementation of arithmetic circuits. In addition, we employed different techniques such as sharing resources to preserve the details of model as well as increasing the network size in addition to keeping the network execution speed close to real time while having high precision. Implementation of a two mini-columns network with 120/30 excitatory/inhibitory neurons is provided to investigate the characteristic of our method in practice. The implementation techniques provide an opportunity to construct large FPGA-based network models to investigate the effect of different neurophysiological mechanisms, like voltage-gated channels and synaptic activities, on the behavior of a neural network in an appropriate execution time. Additional to inherent properties of FPGA, like parallelism and re-configurability, our approach makes the FPGA-based system a proper candidate for study on neural control of cognitive robots and systems as well.

Supervised artificial neural network-based method for conversion of solar radiation data (case study: Algeria)

NASA Astrophysics Data System (ADS)

Laidi, Maamar; Hanini, Salah; Rezrazi, Ahmed; Yaiche, Mohamed Redha; El Hadj, Abdallah Abdallah; Chellali, Farouk

2017-04-01

In this study, a backpropagation artificial neural network (BP-ANN) model is used as an alternative approach to predict solar radiation on tilted surfaces (SRT) using a number of variables involved in physical process. These variables are namely the latitude of the site, mean temperature and relative humidity, Linke turbidity factor and Angstrom coefficient, extraterrestrial solar radiation, solar radiation data measured on horizontal surfaces (SRH), and solar zenith angle. Experimental solar radiation data from 13 stations spread all over Algeria around the year (2004) were used for training/validation and testing the artificial neural networks (ANNs), and one station was used to make the interpolation of the designed ANN. The ANN model was trained, validated, and tested using 60, 20, and 20 % of all data, respectively. The configuration 8-35-1 (8 inputs, 35 hidden, and 1 output neurons) presented an excellent agreement between the prediction and the experimental data during the test stage with determination coefficient of 0.99 and root meat squared error of 5.75 Wh/m2, considering a three-layer feedforward backpropagation neural network with Levenberg-Marquardt training algorithm, a hyperbolic tangent sigmoid and linear transfer function at the hidden and the output layer, respectively. This novel model could be used by researchers or scientists to design high-efficiency solar devices that are usually tilted at an optimum angle to increase the solar incident on the surface.

A Comparison of Synoptic Classification Methods for Application to Wind Power Prediction

NASA Astrophysics Data System (ADS)

Fowler, P.; Basu, S.

2008-12-01

Wind energy is a highly variable resource. To make it competitive with other sources of energy for integration on the power grid, at the very least, a day-ahead forecast of power output must be available. In many grid operations worldwide, next-day power output is scheduled in 30 minute intervals and grid management routinely occurs at real time. Maintenance and repairs require costly time to complete and must be scheduled along with normal operations. Revenue is dependent on the reliability of the entire system. In other words, there is financial and managerial benefit to short-term prediction of wind power. One approach to short-term forecasting is to combine a data centric method such as an artificial neural network with a physically based approach like numerical weather prediction (NWP). The key is in associating high-dimensional NWP model output with the most appropriately trained neural network. Because neural networks perform the best in the situations they are designed for, one can hypothesize that if one can identify similar recurring states in historical weather data, this data can be used to train multiple custom designed neural networks to be used when called upon by numerical prediction. Identifying similar recurring states may offer insight to how a neural network forecast can be improved, but amassing the knowledge and utilizing it efficiently in the time required for power prediction would be difficult for a human to master, thus showing the advantage of classification. Classification methods are important tools for short-term forecasting because they can be unsupervised, objective, and computationally quick. They primarily involve categorizing data sets in to dominant weather classes, but there are numerous ways to define a class and a great variety in interpretation of the results. In the present study a collection of classification methods are used on a sampling of atmospheric variables from the North American Regional Reanalysis data set. The results will be discussed in relation to their use for short-term wind power forecasting by neural networks.

Neural network feedforward control of a closed-circuit wind tunnel

NASA Astrophysics Data System (ADS)

Sutcliffe, Peter

Accurate control of wind-tunnel test conditions can be dramatically enhanced using feedforward control architectures which allow operating conditions to be maintained at a desired setpoint through the use of mathematical models as the primary source of prediction. However, as the desired accuracy of the feedforward prediction increases, the model complexity also increases, so that an ever increasing computational load is incurred. This drawback can be avoided by employing a neural network that is trained offline using the output of a high fidelity wind-tunnel mathematical model, so that the neural network can rapidly reproduce the predictions of the model with a greatly reduced computational overhead. A novel neural network database generation method, developed through the use of fractional factorial arrays, was employed such that a neural network can accurately predict wind-tunnel parameters across a wide range of operating conditions whilst trained upon a highly efficient database. The subsequent network was incorporated into a Neural Network Model Predictive Control (NNMPC) framework to allow an optimised output schedule capable of providing accurate control of the wind-tunnel operating parameters. Facilitation of an optimised path through the solution space is achieved through the use of a chaos optimisation algorithm such that a more globally optimum solution is likely to be found with less computational expense than the gradient descent method. The parameters associated with the NNMPC such as the control horizon are determined through the use of a Taguchi methodology enabling the minimum number of experiments to be carried out to determine the optimal combination. The resultant NNMPC scheme was employed upon the Hessert Low Speed Wind Tunnel at the University of Notre Dame to control the test-section temperature such that it follows a pre-determined reference trajectory during changes in the test-section velocity. Experimental testing revealed that the derived NNMPC controller provided an excellent level of control over the test-section temperature in adherence to a reference trajectory even when faced with unforeseen disturbances such as rapid changes in the operating environment.

Conductive nanogel-interfaced neural microelectrode arrays with electrically controlled in-situ delivery of manganese ions enabling high-resolution MEMRI for synchronous neural tracing with deep brain stimulation.

PubMed

Huang, Wei-Chen; Lo, Yu-Chih; Chu, Chao-Yi; Lai, Hsin-Yi; Chen, You-Yin; Chen, San-Yuan

2017-04-01

Chronic brain stimulation has become a promising physical therapy with increased efficacy and efficiency in the treatment of neurodegenerative diseases. The application of deep brain electrical stimulation (DBS) combined with manganese-enhanced magnetic resonance imaging (MEMRI) provides an unbiased representation of the functional anatomy, which shows the communication between areas of the brain responding to the therapy. However, it is challenging for the current system to provide a real-time high-resolution image because the incorporated MnCl 2 solution through microinjection usually results in image blurring or toxicity due to the uncontrollable diffusion of Mn 2+ . In this study, we developed a new type of conductive nanogel-based neural interface composed of amphiphilic chitosan-modified poly(3,4 -ethylenedioxythiophene) (PMSDT) that can exhibit biomimic structural/mechanical properties and ionic/electrical conductivity comparable to that of Au. More importantly, the PMSDT enables metal-ligand bonding with Mn 2+ ions, so that the system can release Mn 2+ ions rather than MnCl 2 solution directly and precisely controlled by electrical stimulation (ES) to achieve real-time high-resolution MEMRI. With the integration of PMSDT nanogel-based coating in polyimide-based microelectrode arrays, the post-implantation DBS enables frequency-dependent MR imaging in vivo, as well as small focal imaging in response to channel site-specific stimulation on the implant. The MR imaging of the implanted brain treated with 5-min electrical stimulation showed a thalamocortical neuronal pathway after 36 h, confirming the effective activation of a downstream neuronal circuit following DBS. By eliminating the susceptibility to artifact and toxicity, this system, in combination with a MR-compatible implant and a bio-compliant neural interface, provides a harmless and synchronic functional anatomy for DBS. The study demonstrates a model of MEMRI-functionalized DBS based on functional neural interface engineering and controllable delivery technology, which can be utilized in more detailed exploration of the functional anatomy in the treatment of neurodegenerative diseases. Copyright © 2017 Elsevier Ltd. All rights reserved.

Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding

PubMed Central

Gardner, Brian; Grüning, André

2016-01-01

Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule’s error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism. PMID:27532262

Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding.

PubMed

Gardner, Brian; Grüning, André

2016-01-01

Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule's error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism.

Effects of Electrical and Optogenetic Deep Brain Stimulation on Synchronized Oscillatory Activity in Parkinsonian Basal Ganglia.

PubMed

Ratnadurai-Giridharan, Shivakeshavan; Cheung, Chung C; Rubchinsky, Leonid L

2017-11-01

Conventional deep brain stimulation of basal ganglia uses high-frequency regular electrical pulses to treat Parkinsonian motor symptoms but has a series of limitations. Relatively new and not yet clinically tested, optogenetic stimulation is an effective experimental stimulation technique to affect pathological network dynamics. We compared the effects of electrical and optogenetic stimulation of the basal gangliaon the pathologicalParkinsonian rhythmic neural activity. We studied the network response to electrical stimulation and excitatory and inhibitory optogenetic stimulations. Different stimulations exhibit different interactions with pathological activity in the network. We studied these interactions for different network and stimulation parameter values. Optogenetic stimulation was found to be more efficient than electrical stimulation in suppressing pathological rhythmicity. Our findings indicate that optogenetic control of neural synchrony may be more efficacious than electrical control because of the different ways of how stimulations interact with network dynamics.

Artificial metaplasticity neural network applied to credit scoring.

PubMed

Marcano-Cedeño, Alexis; Marin-de-la-Barcena, A; Jimenez-Trillo, J; Piñuela, J A; Andina, D

2011-08-01

The assessment of the risk of default on credit is important for financial institutions. Different Artificial Neural Networks (ANN) have been suggested to tackle the credit scoring problem, however, the obtained error rates are often high. In the search for the best ANN algorithm for credit scoring, this paper contributes with the application of an ANN Training Algorithm inspired by the neurons' biological property of metaplasticity. This algorithm is especially efficient when few patterns of a class are available, or when information inherent to low probability events is crucial for a successful application, as weight updating is overemphasized in the less frequent activations than in the more frequent ones. Two well-known and readily available such as: Australia and German data sets has been used to test the algorithm. The results obtained by AMMLP shown have been superior to state-of-the-art classification algorithms in credit scoring.

Behavior and neural basis of near-optimal visual search

PubMed Central

Ma, Wei Ji; Navalpakkam, Vidhya; Beck, Jeffrey M; van den Berg, Ronald; Pouget, Alexandre

2013-01-01

The ability to search efficiently for a target in a cluttered environment is one of the most remarkable functions of the nervous system. This task is difficult under natural circumstances, as the reliability of sensory information can vary greatly across space and time and is typically a priori unknown to the observer. In contrast, visual-search experiments commonly use stimuli of equal and known reliability. In a target detection task, we randomly assigned high or low reliability to each item on a trial-by-trial basis. An optimal observer would weight the observations by their trial-to-trial reliability and combine them using a specific nonlinear integration rule. We found that humans were near-optimal, regardless of whether distractors were homogeneous or heterogeneous and whether reliability was manipulated through contrast or shape. We present a neural-network implementation of near-optimal visual search based on probabilistic population coding. The network matched human performance. PMID:21552276

Hybrid neural network for density limit disruption prediction and avoidance on J-TEXT tokamak

NASA Astrophysics Data System (ADS)

Zheng, W.; Hu, F. R.; Zhang, M.; Chen, Z. Y.; Zhao, X. Q.; Wang, X. L.; Shi, P.; Zhang, X. L.; Zhang, X. Q.; Zhou, Y. N.; Wei, Y. N.; Pan, Y.; J-TEXT team

2018-05-01

Increasing the plasma density is one of the key methods in achieving an efficient fusion reaction. High-density operation is one of the hot topics in tokamak plasmas. Density limit disruptions remain an important issue for safe operation. An effective density limit disruption prediction and avoidance system is the key to avoid density limit disruptions for long pulse steady state operations. An artificial neural network has been developed for the prediction of density limit disruptions on the J-TEXT tokamak. The neural network has been improved from a simple multi-layer design to a hybrid two-stage structure. The first stage is a custom network which uses time series diagnostics as inputs to predict plasma density, and the second stage is a three-layer feedforward neural network to predict the probability of density limit disruptions. It is found that hybrid neural network structure, combined with radiation profile information as an input can significantly improve the prediction performance, especially the average warning time ({{T}warn} ). In particular, the {{T}warn} is eight times better than that in previous work (Wang et al 2016 Plasma Phys. Control. Fusion 58 055014) (from 5 ms to 40 ms). The success rate for density limit disruptive shots is above 90%, while, the false alarm rate for other shots is below 10%. Based on the density limit disruption prediction system and the real-time density feedback control system, the on-line density limit disruption avoidance system has been implemented on the J-TEXT tokamak.

Neural mechanism of optimal limb coordination in crustacean swimming

PubMed Central

Zhang, Calvin; Guy, Robert D.; Mulloney, Brian; Zhang, Qinghai; Lewis, Timothy J.

2014-01-01

A fundamental challenge in neuroscience is to understand how biologically salient motor behaviors emerge from properties of the underlying neural circuits. Crayfish, krill, prawns, lobsters, and other long-tailed crustaceans swim by rhythmically moving limbs called swimmerets. Over the entire biological range of animal size and paddling frequency, movements of adjacent swimmerets maintain an approximate quarter-period phase difference with the more posterior limbs leading the cycle. We use a computational fluid dynamics model to show that this frequency-invariant stroke pattern is the most effective and mechanically efficient paddling rhythm across the full range of biologically relevant Reynolds numbers in crustacean swimming. We then show that the organization of the neural circuit underlying swimmeret coordination provides a robust mechanism for generating this stroke pattern. Specifically, the wave-like limb coordination emerges robustly from a combination of the half-center structure of the local central pattern generating circuits (CPGs) that drive the movements of each limb, the asymmetric network topology of the connections between local CPGs, and the phase response properties of the local CPGs, which we measure experimentally. Thus, the crustacean swimmeret system serves as a concrete example in which the architecture of a neural circuit leads to optimal behavior in a robust manner. Furthermore, we consider all possible connection topologies between local CPGs and show that the natural connectivity pattern generates the biomechanically optimal stroke pattern most robustly. Given the high metabolic cost of crustacean swimming, our results suggest that natural selection has pushed the swimmeret neural circuit toward a connection topology that produces optimal behavior. PMID:25201976

Signature neural networks: definition and application to multidimensional sorting problems.

PubMed

Latorre, Roberto; de Borja Rodriguez, Francisco; Varona, Pablo

2011-01-01

In this paper we present a self-organizing neural network paradigm that is able to discriminate information locally using a strategy for information coding and processing inspired in recent findings in living neural systems. The proposed neural network uses: 1) neural signatures to identify each unit in the network; 2) local discrimination of input information during the processing; and 3) a multicoding mechanism for information propagation regarding the who and the what of the information. The local discrimination implies a distinct processing as a function of the neural signature recognition and a local transient memory. In the context of artificial neural networks none of these mechanisms has been analyzed in detail, and our goal is to demonstrate that they can be used to efficiently solve some specific problems. To illustrate the proposed paradigm, we apply it to the problem of multidimensional sorting, which can take advantage of the local information discrimination. In particular, we compare the results of this new approach with traditional methods to solve jigsaw puzzles and we analyze the situations where the new paradigm improves the performance.

Top-Down Inhibition of BMP Signaling Enables Robust Induction of hPSCs Into Neural Crest in Fully Defined, Xeno-free Conditions.

PubMed

Hackland, James O S; Frith, Tom J R; Thompson, Oliver; Marin Navarro, Ana; Garcia-Castro, Martin I; Unger, Christian; Andrews, Peter W

2017-10-10

Defects in neural crest development have been implicated in many human disorders, but information about human neural crest formation mostly depends on extrapolation from model organisms. Human pluripotent stem cells (hPSCs) can be differentiated into in vitro counterparts of the neural crest, and some of the signals known to induce neural crest formation in vivo are required during this process. However, the protocols in current use tend to produce variable results, and there is no consensus as to the precise signals required for optimal neural crest differentiation. Using a fully defined culture system, we have now found that the efficient differentiation of hPSCs to neural crest depends on precise levels of BMP signaling, which are vulnerable to fluctuations in endogenous BMP production. We present a method that controls for this phenomenon and could be applied to other systems where endogenous signaling can also affect the outcome of differentiation protocols. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Materials for Neural Differentiation, Trans-Differentiation, and Modeling of Neurological Disease.

PubMed

Gong, Lulu; Cao, Lining; Shen, Zhenmin; Shao, Li; Gao, Shaorong; Zhang, Chao; Lu, Jianfeng; Li, Weida

2018-04-01

Neuron regeneration from pluripotent stem cells (PSCs) differentiation or somatic cells trans-differentiation is a promising approach for cell replacement in neurodegenerative diseases and provides a powerful tool for investigating neural development, modeling neurological diseases, and uncovering the mechanisms that underlie diseases. Advancing the materials that are applied in neural differentiation and trans-differentiation promotes the safety, efficiency, and efficacy of neuron regeneration. In the neural differentiation process, matrix materials, either natural or synthetic, not only provide a structural and biochemical support for the monolayer or three-dimensional (3D) cultured cells but also assist in cell adhesion and cell-to-cell communication. They play important roles in directing the differentiation of PSCs into neural cells and modeling neurological diseases. For the trans-differentiation of neural cells, several materials have been used to make the conversion feasible for future therapy. Here, the most current applications of materials for neural differentiation for PSCs, neuronal trans-differentiation, and neurological disease modeling is summarized and discussed. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A neural net based architecture for the segmentation of mixed gray-level and binary pictures

NASA Technical Reports Server (NTRS)

Tabatabai, Ali; Troudet, Terry P.

1991-01-01

A neural-net-based architecture is proposed to perform segmentation in real time for mixed gray-level and binary pictures. In this approach, the composite picture is divided into 16 x 16 pixel blocks, which are identified as character blocks or image blocks on the basis of a dichotomy measure computed by an adaptive 16 x 16 neural net. For compression purposes, each image block is further divided into 4 x 4 subblocks; a one-bit nonparametric quantizer is used to encode 16 x 16 character and 4 x 4 image blocks; and the binary map and quantizer levels are obtained through a neural net segmentor over each block. The efficiency of the neural segmentation in terms of computational speed, data compression, and quality of the compressed picture is demonstrated. The effect of weight quantization is also discussed. VLSI implementations of such adaptive neural nets in CMOS technology are described and simulated in real time for a maximum block size of 256 pixels.

Using Neural Networks to Describe Tracer Correlations

NASA Technical Reports Server (NTRS)

Lary, D. J.; Mueller, M. D.; Mussa, H. Y.

2003-01-01

Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). In this study a neural network using Quickprop learning and one hidden layer with eight nodes was able to reproduce the CH4-N2O correlation with a correlation co- efficient of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. Such as the dataset from the Halogen Occultation Experiment (HALOE) which has continuously observed CH4, (but not N2O) from 1991 till the present. The neural network Fortran code used is available for download.

Nonlinear recurrent neural networks for finite-time solution of general time-varying linear matrix equations.

PubMed

Xiao, Lin; Liao, Bolin; Li, Shuai; Chen, Ke

2018-02-01

In order to solve general time-varying linear matrix equations (LMEs) more efficiently, this paper proposes two nonlinear recurrent neural networks based on two nonlinear activation functions. According to Lyapunov theory, such two nonlinear recurrent neural networks are proved to be convergent within finite-time. Besides, by solving differential equation, the upper bounds of the finite convergence time are determined analytically. Compared with existing recurrent neural networks, the proposed two nonlinear recurrent neural networks have a better convergence property (i.e., the upper bound is lower), and thus the accurate solutions of general time-varying LMEs can be obtained with less time. At last, various different situations have been considered by setting different coefficient matrices of general time-varying LMEs and a great variety of computer simulations (including the application to robot manipulators) have been conducted to validate the better finite-time convergence of the proposed two nonlinear recurrent neural networks. Copyright © 2017 Elsevier Ltd. All rights reserved.

Exploring the neural bases of goal-directed motor behavior using fully resolved simulations

NASA Astrophysics Data System (ADS)

Patel, Namu; Patankar, Neelesh A.

2016-11-01

Undulatory swimming is an ideal problem for understanding the neural architecture for motor control and movement; a vertebrate's robust morphology and adaptive locomotive gait allows the swimmer to navigate complex environments. Simple mathematical models for neurally activated muscle contractions have been incorporated into a swimmer immersed in fluid. Muscle contractions produce bending moments which determine the swimming kinematics. The neurobiology of goal-directed locomotion is explored using fast, efficient, and fully resolved constraint-based immersed boundary simulations. Hierarchical control systems tune the strength, frequency, and duty cycle for neural activation waves to produce multifarious swimming gaits or synergies. Simulation results are used to investigate why the basal ganglia and other control systems may command a particular neural pattern to accomplish a task. Using simple neural models, the effect of proprioceptive feedback on refining the body motion is demonstrated. Lastly, the ability for a learned swimmer to successfully navigate a complex environment is tested. This work is supported by NSF CBET 1066575 and NSF CMMI 0941674.

A neural-network-based model for the dynamic simulation of the tire/suspension system while traversing road irregularities.

PubMed

Guarneri, Paolo; Rocca, Gianpiero; Gobbi, Massimiliano

2008-09-01

This paper deals with the simulation of the tire/suspension dynamics by using recurrent neural networks (RNNs). RNNs are derived from the multilayer feedforward neural networks, by adding feedback connections between output and input layers. The optimal network architecture derives from a parametric analysis based on the optimal tradeoff between network accuracy and size. The neural network can be trained with experimental data obtained in the laboratory from simulated road profiles (cleats). The results obtained from the neural network demonstrate good agreement with the experimental results over a wide range of operation conditions. The NN model can be effectively applied as a part of vehicle system model to accurately predict elastic bushings and tire dynamics behavior. Although the neural network model, as a black-box model, does not provide a good insight of the physical behavior of the tire/suspension system, it is a useful tool for assessing vehicle ride and noise, vibration, harshness (NVH) performance due to its good computational efficiency and accuracy.

Are artificial neural networks black boxes?

PubMed

Benitez, J M; Castro, J L; Requena, I

1997-01-01

Artificial neural networks are efficient computing models which have shown their strengths in solving hard problems in artificial intelligence. They have also been shown to be universal approximators. Notwithstanding, one of the major criticisms is their being black boxes, since no satisfactory explanation of their behavior has been offered. In this paper, we provide such an interpretation of neural networks so that they will no longer be seen as black boxes. This is stated after establishing the equality between a certain class of neural nets and fuzzy rule-based systems. This interpretation is built with fuzzy rules using a new fuzzy logic operator which is defined after introducing the concept of f-duality. In addition, this interpretation offers an automated knowledge acquisition procedure.

A quantum-implementable neural network model

NASA Astrophysics Data System (ADS)

Chen, Jialin; Wang, Lingli; Charbon, Edoardo

2017-10-01

A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is proposed in this paper. QPNN can use quantum parallelism to trace all possible network states to improve the result. Due to its unique quantum nature, this model is robust to several quantum noises under certain conditions, which can be efficiently implemented by the qubus quantum computer. Another advantage is that QPNN can be used as memory to retrieve the most relevant data and even to generate new data. The MATLAB experimental results of Iris data classification and MNIST handwriting recognition show that much less neuron resources are required in QPNN to obtain a good result than the classical feedforward neural network. The proposed QPNN model indicates that quantum effects are useful for real-life classification tasks.

Warm Body Temperature Facilitates Energy Efficient Cortical Action Potentials

PubMed Central

Yu, Yuguo; Hill, Adam P.; McCormick, David A.

2012-01-01

The energy efficiency of neural signal transmission is important not only as a limiting factor in brain architecture, but it also influences the interpretation of functional brain imaging signals. Action potential generation in mammalian, versus invertebrate, axons is remarkably energy efficient. Here we demonstrate that this increase in energy efficiency is due largely to a warmer body temperature. Increases in temperature result in an exponential increase in energy efficiency for single action potentials by increasing the rate of Na+ channel inactivation, resulting in a marked reduction in overlap of the inward Na+, and outward K+, currents and a shortening of action potential duration. This increase in single spike efficiency is, however, counterbalanced by a temperature-dependent decrease in the amplitude and duration of the spike afterhyperpolarization, resulting in a nonlinear increase in the spike firing rate, particularly at temperatures above approximately 35°C. Interestingly, the total energy cost, as measured by the multiplication of total Na+ entry per spike and average firing rate in response to a constant input, reaches a global minimum between 37–42°C. Our results indicate that increases in temperature result in an unexpected increase in energy efficiency, especially near normal body temperature, thus allowing the brain to utilize an energy efficient neural code. PMID:22511855

Three-Dimensional Stereoscopic Tracking Velocimetry and Experimental/Numerical Comparison of Directional Solidification

NASA Technical Reports Server (NTRS)

Lee, David; Ge, Yi; Cha, Soyoung Stephen; Ramachandran, Narayanan; Rose, M. Franklin (Technical Monitor)

2001-01-01

Measurement of three-dimensional (3-D) three-component velocity fields is of great importance in both ground and space experiments for understanding materials processing and fluid physics. The experiments in these fields most likely inhibit the application of conventional planar probes for observing 3-D phenomena. Here, we present the investigation results of stereoscopic tracking velocimetry (STV) for measuring 3-D velocity fields, which include diagnostic technology development, experimental velocity measurement, and comparison with analytical and numerical computation. STV is advantageous in system simplicity for building compact hardware and in software efficiency for continual near-real-time monitoring. It has great freedom in illuminating and observing volumetric fields from arbitrary directions. STV is based on stereoscopic observation of particles-Seeded in a flow by CCD sensors. In the approach, part of the individual particle images that provide data points is likely to be lost or cause errors when their images overlap and crisscross each other especially under a high particle density. In order to maximize the valid recovery of data points, neural networks are implemented for these two important processes. For the step of particle overlap decomposition, the back propagation neural network is utilized because of its ability in pattern recognition with pertinent particle image feature parameters. For the step of particle tracking, the Hopfield neural network is employed to find appropriate particle tracks based on global optimization. Our investigation indicates that the neural networks are very efficient and useful for stereoscopically tracking particles. As an initial assessment of the diagnostic technology performance, laminar water jets with and without pulsation are measured. The jet tip velocity profiles are in good agreement with analytical predictions. Finally, for testing in material processing applications, a simple directional solidification apparatus is built for experimenting with a metal analog of succinonitrile. Its 3-D velocity field at the liquid phase is then measured to be compared with those from numerical computation. Our theoretical, numerical, and experimental investigations have proven STV to be a viable candidate for reliably measuring 3-D flow velocities. With current activities are focused on further improving the processing efficiency, overall accuracy, and automation, the eventual efforts of broad experimental applications and concurrent numerical modeling validation will be vital to many areas in fluid flow and materials processing.

Structural reliability calculation method based on the dual neural network and direct integration method.

PubMed

Li, Haibin; He, Yun; Nie, Xiaobo

2018-01-01

Structural reliability analysis under uncertainty is paid wide attention by engineers and scholars due to reflecting the structural characteristics and the bearing actual situation. The direct integration method, started from the definition of reliability theory, is easy to be understood, but there are still mathematics difficulties in the calculation of multiple integrals. Therefore, a dual neural network method is proposed for calculating multiple integrals in this paper. Dual neural network consists of two neural networks. The neural network A is used to learn the integrand function, and the neural network B is used to simulate the original function. According to the derivative relationships between the network output and the network input, the neural network B is derived from the neural network A. On this basis, the performance function of normalization is employed in the proposed method to overcome the difficulty of multiple integrations and to improve the accuracy for reliability calculations. The comparisons between the proposed method and Monte Carlo simulation method, Hasofer-Lind method, the mean value first-order second moment method have demonstrated that the proposed method is an efficient and accurate reliability method for structural reliability problems.

Combination of counterpropagation artificial neural networks and antioxidant activities for comprehensive evaluation of associated-extraction efficiency of various cyclodextrins in the traditional Chinese formula Xue-Zhi-Ning.

PubMed

Sun, Lili; Yang, Jianwen; Wang, Meng; Zhang, Huijie; Liu, Yanan; Ren, Xiaoliang; Qi, Aidi

2015-11-10

Xue-Zhi-Ning (XZN) is a widely used traditional Chinese medicine formula to treat hyperlipidemia. Recently, cyclodextrins (CDs) have been extensively used to minimize problems relative to medicine bioavailability, such as low solubility and poor stability. The objective of this study was to determine the associated-extraction efficiency of various CDs in XZN. Three various type CDs were evaluated, including native CDs (α-CD, β-CD), hydrophilic CD derivatives (HP-β-CD and Me-β-CD), and ionic CD derivatives (SBE-β-CD and CM-β-CD). An ultra high-performance liquid chromatography (UHPLC) fingerprint was applied to determine the components in CD extracts and original aqueous extract (OAE). A counterpropagation artificial neural network (CP-ANN) was used to analyze the components in different extracts and compare the selective extraction of various CDs. Extraction efficiencies of the various CDs in terms of extracted components follow the ranking, ionic CD derivatives>hydrophilic CD derivatives>native CDs>OAE. Besides, different types of CDs have their own selective extraction and ionic CD derivatives present the strongest associated-extraction efficiency. Antioxidant potentials of various extracts were evaluated by determining the inhibition of spontaneous, H2O2-induced, CCl4-induced and Fe(2+)/ascorbic acid-induced lipid peroxidation (LPO) and analyzing the scavenging capacity for DPPH and hydroxyl radicals. The order of extraction efficiencies of the various CDs relative to antioxidant activities is as follows: SBE-β-CD>CM-β-CD>HP-β-CD>Me-β-CD>β-CD>α-CD. It can be demonstrated that all of the CDs studied increase the extraction efficiency and that ionic CD derivatives (SBE-β-CD and CM-β-CD) present the highest extraction capability in terms of amount extracted and antioxidant activities of extracts. Copyright © 2015 Elsevier B.V. All rights reserved.

Direct and efficient transfection of mouse neural stem cells and mature neurons by in vivo mRNA electroporation.

PubMed

Bugeon, Stéphane; de Chevigny, Antoine; Boutin, Camille; Coré, Nathalie; Wild, Stefan; Bosio, Andreas; Cremer, Harold; Beclin, Christophe

2017-11-01

In vivo brain electroporation of DNA expression vectors is a widely used method for lineage and gene function studies in the developing and postnatal brain. However, transfection efficiency of DNA is limited and adult brain tissue is refractory to electroporation. Here, we present a systematic study of mRNA as a vector for acute genetic manipulation in the developing and adult brain. We demonstrate that mRNA electroporation is far more efficient than DNA electroporation, and leads to faster and more homogeneous protein expression in vivo Importantly, mRNA electroporation allows the manipulation of neural stem cells and postmitotic neurons in the adult brain using minimally invasive procedures. Finally, we show that this approach can be efficiently used for functional studies, as exemplified by transient overexpression of the neurogenic factor Myt1l and by stably inactivating Dicer nuclease in vivo in adult born olfactory bulb interneurons and in fully integrated cortical projection neurons. © 2017. Published by The Company of Biologists Ltd.

Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches.

PubMed

Singh, Kunwar P; Gupta, Shikha; Rai, Premanjali

2013-09-01

The research aims to develop global modeling tools capable of categorizing structurally diverse chemicals in various toxicity classes according to the EEC and European Community directives, and to predict their acute toxicity in fathead minnow using set of selected molecular descriptors. Accordingly, artificial intelligence approach based classification and regression models, such as probabilistic neural networks (PNN), generalized regression neural networks (GRNN), multilayer perceptron neural network (MLPN), radial basis function neural network (RBFN), support vector machines (SVM), gene expression programming (GEP), and decision tree (DT) were constructed using the experimental toxicity data. Diversity and non-linearity in the chemicals' data were tested using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Predictive and generalization abilities of various models constructed here were compared using several statistical parameters. PNN and GRNN models performed relatively better than MLPN, RBFN, SVM, GEP, and DT. Both in two and four category classifications, PNN yielded a considerably high accuracy of classification in training (95.85 percent and 90.07 percent) and validation data (91.30 percent and 86.96 percent), respectively. GRNN rendered a high correlation between the measured and model predicted -log LC50 values both for the training (0.929) and validation (0.910) data and low prediction errors (RMSE) of 0.52 and 0.49 for two sets. Efficiency of the selected PNN and GRNN models in predicting acute toxicity of new chemicals was adequately validated using external datasets of different fish species (fathead minnow, bluegill, trout, and guppy). The PNN and GRNN models showed good predictive and generalization abilities and can be used as tools for predicting toxicities of structurally diverse chemical compounds. Copyright © 2013 Elsevier Inc. All rights reserved.

High-Density Stretchable Electrode Grids for Chronic Neural Recording

PubMed Central

Tybrandt, Klas; Khodagholy, Dion; Dielacher, Bernd; Stauffer, Flurin; Renz, Aline F.; Buzsáki, György; Vörös, János

2018-01-01

Electrical interfacing with neural tissue is key to advancing diagnosis and therapies for neurological disorders, as well as providing detailed information about neural signals. A challenge for creating long-term stable interfaces between electronics and neural tissue is the huge mechanical mismatch between the systems. So far, materials and fabrication processes have restricted the development of soft electrode grids able to combine high performance, long-term stability, and high electrode density, aspects all essential for neural interfacing. Here, this challenge is addressed by developing a soft, high-density, stretchable electrode grid based on an inert, high-performance composite material comprising gold-coated titanium dioxide nanowires embedded in a silicone matrix. The developed grid can resolve high spatiotemporal neural signals from the surface of the cortex in freely moving rats with stable neural recording quality and preserved electrode signal coherence during 3 months of implantation. Due to its flexible and stretchable nature, it is possible to minimize the size of the craniotomy required for placement, further reducing the level of invasiveness. The material and device technology presented herein have potential for a wide range of emerging biomedical applications. PMID:29488263

Image Tracking for the High Similarity Drug Tablets Based on Light Intensity Reflective Energy and Artificial Neural Network

PubMed Central

Liang, Zhongwei; Zhou, Liang; Liu, Xiaochu; Wang, Xiaogang

2014-01-01

It is obvious that tablet image tracking exerts a notable influence on the efficiency and reliability of high-speed drug mass production, and, simultaneously, it also emerges as a big difficult problem and targeted focus during production monitoring in recent years, due to the high similarity shape and random position distribution of those objectives to be searched for. For the purpose of tracking tablets accurately in random distribution, through using surface fitting approach and transitional vector determination, the calibrated surface of light intensity reflective energy can be established, describing the shape topology and topography details of objective tablet. On this basis, the mathematical properties of these established surfaces have been proposed, and thereafter artificial neural network (ANN) has been employed for classifying those moving targeted tablets by recognizing their different surface properties; therefore, the instantaneous coordinate positions of those drug tablets on one image frame can then be determined. By repeating identical pattern recognition on the next image frame, the real-time movements of objective tablet templates were successfully tracked in sequence. This paper provides reliable references and new research ideas for the real-time objective tracking in the case of drug production practices. PMID:25143781

Neural Information Processing in Cognition: We Start to Understand the Orchestra, but Where is the Conductor?

PubMed Central

Palm, Günther

2016-01-01

Research in neural information processing has been successful in the past, providing useful approaches both to practical problems in computer science and to computational models in neuroscience. Recent developments in the area of cognitive neuroscience present new challenges for a computational or theoretical understanding asking for neural information processing models that fulfill criteria or constraints from cognitive psychology, neuroscience and computational efficiency. The most important of these criteria for the evaluation of present and future contributions to this new emerging field are listed at the end of this article. PMID:26858632

Computational neural learning formalisms for manipulator inverse kinematics

NASA Technical Reports Server (NTRS)

Gulati, Sandeep; Barhen, Jacob; Iyengar, S. Sitharama

1989-01-01

An efficient, adaptive neural learning paradigm for addressing the inverse kinematics of redundant manipulators is presented. The proposed methodology exploits the infinite local stability of terminal attractors - a new class of mathematical constructs which provide unique information processing capabilities to artificial neural systems. For robotic applications, synaptic elements of such networks can rapidly acquire the kinematic invariances embedded within the presented samples. Subsequently, joint-space configurations, required to follow arbitrary end-effector trajectories, can readily be computed. In a significant departure from prior neuromorphic learning algorithms, this methodology provides mechanisms for incorporating an in-training skew to handle kinematics and environmental constraints.

Evolvable synthetic neural system

NASA Technical Reports Server (NTRS)

Curtis, Steven A. (Inventor)

2009-01-01

An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

New Passivity Criteria for Fuzzy Bam Neural Networks with Markovian Jumping Parameters and Time-Varying Delays

NASA Astrophysics Data System (ADS)

Vadivel, P.; Sakthivel, R.; Mathiyalagan, K.; Thangaraj, P.

2013-02-01

This paper addresses the problem of passivity analysis issue for a class of fuzzy bidirectional associative memory (BAM) neural networks with Markovian jumping parameters and time varying delays. A set of sufficient conditions for the passiveness of the considered fuzzy BAM neural network model is derived in terms of linear matrix inequalities by using the delay fractioning technique together with the Lyapunov function approach. In addition, the uncertainties are inevitable in neural networks because of the existence of modeling errors and external disturbance. Further, this result is extended to study the robust passivity criteria for uncertain fuzzy BAM neural networks with time varying delays and uncertainties. These criteria are expressed in the form of linear matrix inequalities (LMIs), which can be efficiently solved via standard numerical software. Two numerical examples are provided to demonstrate the effectiveness of the obtained results.

Learning in neural networks based on a generalized fluctuation theorem

NASA Astrophysics Data System (ADS)

Hayakawa, Takashi; Aoyagi, Toshio

2015-11-01

Information maximization has been investigated as a possible mechanism of learning governing the self-organization that occurs within the neural systems of animals. Within the general context of models of neural systems bidirectionally interacting with environments, however, the role of information maximization remains to be elucidated. For bidirectionally interacting physical systems, universal laws describing the fluctuation they exhibit and the information they possess have recently been discovered. These laws are termed fluctuation theorems. In the present study, we formulate a theory of learning in neural networks bidirectionally interacting with environments based on the principle of information maximization. Our formulation begins with the introduction of a generalized fluctuation theorem, employing an interpretation appropriate for the present application, which differs from the original thermodynamic interpretation. We analytically and numerically demonstrate that the learning mechanism presented in our theory allows neural networks to efficiently explore their environments and optimally encode information about them.

MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning

PubMed Central

Yang, Jie; Huang, Yuan; Xu, Lixiong; Li, Siguang; Qi, Man

2015-01-01

Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation. PMID:26681933

Neural basis of nonanalytical reasoning expertise during clinical evaluation.

PubMed

Durning, Steven J; Costanzo, Michelle E; Artino, Anthony R; Graner, John; van der Vleuten, Cees; Beckman, Thomas J; Wittich, Christopher M; Roy, Michael J; Holmboe, Eric S; Schuwirth, Lambert

2015-03-01

Understanding clinical reasoning is essential for patient care and medical education. Dual-processing theory suggests that nonanalytic reasoning is an essential aspect of expertise; however, assessing nonanalytic reasoning is challenging because it is believed to occur on the subconscious level. This assumption makes concurrent verbal protocols less reliable assessment tools. Functional magnetic resonance imaging was used to explore the neural basis of nonanalytic reasoning in internal medicine interns (novices) and board-certified staff internists (experts) while completing United States Medical Licensing Examination and American Board of Internal Medicine multiple-choice questions. The results demonstrated that novices and experts share a common neural network in addition to nonoverlapping neural resources. However, experts manifested greater neural processing efficiency in regions such as the prefrontal cortex during nonanalytical reasoning. These findings reveal a multinetwork system that supports the dual-process mode of expert clinical reasoning during medical evaluation.

QSAR modelling using combined simple competitive learning networks and RBF neural networks.

PubMed

Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E

2018-04-01

The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning.

PubMed

Liu, Yang; Yang, Jie; Huang, Yuan; Xu, Lixiong; Li, Siguang; Qi, Man

2015-01-01

Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

A comparison of neural tube defects identified by two independent routine recording systems for congenital malformations in Northern Ireland.

PubMed

Nevin, N C; McDonald, J R; Walby, A L

1978-12-01

The efficiency of two systems for recording congenital malformations has been compared; one system, the Registrar General's Congenital Malformation Notification, is based on registering all malformed infants, and the other, the Child Health System, records all births. In Northern Ireland for three years [1974--1976], using multiple sources of ascertainment, a total of 686 infants with neural tube defects was identified among 79 783 live and stillbirths. The incidence for all neural tube defects in 8 60 per 1 000 births. The Registrar General's Congenital Malformation Notification System identified 83.6% whereas the Child Health System identified only 63.3% of all neural tube defects. Both systems together identified 86.2% of all neural tube defects. The two systems are suitable for monitoring of malformations and the addition of information from the Genetic Counselling Clinics would enhance the data for epidemiological studies.

Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree.

PubMed

Özdemir, Merve Erkınay; Telatar, Ziya; Eroğul, Osman; Tunca, Yusuf

2018-05-01

Dysmorphic syndromes have different facial malformations. These malformations are significant to an early diagnosis of dysmorphic syndromes and contain distinctive information for face recognition. In this study we define the certain features of each syndrome by considering facial malformations and classify Fragile X, Hurler, Prader Willi, Down, Wolf Hirschhorn syndromes and healthy groups automatically. The reference points are marked on the face images and ratios between the points' distances are taken into consideration as features. We suggest a neural network based hierarchical decision tree structure in order to classify the syndrome types. We also implement k-nearest neighbor (k-NN) and artificial neural network (ANN) classifiers to compare classification accuracy with our hierarchical decision tree. The classification accuracy is 50, 73 and 86.7% with k-NN, ANN and hierarchical decision tree methods, respectively. Then, the same images are shown to a clinical expert who achieve a recognition rate of 46.7%. We develop an efficient system to recognize different syndrome types automatically in a simple, non-invasive imaging data, which is independent from the patient's age, sex and race at high accuracy. The promising results indicate that our method can be used for pre-diagnosis of the dysmorphic syndromes by clinical experts.

A DSP-based neural network non-uniformity correction algorithm for IRFPA

NASA Astrophysics Data System (ADS)

Liu, Chong-liang; Jin, Wei-qi; Cao, Yang; Liu, Xiu

2009-07-01

An effective neural network non-uniformity correction (NUC) algorithm based on DSP is proposed in this paper. The non-uniform response in infrared focal plane array (IRFPA) detectors produces corrupted images with a fixed-pattern noise(FPN).We introduced and analyzed the artificial neural network scene-based non-uniformity correction (SBNUC) algorithm. A design of DSP-based NUC development platform for IRFPA is described. The DSP hardware platform designed is of low power consumption, with 32-bit fixed point DSP TMS320DM643 as the kernel processor. The dependability and expansibility of the software have been improved by DSP/BIOS real-time operating system and Reference Framework 5. In order to realize real-time performance, the calibration parameters update is set at a lower task priority then video input and output in DSP/BIOS. In this way, calibration parameters updating will not affect video streams. The work flow of the system and the strategy of real-time realization are introduced. Experiments on real infrared imaging sequences demonstrate that this algorithm requires only a few frames to obtain high quality corrections. It is computationally efficient and suitable for all kinds of non-uniformity.

Implementation of an Adaptive Controller System from Concept to Flight Test

NASA Technical Reports Server (NTRS)

Larson, Richard R.; Burken, John J.; Butler, Bradley S.; Yokum, Steve

2009-01-01

The National Aeronautics and Space Administration Dryden Flight Research Center (Edwards, California) is conducting ongoing flight research using adaptive controller algorithms. A highly modified McDonnell-Douglas NF-15B airplane called the F-15 Intelligent Flight Control System (IFCS) is used to test and develop these algorithms. Modifications to this airplane include adding canards and changing the flight control systems to interface a single-string research controller processor for neural network algorithms. Research goals include demonstration of revolutionary control approaches that can efficiently optimize aircraft performance in both normal and failure conditions and advancement of neural-network-based flight control technology for new aerospace system designs. This report presents an overview of the processes utilized to develop adaptive controller algorithms during a flight-test program, including a description of initial adaptive controller concepts and a discussion of modeling formulation and performance testing. Design finalization led to integration with the system interfaces, verification of the software, validation of the hardware to the requirements, design of failure detection, development of safety limiters to minimize the effect of erroneous neural network commands, and creation of flight test control room displays to maximize human situational awareness; these are also discussed.

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.

PubMed

Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

2014-01-01

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting

PubMed Central

Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

2014-01-01

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds. PMID:27382627

Near-instant automatic access to visually presented words in the human neocortex: neuromagnetic evidence.

PubMed

Shtyrov, Yury; MacGregor, Lucy J

2016-05-24

Rapid and efficient processing of external information by the brain is vital to survival in a highly dynamic environment. The key channel humans use to exchange information is language, but the neural underpinnings of its processing are still not fully understood. We investigated the spatio-temporal dynamics of neural access to word representations in the brain by scrutinising the brain's activity elicited in response to psycholinguistically, visually and phonologically matched groups of familiar words and meaningless pseudowords. Stimuli were briefly presented on the visual-field periphery to experimental participants whose attention was occupied with a non-linguistic visual feature-detection task. The neural activation elicited by these unattended orthographic stimuli was recorded using multi-channel whole-head magnetoencephalography, and the timecourse of lexically-specific neuromagnetic responses was assessed in sensor space as well as at the level of cortical sources, estimated using individual MR-based distributed source reconstruction. Our results demonstrate a neocortical signature of automatic near-instant access to word representations in the brain: activity in the perisylvian language network characterised by specific activation enhancement for familiar words, starting as early as ~70 ms after the onset of unattended word stimuli and underpinned by temporal and inferior-frontal cortices.

Computational models of epileptiform activity.

PubMed

Wendling, Fabrice; Benquet, Pascal; Bartolomei, Fabrice; Jirsa, Viktor

2016-02-15

We reviewed computer models that have been developed to reproduce and explain epileptiform activity. Unlike other already-published reviews on computer models of epilepsy, the proposed overview starts from the various types of epileptiform activity encountered during both interictal and ictal periods. Computational models proposed so far in the context of partial and generalized epilepsies are classified according to the following taxonomy: neural mass, neural field, detailed network and formal mathematical models. Insights gained about interictal epileptic spikes and high-frequency oscillations, about fast oscillations at seizure onset, about seizure initiation and propagation, about spike-wave discharges and about status epilepticus are described. This review shows the richness and complementarity of the various modeling approaches as well as the fruitful contribution of the computational neuroscience community in the field of epilepsy research. It shows that models have progressively gained acceptance and are now considered as an efficient way of integrating structural, functional and pathophysiological data about neural systems into "coherent and interpretable views". The advantages, limitations and future of modeling approaches are discussed. Perspectives in epilepsy research and clinical epileptology indicate that very promising directions are foreseen, like model-guided experiments or model-guided therapeutic strategy, among others. Copyright © 2015 Elsevier B.V. All rights reserved.

Disrupted Topological Patterns of Large-Scale Network in Conduct Disorder

PubMed Central

Jiang, Yali; Liu, Weixiang; Ming, Qingsen; Gao, Yidian; Ma, Ren; Zhang, Xiaocui; Situ, Weijun; Wang, Xiang; Yao, Shuqiao; Huang, Bingsheng

2016-01-01

Regional abnormalities in brain structure and function, as well as disrupted connectivity, have been found repeatedly in adolescents with conduct disorder (CD). Yet, the large-scale brain topology associated with CD is not well characterized, and little is known about the systematic neural mechanisms of CD. We employed graphic theory to investigate systematically the structural connectivity derived from cortical thickness correlation in a group of patients with CD (N = 43) and healthy controls (HCs, N = 73). Nonparametric permutation tests were applied for between-group comparisons of graphical metrics. Compared with HCs, network measures including global/local efficiency and modularity all pointed to hypo-functioning in CD, despite of preserved small-world organization in both groups. The hubs distribution is only partially overlapped with each other. These results indicate that CD is accompanied by both impaired integration and segregation patterns of brain networks, and the distribution of highly connected neural network ‘hubs’ is also distinct between groups. Such misconfiguration extends our understanding regarding how structural neural network disruptions may underlie behavioral disturbances in adolescents with CD, and potentially, implicates an aberrant cytoarchitectonic profiles in the brain of CD patients. PMID:27841320

Neural network architectures to analyze OPAD data

NASA Technical Reports Server (NTRS)

Whitaker, Kevin W.

1992-01-01

A prototype Optical Plume Anomaly Detection (OPAD) system is now installed on the space shuttle main engine (SSME) Technology Test Bed (TTB) at MSFC. The OPAD system requirements dictate the need for fast, efficient data processing techniques. To address this need of the OPAD system, a study was conducted into how artificial neural networks could be used to assist in the analysis of plume spectral data.

Two-photon imaging in living brain slices.

PubMed

Mainen, Z F; Maletic-Savatic, M; Shi, S H; Hayashi, Y; Malinow, R; Svoboda, K

1999-06-01

Two-photon excitation laser scanning microscopy (TPLSM) has become the tool of choice for high-resolution fluorescence imaging in intact neural tissues. Compared with other optical techniques, TPLSM allows high-resolution imaging and efficient detection of fluorescence signal with minimal photobleaching and phototoxicity. The advantages of TPLSM are especially pronounced in highly scattering environments such as the brain slice. Here we describe our approaches to imaging various aspects of synaptic function in living brain slices. To combine several imaging modes together with patch-clamp electrophysiological recordings we found it advantageous to custom-build an upright microscope. Our design goals were primarily experimental convenience and efficient collection of fluorescence. We describe our TPLSM imaging system and its performance in detail. We present dynamic measurements of neuronal morphology of neurons expressing green fluorescent protein (GFP) and GFP fusion proteins as well as functional imaging of calcium dynamics in individual dendritic spines. Although our microscope is a custom instrument, its key advantages can be easily implemented as a modification of commercial laser scanning microscopes. Copyright 1999 Academic Press.

Hardware-Efficient On-line Learning through Pipelined Truncated-Error Backpropagation in Binary-State Networks

PubMed Central

Mostafa, Hesham; Pedroni, Bruno; Sheik, Sadique; Cauwenberghs, Gert

2017-01-01

Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the training phase, however, has attracted relatively little attention. In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation. Learning is performed in parallel with inference in the forward pass, removing the need for an explicit backward pass and requiring no extra weight lookup. By using binary state variables in the feedforward network and ternary errors in truncated-error backpropagation, the need for any multiplications in the forward and backward passes is removed, and memory requirements for the pipelining are drastically reduced. Further reduction in addition operations owing to the sparsity in the forward neural and backpropagating error signal paths contributes to highly efficient hardware implementation. For proof-of-concept validation, we demonstrate on-line learning of MNIST handwritten digit classification on a Spartan 6 FPGA interfacing with an external 1Gb DDR2 DRAM, that shows small degradation in test error performance compared to an equivalently sized binary ANN trained off-line using standard back-propagation and exact errors. Our results highlight an attractive synergy between pipelined backpropagation and binary-state networks in substantially reducing computation and memory requirements, making pipelined on-line learning practical in deep networks. PMID:28932180

Hardware-Efficient On-line Learning through Pipelined Truncated-Error Backpropagation in Binary-State Networks.

PubMed

Mostafa, Hesham; Pedroni, Bruno; Sheik, Sadique; Cauwenberghs, Gert

2017-01-01

Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the training phase, however, has attracted relatively little attention. In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation. Learning is performed in parallel with inference in the forward pass, removing the need for an explicit backward pass and requiring no extra weight lookup. By using binary state variables in the feedforward network and ternary errors in truncated-error backpropagation, the need for any multiplications in the forward and backward passes is removed, and memory requirements for the pipelining are drastically reduced. Further reduction in addition operations owing to the sparsity in the forward neural and backpropagating error signal paths contributes to highly efficient hardware implementation. For proof-of-concept validation, we demonstrate on-line learning of MNIST handwritten digit classification on a Spartan 6 FPGA interfacing with an external 1Gb DDR2 DRAM, that shows small degradation in test error performance compared to an equivalently sized binary ANN trained off-line using standard back-propagation and exact errors. Our results highlight an attractive synergy between pipelined backpropagation and binary-state networks in substantially reducing computation and memory requirements, making pipelined on-line learning practical in deep networks.

Exact event-driven implementation for recurrent networks of stochastic perfect integrate-and-fire neurons.

PubMed

Taillefumier, Thibaud; Touboul, Jonathan; Magnasco, Marcelo

2012-12-01

In vivo cortical recording reveals that indirectly driven neural assemblies can produce reliable and temporally precise spiking patterns in response to stereotyped stimulation. This suggests that despite being fundamentally noisy, the collective activity of neurons conveys information through temporal coding. Stochastic integrate-and-fire models delineate a natural theoretical framework to study the interplay of intrinsic neural noise and spike timing precision. However, there are inherent difficulties in simulating their networks' dynamics in silico with standard numerical discretization schemes. Indeed, the well-posedness of the evolution of such networks requires temporally ordering every neuronal interaction, whereas the order of interactions is highly sensitive to the random variability of spiking times. Here, we answer these issues for perfect stochastic integrate-and-fire neurons by designing an exact event-driven algorithm for the simulation of recurrent networks, with delayed Dirac-like interactions. In addition to being exact from the mathematical standpoint, our proposed method is highly efficient numerically. We envision that our algorithm is especially indicated for studying the emergence of polychronized motifs in networks evolving under spike-timing-dependent plasticity with intrinsic noise.

Village Building Identification Based on Ensemble Convolutional Neural Networks

PubMed Central

Guo, Zhiling; Chen, Qi; Xu, Yongwei; Shibasaki, Ryosuke; Shao, Xiaowei

2017-01-01

In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86. PMID:29084154

Neural correlates of math anxiety - an overview and implications.

PubMed

Artemenko, Christina; Daroczy, Gabriella; Nuerk, Hans-Christoph

2015-01-01

Math anxiety is a common phenomenon which can have a negative impact on numerical and arithmetic performance. However, so far little is known about the underlying neurocognitive mechanisms. This mini review provides an overview of studies investigating the neural correlates of math anxiety which provide several hints regarding its influence on math performance: while behavioral studies mostly observe an influence of math anxiety on difficult math tasks, neurophysiological studies show that processing efficiency is already affected in basic number processing. Overall, the neurocognitive literature suggests that (i) math anxiety elicits emotion- and pain-related activation during and before math activities, (ii) that the negative emotional response to math anxiety impairs processing efficiency, and (iii) that math deficits triggered by math anxiety may be compensated for by modulating the cognitive control or emotional regulation network. However, activation differs strongly between studies, depending on tasks, paradigms, and samples. We conclude that neural correlates can help to understand and explore the processes underlying math anxiety, but the data are not very consistent yet.

Neural correlates of math anxiety – an overview and implications

PubMed Central

Artemenko, Christina; Daroczy, Gabriella; Nuerk, Hans-Christoph

2015-01-01

Math anxiety is a common phenomenon which can have a negative impact on numerical and arithmetic performance. However, so far little is known about the underlying neurocognitive mechanisms. This mini review provides an overview of studies investigating the neural correlates of math anxiety which provide several hints regarding its influence on math performance: while behavioral studies mostly observe an influence of math anxiety on difficult math tasks, neurophysiological studies show that processing efficiency is already affected in basic number processing. Overall, the neurocognitive literature suggests that (i) math anxiety elicits emotion- and pain-related activation during and before math activities, (ii) that the negative emotional response to math anxiety impairs processing efficiency, and (iii) that math deficits triggered by math anxiety may be compensated for by modulating the cognitive control or emotional regulation network. However, activation differs strongly between studies, depending on tasks, paradigms, and samples. We conclude that neural correlates can help to understand and explore the processes underlying math anxiety, but the data are not very consistent yet. PMID:26388824

A nanostructure based on metasurfaces for optical interconnects

NASA Astrophysics Data System (ADS)

Lin, Shulang; Gu, Huarong

2017-08-01

Optical-electronic Integrated Neural Co-processor takes vital part in optical neural network, which is mainly realized by optical interconnects. Because of the accuracy requirement and long-term goal of integration, optical interconnects should be effective and pint-size. In traditional solutions of optical interconnects, holography built on crystalloid or law of Fresnel diffraction exploited on zone plate was used. However, holographic method cannot meet the efficiency requirement and zone plate is too bulk to make the optical neural unit miniaturization. Thus, this paper aims to find a way to replace holographic method or zone plate with enough diffraction efficiency and smaller size. Metasurfaces are composed of subwavelength-spaced phase shifters at an interface of medium. Metasurfaces allow for unprecedented control of light properties. They also have advanced optical technology of enabling versatile functionalities in a planar structure. In this paper, a nanostructure is presented for optical interconnects. The comparisons of light splitting ability and simulated crosstalk between nanostructure and zone plate are also made.

Fitting Neuron Models to Spike Trains

PubMed Central

Rossant, Cyrille; Goodman, Dan F. M.; Fontaine, Bertrand; Platkiewicz, Jonathan; Magnusson, Anna K.; Brette, Romain

2011-01-01

Computational modeling is increasingly used to understand the function of neural circuits in systems neuroscience. These studies require models of individual neurons with realistic input–output properties. Recently, it was found that spiking models can accurately predict the precisely timed spike trains produced by cortical neurons in response to somatically injected currents, if properly fitted. This requires fitting techniques that are efficient and flexible enough to easily test different candidate models. We present a generic solution, based on the Brian simulator (a neural network simulator in Python), which allows the user to define and fit arbitrary neuron models to electrophysiological recordings. It relies on vectorization and parallel computing techniques to achieve efficiency. We demonstrate its use on neural recordings in the barrel cortex and in the auditory brainstem, and confirm that simple adaptive spiking models can accurately predict the response of cortical neurons. Finally, we show how a complex multicompartmental model can be reduced to a simple effective spiking model. PMID:21415925

Improved Neural Processing Efficiency in a Chronic Aphasia Patient Following Melodic Intonation Therapy: A Neuropsychological and Functional MRI Study

PubMed Central

Tabei, Ken-ichi; Satoh, Masayuki; Nakano, Chizuru; Ito, Ai; Shimoji, Yasuo; Kida, Hirotaka; Sakuma, Hajime; Tomimoto, Hidekazu

2016-01-01

Melodic intonation therapy (MIT) is a treatment program for the rehabilitation of aphasic patients with speech production disorders. We report a case of severe chronic non-fluent aphasia unresponsive to several years of conventional therapy that showed a marked improvement following intensive 9-day training on the Japanese version of MIT (MIT-J). The purpose of this study was to verify the efficacy of MIT-J by functional assessment and examine associated changes in neural processing by functional magnetic resonance imaging. MIT improved language output and auditory comprehension, and decreased the response time for picture naming. Following MIT-J, an area of the right hemisphere was less activated on correct naming trials than compared with before training but similarly activated on incorrect trials. These results suggest that the aphasic symptoms of our patient were improved by increased neural processing efficiency and a concomitant decrease in cognitive load. PMID:27698650

Efficient Simulation of Wing Modal Response: Application of 2nd Order Shape Sensitivities and Neural Networks

NASA Technical Reports Server (NTRS)

Kapania, Rakesh K.; Liu, Youhua

2000-01-01

At the preliminary design stage of a wing structure, an efficient simulation, one needing little computation but yielding adequately accurate results for various response quantities, is essential in the search of optimal design in a vast design space. In the present paper, methods of using sensitivities up to 2nd order, and direct application of neural networks are explored. The example problem is how to decide the natural frequencies of a wing given the shape variables of the structure. It is shown that when sensitivities cannot be obtained analytically, the finite difference approach is usually more reliable than a semi-analytical approach provided an appropriate step size is used. The use of second order sensitivities is proved of being able to yield much better results than the case where only the first order sensitivities are used. When neural networks are trained to relate the wing natural frequencies to the shape variables, a negligible computation effort is needed to accurately determine the natural frequencies of a new design.

A fast new algorithm for a robot neurocontroller using inverse QR decomposition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morris, A.S.; Khemaissia, S.

2000-01-01

A new adaptive neural network controller for robots is presented. The controller is based on direct adaptive techniques. Unlike many neural network controllers in the literature, inverse dynamical model evaluation is not required. A numerically robust, computationally efficient processing scheme for neutral network weight estimation is described, namely, the inverse QR decomposition (INVQR). The inverse QR decomposition and a weighted recursive least-squares (WRLS) method for neural network weight estimation is derived using Cholesky factorization of the data matrix. The algorithm that performs the efficient INVQR of the underlying space-time data matrix may be implemented in parallel on a triangular array.more » Furthermore, its systolic architecture is well suited for VLSI implementation. Another important benefit is well suited for VLSI implementation. Another important benefit of the INVQR decomposition is that it solves directly for the time-recursive least-squares filter vector, while avoiding the sequential back-substitution step required by the QR decomposition approaches.« less

Artificial Neural Network Based Mission Planning Mechanism for Spacecraft

NASA Astrophysics Data System (ADS)

Li, Zhaoyu; Xu, Rui; Cui, Pingyuan; Zhu, Shengying

2018-04-01

The ability to plan and react fast in dynamic space environments is central to intelligent behavior of spacecraft. For space and robotic applications, many planners have been used. But it is difficult to encode the domain knowledge and directly use existing techniques such as heuristic to improve the performance of the application systems. Therefore, regarding planning as an advanced control problem, this paper first proposes an autonomous mission planning and action selection mechanism through a multiple layer perceptron neural network approach to select actions in planning process and improve efficiency. To prove the availability and effectiveness, we use autonomous mission planning problems of the spacecraft, which is a sophisticated system with complex subsystems and constraints as an example. Simulation results have shown that artificial neural networks (ANNs) are usable for planning problems. Compared with the existing planning method in EUROPA, the mechanism using ANNs is more efficient and can guarantee stable performance. Therefore, the mechanism proposed in this paper is more suitable for planning problems of spacecraft that require real time and stability.

Focal versus distributed temporal cortex activity for speech sound category assignment

PubMed Central

Bouton, Sophie; Chambon, Valérian; Tyrand, Rémi; Seeck, Margitta; Karkar, Sami; van de Ville, Dimitri; Giraud, Anne-Lise

2018-01-01

Percepts and words can be decoded from distributed neural activity measures. However, the existence of widespread representations might conflict with the more classical notions of hierarchical processing and efficient coding, which are especially relevant in speech processing. Using fMRI and magnetoencephalography during syllable identification, we show that sensory and decisional activity colocalize to a restricted part of the posterior superior temporal gyrus (pSTG). Next, using intracortical recordings, we demonstrate that early and focal neural activity in this region distinguishes correct from incorrect decisions and can be machine-decoded to classify syllables. Crucially, significant machine decoding was possible from neuronal activity sampled across different regions of the temporal and frontal lobes, despite weak or absent sensory or decision-related responses. These findings show that speech-sound categorization relies on an efficient readout of focal pSTG neural activity, while more distributed activity patterns, although classifiable by machine learning, instead reflect collateral processes of sensory perception and decision. PMID:29363598