MIMO nonlinear ultrasonic tomography by propagation and backpropagation method.

PubMed

Dong, Chengdong; Jin, Yuanwei

2013-03-01

This paper develops a fast ultrasonic tomographic imaging method in a multiple-input multiple-output (MIMO) configuration using the propagation and backpropagation (PBP) method. By this method, ultrasonic excitation signals from multiple sources are transmitted simultaneously to probe the objects immersed in the medium. The scattering signals are recorded by multiple receivers. Utilizing the nonlinear ultrasonic wave propagation equation and the received time domain scattered signals, the objects are to be reconstructed iteratively in three steps. First, the propagation step calculates the predicted acoustic potential data at the receivers using an initial guess. Second, the difference signal between the predicted value and the measured data is calculated. Third, the backpropagation step computes updated acoustical potential data by backpropagating the difference signal to the same medium computationally. Unlike the conventional PBP method for tomographic imaging where each source takes turns to excite the acoustical field until all the sources are used, the developed MIMO-PBP method achieves faster image reconstruction by utilizing multiple source simultaneous excitation. Furthermore, we develop an orthogonal waveform signaling method using a waveform delay scheme to reduce the impact of speckle patterns in the reconstructed images. By numerical experiments we demonstrate that the proposed MIMO-PBP tomographic imaging method results in faster convergence and achieves superior imaging quality.

Heteromeric Kv7.2/7.3 channels differentially regulate action potential initiation and conduction in neocortical myelinated axons.

PubMed

Battefeld, Arne; Tran, Baouyen T; Gavrilis, Jason; Cooper, Edward C; Kole, Maarten H P

2014-03-05

Rapid energy-efficient signaling along vertebrate axons is achieved through intricate subcellular arrangements of voltage-gated ion channels and myelination. One recently appreciated example is the tight colocalization of K(v)7 potassium channels and voltage-gated sodium (Na(v)) channels in the axonal initial segment and nodes of Ranvier. The local biophysical properties of these K(v)7 channels and the functional impact of colocalization with Na(v) channels remain poorly understood. Here, we quantitatively examined K(v)7 channels in myelinated axons of rat neocortical pyramidal neurons using high-resolution confocal imaging and patch-clamp recording. K(v)7.2 and 7.3 immunoreactivity steeply increased within the distal two-thirds of the axon initial segment and was mirrored by the conductance density estimates, which increased from ~12 (proximal) to 150 pS μm(-2) (distal). The axonal initial segment and nodal M-currents were similar in voltage dependence and kinetics, carried by K(v)7.2/7.3 heterotetramers, 4% activated at the resting membrane potential and rapidly activated with single-exponential time constants (~15 ms at 28 mV). Experiments and computational modeling showed that while somatodendritic K(v)7 channels are strongly activated by the backpropagating action potential to attenuate the afterdepolarization and repetitive firing, axonal K(v)7 channels are minimally recruited by the forward-propagating action potential. Instead, in nodal domains K(v)7.2/7.3 channels were found to increase Na(v) channel availability and action potential amplitude by stabilizing the resting membrane potential. Thus, K(v)7 clustering near axonal Na(v) channels serves specific and context-dependent roles, both restraining initiation and enhancing conduction of the action potential.

Heteromeric Kv7.2/7.3 Channels Differentially Regulate Action Potential Initiation and Conduction in Neocortical Myelinated Axons

PubMed Central

Battefeld, Arne; Tran, Baouyen T.; Gavrilis, Jason; Cooper, Edward C.

2014-01-01

Rapid energy-efficient signaling along vertebrate axons is achieved through intricate subcellular arrangements of voltage-gated ion channels and myelination. One recently appreciated example is the tight colocalization of Kv7 potassium channels and voltage-gated sodium (Nav) channels in the axonal initial segment and nodes of Ranvier. The local biophysical properties of these Kv7 channels and the functional impact of colocalization with Nav channels remain poorly understood. Here, we quantitatively examined Kv7 channels in myelinated axons of rat neocortical pyramidal neurons using high-resolution confocal imaging and patch-clamp recording. Kv7.2 and 7.3 immunoreactivity steeply increased within the distal two-thirds of the axon initial segment and was mirrored by the conductance density estimates, which increased from ∼12 (proximal) to 150 pS μm−2 (distal). The axonal initial segment and nodal M-currents were similar in voltage dependence and kinetics, carried by Kv7.2/7.3 heterotetramers, 4% activated at the resting membrane potential and rapidly activated with single-exponential time constants (∼15 ms at 28 mV). Experiments and computational modeling showed that while somatodendritic Kv7 channels are strongly activated by the backpropagating action potential to attenuate the afterdepolarization and repetitive firing, axonal Kv7 channels are minimally recruited by the forward-propagating action potential. Instead, in nodal domains Kv7.2/7.3 channels were found to increase Nav channel availability and action potential amplitude by stabilizing the resting membrane potential. Thus, Kv7 clustering near axonal Nav channels serves specific and context-dependent roles, both restraining initiation and enhancing conduction of the action potential. PMID:24599470

Analysis of Accuracy and Epoch on Back-propagation BFGS Quasi-Newton

NASA Astrophysics Data System (ADS)

Silaban, Herlan; Zarlis, Muhammad; Sawaluddin

2017-12-01

Back-propagation is one of the learning algorithms on artificial neural networks that have been widely used to solve various problems, such as pattern recognition, prediction and classification. The Back-propagation architecture will affect the outcome of learning processed. BFGS Quasi-Newton is one of the functions that can be used to change the weight of back-propagation. This research tested some back-propagation architectures using classical back-propagation and back-propagation with BFGS. There are 7 architectures that have been tested on glass dataset with various numbers of neurons, 6 architectures with 1 hidden layer and 1 architecture with 2 hidden layers. BP with BFGS improves the convergence of the learning process. The average improvement convergence is 98.34%. BP with BFGS is more optimal on architectures with smaller number of neurons with decreased epoch number is 94.37% with the increase of accuracy about 0.5%.

Influence of asymmetric attenuation of single and paired dendritic inputs on summation of synaptic potentials and initiation of action potentials.

PubMed

Fortier, Pierre A; Bray, Chelsea

2013-04-16

Previous studies revealed mechanisms of dendritic inputs leading to action potential initiation at the axon initial segment and backpropagation into the dendritic tree. This interest has recently expanded toward the communication between different parts of the dendritic tree which could preprocess information before reaching the soma. This study tested for effects of asymmetric voltage attenuation between different sites in the dendritic tree on summation of synaptic inputs and action potential initiation using the NEURON simulation environment. Passive responses due to the electrical equivalent circuit of the three-dimensional neuron architecture with leak channels were examined first, followed by the responses after adding voltage-gated channels and finally synaptic noise. Asymmetric attenuation of voltage, which is a function of asymmetric input resistance, was seen between all pairs of dendritic sites but the transfer voltages (voltage recorded at the opposite site from stimulation among a pair of dendritic sites) were equal and also summed linearly with local voltage responses during simultaneous stimulation of both sites. In neurons with voltage-gated channels, we reproduced the observations where a brief stimulus to the proximal ascending dendritic branch of a pyramidal cell triggers a local action potential but a long stimulus triggers a somal action potential. Combined stimulation of a pair of sites in this proximal dendrite did not alter this pattern. The attraction of the action potential onset toward the soma with a long stimulus in the absence of noise was due to the higher density of voltage-gated sodium channels at the axon initial segment. This attraction was, however, negligible at the most remote distal dendritic sites and was replaced by an effect due to high input resistance. Action potential onset occurred at the dendritic site of higher input resistance among a pair of remote dendritic sites, irrespective of which of these two sites received the synaptic input. Exploration of the parameter space showed how the gradient of voltage-gated channel densities and input resistances along a dendrite could draw the action potential onset away from the stimulation site. The attraction of action potential onset toward the higher density of voltage-gated channels in the soma during stimulation of the proximal dendrite was, however, reduced after the addition of synaptic noise. Copyright © 2012 IBRO. Published by Elsevier Ltd. All rights reserved.

Application of backpropagation artificial neural network prediction model for the PAH bioremediation of polluted soil.

PubMed

Olawoyin, Richard

2016-10-01

The backpropagation (BP) artificial neural network (ANN) is a renowned and extensively functional mathematical tool used for time-series predictions and approximations; which also define results for non-linear functions. ANNs are vital tools in the predictions of toxicant levels, such as polycyclic aromatic hydrocarbons (PAH) potentially derived from anthropogenic activities in the microenvironment. In the present work, BP ANN was used as a prediction tool to study the potential toxicity of PAH carcinogens (PAHcarc) in soils. Soil samples (16 × 4 = 64) were collected from locations in South-southern Nigeria. The concentration of PAHcarc in laboratory cultivated white melilot, Melilotus alba roots grown on treated soils was predicted using ANN model training. Results indicated the Levenberg-Marquardt back-propagation training algorithm converged in 2.5E+04 epochs at an average RMSE value of 1.06E-06. The averagedR(2) comparison between the measured and predicted outputs was 0.9994. It may be deduced from this study that, analytical processes involving environmental risk assessment as used in this study can successfully provide prompt prediction and source identification of major soil toxicants. Copyright © 2016 Elsevier Ltd. All rights reserved.

WS-BP: An efficient wolf search based back-propagation algorithm

NASA Astrophysics Data System (ADS)

Nawi, Nazri Mohd; Rehman, M. Z.; Khan, Abdullah

2015-05-01

Wolf Search (WS) is a heuristic based optimization algorithm. Inspired by the preying and survival capabilities of the wolves, this algorithm is highly capable to search large spaces in the candidate solutions. This paper investigates the use of WS algorithm in combination with back-propagation neural network (BPNN) algorithm to overcome the local minima problem and to improve convergence in gradient descent. The performance of the proposed Wolf Search based Back-Propagation (WS-BP) algorithm is compared with Artificial Bee Colony Back-Propagation (ABC-BP), Bat Based Back-Propagation (Bat-BP), and conventional BPNN algorithms. Specifically, OR and XOR datasets are used for training the network. The simulation results show that the WS-BP algorithm effectively avoids the local minima and converge to global minima.

Cortical Interneuron Subtypes Vary in Their Axonal Action Potential Properties

PubMed Central

Casale, Amanda E.; Foust, Amanda J.; Bal, Thierry

2015-01-01

The role of interneurons in cortical microcircuits is strongly influenced by their passive and active electrical properties. Although different types of interneurons exhibit unique electrophysiological properties recorded at the soma, it is not yet clear whether these differences are also manifested in other neuronal compartments. To address this question, we have used voltage-sensitive dye to image the propagation of action potentials into the fine collaterals of axons and dendrites in two of the largest cortical interneuron subtypes in the mouse: fast-spiking interneurons, which are typically basket or chandelier neurons; and somatostatin containing interneurons, which are typically regular spiking Martinotti cells. We found that fast-spiking and somatostatin-expressing interneurons differed in their electrophysiological characteristics along their entire dendrosomatoaxonal extent. The action potentials generated in the somata and axons, including axon collaterals, of somatostatin-expressing interneurons are significantly broader than those generated in the same compartments of fast-spiking inhibitory interneurons. In addition, action potentials back-propagated into the dendrites of somatostatin-expressing interneurons much more readily than fast-spiking interneurons. Pharmacological investigations suggested that axonal action potential repolarization in both cell types depends critically upon Kv1 channels, whereas the axonal and somatic action potentials of somatostatin-expressing interneurons also depend on BK Ca2+-activated K+ channels. These results indicate that the two broad classes of interneurons studied here have expressly different subcellular physiological properties, allowing them to perform unique computational roles in cortical circuit operations. SIGNIFICANCE STATEMENT Neurons in the cerebral cortex are of two major types: excitatory and inhibitory. The proper balance of excitation and inhibition in the brain is critical for its operation. Neurons contain three main compartments: dendritic, somatic, and axonal. How the neurons receive information, process it, and pass on new information depends upon how these three compartments operate. While it has long been assumed that axons are simply for conducting information from the cell body to the synapses, here we demonstrate that the axons of different types of interneurons, the inhibitory cells, possess differing electrophysiological properties. This result implies that differing types of interneurons perform different tasks in the cortex, not only through their anatomical connections, but also through how their axons operate. PMID:26609152

The role of extracellular conductivity profiles in compartmental models for neurons: particulars for layer 5 pyramidal cells.

PubMed

Wang, Kai; Riera, Jorge; Enjieu-Kadji, Herve; Kawashima, Ryuta

2013-07-01

With the rapid increase in the number of technologies aimed at observing electric activity inside the brain, scientists have felt the urge to create proper links between intracellular- and extracellular-based experimental approaches. Biophysical models at both physical scales have been formalized under assumptions that impede the creation of such links. In this work, we address this issue by proposing a multicompartment model that allows the introduction of complex extracellular and intracellular resistivity profiles. This model accounts for the geometrical and electrotonic properties of any type of neuron through the combination of four devices: the integrator, the propagator, the 3D connector, and the collector. In particular, we applied this framework to model the tufted pyramidal cells of layer 5 (PCL5) in the neocortex. Our model was able to reproduce the decay and delay curves of backpropagating action potentials (APs) in this type of cell with better agreement with experimental data. We used the voltage drops of the extracellular resistances at each compartment to approximate the local field potentials generated by a PCL5 located in close proximity to linear microelectrode arrays. Based on the voltage drops produced by backpropagating APs, we were able to estimate the current multipolar moments generated by a PCL5. By adding external current sources in parallel to the extracellular resistances, we were able to create a sensitivity profile of PCL5 to electric current injections from nearby microelectrodes. In our model for PCL5, the kinetics and spatial profile of each ionic current were determined based on a literature survey, and the geometrical properties of these cells were evaluated experimentally. We concluded that the inclusion of the extracellular space in the compartmental models of neurons as an extra electrotonic medium is crucial for the accurate simulation of both the propagation of the electric potentials along the neuronal dendrites and the neuronal reactivity to an electrical stimulation using external microelectrodes.

Investigating the performance of neural network backpropagation algorithms for TEC estimations using South African GPS data

NASA Astrophysics Data System (ADS)

Habarulema, J. B.; McKinnell, L.-A.

2012-05-01

In this work, results obtained by investigating the application of different neural network backpropagation training algorithms are presented. This was done to assess the performance accuracy of each training algorithm in total electron content (TEC) estimations using identical datasets in models development and verification processes. Investigated training algorithms are standard backpropagation (SBP), backpropagation with weight delay (BPWD), backpropagation with momentum (BPM) term, backpropagation with chunkwise weight update (BPC) and backpropagation for batch (BPB) training. These five algorithms are inbuilt functions within the Stuttgart Neural Network Simulator (SNNS) and the main objective was to find out the training algorithm that generates the minimum error between the TEC derived from Global Positioning System (GPS) observations and the modelled TEC data. Another investigated algorithm is the MatLab based Levenberg-Marquardt backpropagation (L-MBP), which achieves convergence after the least number of iterations during training. In this paper, neural network (NN) models were developed using hourly TEC data (for 8 years: 2000-2007) derived from GPS observations over a receiver station located at Sutherland (SUTH) (32.38° S, 20.81° E), South Africa. Verification of the NN models for all algorithms considered was performed on both "seen" and "unseen" data. Hourly TEC values over SUTH for 2003 formed the "seen" dataset. The "unseen" dataset consisted of hourly TEC data for 2002 and 2008 over Cape Town (CPTN) (33.95° S, 18.47° E) and SUTH, respectively. The models' verification showed that all algorithms investigated provide comparable results statistically, but differ significantly in terms of time required to achieve convergence during input-output data training/learning. This paper therefore provides a guide to neural network users for choosing appropriate algorithms based on the availability of computation capabilities used for research.

Proteomic analysis highlights the molecular complexities of native Kv4 channel macromolecular complexes.

PubMed

Marionneau, Céline; Townsend, R Reid; Nerbonne, Jeanne M

2011-04-01

Voltage-gated K(+) (Kv) channels are key determinants of membrane excitability in the nervous and cardiovascular systems, functioning to control resting membrane potentials, shape action potential waveforms and influence the responses to neurotransmitters and neurohormones. Consistent with this functional diversity, multiple types of Kv currents, with distinct biophysical properties and cellular/subcellular distributions, have been identified. Rapidly activating and inactivating Kv currents, typically referred to as I(A) (A-type) in neurons, for example, regulate repetitive firing rates, action potential back-propagation (into dendrites) and modulate synaptic responses. Currents with similar properties, referred to as I(to,f) (fast transient outward), expressed in cardiomyocytes, control the early phase of myocardial action potential repolarization. A number of studies have demonstrated critical roles for pore-forming (α) subunits of the Kv4 subfamily in the generation of native neuronal I(A) and cardiac I(to,f) channels. Studies in heterologous cells have also suggested important roles for a number of Kv channel accessory and regulatory proteins in the generation of functional I(A) and I(to,f) channels. Quantitative mass spectrometry-based proteomic analysis is increasingly recognized as a rapid and, importantly, unbiased, approach to identify the components of native macromolecular protein complexes. The recent application of proteomic approaches to identify the components of native neuronal (and cardiac) Kv4 channel complexes has revealed even greater complexity than anticipated. The continued emphasis on development of improved biochemical and analytical proteomic methods seems certain to accelerate progress and to provide important new insights into the molecular determinants of native ion channel protein complexes. Copyright © 2010 Elsevier Ltd. All rights reserved.

Analog hardware for delta-backpropagation neural networks

NASA Technical Reports Server (NTRS)

Eberhardt, Silvio P. (Inventor)

1992-01-01

This is a fully parallel analog backpropagation learning processor which comprises a plurality of programmable resistive memory elements serving as synapse connections whose values can be weighted during learning with buffer amplifiers, summing circuits, and sample-and-hold circuits arranged in a plurality of neuron layers in accordance with delta-backpropagation algorithms modified so as to control weight changes due to circuit drift.

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.

A genetically encoded fluorescent sensor of ERK activity.

PubMed

Harvey, Christopher D; Ehrhardt, Anka G; Cellurale, Cristina; Zhong, Haining; Yasuda, Ryohei; Davis, Roger J; Svoboda, Karel

2008-12-09

The activity of the ERK has complex spatial and temporal dynamics that are important for the specificity of downstream effects. However, current biochemical techniques do not allow for the measurement of ERK signaling with fine spatiotemporal resolution. We developed a genetically encoded, FRET-based sensor of ERK activity (the extracellular signal-regulated kinase activity reporter, EKAR), optimized for signal-to-noise ratio and fluorescence lifetime imaging. EKAR selectively and reversibly reported ERK activation in HEK293 cells after epidermal growth factor stimulation. EKAR signals were correlated with ERK phosphorylation, required ERK activity, and did not report the activities of JNK or p38. EKAR reported ERK activation in the dendrites and nucleus of hippocampal pyramidal neurons in brain slices after theta-burst stimuli or trains of back-propagating action potentials. EKAR therefore permits the measurement of spatiotemporal ERK signaling dynamics in living cells, including in neuronal compartments in intact tissues.

Trafficking Mechanisms Underlying Neuronal Voltage-gated Ion Channel Localization at the Axon Initial Segment

PubMed Central

Vacher, Helene; Trimmer, James S.

2012-01-01

Summary Voltage-gated ion channels are diverse and fundamental determinants of neuronal intrinsic excitability. Voltage-gated K+ (Kv) and Na+ (Nav) channels play complex yet fundamentally important roles in determining intrinsic excitability. The Kv and Nav channels located at the axon initial segment (AIS) play a unique and especially important role in generating neuronal output in the form of anterograde axonal and backpropagating action potentials, Aberrant intrinsic excitability in individual neurons within networks contributes to synchronous neuronal activity leading to seizures. Mutations in ion channel genes gives rise to a variety of seizure-related “Channelopathies”, and many of the ion channel subunits associated with epilepsy mutations are localized at the AIS, making this a hotspot for epileptogenesis. Here we review the cellular mechanisms that underlie the trafficking of Kv and Nav channels found at the AIS, and how Kv and Nav channel mutations associated with epilepsy can alter these processes. PMID:23216576

Multithreading with separate data to improve the performance of Backpropagation method

NASA Astrophysics Data System (ADS)

Dhamma, Mulia; Zarlis, Muhammad; Budhiarti Nababan, Erna

2017-12-01

Backpropagation is one method of artificial neural network that can make a prediction for a new data with learning by supervised of the past data. The learning process of backpropagation method will become slow if we give too much data for backpropagation method to learn the data. Multithreading with a separate data inside of each thread are being used in order to improve the performance of backpropagtion method . Base on the research for 39 data and also 5 times experiment with separate data into 2 thread, the result showed that the average epoch become 6490 when using 2 thread and 453049 epoch when using only 1 thread. The most lowest epoch for 2 thread is 1295 and 1 thread is 356116. The process of improvement is caused by the minimum error from 2 thread that has been compared to take the weight and bias value. This process will be repeat as long as the backpropagation do learning.

Cortical Interneuron Subtypes Vary in Their Axonal Action Potential Properties.

PubMed

Casale, Amanda E; Foust, Amanda J; Bal, Thierry; McCormick, David A

2015-11-25

The role of interneurons in cortical microcircuits is strongly influenced by their passive and active electrical properties. Although different types of interneurons exhibit unique electrophysiological properties recorded at the soma, it is not yet clear whether these differences are also manifested in other neuronal compartments. To address this question, we have used voltage-sensitive dye to image the propagation of action potentials into the fine collaterals of axons and dendrites in two of the largest cortical interneuron subtypes in the mouse: fast-spiking interneurons, which are typically basket or chandelier neurons; and somatostatin containing interneurons, which are typically regular spiking Martinotti cells. We found that fast-spiking and somatostatin-expressing interneurons differed in their electrophysiological characteristics along their entire dendrosomatoaxonal extent. The action potentials generated in the somata and axons, including axon collaterals, of somatostatin-expressing interneurons are significantly broader than those generated in the same compartments of fast-spiking inhibitory interneurons. In addition, action potentials back-propagated into the dendrites of somatostatin-expressing interneurons much more readily than fast-spiking interneurons. Pharmacological investigations suggested that axonal action potential repolarization in both cell types depends critically upon Kv1 channels, whereas the axonal and somatic action potentials of somatostatin-expressing interneurons also depend on BK Ca(2+)-activated K(+) channels. These results indicate that the two broad classes of interneurons studied here have expressly different subcellular physiological properties, allowing them to perform unique computational roles in cortical circuit operations. Neurons in the cerebral cortex are of two major types: excitatory and inhibitory. The proper balance of excitation and inhibition in the brain is critical for its operation. Neurons contain three main compartments: dendritic, somatic, and axonal. How the neurons receive information, process it, and pass on new information depends upon how these three compartments operate. While it has long been assumed that axons are simply for conducting information from the cell body to the synapses, here we demonstrate that the axons of different types of interneurons, the inhibitory cells, possess differing electrophysiological properties. This result implies that differing types of interneurons perform different tasks in the cortex, not only through their anatomical connections, but also through how their axons operate. Copyright © 2015 the authors 0270-6474/15/3515555-13$15.00/0.

Spine Calcium Transients Induced by Synaptically-Evoked Action Potentials Can Predict Synapse Location and Establish Synaptic Democracy

PubMed Central

Meredith, Rhiannon M.; van Ooyen, Arjen

2012-01-01

CA1 pyramidal neurons receive hundreds of synaptic inputs at different distances from the soma. Distance-dependent synaptic scaling enables distal and proximal synapses to influence the somatic membrane equally, a phenomenon called “synaptic democracy”. How this is established is unclear. The backpropagating action potential (BAP) is hypothesised to provide distance-dependent information to synapses, allowing synaptic strengths to scale accordingly. Experimental measurements show that a BAP evoked by current injection at the soma causes calcium currents in the apical shaft whose amplitudes decay with distance from the soma. However, in vivo action potentials are not induced by somatic current injection but by synaptic inputs along the dendrites, which creates a different excitable state of the dendrites. Due to technical limitations, it is not possible to study experimentally whether distance information can also be provided by synaptically-evoked BAPs. Therefore we adapted a realistic morphological and electrophysiological model to measure BAP-induced voltage and calcium signals in spines after Schaffer collateral synapse stimulation. We show that peak calcium concentration is highly correlated with soma-synapse distance under a number of physiologically-realistic suprathreshold stimulation regimes and for a range of dendritic morphologies. Peak calcium levels also predicted the attenuation of the EPSP across the dendritic tree. Furthermore, we show that peak calcium can be used to set up a synaptic democracy in a homeostatic manner, whereby synapses regulate their synaptic strength on the basis of the difference between peak calcium and a uniform target value. We conclude that information derived from synaptically-generated BAPs can indicate synapse location and can subsequently be utilised to implement a synaptic democracy. PMID:22719238

Embodiment of Learning in Electro-Optical Signal Processors

NASA Astrophysics Data System (ADS)

Hermans, Michiel; Antonik, Piotr; Haelterman, Marc; Massar, Serge

2016-09-01

Delay-coupled electro-optical systems have received much attention for their dynamical properties and their potential use in signal processing. In particular, it has recently been demonstrated, using the artificial intelligence algorithm known as reservoir computing, that photonic implementations of such systems solve complex tasks such as speech recognition. Here, we show how the backpropagation algorithm can be physically implemented on the same electro-optical delay-coupled architecture used for computation with only minor changes to the original design. We find that, compared to when the backpropagation algorithm is not used, the error rate of the resulting computing device, evaluated on three benchmark tasks, decreases considerably. This demonstrates that electro-optical analog computers can embody a large part of their own training process, allowing them to be applied to new, more difficult tasks.

Embodiment of Learning in Electro-Optical Signal Processors.

PubMed

Hermans, Michiel; Antonik, Piotr; Haelterman, Marc; Massar, Serge

2016-09-16

Delay-coupled electro-optical systems have received much attention for their dynamical properties and their potential use in signal processing. In particular, it has recently been demonstrated, using the artificial intelligence algorithm known as reservoir computing, that photonic implementations of such systems solve complex tasks such as speech recognition. Here, we show how the backpropagation algorithm can be physically implemented on the same electro-optical delay-coupled architecture used for computation with only minor changes to the original design. We find that, compared to when the backpropagation algorithm is not used, the error rate of the resulting computing device, evaluated on three benchmark tasks, decreases considerably. This demonstrates that electro-optical analog computers can embody a large part of their own training process, allowing them to be applied to new, more difficult tasks.

Optophysiological Approach to Resolve Neuronal Action Potentials with High Spatial and Temporal Resolution in Cultured Neurons

PubMed Central

Pagès, Stéphane; Côté, Daniel; De Koninck, Paul

2011-01-01

Cell to cell communication in the central nervous system is encoded into transient and local membrane potential changes (ΔVm). Deciphering the rules that govern synaptic transmission and plasticity entails to be able to perform Vm recordings throughout the entire neuronal arborization. Classical electrophysiology is, in most cases, not able to do so within small and fragile neuronal subcompartments. Thus, optical techniques based on the use of fluorescent voltage-sensitive dyes (VSDs) have been developed. However, reporting spontaneous or small ΔVm from neuronal ramifications has been challenging, in part due to the limited sensitivity and phototoxicity of VSD-based optical measurements. Here we demonstrate the use of water soluble VSD, ANNINE-6plus, with laser-scanning microscopy to optically record ΔVm in cultured neurons. We show that the sensitivity (>10% of fluorescence change for 100 mV depolarization) and time response (sub millisecond) of the dye allows the robust detection of action potentials (APs) even without averaging, allowing the measurement of spontaneous neuronal firing patterns. In addition, we show that back-propagating APs can be recorded, along distinct dendritic sites and within dendritic spines. Importantly, our approach does not induce any detectable phototoxic effect on cultured neurons. This optophysiological approach provides a simple, minimally invasive, and versatile optical method to measure electrical activity in cultured neurons with high temporal (ms) resolution and high spatial (μm) resolution. PMID:22016723

Modification Of Learning Rate With Lvq Model Improvement In Learning Backpropagation

NASA Astrophysics Data System (ADS)

Tata Hardinata, Jaya; Zarlis, Muhammad; Budhiarti Nababan, Erna; Hartama, Dedy; Sembiring, Rahmat W.

2017-12-01

One type of artificial neural network is a backpropagation, This algorithm trained with the network architecture used during the training as well as providing the correct output to insert a similar but not the same with the architecture in use at training.The selection of appropriate parameters also affects the outcome, value of learning rate is one of the parameters which influence the process of training, Learning rate affects the speed of learning process on the network architecture.If the learning rate is set too large, then the algorithm will become unstable and otherwise the algorithm will converge in a very long period of time.So this study was made to determine the value of learning rate on the backpropagation algorithm. LVQ models of learning rate is one of the models used in the determination of the value of the learning rate of the algorithm LVQ.By modifying this LVQ model to be applied to the backpropagation algorithm. From the experimental results known to modify the learning rate LVQ models were applied to the backpropagation algorithm learning process becomes faster (epoch less).

Synaptically activated Ca2+ waves in layer 2/3 and layer 5 rat neocortical pyramidal neurons

PubMed Central

Larkum, Matthew E; Watanabe, Shigeo; Nakamura, Takeshi; Lasser-Ross, Nechama; Ross, William N

2003-01-01

Calcium waves in layer 2/3 and layer 5 neocortical somatosensory pyramidal neurons were examined in slices from 2- to 8-week-old rats. Repetitive synaptic stimulation evoked a delayed, all-or-none [Ca2+]i increase primarily on the main dendritic shaft. This component was blocked by 1 mm (R,S)-α-methyl-4-carboxyphenylglycine (MCPG), 10 μm ryanodine, 1 mg ml−1 internal heparin, and was not blocked by 400 μm internal Ruthenium Red, indicating that it was due to Ca2+ release from internal stores by inositol 1,4,5-trisphosphate (IP3) mobilized via activation of metabotropic glutamate receptors. Calcium waves were initiated on the apical shaft at sites between the soma to around the main branch point, mostly at insertion points of oblique dendrites, and spread in both directions along the shaft. In the proximal dendrites the peak amplitude of the resulting [Ca2+]i change was much larger than that evoked by a train of Na+ spikes. In distal dendrites the peak amplitude was comparable to the [Ca2+]i change due to a Ca2+ spike. IP3-mediated Ca2+ release also was observed in the presence of the metabotropic agonists t-ACPD and carbachol when backpropagating spikes were generated. Ca2+ entry through NMDA receptors was observed primarily on the oblique dendrites. The main differences between waves in neocortical neurons and in previously described hippocampal pyramidal neurons were, (a) Ca2+ waves in L5 neurons could be evoked further out along the main shaft, (b) Ca2+ waves extended slightly further out into the oblique dendrites and (c) higher concentrations of bath-applied t-ACPD and carbachol were required to generate Ca2+ release events by backpropagating action potentials. PMID:12692172

Tunneling exit characteristics from classical backpropagation of an ionized electron wave packet

NASA Astrophysics Data System (ADS)

Ni, Hongcheng; Saalmann, Ulf; Rost, Jan-Michael

2018-01-01

We investigate tunneling ionization of a single active electron with a strong and short laser pulse, circularly polarized. With the recently proposed backpropagation method, we can compare different criteria for the tunnel exit as well as popular approximations in strong-field physics on the same footing. Thereby, we trace back discrepancies in the literature regarding the tunneling time to inconsistent tunneling exit criteria. The main source of error is the use of a static ionization potential, which is, however, time dependent for a short laser pulse. A vanishing velocity in the instantaneous field direction as tunneling exit criterion offers a consistent alternative, since it does not require the knowledge of the instantaneous binding energy. Finally, we propose a mapping technique that links observables from attoclock experiments to the intrinsic tunneling exit time.

Trainable hardware for dynamical computing using error backpropagation through physical media.

PubMed

Hermans, Michiel; Burm, Michaël; Van Vaerenbergh, Thomas; Dambre, Joni; Bienstman, Peter

2015-03-24

Neural networks are currently implemented on digital Von Neumann machines, which do not fully leverage their intrinsic parallelism. We demonstrate how to use a novel class of reconfigurable dynamical systems for analogue information processing, mitigating this problem. Our generic hardware platform for dynamic, analogue computing consists of a reciprocal linear dynamical system with nonlinear feedback. Thanks to reciprocity, a ubiquitous property of many physical phenomena like the propagation of light and sound, the error backpropagation-a crucial step for tuning such systems towards a specific task-can happen in hardware. This can potentially speed up the optimization process significantly, offering important benefits for the scalability of neuro-inspired hardware. In this paper, we show, using one experimentally validated and one conceptual example, that such systems may provide a straightforward mechanism for constructing highly scalable, fully dynamical analogue computers.

Analysis of Artificial Neural Network Backpropagation Using Conjugate Gradient Fletcher Reeves In The Predicting Process

NASA Astrophysics Data System (ADS)

Wanto, Anjar; Zarlis, Muhammad; Sawaluddin; Hartama, Dedy

2017-12-01

Backpropagation is a good artificial neural network algorithm used to predict, one of which is to predict the rate of Consumer Price Index (CPI) based on the foodstuff sector. While conjugate gradient fletcher reeves is a suitable optimization method when juxtaposed with backpropagation method, because this method can shorten iteration without reducing the quality of training and testing result. Consumer Price Index (CPI) data that will be predicted to come from the Central Statistics Agency (BPS) Pematangsiantar. The results of this study will be expected to contribute to the government in making policies to improve economic growth. In this study, the data obtained will be processed by conducting training and testing with artificial neural network backpropagation by using parameter learning rate 0,01 and target error minimum that is 0.001-0,09. The training network is built with binary and bipolar sigmoid activation functions. After the results with backpropagation are obtained, it will then be optimized using the conjugate gradient fletcher reeves method by conducting the same training and testing based on 5 predefined network architectures. The result, the method used can increase the speed and accuracy result.

Activity-Dependent Exocytosis of Lysosomes Regulates the Structural Plasticity of Dendritic Spines.

PubMed

Padamsey, Zahid; McGuinness, Lindsay; Bardo, Scott J; Reinhart, Marcia; Tong, Rudi; Hedegaard, Anne; Hart, Michael L; Emptage, Nigel J

2017-01-04

Lysosomes have traditionally been viewed as degradative organelles, although a growing body of evidence suggests that they can function as Ca 2+ stores. Here we examined the function of these stores in hippocampal pyramidal neurons. We found that back-propagating action potentials (bpAPs) could elicit Ca 2+ release from lysosomes in the dendrites. This Ca 2+ release triggered the fusion of lysosomes with the plasma membrane, resulting in the release of Cathepsin B. Cathepsin B increased the activity of matrix metalloproteinase 9 (MMP-9), an enzyme involved in extracellular matrix (ECM) remodelling and synaptic plasticity. Inhibition of either lysosomal Ca 2+ signaling or Cathepsin B release prevented the maintenance of dendritic spine growth induced by Hebbian activity. This impairment could be rescued by exogenous application of active MMP-9. Our findings suggest that activity-dependent exocytosis of Cathepsin B from lysosomes regulates the long-term structural plasticity of dendritic spines by triggering MMP-9 activation and ECM remodelling. Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.

Supersensitive Ras activation in dendrites and spines revealed by two-photon fluorescence lifetime imaging.

PubMed

Yasuda, Ryohei; Harvey, Christopher D; Zhong, Haining; Sobczyk, Aleksander; van Aelst, Linda; Svoboda, Karel

2006-02-01

To understand the biochemical signals regulated by neural activity, it is necessary to measure protein-protein interactions and enzymatic activity in neuronal microcompartments such as axons, dendrites and their spines. We combined two-photon excitation laser scanning with fluorescence lifetime imaging to measure fluorescence resonance energy transfer at high resolutions in brain slices. We also developed sensitive fluorescent protein-based sensors for the activation of the small GTPase protein Ras with slow (FRas) and fast (FRas-F) kinetics. Using FRas-F, we found in CA1 hippocampal neurons that trains of back-propagating action potentials rapidly and reversibly activated Ras in dendrites and spines. The relationship between firing rate and Ras activation was highly nonlinear (Hill coefficient approximately 5). This steep dependence was caused by a highly cooperative interaction between calcium ions (Ca(2+)) and Ras activators. The Ras pathway therefore functions as a supersensitive threshold detector for neural activity and Ca(2+) concentration.

Program Helps Simulate Neural Networks

NASA Technical Reports Server (NTRS)

Villarreal, James; Mcintire, Gary

1993-01-01

Neural Network Environment on Transputer System (NNETS) computer program provides users high degree of flexibility in creating and manipulating wide variety of neural-network topologies at processing speeds not found in conventional computing environments. Supports back-propagation and back-propagation-related algorithms. Back-propagation algorithm used is implementation of Rumelhart's generalized delta rule. NNETS developed on INMOS Transputer(R). Predefines back-propagation network, Jordan network, and reinforcement network to assist users in learning and defining own networks. Also enables users to configure other neural-network paradigms from NNETS basic architecture. Small portion of software written in OCCAM(R) language.

Analysis Resilient Algorithm on Artificial Neural Network Backpropagation

NASA Astrophysics Data System (ADS)

Saputra, Widodo; Tulus; Zarlis, Muhammad; Widia Sembiring, Rahmat; Hartama, Dedy

2017-12-01

Prediction required by decision makers to anticipate future planning. Artificial Neural Network (ANN) Backpropagation is one of method. This method however still has weakness, for long training time. This is a reason to improve a method to accelerate the training. One of Artificial Neural Network (ANN) Backpropagation method is a resilient method. Resilient method of changing weights and bias network with direct adaptation process of weighting based on local gradient information from every learning iteration. Predicting data result of Istanbul Stock Exchange training getting better. Mean Square Error (MSE) value is getting smaller and increasing accuracy.

Two-Photon Na+ Imaging Reports Somatically Evoked Action Potentials in Rat Olfactory Bulb Mitral and Granule Cell Neurites.

PubMed

Ona-Jodar, Tiffany; Gerkau, Niklas J; Sara Aghvami, S; Rose, Christine R; Egger, Veronica

2017-01-01

Dendrodendritic synaptic interactions are a hallmark of neuronal processing in the vertebrate olfactory bulb. Many classes of olfactory bulb neurons including the principal mitral cells (MCs) and the axonless granule cells (GCs) dispose of highly efficient propagation of action potentials (AP) within their dendrites, from where they can release transmitter onto each other. So far, backpropagation in GC dendrites has been investigated indirectly via Ca 2+ imaging. Here, we used two-photon Na + imaging to directly report opening of voltage-gated sodium channels due to AP propagation in both cell types. To this end, neurons in acute slices from juvenile rat bulbs were filled with 1 mM SBFI via whole-cell patch-clamp. Calibration of SBFI signals revealed that a change in fluorescence Δ F / F by 10% corresponded to a Δ[Na + ] i of ∼22 mM. We then imaged proximal axon segments of MCs during somatically evoked APs (sAP). While single sAPs were detectable in ∼50% of axons, trains of 20 sAPs at 50 Hz always resulted in substantial Δ F / F of ∼15% (∼33 mM Δ[Na + ] i ). Δ F / F was significantly larger for 80 Hz vs. 50 Hz trains, and decayed with half-durations τ 1/2 ∼0.6 s for both frequencies. In MC lateral dendrites, AP trains yielded small Δ F / F of ∼3% (∼7 mM Δ[Na + ] i ). In GC apical dendrites and adjacent spines, single sAPs were not detectable. Trains resulted in an average dendritic Δ F / F of 7% (16 mM Δ[Na + ] i ) with τ 1/2 ∼1 s, similar for 50 and 80 Hz. Na + transients were indistinguishable between large GC spines and their adjacent dendrites. Cell-wise analysis revealed two classes of GCs with the first showing a decrease in Δ F / F along the dendrite with distance from the soma and the second an increase. These classes clustered with morphological parameters. Simulations of Δ[Na + ] i replicated these behaviors via negative and positive gradients in Na + current density, assuming faithful AP backpropagation. Such specializations of dendritic excitability might confer specific temporal processing capabilities to bulbar principal cell-GC subnetworks. In conclusion, we show that Na + imaging provides a valuable tool for characterizing AP invasion of MC axons and GC dendrites and spines.

Two-Photon Na+ Imaging Reports Somatically Evoked Action Potentials in Rat Olfactory Bulb Mitral and Granule Cell Neurites

PubMed Central

Ona-Jodar, Tiffany; Gerkau, Niklas J.; Sara Aghvami, S.; Rose, Christine R.; Egger, Veronica

2017-01-01

Dendrodendritic synaptic interactions are a hallmark of neuronal processing in the vertebrate olfactory bulb. Many classes of olfactory bulb neurons including the principal mitral cells (MCs) and the axonless granule cells (GCs) dispose of highly efficient propagation of action potentials (AP) within their dendrites, from where they can release transmitter onto each other. So far, backpropagation in GC dendrites has been investigated indirectly via Ca2+ imaging. Here, we used two-photon Na+ imaging to directly report opening of voltage-gated sodium channels due to AP propagation in both cell types. To this end, neurons in acute slices from juvenile rat bulbs were filled with 1 mM SBFI via whole-cell patch-clamp. Calibration of SBFI signals revealed that a change in fluorescence ΔF/F by 10% corresponded to a Δ[Na+]i of ∼22 mM. We then imaged proximal axon segments of MCs during somatically evoked APs (sAP). While single sAPs were detectable in ∼50% of axons, trains of 20 sAPs at 50 Hz always resulted in substantial ΔF/F of ∼15% (∼33 mM Δ[Na+]i). ΔF/F was significantly larger for 80 Hz vs. 50 Hz trains, and decayed with half-durations τ1/2 ∼0.6 s for both frequencies. In MC lateral dendrites, AP trains yielded small ΔF/F of ∼3% (∼7 mM Δ[Na+]i). In GC apical dendrites and adjacent spines, single sAPs were not detectable. Trains resulted in an average dendritic ΔF/F of 7% (16 mM Δ[Na+]i) with τ1/2 ∼1 s, similar for 50 and 80 Hz. Na+ transients were indistinguishable between large GC spines and their adjacent dendrites. Cell-wise analysis revealed two classes of GCs with the first showing a decrease in ΔF/F along the dendrite with distance from the soma and the second an increase. These classes clustered with morphological parameters. Simulations of Δ[Na+]i replicated these behaviors via negative and positive gradients in Na+ current density, assuming faithful AP backpropagation. Such specializations of dendritic excitability might confer specific temporal processing capabilities to bulbar principal cell-GC subnetworks. In conclusion, we show that Na+ imaging provides a valuable tool for characterizing AP invasion of MC axons and GC dendrites and spines. PMID:28293175

Mechanisms underlying subunit independence in pyramidal neuron dendrites

PubMed Central

Behabadi, Bardia F.; Mel, Bartlett W.

2014-01-01

Pyramidal neuron (PN) dendrites compartmentalize voltage signals and can generate local spikes, which has led to the proposal that their dendrites act as independent computational subunits within a multilayered processing scheme. However, when a PN is strongly activated, back-propagating action potentials (bAPs) sweeping outward from the soma synchronize dendritic membrane potentials many times per second. How PN dendrites maintain the independence of their voltage-dependent computations, despite these repeated voltage resets, remains unknown. Using a detailed compartmental model of a layer 5 PN, and an improved method for quantifying subunit independence that incorporates a more accurate model of dendritic integration, we first established that the output of each dendrite can be almost perfectly predicted by the intensity and spatial configuration of its own synaptic inputs, and is nearly invariant to the rate of bAP-mediated “cross-talk” from other dendrites over a 100-fold range. Then, through an analysis of conductance, voltage, and current waveforms within the model cell, we identify three biophysical mechanisms that together help make independent dendritic computation possible in a firing neuron, suggesting that a major subtype of neocortical neuron has been optimized for layered, compartmentalized processing under in-vivo–like spiking conditions. PMID:24357611

Dynamic mesolimbic dopamine signaling during action sequence learning and expectation violation

PubMed Central

Collins, Anne L.; Greenfield, Venuz Y.; Bye, Jeffrey K.; Linker, Kay E.; Wang, Alice S.; Wassum, Kate M.

2016-01-01

Prolonged mesolimbic dopamine concentration changes have been detected during spatial navigation, but little is known about the conditions that engender this signaling profile or how it develops with learning. To address this, we monitored dopamine concentration changes in the nucleus accumbens core of rats throughout acquisition and performance of an instrumental action sequence task. Prolonged dopamine concentration changes were detected that ramped up as rats executed each action sequence and declined after earned reward collection. With learning, dopamine concentration began to rise increasingly earlier in the execution of the sequence and ultimately backpropagated away from stereotyped sequence actions, becoming only transiently elevated by the most distal and unexpected reward predictor. Action sequence-related dopamine signaling was reactivated in well-trained rats if they became disengaged in the task and in response to an unexpected change in the value, but not identity of the earned reward. Throughout training and test, dopamine signaling correlated with sequence performance. These results suggest that action sequences can engender a prolonged mode of dopamine signaling in the nucleus accumbens core and that such signaling relates to elements of the motivation underlying sequence execution and is dynamic with learning, overtraining and violations in reward expectation. PMID:26869075

Machinery Monitoring and Diagnostics Using Pseudo Wigner-Ville Distribution and Backpropagation Neural Network

DTIC Science & Technology

1993-09-01

frequency, which when used as an input to an artificial neural network will aide in the detection of location and severity of machinery faults...Research is presented where the union of an artificial neural network , utilizing the highly successful backpropagation paradigm, and the pseudo wigner

Fuzzy recognition of noncompact musical objects

NASA Astrophysics Data System (ADS)

Cristobal Salas, Alfredo; Tchernykh, Andrei

1997-03-01

This article describes and compares some techniques to extract attributes from black and white images which contain musical objects. The inertia moment, the central moments and the wavelet transform methods are used to describe the images. Two supervised neural networks are applied to classify the images: backpropagation and fuzzy backpropagation. The results are compared.

A Novel Handwritten Letter Recognizer Using Enhanced Evolutionary Neural Network

NASA Astrophysics Data System (ADS)

Mahmoudi, Fariborz; Mirzashaeri, Mohsen; Shahamatnia, Ehsan; Faridnia, Saed

This paper introduces a novel design for handwritten letter recognition by employing a hybrid back-propagation neural network with an enhanced evolutionary algorithm. Feeding the neural network consists of a new approach which is invariant to translation, rotation, and scaling of input letters. Evolutionary algorithm is used for the global search of the search space and the back-propagation algorithm is used for the local search. The results have been computed by implementing this approach for recognizing 26 English capital letters in the handwritings of different people. The computational results show that the neural network reaches very satisfying results with relatively scarce input data and a promising performance improvement in convergence of the hybrid evolutionary back-propagation algorithms is exhibited.

The A-Current Modulates Learning via NMDA Receptors Containing the NR2B Subunit

PubMed Central

Fontán-Lozano, Ángela; Suárez-Pereira, Irene; González-Forero, David; Carrión, Ángel Manuel

2011-01-01

Synaptic plasticity involves short- and long-term events, although the molecular mechanisms that underlie these processes are not fully understood. The transient A-type K+ current (IA) controls the excitability of the dendrites from CA1 pyramidal neurons by regulating the back-propagation of action potentials and shaping synaptic input. Here, we have studied how decreases in IA affect cognitive processes and synaptic plasticity. Using wild-type mice treated with 4-AP, an IA inhibitor, and mice lacking the DREAM protein, a transcriptional repressor and modulator of the IA, we demonstrate that impairment of IA decreases the stimulation threshold for learning and the induction of early-LTP. Hippocampal electrical recordings in both models revealed alterations in basal electrical oscillatory properties toward low-theta frequencies. In addition, we demonstrated that the facilitated learning induced by decreased IA requires the activation of NMDA receptors containing the NR2B subunit. Together, these findings point to a balance between the IA and the activity of NR2B-containing NMDA receptors in the regulation of learning. PMID:21966384

Dendritic mechanisms underlying the coupling of the dendritic with the axonal action potential initiation zone of adult rat layer 5 pyramidal neurons

PubMed Central

Larkum, M E; Zhu, J J; Sakmann, B

2001-01-01

Double, triple and quadruple whole-cell voltage recordings were made simultaneously from different parts of the apical dendritic arbor and the soma of adult layer 5 (L5) pyramidal neurons. We investigated the membrane mechanisms that support the conduction of dendritic action potentials (APs) between the dendritic and axonal AP initiation zones and their influence on the subsequent AP pattern. The duration of the current injection to the distal dendritic initiation zone controlled the degree of coupling with the axonal initiation zone and the AP pattern. Two components of the distally evoked regenerative potential were pharmacologically distinguished: a rapidly rising peak potential that was TTX sensitive and a slowly rising plateau-like potential that was Cd2+ and Ni2+ sensitive and present only with longer-duration current injection. The amplitude of the faster forward-propagating Na+-dependent component and the amplitude of the back-propagating AP fell into two classes (more distinctly in the forward-propagating case). Current injection into the dendrite altered propagation in both directions. Somatic current injections that elicited single Na+ APs evoked bursts of Na+ APs when current was injected simultaneously into the proximal apical dendrite. The mechanism did not depend on dendritic Na+–Ca2+ APs. A three-compartment model of a L5 pyramidal neuron is proposed. It comprises the distal dendritic and axonal AP initiation zones and the proximal apical dendrite. Each compartment contributes to the initiation and to the pattern of AP discharge in a distinct manner. Input to the three main dendritic arbors (tuft dendrites, apical oblique dendrites and basal dendrites) has a dominant influence on only one of these compartments. Thus, the AP pattern of L5 pyramids reflects the laminar distribution of synaptic activity in a cortical column. PMID:11389204

Training Deep Spiking Neural Networks Using Backpropagation.

PubMed

Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

2016-01-01

Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

Learning in the Machine: Random Backpropagation and the Deep Learning Channel.

PubMed

Baldi, Pierre; Sadowski, Peter; Lu, Zhiqin

2018-07-01

Random backpropagation (RBP) is a variant of the backpropagation algorithm for training neural networks, where the transpose of the forward matrices are replaced by fixed random matrices in the calculation of the weight updates. It is remarkable both because of its effectiveness, in spite of using random matrices to communicate error information, and because it completely removes the taxing requirement of maintaining symmetric weights in a physical neural system. To better understand random backpropagation, we first connect it to the notions of local learning and learning channels. Through this connection, we derive several alternatives to RBP, including skipped RBP (SRPB), adaptive RBP (ARBP), sparse RBP, and their combinations (e.g. ASRBP) and analyze their computational complexity. We then study their behavior through simulations using the MNIST and CIFAR-10 bechnmark datasets. These simulations show that most of these variants work robustly, almost as well as backpropagation, and that multiplication by the derivatives of the activation functions is important. As a follow-up, we study also the low-end of the number of bits required to communicate error information over the learning channel. We then provide partial intuitive explanations for some of the remarkable properties of RBP and its variations. Finally, we prove several mathematical results, including the convergence to fixed points of linear chains of arbitrary length, the convergence to fixed points of linear autoencoders with decorrelated data, the long-term existence of solutions for linear systems with a single hidden layer and convergence in special cases, and the convergence to fixed points of non-linear chains, when the derivative of the activation functions is included.

Fast Back-Propagation Learning Using Steep Activation Functions and Automatic Weight

Treesearch

Tai-Hoon Cho; Richard W. Conners; Philip A. Araman

1992-01-01

In this paper, several back-propagation (BP) learning speed-up algorithms that employ the ãgainä parameter, i.e., steepness of the activation function, are examined. Simulations will show that increasing the gain seemingly increases the speed of convergence and that these algorithms can converge faster than the standard BP learning algorithm on some problems. However,...

An application of artificial neural networks to experimental data approximation

NASA Technical Reports Server (NTRS)

Meade, Andrew J., Jr.

1993-01-01

As an initial step in the evaluation of networks, a feedforward architecture is trained to approximate experimental data by the backpropagation algorithm. Several drawbacks were detected and an alternative learning algorithm was then developed to partially address the drawbacks. This noniterative algorithm has a number of advantages over the backpropagation method and is easily implemented on existing hardware.

An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity

PubMed Central

Whittington, James C. R.; Bogacz, Rafal

2017-01-01

To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output. PMID:28333583

Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization

PubMed Central

Huang, Daizheng; Wu, Zhihui

2017-01-01

Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods. PMID:28222194

Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization.

PubMed

Huang, Daizheng; Wu, Zhihui

2017-01-01

Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.

A Q-backpropagated time delay neural network for diagnosing severity of gait disturbances in Parkinson's disease.

PubMed

Nancy Jane, Y; Khanna Nehemiah, H; Arputharaj, Kannan

2016-04-01

Parkinson's disease (PD) is a movement disorder that affects the patient's nervous system and health-care applications mostly uses wearable sensors to collect these data. Since these sensors generate time stamped data, analyzing gait disturbances in PD becomes challenging task. The objective of this paper is to develop an effective clinical decision-making system (CDMS) that aids the physician in diagnosing the severity of gait disturbances in PD affected patients. This paper presents a Q-backpropagated time delay neural network (Q-BTDNN) classifier that builds a temporal classification model, which performs the task of classification and prediction in CDMS. The proposed Q-learning induced backpropagation (Q-BP) training algorithm trains the Q-BTDNN by generating a reinforced error signal. The network's weights are adjusted through backpropagating the generated error signal. For experimentation, the proposed work uses a PD gait database, which contains gait measures collected through wearable sensors from three different PD research studies. The experimental result proves the efficiency of Q-BP in terms of its improved classification accuracy of 91.49%, 92.19% and 90.91% with three datasets accordingly compared to other neural network training algorithms. Copyright © 2016 Elsevier Inc. All rights reserved.

An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity.

PubMed

Whittington, James C R; Bogacz, Rafal

2017-05-01

To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output.

Automatic telangiectasia analysis in dermoscopy images using adaptive critic design.

PubMed

Cheng, B; Stanley, R J; Stoecker, W V; Hinton, K

2012-11-01

Telangiectasia, tiny skin vessels, are important dermoscopy structures used to discriminate basal cell carcinoma (BCC) from benign skin lesions. This research builds off of previously developed image analysis techniques to identify vessels automatically to discriminate benign lesions from BCCs. A biologically inspired reinforcement learning approach is investigated in an adaptive critic design framework to apply action-dependent heuristic dynamic programming (ADHDP) for discrimination based on computed features using different skin lesion contrast variations to promote the discrimination process. Lesion discrimination results for ADHDP are compared with multilayer perception backpropagation artificial neural networks. This study uses a data set of 498 dermoscopy skin lesion images of 263 BCCs and 226 competitive benign images as the input sets. This data set is extended from previous research [Cheng et al., Skin Research and Technology, 2011, 17: 278]. Experimental results yielded a diagnostic accuracy as high as 84.6% using the ADHDP approach, providing an 8.03% improvement over a standard multilayer perception method. We have chosen BCC detection rather than vessel detection as the endpoint. Although vessel detection is inherently easier, BCC detection has potential direct clinical applications. Small BCCs are detectable early by dermoscopy and potentially detectable by the automated methods described in this research. © 2011 John Wiley & Sons A/S.

Non-binary LDPC-coded modulation for high-speed optical metro networks with backpropagation

NASA Astrophysics Data System (ADS)

Arabaci, Murat; Djordjevic, Ivan B.; Saunders, Ross; Marcoccia, Roberto M.

2010-01-01

To simultaneously mitigate the linear and nonlinear channel impairments in high-speed optical communications, we propose the use of non-binary low-density-parity-check-coded modulation in combination with a coarse backpropagation method. By employing backpropagation, we reduce the memory in the channel and in return obtain significant reductions in the complexity of the channel equalizer which is exponentially proportional to the channel memory. We then compensate for the remaining channel distortions using forward error correction based on non-binary LDPC codes. We propose non-binary-LDPC-coded modulation scheme because, compared to bit-interleaved binary-LDPC-coded modulation scheme employing turbo equalization, the proposed scheme lowers the computational complexity and latency of the overall system while providing impressively larger coding gains.

Ethnomathematics elements in Batik Bali using backpropagation method

NASA Astrophysics Data System (ADS)

Lestari, Mei; Irawan, Ari; Rahayu, Wanti; Wayan Parwati, Ni

2018-05-01

Batik is one of traditional arts that has been established by the UNESCO as Indonesia’s cultural heritage. Batik has varieties and motifs, and each motifs has its own uniqueness but seems similar, that makes it difficult to identify. This study aims to develop an application that can identify typical batik Bali with etnomatematics elements on it. Etnomatematics is a study that shows relation between culture and mathematics concepts. Etnomatematics in Batik Bali is more to geometrical concept in line of strong Balinese culture element. The identification process is use backpropagation method. Steps of backpropagation methods are image processing (including scalling and tresholding image process). Next step is insert the processed image to an artificial neural network. This study resulted an accuracy of identification of batik Bali that has Etnomatematics elements on it.

Improving Maritime Domain Awareness Using Neural Networks for Target of Interest Classification

DTIC Science & Technology

2015-03-01

spreading SCG scaled conjugate gradient xv THIS PAGE INTENTIONALLY LEFT BLANK xvi EXECUTIVE SUMMARY The research detailed in this thesis is a...algorithms were explored for training the neural networks: resilient backpropagation (RP) and scaled conjugate gradient backpropagation ( SCG ). The...results of the neural network training performance are presented using mean squared error convergence plots. In all implementations, the SCG learning

Recognition of Roasted Coffee Bean Levels using Image Processing and Neural Network

NASA Astrophysics Data System (ADS)

Nasution, T. H.; Andayani, U.

2017-03-01

The coffee beans roast levels have some characteristics. However, some people cannot recognize the coffee beans roast level. In this research, we propose to design a method to recognize the coffee beans roast level of images digital by processing the image and classifying with backpropagation neural network. The steps consist of how to collect the images data with image acquisition, pre-processing, feature extraction using Gray Level Co-occurrence Matrix (GLCM) method and finally normalization of data extraction using decimal scaling features. The values of decimal scaling features become an input of classifying in backpropagation neural network. We use the method of backpropagation to recognize the coffee beans roast levels. The results showed that the proposed method is able to identify the coffee roasts beans level with an accuracy of 97.5%.

MicroRNA Expression Profile Selection for Cancer Staging Classification Using Backpropagation

NASA Astrophysics Data System (ADS)

Anjarwati; Wibowo, Adi; Adhy, Satriyo; Kusumaningrum, Retno

2018-05-01

Ovarian cancer, breast cancer, and lung cancer are deadly diseases and require serious treatment. The cancers are among the fifth most common causes of cancer-induced deaths especially for woman. The high mortality rate of cancer is caused by the lack of effective strategies for early detection of the cancer, whereas if its detected in the early stages, the life survival of cancer patients will be 90%, otherwise the survival rate only 30% when the cancers detected on metastasis stages or cancer cells have spread from a primary site of cancer. MicroRNAs can be used as potential biomarkers for cancer due to their profile expression on the cancers. In this paper, we proposed the feature selection of microRNA expression profiles for classification of the cancers stages using Backpropagation Neural Network. The Cancer stages are classified into before metastasis and after metastasis. Several combinations of the microRNA expression profiles from medical references are compared to find the best features for the classification. The accuracy and the mean square errors are used as basis testing the comparison.

Slowly inactivating component of Na+ current in peri-somatic region of hippocampal CA1 pyramidal neurons

PubMed Central

Park, Yul Young; Johnston, Daniel

2013-01-01

The properties of voltage-gated ion channels on the neuronal membrane shape electrical activity such as generation and backpropagation of action potentials, initiation of dendritic spikes, and integration of synaptic inputs. Subthreshold currents mediated by sodium channels are of interest because of their activation near rest, slow inactivation kinetics, and consequent effects on excitability. Modulation of these currents can also perturb physiological responses of a neuron that might underlie pathological states such as epilepsy. Using nucleated patches from the peri-somatic region of hippocampal CA1 neurons, we recorded a slowly inactivating component of the macroscopic Na+ current (which we have called INaS) that shared many biophysical properties with the persistent Na+ current, INaP, but showed distinctively faster inactivating kinetics. Ramp voltage commands with a velocity of 400 mV/s were found to elicit this component of Na+ current reliably. INaS also showed a more hyperpolarized I-V relationship and slower inactivation than those of the fast transient Na+ current (INaT) recorded in the same patches. The peak amplitude of INaS was proportional to the peak amplitude of INaT but was much smaller in amplitude. Hexanol, riluzole, and ranolazine, known Na+ channel blockers, were tested to compare their effects on both INaS and INaT. The peak conductance of INaS was preferentially blocked by hexanol and riluzole, but the shift of half-inactivation voltage (V1/2) was only observed in the presence of riluzole. Current-clamp measurements with hexanol suggested that INaS was involved in generation of an action potential and in upregulation of neuronal excitability. PMID:23236005

Elimination of fast inactivation in Kv4 A-type potassium channels by an auxiliary subunit domain.

PubMed

Holmqvist, Mats H; Cao, Jie; Hernandez-Pineda, Ricardo; Jacobson, Michael D; Carroll, Karen I; Sung, M Amy; Betty, Maria; Ge, Pei; Gilbride, Kevin J; Brown, Melissa E; Jurman, Mark E; Lawson, Deborah; Silos-Santiago, Inmaculada; Xie, Yu; Covarrubias, Manuel; Rhodes, Kenneth J; Distefano, Peter S; An, W Frank

2002-01-22

The Kv4 A-type potassium currents contribute to controlling the frequency of slow repetitive firing and back-propagation of action potentials in neurons and shape the action potential in heart. Kv4 currents exhibit rapid activation and inactivation and are specifically modulated by K-channel interacting proteins (KChIPs). Here we report the discovery and functional characterization of a modular K-channel inactivation suppressor (KIS) domain located in the first 34 aa of an additional KChIP (KChIP4a). Coexpression of KChIP4a with Kv4 alpha-subunits abolishes fast inactivation of the Kv4 currents in various cell types, including cerebellar granule neurons. Kinetic analysis shows that the KIS domain delays Kv4.3 opening, but once the channel is open, it disrupts rapid inactivation and slows Kv4.3 closing. Accordingly, KChIP4a increases the open probability of single Kv4.3 channels. The net effects of KChIP4a and KChIP1-3 on Kv4 gating are quite different. When both KChIP4a and KChIP1 are present, the Kv4.3 current shows mixed inactivation profiles dependent on KChIP4a/KChIP1 ratios. The KIS domain effectively converts the A-type Kv4 current to a slowly inactivating delayed rectifier-type potassium current. This conversion is opposite to that mediated by the Kv1-specific "ball" domain of the Kv beta 1 subunit. Together, these results demonstrate that specific auxiliary subunits with distinct functions actively modulate gating of potassium channels that govern membrane excitability.

Grounding the Meanings in Sensorimotor Behavior using Reinforcement Learning

PubMed Central

Farkaš, Igor; Malík, Tomáš; Rebrová, Kristína

2012-01-01

The recent outburst of interest in cognitive developmental robotics is fueled by the ambition to propose ecologically plausible mechanisms of how, among other things, a learning agent/robot could ground linguistic meanings in its sensorimotor behavior. Along this stream, we propose a model that allows the simulated iCub robot to learn the meanings of actions (point, touch, and push) oriented toward objects in robot’s peripersonal space. In our experiments, the iCub learns to execute motor actions and comment on them. Architecturally, the model is composed of three neural-network-based modules that are trained in different ways. The first module, a two-layer perceptron, is trained by back-propagation to attend to the target position in the visual scene, given the low-level visual information and the feature-based target information. The second module, having the form of an actor-critic architecture, is the most distinguishing part of our model, and is trained by a continuous version of reinforcement learning to execute actions as sequences, based on a linguistic command. The third module, an echo-state network, is trained to provide the linguistic description of the executed actions. The trained model generalizes well in case of novel action-target combinations with randomized initial arm positions. It can also promptly adapt its behavior if the action/target suddenly changes during motor execution. PMID:22393319

A comparison of neural network architectures for the prediction of MRR in EDM

NASA Astrophysics Data System (ADS)

Jena, A. R.; Das, Raja

2017-11-01

The aim of the research work is to predict the material removal rate of a work-piece in electrical discharge machining (EDM). Here, an effort has been made to predict the material removal rate through back-propagation neural network (BPN) and radial basis function neural network (RBFN) for a work-piece of AISI D2 steel. The input parameters for the architecture are discharge-current (Ip), pulse-duration (Ton), and duty-cycle (τ) taken for consideration to obtained the output for material removal rate of the work-piece. In the architecture, it has been observed that radial basis function neural network is comparatively faster than back-propagation neural network but logically back-propagation neural network results more real value. Therefore BPN may consider as a better process in this architecture for consistent prediction to save time and money for conducting experiments.

Prediction of air pollutant concentration based on sparse response back-propagation training feedforward neural networks.

PubMed

Ding, Weifu; Zhang, Jiangshe; Leung, Yee

2016-10-01

In this paper, we predict air pollutant concentration using a feedforward artificial neural network inspired by the mechanism of the human brain as a useful alternative to traditional statistical modeling techniques. The neural network is trained based on sparse response back-propagation in which only a small number of neurons respond to the specified stimulus simultaneously and provide a high convergence rate for the trained network, in addition to low energy consumption and greater generalization. Our method is evaluated on Hong Kong air monitoring station data and corresponding meteorological variables for which five air quality parameters were gathered at four monitoring stations in Hong Kong over 4 years (2012-2015). Our results show that our training method has more advantages in terms of the precision of the prediction, effectiveness, and generalization of traditional linear regression algorithms when compared with a feedforward artificial neural network trained using traditional back-propagation.

Random synaptic feedback weights support error backpropagation for deep learning

NASA Astrophysics Data System (ADS)

Lillicrap, Timothy P.; Cownden, Daniel; Tweed, Douglas B.; Akerman, Colin J.

2016-11-01

The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame by multiplying error signals with all the synaptic weights on each neuron's axon and further downstream. However, this involves a precise, symmetric backward connectivity pattern, which is thought to be impossible in the brain. Here we demonstrate that this strong architectural constraint is not required for effective error propagation. We present a surprisingly simple mechanism that assigns blame by multiplying errors by even random synaptic weights. This mechanism can transmit teaching signals across multiple layers of neurons and performs as effectively as backpropagation on a variety of tasks. Our results help reopen questions about how the brain could use error signals and dispel long-held assumptions about algorithmic constraints on learning.

Back-propagation learning of infinite-dimensional dynamical systems.

PubMed

Tokuda, Isao; Tokunaga, Ryuji; Aihara, Kazuyuki

2003-10-01

This paper presents numerical studies of applying back-propagation learning to a delayed recurrent neural network (DRNN). The DRNN is a continuous-time recurrent neural network having time delayed feedbacks and the back-propagation learning is to teach spatio-temporal dynamics to the DRNN. Since the time-delays make the dynamics of the DRNN infinite-dimensional, the learning algorithm and the learning capability of the DRNN are different from those of the ordinary recurrent neural network (ORNN) having no time-delays. First, two types of learning algorithms are developed for a class of DRNNs. Then, using chaotic signals generated from the Mackey-Glass equation and the Rössler equations, learning capability of the DRNN is examined. Comparing the learning algorithms, learning capability, and robustness against noise of the DRNN with those of the ORNN and time delay neural network, advantages as well as disadvantages of the DRNN are investigated.

Random synaptic feedback weights support error backpropagation for deep learning

PubMed Central

Lillicrap, Timothy P.; Cownden, Daniel; Tweed, Douglas B.; Akerman, Colin J.

2016-01-01

The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame by multiplying error signals with all the synaptic weights on each neuron's axon and further downstream. However, this involves a precise, symmetric backward connectivity pattern, which is thought to be impossible in the brain. Here we demonstrate that this strong architectural constraint is not required for effective error propagation. We present a surprisingly simple mechanism that assigns blame by multiplying errors by even random synaptic weights. This mechanism can transmit teaching signals across multiple layers of neurons and performs as effectively as backpropagation on a variety of tasks. Our results help reopen questions about how the brain could use error signals and dispel long-held assumptions about algorithmic constraints on learning. PMID:27824044

The Slow Dynamics of Intracellular Sodium Concentration Increase the Time Window of Neuronal Integration: A Simulation Study

PubMed Central

Zylbertal, Asaph; Yarom, Yosef; Wagner, Shlomo

2017-01-01

Changes in intracellular Na+ concentration ([Na+]i) are rarely taken into account when neuronal activity is examined. As opposed to Ca2+, [Na+]i dynamics are strongly affected by longitudinal diffusion, and therefore they are governed by the morphological structure of the neurons, in addition to the localization of influx and efflux mechanisms. Here, we examined [Na+]i dynamics and their effects on neuronal computation in three multi-compartmental neuronal models, representing three distinct cell types: accessory olfactory bulb (AOB) mitral cells, cortical layer V pyramidal cells, and cerebellar Purkinje cells. We added [Na+]i as a state variable to these models, and allowed it to modulate the Na+ Nernst potential, the Na+-K+ pump current, and the Na+-Ca2+ exchanger rate. Our results indicate that in most cases [Na+]i dynamics are significantly slower than [Ca2+]i dynamics, and thus may exert a prolonged influence on neuronal computation in a neuronal type specific manner. We show that [Na+]i dynamics affect neuronal activity via three main processes: reduction of EPSP amplitude in repeatedly active synapses due to reduction of the Na+ Nernst potential; activity-dependent hyperpolarization due to increased activity of the Na+-K+ pump; specific tagging of active synapses by extended Ca2+ elevation, intensified by concurrent back-propagating action potentials or complex spikes. Thus, we conclude that [Na+]i dynamics should be considered whenever synaptic plasticity, extensive synaptic input, or bursting activity are examined. PMID:28970791

Spatial Segregation and Interaction of Calcium Signalling Mechanisms in Rat Hippocampal CA1 Pyramidal Neurons

PubMed Central

Nakamura, Takeshi; Lasser-Ross, Nechama; Nakamura, Kyoko; Ross, William N

2002-01-01

Postsynaptic [Ca2+]i increases result from Ca2+ entry through ligand-gated channels, entry through voltage-gated channels, or release from intracellular stores. We found that these sources have distinct spatial distributions in hippocampal CA1 pyramidal neurons. Large amplitude regenerative release of Ca2+ from IP3-sensitive stores in the form of Ca2+ waves were found almost exclusively on the thick apical shaft. Smaller release events did not extend more than 15 μm into the oblique dendrites. These synaptically activated regenerative waves initiated at points where the stimulated oblique dendrites branch from the apical shaft. In contrast, NMDA receptor-mediated increases were observed predominantly in oblique dendrites where spines are found at high density. These [Ca2+]i increases were typically more than eight times larger than [Ca2+]i from this source on the main aspiny apical shaft. Ca2+ entry through voltage-gated channels, activated by backpropagating action potentials, was detected at all dendritic locations. These mechanisms were not independent. Ca2+ entry through NMDA receptor channels or voltage-gated channels (as previously demonstrated) synergistically enhanced Ca2+ release generated by mGluR mobilization of IP3. PMID:12205182

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.

Discovering weighted patterns in intron sequences using self-adaptive harmony search and back-propagation algorithms.

PubMed

Huang, Yin-Fu; Wang, Chia-Ming; Liou, Sing-Wu

2013-01-01

A hybrid self-adaptive harmony search and back-propagation mining system was proposed to discover weighted patterns in human intron sequences. By testing the weights under a lazy nearest neighbor classifier, the numerical results revealed the significance of these weighted patterns. Comparing these weighted patterns with the popular intron consensus model, it is clear that the discovered weighted patterns make originally the ambiguous 5SS and 3SS header patterns more specific and concrete.

Discovering Weighted Patterns in Intron Sequences Using Self-Adaptive Harmony Search and Back-Propagation Algorithms

PubMed Central

Wang, Chia-Ming; Liou, Sing-Wu

2013-01-01

A hybrid self-adaptive harmony search and back-propagation mining system was proposed to discover weighted patterns in human intron sequences. By testing the weights under a lazy nearest neighbor classifier, the numerical results revealed the significance of these weighted patterns. Comparing these weighted patterns with the popular intron consensus model, it is clear that the discovered weighted patterns make originally the ambiguous 5SS and 3SS header patterns more specific and concrete. PMID:23737711

Minimal-scan filtered backpropagation algorithms for diffraction tomography.

PubMed

Pan, X; Anastasio, M A

1999-12-01

The filtered backpropagation (FBPP) algorithm, originally developed by Devaney [Ultrason. Imaging 4, 336 (1982)], has been widely used for reconstructing images in diffraction tomography. It is generally known that the FBPP algorithm requires scattered data from a full angular range of 2 pi for exact reconstruction of a generally complex-valued object function. However, we reveal that one needs scattered data only over the angular range 0 < or = phi < or = 3 pi/2 for exact reconstruction of a generally complex-valued object function. Using this insight, we develop and analyze a family of minimal-scan filtered backpropagation (MS-FBPP) algorithms, which, unlike the FBPP algorithm, use scattered data acquired from view angles over the range 0 < or = phi < or = 3 pi/2. We show analytically that these MS-FBPP algorithms are mathematically identical to the FBPP algorithm. We also perform computer simulation studies for validation, demonstration, and comparison of these MS-FBPP algorithms. The numerical results in these simulation studies corroborate our theoretical assertions.

Development of neural network techniques for finger-vein pattern classification

NASA Astrophysics Data System (ADS)

Wu, Jian-Da; Liu, Chiung-Tsiung; Tsai, Yi-Jang; Liu, Jun-Ching; Chang, Ya-Wen

2010-02-01

A personal identification system using finger-vein patterns and neural network techniques is proposed in the present study. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis and classification. The biometric system for verification consists of a combination of feature extraction using principal component analysis and pattern classification using both back-propagation network and adaptive neuro-fuzzy inference systems. Finger-vein features are first extracted by principal component analysis method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed adaptive neuro-fuzzy inference system in the pattern classification, the back-propagation network is compared with the proposed system. The experimental results indicated the proposed system using adaptive neuro-fuzzy inference system demonstrated a better performance than the back-propagation network for personal identification using the finger-vein patterns.

Algebraic and adaptive learning in neural control systems

NASA Astrophysics Data System (ADS)

Ferrari, Silvia

A systematic approach is developed for designing adaptive and reconfigurable nonlinear control systems that are applicable to plants modeled by ordinary differential equations. The nonlinear controller comprising a network of neural networks is taught using a two-phase learning procedure realized through novel techniques for initialization, on-line training, and adaptive critic design. A critical observation is that the gradients of the functions defined by the neural networks must equal corresponding linear gain matrices at chosen operating points. On-line training is based on a dual heuristic adaptive critic architecture that improves control for large, coupled motions by accounting for actual plant dynamics and nonlinear effects. An action network computes the optimal control law; a critic network predicts the derivative of the cost-to-go with respect to the state. Both networks are algebraically initialized based on prior knowledge of satisfactory pointwise linear controllers and continue to adapt on line during full-scale simulations of the plant. On-line training takes place sequentially over discrete periods of time and involves several numerical procedures. A backpropagating algorithm called Resilient Backpropagation is modified and successfully implemented to meet these objectives, without excessive computational expense. This adaptive controller is as conservative as the linear designs and as effective as a global nonlinear controller. The method is successfully implemented for the full-envelope control of a six-degree-of-freedom aircraft simulation. The results show that the on-line adaptation brings about improved performance with respect to the initialization phase during aircraft maneuvers that involve large-angle and coupled dynamics, and parameter variations.

Learning rules for spike timing-dependent plasticity depend on dendritic synapse location.

PubMed

Letzkus, Johannes J; Kampa, Björn M; Stuart, Greg J

2006-10-11

Previous studies focusing on the temporal rules governing changes in synaptic strength during spike timing-dependent synaptic plasticity (STDP) have paid little attention to the fact that synaptic inputs are distributed across complex dendritic trees. During STDP, propagation of action potentials (APs) back to the site of synaptic input is thought to trigger plasticity. However, in pyramidal neurons, backpropagation of single APs is decremental, whereas high-frequency bursts lead to generation of distal dendritic calcium spikes. This raises the question whether STDP learning rules depend on synapse location and firing mode. Here, we investigate this issue at synapses between layer 2/3 and layer 5 pyramidal neurons in somatosensory cortex. We find that low-frequency pairing of single APs at positive times leads to a distance-dependent shift to long-term depression (LTD) at distal inputs. At proximal sites, this LTD could be converted to long-term potentiation (LTP) by dendritic depolarizations suprathreshold for BAC-firing or by high-frequency AP bursts. During AP bursts, we observed a progressive, distance-dependent shift in the timing requirements for induction of LTP and LTD, such that distal synapses display novel timing rules: they potentiate when inputs are activated after burst onset (negative timing) but depress when activated before burst onset (positive timing). These findings could be explained by distance-dependent differences in the underlying dendritic voltage waveforms driving NMDA receptor activation during STDP induction. Our results suggest that synapse location within the dendritic tree is a crucial determinant of STDP, and that synapses undergo plasticity according to local rather than global learning rules.

Application of structured support vector machine backpropagation to a convolutional neural network for human pose estimation.

PubMed

Witoonchart, Peerajak; Chongstitvatana, Prabhas

2017-08-01

In this study, for the first time, we show how to formulate a structured support vector machine (SSVM) as two layers in a convolutional neural network, where the top layer is a loss augmented inference layer and the bottom layer is the normal convolutional layer. We show that a deformable part model can be learned with the proposed structured SVM neural network by backpropagating the error of the deformable part model to the convolutional neural network. The forward propagation calculates the loss augmented inference and the backpropagation calculates the gradient from the loss augmented inference layer to the convolutional layer. Thus, we obtain a new type of convolutional neural network called an Structured SVM convolutional neural network, which we applied to the human pose estimation problem. This new neural network can be used as the final layers in deep learning. Our method jointly learns the structural model parameters and the appearance model parameters. We implemented our method as a new layer in the existing Caffe library. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Circuit-Based Neural Network with Hybrid Learning of Backpropagation and Random Weight Change Algorithms

PubMed Central

Yang, Changju; Kim, Hyongsuk; Adhikari, Shyam Prasad; Chua, Leon O.

2016-01-01

A hybrid learning method of a software-based backpropagation learning and a hardware-based RWC learning is proposed for the development of circuit-based neural networks. The backpropagation is known as one of the most efficient learning algorithms. A weak point is that its hardware implementation is extremely difficult. The RWC algorithm, which is very easy to implement with respect to its hardware circuits, takes too many iterations for learning. The proposed learning algorithm is a hybrid one of these two. The main learning is performed with a software version of the BP algorithm, firstly, and then, learned weights are transplanted on a hardware version of a neural circuit. At the time of the weight transplantation, a significant amount of output error would occur due to the characteristic difference between the software and the hardware. In the proposed method, such error is reduced via a complementary learning of the RWC algorithm, which is implemented in a simple hardware. The usefulness of the proposed hybrid learning system is verified via simulations upon several classical learning problems. PMID:28025566

Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network.

PubMed

Antwi, Philip; Li, Jianzheng; Boadi, Portia Opoku; Meng, Jia; Shi, En; Deng, Kaiwen; Bondinuba, Francis Kwesi

2017-03-01

Three-layered feedforward backpropagation (BP) artificial neural networks (ANN) and multiple nonlinear regression (MnLR) models were developed to estimate biogas and methane yield in an upflow anaerobic sludge blanket (UASB) reactor treating potato starch processing wastewater (PSPW). Anaerobic process parameters were optimized to identify their importance on methanation. pH, total chemical oxygen demand, ammonium, alkalinity, total Kjeldahl nitrogen, total phosphorus, volatile fatty acids and hydraulic retention time selected based on principal component analysis were used as input variables, whiles biogas and methane yield were employed as target variables. Quasi-Newton method and conjugate gradient backpropagation algorithms were best among eleven training algorithms. Coefficient of determination (R 2 ) of the BP-ANN reached 98.72% and 97.93% whiles MnLR model attained 93.9% and 91.08% for biogas and methane yield, respectively. Compared with the MnLR model, BP-ANN model demonstrated significant performance, suggesting possible control of the anaerobic digestion process with the BP-ANN model. Copyright © 2016 Elsevier Ltd. All rights reserved.

Differential excitability and modulation of striatal medium spiny neuron dendrites

PubMed Central

Day, Michelle; Wokosin, David; Plotkin, Joshua L.; Tian, Xinyoung; Surmeier, D. James

2011-01-01

The loss of striatal dopamine (DA) in Parkinson's disease (PD) models triggers a cell-type specific reduction in the density of dendritic spines in D2 receptor-expressing striatopallidal medium spiny neurons (D2 MSNs). How the intrinsic properties of MSN dendrites, where the vast majority of DA receptors are found, contribute to this adaptation is not clear. To address this question, two-photon laser scanning microscopy (2PLSM) was performed in patch-clamped mouse MSNs identified in striatal slices by expression of green fluorescent protein (eGFP) controlled by DA receptor promoters. These studies revealed that single back-propagating action potentials (bAP) produced more reliable elevations in cytosolic Ca2+ concentration at distal dendritic locations in D2 MSNs than at similar locations in D1 receptor-expressing striatonigral MSNs (D1 MSNs). In both cell types, the dendritic Ca2+ entry elicited by bAPs was enhanced by pharmacological blockade of Kv4, but not Kv1 K+ channels. Local application of DA depressed dendritic bAP-evoked Ca2+ transients, whereas application of ACh increased these Ca2+ transients in D2 MSNs—but not in D1 MSNs. Following DA depletion, bAP-evoked Ca2+ transients were enhanced in distal dendrites and spines in D2 MSNs. Taken together, these results suggest that normally D2 MSN dendrites are more excitable than those of D1 MSNs and that DA depletion exaggerates this asymmetry, potentially contributing to adaptations in PD models. PMID:18987196

Structure-activity relationships for serotonin transporter and dopamine receptor selectivity.

PubMed

Agatonovic-Kustrin, Snezana; Davies, Paul; Turner, Joseph V

2009-05-01

Antipsychotic medications have a diverse pharmacology with affinity for serotonergic, dopaminergic, adrenergic, histaminergic and cholinergic receptors. Their clinical use now also includes the treatment of mood disorders, thought to be mediated by serotonergic receptor activity. The aim of our study was to characterise the molecular properties of antipsychotic agents, and to develop a model that would indicate molecular specificity for the dopamine (D(2)) receptor and the serotonin (5-HT) transporter. Back-propagation artificial neural networks (ANNs) were trained on a dataset of 47 ligands categorically assigned antidepressant or antipsychotic utility. The structure of each compound was encoded with 63 calculated molecular descriptors. ANN parameters including hidden neurons and input descriptors were optimised based on sensitivity analyses, with optimum models containing between four and 14 descriptors. Predicted binding preferences were in excellent agreement with clinical antipsychotic or antidepressant utility. Validated models were further tested by use of an external prediction set of five drugs with unknown mechanism of action. The SAR models developed revealed the importance of simple molecular characteristics for differential binding to the D(2) receptor and the 5-HT transporter. These included molecular size and shape, solubility parameters, hydrogen donating potential, electrostatic parameters, stereochemistry and presence of nitrogen. The developed models and techniques employed are expected to be useful in the rational design of future therapeutic agents.

Chromatic characterization of a three-channel colorimeter using back-propagation neural networks

NASA Astrophysics Data System (ADS)

Pardo, P. J.; Pérez, A. L.; Suero, M. I.

2004-09-01

This work describes a method for the chromatic characterization of a three-channel colorimeter of recent design and construction dedicated to color vision research. The colorimeter consists of two fixed monochromators and a third monochromator interchangeable with a cathode ray tube or any other external light source. Back-propagation neural networks were used for the chromatic characterization to establish the relationship between each monochromator's input parameters and the tristimulus values of each chromatic stimulus generated. The results showed the effectiveness of this type of neural-network-based system for the chromatic characterization of the stimuli produced by any monochromator.

Calcium Dynamics in Basal Dendrites of Layer 5A and 5B Pyramidal Neurons Is Tuned to the Cell-Type Specific Physiological Action Potential Discharge

PubMed Central

Krieger, Patrik; de Kock, Christiaan P. J.; Frick, Andreas

2017-01-01

Layer 5 (L5) is a major neocortical output layer containing L5A slender-tufted (L5A-st) and L5B thick-tufted (L5B-tt) pyramidal neurons. These neuron types differ in their in vivo firing patterns, connectivity and dendritic morphology amongst other features, reflecting their specific functional role within the neocortical circuits. Here, we asked whether the active properties of the basal dendrites that receive the great majority of synaptic inputs within L5 differ between these two pyramidal neuron classes. To quantify their active properties, we measured the efficacy with which action potential (AP) firing patterns backpropagate along the basal dendrites by measuring the accompanying calcium transients using two-photon laser scanning microscopy in rat somatosensory cortex slices. For these measurements we used both “artificial” three-AP patterns and more complex physiological AP patterns that were previously recorded in anesthetized rats in L5A-st and L5B-tt neurons in response to whisker stimulation. We show that AP patterns with relatively few APs (3APs) evoke a calcium response in L5B-tt, but not L5A-st, that is dependent on the temporal pattern of the three APs. With more complex in vivo recorded AP patterns, the average calcium response was similar in the proximal dendrites but with a decay along dendrites (measured up to 100 μm) of L5B-tt but not L5A-st neurons. Interestingly however, the whisker evoked AP patterns—although very different for the two cell types—evoke similar calcium responses. In conclusion, although the effectiveness with which different AP patterns evoke calcium transients vary between L5A-st and L5B-tt cell, the calcium influx appears to be tuned such that whisker-evoked calcium transients are within the same dynamic range for both cell types. PMID:28744201

KChIP1 modulation of Kv4.3-mediated A-type K(+) currents and repetitive firing in hippocampal interneurons.

PubMed

Bourdeau, M L; Laplante, I; Laurent, C E; Lacaille, J-C

2011-03-10

Neuronal A-type K(+) channels regulate action potential waveform, back-propagation and firing frequency. In hippocampal CA1 interneurons located at the stratum lacunosum-moleculare/radiatum junction (LM/RAD), Kv4.3 mediates A-type K(+) currents and a Kv4 β-subunit of the Kv channel interacting protein (KChIP) family, KChIP1, appears specifically expressed in these cells. However, the functional role of this accessory subunit in A-type K(+) currents and interneuron excitability remains largely unknown. Thus, first we studied KChIP1 and Kv4.3 channel interactions in human embryonic kidney 293 (HEK293) cells and determined that KChIP1 coexpression modulated the biophysical properties of Kv4.3 A-type currents (faster recovery from inactivation, leftward shift of activation curve, faster rise time and slower decay) and this modulation was selectively prevented by KChIP1 short interfering RNA (siRNA) knockdown. Next, we evaluated the effects of KChIP1 down-regulation by siRNA on A-type K(+) currents in LM/RAD interneurons in slice cultures. Recovery from inactivation of A-type K(+) currents was slower after KChIP1 down-regulation but other properties were unchanged. In addition, down-regulation of KChIP1 levels did not affect action potential waveform and firing, but increased firing frequency during suprathreshold depolarizations, indicating that KChIP1 regulates interneuron excitability. The effects of KChIP1 down-regulation were cell-specific since CA1 pyramidal cells that do not express KChIP1 were unaffected. Overall, our findings suggest that KChIP1 interacts with Kv4.3 in LM/RAD interneurons, enabling faster recovery from inactivation of A-type currents and thus promoting stronger inhibitory control of firing during sustained activity. Copyright © 2011 IBRO. Published by Elsevier Ltd. All rights reserved.

Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation

PubMed Central

Scellier, Benjamin; Bengio, Yoshua

2017-01-01

We introduce Equilibrium Propagation, a learning framework for energy-based models. It involves only one kind of neural computation, performed in both the first phase (when the prediction is made) and the second phase of training (after the target or prediction error is revealed). Although this algorithm computes the gradient of an objective function just like Backpropagation, it does not need a special computation or circuit for the second phase, where errors are implicitly propagated. Equilibrium Propagation shares similarities with Contrastive Hebbian Learning and Contrastive Divergence while solving the theoretical issues of both algorithms: our algorithm computes the gradient of a well-defined objective function. Because the objective function is defined in terms of local perturbations, the second phase of Equilibrium Propagation corresponds to only nudging the prediction (fixed point or stationary distribution) toward a configuration that reduces prediction error. In the case of a recurrent multi-layer supervised network, the output units are slightly nudged toward their target in the second phase, and the perturbation introduced at the output layer propagates backward in the hidden layers. We show that the signal “back-propagated” during this second phase corresponds to the propagation of error derivatives and encodes the gradient of the objective function, when the synaptic update corresponds to a standard form of spike-timing dependent plasticity. This work makes it more plausible that a mechanism similar to Backpropagation could be implemented by brains, since leaky integrator neural computation performs both inference and error back-propagation in our model. The only local difference between the two phases is whether synaptic changes are allowed or not. We also show experimentally that multi-layer recurrently connected networks with 1, 2, and 3 hidden layers can be trained by Equilibrium Propagation on the permutation-invariant MNIST task. PMID:28522969

Neuromorphic learning of continuous-valued mappings from noise-corrupted data

NASA Technical Reports Server (NTRS)

Troudet, T.; Merrill, W.

1991-01-01

The effect of noise on the learning performance of the backpropagation algorithm is analyzed. A selective sampling of the training set is proposed to maximize the learning of control laws by backpropagation, when the data have been corrupted by noise. The training scheme is applied to the nonlinear control of a cart-pole system in the presence of noise. The neural computation provides the neurocontroller with good noise-filtering properties. In the presence of plant noise, the neurocontroller is found to be more stable than the teacher. A novel perspective on the application of neural network technology to control engineering is presented.

Learning polynomial feedforward neural networks by genetic programming and backpropagation.

PubMed

Nikolaev, N Y; Iba, H

2003-01-01

This paper presents an approach to learning polynomial feedforward neural networks (PFNNs). The approach suggests, first, finding the polynomial network structure by means of a population-based search technique relying on the genetic programming paradigm, and second, further adjustment of the best discovered network weights by an especially derived backpropagation algorithm for higher order networks with polynomial activation functions. These two stages of the PFNN learning process enable us to identify networks with good training as well as generalization performance. Empirical results show that this approach finds PFNN which outperform considerably some previous constructive polynomial network algorithms on processing benchmark time series.

Two papers on feed-forward networks

NASA Technical Reports Server (NTRS)

Buntine, Wray L.; Weigend, Andreas S.

1991-01-01

Connectionist feed-forward networks, trained with back-propagation, can be used both for nonlinear regression and for (discrete one-of-C) classification, depending on the form of training. This report contains two papers on feed-forward networks. The papers can be read independently. They are intended for the theoretically-aware practitioner or algorithm-designer; however, they also contain a review and comparison of several learning theories so they provide a perspective for the theoretician. The first paper works through Bayesian methods to complement back-propagation in the training of feed-forward networks. The second paper addresses a problem raised by the first: how to efficiently calculate second derivatives on feed-forward networks.

Fault-tolerant nonlinear adaptive flight control using sliding mode online learning.

PubMed

Krüger, Thomas; Schnetter, Philipp; Placzek, Robin; Vörsmann, Peter

2012-08-01

An expanded nonlinear model inversion flight control strategy using sliding mode online learning for neural networks is presented. The proposed control strategy is implemented for a small unmanned aircraft system (UAS). This class of aircraft is very susceptible towards nonlinearities like atmospheric turbulence, model uncertainties and of course system failures. Therefore, these systems mark a sensible testbed to evaluate fault-tolerant, adaptive flight control strategies. Within this work the concept of feedback linearization is combined with feed forward neural networks to compensate for inversion errors and other nonlinear effects. Backpropagation-based adaption laws of the network weights are used for online training. Within these adaption laws the standard gradient descent backpropagation algorithm is augmented with the concept of sliding mode control (SMC). Implemented as a learning algorithm, this nonlinear control strategy treats the neural network as a controlled system and allows a stable, dynamic calculation of the learning rates. While considering the system's stability, this robust online learning method therefore offers a higher speed of convergence, especially in the presence of external disturbances. The SMC-based flight controller is tested and compared with the standard gradient descent backpropagation algorithm in the presence of system failures. Copyright © 2012 Elsevier Ltd. All rights reserved.

Predictive analysis effectiveness in determining the epidemic disease infected area

NASA Astrophysics Data System (ADS)

Ibrahim, Najihah; Akhir, Nur Shazwani Md.; Hassan, Fadratul Hafinaz

2017-10-01

Epidemic disease outbreak had caused nowadays community to raise their great concern over the infectious disease controlling, preventing and handling methods to diminish the disease dissemination percentage and infected area. Backpropagation method was used for the counter measure and prediction analysis of the epidemic disease. The predictive analysis based on the backpropagation method can be determine via machine learning process that promotes the artificial intelligent in pattern recognition, statistics and features selection. This computational learning process will be integrated with data mining by measuring the score output as the classifier to the given set of input features through classification technique. The classification technique is the features selection of the disease dissemination factors that likely have strong interconnection between each other in causing infectious disease outbreaks. The predictive analysis of epidemic disease in determining the infected area was introduced in this preliminary study by using the backpropagation method in observation of other's findings. This study will classify the epidemic disease dissemination factors as the features for weight adjustment on the prediction of epidemic disease outbreaks. Through this preliminary study, the predictive analysis is proven to be effective method in determining the epidemic disease infected area by minimizing the error value through the features classification.

Prediction of protein tertiary structure from sequences using a very large back-propagation neural network

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

Liu, X.; Wilcox, G.L.

1993-12-31

We have implemented large scale back-propagation neural networks on a 544 node Connection Machine, CM-5, using the C language in MIMD mode. The program running on 512 processors performs backpropagation learning at 0.53 Gflops, which provides 76 million connection updates per second. We have applied the network to the prediction of protein tertiary structure from sequence information alone. A neural network with one hidden layer and 40 million connections is trained to learn the relationship between sequence and tertiary structure. The trained network yields predicted structures of some proteins on which it has not been trained given only their sequences.more » Presentation of the Fourier transform of the sequences accentuates periodicity in the sequence and yields good generalization with greatly increased training efficiency. Training simulations with a large, heterologous set of protein structures (111 proteins from CM-5 time) to solutions with under 2% RMS residual error within the training set (random responses give an RMS error of about 20%). Presentation of 15 sequences of related proteins in a testing set of 24 proteins yields predicted structures with less than 8% RMS residual error, indicating good apparent generalization.« less

Space shuttle main engine fault detection using neural networks

NASA Technical Reports Server (NTRS)

Bishop, Thomas; Greenwood, Dan; Shew, Kenneth; Stevenson, Fareed

1991-01-01

A method for on-line Space Shuttle Main Engine (SSME) anomaly detection and fault typing using a feedback neural network is described. The method involves the computation of features representing time-variance of SSME sensor parameters, using historical test case data. The network is trained, using backpropagation, to recognize a set of fault cases. The network is then able to diagnose new fault cases correctly. An essential element of the training technique is the inclusion of randomly generated data along with the real data, in order to span the entire input space of potential non-nominal data.

Multiple spatial modes based QKD over marine free-space optical channels in the presence of atmospheric turbulence.

PubMed

Sun, Xiaole; Djordjevic, Ivan B; Neifeld, Mark A

2016-11-28

We investigate a multiple spatial modes based quantum key distribution (QKD) scheme that employs multiple independent parallel beams through a marine free-space optical channel over open ocean. This approach provides the potential to increase secret key rate (SKR) linearly with the number of channels. To improve the SKR performance, we describe a back-propagation mode (BPM) method to mitigate the atmospheric turbulence effects. Our simulation results indicate that the secret key rate can be improved significantly by employing the proposed BPM-based multi-channel QKD scheme.

FDI and Accommodation Using NN Based Techniques

NASA Astrophysics Data System (ADS)

Garcia, Ramon Ferreiro; de Miguel Catoira, Alberto; Sanz, Beatriz Ferreiro

Massive application of dynamic backpropagation neural networks is used on closed loop control FDI (fault detection and isolation) tasks. The process dynamics is mapped by means of a trained backpropagation NN to be applied on residual generation. Process supervision is then applied to discriminate faults on process sensors, and process plant parameters. A rule based expert system is used to implement the decision making task and the corresponding solution in terms of faults accommodation and/or reconfiguration. Results show an efficient and robust FDI system which could be used as the core of an SCADA or alternatively as a complement supervision tool operating in parallel with the SCADA when applied on a heat exchanger.

Privacy-preserving backpropagation neural network learning.

PubMed

Chen, Tingting; Zhong, Sheng

2009-10-01

With the development of distributed computing environment , many learning problems now have to deal with distributed input data. To enhance cooperations in learning, it is important to address the privacy concern of each data holder by extending the privacy preservation notion to original learning algorithms. In this paper, we focus on preserving the privacy in an important learning model, multilayer neural networks. We present a privacy-preserving two-party distributed algorithm of backpropagation which allows a neural network to be trained without requiring either party to reveal her data to the other. We provide complete correctness and security analysis of our algorithms. The effectiveness of our algorithms is verified by experiments on various real world data sets.

Median Filter Noise Reduction of Image and Backpropagation Neural Network Model for Cervical Cancer Classification

NASA Astrophysics Data System (ADS)

Wutsqa, D. U.; Marwah, M.

2017-06-01

In this paper, we consider spatial operation median filter to reduce the noise in the cervical images yielded by colposcopy tool. The backpropagation neural network (BPNN) model is applied to the colposcopy images to classify cervical cancer. The classification process requires an image extraction by using a gray level co-occurrence matrix (GLCM) method to obtain image features that are used as inputs of BPNN model. The advantage of noise reduction is evaluated by comparing the performances of BPNN models with and without spatial operation median filter. The experimental result shows that the spatial operation median filter can improve the accuracy of the BPNN model for cervical cancer classification.

Water Level Prediction of Lake Cascade Mahakam Using Adaptive Neural Network Backpropagation (ANNBP)

NASA Astrophysics Data System (ADS)

Mislan; Gaffar, A. F. O.; Haviluddin; Puspitasari, N.

2018-04-01

A natural hazard information and flood events are indispensable as a form of prevention and improvement. One of the causes is flooding in the areas around the lake. Therefore, forecasting the surface of Lake water level to anticipate flooding is required. The purpose of this paper is implemented computational intelligence method namely Adaptive Neural Network Backpropagation (ANNBP) to forecasting the Lake Cascade Mahakam. Based on experiment, performance of ANNBP indicated that Lake water level prediction have been accurate by using mean square error (MSE) and mean absolute percentage error (MAPE). In other words, computational intelligence method can produce good accuracy. A hybrid and optimization of computational intelligence are focus in the future work.

Application of artificial neural networks with backpropagation technique in the financial data

NASA Astrophysics Data System (ADS)

Jaiswal, Jitendra Kumar; Das, Raja

2017-11-01

The propensity of applying neural networks has been proliferated in multiple disciplines for research activities since the past recent decades because of its powerful control with regulatory parameters for pattern recognition and classification. It is also being widely applied for forecasting in the numerous divisions. Since financial data have been readily available due to the involvement of computers and computing systems in the stock market premises throughout the world, researchers have also developed numerous techniques and algorithms to analyze the data from this sector. In this paper we have applied neural network with backpropagation technique to find the data pattern from finance section and prediction for stock values as well.

Modulation format dependence of digital nonlinearity compensation performance in optical fibre communication systems.

PubMed

Xu, Tianhua; Shevchenko, Nikita A; Lavery, Domaniç; Semrau, Daniel; Liga, Gabriele; Alvarado, Alex; Killey, Robert I; Bayvel, Polina

2017-02-20

The relationship between modulation format and the performance of multi-channel digital back-propagation (MC-DBP) in ideal Nyquist-spaced optical communication systems is investigated. It is found that the nonlinear distortions behave independent of modulation format in the case of full-field DBP, in contrast to the cases of electronic dispersion compensation and partial-bandwidth DBP. It is shown that the minimum number of steps per span required for MC-DBP depends on the chosen modulation format. For any given target information rate, there exists a possible trade-off between modulation format and back-propagated bandwidth, which could be used to reduce the computational complexity requirement of MC-DBP.

Developmental profile of localized spontaneous Ca2+ release events in the dendrites of rat hippocampal pyramidal neurons

PubMed Central

Miyazaki, Kenichi; Manita, Satoshi; Ross, William N.

2012-01-01

Summary Recent experiments demonstrate that localized spontaneous Ca2+ release events can be detected in the dendrites of pyramidal cells in the hippocampus and other neurons (J. Neurosci. 29:7833-7845, 2009). These events have some properties that resemble ryanodine receptor mediated “sparks” in myocytes, and some that resemble IP3 receptor mediated “puffs” in oocytes. They can be detected in the dendrites of rats of all tested ages between P3 and P80 (with sparser sampling in older rats), suggesting that they serve a general signaling function and are not just important in development. However, in younger rats the amplitudes of the events are larger than the amplitudes in older animals and almost as large as the amplitudes of Ca2+ signals from backpropagating action potentials (bAPs). The rise time of the event signal is fast at all ages and is comparable to the rise time of the bAP fluorescence signal at the same dendritic location. The decay time is slower in younger animals, primarily because of weaker Ca2+ extrusion mechanisms at that age. Diffusion away from a brief localized source is the major determinant of decay at all ages. A simple computational model closely simulates these events with extrusion rate the only age dependent variable. PMID:22951184

Loss of Functional A-Type Potassium Channels in the Dendrites of CA1 Pyramidal Neurons from a Mouse Model of Fragile X Syndrome

PubMed Central

Routh, Brandy N.; Johnston, Daniel

2013-01-01

Despite the critical importance of voltage-gated ion channels in neurons, very little is known about their functional properties in Fragile X syndrome: the most common form of inherited cognitive impairment. Using three complementary approaches, we investigated the physiological role of A-type K+ currents (IKA) in hippocampal CA1 pyramidal neurons from fmr1-/y mice. Direct measurement of IKA using cell-attached patch-clamp recordings revealed that there was significantly less IKA in the dendrites of CA1 neurons from fmr1-/y mice. Interestingly, the midpoint of activation for A-type K+ channels was hyperpolarized for fmr1-/y neurons compared with wild-type, which might partially compensate for the lower current density. Because of the rapid time course for recovery from steady-state inactivation, the dendritic A-type K+ current in CA1 neurons from both wild-type and fmr1-/y mice is likely mediated by KV4 containing channels. The net effect of the differences in IKA was that back-propagating action potentials had larger amplitudes producing greater calcium influx in the distal dendrites of fmr1-/y neurons. Furthermore, CA1 pyramidal neurons from fmr1-/y mice had a lower threshold for LTP induction. These data suggest that loss of IKA in hippocampal neurons may contribute to dendritic pathophysiology in Fragile X syndrome. PMID:24336711

Knowledge mining from clinical datasets using rough sets and backpropagation neural network.

PubMed

Nahato, Kindie Biredagn; Harichandran, Khanna Nehemiah; Arputharaj, Kannan

2015-01-01

The availability of clinical datasets and knowledge mining methodologies encourages the researchers to pursue research in extracting knowledge from clinical datasets. Different data mining techniques have been used for mining rules, and mathematical models have been developed to assist the clinician in decision making. The objective of this research is to build a classifier that will predict the presence or absence of a disease by learning from the minimal set of attributes that has been extracted from the clinical dataset. In this work rough set indiscernibility relation method with backpropagation neural network (RS-BPNN) is used. This work has two stages. The first stage is handling of missing values to obtain a smooth data set and selection of appropriate attributes from the clinical dataset by indiscernibility relation method. The second stage is classification using backpropagation neural network on the selected reducts of the dataset. The classifier has been tested with hepatitis, Wisconsin breast cancer, and Statlog heart disease datasets obtained from the University of California at Irvine (UCI) machine learning repository. The accuracy obtained from the proposed method is 97.3%, 98.6%, and 90.4% for hepatitis, breast cancer, and heart disease, respectively. The proposed system provides an effective classification model for clinical datasets.

Implementation of neural network for color properties of polycarbonates

NASA Astrophysics Data System (ADS)

Saeed, U.; Ahmad, S.; Alsadi, J.; Ross, D.; Rizvi, G.

2014-05-01

In present paper, the applicability of artificial neural networks (ANN) is investigated for color properties of plastics. The neural networks toolbox of Matlab 6.5 is used to develop and test the ANN model on a personal computer. An optimal design is completed for 10, 12, 14,16,18 & 20 hidden neurons on single hidden layer with five different algorithms: batch gradient descent (GD), batch variable learning rate (GDX), resilient back-propagation (RP), scaled conjugate gradient (SCG), levenberg-marquardt (LM) in the feed forward back-propagation neural network model. The training data for ANN is obtained from experimental measurements. There were twenty two inputs including resins, additives & pigments while three tristimulus color values L*, a* and b* were used as output layer. Statistical analysis in terms of Root-Mean-Squared (RMS), absolute fraction of variance (R squared), as well as mean square error is used to investigate the performance of ANN. LM algorithm with fourteen neurons on hidden layer in Feed Forward Back-Propagation of ANN model has shown best result in the present study. The degree of accuracy of the ANN model in reduction of errors is proven acceptable in all statistical analysis and shown in results. However, it was concluded that ANN provides a feasible method in error reduction in specific color tristimulus values.

Neural network modeling of a dolphin's sonar discrimination capabilities.

PubMed

Au, W W; Andersen, L N; Rasmussen, A R; Roitblat, H L; Nachtigall, P E

1995-07-01

The capability of an echolocating dolphin to discriminate differences in the wall thickness of cylinders was previously modeled by a counterpropagation neural network using only spectral information from the echoes. In this study, both time and frequency information were used to model the dolphin discrimination capabilities. Echoes from the same cylinders were digitized using a broadband simulated dolphin sonar signal with the transducer mounted on the dolphin's pen. The echoes were filtered by a bank of continuous constant-Q digital filters and the energy from each filter was computed in time increments of 1/bandwidth. Echo features of the standard and each comparison target were analyzed in pairs by a counterpropagation neural network, a backpropagation neural network, and a model using Euclidean distance measures. The backpropagation network performed better than both the counterpropagation network, and the Euclidean model, using either spectral-only features or combined temporal and spectral features. All models performed better using features containing both temporal and spectral information. The backpropagation network was able to perform better than the dolphins for noise-free echoes with Q values as low as 2 and 3. For a Q of 2, only temporal information was available. However, with noisy data, the network required a Q of 8 in order to perform as well as the dolphin.

LVQ and backpropagation neural networks applied to NASA SSME data

NASA Technical Reports Server (NTRS)

Doniere, Timothy F.; Dhawan, Atam P.

1993-01-01

Feedfoward neural networks with backpropagation learning have been used as function approximators for modeling the space shuttle main engine (SSME) sensor signals. The modeling of these sensor signals is aimed at the development of a sensor fault detection system that can be used during ground test firings. The generalization capability of a neural network based function approximator depends on the training vectors which in this application may be derived from a number of SSME ground test-firings. This yields a large number of training vectors. Large training sets can cause the time required to train the network to be very large. Also, the network may not be able to generalize for large training sets. To reduce the size of the training sets, the SSME test-firing data is reduced using the learning vector quantization (LVQ) based technique. Different compression ratios were used to obtain compressed data in training the neural network model. The performance of the neural model trained using reduced sets of training patterns is presented and compared with the performance of the model trained using complete data. The LVQ can also be used as a function approximator. The performance of the LVQ as a function approximator using reduced training sets is presented and compared with the performance of the backpropagation network.

Multilayer perceptron, fuzzy sets, and classification

NASA Technical Reports Server (NTRS)

Pal, Sankar K.; Mitra, Sushmita

1992-01-01

A fuzzy neural network model based on the multilayer perceptron, using the back-propagation algorithm, and capable of fuzzy classification of patterns is described. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of fuzzy class membership values. This allows efficient modeling of fuzzy or uncertain patterns with appropriate weights being assigned to the backpropagated errors depending upon the membership values at the corresponding outputs. During training, the learning rate is gradually decreased in discrete steps until the network converges to a minimum error solution. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of the conventional MLP, the Bayes classifier, and the other related models.

Superior Generalization Capability of Hardware-Learing Algorithm Developed for Self-Learning Neuron-MOS Neural Networks

NASA Astrophysics Data System (ADS)

Kondo, Shuhei; Shibata, Tadashi; Ohmi, Tadahiro

1995-02-01

We have investigated the learning performance of the hardware backpropagation (HBP) algorithm, a hardware-oriented learning algorithm developed for the self-learning architecture of neural networks constructed using neuron MOS (metal-oxide-semiconductor) transistors. The solution to finding a mirror symmetry axis in a 4×4 binary pixel array was tested by computer simulation based on the HBP algorithm. Despite the inherent restrictions imposed on the hardware-learning algorithm, HBP exhibits equivalent learning performance to that of the original backpropagation (BP) algorithm when all the pertinent parameters are optimized. Very importantly, we have found that HBP has a superior generalization capability over BP; namely, HBP exhibits higher performance in solving problems that the network has not yet learnt.

Error vector magnitude based parameter estimation for digital filter back-propagation mitigating SOA distortions in 16-QAM.

PubMed

Amiralizadeh, Siamak; Nguyen, An T; Rusch, Leslie A

2013-08-26

We investigate the performance of digital filter back-propagation (DFBP) using coarse parameter estimation for mitigating SOA nonlinearity in coherent communication systems. We introduce a simple, low overhead method for parameter estimation for DFBP based on error vector magnitude (EVM) as a figure of merit. The bit error rate (BER) penalty achieved with this method has negligible penalty as compared to DFBP with fine parameter estimation. We examine different bias currents for two commercial SOAs used as booster amplifiers in our experiments to find optimum operating points and experimentally validate our method. The coarse parameter DFBP efficiently compensates SOA-induced nonlinearity for both SOA types in 80 km propagation of 16-QAM signal at 22 Gbaud.

Computation of Ground-State Properties in Molecular Systems: Back-Propagation with Auxiliary-Field Quantum Monte Carlo.

PubMed

Motta, Mario; Zhang, Shiwei

2017-11-14

We address the computation of ground-state properties of chemical systems and realistic materials within the auxiliary-field quantum Monte Carlo method. The phase constraint to control the Fermion phase problem requires the random walks in Slater determinant space to be open-ended with branching. This in turn makes it necessary to use back-propagation (BP) to compute averages and correlation functions of operators that do not commute with the Hamiltonian. Several BP schemes are investigated, and their optimization with respect to the phaseless constraint is considered. We propose a modified BP method for the computation of observables in electronic systems, discuss its numerical stability and computational complexity, and assess its performance by computing ground-state properties in several molecular systems, including small organic molecules.

Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer

NASA Astrophysics Data System (ADS)

Werdiningsih, Indah; Zaman, Badrus; Nuqoba, Barry

2017-08-01

This paper presents classification of brain cancer using wavelet transformation and Adaptive Neighborhood Based Modified Backpropagation (ANMBP). Three stages of the processes, namely features extraction, features reduction, and classification process. Wavelet transformation is used for feature extraction and ANMBP is used for classification process. The result of features extraction is feature vectors. Features reduction used 100 energy values per feature and 10 energy values per feature. Classifications of brain cancer are normal, alzheimer, glioma, and carcinoma. Based on simulation results, 10 energy values per feature can be used to classify brain cancer correctly. The correct classification rate of proposed system is 95 %. This research demonstrated that wavelet transformation can be used for features extraction and ANMBP can be used for classification of brain cancer.

Application of Gaussian beam ray-equivalent model and back-propagation artificial neural network in laser diode fast axis collimator assembly.

PubMed

Yu, Hao; Rossi, Giammarco; Braglia, Andrea; Perrone, Guido

2016-08-10

The paper presents the development of a tool based on a back-propagation artificial neural network to assist in the accurate positioning of the lenses used to collimate the beam from semiconductor laser diodes along the so-called fast axis. After training using a Gaussian beam ray-equivalent model, the network is capable of indicating the tilt, decenter, and defocus of such lenses from the measured field distribution, so the operator can determine the errors with respect to the actual lens position and optimize the diode assembly procedure. An experimental validation using a typical configuration exploited in multi-emitter diode module assembly and fast axis collimating lenses with different focal lengths and numerical apertures is reported.

Analysis backpropagation methods with neural network for prediction of children's ability in psychomotoric

NASA Astrophysics Data System (ADS)

Izhari, F.; Dhany, H. W.; Zarlis, M.; Sutarman

2018-03-01

A good age in optimizing aspects of development is at the age of 4-6 years, namely with psychomotor development. Psychomotor is broader, more difficult to monitor but has a meaningful value for the child's life because it directly affects his behavior and deeds. Therefore, there is a problem to predict the child's ability level based on psychomotor. This analysis uses backpropagation method analysis with artificial neural network to predict the ability of the child on the psychomotor aspect by generating predictions of the child's ability on psychomotor and testing there is a mean squared error (MSE) value at the end of the training of 0.001. There are 30% of children aged 4-6 years have a good level of psychomotor ability, excellent, less good, and good enough.

Gradient calculations for dynamic recurrent neural networks: a survey.

PubMed

Pearlmutter, B A

1995-01-01

Surveys learning algorithms for recurrent neural networks with hidden units and puts the various techniques into a common framework. The authors discuss fixed point learning algorithms, namely recurrent backpropagation and deterministic Boltzmann machines, and nonfixed point algorithms, namely backpropagation through time, Elman's history cutoff, and Jordan's output feedback architecture. Forward propagation, an on-line technique that uses adjoint equations, and variations thereof, are also discussed. In many cases, the unified presentation leads to generalizations of various sorts. The author discusses advantages and disadvantages of temporally continuous neural networks in contrast to clocked ones continues with some "tricks of the trade" for training, using, and simulating continuous time and recurrent neural networks. The author presents some simulations, and at the end, addresses issues of computational complexity and learning speed.

An Expedient Study on Back-Propagation (BPN) Neural Networks for Modeling Automated Evaluation of the Answers and Progress of Deaf Students' That Possess Basic Knowledge of the English Language and Computer Skills

NASA Astrophysics Data System (ADS)

Vrettaros, John; Vouros, George; Drigas, Athanasios S.

This article studies the expediency of using neural networks technology and the development of back-propagation networks (BPN) models for modeling automated evaluation of the answers and progress of deaf students' that possess basic knowledge of the English language and computer skills, within a virtual e-learning environment. The performance of the developed neural models is evaluated with the correlation factor between the neural networks' response values and the real value data as well as the percentage measurement of the error between the neural networks' estimate values and the real value data during its training process and afterwards with unknown data that weren't used in the training process.

A comparison of manual neuronal reconstruction from biocytin histology or 2-photon imaging: morphometry and computer modeling

PubMed Central

Blackman, Arne V.; Grabuschnig, Stefan; Legenstein, Robert; Sjöström, P. Jesper

2014-01-01

Accurate 3D reconstruction of neurons is vital for applications linking anatomy and physiology. Reconstructions are typically created using Neurolucida after biocytin histology (BH). An alternative inexpensive and fast method is to use freeware such as Neuromantic to reconstruct from fluorescence imaging (FI) stacks acquired using 2-photon laser-scanning microscopy during physiological recording. We compare these two methods with respect to morphometry, cell classification, and multicompartmental modeling in the NEURON simulation environment. Quantitative morphological analysis of the same cells reconstructed using both methods reveals that whilst biocytin reconstructions facilitate tracing of more distal collaterals, both methods are comparable in representing the overall morphology: automated clustering of reconstructions from both methods successfully separates neocortical basket cells from pyramidal cells but not BH from FI reconstructions. BH reconstructions suffer more from tissue shrinkage and compression artifacts than FI reconstructions do. FI reconstructions, on the other hand, consistently have larger process diameters. Consequently, significant differences in NEURON modeling of excitatory post-synaptic potential (EPSP) forward propagation are seen between the two methods, with FI reconstructions exhibiting smaller depolarizations. Simulated action potential backpropagation (bAP), however, is indistinguishable between reconstructions obtained with the two methods. In our hands, BH reconstructions are necessary for NEURON modeling and detailed morphological tracing, and thus remain state of the art, although they are more labor intensive, more expensive, and suffer from a higher failure rate due to the occasional poor outcome of histological processing. However, for a subset of anatomical applications such as cell type identification, FI reconstructions are superior, because of indistinguishable classification performance with greater ease of use, essentially 100% success rate, and lower cost. PMID:25071470

Reliability analysis of C-130 turboprop engine components using artificial neural network

NASA Astrophysics Data System (ADS)

Qattan, Nizar A.

In this study, we predict the failure rate of Lockheed C-130 Engine Turbine. More than thirty years of local operational field data were used for failure rate prediction and validation. The Weibull regression model and the Artificial Neural Network model including (feed-forward back-propagation, radial basis neural network, and multilayer perceptron neural network model); will be utilized to perform this study. For this purpose, the thesis will be divided into five major parts. First part deals with Weibull regression model to predict the turbine general failure rate, and the rate of failures that require overhaul maintenance. The second part will cover the Artificial Neural Network (ANN) model utilizing the feed-forward back-propagation algorithm as a learning rule. The MATLAB package will be used in order to build and design a code to simulate the given data, the inputs to the neural network are the independent variables, the output is the general failure rate of the turbine, and the failures which required overhaul maintenance. In the third part we predict the general failure rate of the turbine and the failures which require overhaul maintenance, using radial basis neural network model on MATLAB tool box. In the fourth part we compare the predictions of the feed-forward back-propagation model, with that of Weibull regression model, and radial basis neural network model. The results show that the failure rate predicted by the feed-forward back-propagation artificial neural network model is closer in agreement with radial basis neural network model compared with the actual field-data, than the failure rate predicted by the Weibull model. By the end of the study, we forecast the general failure rate of the Lockheed C-130 Engine Turbine, the failures which required overhaul maintenance and six categorical failures using multilayer perceptron neural network (MLP) model on DTREG commercial software. The results also give an insight into the reliability of the engine turbine under actual operating conditions, which can be used by aircraft operators for assessing system and component failures and customizing the maintenance programs recommended by the manufacturer.

Adaptive Critic-based Neurofuzzy Controller for the Steam Generator Water Level

NASA Astrophysics Data System (ADS)

Fakhrazari, Amin; Boroushaki, Mehrdad

2008-06-01

In this paper, an adaptive critic-based neurofuzzy controller is presented for water level regulation of nuclear steam generators. The problem has been of great concern for many years as the steam generator is a highly nonlinear system showing inverse response dynamics especially at low operating power levels. Fuzzy critic-based learning is a reinforcement learning method based on dynamic programming. The only information available for the critic agent is the system feedback which is interpreted as the last action the controller has performed in the previous state. The signal produced by the critic agent is used alongside the backpropagation of error algorithm to tune online conclusion parts of the fuzzy inference rules. The critic agent here has a proportional-derivative structure and the fuzzy rule base has nine rules. The proposed controller shows satisfactory transient responses, disturbance rejection and robustness to model uncertainty. Its simple design procedure and structure, nominates it as one of the suitable controller designs for the steam generator water level control in nuclear power plant industry.

Quantum machine learning with glow for episodic tasks and decision games

NASA Astrophysics Data System (ADS)

Clausen, Jens; Briegel, Hans J.

2018-02-01

We consider a general class of models, where a reinforcement learning (RL) agent learns from cyclic interactions with an external environment via classical signals. Perceptual inputs are encoded as quantum states, which are subsequently transformed by a quantum channel representing the agent's memory, while the outcomes of measurements performed at the channel's output determine the agent's actions. The learning takes place via stepwise modifications of the channel properties. They are described by an update rule that is inspired by the projective simulation (PS) model and equipped with a glow mechanism that allows for a backpropagation of policy changes, analogous to the eligibility traces in RL and edge glow in PS. In this way, the model combines features of PS with the ability for generalization, offered by its physical embodiment as a quantum system. We apply the agent to various setups of an invasion game and a grid world, which serve as elementary model tasks allowing a direct comparison with a basic classical PS agent.

Tropical Timber Identification using Backpropagation Neural Network

NASA Astrophysics Data System (ADS)

Siregar, B.; Andayani, U.; Fatihah, N.; Hakim, L.; Fahmi, F.

2017-01-01

Each and every type of wood has different characteristics. Identifying the type of wood properly is important, especially for industries that need to know the type of timber specifically. However, it requires expertise in identifying the type of wood and only limited experts available. In addition, the manual identification even by experts is rather inefficient because it requires a lot of time and possibility of human errors. To overcome these problems, a digital image based method to identify the type of timber automatically is needed. In this study, backpropagation neural network is used as artificial intelligence component. Several stages were developed: a microscope image acquisition, pre-processing, feature extraction using gray level co-occurrence matrix and normalization of data extraction using decimal scaling features. The results showed that the proposed method was able to identify the timber with an accuracy of 94%.

Classification of epileptiform and wicket spike of EEG pattern using backpropagation neural network

NASA Astrophysics Data System (ADS)

Puspita, Juni Wijayanti; Jaya, Agus Indra; Gunadharma, Suryani

2017-03-01

Epilepsy is characterized by recurrent seizures that is resulted by permanent brain abnormalities. One of tools to support the diagnosis of epilepsy is Electroencephalograph (EEG), which describes the recording of brain electrical activity. Abnormal EEG patterns in epilepsy patients consist of Spike and Sharp waves. While both waves, there is a normal pattern that sometimes misinterpreted as epileptiform by electroenchepalographer (EEGer), namely Wicket Spike. The main difference of the three waves are on the time duration that related to the frequency. In this study, we proposed a method to classify a EEG wave into Sharp wave, Spike wave or Wicket spike group using Backpropagation Neural Network based on the frequency and amplitude of each wave. The results show that the proposed method can classifies the three group of waves with good accuracy.

Digital backpropagation accounting for polarization-mode dispersion.

PubMed

Czegledi, Cristian B; Liga, Gabriele; Lavery, Domaniç; Karlsson, Magnus; Agrell, Erik; Savory, Seb J; Bayvel, Polina

2017-02-06

Digital backpropagation (DBP) is a promising digital-domain technique to mitigate Kerr-induced nonlinear interference. While it successfully removes deterministic signal-signal interactions, the performance of ideal DBP is limited by stochastic effects, such as polarization-mode dispersion (PMD). In this paper, we consider an ideal full-field DBP implementation and modify it to additionally account for PMD; reversing the PMD effects in the backward propagation by passing the reverse propagated signal also through PMD sections, which concatenated equal the inverse of the PMD in the forward propagation. These PMD sections are calculated analytically at the receiver based on the total accumulated PMD of the link estimated from channel equalizers. Numerical simulations show that, accounting for nonlinear polarization-related interactions in the modified DBP algorithm, additional signal-to-noise ratio gains of 1.1 dB are obtained for transmission over 1000 km.

A robust cloud registration method based on redundant data reduction using backpropagation neural network and shift window

NASA Astrophysics Data System (ADS)

Xin, Meiting; Li, Bing; Yan, Xiao; Chen, Lei; Wei, Xiang

2018-02-01

A robust coarse-to-fine registration method based on the backpropagation (BP) neural network and shift window technology is proposed in this study. Specifically, there are three steps: coarse alignment between the model data and measured data, data simplification based on the BP neural network and point reservation in the contour region of point clouds, and fine registration with the reweighted iterative closest point algorithm. In the process of rough alignment, the initial rotation matrix and the translation vector between the two datasets are obtained. After performing subsequent simplification operations, the number of points can be reduced greatly. Therefore, the time and space complexity of the accurate registration can be significantly reduced. The experimental results show that the proposed method improves the computational efficiency without loss of accuracy.

ODTbrain: a Python library for full-view, dense diffraction tomography.

PubMed

Müller, Paul; Schürmann, Mirjam; Guck, Jochen

2015-11-04

Analyzing the three-dimensional (3D) refractive index distribution of a single cell makes it possible to describe and characterize its inner structure in a marker-free manner. A dense, full-view tomographic data set is a set of images of a cell acquired for multiple rotational positions, densely distributed from 0 to 360 degrees. The reconstruction is commonly realized by projection tomography, which is based on the inversion of the Radon transform. The reconstruction quality of projection tomography is greatly improved when first order scattering, which becomes relevant when the imaging wavelength is comparable to the characteristic object size, is taken into account. This advanced reconstruction technique is called diffraction tomography. While many implementations of projection tomography are available today, there is no publicly available implementation of diffraction tomography so far. We present a Python library that implements the backpropagation algorithm for diffraction tomography in 3D. By establishing benchmarks based on finite-difference time-domain (FDTD) simulations, we showcase the superiority of the backpropagation algorithm over the backprojection algorithm. Furthermore, we discuss how measurment parameters influence the reconstructed refractive index distribution and we also give insights into the applicability of diffraction tomography to biological cells. The present software library contains a robust implementation of the backpropagation algorithm. The algorithm is ideally suited for the application to biological cells. Furthermore, the implementation is a drop-in replacement for the classical backprojection algorithm and is made available to the large user community of the Python programming language.

Experiments in Neural-Network Control of a Free-Flying Space Robot

NASA Technical Reports Server (NTRS)

Wilson, Edward

1995-01-01

Four important generic issues are identified and addressed in some depth in this thesis as part of the development of an adaptive neural network based control system for an experimental free flying space robot prototype. The first issue concerns the importance of true system level design of the control system. A new hybrid strategy is developed here, in depth, for the beneficial integration of neural networks into the total control system. A second important issue in neural network control concerns incorporating a priori knowledge into the neural network. In many applications, it is possible to get a reasonably accurate controller using conventional means. If this prior information is used purposefully to provide a starting point for the optimizing capabilities of the neural network, it can provide much faster initial learning. In a step towards addressing this issue, a new generic Fully Connected Architecture (FCA) is developed for use with backpropagation. A third issue is that neural networks are commonly trained using a gradient based optimization method such as backpropagation; but many real world systems have Discrete Valued Functions (DVFs) that do not permit gradient based optimization. One example is the on-off thrusters that are common on spacecraft. A new technique is developed here that now extends backpropagation learning for use with DVFs. The fourth issue is that the speed of adaptation is often a limiting factor in the implementation of a neural network control system. This issue has been strongly resolved in the research by drawing on the above new contributions.

An intelligent switch with back-propagation neural network based hybrid power system

NASA Astrophysics Data System (ADS)

Perdana, R. H. Y.; Fibriana, F.

2018-03-01

The consumption of conventional energy such as fossil fuels plays the critical role in the global warming issues. The carbon dioxide, methane, nitrous oxide, etc. could lead the greenhouse effects and change the climate pattern. In fact, 77% of the electrical energy is generated from fossil fuels combustion. Therefore, it is necessary to use the renewable energy sources for reducing the conventional energy consumption regarding electricity generation. This paper presents an intelligent switch to combine both energy resources, i.e., the solar panels as the renewable energy with the conventional energy from the State Electricity Enterprise (PLN). The artificial intelligence technology with the back-propagation neural network was designed to control the flow of energy that is distributed dynamically based on renewable energy generation. By the continuous monitoring on each load and source, the dynamic pattern of the intelligent switch was better than the conventional switching method. The first experimental results for 60 W solar panels showed the standard deviation of the trial at 0.7 and standard deviation of the experiment at 0.28. The second operation for a 900 W of solar panel obtained the standard deviation of the trial at 0.05 and 0.18 for the standard deviation of the experiment. Moreover, the accuracy reached 83% using this method. By the combination of the back-propagation neural network with the observation of energy usage of the load using wireless sensor network, each load can be evenly distributed and will impact on the reduction of conventional energy usage.

Deconvolution using a neural network

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

Lehman, S.K.

1990-11-15

Viewing one dimensional deconvolution as a matrix inversion problem, we compare a neural network backpropagation matrix inverse with LMS, and pseudo-inverse. This is a largely an exercise in understanding how our neural network code works. 1 ref.

Data fusion in data scarce areas using a back-propagation artificial neural network model: a case study of the South China Sea

NASA Astrophysics Data System (ADS)

Wang, Zheng; Mao, Zhihua; Xia, Junshi; Du, Peijun; Shi, Liangliang; Huang, Haiqing; Wang, Tianyu; Gong, Fang; Zhu, Qiankun

2018-06-01

The cloud cover for the South China Sea and its coastal area is relatively large throughout the year, which limits the potential application of optical remote sensing. A HJ-charge-coupled device (HJ-CCD) has the advantages of wide field, high temporal resolution, and short repeat cycle. However, this instrument suffers from its use of only four relatively low-quality bands which can't adequately resolve the features of long wavelengths. The Landsat Enhanced Thematic Mapper-plus (ETM+) provides high-quality data, however, the Scan Line Corrector (SLC) stopped working and caused striping of remote sensed images, which dramatically reduced the coverage of the ETM+ data. In order to combine the advantages of the HJ-CCD and Landsat ETM+ data, we adopted a back-propagation artificial neural network (BP-ANN) to fuse these two data types for this study. The results showed that the fused output data not only have the advantage of data intactness for the HJ-CCD, but also have the advantages of the multi-spectral and high radiometric resolution of the ETM+ data. Moreover, the fused data were analyzed qualitatively, quantitatively and from a practical application point of view. Experimental studies indicated that the fused data have a full spatial distribution, multi-spectral bands, high radiometric resolution, a small difference between the observed and fused output data, and a high correlation between the observed and fused data. The excellent performance in its practical application is a further demonstration that the fused data are of high quality.

Denoising by coupled partial differential equations and extracting phase by backpropagation neural networks for electronic speckle pattern interferometry.

PubMed

Tang, Chen; Lu, Wenjing; Chen, Song; Zhang, Zhen; Li, Botao; Wang, Wenping; Han, Lin

2007-10-20

We extend and refine previous work [Appl. Opt. 46, 2907 (2007)]. Combining the coupled nonlinear partial differential equations (PDEs) denoising model with the ordinary differential equations enhancement method, we propose the new denoising and enhancing model for electronic speckle pattern interferometry (ESPI) fringe patterns. Meanwhile, we propose the backpropagation neural networks (BPNN) method to obtain unwrapped phase values based on a skeleton map instead of traditional interpolations. We test the introduced methods on the computer-simulated speckle ESPI fringe patterns and experimentally obtained fringe pattern, respectively. The experimental results show that the coupled nonlinear PDEs denoising model is capable of effectively removing noise, and the unwrapped phase values obtained by the BPNN method are much more accurate than those obtained by the well-known traditional interpolation. In addition, the accuracy of the BPNN method is adjustable by changing the parameters of networks such as the number of neurons.

Two States Mapping Based Time Series Neural Network Model for Compensation Prediction Residual Error

NASA Astrophysics Data System (ADS)

Jung, Insung; Koo, Lockjo; Wang, Gi-Nam

2008-11-01

The objective of this paper was to design a model of human bio signal data prediction system for decreasing of prediction error using two states mapping based time series neural network BP (back-propagation) model. Normally, a lot of the industry has been applied neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has got a residual error between real value and prediction result. Therefore, we designed two states of neural network model for compensation residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We determined that most of the simulation cases were satisfied by the two states mapping based time series prediction model. In particular, small sample size of times series were more accurate than the standard MLP model.

Pattern recognition of visible and near-infrared spectroscopy from bayberry juice by use of partial least squares and a backpropagation neural network

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

Cen Haiyan; Bao Yidan; He Yong

2006-10-10

Visible and near-infrared reflectance (visible-NIR) spectroscopy is applied to discriminate different varieties of bayberry juices. The discrimination of visible-NIR spectra from samples is a matter of pattern recognition. By partial least squares (PLS), the spectrum is reduced to certain factors, which are then taken as the input of the backpropagation neural network (BPNN). Through training and prediction, three different varieties of bayberry juice are classified based on the output of the BPNN. In addition, a mathematical model is built and the algorithm is optimized. With proper parameters in the training set,100% accuracy is obtained by the BPNN. Thus it ismore » concluded that the PLS analysis combined with the BPNN is an alternative for pattern recognition based on visible and NIR spectroscopy.« less

Self-organizing neural networks--an alternative way of cluster analysis in clinical chemistry.

PubMed

Reibnegger, G; Wachter, H

1996-04-15

Supervised learning schemes have been employed by several workers for training neural networks designed to solve clinical problems. We demonstrate that unsupervised techniques can also produce interesting and meaningful results. Using a data set on the chemical composition of milk from 22 different mammals, we demonstrate that self-organizing feature maps (Kohonen networks) as well as a modified version of error backpropagation technique yield results mimicking conventional cluster analysis. Both techniques are able to project a potentially multi-dimensional input vector onto a two-dimensional space whereby neighborhood relationships remain conserved. Thus, these techniques can be used for reducing dimensionality of complicated data sets and for enhancing comprehensibility of features hidden in the data matrix.

Ultrasonic/Sonic Jackhammer

NASA Technical Reports Server (NTRS)

Bar-Cohen, Yoseph (Inventor); Herz, Jack L. (Inventor); Sherrit, Stewart (Inventor)

2014-01-01

The invention provides a novel jackhammer that utilizes ultrasonic and/or sonic vibrations as source of power. It is easy to operate and does not require extensive training, requiring substantially less physical capabilities from the user and thereby increasing the pool of potential operators. An important safety benefit is that it does not fracture resilient or compliant materials such as cable channels and conduits, tubing, plumbing, cabling and other embedded fixtures that may be encountered along the impact path. While the ultrasonic/sonic jackhammer of the invention is able to cut concrete and asphalt, it generates little back-propagated shocks or vibrations onto the mounting fixture, and can be operated from an automatic platform or robotic system. PNEUMATICS; ULTRASONICS; IMPACTORS; DRILLING; HAMMERS BRITTLE MATERIALS; DRILL BITS; PROTOTYPES; VIBRATION

Rainfall prediction with backpropagation method

NASA Astrophysics Data System (ADS)

Wahyuni, E. G.; Fauzan, L. M. F.; Abriyani, F.; Muchlis, N. F.; Ulfa, M.

2018-03-01

Rainfall is an important factor in many fields, such as aviation and agriculture. Although it has been assisted by technology but the accuracy can not reach 100% and there is still the possibility of error. Though current rainfall prediction information is needed in various fields, such as agriculture and aviation fields. In the field of agriculture, to obtain abundant and quality yields, farmers are very dependent on weather conditions, especially rainfall. Rainfall is one of the factors that affect the safety of aircraft. To overcome the problems above, then it’s required a system that can accurately predict rainfall. In predicting rainfall, artificial neural network modeling is applied in this research. The method used in modeling this artificial neural network is backpropagation method. Backpropagation methods can result in better performance in repetitive exercises. This means that the weight of the ANN interconnection can approach the weight it should be. Another advantage of this method is the ability in the learning process adaptively and multilayer owned on this method there is a process of weight changes so as to minimize error (fault tolerance). Therefore, this method can guarantee good system resilience and consistently work well. The network is designed using 4 input variables, namely air temperature, air humidity, wind speed, and sunshine duration and 3 output variables ie low rainfall, medium rainfall, and high rainfall. Based on the research that has been done, the network can be used properly, as evidenced by the results of the prediction of the system precipitation is the same as the results of manual calculations.

Time-Reversal Location of the 2004 M6.0 Parkfield Earthquake Using the Vertical Component of Seismic Data.

NASA Astrophysics Data System (ADS)

Larmat, C. S.; Johnson, P.; Huang, L.; Randall, G.; Patton, H.; Montagner, J.

2007-12-01

In this work we describe Time Reversal experiments applying seismic waves recorded from the 2004 M6.0 Parkfield Earthquake. The reverse seismic wavefield is created by time-reversing recorded seismograms and then injecting them from the seismograph locations into a whole entire Earth velocity model. The concept is identical to acoustic Time-Reversal Mirror laboratory experiments except the seismic data are numerically backpropagated through a velocity model (Fink, 1996; Ulrich et al, 2007). Data are backpropagated using the finite element code SPECFEM3D (Komatitsch et al, 2002), employing the velocity model s20rts (Ritsema et al, 2000). In this paper, we backpropagate only the vertical component of seismic data from about 100 broadband surface stations located worldwide (FDSN), using the period band of 23-120s. We use those only waveforms that are highly correlated with forward-propagated synthetics. The focusing quality depends upon the type of waves back- propagated; for the vertical displacement component the possible types include body waves, Rayleigh waves, or their combination. We show that Rayleigh waves, both real and artifact, dominate the reverse movie in all cases. They are created during rebroadcast of the time reverse signals, including body wave phases, because we use point-like-force sources for injection. The artifact waves, termed "ghosts" manifest as surface waves, do not correspond to real wave phases during the forward propagation. The surface ghost waves can significantly blur the focusing at the source. We find that the ghosts cannot be easily eliminated in the manner described by Tsogka&Papanicolaou (2002). It is necessary to understand how they are created in order to remove them during TRM studies, particularly when using only the body waves. For this moderate magnitude of earthquake we demonstrate the robustness of the TRM as an alternative location method despite the restriction to vertical component phases. One advantage of TRM location is that it does not rely on a prior picking of specific phases (Larmat et al, 2006). In future work will be conducted TRM backpropagation using the horizontal displacement components of seismic data as well as study the source complexity (double couples). Our ultimate goal is to determine whether or not Time Reversal offers information about the source that cannot be obtained from other methods, or that complements other methods.

The subcellular distribution of T-type Ca2+ channels in interneurons of the lateral geniculate nucleus.

PubMed

Allken, Vaneeda; Chepkoech, Joy-Loi; Einevoll, Gaute T; Halnes, Geir

2014-01-01

Inhibitory interneurons (INs) in the lateral geniculate nucleus (LGN) provide both axonal and dendritic GABA output to thalamocortical relay cells (TCs). Distal parts of the IN dendrites often enter into complex arrangements known as triadic synapses, where the IN dendrite plays a dual role as postsynaptic to retinal input and presynaptic to TC dendrites. Dendritic GABA release can be triggered by retinal input, in a highly localized process that is functionally isolated from the soma, but can also be triggered by somatically elicited Ca(2+)-spikes and possibly by backpropagating action potentials. Ca(2+)-spikes in INs are predominantly mediated by T-type Ca(2+)-channels (T-channels). Due to the complex nature of the dendritic signalling, the function of the IN is likely to depend critically on how T-channels are distributed over the somatodendritic membrane (T-distribution). To study the relationship between the T-distribution and several IN response properties, we here run a series of simulations where we vary the T-distribution in a multicompartmental IN model with a realistic morphology. We find that the somatic response to somatic current injection is facilitated by a high T-channel density in the soma-region. Conversely, a high T-channel density in the distal dendritic region is found to facilitate dendritic signalling in both the outward direction (increases the response in distal dendrites to somatic input) and the inward direction (the soma responds stronger to distal synaptic input). The real T-distribution is likely to reflect a compromise between several neural functions, involving somatic response patterns and dendritic signalling.

The Subcellular Distribution of T-Type Ca2+ Channels in Interneurons of the Lateral Geniculate Nucleus

PubMed Central

Allken, Vaneeda; Chepkoech, Joy-Loi; Einevoll, Gaute T.; Halnes, Geir

2014-01-01

Inhibitory interneurons (INs) in the lateral geniculate nucleus (LGN) provide both axonal and dendritic GABA output to thalamocortical relay cells (TCs). Distal parts of the IN dendrites often enter into complex arrangements known as triadic synapses, where the IN dendrite plays a dual role as postsynaptic to retinal input and presynaptic to TC dendrites. Dendritic GABA release can be triggered by retinal input, in a highly localized process that is functionally isolated from the soma, but can also be triggered by somatically elicited Ca2+-spikes and possibly by backpropagating action potentials. Ca2+-spikes in INs are predominantly mediated by T-type Ca2+-channels (T-channels). Due to the complex nature of the dendritic signalling, the function of the IN is likely to depend critically on how T-channels are distributed over the somatodendritic membrane (T-distribution). To study the relationship between the T-distribution and several IN response properties, we here run a series of simulations where we vary the T-distribution in a multicompartmental IN model with a realistic morphology. We find that the somatic response to somatic current injection is facilitated by a high T-channel density in the soma-region. Conversely, a high T-channel density in the distal dendritic region is found to facilitate dendritic signalling in both the outward direction (increases the response in distal dendrites to somatic input) and the inward direction (the soma responds stronger to distal synaptic input). The real T-distribution is likely to reflect a compromise between several neural functions, involving somatic response patterns and dendritic signalling. PMID:25268996

GPU-accelerated adjoint algorithmic differentiation

NASA Astrophysics Data System (ADS)

Gremse, Felix; Höfter, Andreas; Razik, Lukas; Kiessling, Fabian; Naumann, Uwe

2016-03-01

Many scientific problems such as classifier training or medical image reconstruction can be expressed as minimization of differentiable real-valued cost functions and solved with iterative gradient-based methods. Adjoint algorithmic differentiation (AAD) enables automated computation of gradients of such cost functions implemented as computer programs. To backpropagate adjoint derivatives, excessive memory is potentially required to store the intermediate partial derivatives on a dedicated data structure, referred to as the ;tape;. Parallelization is difficult because threads need to synchronize their accesses during taping and backpropagation. This situation is aggravated for many-core architectures, such as Graphics Processing Units (GPUs), because of the large number of light-weight threads and the limited memory size in general as well as per thread. We show how these limitations can be mediated if the cost function is expressed using GPU-accelerated vector and matrix operations which are recognized as intrinsic functions by our AAD software. We compare this approach with naive and vectorized implementations for CPUs. We use four increasingly complex cost functions to evaluate the performance with respect to memory consumption and gradient computation times. Using vectorization, CPU and GPU memory consumption could be substantially reduced compared to the naive reference implementation, in some cases even by an order of complexity. The vectorization allowed usage of optimized parallel libraries during forward and reverse passes which resulted in high speedups for the vectorized CPU version compared to the naive reference implementation. The GPU version achieved an additional speedup of 7.5 ± 4.4, showing that the processing power of GPUs can be utilized for AAD using this concept. Furthermore, we show how this software can be systematically extended for more complex problems such as nonlinear absorption reconstruction for fluorescence-mediated tomography.

Identification of lithofacies using Kohonen self-organizing maps

USGS Publications Warehouse

Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.

2002-01-01

Lithofacies identification is a primary task in reservoir characterization. Traditional techniques of lithofacies identification from core data are costly, and it is difficult to extrapolate to non-cored wells. We present a low-cost automated technique using Kohonen self-organizing maps (SOMs) to identify systematically and objectively lithofacies from well log data. SOMs are unsupervised artificial neural networks that map the input space into clusters in a topological form whose organization is related to trends in the input data. A case study used five wells located in Appleton Field, Escambia County, Alabama (Smackover Formation, limestone and dolomite, Oxfordian, Jurassic). A five-input, one-dimensional output approach is employed, assuming the lithofacies are in ascending/descending order with respect to paleoenvironmental energy levels. To consider the possible appearance of new logfacies not seen in training mode, which may potentially appear in test wells, the maximum number of outputs is set to 20 instead of four, the designated number of lithosfacies in the study area. This study found eleven major clusters. The clusters were compared to depositional lithofacies identified by manual core examination. The clusters were ordered by the SOM in a pattern consistent with environmental gradients inferred from core examination: bind/boundstone, grainstone, packstone, and wackestone. This new approach predicted lithofacies identity from well log data with 78.8% accuracy which is more accurate than using a backpropagation neural network (57.3%). The clusters produced by the SOM are ordered with respect to paleoenvironmental energy levels. This energy-related clustering provides geologists and petroleum engineers with valuable geologic information about the logfacies and their interrelationships. This advantage is not obtained in backpropagation neural networks and adaptive resonance theory neural networks. ?? 2002 Elsevier Science Ltd. All rights reserved.

GPU-Accelerated Adjoint Algorithmic Differentiation.

PubMed

Gremse, Felix; Höfter, Andreas; Razik, Lukas; Kiessling, Fabian; Naumann, Uwe

2016-03-01

Many scientific problems such as classifier training or medical image reconstruction can be expressed as minimization of differentiable real-valued cost functions and solved with iterative gradient-based methods. Adjoint algorithmic differentiation (AAD) enables automated computation of gradients of such cost functions implemented as computer programs. To backpropagate adjoint derivatives, excessive memory is potentially required to store the intermediate partial derivatives on a dedicated data structure, referred to as the "tape". Parallelization is difficult because threads need to synchronize their accesses during taping and backpropagation. This situation is aggravated for many-core architectures, such as Graphics Processing Units (GPUs), because of the large number of light-weight threads and the limited memory size in general as well as per thread. We show how these limitations can be mediated if the cost function is expressed using GPU-accelerated vector and matrix operations which are recognized as intrinsic functions by our AAD software. We compare this approach with naive and vectorized implementations for CPUs. We use four increasingly complex cost functions to evaluate the performance with respect to memory consumption and gradient computation times. Using vectorization, CPU and GPU memory consumption could be substantially reduced compared to the naive reference implementation, in some cases even by an order of complexity. The vectorization allowed usage of optimized parallel libraries during forward and reverse passes which resulted in high speedups for the vectorized CPU version compared to the naive reference implementation. The GPU version achieved an additional speedup of 7.5 ± 4.4, showing that the processing power of GPUs can be utilized for AAD using this concept. Furthermore, we show how this software can be systematically extended for more complex problems such as nonlinear absorption reconstruction for fluorescence-mediated tomography.

GPU-Accelerated Adjoint Algorithmic Differentiation

PubMed Central

Gremse, Felix; Höfter, Andreas; Razik, Lukas; Kiessling, Fabian; Naumann, Uwe

2015-01-01

Many scientific problems such as classifier training or medical image reconstruction can be expressed as minimization of differentiable real-valued cost functions and solved with iterative gradient-based methods. Adjoint algorithmic differentiation (AAD) enables automated computation of gradients of such cost functions implemented as computer programs. To backpropagate adjoint derivatives, excessive memory is potentially required to store the intermediate partial derivatives on a dedicated data structure, referred to as the “tape”. Parallelization is difficult because threads need to synchronize their accesses during taping and backpropagation. This situation is aggravated for many-core architectures, such as Graphics Processing Units (GPUs), because of the large number of light-weight threads and the limited memory size in general as well as per thread. We show how these limitations can be mediated if the cost function is expressed using GPU-accelerated vector and matrix operations which are recognized as intrinsic functions by our AAD software. We compare this approach with naive and vectorized implementations for CPUs. We use four increasingly complex cost functions to evaluate the performance with respect to memory consumption and gradient computation times. Using vectorization, CPU and GPU memory consumption could be substantially reduced compared to the naive reference implementation, in some cases even by an order of complexity. The vectorization allowed usage of optimized parallel libraries during forward and reverse passes which resulted in high speedups for the vectorized CPU version compared to the naive reference implementation. The GPU version achieved an additional speedup of 7.5 ± 4.4, showing that the processing power of GPUs can be utilized for AAD using this concept. Furthermore, we show how this software can be systematically extended for more complex problems such as nonlinear absorption reconstruction for fluorescence-mediated tomography. PMID:26941443

Advances in Artificial Neural Networks - Methodological Development and Application

USDA-ARS?s Scientific Manuscript database

Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other ne...

Noise-enhanced convolutional neural networks.

PubMed

Audhkhasi, Kartik; Osoba, Osonde; Kosko, Bart

2016-06-01

Injecting carefully chosen noise can speed convergence in the backpropagation training of a convolutional neural network (CNN). The Noisy CNN algorithm speeds training on average because the backpropagation algorithm is a special case of the generalized expectation-maximization (EM) algorithm and because such carefully chosen noise always speeds up the EM algorithm on average. The CNN framework gives a practical way to learn and recognize images because backpropagation scales with training data. It has only linear time complexity in the number of training samples. The Noisy CNN algorithm finds a special separating hyperplane in the network's noise space. The hyperplane arises from the likelihood-based positivity condition that noise-boosts the EM algorithm. The hyperplane cuts through a uniform-noise hypercube or Gaussian ball in the noise space depending on the type of noise used. Noise chosen from above the hyperplane speeds training on average. Noise chosen from below slows it on average. The algorithm can inject noise anywhere in the multilayered network. Adding noise to the output neurons reduced the average per-iteration training-set cross entropy by 39% on a standard MNIST image test set of handwritten digits. It also reduced the average per-iteration training-set classification error by 47%. Adding noise to the hidden layers can also reduce these performance measures. The noise benefit is most pronounced for smaller data sets because the largest EM hill-climbing gains tend to occur in the first few iterations. This noise effect can assist random sampling from large data sets because it allows a smaller random sample to give the same or better performance than a noiseless sample gives. Copyright © 2015 Elsevier Ltd. All rights reserved.

ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci

PubMed Central

2010-01-01

Background Growing interest and burgeoning technology for discovering genetic mechanisms that influence disease processes have ushered in a flood of genetic association studies over the last decade, yet little heritability in highly studied complex traits has been explained by genetic variation. Non-additive gene-gene interactions, which are not often explored, are thought to be one source of this "missing" heritability. Methods Stochastic methods employing evolutionary algorithms have demonstrated promise in being able to detect and model gene-gene and gene-environment interactions that influence human traits. Here we demonstrate modifications to a neural network algorithm in ATHENA (the Analysis Tool for Heritable and Environmental Network Associations) resulting in clear performance improvements for discovering gene-gene interactions that influence human traits. We employed an alternative tree-based crossover, backpropagation for locally fitting neural network weights, and incorporation of domain knowledge obtainable from publicly accessible biological databases for initializing the search for gene-gene interactions. We tested these modifications in silico using simulated datasets. Results We show that the alternative tree-based crossover modification resulted in a modest increase in the sensitivity of the ATHENA algorithm for discovering gene-gene interactions. The performance increase was highly statistically significant when backpropagation was used to locally fit NN weights. We also demonstrate that using domain knowledge to initialize the search for gene-gene interactions results in a large performance increase, especially when the search space is larger than the search coverage. Conclusions We show that a hybrid optimization procedure, alternative crossover strategies, and incorporation of domain knowledge from publicly available biological databases can result in marked increases in sensitivity and performance of the ATHENA algorithm for detecting and modelling gene-gene interactions that influence a complex human trait. PMID:20875103

A comparison of two neural network schemes for navigation

NASA Technical Reports Server (NTRS)

Munro, Paul W.

1989-01-01

Neural networks have been applied to tasks in several areas of artificial intelligence, including vision, speech, and language. Relatively little work has been done in the area of problem solving. Two approaches to path-finding are presented, both using neural network techniques. Both techniques require a training period. Training under the back propagation (BPL) method was accomplished by presenting representations of (current position, goal position) pairs as input and appropriate actions as output. The Hebbian/interactive activation (HIA) method uses the Hebbian rule to associate points that are nearby. A path to a goal is found by activating a representation of the goal in the network and processing until the current position is activated above some threshold level. BPL, using back-propagation learning, failed to learn, except in a very trivial fashion, that is equivalent to table lookup techniques. HIA, performed much better, and required storage of fewer weights. In drawing a comparison, it is important to note that back propagation techniques depend critically upon the forms of representation used, and can be sensitive to parameters in the simulations; hence the BPL technique may yet yield strong results.

A comparison of two neural network schemes for navigation

NASA Technical Reports Server (NTRS)

Munro, Paul

1990-01-01

Neural networks have been applied to tasks in several areas of artificial intelligence, including vision, speech, and language. Relatively little work has been done in the area of problem solving. Two approaches to path-finding are presented, both using neural network techniques. Both techniques require a training period. Training under the back propagation (BPL) method was accomplished by presenting representations of current position, goal position pairs as input and appropriate actions as output. The Hebbian/interactive activation (HIA) method uses the Hebbian rule to associate points that are nearby. A path to a goal is found by activating a representation of the goal in the network and processing until the current position is activated above some threshold level. BPL, using back-propagation learning, failed to learn, except in a very trivial fashion, that is equivalent to table lookup techniques. HIA, performed much better, and required storage of fewer weights. In drawing a comparison, it is important to note that back propagation techniques depend critically upon the forms of representation used, and can be sensitive to parameters in the simulations; hence the BPL technique may yet yield strong results.

Clipping in neurocontrol by adaptive dynamic programming.

PubMed

Fairbank, Michael; Prokhorov, Danil; Alonso, Eduardo

2014-10-01

In adaptive dynamic programming, neurocontrol, and reinforcement learning, the objective is for an agent to learn to choose actions so as to minimize a total cost function. In this paper, we show that when discretized time is used to model the motion of the agent, it can be very important to do clipping on the motion of the agent in the final time step of the trajectory. By clipping, we mean that the final time step of the trajectory is to be truncated such that the agent stops exactly at the first terminal state reached, and no distance further. We demonstrate that when clipping is omitted, learning performance can fail to reach the optimum, and when clipping is done properly, learning performance can improve significantly. The clipping problem we describe affects algorithms that use explicit derivatives of the model functions of the environment to calculate a learning gradient. These include backpropagation through time for control and methods based on dual heuristic programming. However, the clipping problem does not significantly affect methods based on heuristic dynamic programming, temporal differences learning, or policy-gradient learning algorithms.

Multimodal Deep Autoencoder for Human Pose Recovery.

PubMed

Hong, Chaoqun; Yu, Jun; Wan, Jian; Tao, Dacheng; Wang, Meng

2015-12-01

Video-based human pose recovery is usually conducted by retrieving relevant poses using image features. In the retrieving process, the mapping between 2D images and 3D poses is assumed to be linear in most of the traditional methods. However, their relationships are inherently non-linear, which limits recovery performance of these methods. In this paper, we propose a novel pose recovery method using non-linear mapping with multi-layered deep neural network. It is based on feature extraction with multimodal fusion and back-propagation deep learning. In multimodal fusion, we construct hypergraph Laplacian with low-rank representation. In this way, we obtain a unified feature description by standard eigen-decomposition of the hypergraph Laplacian matrix. In back-propagation deep learning, we learn a non-linear mapping from 2D images to 3D poses with parameter fine-tuning. The experimental results on three data sets show that the recovery error has been reduced by 20%-25%, which demonstrates the effectiveness of the proposed method.

Psycho-acoustic evaluation of the indoor noise in cabins of a naval vessel using a back-propagation neural network algorithm

NASA Astrophysics Data System (ADS)

Han, Hyung-Suk

2012-12-01

The indoor noise of a ship is usually determined using the A-weighted sound pressure level. However, in order to better understand this phenomenon, evaluation parameters that more accurately reflect the human sense of hearing are required. To find the level of the satisfaction index of the noise inside a naval vessel such as "Loudness" and "Annoyance", psycho-acoustic evaluation of various sound recordings from the naval vessel was performed in a laboratory. The objective of this paper is to develop a single index of "Loudness" and "Annoyance" for noise inside a naval vessel according to a psycho-acoustic evaluation by using psychological responses such as Noise Rating (NR), Noise Criterion (NC), Room Criterion (RC), Preferred Speech Interference Level (PSIL) and loudness level. Additionally, in order to determine a single index of satisfaction for noise such as "Loudness" and "Annoyance", with respect to a human's sense of hearing, a back-propagation neural network is applied.

Hypertensive retinopathy identification through retinal fundus image using backpropagation neural network

NASA Astrophysics Data System (ADS)

Syahputra, M. F.; Amalia, C.; Rahmat, R. F.; Abdullah, D.; Napitupulu, D.; Setiawan, M. I.; Albra, W.; Nurdin; Andayani, U.

2018-03-01

Hypertension or high blood pressure can cause damage of blood vessels in the retina of eye called hypertensive retinopathy (HR). In the event Hypertension, it will cause swelling blood vessels and a decrese in retina performance. To detect HR in patients body, it is usually performed through physical examination of opthalmoscope which is still conducted manually by an ophthalmologist. Certainly, in such a manual manner, takes a ong time for a doctor to detetct HR on aa patient based on retina fundus iamge. To overcome ths problem, a method is needed to identify the image of retinal fundus automatically. In this research, backpropagation neural network was used as a method for retinal fundus identification. The steps performed prior to identification were pre-processing (green channel, contrast limited adapative histogram qualization (CLAHE), morphological close, background exclusion, thresholding and connected component analysis), feature extraction using zoning. The results show that the proposed method is able to identify retinal fundus with an accuracy of 95% with maximum epoch of 1500.

Sensorless control for permanent magnet synchronous motor using a neural network based adaptive estimator

NASA Astrophysics Data System (ADS)

Kwon, Chung-Jin; Kim, Sung-Joong; Han, Woo-Young; Min, Won-Kyoung

2005-12-01

The rotor position and speed estimation of permanent-magnet synchronous motor(PMSM) was dealt with. By measuring the phase voltages and currents of the PMSM drive, two diagonally recurrent neural network(DRNN) based observers, a neural current observer and a neural velocity observer were developed. DRNN which has self-feedback of the hidden neurons ensures that the outputs of DRNN contain the whole past information of the system even if the inputs of DRNN are only the present states and inputs of the system. Thus the structure of DRNN may be simpler than that of feedforward and fully recurrent neural networks. If the backpropagation method was used for the training of the DRNN the problem of slow convergence arise. In order to reduce this problem, recursive prediction error(RPE) based learning method for the DRNN was presented. The simulation results show that the proposed approach gives a good estimation of rotor speed and position, and RPE based training has requires a shorter computation time compared to backpropagation based training.

Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm.

PubMed

Sinha, S K; Karray, F

2002-01-01

Pipeline surface defects such as holes and cracks cause major problems for utility managers, particularly when the pipeline is buried under the ground. Manual inspection for surface defects in the pipeline has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection system using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer utility managers an opportunity to significantly improve quality and reduce costs. A recognition and classification of pipe cracks using images analysis and neuro-fuzzy algorithm is proposed. In the preprocessing step the scanned images of pipe are analyzed and crack features are extracted. In the classification step the neuro-fuzzy algorithm is developed that employs a fuzzy membership function and error backpropagation algorithm. The idea behind the proposed approach is that the fuzzy membership function will absorb variation of feature values and the backpropagation network, with its learning ability, will show good classification efficiency.

Applying artificial intelligence technology to support decision-making in nursing: A case study in Taiwan.

PubMed

Liao, Pei-Hung; Hsu, Pei-Ti; Chu, William; Chu, Woei-Chyn

2015-06-01

This study applied artificial intelligence to help nurses address problems and receive instructions through information technology. Nurses make diagnoses according to professional knowledge, clinical experience, and even instinct. Without comprehensive knowledge and thinking, diagnostic accuracy can be compromised and decisions may be delayed. We used a back-propagation neural network and other tools for data mining and statistical analysis. We further compared the prediction accuracy of the previous methods with an adaptive-network-based fuzzy inference system and the back-propagation neural network, identifying differences in the questions and in nurse satisfaction levels before and after using the nursing information system. This study investigated the use of artificial intelligence to generate nursing diagnoses. The percentage of agreement between diagnoses suggested by the information system and those made by nurses was as much as 87 percent. When patients are hospitalized, we can calculate the probability of various nursing diagnoses based on certain characteristics. © The Author(s) 2013.

Dynamic Hebbian Cross-Correlation Learning Resolves the Spike Timing Dependent Plasticity Conundrum.

PubMed

Olde Scheper, Tjeerd V; Meredith, Rhiannon M; Mansvelder, Huibert D; van Pelt, Jaap; van Ooyen, Arjen

2017-01-01

Spike Timing-Dependent Plasticity has been found to assume many different forms. The classic STDP curve, with one potentiating and one depressing window, is only one of many possible curves that describe synaptic learning using the STDP mechanism. It has been shown experimentally that STDP curves may contain multiple LTP and LTD windows of variable width, and even inverted windows. The underlying STDP mechanism that is capable of producing such an extensive, and apparently incompatible, range of learning curves is still under investigation. In this paper, it is shown that STDP originates from a combination of two dynamic Hebbian cross-correlations of local activity at the synapse. The correlation of the presynaptic activity with the local postsynaptic activity is a robust and reliable indicator of the discrepancy between the presynaptic neuron and the postsynaptic neuron's activity. The second correlation is between the local postsynaptic activity with dendritic activity which is a good indicator of matching local synaptic and dendritic activity. We show that this simple time-independent learning rule can give rise to many forms of the STDP learning curve. The rule regulates synaptic strength without the need for spike matching or other supervisory learning mechanisms. Local differences in dendritic activity at the synapse greatly affect the cross-correlation difference which determines the relative contributions of different neural activity sources. Dendritic activity due to nearby synapses, action potentials, both forward and back-propagating, as well as inhibitory synapses will dynamically modify the local activity at the synapse, and the resulting STDP learning rule. The dynamic Hebbian learning rule ensures furthermore, that the resulting synaptic strength is dynamically stable, and that interactions between synapses do not result in local instabilities. The rule clearly demonstrates that synapses function as independent localized computational entities, each contributing to the global activity, not in a simply linear fashion, but in a manner that is appropriate to achieve local and global stability of the neuron and the entire dendritic structure.

A stable second order method for training back propagation networks

NASA Technical Reports Server (NTRS)

Nachtsheim, Philip R.

1993-01-01

A simple method for improving the learning rate of the back-propagation algorithm is described. The basis of the method is that approximate second order corrections can be incorporated in the output units. The extended method leads to significant improvements in the convergence rate.

Coherent detection and digital signal processing for fiber optic communications

NASA Astrophysics Data System (ADS)

Ip, Ezra

The drive towards higher spectral efficiency in optical fiber systems has generated renewed interest in coherent detection. We review different detection methods, including noncoherent, differentially coherent, and coherent detection, as well as hybrid detection methods. We compare the modulation methods that are enabled and their respective performances in a linear regime. An important system parameter is the number of degrees of freedom (DOF) utilized in transmission. Polarization-multiplexed quadrature-amplitude modulation maximizes spectral efficiency and power efficiency as it uses all four available DOF contained in the two field quadratures in the two polarizations. Dual-polarization homodyne or heterodyne downconversion are linear processes that can fully recover the received signal field in these four DOF. When downconverted signals are sampled at the Nyquist rate, compensation of transmission impairments can be performed using digital signal processing (DSP). Software based receivers benefit from the robustness of DSP, flexibility in design, and ease of adaptation to time-varying channels. Linear impairments, including chromatic dispersion (CD) and polarization-mode dispersion (PMD), can be compensated quasi-exactly using finite impulse response filters. In practical systems, sampling the received signal at 3/2 times the symbol rate is sufficient to enable an arbitrary amount of CD and PMD to be compensated for a sufficiently long equalizer whose tap length scales linearly with transmission distance. Depending on the transmitted constellation and the target bit error rate, the analog-to-digital converter (ADC) should have around 5 to 6 bits of resolution. Digital coherent receivers are naturally suited for the implementation of feedforward carrier recovery, which has superior linewidth tolerance than phase-locked loops, and does not suffer from feedback delay constraints. Differential bit encoding can be used to prevent catastrophic receiver failure due to cycle slips. In systems where nonlinear effects are concentrated mostly at fiber locations with small accumulated dispersion, nonlinear phase de-rotation is a low-complexity algorithm that can partially mitigate nonlinear effects. For systems with arbitrary dispersion maps, however, backpropagation is the only universal technique that can jointly compensate dispersion and fiber nonlinearity. Backpropagation requires solving the nonlinear Schrodinger equation at the receiver, and has high computational cost. Backpropagation is most effective when dispersion compensation fibers are removed, and when signal processing is performed at three times oversampling. Backpropagation can improve system performance and increase transmission distance. With anticipated advances in analog-to-digital converters and integrated circuit technology, DSP-based coherent receivers at bit rates up to 100 Gb/s should become practical in the near future.

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

Trainable hardware for dynamical computing using error backpropagation through physical media

NASA Astrophysics Data System (ADS)

Hermans, Michiel; Burm, Michaël; van Vaerenbergh, Thomas; Dambre, Joni; Bienstman, Peter

2015-03-01

Neural networks are currently implemented on digital Von Neumann machines, which do not fully leverage their intrinsic parallelism. We demonstrate how to use a novel class of reconfigurable dynamical systems for analogue information processing, mitigating this problem. Our generic hardware platform for dynamic, analogue computing consists of a reciprocal linear dynamical system with nonlinear feedback. Thanks to reciprocity, a ubiquitous property of many physical phenomena like the propagation of light and sound, the error backpropagation—a crucial step for tuning such systems towards a specific task—can happen in hardware. This can potentially speed up the optimization process significantly, offering important benefits for the scalability of neuro-inspired hardware. In this paper, we show, using one experimentally validated and one conceptual example, that such systems may provide a straightforward mechanism for constructing highly scalable, fully dynamical analogue computers.

Strategies for mapping synaptic inputs on dendrites in vivo by combining two-photon microscopy, sharp intracellular recording, and pharmacology

PubMed Central

Levy, Manuel; Schramm, Adrien E.; Kara, Prakash

2012-01-01

Uncovering the functional properties of individual synaptic inputs on single neurons is critical for understanding the computational role of synapses and dendrites. Previous studies combined whole-cell patch recording to load neurons with a fluorescent calcium indicator and two-photon imaging to map subcellular changes in fluorescence upon sensory stimulation. By hyperpolarizing the neuron below spike threshold, the patch electrode ensured that changes in fluorescence associated with synaptic events were isolated from those caused by back-propagating action potentials. This technique holds promise for determining whether the existence of unique cortical feature maps across different species may be associated with distinct wiring diagrams. However, the use of whole-cell patch for mapping inputs on dendrites is challenging in large mammals, due to brain pulsations and the accumulation of fluorescent dye in the extracellular milieu. Alternatively, sharp intracellular electrodes have been used to label neurons with fluorescent dyes, but the current passing capabilities of these high impedance electrodes may be insufficient to prevent spiking. In this study, we tested whether sharp electrode recording is suitable for mapping functional inputs on dendrites in the cat visual cortex. We compared three different strategies for suppressing visually evoked spikes: (1) hyperpolarization by intracellular current injection, (2) pharmacological blockade of voltage-gated sodium channels by intracellular QX-314, and (3) GABA iontophoresis from a perisomatic electrode glued to the intracellular electrode. We found that functional inputs on dendrites could be successfully imaged using all three strategies. However, the best method for preventing spikes was GABA iontophoresis with low currents (5–10 nA), which minimally affected the local circuit. Our methods advance the possibility of determining functional connectivity in preparations where whole-cell patch may be impractical. PMID:23248588

Convergent phosphomodulation of the major neuronal dendritic potassium channel Kv4.2 by pituitary adenylate cyclase-activating polypeptide.

PubMed

Gupte, Raeesa P; Kadunganattil, Suraj; Shepherd, Andrew J; Merrill, Ronald; Planer, William; Bruchas, Michael R; Strack, Stefan; Mohapatra, Durga P

2016-02-01

The endogenous neuropeptide pituitary adenylate cyclase-activating polypeptide (PACAP) is secreted by both neuronal and non-neuronal cells in the brain and spinal cord, in response to pathological conditions such as stroke, seizures, chronic inflammatory and neuropathic pain. PACAP has been shown to exert various neuromodulatory and neuroprotective effects. However, direct influence of PACAP on the function of intrinsically excitable ion channels that are critical to both hyperexcitation as well as cell death, remain largely unexplored. The major dendritic K(+) channel Kv4.2 is a critical regulator of neuronal excitability, back-propagating action potentials in the dendrites, and modulation of synaptic inputs. We identified, cloned and characterized the downstream signaling originating from the activation of three PACAP receptor (PAC1) isoforms that are expressed in rodent hippocampal neurons that also exhibit abundant expression of Kv4.2 protein. Activation of PAC1 by PACAP leads to phosphorylation of Kv4.2 and downregulation of channel currents, which can be attenuated by inhibition of either PKA or ERK1/2 activity. Mechanistically, this dynamic downregulation of Kv4.2 function is a consequence of reduction in the density of surface channels, without any influence on the voltage-dependence of channel activation. Interestingly, PKA-induced effects on Kv4.2 were mediated by ERK1/2 phosphorylation of the channel at two critical residues, but not by direct channel phosphorylation by PKA, suggesting a convergent phosphomodulatory signaling cascade. Altogether, our findings suggest a novel GPCR-channel signaling crosstalk between PACAP/PAC1 and Kv4.2 channel in a manner that could lead to neuronal hyperexcitability. Copyright © 2015 Elsevier Ltd. All rights reserved.

Neuroplasticity of A-type potassium channel complexes induced by chronic alcohol exposure enhances dendritic calcium transients in hippocampus.

PubMed

Mulholland, Patrick J; Spencer, Kathryn B; Hu, Wei; Kroener, Sven; Chandler, L Judson

2015-06-01

Chronic alcohol-induced cognitive impairments and maladaptive plasticity of glutamatergic synapses are well-documented. However, it is unknown if prolonged alcohol exposure affects dendritic signaling that may underlie hippocampal dysfunction in alcoholics. Back-propagation of action potentials (bAPs) into apical dendrites of hippocampal neurons provides distance-dependent signals that modulate dendritic and synaptic plasticity. The amplitude of bAPs decreases with distance from the soma that is thought to reflect an increase in the density of Kv4.2 channels toward distal dendrites. The aim of this study was to quantify changes in hippocampal Kv4.2 channel function and expression using electrophysiology, Ca(2+) imaging, and western blot analyses in a well-characterized in vitro model of chronic alcohol exposure. Chronic alcohol exposure significantly decreased expression of Kv4.2 channels and KChIP3 in hippocampus. This reduction was associated with an attenuation of macroscopic A-type K(+) currents in CA1 neurons. Chronic alcohol exposure increased bAP-evoked Ca(2+) transients in the distal apical dendrites of CA1 pyramidal neurons. The enhanced bAP-evoked Ca(2+) transients induced by chronic alcohol exposure were not related to synaptic targeting of N-methyl-D-aspartate (NMDA) receptors or morphological adaptations in apical dendritic arborization. These data suggest that chronic alcohol-induced decreases in Kv4.2 channel function possibly mediated by a downregulation of KChIP3 drive the elevated bAP-associated Ca(2+) transients in distal apical dendrites. Alcohol-induced enhancement of bAPs may affect metaplasticity and signal integration in apical dendrites of hippocampal neurons leading to alterations in hippocampal function.

Dendritic A-type potassium channel subunit expression in CA1 hippocampal interneurons.

PubMed

Menegola, M; Misonou, H; Vacher, H; Trimmer, J S

2008-06-26

Voltage-gated potassium (Kv) channels are important and diverse determinants of neuronal excitability and exhibit specific expression patterns throughout the brain. Among Kv channels, Kv4 channels are major determinants of somatodendritic A-type current and are essential in controlling the amplitude of backpropagating action potentials (BAPs) into neuronal dendrites. BAPs have been well studied in a variety of neurons, and have been recently described in hippocampal and cortical interneurons, a heterogeneous population of GABAergic inhibitory cells that regulate activity of principal cells and neuronal networks. We used well-characterized mouse monoclonal antibodies against the Kv4.3 and potassium channel interacting protein (KChIP) 1 subunits of A-type Kv channels, and antibodies against different interneuron markers in single- and double-label immunohistochemistry experiments to analyze the expression patterns of Kv4.3 and KChIP1 in hippocampal Ammon's horn (CA1) neurons. Immunohistochemistry was performed on 40 mum rat brain sections using nickel-enhanced diaminobenzidine staining or multiple-label immunofluorescence. Our results show that Kv4.3 and KChIP1 component subunits of A-type channels are co-localized in the soma and dendrites of a large number of GABAergic hippocampal interneurons. These subunits co-localize extensively but not completely with markers defining the four major interneuron subpopulations tested (parvalbumin, calbindin, calretinin, and somatostatin). These results suggest that CA1 hippocampal interneurons can be divided in two groups according to the expression of Kv4.3/KChIP1 channel subunits. Antibodies against Kv4.3 and KChIP1 represent an important new tool for identifying a subpopulation of hippocampal interneurons with a unique dendritic A-type channel complement and ability to control BAPs.

Where Do Features Come From?

ERIC Educational Resources Information Center

Hinton, Geoffrey

2014-01-01

It is possible to learn multiple layers of non-linear features by backpropagating error derivatives through a feedforward neural network. This is a very effective learning procedure when there is a huge amount of labeled training data, but for many learning tasks very few labeled examples are available. In an effort to overcome the need for…

The power of neural nets

NASA Technical Reports Server (NTRS)

Ryan, J. P.; Shah, B. H.

1987-01-01

Implementation of the Hopfield net which is used in the image processing type of applications where only partial information about the image may be available is discussed. The image classification type of algorithm of Hopfield and other learning algorithms, such as the Boltzmann machine and the back-propagation training algorithm, have many vital applications in space.

Spectrally Shaped DP-16QAM Super-Channel Transmission with Multi-Channel Digital Back-Propagation

PubMed Central

Maher, Robert; Xu, Tianhua; Galdino, Lidia; Sato, Masaki; Alvarado, Alex; Shi, Kai; Savory, Seb J.; Thomsen, Benn C.; Killey, Robert I.; Bayvel, Polina

2015-01-01

The achievable transmission capacity of conventional optical fibre communication systems is limited by nonlinear distortions due to the Kerr effect and the difficulty in modulating the optical field to effectively use the available fibre bandwidth. In order to achieve a high information spectral density (ISD), while simultaneously maintaining transmission reach, multi-channel fibre nonlinearity compensation and spectrally efficient data encoding must be utilised. In this work, we use a single coherent super-receiver to simultaneously receive a DP-16QAM super-channel, consisting of seven spectrally shaped 10GBd sub-carriers spaced at the Nyquist frequency. Effective nonlinearity mitigation is achieved using multi-channel digital back-propagation (MC-DBP) and this technique is combined with an optimised forward error correction implementation to demonstrate a record gain in transmission reach of 85%; increasing the maximum transmission distance from 3190 km to 5890 km, with an ISD of 6.60 b/s/Hz. In addition, this report outlines for the first time, the sensitivity of MC-DBP gain to linear transmission line impairments and defines a trade-off between performance and complexity. PMID:25645457

Neural network recognition of chemical class information in mobility spectra obtained at high temperatures

NASA Technical Reports Server (NTRS)

Bell, S.; Nazarov, E.; Wang, Y. F.; Rodriguez, J. E.; Eiceman, G. A.

2000-01-01

A minimal neural network was applied to a large library of high-temperature mobility spectra drawn from 16 chemical classes including 154 substances with 2000 spectra at various concentrations. A genetic algorithm was used to create a representative subset of points from the mobility spectrum as input to a cascade-type back-propagation network. This network demonstrated that significant information specific to chemical class was located in the spectral region near the reactant ions. This network failed to generalize the solution to unfamiliar compounds necessitating the use of complete spectra in network processing. An extended back-propagation network classified unfamiliar chemicals by functional group with a mean for average values of 0.83 without sulfides and 0.79 with sulfides. Further experiments confirmed that chemical class information was resident in the spectral region near the reactant ions. Deconvolution of spectra demonstrated the presence of ions, merged with the reactant ion peaks that originated from introduced samples. The ability of the neural network to generalize the solution to unfamiliar compounds suggests that these ions are distinct and class specific.

Prediction of Contact Fatigue Life of Alloy Cast Steel Rolls Using Back-Propagation Neural Network

NASA Astrophysics Data System (ADS)

Jin, Huijin; Wu, Sujun; Peng, Yuncheng

2013-12-01

In this study, an artificial neural network (ANN) was employed to predict the contact fatigue life of alloy cast steel rolls (ACSRs) as a function of alloy composition, heat treatment parameters, and contact stress by utilizing the back-propagation algorithm. The ANN was trained and tested using experimental data and a very good performance of the neural network was achieved. The well-trained neural network was then adopted to predict the contact fatigue life of chromium alloyed cast steel rolls with different alloy compositions and heat treatment processes. The prediction results showed that the maximum value of contact fatigue life was obtained with quenching at 960 °C, tempering at 520 °C, and under the contact stress of 2355 MPa. The optimal alloy composition was C-0.54, Si-0.66, Mn-0.67, Cr-4.74, Mo-0.46, V-0.13, Ni-0.34, and Fe-balance (wt.%). Some explanations of the predicted results from the metallurgical viewpoints are given. A convenient and powerful method of optimizing alloy composition and heat treatment parameters of ACSRs has been developed.

PSF estimation for defocus blurred image based on quantum back-propagation neural network

NASA Astrophysics Data System (ADS)

Gao, Kun; Zhang, Yan; Shao, Xiao-guang; Liu, Ying-hui; Ni, Guoqiang

2010-11-01

Images obtained by an aberration-free system are defocused blur due to motion in depth and/or zooming. The precondition of restoring the degraded image is to estimate point spread function (PSF) of the imaging system as precisely as possible. But it is difficult to identify the analytic model of PSF precisely due to the complexity of the degradation process. Inspired by the similarity between the quantum process and imaging process in the probability and statistics fields, one reformed multilayer quantum neural network (QNN) is proposed to estimate PSF of the defocus blurred image. Different from the conventional artificial neural network (ANN), an improved quantum neuron model is used in the hidden layer instead, which introduces a 2-bit controlled NOT quantum gate to control output and adopts 2 texture and edge features as the input vectors. The supervised back-propagation learning rule is adopted to train network based on training sets from the historical images. Test results show that this method owns excellent features of high precision and strong generalization ability.

Quick fuzzy backpropagation algorithm.

PubMed

Nikov, A; Stoeva, S

2001-03-01

A modification of the fuzzy backpropagation (FBP) algorithm called QuickFBP algorithm is proposed, where the computation of the net function is significantly quicker. It is proved that the FBP algorithm is of exponential time complexity, while the QuickFBP algorithm is of polynomial time complexity. Convergence conditions of the QuickFBP, resp. the FBP algorithm are defined and proved for: (1) single output neural networks in case of training patterns with different targets; and (2) multiple output neural networks in case of training patterns with equivalued target vector. They support the automation of the weights training process (quasi-unsupervised learning) establishing the target value(s) depending on the network's input values. In these cases the simulation results confirm the convergence of both algorithms. An example with a large-sized neural network illustrates the significantly greater training speed of the QuickFBP rather than the FBP algorithm. The adaptation of an interactive web system to users on the basis of the QuickFBP algorithm is presented. Since the QuickFBP algorithm ensures quasi-unsupervised learning, this implies its broad applicability in areas of adaptive and adaptable interactive systems, data mining, etc. applications.

Neural network for processing both spatial and temporal data with time based back-propagation

NASA Technical Reports Server (NTRS)

Villarreal, James A. (Inventor); Shelton, Robert O. (Inventor)

1993-01-01

Neural networks are computing systems modeled after the paradigm of the biological brain. For years, researchers using various forms of neural networks have attempted to model the brain's information processing and decision-making capabilities. Neural network algorithms have impressively demonstrated the capability of modeling spatial information. On the other hand, the application of parallel distributed models to the processing of temporal data has been severely restricted. The invention introduces a novel technique which adds the dimension of time to the well known back-propagation neural network algorithm. In the space-time neural network disclosed herein, the synaptic weights between two artificial neurons (processing elements) are replaced with an adaptable-adjustable filter. Instead of a single synaptic weight, the invention provides a plurality of weights representing not only association, but also temporal dependencies. In this case, the synaptic weights are the coefficients to the adaptable digital filters. Novelty is believed to lie in the disclosure of a processing element and a network of the processing elements which are capable of processing temporal as well as spacial data.

Classification of multispectral image data by the Binary Diamond neural network and by nonparametric, pixel-by-pixel methods

NASA Technical Reports Server (NTRS)

Salu, Yehuda; Tilton, James

1993-01-01

The classification of multispectral image data obtained from satellites has become an important tool for generating ground cover maps. This study deals with the application of nonparametric pixel-by-pixel classification methods in the classification of pixels, based on their multispectral data. A new neural network, the Binary Diamond, is introduced, and its performance is compared with a nearest neighbor algorithm and a back-propagation network. The Binary Diamond is a multilayer, feed-forward neural network, which learns from examples in unsupervised, 'one-shot' mode. It recruits its neurons according to the actual training set, as it learns. The comparisons of the algorithms were done by using a realistic data base, consisting of approximately 90,000 Landsat 4 Thematic Mapper pixels. The Binary Diamond and the nearest neighbor performances were close, with some advantages to the Binary Diamond. The performance of the back-propagation network lagged behind. An efficient nearest neighbor algorithm, the binned nearest neighbor, is described. Ways for improving the performances, such as merging categories, and analyzing nonboundary pixels, are addressed and evaluated.

Back-Propagation Operation for Analog Neural Network Hardware with Synapse Components Having Hysteresis Characteristics

PubMed Central

Ueda, Michihito; Nishitani, Yu; Kaneko, Yukihiro; Omote, Atsushi

2014-01-01

To realize an analog artificial neural network hardware, the circuit element for synapse function is important because the number of synapse elements is much larger than that of neuron elements. One of the candidates for this synapse element is a ferroelectric memristor. This device functions as a voltage controllable variable resistor, which can be applied to a synapse weight. However, its conductance shows hysteresis characteristics and dispersion to the input voltage. Therefore, the conductance values vary according to the history of the height and the width of the applied pulse voltage. Due to the difficulty of controlling the accurate conductance, it is not easy to apply the back-propagation learning algorithm to the neural network hardware having memristor synapses. To solve this problem, we proposed and simulated a learning operation procedure as follows. Employing a weight perturbation technique, we derived the error change. When the error reduced, the next pulse voltage was updated according to the back-propagation learning algorithm. If the error increased the amplitude of the next voltage pulse was set in such way as to cause similar memristor conductance but in the opposite voltage scanning direction. By this operation, we could eliminate the hysteresis and confirmed that the simulation of the learning operation converged. We also adopted conductance dispersion numerically in the simulation. We examined the probability that the error decreased to a designated value within a predetermined loop number. The ferroelectric has the characteristics that the magnitude of polarization does not become smaller when voltages having the same polarity are applied. These characteristics greatly improved the probability even if the learning rate was small, if the magnitude of the dispersion is adequate. Because the dispersion of analog circuit elements is inevitable, this learning operation procedure is useful for analog neural network hardware. PMID:25393715

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

PubMed

Morns, I P; Dlay, S S

1999-01-01

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

Neural Network Classifier Architectures for Phoneme Recognition. CRC Technical Note No. CRC-TN-92-001.

ERIC Educational Resources Information Center

Treurniet, William

A study applied artificial neural networks, trained with the back-propagation learning algorithm, to modelling phonemes extracted from the DARPA TIMIT multi-speaker, continuous speech data base. A number of proposed network architectures were applied to the phoneme classification task, ranging from the simple feedforward multilayer network to more…

Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation

ERIC Educational Resources Information Center

Hinton, Geoffrey; Osindero, Simon; Welling, Max; Teh, Yee-Whye

2006-01-01

We describe a way of modeling high-dimensional data vectors by using an unsupervised, nonlinear, multilayer neural network in which the activity of each neuron-like unit makes an additive contribution to a global energy score that indicates how surprised the network is by the data vector. The connection weights that determine how the activity of…

Method Accelerates Training Of Some Neural Networks

NASA Technical Reports Server (NTRS)

Shelton, Robert O.

1992-01-01

Three-layer networks trained faster provided two conditions are satisfied: numbers of neurons in layers are such that majority of work done in synaptic connections between input and hidden layers, and number of neurons in input layer at least as great as number of training pairs of input and output vectors. Based on modified version of back-propagation method.

Locally Bayesian Learning with Applications to Retrospective Revaluation and Highlighting

ERIC Educational Resources Information Center

Kruschke, John K.

2006-01-01

A scheme is described for locally Bayesian parameter updating in models structured as successions of component functions. The essential idea is to back-propagate the target data to interior modules, such that an interior component's target is the input to the next component that maximizes the probability of the next component's target. Each layer…

Optimization of Operation Parameters for Helical Flow Cleanout with Supercritical CO2 in Horizontal Wells Using Back-Propagation Artificial Neural Network.

PubMed

Song, Xianzhi; Peng, Chi; Li, Gensheng; He, Zhenguo; Wang, Haizhu

2016-01-01

Sand production and blockage are common during the drilling and production of horizontal oil and gas wells as a result of formation breakdown. The use of high-pressure rotating jets and annular helical flow is an effective way to enhance horizontal wellbore cleanout. In this paper, we propose the idea of using supercritical CO2 (SC-CO2) as washing fluid in water-sensitive formation. SC-CO2 is manifested to be effective in preventing formation damage and enhancing production rate as drilling fluid, which justifies tis potential in wellbore cleanout. In order to investigate the effectiveness of SC-CO2 helical flow cleanout, we perform the numerical study on the annular flow field, which significantly affects sand cleanout efficiency, of SC-CO2 jets in horizontal wellbore. Based on the field data, the geometry model and mathematical models were built. Then a numerical simulation of the annular helical flow field by SC-CO2 jets was accomplished. The influences of several key parameters were investigated, and SC-CO2 jets were compared to conventional water jets. The results show that flow rate, ambient temperature, jet temperature, and nozzle assemblies play the most important roles on wellbore flow field. Once the difference between ambient temperatures and jet temperatures is kept constant, the wellbore velocity distributions will not change. With increasing lateral nozzle size or decreasing rear/forward nozzle size, suspending ability of SC-CO2 flow improves obviously. A back-propagation artificial neural network (BP-ANN) was successfully employed to match the operation parameters and SC-CO2 flow velocities. A comprehensive model was achieved to optimize the operation parameters according to two strategies: cost-saving strategy and local optimal strategy. This paper can help to understand the distinct characteristics of SC-CO2 flow. And it is the first time that the BP-ANN is introduced to analyze the flow field during wellbore cleanout in horizontal wells.

Non-invasive algorithm for bowel motility estimation using a back-propagation neural network model of bowel sounds.

PubMed

Kim, Keo-Sik; Seo, Jeong-Hwan; Song, Chul-Gyu

2011-08-10

Radiological scoring methods such as colon transit time (CTT) have been widely used for the assessment of bowel motility. However, these radiograph-based methods need cumbersome radiological instruments and their frequent exposure to radiation. Therefore, a non-invasive estimation algorithm of bowel motility, based on a back-propagation neural network (BPNN) model of bowel sounds (BS) obtained by an auscultation, was devised. Twelve healthy males (age: 24.8 ± 2.7 years) and 6 patients with spinal cord injury (6 males, age: 55.3 ± 7.1 years) were examined. BS signals generated during the digestive process were recorded from 3 colonic segments (ascending, descending and sigmoid colon), and then, the acoustical features (jitter and shimmer) of the individual BS segment were obtained. Only 6 features (J1, 3, J3, 3, S1, 2, S2, 1, S2, 2, S3, 2), which are highly correlated to the CTTs measured by the conventional method, were used as the features of the input vector for the BPNN. As a results, both the jitters and shimmers of the normal subjects were relatively higher than those of the patients, whereas the CTTs of the normal subjects were relatively lower than those of the patients (p < 0.01). Also, through k-fold cross validation, the correlation coefficient and mean average error between the CTTs measured by a conventional radiograph and the values estimated by our algorithm were 0.89 and 10.6 hours, respectively. The jitter and shimmer of the BS signals generated during the peristalsis could be clinically useful for the discriminative parameters of bowel motility. Also, the devised algorithm showed good potential for the continuous monitoring and estimation of bowel motility, instead of conventional radiography, and thus, it could be used as a complementary tool for the non-invasive measurement of bowel motility.

Optimization of Operation Parameters for Helical Flow Cleanout with Supercritical CO2 in Horizontal Wells Using Back-Propagation Artificial Neural Network

PubMed Central

Song, Xianzhi; Peng, Chi; Li, Gensheng

2016-01-01

Sand production and blockage are common during the drilling and production of horizontal oil and gas wells as a result of formation breakdown. The use of high-pressure rotating jets and annular helical flow is an effective way to enhance horizontal wellbore cleanout. In this paper, we propose the idea of using supercritical CO2 (SC-CO2) as washing fluid in water-sensitive formation. SC-CO2 is manifested to be effective in preventing formation damage and enhancing production rate as drilling fluid, which justifies tis potential in wellbore cleanout. In order to investigate the effectiveness of SC-CO2 helical flow cleanout, we perform the numerical study on the annular flow field, which significantly affects sand cleanout efficiency, of SC-CO2 jets in horizontal wellbore. Based on the field data, the geometry model and mathematical models were built. Then a numerical simulation of the annular helical flow field by SC-CO2 jets was accomplished. The influences of several key parameters were investigated, and SC-CO2 jets were compared to conventional water jets. The results show that flow rate, ambient temperature, jet temperature, and nozzle assemblies play the most important roles on wellbore flow field. Once the difference between ambient temperatures and jet temperatures is kept constant, the wellbore velocity distributions will not change. With increasing lateral nozzle size or decreasing rear/forward nozzle size, suspending ability of SC-CO2 flow improves obviously. A back-propagation artificial neural network (BP-ANN) was successfully employed to match the operation parameters and SC-CO2 flow velocities. A comprehensive model was achieved to optimize the operation parameters according to two strategies: cost-saving strategy and local optimal strategy. This paper can help to understand the distinct characteristics of SC-CO2 flow. And it is the first time that the BP-ANN is introduced to analyze the flow field during wellbore cleanout in horizontal wells. PMID:27249026

Sparsity Aware Adaptive Radar Sensor Imaging in Complex Scattering Environments

DTIC Science & Technology

2015-06-15

while meeting the requirement on the peak to average power ratio. Third, we study impact of waveform encoding on nonlinear electromagnetic tomographic...Enyue Lu. Time Domain Electromagnetic Tomography Using Propagation and Backpropagation Method, IEEE International Conference on Image Processing...Received Paper 3.00 4.00 Yuanwei Jin, Chengdon Dong, Enyue Lu. Waveform Encoding for Nonlinear Electromagnetic Tomographic Imaging, IEEE Global

Application of the clinical matrix to the diagnosis of leukemia

NASA Astrophysics Data System (ADS)

Pakkala, Sampath Y.; Lin, Frank C.

1992-07-01

A system for diagnosing leukemia subtypes has been formulated using neural networks. The statistical data of the symptoms collected by hematologists is fed into a single training set using a neural network, where the network is trained by using fast backpropagation algorithm, which when done can help the general practitioners for making diagnoses on the basis of signs and symptoms alone.

Predicting Item Difficulty in a Reading Comprehension Test with an Artificial Neural Network.

ERIC Educational Resources Information Center

Perkins, Kyle; And Others

This paper reports the results of using a three-layer backpropagation artificial neural network to predict item difficulty in a reading comprehension test. Two network structures were developed, one with and one without a sigmoid function in the output processing unit. The data set, which consisted of a table of coded test items and corresponding…

Prediction of Prospective Mathematics Teachers' Academic Success in Entering Graduate Education by Using Back-Propagation Neural Network

ERIC Educational Resources Information Center

Bahadir, Elif

2016-01-01

The purpose of this study is to examine a neural network based approach to predict achievement in graduate education for Elementary Mathematics prospective teachers. With the help of this study, it can be possible to make an effective prediction regarding the students' achievement in graduate education with Artificial Neural Networks (ANN). Two…

Convolutional Neural Network Based on Extreme Learning Machine for Maritime Ships Recognition in Infrared Images.

PubMed

Khellal, Atmane; Ma, Hongbin; Fei, Qing

2018-05-09

The success of Deep Learning models, notably convolutional neural networks (CNNs), makes them the favorable solution for object recognition systems in both visible and infrared domains. However, the lack of training data in the case of maritime ships research leads to poor performance due to the problem of overfitting. In addition, the back-propagation algorithm used to train CNN is very slow and requires tuning many hyperparameters. To overcome these weaknesses, we introduce a new approach fully based on Extreme Learning Machine (ELM) to learn useful CNN features and perform a fast and accurate classification, which is suitable for infrared-based recognition systems. The proposed approach combines an ELM based learning algorithm to train CNN for discriminative features extraction and an ELM based ensemble for classification. The experimental results on VAIS dataset, which is the largest dataset of maritime ships, confirm that the proposed approach outperforms the state-of-the-art models in term of generalization performance and training speed. For instance, the proposed model is up to 950 times faster than the traditional back-propagation based training of convolutional neural networks, primarily for low-level features extraction.

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.

Non-proliferative diabetic retinopathy symptoms detection and classification using neural network.

PubMed

Al-Jarrah, Mohammad A; Shatnawi, Hadeel

2017-08-01

Diabetic retinopathy (DR) causes blindness in the working age for people with diabetes in most countries. The increasing number of people with diabetes worldwide suggests that DR will continue to be major contributors to vision loss. Early detection of retinopathy progress in individuals with diabetes is critical for preventing visual loss. Non-proliferative DR (NPDR) is an early stage of DR. Moreover, NPDR can be classified into mild, moderate and severe. This paper proposes a novel morphology-based algorithm for detecting retinal lesions and classifying each case. First, the proposed algorithm detects the three DR lesions, namely haemorrhages, microaneurysms and exudates. Second, we defined and extracted a set of features from detected lesions. The set of selected feature emulates what physicians looked for in classifying NPDR case. Finally, we designed an artificial neural network (ANN) classifier with three layers to classify NPDR to normal, mild, moderate and severe. Bayesian regularisation and resilient backpropagation algorithms are used to train ANN. The accuracy for the proposed classifiers based on Bayesian regularisation and resilient backpropagation algorithms are 96.6 and 89.9, respectively. The obtained results are compared with results of the recent published classifier. Our proposed classifier outperforms the best in terms of sensitivity and specificity.

A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification

NASA Astrophysics Data System (ADS)

Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun

2016-12-01

Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value.

A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification.

PubMed

Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun

2016-12-01

Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value.

Using a binaural biomimetic array to identify bottom objects ensonified by echolocating dolphins

USGS Publications Warehouse

Heiweg, D.A.; Moore, P.W.; Martin, S.W.; Dankiewicz, L.A.

2006-01-01

The development of a unique dolphin biomimetic sonar produced data that were used to study signal processing methods for object identification. Echoes from four metallic objects proud on the bottom, and a substrate-only condition, were generated by bottlenose dolphins trained to ensonify the targets in very shallow water. Using the two-element ('binaural') receive array, object echo spectra were collected and submitted for identification to four neural network architectures. Identification accuracy was evaluated over two receive array configurations, and five signal processing schemes. The four neural networks included backpropagation, learning vector quantization, genetic learning and probabilistic network architectures. The processing schemes included four methods that capitalized on the binaural data, plus a monaural benchmark process. All the schemes resulted in above-chance identification accuracy when applied to learning vector quantization and backpropagation. Beam-forming or concatenation of spectra from both receive elements outperformed the monaural benchmark, with higher sensitivity and lower bias. Ultimately, best object identification performance was achieved by the learning vector quantization network supplied with beam-formed data. The advantages of multi-element signal processing for object identification are clearly demonstrated in this development of a first-ever dolphin biomimetic sonar. ?? 2006 IOP Publishing Ltd.

A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification

PubMed Central

Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun

2016-01-01

Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value. PMID:27905520

Application of principal component regression and artificial neural network in FT-NIR soluble solids content determination of intact pear fruit

NASA Astrophysics Data System (ADS)

Ying, Yibin; Liu, Yande; Fu, Xiaping; Lu, Huishan

2005-11-01

The artificial neural networks (ANNs) have been used successfully in applications such as pattern recognition, image processing, automation and control. However, majority of today's applications of ANNs is back-propagate feed-forward ANN (BP-ANN). In this paper, back-propagation artificial neural networks (BP-ANN) were applied for modeling soluble solid content (SSC) of intact pear from their Fourier transform near infrared (FT-NIR) spectra. One hundred and sixty-four pear samples were used to build the calibration models and evaluate the models predictive ability. The results are compared to the classical calibration approaches, i.e. principal component regression (PCR), partial least squares (PLS) and non-linear PLS (NPLS). The effects of the optimal methods of training parameters on the prediction model were also investigated. BP-ANN combine with principle component regression (PCR) resulted always better than the classical PCR, PLS and Weight-PLS methods, from the point of view of the predictive ability. Based on the results, it can be concluded that FT-NIR spectroscopy and BP-ANN models can be properly employed for rapid and nondestructive determination of fruit internal quality.

Heating value prediction for combustible fraction of municipal solid waste in Semarang using backpropagation neural network

NASA Astrophysics Data System (ADS)

Khuriati, Ainie; Setiabudi, Wahyu; Nur, Muhammad; Istadi, Istadi

2015-12-01

Backpropgation neural network was trained to predict of combustible fraction heating value of MSW from the physical composition. Waste-to-Energy (WtE) is a viable option for municipal solid waste (MSW) management. The influence of the heating value of municipal solid waste (MSW) is very important on the implementation of WtE systems. As MSW is heterogeneous material, direct heating value measurements are often not feasible. In this study an empirical model was developed to describe the heating value of the combustible fraction of municipal solid waste as a function of its physical composition of MSW using backpropagation neural network. Sampling process was carried out at Jatibarang landfill. The weight of each sorting sample taken from each discharged MSW vehicle load is 100 kg. The MSW physical components were grouped into paper wastes, absorbent hygiene product waste, styrofoam waste, HD plastic waste, plastic waste, rubber waste, textile waste, wood waste, yard wastes, kitchen waste, coco waste, and miscellaneous combustible waste. Network was trained by 24 datasets with 1200, 769, and 210 epochs. The results of this analysis showed that the correlation from the physical composition is better than multiple regression method .

One-dimensional inversion of geo-electrical resistivity sounding data using artificial neural networks—a case study

NASA Astrophysics Data System (ADS)

Singh, U. K.; Tiwari, R. K.; Singh, S. B.

2005-02-01

This paper deals with the application of artificial neural networks (ANN) technique for the study of a case history using 1-D inversion of vertical electrical resistivity sounding (VES) data from the Puga valley, Kashmir, India. The study area is important for its rich geothermal resources as well as from the tectonic point of view as it is located near the collision boundary of the Indo-Asian crustal plates. In order to understand the resistivity structure and layer thicknesses, we used here three-layer feedforward neural networks to model and predict measured VES data. Three algorithms, e.g. back-propagation (BP), adaptive back-propagation (ABP) and Levenberg-Marquardt algorithm (LMA) were applied to the synthetic as well as real VES field data and efficiency of supervised training network are compared. Analyses suggest that LMA is computationally faster and give results, which are comparatively more accurate and consistent than BP and ABP. The results obtained using the ANN inversions are remarkably correlated with the available borehole litho-logs. The feasibility study suggests that ANN methods offer an excellent complementary tool for the direct detection of layered resistivity structure.

Development of LC-MS determination method and back-propagation ANN pharmacokinetic model of corynoxeine in rat.

PubMed

Ma, Jianshe; Cai, Jinzhang; Lin, Guanyang; Chen, Huilin; Wang, Xianqin; Wang, Xianchuan; Hu, Lufeng

2014-05-15

Corynoxeine(CX), isolated from the extract of Uncaria rhynchophylla, is a useful and prospective compound in the prevention and treatment for vascular diseases. A simple and selective liquid chromatography mass spectrometry (LC-MS) method was developed to determine the concentration of CX in rat plasma. The chromatographic separation was achieved on a Zorbax SB-C18 (2.1 mm × 150 mm, 5 μm) column with acetonitrile-0.1% formic acid in water as mobile phase. Selective ion monitoring (SIM) mode was used for quantification using target ions m/z 383 for CX and m/z 237 for the carbamazepine (IS). After the LC-MS method was validated, it was applied to a back-propagation artificial neural network (BP-ANN) pharmacokinetic model study of CX in rats. The results showed that after intravenous administration of CX, it was mainly distributed in blood and eliminated quickly, t1/2 was less than 1h. The predicted concentrations generated by BP-ANN model had a high correlation coefficient (R>0.99) with experimental values. The developed BP-ANN pharmacokinetic model can be used to predict the concentration of CX in rats. Copyright © 2014 Elsevier B.V. All rights reserved.

Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications. Volume 29

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2010-01-01

Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…

Classifying features in CT imagery: accuracy for some single- and multiple-species classifiers

Treesearch

Daniel L. Schmoldt; Jing He; A. Lynn Abbott

1998-01-01

Our current approach to automatically label features in CT images of hardwood logs classifies each pixel of an image individually. These feature classifiers use a back-propagation artificial neural network (ANN) and feature vectors that include a small, local neighborhood of pixels and the distance of the target pixel to the center of the log. Initially, this type of...

A Connectionist Model of Stimulus Class Formation with a Yes/No Procedure and Compound Stimuli

ERIC Educational Resources Information Center

Tovar, Angel E.; Chavez, Alvaro Torres

2012-01-01

We analyzed stimulus class formation in a human study and in a connectionist model (CM) with a yes/no procedure, using compound stimuli. In the human study, the participants were six female undergraduate students; the CM was a feed-forward back-propagation network. Two 3-member stimulus classes were trained with a similar procedure in both the…

A CFBPN Artificial Neural Network Model for Educational Qualitative Data Analyses: Example of Students' Attitudes Based on Kellerts' Typologies

ERIC Educational Resources Information Center

Yorek, Nurettin; Ugulu, Ilker

2015-01-01

In this study, artificial neural networks are suggested as a model that can be "trained" to yield qualitative results out of a huge amount of categorical data. It can be said that this is a new approach applied in educational qualitative data analysis. In this direction, a cascade-forward back-propagation neural network (CFBPN) model was…

Predicting Engineering Student Attrition Risk Using a Probabilistic Neural Network and Comparing Results with a Backpropagation Neural Network and Logistic Regression

ERIC Educational Resources Information Center

Mason, Cindi; Twomey, Janet; Wright, David; Whitman, Lawrence

2018-01-01

As the need for engineers continues to increase, a growing focus has been placed on recruiting students into the field of engineering and retaining the students who select engineering as their field of study. As a result of this concentration on student retention, numerous studies have been conducted to identify, understand, and confirm…

A theory of local learning, the learning channel, and the optimality of backpropagation.

PubMed

Baldi, Pierre; Sadowski, Peter

2016-11-01

In a physical neural system, where storage and processing are intimately intertwined, the rules for adjusting the synaptic weights can only depend on variables that are available locally, such as the activity of the pre- and post-synaptic neurons, resulting in local learning rules. A systematic framework for studying the space of local learning rules is obtained by first specifying the nature of the local variables, and then the functional form that ties them together into each learning rule. Such a framework enables also the systematic discovery of new learning rules and exploration of relationships between learning rules and group symmetries. We study polynomial local learning rules stratified by their degree and analyze their behavior and capabilities in both linear and non-linear units and networks. Stacking local learning rules in deep feedforward networks leads to deep local learning. While deep local learning can learn interesting representations, it cannot learn complex input-output functions, even when targets are available for the top layer. Learning complex input-output functions requires local deep learning where target information is communicated to the deep layers through a backward learning channel. The nature of the communicated information about the targets and the structure of the learning channel partition the space of learning algorithms. For any learning algorithm, the capacity of the learning channel can be defined as the number of bits provided about the error gradient per weight, divided by the number of required operations per weight. We estimate the capacity associated with several learning algorithms and show that backpropagation outperforms them by simultaneously maximizing the information rate and minimizing the computational cost. This result is also shown to be true for recurrent networks, by unfolding them in time. The theory clarifies the concept of Hebbian learning, establishes the power and limitations of local learning rules, introduces the learning channel which enables a formal analysis of the optimality of backpropagation, and explains the sparsity of the space of learning rules discovered so far. Copyright © 2016 Elsevier Ltd. All rights reserved.

Determining geophysical properties from well log data using artificial neural networks and fuzzy inference systems

NASA Astrophysics Data System (ADS)

Chang, Hsien-Cheng

Two novel synergistic systems consisting of artificial neural networks and fuzzy inference systems are developed to determine geophysical properties by using well log data. These systems are employed to improve the determination accuracy in carbonate rocks, which are generally more complex than siliciclastic rocks. One system, consisting of a single adaptive resonance theory (ART) neural network and three fuzzy inference systems (FISs), is used to determine the permeability category. The other system, which is composed of three ART neural networks and a single FIS, is employed to determine the lithofacies. The geophysical properties studied in this research, permeability category and lithofacies, are treated as categorical data. The permeability values are transformed into a "permeability category" to account for the effects of scale differences between core analyses and well logs, and heterogeneity in the carbonate rocks. The ART neural networks dynamically cluster the input data sets into different groups. The FIS is used to incorporate geologic experts' knowledge, which is usually in linguistic forms, into systems. These synergistic systems thus provide viable alternative solutions to overcome the effects of heterogeneity, the uncertainties of carbonate rock depositional environments, and the scarcity of well log data. The results obtained in this research show promising improvements over backpropagation neural networks. For the permeability category, the prediction accuracies are 68.4% and 62.8% for the multiple-single ART neural network-FIS and a single backpropagation neural network, respectively. For lithofacies, the prediction accuracies are 87.6%, 79%, and 62.8% for the single-multiple ART neural network-FIS, a single ART neural network, and a single backpropagation neural network, respectively. The sensitivity analysis results show that the multiple-single ART neural networks-FIS and a single ART neural network possess the same matching trends in determining lithofacies. This research shows that the adaptive resonance theory neural networks enable decision-makers to clearly distinguish the importance of different pieces of data which are useful in three-dimensional subsurface modeling. Geologic experts' knowledge can be easily applied and maintained by using the fuzzy inference systems.

Stabilization of burn conditions in a thermonuclear reactor using artificial neural networks

NASA Astrophysics Data System (ADS)

Vitela, Javier E.; Martinell, Julio J.

1998-02-01

In this work we develop an artificial neural network (ANN) for the feedback stabilization of a thermonuclear reactor at nearly ignited burn conditions. A volume-averaged zero-dimensional nonlinear model is used to represent the time evolution of the electron density, the relative density of alpha particles and the temperature of the plasma, where a particular scaling law for the energy confinement time previously used by other authors, was adopted. The control actions include the concurrent modulation of the D-T refuelling rate, the injection of a neutral He-4 beam and an auxiliary heating power modulation, which are constrained to take values within a maximum and minimum levels. For this purpose a feedforward multilayer artificial neural network with sigmoidal activation function is trained using a back-propagation through-time technique. Numerical examples are used to illustrate the behaviour of the resulting ANN-dynamical system configuration. It is concluded that the resulting ANN can successfully stabilize the nonlinear model of the thermonuclear reactor at nearly ignited conditions for temperature and density departures significantly far from their nominal operating values. The NN-dynamical system configuration is shown to be robust with respect to the thermalization time of the alpha particles for perturbations within the region used to train the NN.

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

PubMed Central

Neftci, Emre O.; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning. PMID:28680387

Adaptive neuro fuzzy inference system-based power estimation method for CMOS VLSI circuits

NASA Astrophysics Data System (ADS)

Vellingiri, Govindaraj; Jayabalan, Ramesh

2018-03-01

Recent advancements in very large scale integration (VLSI) technologies have made it feasible to integrate millions of transistors on a single chip. This greatly increases the circuit complexity and hence there is a growing need for less-tedious and low-cost power estimation techniques. The proposed work employs Back-Propagation Neural Network (BPNN) and Adaptive Neuro Fuzzy Inference System (ANFIS), which are capable of estimating the power precisely for the complementary metal oxide semiconductor (CMOS) VLSI circuits, without requiring any knowledge on circuit structure and interconnections. The ANFIS to power estimation application is relatively new. Power estimation using ANFIS is carried out by creating initial FIS modes using hybrid optimisation and back-propagation (BP) techniques employing constant and linear methods. It is inferred that ANFIS with the hybrid optimisation technique employing the linear method produces better results in terms of testing error that varies from 0% to 0.86% when compared to BPNN as it takes the initial fuzzy model and tunes it by means of a hybrid technique combining gradient descent BP and mean least-squares optimisation algorithms. ANFIS is the best suited for power estimation application with a low RMSE of 0.0002075 and a high coefficient of determination (R) of 0.99961.

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.

PubMed

Neftci, Emre O; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

Understanding the Convolutional Neural Networks with Gradient Descent and Backpropagation

NASA Astrophysics Data System (ADS)

Zhou, XueFei

2018-04-01

With the development of computer technology, the applications of machine learning are more and more extensive. And machine learning is providing endless opportunities to develop new applications. One of those applications is image recognition by using Convolutional Neural Networks (CNNs). CNN is one of the most common algorithms in image recognition. It is significant to understand its theory and structure for every scholar who is interested in this field. CNN is mainly used in computer identification, especially in voice, text recognition and other aspects of the application. It utilizes hierarchical structure with different layers to accelerate computing speed. In addition, the greatest features of CNNs are the weight sharing and dimension reduction. And all of these consolidate the high effectiveness and efficiency of CNNs with idea computing speed and error rate. With the help of other learning altruisms, CNNs could be used in several scenarios for machine learning, especially for deep learning. Based on the general introduction to the background and the core solution CNN, this paper is going to focus on summarizing how Gradient Descent and Backpropagation work, and how they contribute to the high performances of CNNs. Also, some practical applications will be discussed in the following parts. The last section exhibits the conclusion and some perspectives of future work.

Repeatability and Reliability Characterization of Phonocardiograph Systems Using Wavelet and Backpropagation Neural Network

NASA Astrophysics Data System (ADS)

Sumarna; Astono, J.; Purwanto, A.; Agustika, D. K.

2018-04-01

Phonocardiograph (PCG) system consisting of an electronic stethoscope, mic condenser, mic preamp, and the battery has been developed. PCG system is used to detect heart abnormalities. Although PCG is not popular because of many things that affect its performance, in this research we try to reduce the factors that affecting its consistency To find out whether the system is repeatable and reliable the system have to be characterized first. This research aims to see whether the PCG system can provide the same results for measurements of the same patient. Characterization of the system is done by analyzing whether the PCG system can recognize the S1 and S2 part of the same person. From the recording result, S1 and S2 then transformed by using Discrete Wavelet Transform of Haar mother wavelet of level 1 and extracted the feature by using data range of approximation coefficients. The result was analyzed by using pattern recognition system of backpropagation neural network. Partially obtained data used as training data and partly used as test data. From the results of the pattern recognition system, it can be concluded that the system accuracy in recognizing S1 reach 87.5% and S2 only hit 67%.

Leukemia and colon tumor detection based on microarray data classification using momentum backpropagation and genetic algorithm as a feature selection method

NASA Astrophysics Data System (ADS)

Wisesty, Untari N.; Warastri, Riris S.; Puspitasari, Shinta Y.

2018-03-01

Cancer is one of the major causes of mordibility and mortality problems in the worldwide. Therefore, the need of a system that can analyze and identify a person suffering from a cancer by using microarray data derived from the patient’s Deoxyribonucleic Acid (DNA). But on microarray data has thousands of attributes, thus making the challenges in data processing. This is often referred to as the curse of dimensionality. Therefore, in this study built a system capable of detecting a patient whether contracted cancer or not. The algorithm used is Genetic Algorithm as feature selection and Momentum Backpropagation Neural Network as a classification method, with data used from the Kent Ridge Bio-medical Dataset. Based on system testing that has been done, the system can detect Leukemia and Colon Tumor with best accuracy equal to 98.33% for colon tumor data and 100% for leukimia data. Genetic Algorithm as feature selection algorithm can improve system accuracy, which is from 64.52% to 98.33% for colon tumor data and 65.28% to 100% for leukemia data, and the use of momentum parameters can accelerate the convergence of the system in the training process of Neural Network.

Displacement back analysis for a high slope of the Dagangshan Hydroelectric Power Station based on BP neural network and particle swarm optimization.

PubMed

Liang, Zhengzhao; Gong, Bin; Tang, Chunan; Zhang, Yongbin; Ma, Tianhui

2014-01-01

The right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacement back analysis for the slope using an artificial neural network model (ANN) and particle swarm optimization model (PSO). A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model. The backpropagation ANN model was used to establish a mapping function between the mechanical parameters and the monitoring displacements. The PSO model was applied to initialize the weights and thresholds of the backpropagation (BP) network model and determine suitable values of the mechanical parameters. Then the elastic moduli of the rock masses were obtained according to the monitoring displacement data at different excavation stages, and the BP neural network model was proved to be valid by comparing the measured displacements, the displacements predicted by the BP neural network model, and the numerical simulation using the back-analyzed parameters. The proposed model is useful for rock mechanical parameters determination and instability investigation of rock slopes.

Simulation of ultrasonic focus aberration and correction through human tissue.

PubMed

Tabei, Makoto; Mast, T Douglas; Waag, Robert C

2003-02-01

Ultrasonic focusing in two dimensions has been investigated by calculating the propagation of ultrasonic pulses through cross-sectional models of human abdominal wall and breast. Propagation calculations used a full-wave k-space method that accounts for spatial variations in density, sound speed, and frequency-dependent absorption and includes perfectly matched layer absorbing boundary conditions. To obtain a distorted receive wavefront, propagation from a point source through the tissue path was computed. Receive focusing used an angular spectrum method. Transmit focusing was accomplished by propagating a pressure wavefront from a virtual array through the tissue path. As well as uncompensated focusing, focusing that employed time-shift compensation and time-shift compensation after backpropagation was investigated in both transmit and receive and time reversal was investigated for transmit focusing in addition. The results indicate, consistent with measurements, that breast causes greater focus degradation than abdominal wall. The investigated compensation methods corrected the receive focus better than the transmit focus. Time-shift compensation after backpropagation improved the focus from that obtained using time-shift compensation alone but the improvement was less in transmit focusing than in receive focusing. Transmit focusing by time reversal resulted in lower sidelobes but larger mainlobes than the other investigated transmit focus compensation methods.

Displacement Back Analysis for a High Slope of the Dagangshan Hydroelectric Power Station Based on BP Neural Network and Particle Swarm Optimization

PubMed Central

Liang, Zhengzhao; Gong, Bin; Tang, Chunan; Zhang, Yongbin; Ma, Tianhui

2014-01-01

The right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacement back analysis for the slope using an artificial neural network model (ANN) and particle swarm optimization model (PSO). A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model. The backpropagation ANN model was used to establish a mapping function between the mechanical parameters and the monitoring displacements. The PSO model was applied to initialize the weights and thresholds of the backpropagation (BP) network model and determine suitable values of the mechanical parameters. Then the elastic moduli of the rock masses were obtained according to the monitoring displacement data at different excavation stages, and the BP neural network model was proved to be valid by comparing the measured displacements, the displacements predicted by the BP neural network model, and the numerical simulation using the back-analyzed parameters. The proposed model is useful for rock mechanical parameters determination and instability investigation of rock slopes. PMID:25140345

Membership generation using multilayer neural network

NASA Technical Reports Server (NTRS)

Kim, Jaeseok

1992-01-01

There has been intensive research in neural network applications to pattern recognition problems. Particularly, the back-propagation network has attracted many researchers because of its outstanding performance in pattern recognition applications. In this section, we describe a new method to generate membership functions from training data using a multilayer neural network. The basic idea behind the approach is as follows. The output values of a sigmoid activation function of a neuron bear remarkable resemblance to membership values. Therefore, we can regard the sigmoid activation values as the membership values in fuzzy set theory. Thus, in order to generate class membership values, we first train a suitable multilayer network using a training algorithm such as the back-propagation algorithm. After the training procedure converges, the resulting network can be treated as a membership generation network, where the inputs are feature values and the outputs are membership values in the different classes. This method allows fairly complex membership functions to be generated because the network is highly nonlinear in general. Also, it is to be noted that the membership functions are generated from a classification point of view. For pattern recognition applications, this is highly desirable, although the membership values may not be indicative of the degree of typicality of a feature value in a particular class.

A Sequential Monte Carlo Approach for Streamflow Forecasting

NASA Astrophysics Data System (ADS)

Hsu, K.; Sorooshian, S.

2008-12-01

As alternatives to traditional physically-based models, Artificial Neural Network (ANN) models offer some advantages with respect to the flexibility of not requiring the precise quantitative mechanism of the process and the ability to train themselves from the data directly. In this study, an ANN model was used to generate one-day-ahead streamflow forecasts from the precipitation input over a catchment. Meanwhile, the ANN model parameters were trained using a Sequential Monte Carlo (SMC) approach, namely Regularized Particle Filter (RPF). The SMC approaches are known for their capabilities in tracking the states and parameters of a nonlinear dynamic process based on the Baye's rule and the proposed effective sampling and resampling strategies. In this study, five years of daily rainfall and streamflow measurement were used for model training. Variable sample sizes of RPF, from 200 to 2000, were tested. The results show that, after 1000 RPF samples, the simulation statistics, in terms of correlation coefficient, root mean square error, and bias, were stabilized. It is also shown that the forecasted daily flows fit the observations very well, with the correlation coefficient of higher than 0.95. The results of RPF simulations were also compared with those from the popular back-propagation ANN training approach. The pros and cons of using SMC approach and the traditional back-propagation approach will be discussed.

Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks.

PubMed

Liu, Da; Xu, Ming; Niu, Dongxiao; Wang, Shoukai; Liang, Sai

2016-01-01

Traditional forecasting models fit a function approximation from dependent invariables to independent variables. However, they usually get into trouble when date are presented in various formats, such as text, voice and image. This study proposes a novel image-encoded forecasting method that input and output binary digital two-dimensional (2D) images are transformed from decimal data. Omitting any data analysis or cleansing steps for simplicity, all raw variables were selected and converted to binary digital images as the input of a deep learning model, convolutional neural network (CNN). Using shared weights, pooling and multiple-layer back-propagation techniques, the CNN was adopted to locate the nexus among variations in local binary digital images. Due to the computing capability that was originally developed for binary digital bitmap manipulation, this model has significant potential for forecasting with vast volume of data. The model was validated by a power loads predicting dataset from the Global Energy Forecasting Competition 2012.

Artificial neural network modelling of a large-scale wastewater treatment plant operation.

PubMed

Güçlü, Dünyamin; Dursun, Sükrü

2010-11-01

Artificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41 mg/L and 5.03% for COD; 1.59, 1.21 mg/L and 17.10% for SS; 52.51, 44.91 mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.

Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks

PubMed Central

Xu, Ming; Niu, Dongxiao; Wang, Shoukai; Liang, Sai

2016-01-01

Traditional forecasting models fit a function approximation from dependent invariables to independent variables. However, they usually get into trouble when date are presented in various formats, such as text, voice and image. This study proposes a novel image-encoded forecasting method that input and output binary digital two-dimensional (2D) images are transformed from decimal data. Omitting any data analysis or cleansing steps for simplicity, all raw variables were selected and converted to binary digital images as the input of a deep learning model, convolutional neural network (CNN). Using shared weights, pooling and multiple-layer back-propagation techniques, the CNN was adopted to locate the nexus among variations in local binary digital images. Due to the computing capability that was originally developed for binary digital bitmap manipulation, this model has significant potential for forecasting with vast volume of data. The model was validated by a power loads predicting dataset from the Global Energy Forecasting Competition 2012. PMID:27281032

Functional properties of granule cells with hilar basal dendrites in the epileptic dentate gyrus.

PubMed

Kelly, Tony; Beck, Heinz

2017-01-01

The maturation of adult-born granule cells and their functional integration into the network is thought to play a key role in the proper functioning of the dentate gyrus. In temporal lobe epilepsy, adult-born granule cells in the dentate gyrus develop abnormally and possess a hilar basal dendrite (HBD). Although morphological studies have shown that these HBDs have synapses, little is known about the functional properties of these HBDs or the intrinsic and network properties of the granule cells that possess these aberrant dendrites. We performed patch-clamp recordings of granule cells within the granule cell layer "normotopic" from sham-control and status epilepticus (SE) animals. Normotopic granule cells from SE animals possessed an HBD (SE + HBD + cells) or not (SE + HBD - cells). Apical and basal dendrites were stimulated using multiphoton uncaging of glutamate. Two-photon Ca 2+ imaging was used to measure Ca 2+ transients associated with back-propagating action potentials (bAPs). Near-synchronous synaptic input integrated linearly in apical dendrites from sham-control animals and was not significantly different in apical dendrites of SE + HBD - cells. The majority of HBDs integrated input linearly, similar to apical dendrites. However, 2 of 11 HBDs were capable of supralinear integration mediated by a dendritic spike. Furthermore, the bAP-evoked Ca 2+ transients were relatively well maintained along HBDs, compared with apical dendrites. This further suggests an enhanced electrogenesis in HBDs. In addition, the output of granule cells from epileptic tissue was enhanced, with both SE + HBD - and SE + HBD + cells displaying increased high-frequency (>100 Hz) burst-firing. Finally, both SE + HBD - and SE + HBD + cells received recurrent excitatory input that was capable of generating APs, especially in the absence of feedback inhibition. Taken together, these data suggest that the enhanced excitability of HBDs combined with the altered intrinsic and network properties of granule cells collude to promote excitability and synchrony in the epileptic dentate gyrus. Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.

Synaptically activated Ca2+ waves and NMDA spikes locally suppress voltage-dependent Ca2+ signalling in rat pyramidal cell dendrites

PubMed Central

Manita, Satoshi; Miyazaki, Kenichi; Ross, William N

2011-01-01

Abstract Postsynaptic [Ca2+]i changes contribute to several kinds of plasticity in pyramidal neurons. We examined the effects of synaptically activated Ca2+ waves and NMDA spikes on subsequent Ca2+ signalling in CA1 pyramidal cell dendrites in hippocampal slices. Tetanic synaptic stimulation evoked a localized Ca2+ wave in the primary apical dendrites. The [Ca2+]i increase from a backpropagating action potential (bAP) or subthreshold depolarization was reduced if it was generated immediately after the wave. The suppression had a recovery time of 30–60 s. The suppression only occurred where the wave was generated and was not due to a change in bAP amplitude or shape. The suppression also could be generated by Ca2+ waves evoked by uncaging IP3, showing that other signalling pathways activated by the synaptic tetanus were not required. The suppression was proportional to the amplitude of the [Ca2+]i change of the Ca2+ wave and was not blocked by a spectrum of kinase or phosphatase inhibitors, consistent with suppression due to Ca2+-dependent inactivation of Ca2+ channels. The waves also reduced the frequency and amplitude of spontaneous, localized Ca2+ release events in the dendrites by a different mechanism, probably by depleting the stores at the site of wave generation. The same synaptic tetanus often evoked NMDA spike-mediated [Ca2+]i increases in the oblique dendrites where Ca2+ waves do not propagate. These NMDA spikes suppressed the [Ca2+]i increase caused by bAPs in those regions. [Ca2+]i increases by Ca2+ entry through voltage-gated Ca2+ channels also suppressed the [Ca2+]i increases from subsequent bAPs in regions where the voltage-gated [Ca2+]i increases were largest, showing that all ways of raising [Ca2+]i could cause suppression. PMID:21844002

Electronic Neural Networks

NASA Technical Reports Server (NTRS)

Thakoor, Anil

1990-01-01

Viewgraphs on electronic neural networks for space station are presented. Topics covered include: electronic neural networks; electronic implementations; VLSI/thin film hybrid hardware for neurocomputing; computations with analog parallel processing; features of neuroprocessors; applications of neuroprocessors; neural network hardware for terrain trafficability determination; a dedicated processor for path planning; neural network system interface; neural network for robotic control; error backpropagation algorithm for learning; resource allocation matrix; global optimization neuroprocessor; and electrically programmable read only thin-film synaptic array.

Modular Neural Networks for Speech Recognition.

DTIC Science & Technology

1996-08-01

automatic speech rccogni- tion, understanding and translation since the early 1950’ s . Although researchers have demonstrated impressive results with...nodes. It serves only as a data source for the following hidden layer( s ). Finally, the networks output is computed by neurons in the output layer. The...following update rule for weights in the hidden layer: w (,,•+I) ("’) E/V S (W W k- = wj, -- 7 - / v It is easy to generalize the backpropagation

Action potentials reliably invade axonal arbors of rat neocortical neurons

PubMed Central

Cox, Charles L.; Denk, Winfried; Tank, David W.; Svoboda, Karel

2000-01-01

Neocortical pyramidal neurons have extensive axonal arborizations that make thousands of synapses. Action potentials can invade these arbors and cause calcium influx that is required for neurotransmitter release and excitation of postsynaptic targets. Thus, the regulation of action potential invasion in axonal branches might shape the spread of excitation in cortical neural networks. To measure the reliability and extent of action potential invasion into axonal arbors, we have used two-photon excitation laser scanning microscopy to directly image action-potential-mediated calcium influx in single varicosities of layer 2/3 pyramidal neurons in acute brain slices. Our data show that single action potentials or bursts of action potentials reliably invade axonal arbors over a range of developmental ages (postnatal 10–24 days) and temperatures (24°C-30°C). Hyperpolarizing current steps preceding action potential initiation, protocols that had previously been observed to produce failures of action potential propagation in cultured preparations, were ineffective in modulating the spread of action potentials in acute slices. Our data show that action potentials reliably invade the axonal arbors of neocortical pyramidal neurons. Failures in synaptic transmission must therefore originate downstream of action potential invasion. We also explored the function of modulators that inhibit presynaptic calcium influx. Consistent with previous studies, we find that adenosine reduces action-potential-mediated calcium influx in presynaptic terminals. This reduction was observed in all terminals tested, suggesting that some modulatory systems are expressed homogeneously in most terminals of the same neuron. PMID:10931955

Novel maximum-margin training algorithms for supervised neural networks.

PubMed

Ludwig, Oswaldo; Nunes, Urbano

2010-06-01

This paper proposes three novel training methods, two of them based on the backpropagation approach and a third one based on information theory for multilayer perceptron (MLP) binary classifiers. Both backpropagation methods are based on the maximal-margin (MM) principle. The first one, based on the gradient descent with adaptive learning rate algorithm (GDX) and named maximum-margin GDX (MMGDX), directly increases the margin of the MLP output-layer hyperplane. The proposed method jointly optimizes both MLP layers in a single process, backpropagating the gradient of an MM-based objective function, through the output and hidden layers, in order to create a hidden-layer space that enables a higher margin for the output-layer hyperplane, avoiding the testing of many arbitrary kernels, as occurs in case of support vector machine (SVM) training. The proposed MM-based objective function aims to stretch out the margin to its limit. An objective function based on Lp-norm is also proposed in order to take into account the idea of support vectors, however, overcoming the complexity involved in solving a constrained optimization problem, usually in SVM training. In fact, all the training methods proposed in this paper have time and space complexities O(N) while usual SVM training methods have time complexity O(N (3)) and space complexity O(N (2)) , where N is the training-data-set size. The second approach, named minimization of interclass interference (MICI), has an objective function inspired on the Fisher discriminant analysis. Such algorithm aims to create an MLP hidden output where the patterns have a desirable statistical distribution. In both training methods, the maximum area under ROC curve (AUC) is applied as stop criterion. The third approach offers a robust training framework able to take the best of each proposed training method. The main idea is to compose a neural model by using neurons extracted from three other neural networks, each one previously trained by MICI, MMGDX, and Levenberg-Marquard (LM), respectively. The resulting neural network was named assembled neural network (ASNN). Benchmark data sets of real-world problems have been used in experiments that enable a comparison with other state-of-the-art classifiers. The results provide evidence of the effectiveness of our methods regarding accuracy, AUC, and balanced error rate.

[Patterns of action potential firing in cortical neurons of neonatal mice and their electrophysiological property].

PubMed

Furong, Liu; Shengtian, L I

2016-05-25

To investigate patterns of action potential firing in cortical heurons of neonatal mice and their electrophysiological properties. The passive and active membrane properties of cortical neurons from 3-d neonatal mice were observed by whole-cell patch clamp with different voltage and current mode. Three patterns of action potential firing were identified in response to depolarized current injection. The effects of action potential firing patterns on voltage-dependent inward and outward current were found. Neurons with three different firing patterns had different thresholds of depolarized current. In the morphology analysis of action potential, the three type neurons were different in rise time, duration, amplitude and threshold of the first action potential evoked by 80 pA current injection. The passive properties were similar in three patterns of action potential firing. These results indicate that newborn cortical neurons exhibit different patterns of action potential firing with different action potential parameters such as shape and threshold.

Role of action potential configuration and the contribution of Ca2+ and K+ currents to isoprenaline-induced changes in canine ventricular cells

PubMed Central

Szentandrássy, N; Farkas, V; Bárándi, L; Hegyi, B; Ruzsnavszky, F; Horváth, B; Bányász, T; Magyar, J; Márton, I; Nánási, PP

2012-01-01

BACKGROUND AND PURPOSE Although isoprenaline (ISO) is known to activate several ion currents in mammalian myocardium, little is known about the role of action potential morphology in the ISO-induced changes in ion currents. Therefore, the effects of ISO on action potential configuration, L-type Ca2+ current (ICa), slow delayed rectifier K+ current (IKs) and fast delayed rectifier K+ current (IKr) were studied and compared in a frequency-dependent manner using canine isolated ventricular myocytes from various transmural locations. EXPERIMENTAL APPROACH Action potentials were recorded with conventional sharp microelectrodes; ion currents were measured using conventional and action potential voltage clamp techniques. KEY RESULTS In myocytes displaying a spike-and-dome action potential configuration (epicardial and midmyocardial cells), ISO caused reversible shortening of action potentials accompanied by elevation of the plateau. ISO-induced action potential shortening was absent in endocardial cells and in myocytes pretreated with 4-aminopyridine. Application of the IKr blocker E-4031 failed to modify the ISO effect, while action potentials were lengthened by ISO in the presence of the IKs blocker HMR-1556. Both action potential shortening and elevation of the plateau were prevented by pretreatment with the ICa blocker nisoldipine. Action potential voltage clamp experiments revealed a prominent slowly inactivating ICa followed by a rise in IKs, both currents increased with increasing the cycle length. CONCLUSIONS AND IMPLICATIONS The effect of ISO in canine ventricular cells depends critically on action potential configuration, and the ISO-induced activation of IKs– but not IKr– may be responsible for the observed shortening of action potentials. PMID:22563726

Role of action potential configuration and the contribution of C²⁺a and K⁺ currents to isoprenaline-induced changes in canine ventricular cells.

PubMed

Szentandrássy, N; Farkas, V; Bárándi, L; Hegyi, B; Ruzsnavszky, F; Horváth, B; Bányász, T; Magyar, J; Márton, I; Nánási, P P

2012-10-01

Although isoprenaline (ISO) is known to activate several ion currents in mammalian myocardium, little is known about the role of action potential morphology in the ISO-induced changes in ion currents. Therefore, the effects of ISO on action potential configuration, L-type Ca²⁺ current (I(Ca)), slow delayed rectifier K⁺ current (I(Ks)) and fast delayed rectifier K⁺ current (I(Kr)) were studied and compared in a frequency-dependent manner using canine isolated ventricular myocytes from various transmural locations. Action potentials were recorded with conventional sharp microelectrodes; ion currents were measured using conventional and action potential voltage clamp techniques. In myocytes displaying a spike-and-dome action potential configuration (epicardial and midmyocardial cells), ISO caused reversible shortening of action potentials accompanied by elevation of the plateau. ISO-induced action potential shortening was absent in endocardial cells and in myocytes pretreated with 4-aminopyridine. Application of the I(Kr) blocker E-4031 failed to modify the ISO effect, while action potentials were lengthened by ISO in the presence of the I(Ks) blocker HMR-1556. Both action potential shortening and elevation of the plateau were prevented by pretreatment with the I(Ca) blocker nisoldipine. Action potential voltage clamp experiments revealed a prominent slowly inactivating I(Ca) followed by a rise in I(Ks) , both currents increased with increasing the cycle length. The effect of ISO in canine ventricular cells depends critically on action potential configuration, and the ISO-induced activation of I(Ks) - but not I(Kr) - may be responsible for the observed shortening of action potentials. © 2012 The Authors. British Journal of Pharmacology © 2012 The British Pharmacological Society.

Microphone Array

NASA Astrophysics Data System (ADS)

Bader, Rolf

This chapter deals with microphone arrays. It is arranged according to the different methods available to proceed through the different problems and through the different mathematical methods. After discussing general properties of different array types, such as plane arrays, spherical arrays, or scanning arrays, it proceeds to the signal processing tools that are most used in speech processing. In the third section, backpropagating methods based on the Helmholtz-Kirchhoff integral are discussed, which result in spatial radiation patterns of vibrating bodies or air.

Invariant object recognition based on the generalized discrete radon transform

NASA Astrophysics Data System (ADS)

Easley, Glenn R.; Colonna, Flavia

2004-04-01

We introduce a method for classifying objects based on special cases of the generalized discrete Radon transform. We adjust the transform and the corresponding ridgelet transform by means of circular shifting and a singular value decomposition (SVD) to obtain a translation, rotation and scaling invariant set of feature vectors. We then use a back-propagation neural network to classify the input feature vectors. We conclude with experimental results and compare these with other invariant recognition methods.

Spatial-Temporal Reasoning Applications of Computational Intelligence in the Game of Go and Computer Networks

DTIC Science & Technology

2012-01-01

dimensionality, Tesauro used a backpropagation- based , three-layer neural network and implemented the outcome from a self-play game as the reinforcement signal...a school of fish, flock of birds, and colony of ants. Our literature review reveals that no one has used PSO to train the neural network ...trained with a variant of PSO called cellular PSO (CPSO). CSRN is a supervised learning neural network (SLNN). The proposed algorithm for the

Training product unit neural networks with genetic algorithms

NASA Technical Reports Server (NTRS)

Janson, D. J.; Frenzel, J. F.; Thelen, D. C.

1991-01-01

The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.

Development of an Efficient Identifier for Nuclear Power Plant Transients Based on Latest Advances of Error Back-Propagation Learning Algorithm

NASA Astrophysics Data System (ADS)

Moshkbar-Bakhshayesh, Khalil; Ghofrani, Mohammad B.

2014-02-01

This study aims to improve the performance of nuclear power plants (NPPs) transients training and identification using the latest advances of error back-propagation (EBP) learning algorithm. To this end, elements of EBP, including input data, initial weights, learning rate, cost function, activation function, and weights updating procedure are investigated and an efficient neural network is developed. Usefulness of modular networks is also examined and appropriate identifiers, one for each transient, are employed. Furthermore, the effect of transient type on transient identifier performance is illustrated. Subsequently, the developed transient identifier is applied to Bushehr nuclear power plant (BNPP). Seven types of the plant events are probed to analyze the ability of the proposed identifier. The results reveal that identification occurs very early with only five plant variables, whilst in the previous studies a larger number of variables (typically 15 to 20) were required. Modular networks facilitated identification due to its sole dependency on the sign of each network output signal. Fast training of input patterns, extendibility for identification of more transients and reduction of false identification are other advantageous of the proposed identifier. Finally, the balance between the correct answer to the trained transients (memorization) and reasonable response to the test transients (generalization) is improved, meeting one of the primary design criteria of identifiers.

Equalization enhanced phase noise in Nyquist-spaced superchannel transmission systems using multi-channel digital back-propagation

PubMed Central

Xu, Tianhua; Liga, Gabriele; Lavery, Domaniç; Thomsen, Benn C.; Savory, Seb J.; Killey, Robert I.; Bayvel, Polina

2015-01-01

Superchannel transmission spaced at the symbol rate, known as Nyquist spacing, has been demonstrated for effectively maximizing the optical communication channel capacity and spectral efficiency. However, the achievable capacity and reach of transmission systems using advanced modulation formats are affected by fibre nonlinearities and equalization enhanced phase noise (EEPN). Fibre nonlinearities can be effectively compensated using digital back-propagation (DBP). However EEPN which arises from the interaction between laser phase noise and dispersion cannot be efficiently mitigated, and can significantly degrade the performance of transmission systems. Here we report the first investigation of the origin and the impact of EEPN in Nyquist-spaced superchannel system, employing electronic dispersion compensation (EDC) and multi-channel DBP (MC-DBP). Analysis was carried out in a Nyquist-spaced 9-channel 32-Gbaud DP-64QAM transmission system. Results confirm that EEPN significantly degrades the performance of all sub-channels of the superchannel system and that the distortions are more severe for the outer sub-channels, both using EDC and MC-DBP. It is also found that the origin of EEPN depends on the relative position between the carrier phase recovery module and the EDC (or MC-DBP) module. Considering EEPN, diverse coding techniques and modulation formats have to be applied for optimizing different sub-channels in superchannel systems. PMID:26365422

Method and system for training dynamic nonlinear adaptive filters which have embedded memory

NASA Technical Reports Server (NTRS)

Rabinowitz, Matthew (Inventor)

2002-01-01

Described herein is a method and system for training nonlinear adaptive filters (or neural networks) which have embedded memory. Such memory can arise in a multi-layer finite impulse response (FIR) architecture, or an infinite impulse response (IIR) architecture. We focus on filter architectures with separate linear dynamic components and static nonlinear components. Such filters can be structured so as to restrict their degrees of computational freedom based on a priori knowledge about the dynamic operation to be emulated. The method is detailed for an FIR architecture which consists of linear FIR filters together with nonlinear generalized single layer subnets. For the IIR case, we extend the methodology to a general nonlinear architecture which uses feedback. For these dynamic architectures, we describe how one can apply optimization techniques which make updates closer to the Newton direction than those of a steepest descent method, such as backpropagation. We detail a novel adaptive modified Gauss-Newton optimization technique, which uses an adaptive learning rate to determine both the magnitude and direction of update steps. For a wide range of adaptive filtering applications, the new training algorithm converges faster and to a smaller value of cost than both steepest-descent methods such as backpropagation-through-time, and standard quasi-Newton methods. We apply the algorithm to modeling the inverse of a nonlinear dynamic tracking system 5, as well as a nonlinear amplifier 6.

Inversion of Density Interfaces Using the Pseudo-Backpropagation Neural Network Method

NASA Astrophysics Data System (ADS)

Chen, Xiaohong; Du, Yukun; Liu, Zhan; Zhao, Wenju; Chen, Xiaocheng

2018-05-01

This paper presents a new pseudo-backpropagation (BP) neural network method that can invert multi-density interfaces at one time. The new method is based on the conventional forward modeling and inverse modeling theories in addition to conventional pseudo-BP neural network arithmetic. A 3D inversion model for gravity anomalies of multi-density interfaces using the pseudo-BP neural network method is constructed after analyzing the structure and function of the artificial neural network. The corresponding iterative inverse formula of the space field is presented at the same time. Based on trials of gravity anomalies and density noise, the influence of the two kinds of noise on the inverse result is discussed and the scale of noise requested for the stability of the arithmetic is analyzed. The effects of the initial model on the reduction of the ambiguity of the result and improvement of the precision of inversion are discussed. The correctness and validity of the method were verified by the 3D model of the three interfaces. 3D inversion was performed on the observed gravity anomaly data of the Okinawa trough using the program presented herein. The Tertiary basement and Moho depth were obtained from the inversion results, which also testify the adaptability of the method. This study has made a useful attempt for the inversion of gravity density interfaces.

The probabilistic neural network architecture for high speed classification of remotely sensed imagery

NASA Technical Reports Server (NTRS)

Chettri, Samir R.; Cromp, Robert F.

1993-01-01

In this paper we discuss a neural network architecture (the Probabilistic Neural Net or the PNN) that, to the best of our knowledge, has not previously been applied to remotely sensed data. The PNN is a supervised non-parametric classification algorithm as opposed to the Gaussian maximum likelihood classifier (GMLC). The PNN works by fitting a Gaussian kernel to each training point. The width of the Gaussian is controlled by a tuning parameter called the window width. If very small widths are used, the method is equivalent to the nearest neighbor method. For large windows, the PNN behaves like the GMLC. The basic implementation of the PNN requires no training time at all. In this respect it is far better than the commonly used backpropagation neural network which can be shown to take O(N6) time for training where N is the dimensionality of the input vector. In addition the PNN can be implemented in a feed forward mode in hardware. The disadvantage of the PNN is that it requires all the training data to be stored. Some solutions to this problem are discussed in the paper. Finally, we discuss the accuracy of the PNN with respect to the GMLC and the backpropagation neural network (BPNN). The PNN is shown to be better than GMLC and not as good as the BPNN with regards to classification accuracy.

Reflection-artifact-free photoacoustic imaging using PAFUSion (photoacoustic-guided focused ultrasound)

NASA Astrophysics Data System (ADS)

Kuniyil Ajith Singh, Mithun; Jaeger, Michael; Frenz, Martin; Steenbergen, Wiendelt

2016-03-01

Reflection artifacts caused by acoustic inhomogeneities are a main challenge to deep-tissue photoacoustic imaging. Photoacoustic transients generated by the skin surface and superficial vasculature will propagate into the tissue and reflect back from echogenic structures to generate reflection artifacts. These artifacts can cause problems in image interpretation and limit imaging depth. In its basic version, PAFUSion mimics the inward travelling wave-field from blood vessel-like PA sources by applying focused ultrasound pulses, and thus provides a way to identify reflection artifacts. In this work, we demonstrate reflection artifact correction in addition to identification, towards obtaining an artifact-free photoacoustic image. In view of clinical applications, we implemented an improved version of PAFUSion in which photoacoustic data is backpropagated to imitate the inward travelling wave-field and thus the reflection artifacts of a more arbitrary distribution of PA sources that also includes the skin melanin layer. The backpropagation is performed in a synthetic way based on the pulse-echo acquisitions after transmission on each single element of the transducer array. We present a phantom experiment and initial in vivo measurements on human volunteers where we demonstrate significant reflection artifact reduction using our technique. The results provide a direct confirmation that reflection artifacts are prominent in clinical epi-photoacoustic imaging, and that PAFUSion can reduce these artifacts significantly to improve the deep-tissue photoacoustic imaging.

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.

Characteristics of action potentials and their underlying outward currents in rat taste receptor cells.

PubMed

Chen, Y; Sun, X D; Herness, S

1996-02-01

1. Taste receptor cells produce action potentials as a result of transduction mechanisms that occur when these cells are stimulated with tastants. These action potentials are thought to be key signaling events in relaying information to the central nervous system. We explored the ionic basis of action potentials from dissociated posterior rat taste cells using the patch-clamp recording technique in both voltage-clamp and current-clamp modes. 2. Action potentials were evoked by intracellular injection of depolarizing current pulses from a holding potential of -80 mV. The threshold potential for firing of action potentials was approximately -35 mV; the input resistance of these cells averaged 6.9 G omega. With long depolarizing pulses, two or three action potentials could be elicited with successive attenuation of the spike height. Afterhyperpolarizations were observed often. 3. Both sodium and calcium currents contribute to depolarizing phases of the action potential. Action potentials were blocked completely in the presence of the sodium channel blocker tetrodotoxin. Calcium contributions could be visualized as prolonged calcium plateaus when repolarizing potassium currents were blocked and barium was used as a charge carrier. 4. Outward currents were composed of sustained delayed rectifier current, transient potassium current, and calcium-activated potassium current. Transient and sustained potassium currents activated close to -30 mV and increased monotonically with further depolarization. Up to half the outward current inactivated with decay constants on the order of seconds. Sustained and transient currents displayed steep voltage dependence in conductance and inactivation curves. Half inactivation occurred at -20 +/- 3.1 mV (mean +/- SE) with a decrease of 11.2 +/- 0.5 mV per e-fold. Half maximal conductance occurred at 3.6 +/- 1.8 mV and increased 12.2 +/- 0.6 mV per e-fold. Calcium-activated potassium current was evidenced by application of apamin and the use of calcium-free bathing solution. It was most obvious at more depolarized holding potentials that inactivated much of the transient and sustained outward currents. 5. Potassium currents contribute to both the repolarization and afterhyperpolarization phases of the action potential. These currents were blocked by bath application of tetraethylammonium, which also substantially broadened the action potential. Application of 4-aminopyridine was able to selectively block transient potassium currents without affecting sustained currents. This also broadened the action potential as well as eliminated the afterhyperpolarization. 6. A second type of action potential was observed that differed in duration. These slow action potentials had t1/2 durations of 9.6 ms compared with 1.4 ms for fast action potentials. Input resistances of the two groups were indistinguishable. Approximately one-fourth of the cells eliciting action potentials were of the slow type. 7. Cells eliciting fast action potentials had large outward currents capable of producing a quick repolarization, whereas cells with slow action potentials had small outward currents by comparison. The average values of fast cells were 2,563 pA and 1.4 ms compared with 373 pA and 9.6 ms for slow cells. Current and duration values were related exponentially. No significant difference was noted for inward currents. 8. These results suggest that many taste receptor cells conduct action potentials, which may be classified broadly into two groups on the basis of action potential duration and potassium current magnitude. These groups may be related to cell turnover. The physiological role of action potentials remains to be elucidated but may be important for communication within the taste bud as well as to the afferent nerve.

Study on Temperature and Synthetic Compensation of Piezo-Resistive Differential Pressure Sensors by Coupled Simulated Annealing and Simplex Optimized Kernel Extreme Learning Machine

PubMed Central

Li, Ji; Hu, Guoqing; Zhou, Yonghong; Zou, Chong; Peng, Wei; Alam SM, Jahangir

2017-01-01

As a high performance-cost ratio solution for differential pressure measurement, piezo-resistive differential pressure sensors are widely used in engineering processes. However, their performance is severely affected by the environmental temperature and the static pressure applied to them. In order to modify the non-linear measuring characteristics of the piezo-resistive differential pressure sensor, compensation actions should synthetically consider these two aspects. Advantages such as nonlinear approximation capability, highly desirable generalization ability and computational efficiency make the kernel extreme learning machine (KELM) a practical approach for this critical task. Since the KELM model is intrinsically sensitive to the regularization parameter and the kernel parameter, a searching scheme combining the coupled simulated annealing (CSA) algorithm and the Nelder-Mead simplex algorithm is adopted to find an optimal KLEM parameter set. A calibration experiment at different working pressure levels was conducted within the temperature range to assess the proposed method. In comparison with other compensation models such as the back-propagation neural network (BP), radius basis neural network (RBF), particle swarm optimization optimized support vector machine (PSO-SVM), particle swarm optimization optimized least squares support vector machine (PSO-LSSVM) and extreme learning machine (ELM), the compensation results show that the presented compensation algorithm exhibits a more satisfactory performance with respect to temperature compensation and synthetic compensation problems. PMID:28422080

Study on Temperature and Synthetic Compensation of Piezo-Resistive Differential Pressure Sensors by Coupled Simulated Annealing and Simplex Optimized Kernel Extreme Learning Machine.

PubMed

Li, Ji; Hu, Guoqing; Zhou, Yonghong; Zou, Chong; Peng, Wei; Alam Sm, Jahangir

2017-04-19

As a high performance-cost ratio solution for differential pressure measurement, piezo-resistive differential pressure sensors are widely used in engineering processes. However, their performance is severely affected by the environmental temperature and the static pressure applied to them. In order to modify the non-linear measuring characteristics of the piezo-resistive differential pressure sensor, compensation actions should synthetically consider these two aspects. Advantages such as nonlinear approximation capability, highly desirable generalization ability and computational efficiency make the kernel extreme learning machine (KELM) a practical approach for this critical task. Since the KELM model is intrinsically sensitive to the regularization parameter and the kernel parameter, a searching scheme combining the coupled simulated annealing (CSA) algorithm and the Nelder-Mead simplex algorithm is adopted to find an optimal KLEM parameter set. A calibration experiment at different working pressure levels was conducted within the temperature range to assess the proposed method. In comparison with other compensation models such as the back-propagation neural network (BP), radius basis neural network (RBF), particle swarm optimization optimized support vector machine (PSO-SVM), particle swarm optimization optimized least squares support vector machine (PSO-LSSVM) and extreme learning machine (ELM), the compensation results show that the presented compensation algorithm exhibits a more satisfactory performance with respect to temperature compensation and synthetic compensation problems.

Voltage-gated sodium channel expression and action potential generation in differentiated NG108-15 cells.

PubMed

Liu, Jinxu; Tu, Huiyin; Zhang, Dongze; Zheng, Hong; Li, Yu-Long

2012-10-25

The generation of action potential is required for stimulus-evoked neurotransmitter release in most neurons. Although various voltage-gated ion channels are involved in action potential production, the initiation of the action potential is mainly mediated by voltage-gated Na+ channels. In the present study, differentiation-induced changes of mRNA and protein expression of Na+ channels, Na+ currents, and cell membrane excitability were investigated in NG108-15 cells. Whole-cell patch-clamp results showed that differentiation (9 days) didn't change cell membrane excitability, compared to undifferentiated state. But differentiation (21 days) induced the action potential generation in 45.5% of NG108-15 cells (25/55 cells). In 9-day-differentiated cells, Na+ currents were mildly increased, which was also found in 21-day differentiated cells without action potential. In 21-day differentiated cells with action potential, Na+ currents were significantly enhanced. Western blot data showed that the expression of Na+ channels was increased with differentiated-time dependent manner. Single-cell real-time PCR data demonstrated that the expression of Na+ channel mRNA was increased by 21 days of differentiation in NG108-15 cells. More importantly, the mRNA level of Na+ channels in cells with action potential was higher than that in cells without action potential. Differentiation induces expression of voltage-gated Na+ channels and action potential generation in NG108-15 cells. A high level of the Na+ channel density is required for differentiation-triggered action potential generation.

Effects of premature stimulation on HERG K+ channels

PubMed Central

Lu, Yu; Mahaut-Smith, Martyn P; Varghese, Anthony; Huang, Christopher L-H; Kemp, Paul R; Vandenberg, Jamie I

2001-01-01

The unusual kinetics of human ether-à-go-go-related gene (HERG) K+ channels are consistent with a role in the suppression of arrhythmias initiated by premature beats. Action potential clamp protocols were used to investigate the effect of premature stimulation on HERG K+ channels, transfected in Chinese hamster ovary cells, at 37 °C. HERG K+ channel currents peaked during the terminal repolarization phase of normally paced action potential waveforms. However, the magnitude of the current and the time point at which conductance was maximal depended on the type of action potential waveform used (epicardial, endocardial, Purkinje fibre or atrial). HERG K+ channel currents recorded during premature action potentials consisted of an early transient outward current followed by a sustained outward current. The magnitude of the transient current component showed a biphasic dependence on the coupling interval between the normally paced and premature action potentials and was maximal at a coupling interval equivalent to 90% repolarization (APD90) for ventricular action potentials. The largest transient current response occurred at shorter coupling intervals for Purkinje fibre (APD90– 20 ms) and atrial (APD90– 30 ms) action potentials. The magnitude of the sustained current response following premature stimulation was similar to that recorded during the first action potential for ventricular action potential waveforms. However, for Purkinje and atrial action potentials the sustained current response was significantly larger during the premature action potential than during the normally paced action potential. A Markov model that included three closed states, one open and one inactivated state with transitions permitted between the pre-open closed state and the inactivated state, successfully reproduced our results for the effects of premature stimuli, both during square pulse and action potential clamp waveforms. These properties of HERG K+ channels may help to suppress arrhythmias initiated by early afterdepolarizations and premature beats in the ventricles, Purkinje fibres or atria. PMID:11744759

Steepest descent with momentum for quadratic functions is a version of the conjugate gradient method.

PubMed

Bhaya, Amit; Kaszkurewicz, Eugenius

2004-01-01

It is pointed out that the so called momentum method, much used in the neural network literature as an acceleration of the backpropagation method, is a stationary version of the conjugate gradient method. Connections with the continuous optimization method known as heavy ball with friction are also made. In both cases, adaptive (dynamic) choices of the so called learning rate and momentum parameters are obtained using a control Liapunov function analysis of the system.

Neural-Network-Development Program

NASA Technical Reports Server (NTRS)

Phillips, Todd A.

1993-01-01

NETS, software tool for development and evaluation of neural networks, provides simulation of neural-network algorithms plus computing environment for development of such algorithms. Uses back-propagation learning method for all of networks it creates. Enables user to customize patterns of connections between layers of network. Also provides features for saving, during learning process, values of weights, providing more-precise control over learning process. Written in ANSI standard C language. Machine-independent version (MSC-21588) includes only code for command-line-interface version of NETS 3.0.

Time-reversal imaging techniques applied to tremor waveforms near Cholame, California to locate tectonic tremor

NASA Astrophysics Data System (ADS)

Horstmann, T.; Harrington, R. M.; Cochran, E. S.

2012-12-01

Frequently, the lack of distinctive phase arrivals makes locating tectonic tremor more challenging than locating earthquakes. Classic location algorithms based on travel times cannot be directly applied because impulsive phase arrivals are often difficult to recognize. Traditional location algorithms are often modified to use phase arrivals identified from stacks of recurring low-frequency events (LFEs) observed within tremor episodes, rather than single events. Stacking the LFE waveforms improves the signal-to-noise ratio for the otherwise non-distinct phase arrivals. In this study, we apply a different method to locate tectonic tremor: a modified time-reversal imaging approach that potentially exploits the information from the entire tremor waveform instead of phase arrivals from individual LFEs. Time reversal imaging uses the waveforms of a given seismic source recorded by multiple seismometers at discrete points on the surface and a 3D velocity model to rebroadcast the waveforms back into the medium to identify the seismic source location. In practice, the method works by reversing the seismograms recorded at each of the stations in time, and back-propagating them from the receiver location individually into the sub-surface as a new source time function. We use a staggered-grid, finite-difference code with 2.5 ms time steps and a grid node spacing of 50 m to compute the rebroadcast wavefield. We calculate the time-dependent curl field at each grid point of the model volume for each back-propagated seismogram. To locate the tremor, we assume that the source time function back-propagated from each individual station produces a similar curl field at the source position. We then cross-correlate the time dependent curl field functions and calculate a median cross-correlation coefficient at each grid point. The highest median cross-correlation coefficient in the model volume is expected to represent the source location. For our analysis, we use the velocity model of Thurber et al. (2006) interpolated to a grid spacing of 50 m. Such grid spacing corresponds to frequencies of up to 8 Hz, which is suitable to calculate the wave propagation of tremor. Our dataset contains continuous broadband data from 13 STS-2 seismometers deployed from May 2010 to July 2011 along the Cholame segment of the San Andreas Fault as well as data from the HRSN and PBO networks. Initial synthetic results from tests on a 2D plane using a line of 15 receivers suggest that we are able to recover accurate event locations to within 100 m horizontally and 300 m depth. We conduct additional synthetic tests to determine the influence of signal-to-noise ratio, number of stations used, and the uncertainty in the velocity model on the location result by adding noise to the seismograms and perturbations to the velocity model. Preliminary results show accurate show location results to within 400 m with a median signal-to-noise ratio of 3.5 and 5% perturbations in the velocity model. The next steps will entail performing the synthetic tests on the 3D velocity model, and applying the method to tremor waveforms. Furthermore, we will determine the spatial and temporal distribution of the source locations and compare our results to those by Sumy and others.

Electrophysiology of neurones of the inferior mesenteric ganglion of the cat.

PubMed Central

Julé, Y; Szurszewski, J H

1983-01-01

Intracellular recordings were obtained from cells in vitro in the inferior mesenteric ganglia of the cat. Neurones could be classified into three types: non-spontaneous, irregular discharging and regular discharging neurones. Non-spontaneous neurones had a stable resting membrane potential and responded with action potentials to indirect preganglionic nerve stimulation and to intracellular injection of depolarizing current. Irregular discharging neurones were characterized by a discharge of excitatory post-synaptic potentials (e.p.s.p.s.) which sometimes gave rise to action potentials. This activity was abolished by hexamethonium bromide, chlorisondamine and d-tubocurarine chloride. Tetrodotoxin and a low Ca2+ -high Mg2+ solution also blocked on-going activity in irregular discharging neurones. Regular discharging neurones were characterized by a rhythmic discharge of action potentials. Each action potential was preceded by a gradual depolarization of the intracellularly recorded membrane potential. Intracellular injection of hyperpolarizing current abolished the regular discharge of action potential. No synaptic potentials were observed during hyperpolarization of the membrane potential. Nicotinic, muscarinic and adrenergic receptor blocking drugs did not modify the discharge of action potentials in regular discharging neurones. A low Ca2+ -high Mg2+ solution also had no effect on the regular discharge of action potentials. Interpolation of an action potential between spontaneous action potentials in regular discharging neurones reset the rhythm of discharge. It is suggested that regular discharging neurones were endogenously active and that these neurones provided synaptic input to irregular discharging neurones. PMID:6140310

Electrophysiology of neurones of the inferior mesenteric ganglion of the cat.

PubMed

Julé, Y; Szurszewski, J H

1983-11-01

Intracellular recordings were obtained from cells in vitro in the inferior mesenteric ganglia of the cat. Neurones could be classified into three types: non-spontaneous, irregular discharging and regular discharging neurones. Non-spontaneous neurones had a stable resting membrane potential and responded with action potentials to indirect preganglionic nerve stimulation and to intracellular injection of depolarizing current. Irregular discharging neurones were characterized by a discharge of excitatory post-synaptic potentials (e.p.s.p.s.) which sometimes gave rise to action potentials. This activity was abolished by hexamethonium bromide, chlorisondamine and d-tubocurarine chloride. Tetrodotoxin and a low Ca2+ -high Mg2+ solution also blocked on-going activity in irregular discharging neurones. Regular discharging neurones were characterized by a rhythmic discharge of action potentials. Each action potential was preceded by a gradual depolarization of the intracellularly recorded membrane potential. Intracellular injection of hyperpolarizing current abolished the regular discharge of action potential. No synaptic potentials were observed during hyperpolarization of the membrane potential. Nicotinic, muscarinic and adrenergic receptor blocking drugs did not modify the discharge of action potentials in regular discharging neurones. A low Ca2+ -high Mg2+ solution also had no effect on the regular discharge of action potentials. Interpolation of an action potential between spontaneous action potentials in regular discharging neurones reset the rhythm of discharge. It is suggested that regular discharging neurones were endogenously active and that these neurones provided synaptic input to irregular discharging neurones.

Dynamics of action potential initiation in the GABAergic thalamic reticular nucleus in vivo.

PubMed

Muñoz, Fabián; Fuentealba, Pablo

2012-01-01

Understanding the neural mechanisms of action potential generation is critical to establish the way neural circuits generate and coordinate activity. Accordingly, we investigated the dynamics of action potential initiation in the GABAergic thalamic reticular nucleus (TRN) using in vivo intracellular recordings in cats in order to preserve anatomically-intact axo-dendritic distributions and naturally-occurring spatiotemporal patterns of synaptic activity in this structure that regulates the thalamic relay to neocortex. We found a wide operational range of voltage thresholds for action potentials, mostly due to intrinsic voltage-gated conductances and not synaptic activity driven by network oscillations. Varying levels of synchronous synaptic inputs produced fast rates of membrane potential depolarization preceding the action potential onset that were associated with lower thresholds and increased excitability, consistent with TRN neurons performing as coincidence detectors. On the other hand the presence of action potentials preceding any given spike was associated with more depolarized thresholds. The phase-plane trajectory of the action potential showed somato-dendritic propagation, but no obvious axon initial segment component, prominent in other neuronal classes and allegedly responsible for the high onset speed. Overall, our results suggest that TRN neurons could flexibly integrate synaptic inputs to discharge action potentials over wide voltage ranges, and perform as coincidence detectors and temporal integrators, supported by a dynamic action potential threshold.

A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network

PubMed Central

Marto, Aminaton; Jahed Armaghani, Danial; Tonnizam Mohamad, Edy; Makhtar, Ahmad Mahir

2014-01-01

Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches. PMID:25147856

Predictive analysis of beer quality by correlating sensory evaluation with higher alcohol and ester production using multivariate statistics methods.

PubMed

Dong, Jian-Jun; Li, Qing-Liang; Yin, Hua; Zhong, Cheng; Hao, Jun-Guang; Yang, Pan-Fei; Tian, Yu-Hong; Jia, Shi-Ru

2014-10-15

Sensory evaluation is regarded as a necessary procedure to ensure a reproducible quality of beer. Meanwhile, high-throughput analytical methods provide a powerful tool to analyse various flavour compounds, such as higher alcohol and ester. In this study, the relationship between flavour compounds and sensory evaluation was established by non-linear models such as partial least squares (PLS), genetic algorithm back-propagation neural network (GA-BP), support vector machine (SVM). It was shown that SVM with a Radial Basis Function (RBF) had a better performance of prediction accuracy for both calibration set (94.3%) and validation set (96.2%) than other models. Relatively lower prediction abilities were observed for GA-BP (52.1%) and PLS (31.7%). In addition, the kernel function of SVM played an essential role of model training when the prediction accuracy of SVM with polynomial kernel function was 32.9%. As a powerful multivariate statistics method, SVM holds great potential to assess beer quality. Copyright © 2014 Elsevier Ltd. All rights reserved.

A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network.

PubMed

Marto, Aminaton; Hajihassani, Mohsen; Armaghani, Danial Jahed; Mohamad, Edy Tonnizam; Makhtar, Ahmad Mahir

2014-01-01

Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches.

Predicting high-risk preterm birth using artificial neural networks.

PubMed

Catley, Christina; Frize, Monique; Walker, C Robin; Petriu, Dorina C

2006-07-01

A reengineered approach to the early prediction of preterm birth is presented as a complimentary technique to the current procedure of using costly and invasive clinical testing on high-risk maternal populations. Artificial neural networks (ANNs) are employed as a screening tool for preterm birth on a heterogeneous maternal population; risk estimations use obstetrical variables available to physicians before 23 weeks gestation. The objective was to assess if ANNs have a potential use in obstetrical outcome estimations in low-risk maternal populations. The back-propagation feedforward ANN was trained and tested on cases with eight input variables describing the patient's obstetrical history; the output variables were: 1) preterm birth; 2) high-risk preterm birth; and 3) a refined high-risk preterm birth outcome excluding all cases where resuscitation was delivered in the form of free flow oxygen. Artificial training sets were created to increase the distribution of the underrepresented class to 20%. Training on the refined high-risk preterm birth model increased the network's sensitivity to 54.8%, compared to just over 20% for the nonartificially distributed preterm birth model.

Rapid and non-destructive determination of rancidity levels in butter cookies by multi-spectral imaging.

PubMed

Xia, Qing; Liu, Changhong; Liu, Jinxia; Pan, Wenjuan; Lu, Xuzhong; Yang, Jianbo; Chen, Wei; Zheng, Lei

2016-03-30

Rancidity is an important attribute for quality assessment of butter cookies, while traditional methods for rancidity measurement are usually laborious, destructive and prone to operational error. In the present paper, the potential of applying multi-spectral imaging (MSI) technology with 19 wavelengths in the range of 405-970 nm to evaluate the rancidity in butter cookies was investigated. Moisture content, acid value and peroxide value were determined by traditional methods and then related with the spectral information by partial least squares regression (PLSR) and back-propagation artificial neural network (BP-ANN). The optimal models for predicting moisture content, acid value and peroxide value were obtained by PLSR. The correlation coefficient (r) obtained by PLSR models revealed that MSI had a perfect ability to predict moisture content (r = 0.909), acid value (r = 0.944) and peroxide value (r = 0.971). The study demonstrated that the rancidity level of butter cookies can be continuously monitored and evaluated in real-time by the multi-spectral imaging, which is of great significance for developing online food safety monitoring solutions. © 2015 Society of Chemical Industry.

Mechanisms and consequences of action potential burst firing in rat neocortical pyramidal neurons

PubMed Central

Williams, Stephen R; Stuart, Greg J

1999-01-01

Electrophysiological recordings and pharmacological manipulations were used to investigate the mechanisms underlying the generation of action potential burst firing and its postsynaptic consequences in visually identified rat layer 5 pyramidal neurons in vitro.Based upon repetitive firing properties and subthreshold membrane characteristics, layer 5 pyramidal neurons were separated into three classes: regular firing and weak and strong intrinsically burst firing.High frequency (330 ± 10 Hz) action potential burst firing was abolished or greatly weakened by the removal of Ca2+ (n = 5) from, or by the addition of the Ca2+ channel antagonist Ni2+ (250–500 μm; n = 8) to, the perfusion medium.The blockade of apical dendritic sodium channels by the local dendritic application of TTX (100 nm; n = 5) abolished or greatly weakened action potential burst firing, as did the local apical dendritic application of Ni2+ (1 mm; n = 5).Apical dendritic depolarisation resulted in low frequency (157 ± 26 Hz; n = 6) action potential burst firing in regular firing neurons, as classified by somatic current injection. The intensity of action potential burst discharges in intrinsically burst firing neurons was facilitated by dendritic depolarisation (n = 11).Action potential amplitude decreased throughout a burst when recorded somatically, suggesting that later action potentials may fail to propagate axonally. Axonal recordings demonstrated that each action potential in a burst is axonally initiated and that no decrement in action potential amplitude is apparent in the axon > 30 μm from the soma.Paired recordings (n = 16) from synaptically coupled neurons indicated that each action potential in a burst could cause transmitter release. EPSPs or EPSCs evoked by a presynaptic burst of action potentials showed use-dependent synaptic depression.A postsynaptic, TTX-sensitive voltage-dependent amplification process ensured that later EPSPs in a burst were amplified when generated from membrane potentials positive to -60 mV, providing a postsynaptic mechanism that counteracts use-dependent depression at synapses between layer 5 pyramidal neurons. PMID:10581316

Treatment of reverse-osmosis concentrate of printing and dyeing wastewater by electro-oxidation process with controlled oxidation-reduction potential (ORP).

PubMed

Wang, Jiade; Zhang, Tian; Mei, Yu; Pan, Bingjun

2018-06-01

Reverse osmosis concentrate (ROC) of printing and dyeing wastewater remains as a daunting environmental issue, which is characterized by high salinity, chemical oxygen demand (COD), chroma and low biodegradability. In this study electro-oxidation process (PbO 2 /Ti electrode) coupled with oxidation-reduction potential (ORP) online monitor was applied to treat such a ROC effluent. The results show that with the increase of specific electrical charge (Q sp ), the removal efficiencies of COD, TN and chroma increased significantly at the incipience and then reached a gentle stage; the optimal total current efficiency (12.04 kWh m -3 ) was obtained with the current density of 10 mA cm -2 (Q sp , 3.0 Ah L -1 ). Meanwhile, some inorganic ions can be simultaneously removed to varying degrees. FTIR analyses indicated that the macromolecular organics were decomposed into smaller molecules. A multi-parameter linear relationship between ORP and Q sp , COD and Cl - concentration was established, which can quantitatively reflect the effect of current density, chloride ion concentration, pollutants and reaction time on the performance of the electro-oxidation system. As compared to a traditional constant-current system, the constant-ORP system developed in this study (through the back-propagation neural network [BPN] model with ORP monitoring) approximately reduced the energy cost by 24-29%. The present work is expected to provide a potential alternative in optimizing the electro-oxidation process. Copyright © 2018 Elsevier Ltd. All rights reserved.

The human ether-a-go-go-related gene (hERG) current inhibition selectively prolongs action potential of midmyocardial cells to augment transmural dispersion.

PubMed

Yasuda, C; Yasuda, S; Yamashita, H; Okada, J; Hisada, T; Sugiura, S

2015-08-01

The majority of drug induced arrhythmias are related to the prolongation of action potential duration following inhibition of rapidly activating delayed rectifier potassium current (I(Kr)) mediated by the hERG channel. However, for arrhythmias to develop and be sustained, not only the prolongation of action potential duration but also its transmural dispersion are required. Herein, we evaluated the effect of hERG inhibition on transmural dispersion of action potential duration using the action potential clamp technique that combined an in silico myocyte model with the actual I(Kr) measurement. Whole cell I(Kr) current was measured in Chinese hamster ovary cells stably expressing the hERG channel. The measured current was coupled with models of ventricular endocardial, M-, and epicardial cells to calculate the action potentials. Action potentials were evaluated under control condition and in the presence of 1, 10, or 100 μM disopyramide, an hERG inhibitor. Disopyramide dose-dependently increased the action potential durations of the three cell types. However, action potential duration of M-cells increased disproportionately at higher doses, and was significantly different from that of epicardial and endocardial cells (dispersion of repolarization). By contrast, the effects of disopyramide on peak I(Kr) and instantaneous current-voltage relation were similar in all cell types. Simulation study suggested that the reduced repolarization reserve of M-cell with smaller amount of slowly activating delayed rectifier potassium current levels off at longer action potential duration to make such differences. The action potential clamp technique is useful for studying the mechanism of arrhythmogenesis by hERG inhibition through the transmural dispersion of repolarization.

Dynamics of Action Potential Initiation in the GABAergic Thalamic Reticular Nucleus In Vivo

PubMed Central

Muñoz, Fabián; Fuentealba, Pablo

2012-01-01

Understanding the neural mechanisms of action potential generation is critical to establish the way neural circuits generate and coordinate activity. Accordingly, we investigated the dynamics of action potential initiation in the GABAergic thalamic reticular nucleus (TRN) using in vivo intracellular recordings in cats in order to preserve anatomically-intact axo-dendritic distributions and naturally-occurring spatiotemporal patterns of synaptic activity in this structure that regulates the thalamic relay to neocortex. We found a wide operational range of voltage thresholds for action potentials, mostly due to intrinsic voltage-gated conductances and not synaptic activity driven by network oscillations. Varying levels of synchronous synaptic inputs produced fast rates of membrane potential depolarization preceding the action potential onset that were associated with lower thresholds and increased excitability, consistent with TRN neurons performing as coincidence detectors. On the other hand the presence of action potentials preceding any given spike was associated with more depolarized thresholds. The phase-plane trajectory of the action potential showed somato-dendritic propagation, but no obvious axon initial segment component, prominent in other neuronal classes and allegedly responsible for the high onset speed. Overall, our results suggest that TRN neurons could flexibly integrate synaptic inputs to discharge action potentials over wide voltage ranges, and perform as coincidence detectors and temporal integrators, supported by a dynamic action potential threshold. PMID:22279567

Decision making and action implementation: evidence for an early visually triggered motor activation specific to potential actions.

PubMed

Tandonnet, Christophe; Garry, Michael I; Summers, Jeffery J

2013-07-01

To make a decision may rely on accumulating evidence in favor of one alternative until a threshold is reached. Sequential-sampling models differ by the way of accumulating evidence and the link with action implementation. Here, we tested a model's prediction of an early action implementation specific to potential actions. We assessed the dynamics of action implementation in go/no-go and between-hand choice tasks by transcranial magnetic stimulation of the motor cortex (single- or paired-pulse TMS; 3-ms interstimulus interval). Prior to implementation of the selected action, the amplitude of the motor evoked potential first increased whatever the visual stimulus but only for the hand potentially involved in the to-be-produced action. These findings suggest that visual stimuli can trigger an early motor activation specific to potential actions, consistent with race-like models with continuous transmission between decision making and action implementation. Copyright © 2013 Society for Psychophysiological Research.

Typing SNP based on the near-infrared spectroscopy and artificial neural network

NASA Astrophysics Data System (ADS)

Ren, Li; Wang, Wei-Peng; Gao, Yu-Zhen; Yu, Xiao-Wei; Xie, Hong-Ping

2009-07-01

Based on the near-infrared spectra (NIRS) of the measured samples as the discriminant variables of their genotypes, the genotype discriminant model of SNP has been established by using back-propagation artificial neural network (BP-ANN). Taking a SNP (857G > A) of N-acetyltransferase 2 (NAT2) as an example, DNA fragments containing the SNP site were amplified by the PCR method based on a pair of primers to obtain the three-genotype (GG, AA, and GA) modeling samples. The NIRS-s of the amplified samples were directly measured in transmission by using quartz cell. Based on the sample spectra measured, the two BP-ANN-s were combined to obtain the stronger ability of the three-genotype classification. One of them was established to compress the measured NIRS variables by using the resilient back-propagation algorithm, and another network established by Levenberg-Marquardt algorithm according to the compressed NIRS-s was used as the discriminant model of the three-genotype classification. For the established model, the root mean square error for the training and the prediction sample sets were 0.0135 and 0.0132, respectively. Certainly, this model could rightly predict the three genotypes (i.e. the accuracy of prediction samples was up to100%) and had a good robust for the prediction of unknown samples. Since the three genotypes of SNP could be directly determined by using the NIRS-s without any preprocessing for the analyzed samples after PCR, this method is simple, rapid and low-cost.

The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach.

PubMed

Fiyadh, Seef Saadi; AlSaadi, Mohammed Abdulhakim; AlOmar, Mohamed Khalid; Fayaed, Sabah Saadi; Hama, Ako R; Bee, Sharifah; El-Shafie, Ahmed

2017-11-01

The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb 2+ . Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb 2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R 2 ) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R 2 of 0.9956 with MSE of 1.66 × 10 -4 . The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.

Programming an Artificial Neural Network Tool for Spatial Interpolation in GIS - A Case Study for Indoor Radio Wave Propagation of WLAN.

PubMed

Sen, Alper; Gümüsay, M Umit; Kavas, Aktül; Bulucu, Umut

2008-09-25

Wireless communication networks offer subscribers the possibilities of free mobility and access to information anywhere at any time. Therefore, electromagnetic coverage calculations are important for wireless mobile communication systems, especially in Wireless Local Area Networks (WLANs). Before any propagation computation is performed, modeling of indoor radio wave propagation needs accurate geographical information in order to avoid the interruption of data transmissions. Geographic Information Systems (GIS) and spatial interpolation techniques are very efficient for performing indoor radio wave propagation modeling. This paper describes the spatial interpolation of electromagnetic field measurements using a feed-forward back-propagation neural network programmed as a tool in GIS. The accuracy of Artificial Neural Networks (ANN) and geostatistical Kriging were compared by adjusting procedures. The feedforward back-propagation ANN provides adequate accuracy for spatial interpolation, but the predictions of Kriging interpolation are more accurate than the selected ANN. The proposed GIS ensures indoor radio wave propagation model and electromagnetic coverage, the number, position and transmitter power of access points and electromagnetic radiation level. Pollution analysis in a given propagation environment was done and it was demonstrated that WLAN (2.4 GHz) electromagnetic coverage does not lead to any electromagnetic pollution due to the low power levels used. Example interpolated electromagnetic field values for WLAN system in a building of Yildiz Technical University, Turkey, were generated using the selected network architectures to illustrate the results with an ANN.

Programming an Artificial Neural Network Tool for Spatial Interpolation in GIS - A Case Study for Indoor Radio Wave Propagation of WLAN

PubMed Central

Şen, Alper; Gümüşay, M. Ümit; Kavas, Aktül; Bulucu, Umut

2008-01-01

Wireless communication networks offer subscribers the possibilities of free mobility and access to information anywhere at any time. Therefore, electromagnetic coverage calculations are important for wireless mobile communication systems, especially in Wireless Local Area Networks (WLANs). Before any propagation computation is performed, modeling of indoor radio wave propagation needs accurate geographical information in order to avoid the interruption of data transmissions. Geographic Information Systems (GIS) and spatial interpolation techniques are very efficient for performing indoor radio wave propagation modeling. This paper describes the spatial interpolation of electromagnetic field measurements using a feed-forward back-propagation neural network programmed as a tool in GIS. The accuracy of Artificial Neural Networks (ANN) and geostatistical Kriging were compared by adjusting procedures. The feedforward back-propagation ANN provides adequate accuracy for spatial interpolation, but the predictions of Kriging interpolation are more accurate than the selected ANN. The proposed GIS ensures indoor radio wave propagation model and electromagnetic coverage, the number, position and transmitter power of access points and electromagnetic radiation level. Pollution analysis in a given propagation environment was done and it was demonstrated that WLAN (2.4 GHz) electromagnetic coverage does not lead to any electromagnetic pollution due to the low power levels used. Example interpolated electromagnetic field values for WLAN system in a building of Yildiz Technical University, Turkey, were generated using the selected network architectures to illustrate the results with an ANN. PMID:27873854

New model for prediction binary mixture of antihistamine decongestant using artificial neural networks and least squares support vector machine by spectrophotometry method

NASA Astrophysics Data System (ADS)

Mofavvaz, Shirin; Sohrabi, Mahmoud Reza; Nezamzadeh-Ejhieh, Alireza

2017-07-01

In the present study, artificial neural networks (ANNs) and least squares support vector machines (LS-SVM) as intelligent methods based on absorption spectra in the range of 230-300 nm have been used for determination of antihistamine decongestant contents. In the first step, one type of network (feed-forward back-propagation) from the artificial neural network with two different training algorithms, Levenberg-Marquardt (LM) and gradient descent with momentum and adaptive learning rate back-propagation (GDX) algorithm, were employed and their performance was evaluated. The performance of the LM algorithm was better than the GDX algorithm. In the second one, the radial basis network was utilized and results compared with the previous network. In the last one, the other intelligent method named least squares support vector machine was proposed to construct the antihistamine decongestant prediction model and the results were compared with two of the aforementioned networks. The values of the statistical parameters mean square error (MSE), Regression coefficient (R2), correlation coefficient (r) and also mean recovery (%), relative standard deviation (RSD) used for selecting the best model between these methods. 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.

Classification of tumor based on magnetic resonance (MR) brain images using wavelet energy feature and neuro-fuzzy model

NASA Astrophysics Data System (ADS)

Damayanti, A.; Werdiningsih, I.

2018-03-01

The brain is the organ that coordinates all the activities that occur in our bodies. Small abnormalities in the brain will affect body activity. Tumor of the brain is a mass formed a result of cell growth not normal and unbridled in the brain. MRI is a non-invasive medical test that is useful for doctors in diagnosing and treating medical conditions. The process of classification of brain tumor can provide the right decision and correct treatment and right on the process of treatment of brain tumor. In this study, the classification process performed to determine the type of brain tumor disease, namely Alzheimer’s, Glioma, Carcinoma and normal, using energy coefficient and ANFIS. Process stages in the classification of images of MR brain are the extraction of a feature, reduction of a feature, and process of classification. The result of feature extraction is a vector approximation of each wavelet decomposition level. The feature reduction is a process of reducing the feature by using the energy coefficients of the vector approximation. The feature reduction result for energy coefficient of 100 per feature is 1 x 52 pixels. This vector will be the input on the classification using ANFIS with Fuzzy C-Means and FLVQ clustering process and LM back-propagation. Percentage of success rate of MR brain images recognition using ANFIS-FLVQ, ANFIS, and LM back-propagation was obtained at 100%.

The effects of saxitoxin and tetrodotoxin on nerve conduction in the presence of lithium ions and of magnesium ions

PubMed Central

Evans, M. H.

1969-01-01

1. It has been shown that nerve fibres from rat cauda equina will conduct action potentials after immersion in saline in which lithium chloride is substituted for sodium chloride. 2. Both saxitoxin and tetrodotoxin inhibit lithium-generated action potentials. The concentration of toxin needed to inhibit the lithium-generated action potentials is similar to that needed to inhibit sodium-generated action potentials. 3. If magnesium chloride is added to the saline to give a concentration of 10-15 mM there is usually a slight fall in amplitude of the compound action potential. Saxitoxin and tetrodotoxin now inhibit the action potential to a greater degree than in the absence of magnesium ions. PMID:5789802

Actions and mechanisms of action of novel analogues of sotalol on guinea-pig and rabbit ventricular cells.

PubMed Central

Connors, S. P.; Gill, E. W.; Terrar, D. A.

1992-01-01

1. The actions and mechanisms of action of novel analogues of sotalol which prolong cardiac action potentials were investigated in guinea-pig and rabbit isolated ventricular cells. 2. In guinea-pig and rabbit cells the compounds significantly prolonged action potential duration at 20% and 90% repolarization levels without affecting resting membrane potential. In guinea-pig but not rabbit cells there was an increase in action potential amplitude and in rabbit cells there was no change in the shape or position of the 'notch' in the action potential. 3. Possible mechanisms of action were studied in more detail in the case of compound II (1-(4-methanesulphonamidophenoxy)-3-(N-methyl 3,4 dichlorophenylethylamino)-2-propanol). Prolongation of action potential duration continued to occur in the presence of nisoldipine, and calcium currents recorded under voltage-clamp conditions were not reduced by compound II (1 microM). Action potential prolongation by compound II was also unaffected in the presence of 10 microM tetrodotoxin. 4. Compound II (1 microM) did not influence IK1 assessed from the current during ramp changes in membrane potential (20 mV s-1) over the range -90 to -10 mV. 5. Compound II (1 microM) blocked time-dependent delayed rectifier potassium current (IK) activated by step depolarizations and recorded as an outward tail following repolarization. When a submaximal concentration (50 nM) was applied there was no change in the apparent reversal potential of IK.(ABSTRACT TRUNCATED AT 250 WORDS) PMID:1393293

Alteration of neural action potential patterns by axonal stimulation: the importance of stimulus location.

PubMed

Crago, Patrick E; Makowski, Nathaniel S

2014-10-01

Stimulation of peripheral nerves is often superimposed on ongoing motor and sensory activity in the same axons, without a quantitative model of the net action potential train at the axon endpoint. We develop a model of action potential patterns elicited by superimposing constant frequency axonal stimulation on the action potentials arriving from a physiologically activated neural source. The model includes interactions due to collision block, resetting of the neural impulse generator, and the refractory period of the axon at the point of stimulation. Both the mean endpoint firing rate and the probability distribution of the action potential firing periods depend strongly on the relative firing rates of the two sources and the intersite conduction time between them. When the stimulus rate exceeds the neural rate, neural action potentials do not reach the endpoint and the rate of endpoint action potentials is the same as the stimulus rate, regardless of the intersite conduction time. However, when the stimulus rate is less than the neural rate, and the intersite conduction time is short, the two rates partially sum. Increases in stimulus rate produce non-monotonic increases in endpoint rate and continuously increasing block of neurally generated action potentials. Rate summation is reduced and more neural action potentials are blocked as the intersite conduction time increases. At long intersite conduction times, the endpoint rate simplifies to being the maximum of either the neural or the stimulus rate. This study highlights the potential of increasing the endpoint action potential rate and preserving neural information transmission by low rate stimulation with short intersite conduction times. Intersite conduction times can be decreased with proximal stimulation sites for muscles and distal stimulation sites for sensory endings. The model provides a basis for optimizing experiments and designing neuroprosthetic interventions involving motor or sensory stimulation.

The role of Na-Ca exchange current in the cardiac action potential.

PubMed

Janvier, N C; Boyett, M R

1996-07-01

Since 1981, when Mullins published his provocative book proposing that the Na-Ca exchanger is electrogenic, it has been shown, first by computer simulation by Noble and later by experiment by various investigators, that inward iNaCa triggered by the Ca2+ transient is responsible for the low plateau of the atrial action potential and contributes to the high plateau of the ventricular action potential. Reduction or complete block of inward iNaCa by buffering intracellular Ca2+ with EGTA or BAPTA, by blocking SR Ca2+ release or by substituting extracellular Na+ with Li+ can result in a shortening of the action potential. The effect of block of outward iNaCa or complete block of both inward and outward iNaCa on the action potential has not been investigated experimentally, because of the lack of a suitable blocker, and remains a goal for the future. An increase in the intracellular Na+ concentration (after the application of cardiac glycoside or an increase in heart rate) or an increase in extracellular Ca2+ are believed to lead to an outward shift in iNaCa at plateau potentials and a shortening of the action potential. Changes in the Ca2+ transient are expected to result in changes in inward iNaCa and thus the action potential. This may explain the shortening of the premature action potential as well as the prolongation of the action potential when a muscle is allowed to shorten during the action potential. Inward iNaCa may play an important role in both normal and abnormal pacemaker activity in the heart.

Simulation of action potential propagation in plants.

PubMed

Sukhov, Vladimir; Nerush, Vladimir; Orlova, Lyubov; Vodeneev, Vladimir

2011-12-21

Action potential is considered to be one of the primary responses of a plant to action of various environmental factors. Understanding plant action potential propagation mechanisms requires experimental investigation and simulation; however, a detailed mathematical model of plant electrical signal transmission is absent. Here, the mathematical model of action potential propagation in plants has been worked out. The model is a two-dimensional system of excitable cells; each of them is electrically coupled with four neighboring ones. Ion diffusion between excitable cell apoplast areas is also taken into account. The action potential generation in a single cell has been described on the basis of our previous model. The model simulates active and passive signal transmission well enough. It has been used to analyze theoretically the influence of cell to cell electrical conductivity and H(+)-ATPase activity on the signal transmission in plants. An increase in cell to cell electrical conductivity has been shown to stimulate an increase in the length constant, the action potential propagation velocity and the temperature threshold, while the membrane potential threshold being weakly changed. The growth of H(+)-ATPase activity has been found to induce the increase of temperature and membrane potential thresholds and the reduction of the length constant and the action potential propagation velocity. Copyright © 2011 Elsevier Ltd. All rights reserved.

[Loudness optimized registration of compound action potential in cochlear implant recipients].

PubMed

Berger, Klaus; Hocke, Thomas; Hessel, Horst

2017-11-01

Background Postoperative measurements of compound action potentials are not always possible due to the insufficient acceptance of the CI-recipients. This study investigated the impact of different parameters on the acceptance of the measurements. Methods Compound action potentials of 16 CI recipients were measured with different pulse-widths. Recipients performed a loudness rating at the potential thresholds with the different sequences. Results Compound action potentials obtained with higher pulse-widths were rated softer than those obtained with smaller pulse-widths. Conclusions Compound action potentials measured with higher pulse-widths generate a gap between loudest acceptable presentation level and potential threshold. This gap contributes to a higher acceptance of postoperative measurements. Georg Thieme Verlag KG Stuttgart · New York.

Inducing repetitive action potential firing in neurons via synthesized photoresponsive nanoscale cellular prostheses.

PubMed

Lu, Siyuan; Madhukar, Anupam

2013-02-01

Recently we reported an analysis that examined the potential of synthesized photovoltaic functional abiotic nanosystems (PVFANs) to modulate membrane potential and activate action potential firing in neurons. Here we extend the analysis to delineate the requirements on the electronic energy levels and the attendant photophysical properties of the PVFANs to induce repetitive action potential under continuous light, a capability essential for the proposed potential application of PVFANs as retinal cellular prostheses to compensate for loss of photoreceptors. We find that repetitive action potential firing demands two basic characteristics in the electronic response of the PVFANs: an exponential dependence of the PVFAN excited state decay rate on the membrane potential and a three-state system such that, following photon absorption, the electron decay from the excited state to the ground state is via intermediate state(s) whose lifetime is comparable to the refractory time following an action potential. In this study, the potential of synthetic photovoltaic functional abiotic nanosystems (PVFANs) is examined under continuous light to modulate membrane potential and activate action potential firing in neurons with the proposed potential application of PVFANs as retinal cellular prostheses. Copyright © 2013 Elsevier Inc. All rights reserved.

Effects of K(+) channel openers on spontaneous action potentials in detrusor smooth muscle of the guinea-pig urinary bladder.

PubMed

Takagi, Hiroaki; Hashitani, Hikaru

2016-10-15

The modulation of spontaneous excitability in detrusor smooth muscle (DSM) upon the pharmacological activation of different populations of K(+) channels was investigated. Effects of distinct K(+) channel openers on spontaneous action potentials in DSM of the guinea-pig bladder were examined using intracellular microelectrode techniques. NS1619 (10μM), a large conductance Ca(2+)-activated K(+) (BK) channel opener, transiently increased action potential frequency and then prevented their generation without hyperpolarizing the membrane in a manner sensitive to iberiotoxin (IbTX, 100nM). A higher concentration of NS1619 (30μM) hyperpolarized the membrane and abolished action potential firing. NS309 (10μM) and SKA31 (100μM), small conductance Ca(2+)-activated K(+) (SK) channel openers, dramatically increased the duration of the after-hyperpolarization and then abolished action potential firing in an apamin (100nM)-sensitive manner. Flupirtine (10μM), a Kv7 channel opener, inhibited action potential firing without hyperpolarizing the membrane in a manner sensitive to XE991 (10μM), a Kv7 channel blocker. BRL37344 (10μM), a β3-adrenceptor agonist, or rolipram (10nM), a phosphodiesterase 4 inhibitor, also inhibited action potential firing. A higher concentration of rolipram (100nM) hyperpolarized the DSM and abolished the action potentials. IbTX (100nM) prevented the rolipram-induced blockade of action potentials but not the hyperpolarization. BK and Kv7 channels appear to predominantly contribute to the stabilization of DSM excitability. Spare SK channels could be pharmacologically activated to suppress DSM excitability. BK channels appear to be involved in the cyclic AMP-induced inhibition of action potentials but not the membrane hyperpolarization. Copyright © 2016 Elsevier B.V. All rights reserved.

Conjugate-Gradient Neural Networks in Classification of Multisource and Very-High-Dimensional Remote Sensing Data

NASA Technical Reports Server (NTRS)

Benediktsson, J. A.; Swain, P. H.; Ersoy, O. K.

1993-01-01

Application of neural networks to classification of remote sensing data is discussed. Conventional two-layer backpropagation is found to give good results in classification of remote sensing data but is not efficient in training. A more efficient variant, based on conjugate-gradient optimization, is used for classification of multisource remote sensing and geographic data and very-high-dimensional data. The conjugate-gradient neural networks give excellent performance in classification of multisource data, but do not compare as well with statistical methods in classification of very-high-dimentional data.

Nonlinear adaptive networks: A little theory, a few applications

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

Jones, R.D.; Qian, S.; Barnes, C.W.

1990-01-01

We present the theory of nonlinear adaptive networks and discuss a few applications. In particular, we review the theory of feedforward backpropagation networks. We than present the theory of the Connectionist Normalized Linear Spline network in both its feedforward and iterated modes. Also, we briefly discuss the theory of stochastic cellular automata. We then discuss applications to chaotic time series tidal prediction in Venice Lagoon, sonar transient detection, control of nonlinear processes, balancing a double inverted pendulum and design advice for free electron lasers. 26 refs., 23 figs.

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.

Forecasting the mortality rates of Indonesian population by using neural network

NASA Astrophysics Data System (ADS)

Safitri, Lutfiani; Mardiyati, Sri; Rahim, Hendrisman

2018-03-01

A model that can represent a problem is required in conducting a forecasting. One of the models that has been acknowledged by the actuary community in forecasting mortality rate is the Lee-Certer model. Lee Carter model supported by Neural Network will be used to calculate mortality forecasting in Indonesia. The type of Neural Network used is feedforward neural network aligned with backpropagation algorithm in python programming language. And the final result of this study is mortality rate in forecasting Indonesia for the next few years

Predicting cloud-to-ground lightning with neural networks

NASA Technical Reports Server (NTRS)

Barnes, Arnold A., Jr.; Frankel, Donald; Draper, James Stark

1991-01-01

A neural network is being trained to predict lightning at Cape Canaveral for periods up to two hours in advance. Inputs consist of ground based field mill data, meteorological tower data, lightning location data, and radiosonde data. High values of the field mill data and rapid changes in the field mill data, offset in time, provide the forecasts or desired output values used to train the neural network through backpropagation. Examples of input data are shown and an example of data compression using a hidden layer in the neural network is discussed.

Supervised learning of probability distributions by neural networks

NASA Technical Reports Server (NTRS)

Baum, Eric B.; Wilczek, Frank

1988-01-01

Supervised learning algorithms for feedforward neural networks are investigated analytically. The back-propagation algorithm described by Werbos (1974), Parker (1985), and Rumelhart et al. (1986) is generalized by redefining the values of the input and output neurons as probabilities. The synaptic weights are then varied to follow gradients in the logarithm of likelihood rather than in the error. This modification is shown to provide a more rigorous theoretical basis for the algorithm and to permit more accurate predictions. A typical application involving a medical-diagnosis expert system is discussed.

Frequency domain, waveform inversion of laboratory crosswell radar data

USGS Publications Warehouse

Ellefsen, Karl J.; Mazzella, Aldo T.; Horton, Robert J.; McKenna, Jason R.

2010-01-01

A new waveform inversion for crosswell radar is formulated in the frequency-domain for a 2.5D model. The inversion simulates radar waves using the vector Helmholtz equation for electromagnetic waves. The objective function is minimized using a backpropagation method suitable for a 2.5D model. The inversion is tested by processing crosswell radar data collected in a laboratory tank. The estimated model is consistent with the known electromagnetic properties of the tank. The formulation for the 2.5D model can be extended to inversions of acoustic and elastic data.

Neural network-based run-to-run controller using exposure and resist thickness adjustment

NASA Astrophysics Data System (ADS)

Geary, Shane; Barry, Ronan

2003-06-01

This paper describes the development of a run-to-run control algorithm using a feedforward neural network, trained using the backpropagation training method. The algorithm is used to predict the critical dimension of the next lot using previous lot information. It is compared to a common prediction algorithm - the exponentially weighted moving average (EWMA) and is shown to give superior prediction performance in simulations. The manufacturing implementation of the final neural network showed significantly improved process capability when compared to the case where no run-to-run control was utilised.

Neural network error correction for solving coupled ordinary differential equations

NASA Technical Reports Server (NTRS)

Shelton, R. O.; Darsey, J. A.; Sumpter, B. G.; Noid, D. W.

1992-01-01

A neural network is presented to learn errors generated by a numerical algorithm for solving coupled nonlinear differential equations. The method is based on using a neural network to correctly learn the error generated by, for example, Runge-Kutta on a model molecular dynamics (MD) problem. The neural network programs used in this study were developed by NASA. Comparisons are made for training the neural network using backpropagation and a new method which was found to converge with fewer iterations. The neural net programs, the MD model and the calculations are discussed.

Identification of species and geographical strains of Sitophilus oryzae and Sitophilus zeamais using the visible/near-infrared hyperspectral imaging technique.

PubMed

Cao, Yang; Zhang, Chaojie; Chen, Quansheng; Li, Yanyu; Qi, Shuai; Tian, Lin; Ren, YongLin

2015-08-01

Identifying stored-product insects is essential for granary management. Automated, computer-based classification methods are rapidly developing in many areas. A hyperspectral imaging technique could potentially be developed to identify stored-product insect species and geographical strains. This study tested and adapted the technique using four geographical strains of each of two insect species, the rice weevil and maize weevil, to collect and analyse the resultant hyperspectral data. Three characteristic images that corresponded to the dominant wavelengths, 505, 659 and 955 nm, were selected by multivariate image analysis. Each image was processed, and 22 morphological and textural features from regions of interest were extracted as the inputs for an identification model. We found the backpropagation neural network model to be the superior method for distinguishing between the insect species and geographical strains. The overall recognition rates of the classification model for insect species were 100 and 98.13% for the calibration and prediction sets respectively, while the rates of the model for geographical strains were 94.17 and 86.88% respectively. This study has demonstrated that hyperspectral imaging, together with the appropriate recognition method, could provide a potential instrument for identifying insects and could become a useful tool for identification of Sitophilus oryzae and Sitophilus zeamais to aid in the management of stored-product insects. © 2014 Society of Chemical Industry.

Use of qualitative and quantitative information in neural networks for assessing agricultural chemical contamination of domestic wells

USGS Publications Warehouse

Mishra, A.; Ray, C.; Kolpin, D.W.

2004-01-01

A neural network analysis of agrichemical occurrence in groundwater was conducted using data from a pilot study of 192 small-diameter drilled and driven wells and 115 dug and bored wells in Illinois, a regional reconnaissance network of 303 wells across 12 Midwestern states, and a study of 687 domestic wells across Iowa. Potential factors contributing to well contamination (e.g., depth to aquifer material, well depth, and distance to cropland) were investigated. These contributing factors were available in either numeric (actual or categorical) or descriptive (yes or no) format. A method was devised to use the numeric and descriptive values simultaneously. Training of the network was conducted using a standard backpropagation algorithm. Approximately 15% of the data was used for testing. Analysis indicated that training error was quite low for most data. Testing results indicated that it was possible to predict the contamination potential of a well with pesticides. However, predicting the actual level of contamination was more difficult. For pesticide occurrence in drilled and driven wells, the network predictions were good. The performance of the network was poorer for predicting nitrate occurrence in dug and bored wells. Although the data set for Iowa was large, the prediction ability of the trained network was poor, due to descriptive or categorical input parameters, compared with smaller data sets such as that for Illinois, which contained more numeric information.

Imaging Action Potential in Single Mammalian Neurons by Tracking the Accompanying Sub-Nanometer Mechanical Motion.

PubMed

Yang, Yunze; Liu, Xian-Wei; Wang, Hui; Yu, Hui; Guan, Yan; Wang, Shaopeng; Tao, Nongjian

2018-03-28

Action potentials in neurons have been studied traditionally by intracellular electrophysiological recordings and more recently by the fluorescence detection methods. Here we describe a label-free optical imaging method that can measure mechanical motion in single cells with a sub-nanometer detection limit. Using the method, we have observed sub-nanometer mechanical motion accompanying the action potential in single mammalian neurons by averaging the repeated action potential spikes. The shape and width of the transient displacement are similar to those of the electrically recorded action potential, but the amplitude varies from neuron to neuron, and from one region of a neuron to another, ranging from 0.2-0.4 nm. The work indicates that action potentials may be studied noninvasively in single mammalian neurons by label-free imaging of the accompanying sub-nanometer mechanical motion.

Pathological prolongation of action potential duration as a cause of the reduced alpha-adrenoceptor-mediated negative inotropy in streptozotocin-induced diabetic mice myocardium.

PubMed

Kanae, Haruna; Hamaguchi, Shogo; Wakasugi, Yumi; Kusakabe, Taichi; Kato, Keisuke; Namekata, Iyuki; Tanaka, Hikaru

2017-11-01

Effect of pathological prolongation of action potential duration on the α-adrenoceptor-mediated negative inotropy was studied in streptozotocin-induced diabetic mice myocardium. In streptozotocin-treated mouse ventricular myocardium, which had longer duration of action potential than that in control mice, the negative inotropic response induced by phenylephrine was smaller than that in control mice. 4-Aminopyridine prolonged the action potential duration and decreased the negative inotropy in control mice. Cromakalim shortened the action potential duration and increased the negative inotropy in streptozotocin-treated mice. These results suggest that the reduced α-adrenoceptor-mediated inotropy in the diabetic mouse myocardium is partly due to its prolonged action potential. Copyright © 2017 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

Determination of cable parameters in skeletal muscle fibres during repetitive firing of action potentials.

PubMed

Riisager, Anders; Duehmke, Rudy; Nielsen, Ole Bækgaard; Huang, Christopher L; Pedersen, Thomas Holm

2014-10-15

Recent studies in rat muscle fibres show that repetitive firing of action potentials causes changes in fibre resting membrane conductance (Gm) that reflect regulation of ClC-1 Cl(-) and KATP K(+) ion channels. Methodologically, these findings were obtained by inserting two microelectrodes at close proximity in the same fibres enabling measurements of fibre input resistance (Rin) in between action potential trains. Since the fibre length constant (λ) could not be determined, however, the calculation of Gm relied on the assumptions that the specific cytosolic resistivity (Ri) and muscle fibre volume remained constant during the repeated action potential firing. Here we present a three-microelectrode technique that enables determinations of multiple cable parameters in action potential-firing fibres including Rin and λ as well as waveform and conduction velocities of fully propagating action potentials. It is shown that in both rat and mouse extensor digitorum longus (EDL) fibres, action potential firing leads to substantial changes in both muscle fibre volume and Ri. The analysis also showed, however, that regardless of these changes, rat and mouse EDL fibres both exhibited initial decreases in Gm that were eventually followed by a ∼3-fold, fully reversible increase in Gm after the firing of 1450-1800 action potentials. Using this three-electrode method we further show that the latter rise in Gm was closely associated with excitation failures and loss of action potential signal above -20 mV. © 2014 The Authors. The Journal of Physiology © 2014 The Physiological Society.

Determination of cable parameters in skeletal muscle fibres during repetitive firing of action potentials

PubMed Central

Riisager, Anders; Duehmke, Rudy; Nielsen, Ole Bækgaard; Huang, Christopher L; Pedersen, Thomas Holm

2014-01-01

Recent studies in rat muscle fibres show that repetitive firing of action potentials causes changes in fibre resting membrane conductance (Gm) that reflect regulation of ClC-1 Cl− and KATP K+ ion channels. Methodologically, these findings were obtained by inserting two microelectrodes at close proximity in the same fibres enabling measurements of fibre input resistance (Rin) in between action potential trains. Since the fibre length constant (λ) could not be determined, however, the calculation of Gm relied on the assumptions that the specific cytosolic resistivity (Ri) and muscle fibre volume remained constant during the repeated action potential firing. Here we present a three-microelectrode technique that enables determinations of multiple cable parameters in action potential-firing fibres including Rin and λ as well as waveform and conduction velocities of fully propagating action potentials. It is shown that in both rat and mouse extensor digitorum longus (EDL) fibres, action potential firing leads to substantial changes in both muscle fibre volume and Ri. The analysis also showed, however, that regardless of these changes, rat and mouse EDL fibres both exhibited initial decreases in Gm that were eventually followed by a ∼3-fold, fully reversible increase in Gm after the firing of 1450–1800 action potentials. Using this three-electrode method we further show that the latter rise in Gm was closely associated with excitation failures and loss of action potential signal above −20 mV. PMID:25128573

Cardiac action potential imaging

NASA Astrophysics Data System (ADS)

Tian, Qinghai; Lipp, Peter; Kaestner, Lars

2013-06-01

Action potentials in cardiac myocytes have durations in the order of magnitude of 100 milliseconds. In biomedical investigations the documentation of the occurrence of action potentials is often not sufficient, but a recording of the shape of an action potential allows a functional estimation of several molecular players. Therefore a temporal resolution of around 500 images per second is compulsory. In the past such measurements have been performed with photometric approaches limiting the measurement to one cell at a time. In contrast, imaging allows reading out several cells at a time with additional spatial information. Recent developments in camera technologies allow the acquisition with the required speed and sensitivity. We performed action potential imaging on isolated adult cardiomyocytes of guinea pigs utilizing the fluorescent membrane potential sensor di-8-ANEPPS and latest electron-multiplication CCD as well as scientific CMOS cameras of several manufacturers. Furthermore, we characterized the signal to noise ratio of action potential signals of varying sets of cameras, dye concentrations and objective lenses. We ensured that di-8-ANEPPS itself did not alter action potentials by avoiding concentrations above 5 μM. Based on these results we can conclude that imaging is a reliable method to read out action potentials. Compared to conventional current-clamp experiments, this optical approach allows a much higher throughput and due to its contact free concept leaving the cell to a much higher degree undisturbed. Action potential imaging based on isolated adult cardiomyocytes can be utilized in pharmacological cardiac safety screens bearing numerous advantages over approaches based on heterologous expression of hERG channels in cell lines.

A simple model for the generation of the vestibular evoked myogenic potential (VEMP).

PubMed

Wit, Hero P; Kingma, Charlotte M

2006-06-01

To describe the mechanism by which the vestibular evoked myogenic potential is generated. Vestibular evoked myogenic potential generation is modeled by adding a large number of muscle motor unit action potentials. These action potentials occur randomly in time along a 100 ms long time axis. But because between approximately 15 and 20 ms after a loud short sound stimulus (almost) no action potentials are generated during VEMP measurements in human subjects, no action potentials are present in the model during this time. The evoked potential is the result of the lack of amplitude cancellation in the averaged surface electromyogram at the edges of this 5 ms long time interval. The relatively simple model describes generation and some properties of the vestibular evoked myogenic potential very well. It is shown that, in contrast with other evoked potentials (BAEPs, VERs), the vestibular evoked myogenic potential is the result of an interruption of activity and not that of summed synchronized neural action potentials.

A rabbit ventricular action potential model replicating cardiac dynamics at rapid heart rates.

PubMed

Mahajan, Aman; Shiferaw, Yohannes; Sato, Daisuke; Baher, Ali; Olcese, Riccardo; Xie, Lai-Hua; Yang, Ming-Jim; Chen, Peng-Sheng; Restrepo, Juan G; Karma, Alain; Garfinkel, Alan; Qu, Zhilin; Weiss, James N

2008-01-15

Mathematical modeling of the cardiac action potential has proven to be a powerful tool for illuminating various aspects of cardiac function, including cardiac arrhythmias. However, no currently available detailed action potential model accurately reproduces the dynamics of the cardiac action potential and intracellular calcium (Ca(i)) cycling at rapid heart rates relevant to ventricular tachycardia and fibrillation. The aim of this study was to develop such a model. Using an existing rabbit ventricular action potential model, we modified the L-type calcium (Ca) current (I(Ca,L)) and Ca(i) cycling formulations based on new experimental patch-clamp data obtained in isolated rabbit ventricular myocytes, using the perforated patch configuration at 35-37 degrees C. Incorporating a minimal seven-state Markovian model of I(Ca,L) that reproduced Ca- and voltage-dependent kinetics in combination with our previously published dynamic Ca(i) cycling model, the new model replicates experimentally observed action potential duration and Ca(i) transient alternans at rapid heart rates, and accurately reproduces experimental action potential duration restitution curves obtained by either dynamic or S1S2 pacing.

Typical gray matter axons in mammalian brain fail to conduct action potentials faithfully at fever-like temperatures.

PubMed

Pekala, Dobromila; Szkudlarek, Hanna; Raastad, Morten

2016-10-01

We studied the ability of typical unmyelinated cortical axons to conduct action potentials at fever-like temperatures because fever often gives CNS symptoms. We investigated such axons in cerebellar and hippocampal slices from 10 to 25 days old rats at temperatures between 30 and 43°C. By recording with two electrodes along axonal pathways, we confirmed that the axons were able to initiate action potentials, but at temperatures >39°C, the propagation of the action potentials to a more distal recording site was reduced. This temperature-sensitive conduction may be specific for the very thin unmyelinated axons because similar recordings from myelinated CNS axons did not show conduction failures. We found that the conduction fidelity improved with 1 mmol/L TEA in the bath, probably due to block of voltage-sensitive potassium channels responsible for the fast repolarization of action potentials. Furthermore, by recording electrically activated antidromic action potentials from the soma of cerebellar granule cells, we showed that the axons failed less if they were triggered 10-30 msec after another action potential. This was because individual action potentials were followed by a depolarizing after-potential, of constant amplitude and shape, which facilitated conduction of the following action potentials. The temperature-sensitive conduction failures above, but not below, normal body temperature, and the failure-reducing effect of the spike's depolarizing after-potential, are two intrinsic mechanisms in normal gray matter axons that may help us understand how the hyperthermic brain functions. © 2016 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of the American Physiological Society and The Physiological Society.

Alteration of neural action potential patterns by axonal stimulation: the importance of stimulus location

PubMed Central

Crago, Patrick E; Makowski, Nathan S

2014-01-01

Objective Stimulation of peripheral nerves is often superimposed on ongoing motor and sensory activity in the same axons, without a quantitative model of the net action potential train at the axon endpoint. Approach We develop a model of action potential patterns elicited by superimposing constant frequency axonal stimulation on the action potentials arriving from a physiologically activated neural source. The model includes interactions due to collision block, resetting of the neural impulse generator, and the refractory period of the axon at the point of stimulation. Main Results Both the mean endpoint firing rate and the probability distribution of the action potential firing periods depend strongly on the relative firing rates of the two sources and the intersite conduction time between them. When the stimulus rate exceeds the neural rate, neural action potentials do not reach the endpoint and the rate of endpoint action potentials is the same as the stimulus rate, regardless of the intersite conduction time. However, when the stimulus rate is less than the neural rate, and the intersite conduction time is short, the two rates partially sum. Increases in stimulus rate produce non-monotonic increases in endpoint rate and continuously increasing block of neurally generated action potentials. Rate summation is reduced and more neural action potentials are blocked as the intersite conduction time increases.. At long intersite conduction times, the endpoint rate simplifies to being the maximum of either the neural or the stimulus rate. Significance This study highlights the potential of increasing the endpoint action potential rate and preserving neural information transmission by low rate stimulation with short intersite conduction times. Intersite conduction times can be decreased with proximal stimulation sites for muscles and distal stimulation sites for sensory endings. The model provides a basis for optimizing experiments and designing neuroprosthetic interventions involving motor or sensory stimulation. PMID:25161163

Alteration of neural action potential patterns by axonal stimulation: the importance of stimulus location

NASA Astrophysics Data System (ADS)

Crago, Patrick E.; Makowski, Nathaniel S.

2014-10-01

Objective. Stimulation of peripheral nerves is often superimposed on ongoing motor and sensory activity in the same axons, without a quantitative model of the net action potential train at the axon endpoint. Approach. We develop a model of action potential patterns elicited by superimposing constant frequency axonal stimulation on the action potentials arriving from a physiologically activated neural source. The model includes interactions due to collision block, resetting of the neural impulse generator, and the refractory period of the axon at the point of stimulation. Main results. Both the mean endpoint firing rate and the probability distribution of the action potential firing periods depend strongly on the relative firing rates of the two sources and the intersite conduction time between them. When the stimulus rate exceeds the neural rate, neural action potentials do not reach the endpoint and the rate of endpoint action potentials is the same as the stimulus rate, regardless of the intersite conduction time. However, when the stimulus rate is less than the neural rate, and the intersite conduction time is short, the two rates partially sum. Increases in stimulus rate produce non-monotonic increases in endpoint rate and continuously increasing block of neurally generated action potentials. Rate summation is reduced and more neural action potentials are blocked as the intersite conduction time increases. At long intersite conduction times, the endpoint rate simplifies to being the maximum of either the neural or the stimulus rate. Significance. This study highlights the potential of increasing the endpoint action potential rate and preserving neural information transmission by low rate stimulation with short intersite conduction times. Intersite conduction times can be decreased with proximal stimulation sites for muscles and distal stimulation sites for sensory endings. The model provides a basis for optimizing experiments and designing neuroprosthetic interventions involving motor or sensory stimulation.

Effect of an educational game on university students' learning about action potentials.

PubMed

Luchi, Kelly Cristina Gaviao; Montrezor, Luís Henrique; Marcondes, Fernanda K

2017-06-01

The aim of this study was to evaluate the effect of an educational game that is used for teaching the mechanisms of the action potentials in cell membranes. The game was composed of pieces representing the intracellular and extracellular environments, ions, ion channels, and the Na + -K + -ATPase pump. During the game activity, the students arranged the pieces to demonstrate how the ions move through the membrane in a resting state and during an action potential, linking the ion movement with a graph of the action potential. To test the effect of the game activity on student understanding, first-year dental students were given the game to play at different times in a series of classes teaching resting membrane potential and action potentials. In all experiments, students who played the game performed better in assessments. According to 98% of the students, the game supported the learning process. The data confirm the students' perception, indicating that the educational game improved their understanding about action potentials. Copyright © 2017 the American Physiological Society.

Understanding the electrical behavior of the action potential in terms of elementary electrical sources.

PubMed

Rodriguez-Falces, Javier

2015-03-01

A concept of major importance in human electrophysiology studies is the process by which activation of an excitable cell results in a rapid rise and fall of the electrical membrane potential, the so-called action potential. Hodgkin and Huxley proposed a model to explain the ionic mechanisms underlying the formation of action potentials. However, this model is unsuitably complex for teaching purposes. In addition, the Hodgkin and Huxley approach describes the shape of the action potential only in terms of ionic currents, i.e., it is unable to explain the electrical significance of the action potential or describe the electrical field arising from this source using basic concepts of electromagnetic theory. The goal of the present report was to propose a new model to describe the electrical behaviour of the action potential in terms of elementary electrical sources (in particular, dipoles). The efficacy of this model was tested through a closed-book written exam. The proposed model increased the ability of students to appreciate the distributed character of the action potential and also to recognize that this source spreads out along the fiber as function of space. In addition, the new approach allowed students to realize that the amplitude and sign of the extracellular electrical potential arising from the action potential are determined by the spatial derivative of this intracellular source. The proposed model, which incorporates intuitive graphical representations, has improved students' understanding of the electrical potentials generated by bioelectrical sources and has heightened their interest in bioelectricity. Copyright © 2015 The American Physiological Society.

Selective activation of heteromeric SK channels contributes to action potential repolarization in mouse atrial myocytes.

PubMed

Hancock, Jane M; Weatherall, Kate L; Choisy, Stéphanie C; James, Andrew F; Hancox, Jules C; Marrion, Neil V

2015-05-01

Activation of small conductance calcium-activated potassium (SK) channels is proposed to contribute to repolarization of the action potential in atrial myocytes. This role is controversial, as these cardiac SK channels appear to exhibit an uncharacteristic pharmacology. The objectives of this study were to resolve whether activation of SK channels contributes to atrial action potential repolarization and to determine the likely subunit composition of the channel. The effect of 2 SK channel inhibitors was assessed on outward current evoked in voltage clamp and on action potential duration in perforated patch and whole-cell current clamp recording from acutely isolated mouse atrial myocytes. The presence of SK channel subunits was assessed using immunocytochemistry. A significant component of outward current was reduced by the SK channel blockers apamin and UCL1684. Block by apamin displayed a sensitivity indicating that this current was carried by homomeric SK2 channels. Action potential duration was significantly prolonged by UCL1684, but not by apamin. This effect was accompanied by an increase in beat-to-beat variability and action potential triangulation. This pharmacology was matched by that of expressed heteromeric SK2-SK3 channels in HEK293 cells. Immunocytochemistry showed that atrial myocytes express both SK2 and SK3 channels with an overlapping expression pattern. Only proposed heteromeric SK2-SK3 channels are physiologically activated to contribute to action potential repolarization, which is indicated by the difference in pharmacology of evoked outward current and prolongation of atrial action potential duration. The effect of blocking this channel on the action potential suggests that SK channel inhibition during cardiac function has the potential to be proarrhythmic. Copyright © 2015 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

Urocortin2 prolongs action potential duration and modulates potassium currents in guinea pig myocytes and HEK293 cells.

PubMed

Yang, Li-Zhen; Zhu, Yi-Chun

2015-07-05

We previously reported that activation of corticotropin releasing factor receptor type 2 by urocortin2 up-regulates both L-type Ca(2+) channels and intracellular Ca(2+) concentration in ventricular myocytes and plays an important role in cardiac contractility and arrhythmogenesis. This study goal was to further test the hypothesis that urocortin2 may modulate action potentials as well as rapidly and slowly activating delayed rectifier potassium currents. With whole cell patch-clamp techniques, action potentials and slowly activating delayed rectifier potassium currents were recorded in isolated guinea pig ventricular myocytes, respectively. And rapidly activating delayed rectifier potassium currents were tested in hERG-HEK293 cells. Urocortin2 produced a time- and concentration-dependent prolongation of action potential duration. The EC50 values of action potential duration and action potential duration at 90% of repolarization were 14.73 and 24.3nM respectively. The prolongation of action potential duration of urocortin2 was almost completely or partly abolished by H-89 (protein kinase A inhibitor) or KB-R7943 (Na(+)/Ca(2+) exchange inhibitor) pretreatment respectively. And urocortin2 caused reduction of rapidly activating delayed rectifier potassium currents in hERG-HEK293 cells. In addition, urocortin2 slowed the rate of slowly activating delayed rectifier potassium channel activation, and rightward shifted the threshold of slowly activating delayed rectifier potassium currents to more positive potentials. Urocortin2 prolonged action potential duration via activation of protein kinase A and Na(+)/ Ca(2+) exchange in isolated guinea pig ventricular myocytes in a time- and concentration- dependent manner. In hERG-HEK293 cells, urocortin2 reduced rapidly activating delayed rectifier potassium current density which may contribute to action potential duration prolongation. Copyright © 2015 Elsevier B.V. All rights reserved.

Population of computational rabbit-specific ventricular action potential models for investigating sources of variability in cellular repolarisation.

PubMed

Gemmell, Philip; Burrage, Kevin; Rodriguez, Blanca; Quinn, T Alexander

2014-01-01

Variability is observed at all levels of cardiac electrophysiology. Yet, the underlying causes and importance of this variability are generally unknown, and difficult to investigate with current experimental techniques. The aim of the present study was to generate populations of computational ventricular action potential models that reproduce experimentally observed intercellular variability of repolarisation (represented by action potential duration) and to identify its potential causes. A systematic exploration of the effects of simultaneously varying the magnitude of six transmembrane current conductances (transient outward, rapid and slow delayed rectifier K(+), inward rectifying K(+), L-type Ca(2+), and Na(+)/K(+) pump currents) in two rabbit-specific ventricular action potential models (Shannon et al. and Mahajan et al.) at multiple cycle lengths (400, 600, 1,000 ms) was performed. This was accomplished with distributed computing software specialised for multi-dimensional parameter sweeps and grid execution. An initial population of 15,625 parameter sets was generated for both models at each cycle length. Action potential durations of these populations were compared to experimentally derived ranges for rabbit ventricular myocytes. 1,352 parameter sets for the Shannon model and 779 parameter sets for the Mahajan model yielded action potential duration within the experimental range, demonstrating that a wide array of ionic conductance values can be used to simulate a physiological rabbit ventricular action potential. Furthermore, by using clutter-based dimension reordering, a technique that allows visualisation of multi-dimensional spaces in two dimensions, the interaction of current conductances and their relative importance to the ventricular action potential at different cycle lengths were revealed. Overall, this work represents an important step towards a better understanding of the role that variability in current conductances may play in experimentally observed intercellular variability of rabbit ventricular action potential repolarisation.

Population of Computational Rabbit-Specific Ventricular Action Potential Models for Investigating Sources of Variability in Cellular Repolarisation

PubMed Central

Gemmell, Philip; Burrage, Kevin; Rodriguez, Blanca; Quinn, T. Alexander

2014-01-01

Variability is observed at all levels of cardiac electrophysiology. Yet, the underlying causes and importance of this variability are generally unknown, and difficult to investigate with current experimental techniques. The aim of the present study was to generate populations of computational ventricular action potential models that reproduce experimentally observed intercellular variability of repolarisation (represented by action potential duration) and to identify its potential causes. A systematic exploration of the effects of simultaneously varying the magnitude of six transmembrane current conductances (transient outward, rapid and slow delayed rectifier K+, inward rectifying K+, L-type Ca2+, and Na+/K+ pump currents) in two rabbit-specific ventricular action potential models (Shannon et al. and Mahajan et al.) at multiple cycle lengths (400, 600, 1,000 ms) was performed. This was accomplished with distributed computing software specialised for multi-dimensional parameter sweeps and grid execution. An initial population of 15,625 parameter sets was generated for both models at each cycle length. Action potential durations of these populations were compared to experimentally derived ranges for rabbit ventricular myocytes. 1,352 parameter sets for the Shannon model and 779 parameter sets for the Mahajan model yielded action potential duration within the experimental range, demonstrating that a wide array of ionic conductance values can be used to simulate a physiological rabbit ventricular action potential. Furthermore, by using clutter-based dimension reordering, a technique that allows visualisation of multi-dimensional spaces in two dimensions, the interaction of current conductances and their relative importance to the ventricular action potential at different cycle lengths were revealed. Overall, this work represents an important step towards a better understanding of the role that variability in current conductances may play in experimentally observed intercellular variability of rabbit ventricular action potential repolarisation. PMID:24587229

Action potential propagation: ion current or intramembrane electric field?

PubMed

Martí, Albert; Pérez, Juan J; Madrenas, Jordi

2018-01-01

The established action potential propagation mechanisms do not satisfactorily explain propagation on myelinated axons given the current knowledge of biological channels and membranes. The flow across ion channels presents two possible effects: the electric potential variations across the lipid bilayers (action potential) and the propagation of an electric field through the membrane inner part. The proposed mechanism is based on intra-membrane electric field propagation, this propagation can explain the action potential saltatory propagation and its constant delay independent of distance between Ranvier nodes in myelinated axons.

Antidromic propagation of action potentials in branched axons: implications for the mechanisms of action of deep brain stimulation.

PubMed

Grill, Warren M; Cantrell, Meredith B; Robertson, Matthew S

2008-02-01

Electrical stimulation of the central nervous system creates both orthodromically propagating action potentials, by stimulation of local cells and passing axons, and antidromically propagating action potentials, by stimulation of presynaptic axons and terminals. Our aim was to understand how antidromic action potentials navigate through complex arborizations, such as those of thalamic and basal ganglia afferents-sites of electrical activation during deep brain stimulation. We developed computational models to study the propagation of antidromic action potentials past the bifurcation in branched axons. In both unmyelinated and myelinated branched axons, when the diameters of each axon branch remained under a specific threshold (set by the antidromic geometric ratio), antidromic propagation occurred robustly; action potentials traveled both antidromically into the primary segment as well as "re-orthodromically" into the terminal secondary segment. Propagation occurred across a broad range of stimulation frequencies, axon segment geometries, and concentrations of extracellular potassium, but was strongly dependent on the geometry of the node of Ranvier at the axonal bifurcation. Thus, antidromic activation of axon terminals can, through axon collaterals, lead to widespread activation or inhibition of targets remote from the site of stimulation. These effects should be included when interpreting the results of functional imaging or evoked potential studies on the mechanisms of action of DBS.

Minocycline inhibits D-amphetamine-elicited action potential bursts in a central snail neuron.

PubMed

Chen, Y-H; Lin, P-L; Wong, R-W; Wu, Y-T; Hsu, H-Y; Tsai, M-C; Lin, M-J; Hsu, Y-C; Lin, C-H

2012-10-25

Minocycline is a second-generation tetracycline that has been reported to have powerful neuroprotective properties. In our previous studies, we found that d-amphetamine (AMPH) elicited action potential bursts in an identifiable RP4 neuron of the African snail, Achatina fulica Ferussac. This study sought to determine the effects of minocycline on the AMPH-elicited action potential pattern changes in the central snail neuron, using the two-electrode voltage clamping method. Extracellular application of AMPH at 300 μM elicited action potential bursts in the RP4 neuron. Minocycline dose-dependently (300-900 μM) inhibited the action potential bursts elicited by AMPH. The inhibitory effects of minocycline on AMPH-elicited action potential bursts were restored by forskolin (50 μM), an adenylate cyclase activator, and by dibutyryl cAMP (N(6),2'-O-Dibutyryladenosine 3',5'-cyclic monophosphate; 1mM), a membrane-permeable cAMP analog. Co-administration of forskolin (50 μM) plus tetraethylammonium chloride (TEA; 5mM) or co-administration of TEA (5mM) plus dibutyryl cAMP (1mM) also elicited action potential bursts, which were prevented and inhibited by minocycline. In addition, minocycline prevented and inhibited forskolin (100 μM)-elicited action potential bursts. Notably, TEA (50mM)-elicited action potential bursts in the RP4 neuron were not affected by minocycline. Minocycline did not affect steady-state outward currents of the RP4 neuron. However, minocycline did decrease the AMPH-elicited steady-state current changes. Similarly, minocycline decreased the effects of forskolin-elicited steady-state current changes. Pretreatment with H89 (N-[2-(p-Bromocinnamylamino)ethyl]-5-isoquinolinesulfonamide dihydrochloride; 10 μM), a protein kinase A inhibitor, inhibited AMPH-elicited action potential bursts and decreased AMPH-elicited steady-state current changes. These results suggest that the cAMP-protein kinase A signaling pathway and the steady-state current are involved in the inhibitory effects of minocycline upon AMPH-elicited action potential bursts. Copyright © 2012 IBRO. Published by Elsevier Ltd. All rights reserved.

A neural network approach for enhancing information extraction from multispectral image data

USGS Publications Warehouse

Liu, J.; Shao, G.; Zhu, H.; Liu, S.

2005-01-01

A back-propagation artificial neural network (ANN) was applied to classify multispectral remote sensing imagery data. The classification procedure included four steps: (i) noisy training that adds minor random variations to the sampling data to make the data more representative and to reduce the training sample size; (ii) iterative or multi-tier classification that reclassifies the unclassified pixels by making a subset of training samples from the original training set, which means the neural model can focus on fewer classes; (iii) spectral channel selection based on neural network weights that can distinguish the relative importance of each channel in the classification process to simplify the ANN model; and (iv) voting rules that adjust the accuracy of classification and produce outputs of different confidence levels. The Purdue Forest, located west of Purdue University, West Lafayette, Indiana, was chosen as the test site. The 1992 Landsat thematic mapper imagery was used as the input data. High-quality airborne photographs of the same Lime period were used for the ground truth. A total of 11 land use and land cover classes were defined, including water, broadleaved forest, coniferous forest, young forest, urban and road, and six types of cropland-grassland. The experiment, indicated that the back-propagation neural network application was satisfactory in distinguishing different land cover types at US Geological Survey levels II-III. The single-tier classification reached an overall accuracy of 85%. and the multi-tier classification an overall accuracy of 95%. For the whole test, region, the final output of this study reached an overall accuracy of 87%. ?? 2005 CASI.

Three-Dimensional Passive-Source Reverse-Time Migration of Converted Waves: The Method

NASA Astrophysics Data System (ADS)

Li, Jiahang; Shen, Yang; Zhang, Wei

2018-02-01

At seismic discontinuities in the crust and mantle, part of the compressional wave energy converts to shear wave, and vice versa. These converted waves have been widely used in receiver function (RF) studies to image discontinuity structures in the Earth. While generally successful, the conventional RF method has its limitations and is suited mostly to flat or gently dipping structures. Among the efforts to overcome the limitations of the conventional RF method is the development of the wave-theory-based, passive-source reverse-time migration (PS-RTM) for imaging complex seismic discontinuities and scatters. To date, PS-RTM has been implemented only in 2D in the Cartesian coordinate for local problems and thus has limited applicability. In this paper, we introduce a 3D PS-RTM approach in the spherical coordinate, which is better suited for regional and global problems. New computational procedures are developed to reduce artifacts and enhance migrated images, including back-propagating the main arrival and the coda containing the converted waves separately, using a modified Helmholtz decomposition operator to separate the P and S modes in the back-propagated wavefields, and applying an imaging condition that maintains a consistent polarity for a given velocity contrast. Our new approach allows us to use migration velocity models with realistic velocity discontinuities, improving accuracy of the migrated images. We present several synthetic experiments to demonstrate the method, using regional and teleseismic sources. The results show that both regional and teleseismic sources can illuminate complex structures and this method is well suited for imaging dipping interfaces and sharp lateral changes in discontinuity structures.

Optimization of Turbine Blade Design for Reusable Launch Vehicles

NASA Technical Reports Server (NTRS)

Shyy, Wei

1998-01-01

To facilitate design optimization of turbine blade shape for reusable launching vehicles, appropriate techniques need to be developed to process and estimate the characteristics of the design variables and the response of the output with respect to the variations of the design variables. The purpose of this report is to offer insight into developing appropriate techniques for supporting such design and optimization needs. Neural network and polynomial-based techniques are applied to process aerodynamic data obtained from computational simulations for flows around a two-dimensional airfoil and a generic three- dimensional wing/blade. For the two-dimensional airfoil, a two-layered radial-basis network is designed and trained. The performances of two different design functions for radial-basis networks, one based on the accuracy requirement, whereas the other one based on the limit on the network size. While the number of neurons needed to satisfactorily reproduce the information depends on the size of the data, the neural network technique is shown to be more accurate for large data set (up to 765 simulations have been used) than the polynomial-based response surface method. For the three-dimensional wing/blade case, smaller aerodynamic data sets (between 9 to 25 simulations) are considered, and both the neural network and the polynomial-based response surface techniques improve their performance as the data size increases. It is found while the relative performance of two different network types, a radial-basis network and a back-propagation network, depends on the number of input data, the number of iterations required for radial-basis network is less than that for the back-propagation network.

Cascade Back-Propagation Learning in Neural Networks

NASA Technical Reports Server (NTRS)

Duong, Tuan A.

2003-01-01

The cascade back-propagation (CBP) algorithm is the basis of a conceptual design for accelerating learning in artificial neural networks. The neural networks would be implemented as analog very-large-scale integrated (VLSI) circuits, and circuits to implement the CBP algorithm would be fabricated on the same VLSI circuit chips with the neural networks. Heretofore, artificial neural networks have learned slowly because it has been necessary to train them via software, for lack of a good on-chip learning technique. The CBP algorithm is an on-chip technique that provides for continuous learning in real time. Artificial neural networks are trained by example: A network is presented with training inputs for which the correct outputs are known, and the algorithm strives to adjust the weights of synaptic connections in the network to make the actual outputs approach the correct outputs. The input data are generally divided into three parts. Two of the parts, called the "training" and "cross-validation" sets, respectively, must be such that the corresponding input/output pairs are known. During training, the cross-validation set enables verification of the status of the input-to-output transformation learned by the network to avoid over-learning. The third part of the data, termed the "test" set, consists of the inputs that are required to be transformed into outputs; this set may or may not include the training set and/or the cross-validation set. Proposed neural-network circuitry for on-chip learning would be divided into two distinct networks; one for training and one for validation. Both networks would share the same synaptic weights.

Scene segmentation of natural images using texture measures and back-propagation

NASA Technical Reports Server (NTRS)

Sridhar, Banavar; Phatak, Anil; Chatterji, Gano

1993-01-01

Knowledge of the three-dimensional world is essential for many guidance and navigation applications. A sequence of images from an electro-optical sensor can be processed using optical flow algorithms to provide a sparse set of ranges as a function of azimuth and elevation. A natural way to enhance the range map is by interpolation. However, this should be undertaken with care since interpolation assumes continuity of range. The range is continuous in certain parts of the image and can jump at object boundaries. In such situations, the ability to detect homogeneous object regions by scene segmentation can be used to determine regions in the range map that can be enhanced by interpolation. The use of scalar features derived from the spatial gray-level dependence matrix for texture segmentation is explored. Thresholding of histograms of scalar texture features is done for several images to select scalar features which result in a meaningful segmentation of the images. Next, the selected scalar features are used with a neural net to automate the segmentation procedure. Back-propagation is used to train the feed forward neural network. The generalization of the network approach to subsequent images in the sequence is examined. It is shown that the use of multiple scalar features as input to the neural network result in a superior segmentation when compared with a single scalar feature. It is also shown that the scalar features, which are not useful individually, result in a good segmentation when used together. The methodology is applied to both indoor and outdoor images.

Learning in the machine: The symmetries of the deep learning channel.

PubMed

Baldi, Pierre; Sadowski, Peter; Lu, Zhiqin

2017-11-01

In a physical neural system, learning rules must be local both in space and time. In order for learning to occur, non-local information must be communicated to the deep synapses through a communication channel, the deep learning channel. We identify several possible architectures for this learning channel (Bidirectional, Conjoined, Twin, Distinct) and six symmetry challenges: (1) symmetry of architectures; (2) symmetry of weights; (3) symmetry of neurons; (4) symmetry of derivatives; (5) symmetry of processing; and (6) symmetry of learning rules. Random backpropagation (RBP) addresses the second and third symmetry, and some of its variations, such as skipped RBP (SRBP) address the first and the fourth symmetry. Here we address the last two desirable symmetries showing through simulations that they can be achieved and that the learning channel is particularly robust to symmetry variations. Specifically, random backpropagation and its variations can be performed with the same non-linear neurons used in the main input-output forward channel, and the connections in the learning channel can be adapted using the same algorithm used in the forward channel, removing the need for any specialized hardware in the learning channel. Finally, we provide mathematical results in simple cases showing that the learning equations in the forward and backward channels converge to fixed points, for almost any initial conditions. In symmetric architectures, if the weights in both channels are small at initialization, adaptation in both channels leads to weights that are essentially symmetric during and after learning. Biological connections are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Optimization of neural network architecture for classification of radar jamming FM signals

NASA Astrophysics Data System (ADS)

Soto, Alberto; Mendoza, Ariadna; Flores, Benjamin C.

2017-05-01

The purpose of this study is to investigate several artificial Neural Network (NN) architectures in order to design a cognitive radar system capable of optimally distinguishing linear Frequency-Modulated (FM) signals from bandlimited Additive White Gaussian Noise (AWGN). The goal is to create a theoretical framework to determine an optimal NN architecture to achieve a Probability of Detection (PD) of 95% or higher and a Probability of False Alarm (PFA) of 1.5% or lower at 5 dB Signal to Noise Ratio (SNR). Literature research reveals that the frequency-domain power spectral densities characterize a signal more efficiently than its time-domain counterparts. Therefore, the input data is preprocessed by calculating the magnitude square of the Discrete Fourier Transform of the digitally sampled bandlimited AWGN and linear FM signals to populate a matrix containing N number of samples and M number of spectra. This matrix is used as input for the NN, and the spectra are divided as follows: 70% for training, 15% for validation, and 15% for testing. The study begins by experimentally deducing the optimal number of hidden neurons (1-40 neurons), then the optimal number of hidden layers (1-5 layers), and lastly, the most efficient learning algorithm. The training algorithms examined are: Resilient Backpropagation, Scaled Conjugate Gradient, Conjugate Gradient with Powell/Beale Restarts, Polak-Ribiére Conjugate Gradient, and Variable Learning Rate Backpropagation. We determine that an architecture with ten hidden neurons (or higher), one hidden layer, and a Scaled Conjugate Gradient for training algorithm encapsulates an optimal architecture for our application.

Selective effects of an octopus toxin on action potentials

PubMed Central

Dulhunty, Angela; Gage, Peter W.

1971-01-01

1. A lethal, water soluble toxin (Maculotoxin, MTX) with a molecular weight less than 540, can be extracted from the salivary glands of an octopus (Hapalochlaena maculosa). 2. MTX blocks action potentials in sartorius muscle fibres of toads without affecting the membrane potential. Delayed rectification is not inhibited by the toxin. 3. At low concentrations (10-6-10-5 g/ml.) MTX blocks action potentials only after a certain number have been elicited. The number of action potentials, which can be defined accurately, depends on the concentration of MTX and the concentration of sodium ions in the extracellular solution. 4. The toxin has no post-synaptic effect at the neuromuscular junction and it is concluded that it blocks neuromuscular transmission by inhibiting action potentials in motor nerve terminals. PMID:4330930

Relationship between size and latency of action potentials in human muscle sympathetic nerve activity.

PubMed

Salmanpour, Aryan; Brown, Lyndon J; Steinback, Craig D; Usselman, Charlotte W; Goswami, Ruma; Shoemaker, J Kevin

2011-06-01

We employed a novel action potential detection and classification technique to study the relationship between the recruitment of sympathetic action potentials (i.e., neurons) and the size of integrated sympathetic bursts in human muscle sympathetic nerve activity (MSNA). Multifiber postganglionic sympathetic nerve activity from the common fibular nerve was collected using microneurography in 10 healthy subjects at rest and during activation of sympathetic outflow using lower body negative pressure (LBNP). Burst occurrence increased with LBNP. Integrated burst strength (size) varied from 0.22 ± 0.07 V at rest to 0.28 ± 0.09 V during LBNP. Sympathetic burst size (i.e., peak height) was directly related to the number of action potentials within a sympathetic burst both at baseline (r = 0.75 ± 0.13; P < 0.001) and LBNP (r = 0.75 ± 0.12; P < 0.001). Also, the amplitude of detected action potentials within sympathetic bursts was directly related to the increased burst size at both baseline (r = 0.59 ± 0.16; P < 0.001) and LBNP (r = 0.61 ± 0.12; P < 0.001). In addition, the number of detected action potentials and the number of distinct action potential clusters within a given sympathetic burst were correlated at baseline (r = 0.7 ± 0.1; P < 0.001) and during LBNP (r = 0.74 ± 0.03; P < 0.001). Furthermore, action potential latency (i.e., an inverse index of neural conduction velocity) was decreased as a function of action potential size at baseline and LBNP. LBNP did not change the number of action potentials and unique clusters per sympathetic burst. It was concluded that there exists a hierarchical pattern of recruitment of additional faster conducting neurons of larger amplitude as the sympathetic bursts become stronger (i.e., larger amplitude bursts). This fundamental pattern was evident at rest and was not altered by the level of baroreceptor unloading applied in this study.

All optical experimental design for neuron excitation, inhibition, and action potential detection

NASA Astrophysics Data System (ADS)

Walsh, Alex J.; Tolstykh, Gleb; Martens, Stacey; Sedelnikova, Anna; Ibey, Bennett L.; Beier, Hope T.

2016-03-01

Recently, infrared light has been shown to both stimulate and inhibit excitatory cells. However, studies of infrared light for excitatory cell inhibition have been constrained by the use of invasive and cumbersome electrodes for cell excitation and action potential recording. Here, we present an all optical experimental design for neuronal excitation, inhibition, and action potential detection. Primary rat neurons were transfected with plasmids containing the light sensitive ion channel CheRiff. CheRiff has a peak excitation around 450 nm, allowing excitation of transfected neurons with pulsed blue light. Additionally, primary neurons were transfected with QuasAr2, a fast and sensitive fluorescent voltage indicator. QuasAr2 is excited with yellow or red light and therefore does not spectrally overlap CheRiff, enabling imaging and action potential activation, simultaneously. Using an optic fiber, neurons were exposed to blue light sequentially to generate controlled action potentials. A second optic fiber delivered a single pulse of 1869nm light to the neuron causing inhibition of the evoked action potentials (by the blue light). When used in concert, these optical techniques enable electrode free neuron excitation, inhibition, and action potential recording, allowing research into neuronal behaviors with high spatial fidelity.

Median and ulnar muscle and sensory evoked potentials.

PubMed

Felsenthal, G

1978-08-01

The medical literature was reviewed to find suggested clinical applications of the study of the amplitude of evoked muscle action potentials (MAP) and sensory action potentials (SAP). In addition, the literature was reviewed to ascertain the normal amplitude and duration of the evoked MAP and SAP as well as the factors affecting the amplitude: age, sex, temperature, ischemia. The present study determined the normal amplitude and duration of the median and ulnar MAP and SAP in fifty normal subjects. The amplitude of evoked muscle or sensory action potentials depends on multiple factors. Increased skin resistance, capacitance, and impedance at the surface of the recording electrode diminishes the amplitude. Similarly, increased distance from the source of the action potential diminishes its amplitude. Increased interelectrode distance increases the amplitude of the bipolarly recorded sensory action potential until a certain interelectrode distance is exceeded and the diphasic response becomes tri- or tetraphasic. Artifact or poor technique may reduce the potential difference between the recording electrodes or obscure the late positive phase of the action potential and thus diminish the peak to peak amplitude measurement. Intraindividual comparison indicated a marked difference of amplitude in opposite hands. The range of the MAP of the abductor pollicis brevis in one hand was 40.0--100% of the response in the opposite hand. For the abductor digiti minimi, the MAP was 58.5--100% of the response of the opposite hand. The median and ulnar SAP was between 50--100% of the opposite SAP. Consequent to these findings the effect of hand dominance on the amplitude of median and ulnar evoked muscle and sensory action potentials was studied in 41 right handed volunteers. The amplitudes of the median muscle action potential (p less than 0.02) and the median and ulnar sensory action potentials (p less than 0.001) were significantly less in the dominant hand. There was no significant difference between the ulnar muscle action potentials or for the median and ulnar distal motor and sensory latencies in the right and left hands of this group of volunteers.

Ionotropic glutamate receptor GluA4 and T-type calcium channel Cav 3.1 subunits control key aspects of synaptic transmission at the mouse L5B-POm giant synapse.

PubMed

Seol, Min; Kuner, Thomas

2015-12-01

The properties and molecular determinants of synaptic transmission at giant synapses connecting layer 5B (L5B) neurons of the somatosensory cortex (S1) with relay neurons of the posteriomedial nucleus (POm) of the thalamus have not been investigated in mice. We addressed this by using direct electrical stimulation of fluorescently labelled single corticothalamic terminals combined with molecular perturbations and whole-cell recordings from POm relay neurons. Consistent with their function as drivers, we found large-amplitude excitatory postsynaptic currents (EPSCs) and multiple postsynaptic action potentials triggered by a single presynaptic action potential. To study the molecular basis of these two features, ionotropic glutamate receptors and low voltage-gated T-type calcium channels were probed by virus-mediated genetic perturbation. Loss of GluA4 almost abolished the EPSC amplitude, strongly delaying the onset of action potential generation, but maintaining the number of action potentials generated per presynaptic action potential. In contrast, knockdown of the Cav 3.1 subunit abrogated the driver function of the synapse at a typical resting membrane potential of -70 mV. However, when depolarizing the membrane potential to -60 mV, the synapse relayed single action potentials. Hence, GluA4 subunits are required to produce an EPSC sufficiently large to trigger postsynaptic action potentials within a defined time window after the presynaptic action potential, while Cav 3.1 expression is essential to establish the driver function of L5B-POm synapses at hyperpolarized membrane potentials. © 2015 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Low concentrations of procaine and diethylaminoethanol reduce the excitability but not the action potential amplitude of hippocampal pyramidal cells.

PubMed

Butterworth, J F; Cole, L R

1990-10-01

To determine whether concentrations of diethylaminoethanol (DEAE) and procaine below those that reduce the amplitude of action potentials might alter the excitability of brain cells, a single microelectrode intracellular recording technique was used to measure firing threshold and action potential amplitude of pyramidal cells in rat hippocampal slices. At low concentrations of both DEAE (less than or equal to 5 mM) and procaine (less than or equal to 0.5 mM), firing threshold was significantly increased (P less than 0.01), whereas action potential spike amplitude was minimally altered. At higher concentrations, both drugs significantly decreased action potential spike amplitude (P less than 0.025) as well as increased firing threshold (P less than 0.001). Diethylaminoethanol tended to increase threshold relatively more than procaine, when drug concentrations that similarly reduced action potential amplitude were compared. All actions of DEAE and procaine were reversible. Inhibition of action potentials by DEAE and procaine was clearly concentration-dependent (P less than or equal to 0.015). Diethylaminoethanol effects on threshold were marginally concentration-dependent (P = 0.08); procaine did not demonstrate clear concentration-dependent effects (P = 0.33) over the concentrations tested in this study. These similar actions of procaine and DEAE on brain cells suggest a mechanism by which intravenous local anesthetics may contribute to the general anesthetic state. Moreover, it appears possible that procaine metabolism and DEAE accumulation may underlie the prolonged effects sometimes seen after intravenous procaine administration.

Review On Applications Of Neural Network To Computer Vision

NASA Astrophysics Data System (ADS)

Li, Wei; Nasrabadi, Nasser M.

1989-03-01

Neural network models have many potential applications to computer vision due to their parallel structures, learnability, implicit representation of domain knowledge, fault tolerance, and ability of handling statistical data. This paper demonstrates the basic principles, typical models and their applications in this field. Variety of neural models, such as associative memory, multilayer back-propagation perceptron, self-stabilized adaptive resonance network, hierarchical structured neocognitron, high order correlator, network with gating control and other models, can be applied to visual signal recognition, reinforcement, recall, stereo vision, motion, object tracking and other vision processes. Most of the algorithms have been simulated on com-puters. Some have been implemented with special hardware. Some systems use features, such as edges and profiles, of images as the data form for input. Other systems use raw data as input signals to the networks. We will present some novel ideas contained in these approaches and provide a comparison of these methods. Some unsolved problems are mentioned, such as extracting the intrinsic properties of the input information, integrating those low level functions to a high-level cognitive system, achieving invariances and other problems. Perspectives of applications of some human vision models and neural network models are analyzed.

Predicting ovarian malignancy: application of artificial neural networks to transvaginal and color Doppler flow US.

PubMed

Biagiotti, R; Desii, C; Vanzi, E; Gacci, G

1999-02-01

To compare the performance of artificial neural networks (ANNs) with that of multiple logistic regression (MLR) models for predicting ovarian malignancy in patients with adnexal masses by using transvaginal B-mode and color Doppler flow ultrasonography (US). A total of 226 adnexal masses were examined before surgery: Fifty-one were malignant and 175 were benign. The data were divided into training and testing subsets by using a "leave n out method." The training subsets were used to compute the optimum MLR equations and to train the ANNs. The cross-validation subsets were used to estimate the performance of each of the two models in predicting ovarian malignancy. At testing, three-layer back-propagation networks, based on the same input variables selected by using MLR (i.e., women's ages, papillary projections, random echogenicity, peak systolic velocity, and resistance index), had a significantly higher sensitivity than did MLR (96% vs 84%; McNemar test, p = .04). The Brier scores for ANNs were significantly lower than those calculated for MLR (Student t test for paired samples, P = .004). ANNs might have potential for categorizing adnexal masses as either malignant or benign on the basis of multiple variables related to demographic and US features.

Deep Learning: A Primer for Radiologists.

PubMed

Chartrand, Gabriel; Cheng, Phillip M; Vorontsov, Eugene; Drozdzal, Michal; Turcotte, Simon; Pal, Christopher J; Kadoury, Samuel; Tang, An

2017-01-01

Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance. These models are multilayer artificial neural networks, loosely inspired by biologic neural systems. Weighted connections between nodes (neurons) in the network are iteratively adjusted based on example pairs of inputs and target outputs by back-propagating a corrective error signal through the network. For computer vision tasks, convolutional neural networks (CNNs) have proven to be effective. Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. This article reviews the key concepts of deep learning for clinical radiologists, discusses technical requirements, describes emerging applications in clinical radiology, and outlines limitations and future directions in this field. Radiologists should become familiar with the principles and potential applications of deep learning in medical imaging. © RSNA, 2017.

Relative optical navigation around small bodies via Extreme Learning Machine

NASA Astrophysics Data System (ADS)

Law, Andrew M.

To perform close proximity operations under a low-gravity environment, relative and absolute positions are vital information to the maneuver. Hence navigation is inseparably integrated in space travel. Extreme Learning Machine (ELM) is presented as an optical navigation method around small celestial bodies. Optical Navigation uses visual observation instruments such as a camera to acquire useful data and determine spacecraft position. The required input data for operation is merely a single image strip and a nadir image. ELM is a machine learning Single Layer feed-Forward Network (SLFN), a type of neural network (NN). The algorithm is developed on the predicate that input weights and biases can be randomly assigned and does not require back-propagation. The learned model is the output layer weights which are used to calculate a prediction. Together, Extreme Learning Machine Optical Navigation (ELM OpNav) utilizes optical images and ELM algorithm to train the machine to navigate around a target body. In this thesis the asteroid, Vesta, is the designated celestial body. The trained ELMs estimate the position of the spacecraft during operation with a single data set. The results show the approach is promising and potentially suitable for on-board navigation.

Thermal Conductivity Prediction of Soil in Complex Plant Soil System using Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Wardani, A. K.; Purqon, A.

2016-08-01

Thermal conductivity is one of thermal properties of soil in seed germination and plants growth. Different soil types have different thermal conductivity. One of soft-computing promising method to predict thermal conductivity of soil types is Artificial Neural Network (ANN). In this study, we estimate the thermal conductivity of soil prediction in a soil-plant complex systems using ANN. With a feed-forward multilayer trained with back-propagation with 4, 10 and 1 on the input, hidden and output layers respectively. Our input are heating time, temperature and thermal resistance with thermal conductivity of soil as a target. ANN prediction demonstrates a good agreement with Mean Squared Error-testing (MSEte) of 9.56 x 10-7 for soils with green beans and those of bare soils is 7.00 × 10-7 respectively Green beans grow only on black-clay soil with a thermal conductivity of 0.7 W/m K with a sufficient water content. Our results demonstrate that temperature, moisture content, colour, texture and structure of soil are greatly affect to the thermal conductivity of soil in seed germination and plant growth. In future, it is potentially applied to estimate more complex compositions of plant-soil systems.

Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment

NASA Astrophysics Data System (ADS)

Sahoo, Sasmita; Jha, Madan K.

2013-12-01

The potential of multiple linear regression (MLR) and artificial neural network (ANN) techniques in predicting transient water levels over a groundwater basin were compared. MLR and ANN modeling was carried out at 17 sites in Japan, considering all significant inputs: rainfall, ambient temperature, river stage, 11 seasonal dummy variables, and influential lags of rainfall, ambient temperature, river stage and groundwater level. Seventeen site-specific ANN models were developed, using multi-layer feed-forward neural networks trained with Levenberg-Marquardt backpropagation algorithms. The performance of the models was evaluated using statistical and graphical indicators. Comparison of the goodness-of-fit statistics of the MLR models with those of the ANN models indicated that there is better agreement between the ANN-predicted groundwater levels and the observed groundwater levels at all the sites, compared to the MLR. This finding was supported by the graphical indicators and the residual analysis. Thus, it is concluded that the ANN technique is superior to the MLR technique in predicting spatio-temporal distribution of groundwater levels in a basin. However, considering the practical advantages of the MLR technique, it is recommended as an alternative and cost-effective groundwater modeling tool.

High resolution near on-axis digital holography using constrained optimization approach with faster convergence

NASA Astrophysics Data System (ADS)

Pandiyan, Vimal Prabhu; Khare, Kedar; John, Renu

2017-09-01

A constrained optimization approach with faster convergence is proposed to recover the complex object field from a near on-axis digital holography (DH). We subtract the DC from the hologram after recording the object beam and reference beam intensities separately. The DC-subtracted hologram is used to recover the complex object information using a constrained optimization approach with faster convergence. The recovered complex object field is back propagated to the image plane using the Fresnel back-propagation method. The results reported in this approach provide high-resolution images compared with the conventional Fourier filtering approach and is 25% faster than the previously reported constrained optimization approach due to the subtraction of two DC terms in the cost function. We report this approach in DH and digital holographic microscopy using the U.S. Air Force resolution target as the object to retrieve the high-resolution image without DC and twin image interference. We also demonstrate the high potential of this technique in transparent microelectrode patterned on indium tin oxide-coated glass, by reconstructing a high-resolution quantitative phase microscope image. We also demonstrate this technique by imaging yeast cells.

A study on using texture analysis methods for identifying lobar fissure regions in isotropic CT images.

PubMed

Wei, Q; Hu, Y

2009-01-01

The major hurdle for segmenting lung lobes in computed tomographic (CT) images is to identify fissure regions, which encase lobar fissures. Accurate identification of these regions is difficult due to the variable shape and appearance of the fissures, along with the low contrast and high noise associated with CT images. This paper studies the effectiveness of two texture analysis methods - the gray level co-occurrence matrix (GLCM) and the gray level run length matrix (GLRLM) - in identifying fissure regions from isotropic CT image stacks. To classify GLCM and GLRLM texture features, we applied a feed-forward back-propagation neural network and achieved the best classification accuracy utilizing 16 quantized levels for computing the GLCM and GLRLM texture features and 64 neurons in the input/hidden layers of the neural network. Tested on isotropic CT image stacks of 24 patients with the pathologic lungs, we obtained accuracies of 86% and 87% for identifying fissure regions using the GLCM and GLRLM methods, respectively. These accuracies compare favorably with surgeons/radiologists' accuracy of 80% for identifying fissure regions in clinical settings. This shows promising potential for segmenting lung lobes using the GLCM and GLRLM methods.

78 FR 34031 - Burned Area Emergency Response, Forest Service

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-06

...) Evaluate potential threats to critical values; (2) determine the risk level for each threat; (3) identify... actions that meet the objectives; (6) evaluate potential response actions on likelihood for timely... stabilization actions. Improved the descriptive guidelines for employing response actions involving...

Conduction velocity of action potentials measured from unidimensional latency-topography in human and frog skeletal muscle fibers.

PubMed

Homma, S; Nakajima, Y; Hayashi, K; Toma, S

1986-01-01

Conduction of an action potential along skeletal muscle fibers was graphically displayed by unidimensional latency-topography, UDLT. Since the slopes of the equipotential line were linear and the width of the line was constant, it was possible to calculate conduction velocity from the slope. To determine conduction direction of the muscle action potential elicited by electric stimulation applied directly to the muscle, surface recording electrodes were placed on a two-dimensional plane over a human muscle. Thus a bi-dimensional topography was obtained. Then, twelve or sixteen surface electrodes were placed linearly along the longitudinal direction of the action potential conduction which was disclosed by the bi-dimensional topography. Thus conduction velocity of muscle action potential in man, calculated from the slope, was for m. brachioradialis, 3.9 +/- 0.4 m/s; for m. biceps brachii, 3.6 +/- 0.2 m/s; for m. sternocleidomastoideus, 3.6 +/- 0.4 m/s. By using a tungsten microelectrode to stimulate the motor axons, a convex-like equipotential line of an action potential in UDLT was obtained from human muscle fibers. Since a similar pattern of UDLT was obtained from experiments on isolated frog muscles, in which the muscle action potential was elicited by stimulating the motor axon, it was assumed that the maximum of the curve corresponds to the end-plate region, and that the slopes on both sides indicate bi-directional conduction of the action potential.

Event-Related Potentials Discriminate Familiar and Unusual Goal Outcomes in 5-Month-Olds and Adults

ERIC Educational Resources Information Center

Michel, Christine; Kaduk, Katharina; Ní Choisdealbha, Áine; Reid, Vincent M.

2017-01-01

Previous event-related potential (ERP) work has indicated that the neural processing of action sequences develops with age. Although adults and 9-month-olds use a semantic processing system, perceiving actions activates attentional processes in 7-month-olds. However, presenting a sequence of action context, action execution and action conclusion…

Understanding the Electrical Behavior of the Action Potential in Terms of Elementary Electrical Sources

ERIC Educational Resources Information Center

Rodriguez-Falces, Javier

2015-01-01

A concept of major importance in human electrophysiology studies is the process by which activation of an excitable cell results in a rapid rise and fall of the electrical membrane potential, the so-called action potential. Hodgkin and Huxley proposed a model to explain the ionic mechanisms underlying the formation of action potentials. However,…

Detachable glass microelectrodes for recording action potentials in active moving organs.

PubMed

Barbic, Mladen; Moreno, Angel; Harris, Tim D; Kay, Matthew W

2017-06-01

Here, we describe new detachable floating glass micropipette electrode devices that provide targeted action potential recordings in active moving organs without requiring constant mechanical constraint or pharmacological inhibition of tissue motion. The technology is based on the concept of a glass micropipette electrode that is held firmly during cell targeting and intracellular insertion, after which a 100-µg glass microelectrode, a "microdevice," is gently released to remain within the moving organ. The microdevices provide long-term recordings of action potentials, even during millimeter-scale movement of tissue in which the device is embedded. We demonstrate two different glass micropipette electrode holding and detachment designs appropriate for the heart (sharp glass microdevices for cardiac myocytes in rats, guinea pigs, and humans) and the brain (patch glass microdevices for neurons in rats). We explain how microdevices enable measurements of multiple cells within a moving organ that are typically difficult with other technologies. Using sharp microdevices, action potential duration was monitored continuously for 15 min in unconstrained perfused hearts during global ischemia-reperfusion, providing beat-to-beat measurements of changes in action potential duration. Action potentials from neurons in the hippocampus of anesthetized rats were measured with patch microdevices, which provided stable base potentials during long-term recordings. Our results demonstrate that detachable microdevices are an elegant and robust tool to record electrical activity with high temporal resolution and cellular level localization without disturbing the physiological working conditions of the organ. NEW & NOTEWORTHY Cellular action potential measurements within tissue using glass micropipette electrodes usually require tissue immobilization, potentially influencing the physiological relevance of the measurement. Here, we addressed this limitation with novel 100-µg detachable glass microelectrodes that can be precisely positioned to provide long-term measurements of action potential duration during unconstrained tissue movement. Copyright © 2017 the American Physiological Society.

Prediction and control of chaotic processes using nonlinear adaptive networks

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

Jones, R.D.; Barnes, C.W.; Flake, G.W.

1990-01-01

We present the theory of nonlinear adaptive networks and discuss a few applications. In particular, we review the theory of feedforward backpropagation networks. We then present the theory of the Connectionist Normalized Linear Spline network in both its feedforward and iterated modes. Also, we briefly discuss the theory of stochastic cellular automata. We then discuss applications to chaotic time series, tidal prediction in Venice lagoon, finite differencing, sonar transient detection, control of nonlinear processes, control of a negative ion source, balancing a double inverted pendulum and design advice for free electron lasers and laser fusion targets.

Application of artificial neural networks in nonlinear analysis of trusses

NASA Technical Reports Server (NTRS)

Alam, J.; Berke, L.

1991-01-01

A method is developed to incorporate neural network model based upon the Backpropagation algorithm for material response into nonlinear elastic truss analysis using the initial stiffness method. Different network configurations are developed to assess the accuracy of neural network modeling of nonlinear material response. In addition to this, a scheme based upon linear interpolation for material data, is also implemented for comparison purposes. It is found that neural network approach can yield very accurate results if used with care. For the type of problems under consideration, it offers a viable alternative to other material modeling methods.

Neuromorphic Learning From Noisy Data

NASA Technical Reports Server (NTRS)

Merrill, Walter C.; Troudet, Terry

1993-01-01

Two reports present numerical study of performance of feedforward neural network trained by back-propagation algorithm in learning continuous-valued mappings from data corrupted by noise. Two types of noise considered: plant noise which affects dynamics of controlled process and data-processing noise, which occurs during analog processing and digital sampling of signals. Study performed with view toward use of neural networks as neurocontrollers to substitute for, or enhance, performances of human experts in controlling mechanical devices in presence of sensor and actuator noise and to enhance performances of more-conventional digital feedback electronic process controllers in noisy environments.

Enhanced online convolutional neural networks for object tracking

NASA Astrophysics Data System (ADS)

Zhang, Dengzhuo; Gao, Yun; Zhou, Hao; Li, Tianwen

2018-04-01

In recent several years, object tracking based on convolution neural network has gained more and more attention. The initialization and update of convolution filters can directly affect the precision of object tracking effective. In this paper, a novel object tracking via an enhanced online convolution neural network without offline training is proposed, which initializes the convolution filters by a k-means++ algorithm and updates the filters by an error back-propagation. The comparative experiments of 7 trackers on 15 challenging sequences showed that our tracker can perform better than other trackers in terms of AUC and precision.

Design of neural networks for classification of remotely sensed imagery

NASA Technical Reports Server (NTRS)

Chettri, Samir R.; Cromp, Robert F.; Birmingham, Mark

1992-01-01

Classification accuracies of a backpropagation neural network are discussed and compared with a maximum likelihood classifier (MLC) with multivariate normal class models. We have found that, because of its nonparametric nature, the neural network outperforms the MLC in this area. In addition, we discuss techniques for constructing optimal neural nets on parallel hardware like the MasPar MP-1 currently at GSFC. Other important discussions are centered around training and classification times of the two methods, and sensitivity to the training data. Finally, we discuss future work in the area of classification and neural nets.

Neural networks for function approximation in nonlinear control

NASA Technical Reports Server (NTRS)

Linse, Dennis J.; Stengel, Robert F.

1990-01-01

Two neural network architectures are compared with a classical spline interpolation technique for the approximation of functions useful in a nonlinear control system. A standard back-propagation feedforward neural network and a cerebellar model articulation controller (CMAC) neural network are presented, and their results are compared with a B-spline interpolation procedure that is updated using recursive least-squares parameter identification. Each method is able to accurately represent a one-dimensional test function. Tradeoffs between size requirements, speed of operation, and speed of learning indicate that neural networks may be practical for identification and adaptation in a nonlinear control environment.

Gross domestic product estimation based on electricity utilization by artificial neural network

NASA Astrophysics Data System (ADS)

Stevanović, Mirjana; Vujičić, Slađana; Gajić, Aleksandar M.

2018-01-01

The main goal of the paper was to estimate gross domestic product (GDP) based on electricity estimation by artificial neural network (ANN). The electricity utilization was analyzed based on different sources like renewable, coal and nuclear sources. The ANN network was trained with two training algorithms namely extreme learning method and back-propagation algorithm in order to produce the best prediction results of the GDP. According to the results it can be concluded that the ANN model with extreme learning method could produce the acceptable prediction of the GDP based on the electricity utilization.

A knowledge-base generating hierarchical fuzzy-neural controller.

PubMed

Kandadai, R M; Tien, J M

1997-01-01

We present an innovative fuzzy-neural architecture that is able to automatically generate a knowledge base, in an extractable form, for use in hierarchical knowledge-based controllers. The knowledge base is in the form of a linguistic rule base appropriate for a fuzzy inference system. First, we modify Berenji and Khedkar's (1992) GARIC architecture to enable it to automatically generate a knowledge base; a pseudosupervised learning scheme using reinforcement learning and error backpropagation is employed. Next, we further extend this architecture to a hierarchical controller that is able to generate its own knowledge base. Example applications are provided to underscore its viability.

Quadratic adaptive algorithm for solving cardiac action potential models.

PubMed

Chen, Min-Hung; Chen, Po-Yuan; Luo, Ching-Hsing

2016-10-01

An adaptive integration method is proposed for computing cardiac action potential models accurately and efficiently. Time steps are adaptively chosen by solving a quadratic formula involving the first and second derivatives of the membrane action potential. To improve the numerical accuracy, we devise an extremum-locator (el) function to predict the local extremum when approaching the peak amplitude of the action potential. In addition, the time step restriction (tsr) technique is designed to limit the increase in time steps, and thus prevent the membrane potential from changing abruptly. The performance of the proposed method is tested using the Luo-Rudy phase 1 (LR1), dynamic (LR2), and human O'Hara-Rudy dynamic (ORd) ventricular action potential models, and the Courtemanche atrial model incorporating a Markov sodium channel model. Numerical experiments demonstrate that the action potential generated using the proposed method is more accurate than that using the traditional Hybrid method, especially near the peak region. The traditional Hybrid method may choose large time steps near to the peak region, and sometimes causes the action potential to become distorted. In contrast, the proposed new method chooses very fine time steps in the peak region, but large time steps in the smooth region, and the profiles are smoother and closer to the reference solution. In the test on the stiff Markov ionic channel model, the Hybrid blows up if the allowable time step is set to be greater than 0.1ms. In contrast, our method can adjust the time step size automatically, and is stable. Overall, the proposed method is more accurate than and as efficient as the traditional Hybrid method, especially for the human ORd model. The proposed method shows improvement for action potentials with a non-smooth morphology, and it needs further investigation to determine whether the method is helpful during propagation of the action potential. Copyright © 2016 Elsevier Ltd. All rights reserved.

Effects of tacrolimus on action potential configuration and transmembrane ion currents in canine ventricular cells.

PubMed

Szabó, László; Szentandrássy, Norbert; Kistamás, Kornél; Hegyi, Bence; Ruzsnavszky, Ferenc; Váczi, Krisztina; Horváth, Balázs; Magyar, János; Bányász, Tamás; Pál, Balázs; Nánási, Péter P

2013-03-01

Tacrolimus is a commonly used immunosuppressive agent which causes cardiovascular complications, e.g., hypertension and hypertrophic cardiomyopathy. In spite of it, there is little information on the cellular cardiac effects of the immunosuppressive agent tacrolimus in larger mammals. In the present study, therefore, the concentration-dependent effects of tacrolimus on action potential morphology and the underlying ion currents were studied in canine ventricular cardiomyocytes. Standard microelectrode, conventional whole cell patch clamp, and action potential voltage clamp techniques were applied in myocytes enzymatically dispersed from canine ventricular myocardium. Tacrolimus (3-30 μM) caused a concentration-dependent reduction of maximum velocity of depolarization and repolarization, action potential amplitude, phase-1 repolarization, action potential duration, and plateau potential, while no significant change in the resting membrane potential was observed. Conventional voltage clamp experiments revealed that tacrolimus concentrations ≥3 μM blocked a variety of ion currents, including I(Ca), I(to), I(K1), I(Kr), and I(Ks). Similar results were obtained under action potential voltage clamp conditions. These effects of tacrolimus developed rapidly and were fully reversible upon washout. The blockade of inward currents with the concomitant shortening of action potential duration in canine myocytes is the opposite of those observed previously with tacrolimus in small rodents. It is concluded that although tacrolimus blocks several ion channels at higher concentrations, there is no risk of direct interaction with cardiac ion channels when applying tacrolimus in therapeutic concentrations.

TRPM4 non-selective cation channels influence action potentials in rabbit Purkinje fibres.

PubMed

Hof, Thomas; Sallé, Laurent; Coulbault, Laurent; Richer, Romain; Alexandre, Joachim; Rouet, René; Manrique, Alain; Guinamard, Romain

2016-01-15

The transient receptor potential melastatin 4 (TRPM4) inhibitor 9-phenanthrol reduces action potential duration in rabbit Purkinje fibres but not in ventricle. TRPM4-like single channel activity is observed in isolated rabbit Purkinje cells but not in ventricular cells. The TRPM4-like current develops during the notch and early repolarization phases of the action potential in Purkinje cells. Transient receptor potential melastatin 4 (TRPM4) Ca(2+)-activated non-selective cation channel activity has been recorded in cardiomyocytes and sinus node cells from mammals. In addition, TRPM4 gene mutations are associated with human diseases of cardiac conduction, suggesting that TRPM4 plays a role in this aspect of cardiac function. Here we evaluate the TRPM4 contribution to cardiac electrophysiology of Purkinje fibres. Ventricular strips with Purkinje fibres were isolated from rabbit hearts. Intracellular microelectrodes recorded Purkinje fibre activity and the TRPM4 inhibitor 9-phenanthrol was applied to unmask potential TRPM4 contributions to the action potential. 9-Phenanthrol reduced action potential duration measured at the point of 50 and 90% repolarization with an EC50 of 32.8 and 36.1×10(-6) mol l(-1), respectively, but did not modulate ventricular action potentials. Inside-out patch-clamp recordings were used to monitor TRPM4 activity in isolated Purkinje cells. TRPM4-like single channel activity (conductance = 23.8 pS; equal permeability for Na(+) and K(+); sensitivity to voltage, Ca(2+) and 9-phenanthrol) was observed in 43% of patches from Purkinje cells but not from ventricular cells (0/16). Action potential clamp experiments performed in the whole-cell configuration revealed a transient inward 9-phenanthrol-sensitive current (peak density = -0.65 ± 0.15 pA pF(-1); n = 5) during the plateau phases of the Purkinje fibre action potential. These results show that TRPM4 influences action potential characteristics in rabbit Purkinje fibres and thus could modulate cardiac conduction and be involved in triggering arrhythmias. © 2015 The Authors. The Journal of Physiology © 2015 The Physiological Society.

14 CFR 1216.306 - Actions normally requiring an EIS.

Code of Federal Regulations, 2013 CFR

2013-01-01

... normally requiring an EIS. (a) NASA will prepare an EIS for actions with the potential to significantly... action or mitigation of its potentially significant impacts. (b) Typical NASA actions normally requiring... material greater than the quantity for which the NASA Nuclear Flight Safety Assurance Manager may grant...

Cell-type-dependent action potentials and voltage-gated currents in mouse fungiform taste buds.

PubMed

Kimura, Kenji; Ohtubo, Yoshitaka; Tateno, Katsumi; Takeuchi, Keita; Kumazawa, Takashi; Yoshii, Kiyonori

2014-01-01

Taste receptor cells fire action potentials in response to taste substances to trigger non-exocytotic neurotransmitter release in type II cells and exocytotic release in type III cells. We investigated possible differences between these action potentials fired by mouse taste receptor cells using in situ whole-cell recordings, and subsequently we identified their cell types immunologically with cell-type markers, an IP3 receptor (IP3 R3) for type II cells and a SNARE protein (SNAP-25) for type III cells. Cells not immunoreactive to these antibodies were examined as non-IRCs. Here, we show that type II cells and type III cells fire action potentials using different ionic mechanisms, and that non-IRCs also fire action potentials with either of the ionic mechanisms. The width of action potentials was significantly narrower and their afterhyperpolarization was deeper in type III cells than in type II cells. Na(+) current density was similar in type II cells and type III cells, but it was significantly smaller in non-IRCs than in the others. Although outwardly rectifying current density was similar between type II cells and type III cells, tetraethylammonium (TEA) preferentially suppressed the density in type III cells and the majority of non-IRCs. Our mathematical model revealed that the shape of action potentials depended on the ratio of TEA-sensitive current density and TEA-insensitive current one. The action potentials of type II cells and type III cells under physiological conditions are discussed. © 2013 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

The optimal distance between two electrode tips during recording of compound nerve action potentials in the rat median nerve

PubMed Central

Li, Yongping; Lao, Jie; Zhao, Xin; Tian, Dong; Zhu, Yi; Wei, Xiaochun

2014-01-01

The distance between the two electrode tips can greatly influence the parameters used for recording compound nerve action potentials. To investigate the optimal parameters for these recordings in the rat median nerve, we dissociated the nerve using different methods and compound nerve action potentials were orthodromically or antidromically recorded with different electrode spacings. Compound nerve action potentials could be consistently recorded using a method in which the middle part of the median nerve was intact, with both ends dissociated from the surrounding fascia and a ground wire inserted into the muscle close to the intact part. When the distance between two stimulating electrode tips was increased, the threshold and supramaximal stimulating intensity of compound nerve action potentials were gradually decreased, but the amplitude was not changed significantly. When the distance between two recording electrode tips was increased, the amplitude was gradually increased, but the threshold and supramaximal stimulating intensity exhibited no significant change. Different distances between recording and stimulating sites did not produce significant effects on the aforementioned parameters. A distance of 5 mm between recording and stimulating electrodes and a distance of 10 mm between recording and stimulating sites were found to be optimal for compound nerve action potential recording in the rat median nerve. In addition, the orthodromic compound action potential, with a biphasic waveform that was more stable and displayed less interference (however also required a higher threshold and higher supramaximal stimulus), was found to be superior to the antidromic compound action potential. PMID:25206798

Modulation of hERG potassium channel gating normalizes action potential duration prolonged by dysfunctional KCNQ1 potassium channel

PubMed Central

Zhang, Hongkang; Zou, Beiyan; Yu, Haibo; Moretti, Alessandra; Wang, Xiaoying; Yan, Wei; Babcock, Joseph J.; Bellin, Milena; McManus, Owen B.; Tomaselli, Gordon; Nan, Fajun; Laugwitz, Karl-Ludwig; Li, Min

2012-01-01

Long QT syndrome (LQTS) is a genetic disease characterized by a prolonged QT interval in an electrocardiogram (ECG), leading to higher risk of sudden cardiac death. Among the 12 identified genes causal to heritable LQTS, ∼90% of affected individuals harbor mutations in either KCNQ1 or human ether-a-go-go related genes (hERG), which encode two repolarizing potassium currents known as IKs and IKr. The ability to quantitatively assess contributions of different current components is therefore important for investigating disease phenotypes and testing effectiveness of pharmacological modulation. Here we report a quantitative analysis by simulating cardiac action potentials of cultured human cardiomyocytes to match the experimental waveforms of both healthy control and LQT syndrome type 1 (LQT1) action potentials. The quantitative evaluation suggests that elevation of IKr by reducing voltage sensitivity of inactivation, not via slowing of deactivation, could more effectively restore normal QT duration if IKs is reduced. Using a unique specific chemical activator for IKr that has a primary effect of causing a right shift of V1/2 for inactivation, we then examined the duration changes of autonomous action potentials from differentiated human cardiomyocytes. Indeed, this activator causes dose-dependent shortening of the action potential durations and is able to normalize action potentials of cells of patients with LQT1. In contrast, an IKr chemical activator of primary effects in slowing channel deactivation was not effective in modulating action potential durations. Our studies provide both the theoretical basis and experimental support for compensatory normalization of action potential duration by a pharmacological agent. PMID:22745159

Short infrared laser pulses block action potentials in neurons

NASA Astrophysics Data System (ADS)

Walsh, Alex J.; Tolstykh, Gleb P.; Martens, Stacey L.; Ibey, Bennett L.; Beier, Hope T.

2017-02-01

Short infrared laser pulses have many physiological effects on cells including the ability to stimulate action potentials in neurons. Here we show that short infrared laser pulses can also reversibly block action potentials. Primary rat hippocampal neurons were transfected with the Optopatch2 plasmid, which contains both a blue-light activated channel rhodopsin (CheRiff) and a red-light fluorescent membrane voltage reporter (QuasAr2). This optogenetic platform allows robust stimulation and recording of action potential activity in neurons in a non-contact, low noise manner. For all experiments, QuasAr2 was imaged continuously on a wide-field fluorescent microscope using a Krypton laser (647 nm) as the excitation source and an EMCCD camera operating at 1000 Hz to collect emitted fluorescence. A co-aligned Argon laser (488 nm, 5 ms at 10Hz) provided activation light for CheRiff. A 200 mm fiber delivered infrared light locally to the target neuron. Reversible action potential block in neurons was observed following a short infrared laser pulse (0.26-0.96 J/cm2; 1.37-5.01 ms; 1869 nm), with the block persisting for more than 1 s with exposures greater than 0.69 J/cm2. Action potential block was sustained for 30 s with the short infrared laser pulsed at 1-7 Hz. Full recovery of neuronal activity was observed 5-30s post-infrared exposure. These results indicate that optogenetics provides a robust platform for the study of action potential block and that short infrared laser pulses can be used for non-contact, reversible action potential block.

Action potentials and ion conductances in wild-type and CALHM1-knockout type II taste cells

PubMed Central

Saung, Wint Thu; Foskett, J. Kevin

2017-01-01

Taste bud type II cells fire action potentials in response to tastants, triggering nonvesicular ATP release to gustatory neurons via voltage-gated CALHM1-associated ion channels. Whereas CALHM1 regulates mouse cortical neuron excitability, its roles in regulating type II cell excitability are unknown. In this study, we compared membrane conductances and action potentials in single identified TRPM5-GFP-expressing circumvallate papillae type II cells acutely isolated from wild-type (WT) and Calhm1 knockout (KO) mice. The activation kinetics of large voltage-gated outward currents were accelerated in cells from Calhm1 KO mice, and their associated nonselective tail currents, previously shown to be highly correlated with ATP release, were completely absent in Calhm1 KO cells, suggesting that CALHM1 contributes to all of these currents. Calhm1 deletion did not significantly alter resting membrane potential or input resistance, the amplitudes and kinetics of Na+ currents either estimated from action potentials or recorded from steady-state voltage pulses, or action potential threshold, overshoot peak, afterhyperpolarization, and firing frequency. However, Calhm1 deletion reduced the half-widths of action potentials and accelerated the deactivation kinetics of transient outward currents, suggesting that the CALHM1-associated conductance becomes activated during the repolarization phase of action potentials. NEW & NOTEWORTHY CALHM1 is an essential ion channel component of the ATP neurotransmitter release mechanism in type II taste bud cells. Its contribution to type II cell resting membrane properties and excitability is unknown. Nonselective voltage-gated currents, previously associated with ATP release, were absent in cells lacking CALHM1. Calhm1 deletion was without effects on resting membrane properties or voltage-gated Na+ and K+ channels but contributed modestly to the kinetics of action potentials. PMID:28202574

Action potentials and ion conductances in wild-type and CALHM1-knockout type II taste cells.

PubMed

Ma, Zhongming; Saung, Wint Thu; Foskett, J Kevin

2017-05-01

Taste bud type II cells fire action potentials in response to tastants, triggering nonvesicular ATP release to gustatory neurons via voltage-gated CALHM1-associated ion channels. Whereas CALHM1 regulates mouse cortical neuron excitability, its roles in regulating type II cell excitability are unknown. In this study, we compared membrane conductances and action potentials in single identified TRPM5-GFP-expressing circumvallate papillae type II cells acutely isolated from wild-type (WT) and Calhm1 knockout (KO) mice. The activation kinetics of large voltage-gated outward currents were accelerated in cells from Calhm1 KO mice, and their associated nonselective tail currents, previously shown to be highly correlated with ATP release, were completely absent in Calhm1 KO cells, suggesting that CALHM1 contributes to all of these currents. Calhm1 deletion did not significantly alter resting membrane potential or input resistance, the amplitudes and kinetics of Na + currents either estimated from action potentials or recorded from steady-state voltage pulses, or action potential threshold, overshoot peak, afterhyperpolarization, and firing frequency. However, Calhm1 deletion reduced the half-widths of action potentials and accelerated the deactivation kinetics of transient outward currents, suggesting that the CALHM1-associated conductance becomes activated during the repolarization phase of action potentials. NEW & NOTEWORTHY CALHM1 is an essential ion channel component of the ATP neurotransmitter release mechanism in type II taste bud cells. Its contribution to type II cell resting membrane properties and excitability is unknown. Nonselective voltage-gated currents, previously associated with ATP release, were absent in cells lacking CALHM1. Calhm1 deletion was without effects on resting membrane properties or voltage-gated Na + and K + channels but contributed modestly to the kinetics of action potentials. Copyright © 2017 the American Physiological Society.

The activity of spontaneous action potentials in developing hair cells is regulated by Ca(2+)-dependence of a transient K+ current.

PubMed

Levic, Snezana; Lv, Ping; Yamoah, Ebenezer N

2011-01-01

Spontaneous action potentials have been described in developing sensory systems. These rhythmic activities may have instructional roles for the functional development of synaptic connections. The importance of spontaneous action potentials in the developing auditory system is underpinned by the stark correlation between the time of auditory system functional maturity, and the cessation of spontaneous action potentials. A prominent K(+) current that regulates patterning of action potentials is I(A). This current undergoes marked changes in expression during chicken hair cell development. Although the properties of I(A) are not normally classified as Ca(2+)-dependent, we demonstrate that throughout the development of chicken hair cells, I(A) is greatly reduced by acute alterations of intracellular Ca(2+). As determinants of spike timing and firing frequency, intracellular Ca(2+) buffers shift the activation and inactivation properties of the current to more positive potentials. Our findings provide evidence to demonstrate that the kinetics and functional expression of I(A) are tightly regulated by intracellular Ca(2+). Such feedback mechanism between the functional expression of I(A) and intracellular Ca(2+) may shape the activity of spontaneous action potentials, thus potentially sculpting synaptic connections in an activity-dependent manner in the developing cochlea. © 2011 Levic et al.

78 FR 23740 - Notice of Availability of a Swine Brucellosis and Pseudorabies Proposed Action Plan

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-22

...] Notice of Availability of a Swine Brucellosis and Pseudorabies Proposed Action Plan AGENCY: Animal and... proposed action plan describing a potential new approach to managing swine brucellosis and pseudorabies...-0086) a notice that made a proposed action plan describing a potential new approach to managing swine...

Modulating anosognosia for hemiplegia: The role of dangerous actions in emergent awareness.

PubMed

D'Imperio, Daniela; Bulgarelli, Cristina; Bertagnoli, Sara; Avesani, Renato; Moro, Valentina

2017-07-01

Anosognosia for hemiplegia is a lack of awareness of motor deficits following a right hemisphere lesion. Residual forms of awareness co-occur with an explicit denial of hemiplegia. The term emergent awareness refers to a condition in which awareness of motor deficits is reported verbally during the actual performance of an action involving the affected body part. In this study, two tasks were used to explore the potential effects of i) attempting actions which are impossible for sufferers of hemiplegia and ii) attempting actions which are potentially dangerous. Sixteen hemiplegic patients (8 anosognosic, and 8 non-anosognosic) were asked to perform both potentially dangerous and neutral actions. Our results confirm an increase in emergent awareness in anosognosic patients during the execution of both of these types of action. Moreover, actions that are potentially dangerous improved the degree of awareness. However, lesions in the fronto-temporal areas appear to be associated with a reduced effect of action execution (emergent awareness) while lesions in the basal ganglia and amygdale and the white matter underlying the insula and fronto-temporal areas are associated with a lesser degree of improvement resulting from attempting to perform dangerous actions. Copyright © 2017 Elsevier Ltd. All rights reserved.

GABAergic transmission facilitates ictogenesis and synchrony between CA3, hilus, and dentate gyrus in slices from epileptic rats

PubMed Central

Gafurov, Boris

2013-01-01

The impact of regional hippocampal interactions and GABAergic transmission on ictogenesis remain unclear. Cortico-hippocampal slices from pilocarpine-treated epileptic rats were compared with controls to investigate associations between seizurelike events (SLE), GABAergic transmission, and neuronal synchrony within and between cortico-hippocampal regions. Multielectrode array recordings revealed more prevalent hippocampal SLE in epileptic tissue when excitatory transmission was enhanced and GABAergic transmission was intact [removal of Mg2+ (0Mg)] than when GABAergic transmission was blocked [removal of Mg2+ + bicuculline methiodide (0Mg+BMI)]. When activity within individual regions was analyzed, spectral and temporal slow oscillation/SLE correlations and cross-correlations were highest within the hilus of epileptic tissue during SLE but were similar in 0Mg and 0Mg+BMI. GABAergic facilitation of spectral “slow” oscillation and ripple correlations was most prominent within CA3 of epileptic tissue during SLE. When activity between regions was analyzed, slow oscillation and ripple coherence was highest between the hilus and dentate gyrus as well as between the hilus and CA3 of epileptic tissue during SLE and was significantly higher in 0Mg than 0Mg+BMI. High 0Mg-induced SLE cross-correlations between the hilus and dentate gyrus as well as between the hilus and CA3 were reduced or abolished in 0Mg+BMI. SLE cross-correlation lag measurements provided evidence for a monosynaptic connection from the hilus to the dentate gyrus during SLE. Findings implicate the hilus as an oscillation generator, whose impact on other cortico-hippocampal regions is mediated by GABAergic transmission. Data also suggest that GABAA receptor-mediated transmission facilitates back-propagation from CA3/hilus to the dentate gyrus and that this back-propagation augments SLE in epileptic hippocampus. PMID:23615549

Inferring the lithology of borehole rocks by applying neural network classifiers to downhole logs: an example from the Ocean Drilling Program

NASA Astrophysics Data System (ADS)

Benaouda, D.; Wadge, G.; Whitmarsh, R. B.; Rothwell, R. G.; MacLeod, C.

1999-02-01

In boreholes with partial or no core recovery, interpretations of lithology in the remainder of the hole are routinely attempted using data from downhole geophysical sensors. We present a practical neural net-based technique that greatly enhances lithological interpretation in holes with partial core recovery by using downhole data to train classifiers to give a global classification scheme for those parts of the borehole for which no core was retrieved. We describe the system and its underlying methods of data exploration, selection and classification, and present a typical example of the system in use. Although the technique is equally applicable to oil industry boreholes, we apply it here to an Ocean Drilling Program (ODP) borehole (Hole 792E, Izu-Bonin forearc, a mixture of volcaniclastic sandstones, conglomerates and claystones). The quantitative benefits of quality-control measures and different subsampling strategies are shown. Direct comparisons between a number of discriminant analysis methods and the use of neural networks with back-propagation of error are presented. The neural networks perform better than the discriminant analysis techniques both in terms of performance rates with test data sets (2-3 per cent better) and in qualitative correlation with non-depth-matched core. We illustrate with the Hole 792E data how vital it is to have a system that permits the number and membership of training classes to be changed as analysis proceeds. The initial classification for Hole 792E evolved from a five-class to a three-class and then to a four-class scheme with resultant classification performance rates for the back-propagation neural network method of 83, 84 and 93 per cent respectively.

Transmission Risks of Schistosomiasis Japonica: Extraction from Back-propagation Artificial Neural Network and Logistic Regression Model

PubMed Central

Xu, Jun-Fang; Xu, Jing; Li, Shi-Zhu; Jia, Tia-Wu; Huang, Xi-Bao; Zhang, Hua-Ming; Chen, Mei; Yang, Guo-Jing; Gao, Shu-Jing; Wang, Qing-Yun; Zhou, Xiao-Nong

2013-01-01

Background The transmission of schistosomiasis japonica in a local setting is still poorly understood in the lake regions of the People's Republic of China (P. R. China), and its transmission patterns are closely related to human, social and economic factors. Methodology/Principal Findings We aimed to apply the integrated approach of artificial neural network (ANN) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data collected from 2339 villagers from 1247 households in six villages of Jiangling County, P.R. China. By using the back-propagation (BP) of the ANN model, 16 factors out of 27 factors were screened, and the top five factors ranked by the absolute value of mean impact value (MIV) were mainly related to human behavior, i.e. integration of water contact history and infection history, family with past infection, history of water contact, infection history, and infection times. The top five factors screened by the logistic regression model were mainly related to the social economics, i.e. village level, economic conditions of family, age group, education level, and infection times. The risk of human infection with S. japonicum is higher in the population who are at age 15 or younger, or with lower education, or with the higher infection rate of the village, or with poor family, and in the population with more than one time to be infected. Conclusion/Significance Both BP artificial neural network and logistic regression model established in a small scale suggested that individual behavior and socioeconomic status are the most important risk factors in the transmission of schistosomiasis japonica. It was reviewed that the young population (≤15) in higher-risk areas was the main target to be intervened for the disease transmission control. PMID:23556015

Monthly evaporation forecasting using artificial neural networks and support vector machines

NASA Astrophysics Data System (ADS)

Tezel, Gulay; Buyukyildiz, Meral

2016-04-01

Evaporation is one of the most important components of the hydrological cycle, but is relatively difficult to estimate, due to its complexity, as it can be influenced by numerous factors. Estimation of evaporation is important for the design of reservoirs, especially in arid and semi-arid areas. Artificial neural network methods and support vector machines (SVM) are frequently utilized to estimate evaporation and other hydrological variables. In this study, usability of artificial neural networks (ANNs) (multilayer perceptron (MLP) and radial basis function network (RBFN)) and ɛ-support vector regression (SVR) artificial intelligence methods was investigated to estimate monthly pan evaporation. For this aim, temperature, relative humidity, wind speed, and precipitation data for the period 1972 to 2005 from Beysehir meteorology station were used as input variables while pan evaporation values were used as output. The Romanenko and Meyer method was also considered for the comparison. The results were compared with observed class A pan evaporation data. In MLP method, four different training algorithms, gradient descent with momentum and adaptive learning rule backpropagation (GDX), Levenberg-Marquardt (LVM), scaled conjugate gradient (SCG), and resilient backpropagation (RBP), were used. Also, ɛ-SVR model was used as SVR model. The models were designed via 10-fold cross-validation (CV); algorithm performance was assessed via mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2). According to the performance criteria, the ANN algorithms and ɛ-SVR had similar results. The ANNs and ɛ-SVR methods were found to perform better than the Romanenko and Meyer methods. Consequently, the best performance using the test data was obtained using SCG(4,2,2,1) with R 2 = 0.905.

PONS2train: tool for testing the MLP architecture and local traning methods for runoff forecast

NASA Astrophysics Data System (ADS)

Maca, P.; Pavlasek, J.; Pech, P.

2012-04-01

The purpose of presented poster is to introduce the PONS2train developed for runoff prediction via multilayer perceptron - MLP. The software application enables the implementation of 12 different MLP's transfer functions, comparison of 9 local training algorithms and finally the evaluation the MLP performance via 17 selected model evaluation metrics. The PONS2train software is written in C++ programing language. Its implementation consists of 4 classes. The NEURAL_NET and NEURON classes implement the MLP, the CRITERIA class estimates model evaluation metrics and for model performance evaluation via testing and validation datasets. The DATA_PATTERN class prepares the validation, testing and calibration datasets. The software application uses the LAPACK, BLAS and ARMADILLO C++ linear algebra libraries. The PONS2train implements the first order local optimization algorithms: standard on-line and batch back-propagation with learning rate combined with momentum and its variants with the regularization term, Rprop and standard batch back-propagation with variable momentum and learning rate. The second order local training algorithms represents: the Levenberg-Marquardt algorithm with and without regularization and four variants of scaled conjugate gradients. The other important PONS2train features are: the multi-run, the weight saturation control, early stopping of trainings, and the MLP weights analysis. The weights initialization is done via two different methods: random sampling from uniform distribution on open interval or Nguyen Widrow method. The data patterns can be transformed via linear and nonlinear transformation. The runoff forecast case study focuses on PONS2train implementation and shows the different aspects of the MLP training, the MLP architecture estimation, the neural network weights analysis and model uncertainty estimation.

[Effect of pulse magnetic field on distribution of neuronal action potential].

PubMed

Zheng, Yu; Cai, Di; Wang, Jin-Hai; Li, Gang; Lin, Ling

2014-08-25

The biological effect on the organism generated by magnetic field is widely studied. The present study was aimed to observe the change of sodium channel under magnetic field in neurons. Cortical neurons of Kunming mice were isolated, subjected to 15 Hz, 1 mT pulse magnetic stimulation, and then the currents of neurons were recorded by whole-cell patch clamp. The results showed that, under magnetic stimulation, the activation process of Na(+) channel was delayed, and the inactivation process was accelerated. Given the classic three-layer model, the polarization diagram of cell membrane potential distribution under pulse magnetic field was simulated, and it was found that the membrane potential induced was associated with the frequency and intensity of magnetic field. Also the effect of magnetic field-induced current on action potential was simulated by Hodgkin-Huxley (H-H) model. The result showed that the generation of action potential was delayed, and frequency and the amplitudes were decreased when working current was between -1.32 μA and 0 μA. When the working current was higher than 0 μA, the generation frequency of action potential was increased, and the change of amplitudes was not obvious, and when the working current was lower than -1.32 μA, the time of rising edge and amplitudes of action potential were decreased drastically, and the action potential was unable to generate. These results suggest that the magnetic field simulation can affect the distribution frequency and amplitude of action potential of neuron via sodium channel mediation.

Intracellular recording of action potentials by nanopillar electroporation.

PubMed

Xie, Chong; Lin, Ziliang; Hanson, Lindsey; Cui, Yi; Cui, Bianxiao

2012-02-12

Action potentials have a central role in the nervous system and in many cellular processes, notably those involving ion channels. The accurate measurement of action potentials requires efficient coupling between the cell membrane and the measuring electrodes. Intracellular recording methods such as patch clamping involve measuring the voltage or current across the cell membrane by accessing the cell interior with an electrode, allowing both the amplitude and shape of the action potentials to be recorded faithfully with high signal-to-noise ratios. However, the invasive nature of intracellular methods usually limits the recording time to a few hours, and their complexity makes it difficult to simultaneously record more than a few cells. Extracellular recording methods, such as multielectrode arrays and multitransistor arrays, are non-invasive and allow long-term and multiplexed measurements. However, extracellular recording sacrifices the one-to-one correspondence between the cells and electrodes, and also suffers from significantly reduced signal strength and quality. Extracellular techniques are not, therefore, able to record action potentials with the accuracy needed to explore the properties of ion channels. As a result, the pharmacological screening of ion-channel drugs is usually performed by low-throughput intracellular recording methods. The use of nanowire transistors, nanotube-coupled transistors and micro gold-spine and related electrodes can significantly improve the signal strength of recorded action potentials. Here, we show that vertical nanopillar electrodes can record both the extracellular and intracellular action potentials of cultured cardiomyocytes over a long period of time with excellent signal strength and quality. Moreover, it is possible to repeatedly switch between extracellular and intracellular recording by nanoscale electroporation and resealing processes. Furthermore, vertical nanopillar electrodes can detect subtle changes in action potentials induced by drugs that target ion channels.

Intracellular recording of action potentials by nanopillar electroporation

NASA Astrophysics Data System (ADS)

Xie, Chong; Lin, Ziliang; Hanson, Lindsey; Cui, Yi; Cui, Bianxiao

2012-03-01

Action potentials have a central role in the nervous system and in many cellular processes, notably those involving ion channels. The accurate measurement of action potentials requires efficient coupling between the cell membrane and the measuring electrodes. Intracellular recording methods such as patch clamping involve measuring the voltage or current across the cell membrane by accessing the cell interior with an electrode, allowing both the amplitude and shape of the action potentials to be recorded faithfully with high signal-to-noise ratios. However, the invasive nature of intracellular methods usually limits the recording time to a few hours, and their complexity makes it difficult to simultaneously record more than a few cells. Extracellular recording methods, such as multielectrode arrays and multitransistor arrays, are non-invasive and allow long-term and multiplexed measurements. However, extracellular recording sacrifices the one-to-one correspondence between the cells and electrodes, and also suffers from significantly reduced signal strength and quality. Extracellular techniques are not, therefore, able to record action potentials with the accuracy needed to explore the properties of ion channels. As a result, the pharmacological screening of ion-channel drugs is usually performed by low-throughput intracellular recording methods. The use of nanowire transistors, nanotube-coupled transistors and micro gold-spine and related electrodes can significantly improve the signal strength of recorded action potentials. Here, we show that vertical nanopillar electrodes can record both the extracellular and intracellular action potentials of cultured cardiomyocytes over a long period of time with excellent signal strength and quality. Moreover, it is possible to repeatedly switch between extracellular and intracellular recording by nanoscale electroporation and resealing processes. Furthermore, vertical nanopillar electrodes can detect subtle changes in action potentials induced by drugs that target ion channels.

Action potential bursts in central snail neurons elicited by paeonol: roles of ionic currents

PubMed Central

Chen, Yi-hung; Lin, Pei-lin; Hsu, Hui-yu; Wu, Ya-ting; Yang, Han-yin; Lu, Dah-yuu; Huang, Shiang-suo; Hsieh, Ching-liang; Lin, Jaung-geng

2010-01-01

Aim: To investigate the effects of 2′-hydroxy-4′-methoxyacetophenone (paeonol) on the electrophysiological behavior of a central neuron (right parietal 4; RP4) of the giant African snail (Achatina fulica Ferussac). Methods: Intracellular recordings and the two-electrode voltage clamp method were used to study the effects of paeonol on the RP4 neuron. Results: The RP4 neuron generated spontaneous action potentials. Bath application of paeonol at a concentration of ≥500 μmol/L reversibly elicited action potential bursts in a concentration-dependent manner. Immersing the neurons in Co2+-substituted Ca2+-free solution did not block paeonol-elicited bursting. Pretreatment with the protein kinase A (PKA) inhibitor KT-5720 or the protein kinase C (PKC) inhibitor Ro 31-8220 did not affect the action potential bursts. Voltage-clamp studies revealed that paeonol at a concentration of 500 μmol/L had no remarkable effects on the total inward currents, whereas paeonol decreased the delayed rectifying K+ current (IKD) and the fast-inactivating K+ current (IA). Application of 4-aminopyridine (4-AP 5 mmol/L), an inhibitor of IA, or charybdotoxin 250 nmol/L, an inhibitor of the Ca2+-activated K+ current (IK(Ca)), failed to elicit action potential bursts, whereas tetraethylammonium chloride (TEA 50 mmol/L), an IKD blocker, successfully elicited action potential bursts. At a lower concentration of 5 mmol/L, TEA facilitated the induction of action potential bursts elicited by paeonol. Conclusion: Paeonol elicited a bursting firing pattern of action potentials in the RP4 neuron and this activity relates closely to the inhibitory effects of paeonol on the IKD. PMID:21042287

Consequences of converting graded to action potentials upon neural information coding and energy efficiency.

PubMed

Sengupta, Biswa; Laughlin, Simon Barry; Niven, Jeremy Edward

2014-01-01

Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied by information loss and a drop in energy efficiency. We investigate the biophysical causes of this loss of information and efficiency by comparing spiking neuron models, containing stochastic voltage-gated Na(+) and K(+) channels, with generator potential and graded potential models lacking voltage-gated Na(+) channels. We identify three causes of information loss in the generator potential that are the by-product of action potential generation: (1) the voltage-gated Na(+) channels necessary for action potential generation increase intrinsic noise and (2) introduce non-linearities, and (3) the finite duration of the action potential creates a 'footprint' in the generator potential that obscures incoming signals. These three processes reduce information rates by ∼50% in generator potentials, to ∼3 times that of spike trains. Both generator potentials and graded potentials consume almost an order of magnitude less energy per second than spike trains. Because of the lower information rates of generator potentials they are substantially less energy efficient than graded potentials. However, both are an order of magnitude more efficient than spike trains due to the higher energy costs and low information content of spikes, emphasizing that there is a two-fold cost of converting analogue to digital; information loss and cost inflation.

Consequences of Converting Graded to Action Potentials upon Neural Information Coding and Energy Efficiency

PubMed Central

Sengupta, Biswa; Laughlin, Simon Barry; Niven, Jeremy Edward

2014-01-01

Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied by information loss and a drop in energy efficiency. We investigate the biophysical causes of this loss of information and efficiency by comparing spiking neuron models, containing stochastic voltage-gated Na+ and K+ channels, with generator potential and graded potential models lacking voltage-gated Na+ channels. We identify three causes of information loss in the generator potential that are the by-product of action potential generation: (1) the voltage-gated Na+ channels necessary for action potential generation increase intrinsic noise and (2) introduce non-linearities, and (3) the finite duration of the action potential creates a ‘footprint’ in the generator potential that obscures incoming signals. These three processes reduce information rates by ∼50% in generator potentials, to ∼3 times that of spike trains. Both generator potentials and graded potentials consume almost an order of magnitude less energy per second than spike trains. Because of the lower information rates of generator potentials they are substantially less energy efficient than graded potentials. However, both are an order of magnitude more efficient than spike trains due to the higher energy costs and low information content of spikes, emphasizing that there is a two-fold cost of converting analogue to digital; information loss and cost inflation. PMID:24465197

Synchronization of action potentials during low-magnesium-induced bursting

PubMed Central

Johnson, Sarah E.; Hudson, John L.

2015-01-01

The relationship between mono- and polysynaptic strength and action potential synchronization was explored using a reduced external Mg2+ model. Single and dual whole cell patch-clamp recordings were performed in hippocampal cultures in three concentrations of external Mg2+. In decreased Mg2+ medium, the individual cells transitioned to spontaneous bursting behavior. In lowered Mg2+ media the larger excitatory synaptic events were observed more frequently and fewer transmission failures occurred, suggesting strengthened synaptic transmission. The event synchronization was calculated for the neural action potentials of the cell pairs, and it increased in media where Mg2+ concentration was lowered. Analysis of surrogate data where bursting was present, but no direct or indirect connections existed between the neurons, showed minimal action potential synchronization. This suggests the synchronization of action potentials is a product of the strengthening synaptic connections within neuronal networks. PMID:25609103

Synchronization of action potentials during low-magnesium-induced bursting.

PubMed

Johnson, Sarah E; Hudson, John L; Kapur, Jaideep

2015-04-01

The relationship between mono- and polysynaptic strength and action potential synchronization was explored using a reduced external Mg(2+) model. Single and dual whole cell patch-clamp recordings were performed in hippocampal cultures in three concentrations of external Mg(2+). In decreased Mg(2+) medium, the individual cells transitioned to spontaneous bursting behavior. In lowered Mg(2+) media the larger excitatory synaptic events were observed more frequently and fewer transmission failures occurred, suggesting strengthened synaptic transmission. The event synchronization was calculated for the neural action potentials of the cell pairs, and it increased in media where Mg(2+) concentration was lowered. Analysis of surrogate data where bursting was present, but no direct or indirect connections existed between the neurons, showed minimal action potential synchronization. This suggests the synchronization of action potentials is a product of the strengthening synaptic connections within neuronal networks. Copyright © 2015 the American Physiological Society.

Millisecond infrared laser pulses depolarize and elicit action potentials on in-vitro dorsal root ganglion neurons

PubMed Central

Paris, Lambert; Marc, Isabelle; Charlot, Benoit; Dumas, Michel; Valmier, Jean; Bardin, Fabrice

2017-01-01

This work focuses on the optical stimulation of dorsal root ganglion (DRG) neurons through infrared laser light stimulation. We show that a few millisecond laser pulse at 1875 nm induces a membrane depolarization, which was observed by the patch-clamp technique. This stimulation led to action potentials firing on a minority of neurons beyond an energy threshold. A depolarization without action potential was observed for the majority of DRG neurons, even beyond the action potential energy threshold. The use of ruthenium red, a thermal channel blocker, stops the action potential generation, but has no effects on membrane depolarization. Local temperature measurements reveal that the depolarization amplitude is sensitive to the amplitude of the temperature rise as well as to the time rate of change of temperature, but in a way which may not fully follow a photothermal capacitive mechanism, suggesting that more complex mechanisms are involved. PMID:29082085

[Effects of dauricine on action potentials and slow inward currents of guinea pig ventricular papillary muscles].

PubMed

Li, S N; Zhang, K Y

1992-11-01

Effects of dauricine (Dau) on the action potentials (AP), the slow action potentials (SAP), and the slow inward currents (Isi) of guinea pig ventricular papillary muscles were observed by means of intracellular microelectrode and single sucrose gap voltage clamp technique. In the early stage, Dau shortened action potential duration 100 (APD100) and effective refractory period (ERP) (ERP/APD < 1; P < 0.01), but did not affect APD20 and other parameters. In the late stage, Dau prolonged APD100, ERP, and APD20, significantly decreased action potential amplitude (APA), maximum velocity (Vmax), and overshot (OS) (ERP/APD > 1; P < 0.01), greatly diminished APA and OS of SAP induced by isoprenaline (P < 0.01), and remarkably inhibited Isi (P < 0.01). The results suggested that Dau exerted an inhibitory effect on Na+, Ca2+, and K+ channels.

Simulation of axonal excitability using a Spreadsheet template created in Microsoft Excel.

PubMed

Brown, A M

2000-08-01

The objective of this present study was to implement an established simulation protocol (A.M. Brown, A methodology for simulating biological systems using Microsoft Excel, Comp. Methods Prog. Biomed. 58 (1999) 181-90) to model axonal excitability. The simulation protocol involves the use of in-cell formulas directly typed into a spreadsheet and does not require any programming skills or use of the macro language. Once the initial spreadsheet template has been set up the simulations described in this paper can be executed with a few simple keystrokes. The model axon contained voltage-gated ion channels that were modeled using Hodgkin Huxley style kinetics. The basic properties of axonal excitability modeled were: (1) threshold of action potential firing, demonstrating that not only are the stimulus amplitude and duration critical in the generation of an action potential, but also the resting membrane potential; (2) refractoriness, the phenomenon of reduced excitability immediately following an action potential. The difference between the absolute refractory period, when no amount of stimulus will elicit an action potential, and relative refractory period, when an action potential may be generated by applying increased stimulus, was demonstrated with regard to the underlying state of the Na(+) and K(+) channels; (3) temporal summation, a process by which two sub-threshold stimuli can unite to elicit an action potential was shown to be due to conductance changes outlasting the first stimulus and summing with the second stimulus-induced conductance changes to drive the membrane potential past threshold; (4) anode break excitation, where membrane hyperpolarization was shown to produce an action potential by removing Na(+) channel inactivation that is present at resting membrane potential. The simulations described in this paper provide insights into mechanisms of axonal excitation that can be carried out by following an easily understood protocol.

Optical mapping of optogenetically shaped cardiac action potentials.

PubMed

Park, Sarah A; Lee, Shin-Rong; Tung, Leslie; Yue, David T

2014-08-19

Light-mediated silencing and stimulation of cardiac excitability, an important complement to electrical stimulation, promises important discoveries and therapies. To date, cardiac optogenetics has been studied with patch-clamp, multielectrode arrays, video microscopy, and an all-optical system measuring calcium transients. The future lies in achieving simultaneous optical acquisition of excitability signals and optogenetic control, both with high spatio-temporal resolution. Here, we make progress by combining optical mapping of action potentials with concurrent activation of channelrhodopsin-2 (ChR2) or halorhodopsin (eNpHR3.0), via an all-optical system applied to monolayers of neonatal rat ventricular myocytes (NRVM). Additionally, we explore the capability of ChR2 and eNpHR3.0 to shape action-potential waveforms, potentially aiding the study of short/long QT syndromes that result from abnormal changes in action potential duration (APD). These results show the promise of an all-optical system to acquire action potentials with precise temporal optogenetics control, achieving a long-sought flexibility beyond the means of conventional electrical stimulation.

Optical mapping of optogenetically shaped cardiac action potentials

PubMed Central

Park, Sarah A.; Lee, Shin-Rong; Tung, Leslie; Yue, David T.

2014-01-01

Light-mediated silencing and stimulation of cardiac excitability, an important complement to electrical stimulation, promises important discoveries and therapies. To date, cardiac optogenetics has been studied with patch-clamp, multielectrode arrays, video microscopy, and an all-optical system measuring calcium transients. The future lies in achieving simultaneous optical acquisition of excitability signals and optogenetic control, both with high spatio-temporal resolution. Here, we make progress by combining optical mapping of action potentials with concurrent activation of channelrhodopsin-2 (ChR2) or halorhodopsin (eNpHR3.0), via an all-optical system applied to monolayers of neonatal rat ventricular myocytes (NRVM). Additionally, we explore the capability of ChR2 and eNpHR3.0 to shape action-potential waveforms, potentially aiding the study of short/long QT syndromes that result from abnormal changes in action potential duration (APD). These results show the promise of an all-optical system to acquire action potentials with precise temporal optogenetics control, achieving a long-sought flexibility beyond the means of conventional electrical stimulation. PMID:25135113

Injury risk associated with playing actions during competitive soccer

PubMed Central

Rahnama, N; Reilly, T; Lees, A

2002-01-01

Objective: To assess the exposure of players to injury risk during English Premier League soccer matches in relation to selected factors. Methods: Injury risk was assessed by rating the injury potential of playing actions during competition with respect to (a) type of playing action, (b) period of the game, (c) zone of the pitch, and (d) playing either at home or away. In all, 10 games from the English Premier League 1999–2000 were chosen for analysis. A notation system was used whereby 16 soccer specific playing actions were classified into three categories: those inducing actual injury, those with a potential for injury (graded as mild, moderate, or high), and those deemed to have no potential for injury. The pitch was divided into 18 zones, and the position of each event was recorded along with time elapsed in the game, enabling six 15 minute periods to be defined. Results: Close to 18 000 actions were notated. On average (mean (SD)), 1788 (73) events (one every three seconds), 767 (99) events with injury potential (one every six seconds), and 2 (1) injuries (one every 45 minutes) per game were recorded. An overall injury incidence of 53 per 1000 playing hours was calculated. Receiving a tackle, receiving a "charge", and making a tackle were categorised as having a substantial injury risk, and goal catch, goal punch, kicking the ball, shot on goal, set kick, and heading the ball were all categorised as having a significant injury risk. All other actions were deemed low in risk. The first 15 minutes of each half contained the highest number of actions with mild injury potential, the last 15 minutes having the highest number of actions with moderate injury potential (p<0.01). The first and last 15 minutes of the game had the highest number of actions with high injury potential, although not significant. More actions with mild injury potential occurred in the goal area, and more actions with moderate and high injury potential occurred in the zone adjacent to the goal area (p<0.001). There was no significant difference between home and away with regard to injury potential. Conclusions: Playing actions with high injury risk were linked to contesting possession. Injury risk was highest in the first and last 15 minutes of the game, reflecting the intense engagements in the opening period and the possible effect of fatigue in the closing period. Injury risk was concentrated in the areas of the pitch where possession of the ball is most vigorously contested, which were specific attacking and defending zones close to the goal. Injury potential was no greater in away matches than at home. PMID:12351333

Associative spike timing-dependent potentiation of the basal dendritic excitatory synapses in the hippocampus in vivo.

PubMed

Fung, Thomas K; Law, Clayton S; Leung, L Stan

2016-06-01

Spike timing-dependent plasticity in the hippocampus has rarely been studied in vivo. Using extracellular potential and current source density analysis in urethane-anesthetized adult rats, we studied synaptic plasticity at the basal dendritic excitatory synapse in CA1 after excitation-spike (ES) pairing; E was a weak basal dendritic excitation evoked by stratum oriens stimulation, and S was a population spike evoked by stratum radiatum apical dendritic excitation. We hypothesize that positive ES pairing-generating synaptic excitation before a spike-results in long-term potentiation (LTP) while negative ES pairing results in long-term depression (LTD). Pairing (50 pairs at 5 Hz) at ES intervals of -10 to 0 ms resulted in significant input-specific LTP of the basal dendritic excitatory sink, lasting 60-120 min. Pairing at +10- to +20-ms ES intervals, or unpaired 5-Hz stimulation, did not induce significant basal dendritic or apical dendritic LTP or LTD. No basal dendritic LTD was found after stimulation of stratum oriens with 200 pairs of high-intensity pulses at 25-ms interval. Pairing-induced LTP was abolished by pretreatment with an N-methyl-d-aspartate receptor antagonist, 3-(2-carboxypiperazin-4-yl)-propyl-1-phosphonic acid (CPP), which also reduced spike bursting during 5-Hz pairing. Pairing at 0.5 Hz did not induce spike bursts or basal dendritic LTP. In conclusion, ES pairing at 5 Hz resulted in input-specific basal dendritic LTP at ES intervals of -10 ms to 0 ms but no LTD at ES intervals of -20 to +20 ms. Associative LTP likely occurred because of theta-rhythmic coincidence of subthreshold excitation with a backpropagated spike burst, which are conditions that can occur naturally in the hippocampus. Copyright © 2016 the American Physiological Society.

Associative spike timing-dependent potentiation of the basal dendritic excitatory synapses in the hippocampus in vivo

PubMed Central

Fung, Thomas K.; Law, Clayton S.

2016-01-01

Spike timing-dependent plasticity in the hippocampus has rarely been studied in vivo. Using extracellular potential and current source density analysis in urethane-anesthetized adult rats, we studied synaptic plasticity at the basal dendritic excitatory synapse in CA1 after excitation-spike (ES) pairing; E was a weak basal dendritic excitation evoked by stratum oriens stimulation, and S was a population spike evoked by stratum radiatum apical dendritic excitation. We hypothesize that positive ES pairing—generating synaptic excitation before a spike—results in long-term potentiation (LTP) while negative ES pairing results in long-term depression (LTD). Pairing (50 pairs at 5 Hz) at ES intervals of −10 to 0 ms resulted in significant input-specific LTP of the basal dendritic excitatory sink, lasting 60–120 min. Pairing at +10- to +20-ms ES intervals, or unpaired 5-Hz stimulation, did not induce significant basal dendritic or apical dendritic LTP or LTD. No basal dendritic LTD was found after stimulation of stratum oriens with 200 pairs of high-intensity pulses at 25-ms interval. Pairing-induced LTP was abolished by pretreatment with an N-methyl-d-aspartate receptor antagonist, 3-(2-carboxypiperazin-4-yl)-propyl-1-phosphonic acid (CPP), which also reduced spike bursting during 5-Hz pairing. Pairing at 0.5 Hz did not induce spike bursts or basal dendritic LTP. In conclusion, ES pairing at 5 Hz resulted in input-specific basal dendritic LTP at ES intervals of −10 ms to 0 ms but no LTD at ES intervals of −20 to +20 ms. Associative LTP likely occurred because of theta-rhythmic coincidence of subthreshold excitation with a backpropagated spike burst, which are conditions that can occur naturally in the hippocampus. PMID:27052581

Spontaneous action potentials and neural coding in unmyelinated axons.

PubMed

O'Donnell, Cian; van Rossum, Mark C W

2015-04-01

The voltage-gated Na and K channels in neurons are responsible for action potential generation. Because ion channels open and close in a stochastic fashion, spontaneous (ectopic) action potentials can result even in the absence of stimulation. While spontaneous action potentials have been studied in detail in single-compartment models, studies on spatially extended processes have been limited. The simulations and analysis presented here show that spontaneous rate in unmyelinated axon depends nonmonotonically on the length of the axon, that the spontaneous activity has sub-Poisson statistics, and that neural coding can be hampered by the spontaneous spikes by reducing the probability of transmitting the first spike in a train.

Improving Cardiac Action Potential Measurements: 2D and 3D Cell Culture.

PubMed

Daily, Neil J; Yin, Yue; Kemanli, Pinar; Ip, Brian; Wakatsuki, Tetsuro

2015-11-01

Progress in the development of assays for measuring cardiac action potential is crucial for the discovery of drugs for treating cardiac disease and assessing cardiotoxicity. Recently, high-throughput methods for assessing action potential using induced pluripotent stem cell (iPSC) derived cardiomyocytes in both two-dimensional monolayer cultures and three-dimensional tissues have been developed. We describe an improved method for assessing cardiac action potential using an ultra-fast cost-effective plate reader with commercially available dyes. Our methods improve dramatically the detection of the fluorescence signal from these dyes and make way for the development of more high-throughput methods for cardiac drug discovery and cardiotoxicity.

Action potentials contribute to epileptic high-frequency oscillations recorded with electrodes remote from neurons.

PubMed

Kobayashi, Katsuhiro; Akiyama, Tomoyuki; Ohmori, Iori; Yoshinaga, Harumi; Gotman, Jean

2015-05-01

The importance of epileptic high-frequency oscillations (HFOs) in electroencephalogram (EEG) is growing. Action potentials generating some HFOs are observed in the vicinity of neurons in experimental animals. However electrodes that are remote from neurons, as in case of clinical situations, should not record action potentials. We propose to resolve this question by a realistic simulation of epileptic neuronal network. The rat dentate gyrus with sclerosis was simulated in silico. We computed the current dipole moment generated by each granule cell and the field potentials in a measurement area far from neurons. The dentate gyrus was stimulated through synaptic input to evoke discharges resembling interictal epileptiform discharges, which had superimposed HFOs⩽295Hz that were recordable with remote electrodes and represented bursts of action potentials of granule cells. The increase in power of HFOs was associated with the progression of sclerosis, the reduction of GABAergic inhibition, and the increase in cell connectivity. Spectral frequency of HFOs had similar tendencies. HFOs recorded with electrodes remote from neurons could actually be generated by clusters of action potentials. The phenomenon of action potentials recorded with remote electrodes can possibly extend the clinical meaning of EEG. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

A physical action potential generator: design, implementation and evaluation.

PubMed

Latorre, Malcolm A; Chan, Adrian D C; Wårdell, Karin

2015-01-01

The objective was to develop a physical action potential generator (Paxon) with the ability to generate a stable, repeatable, programmable, and physiological-like action potential. The Paxon has an equivalent of 40 nodes of Ranvier that were mimicked using resin embedded gold wires (Ø = 20 μm). These nodes were software controlled and the action potentials were initiated by a start trigger. Clinically used Ag-AgCl electrodes were coupled to the Paxon for functional testing. The Paxon's action potential parameters were tunable using a second order mathematical equation to generate physiologically relevant output, which was accomplished by varying the number of nodes involved (1-40 in incremental steps of 1) and the node drive potential (0-2.8 V in 0.7 mV steps), while keeping a fixed inter-nodal timing and test electrode configuration. A system noise floor of 0.07 ± 0.01 μV was calculated over 50 runs. A differential test electrode recorded a peak positive amplitude of 1.5 ± 0.05 mV (gain of 40x) at time 196.4 ± 0.06 ms, including a post trigger delay. The Paxon's programmable action potential like signal has the possibility to be used as a validation test platform for medical surface electrodes and their attached systems.

Direct detection of a single evoked action potential with MRS in Lumbricus terrestris.

PubMed

Poplawsky, Alexander J; Dingledine, Raymond; Hu, Xiaoping P

2012-01-01

Functional MRI (fMRI) measures neural activity indirectly by detecting the signal change associated with the hemodynamic response following brain activation. In order to alleviate the temporal and spatial specificity problems associated with fMRI, a number of attempts have been made to detect neural magnetic fields (NMFs) with MRI directly, but have thus far provided conflicting results. In this study, we used MR to detect axonal NMFs in the median giant fiber of the earthworm, Lumbricus terrestris, by examining the free induction decay (FID) with a sampling interval of 0.32 ms. The earthworm nerve cords were isolated from the vasculature and stimulated at the threshold of action potential generation. FIDs were acquired shortly after the stimulation, and simultaneous field potential recordings identified the presence or absence of single evoked action potentials. FIDs acquired when the stimulus did not evoke an action potential were summed as background. The phase of the background-subtracted FID exhibited a systematic change, with a peak phase difference of (-1.2 ± 0.3) × 10(-5) radians occurring at a time corresponding to the timing of the action potential. In addition, we calculated the possible changes in the FID magnitude and phase caused by a simulated action potential using a volume conductor model. The measured phase difference matched the theoretical prediction well in both amplitude and temporal characteristics. This study provides the first evidence for the direct detection of a magnetic field from an evoked action potential using MR. Copyright © 2011 John Wiley & Sons, Ltd.

The Direct Detection of a Single Evoked Action Potential with Magnetic Resonance Spectroscopy in Lumbricus Terrestris

PubMed Central

Poplawsky, Alexander J.; Dingledine, Raymond

2011-01-01

Functional MRI (fMRI) indirectly measures neural activity by detecting the signal change associated with the hemodynamic response following brain activation. In order to alleviate the temporal and spatial specificity problems associated with fMRI, a number of attempts have been made to detect neural magnetic fields (NMFs) with MRI directly, but have thus far provided conflicting results. In the present study, we used magnetic resonance to detect axonal NMFs in the median giant fiber of the earthworm, Lumbricus terrestris, by examining the free-induction decay (FID) with a sampling interval of 0.32 ms. The earthworm nerve cords were isolated from the vasculature and stimulated at the threshold of action potential generation. FIDs were acquired shortly after the stimulation and simultaneous field potential recordings identified the presence or absence of single evoked action potentials. FIDs acquired when the stimulus did not evoke an action potential were summed as background. The phase of the background-subtracted FID exhibited a systematic change, with a peak phase difference of [-1.2 ± 0.3] ×10-5 radians occurring at a time corresponding to the timing of the action potential. In addition, we calculated the possible changes in the FID magnitude and phase due to a simulated action potential using a volume conductor model. The measured phase difference matched the theoretical prediction well in both amplitude and temporal characteristics. This study provides the first evidence for the direct detection of a magnetic field from an evoked action potential using magnetic resonance. PMID:21728204

Channel sialic acids limit hERG channel activity during the ventricular action potential.

PubMed

Norring, Sarah A; Ednie, Andrew R; Schwetz, Tara A; Du, Dongping; Yang, Hui; Bennett, Eric S

2013-02-01

Activity of human ether-a-go-go-related gene (hERG) 1 voltage-gated K(+) channels is responsible for portions of phase 2 and phase 3 repolarization of the human ventricular action potential. Here, we questioned whether and how physiologically and pathophysiologically relevant changes in surface N-glycosylation modified hERG channel function. Voltage-dependent hERG channel gating and activity were evaluated as expressed in a set of Chinese hamster ovary (CHO) cell lines under conditions of full glycosylation, no sialylation, no complex N-glycans, and following enzymatic deglycosylation of surface N-glycans. For each condition of reduced glycosylation, hERG channel steady-state activation and inactivation relationships were shifted linearly by significant depolarizing ∼9 and ∼18 mV, respectively. The hERG window current increased significantly by 50-150%, and the peak shifted by a depolarizing ∼10 mV. There was no significant change in maximum hERG current density. Deglycosylated channels were significantly more active (20-80%) than glycosylated controls during phases 2 and 3 of action potential clamp protocols. Simulations of hERG current and ventricular action potentials corroborated experimental data and predicted reduced sialylation leads to a 50-70-ms decrease in action potential duration. The data describe a novel mechanism by which hERG channel gating is modulated through physiologically and pathophysiologically relevant changes in N-glycosylation; reduced channel sialylation increases hERG channel activity during the action potential, thereby increasing the rate of action potential repolarization.

Sodium and calcium currents shape action potentials in immature mouse inner hair cells

PubMed Central

Marcotti, Walter; Johnson, Stuart L; Rüsch, Alfons; Kros, Corné J

2003-01-01

Before the onset of hearing at postnatal day 12, mouse inner hair cells (IHCs) produce spontaneous and evoked action potentials. These spikes are likely to induce neurotransmitter release onto auditory nerve fibres. Since immature IHCs express both α1D (Cav1.3) Ca2+ and Na+ currents that activate near the resting potential, we examined whether these two conductances are involved in shaping the action potentials. Both had extremely rapid activation kinetics, followed by fast and complete voltage-dependent inactivation for the Na+ current, and slower, partially Ca2+-dependent inactivation for the Ca2+ current. Only the Ca2+ current is necessary for spontaneous and induced action potentials, and 29 % of cells lacked a Na+ current. The Na+ current does, however, shorten the time to reach the action-potential threshold, whereas the Ca2+ current is mainly involved, together with the K+ currents, in determining the speed and size of the spikes. Both currents increased in size up to the end of the first postnatal week. After this, the Ca2+ current reduced to about 30 % of its maximum size and persisted in mature IHCs. The Na+ current was downregulated around the onset of hearing, when the spiking is also known to disappear. Although the Na+ current was observed as early as embryonic day 16.5, its role in action-potential generation was only evident from just after birth, when the resting membrane potential became sufficiently negative to remove a sizeable fraction of the inactivation (half inactivation was at −71 mV). The size of both currents was positively correlated with the developmental change in action-potential frequency. PMID:12937295

The dependence of the action potential of the frog's heart on the external and intracellular sodium concentration

PubMed Central

Niedergerke, R.; Orkand, R. K.

1966-01-01

1. The overshoot of the action potential of the frog's heart was reduced when external sodium chloride was replaced by sucrose. However, the potential decrement was only 17·3 mV for a 10-fold reduction of sodium as compared with 58 mV expected on the basis of the sodium hypothesis of excitation. 2. Replacement of up to 75% of the external sodium by choline did not reduce the overshoot, provided atropine was present in sufficient concentrations to suppress any parasympathomimetic action. 3. The maximum rate of rise of the action potential markedly declined in low sodium fluids whether sucrose or choline chloride was used to replace sodium chloride. 4. The maximum rate of rise was reduced to only a small extent when external sodium was replaced by lithium. 5. Increasing the intracellular sodium concentration in exchange for lost potassium caused overshoots to decline. The effects resembled those obtained in similar experiments with skeletal muscle fibres (Desmedt, 1953). 6. Action potentials occurring under certain conditions even in the presence of very low external sodium concentrations (≤ 5% normal) also declined in height when the intracellular sodium concentration was increased. 7. The behaviour of the action potential in low external sodium concentrations may be explained by an action of calcium on the excitable membrane. PMID:5921833

Neural Network Compensation for Frequency Cross-Talk in Laser Interferometry

NASA Astrophysics Data System (ADS)

Lee, Wooram; Heo, Gunhaeng; You, Kwanho

The heterodyne laser interferometer acts as an ultra-precise measurement apparatus in semiconductor manufacture. However the periodical nonlinearity property caused from frequency cross-talk is an obstacle to improve the high measurement accuracy in nanometer scale. In order to minimize the nonlinearity error of the heterodyne interferometer, we propose a frequency cross-talk compensation algorithm using an artificial intelligence method. The feedforward neural network trained by back-propagation compensates the nonlinearity error and regulates to minimize the difference with the reference signal. With some experimental results, the improved accuracy is proved through comparison with the position value from a capacitive displacement sensor.

Generative Adversarial Networks: An Overview

NASA Astrophysics Data System (ADS)

Creswell, Antonia; White, Tom; Dumoulin, Vincent; Arulkumaran, Kai; Sengupta, Biswa; Bharath, Anil A.

2018-01-01

Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image synthesis, semantic image editing, style transfer, image super-resolution and classification. The aim of this review paper is to provide an overview of GANs for the signal processing community, drawing on familiar analogies and concepts where possible. In addition to identifying different methods for training and constructing GANs, we also point to remaining challenges in their theory and application.

The DSFPN, a new neural network for optical character recognition.

PubMed

Morns, L P; Dlay, S S

1999-01-01

A new type of neural network for recognition tasks is presented in this paper. The network, called the dynamic supervised forward-propagation network (DSFPN), is based on the forward only version of the counterpropagation network (CPN). The DSFPN, trains using a supervised algorithm and can grow dynamically during training, allowing subclasses in the training data to be learnt in an unsupervised manner. It is shown to train in times comparable to the CPN while giving better classification accuracies than the popular backpropagation network. Both Fourier descriptors and wavelet descriptors are used for image preprocessing and the wavelets are proven to give a far better performance.

Intelligent Surveillance Robot with Obstacle Avoidance Capabilities Using Neural Network

PubMed Central

2015-01-01

For specific purpose, vision-based surveillance robot that can be run autonomously and able to acquire images from its dynamic environment is very important, for example, in rescuing disaster victims in Indonesia. In this paper, we propose architecture for intelligent surveillance robot that is able to avoid obstacles using 3 ultrasonic distance sensors based on backpropagation neural network and a camera for face recognition. 2.4 GHz transmitter for transmitting video is used by the operator/user to direct the robot to the desired area. Results show the effectiveness of our method and we evaluate the performance of the system. PMID:26089863

Application of DBNs for concerned internet information detecting

NASA Astrophysics Data System (ADS)

Wang, Yanfang; Gao, Song

2017-03-01

In recent years, deep learning has achieved great success in many fields, ranging from voice recognition and image classification to computer vision. In this study we apply DBNs to concerned internet information in Chinese detecting problem, since there are inherent differences between English and Chinese. Contrastive divergence (CD) is employed in the DBNs to learn a multi-layer generative model from numerous unlabeled data. The features obtained by this model are used to initialize the feed-forward neural network, which can be fine-tuned with backpropagation. Experiment results indicate that, the model and training method we proposed can be used to detect the concerned internet information effectively and accurately.

Size invariance does not hold for connectionist models: dangers of using a toy model.

PubMed

Yamaguchi, Makoto

2004-03-01

Connectionist models with backpropagation learning rule are known to have a serious problem called catastrophic interference or forgetting, although there have been several reports showing that the interference can be relatively mild with orthogonal inputs. The present study investigated the extent of interference using orthogonal inputs with varying network sizes. One would naturally assume that results obtained from small networks could be extrapolated for larger networks. Unexpectedly, the use of small networks was shown to worsen performance. This result has important implications for interpreting some data in the literature and cautions against the use of a toy model. Copyright 2004 Lippincott Williams & Wilkins

Polar cloud and surface classification using AVHRR imagery - An intercomparison of methods

NASA Technical Reports Server (NTRS)

Welch, R. M.; Sengupta, S. K.; Goroch, A. K.; Rabindra, P.; Rangaraj, N.; Navar, M. S.

1992-01-01

Six Advanced Very High-Resolution Radiometer local area coverage (AVHRR LAC) arctic scenes are classified into ten classes. Three different classifiers are examined: (1) the traditional stepwise discriminant analysis (SDA) method; (2) the feed-forward back-propagation (FFBP) neural network; and (3) the probabilistic neural network (PNN). More than 200 spectral and textural measures are computed. These are reduced to 20 features using sequential forward selection. Theoretical accuracy of the classifiers is determined using the bootstrap approach. Overall accuracy is 85.6 percent, 87.6 percent, and 87.0 percent for the SDA, FFBP, and PNN classifiers, respectively, with standard deviations of approximately 1 percent.

Gradient descent learning algorithm overview: a general dynamical systems perspective.

PubMed

Baldi, P

1995-01-01

Gives a unified treatment of gradient descent learning algorithms for neural networks using a general framework of dynamical systems. This general approach organizes and simplifies all the known algorithms and results which have been originally derived for different problems (fixed point/trajectory learning), for different models (discrete/continuous), for different architectures (forward/recurrent), and using different techniques (backpropagation, variational calculus, adjoint methods, etc.). The general approach can also be applied to derive new algorithms. The author then briefly examines some of the complexity issues and limitations intrinsic to gradient descent learning. Throughout the paper, the author focuses on the problem of trajectory learning.

Application of artificial neural network for heat transfer in porous cone

NASA Astrophysics Data System (ADS)

Athani, Abdulgaphur; Ahamad, N. Ameer; Badruddin, Irfan Anjum

2018-05-01

Heat transfer in porous medium is one of the classical areas of research that has been active for many decades. The heat transfer in porous medium is generally studied by using numerical methods such as finite element method; finite difference method etc. that solves coupled partial differential equations by converting them into simpler forms. The current work utilizes an alternate method known as artificial neural network that mimics the learning characteristics of neurons. The heat transfer in porous medium fixed in a cone is predicted using backpropagation neural network. The artificial neural network is able to predict this behavior quite accurately.

Heat transfer prediction in a square porous medium using artificial neural network

NASA Astrophysics Data System (ADS)

Ahamad, N. Ameer; Athani, Abdulgaphur; Badruddin, Irfan Anjum

2018-05-01

Heat transfer in porous media has been investigated extensively because of its applications in various important fields. Neural network approach is applied to analyze steady two dimensional free convection flows through a porous medium fixed in a square cavity. The backpropagation neural network is trained and used to predict the heat transfer. The results are compared with available information in the literature. It is found that the heat transfer increases with increase in Rayleigh number. It is further found that the local Nusselt number decreases along the height of cavity. The neural network is found to predict the heat transfer behavior accurately for given parameters.

Robust Bioinformatics Recognition with VLSI Biochip Microsystem

NASA Technical Reports Server (NTRS)

Lue, Jaw-Chyng L.; Fang, Wai-Chi

2006-01-01

A microsystem architecture for real-time, on-site, robust bioinformatic patterns recognition and analysis has been proposed. This system is compatible with on-chip DNA analysis means such as polymerase chain reaction (PCR)amplification. A corresponding novel artificial neural network (ANN) learning algorithm using new sigmoid-logarithmic transfer function based on error backpropagation (EBP) algorithm is invented. Our results show the trained new ANN can recognize low fluorescence patterns better than the conventional sigmoidal ANN does. A differential logarithmic imaging chip is designed for calculating logarithm of relative intensities of fluorescence signals. The single-rail logarithmic circuit and a prototype ANN chip are designed, fabricated and characterized.

Effect of filtration of signals of brain activity on quality of recognition of brain activity patterns using artificial intelligence methods

NASA Astrophysics Data System (ADS)

Hramov, Alexander E.; Frolov, Nikita S.; Musatov, Vyachaslav Yu.

2018-02-01

In present work we studied features of the human brain states classification, corresponding to the real movements of hands and legs. For this purpose we used supervised learning algorithm based on feed-forward artificial neural networks (ANNs) with error back-propagation along with the support vector machine (SVM) method. We compared the quality of operator movements classification by means of EEG signals obtained experimentally in the absence of preliminary processing and after filtration in different ranges up to 25 Hz. It was shown that low-frequency filtering of multichannel EEG data significantly improved accuracy of operator movements classification.

Rapid Ca2+ flux through the transverse tubular membrane, activated by individual action potentials in mammalian skeletal muscle

PubMed Central

Launikonis, Bradley S; Stephenson, D George; Friedrich, Oliver

2009-01-01

Periods of low frequency stimulation are known to increase the net Ca2+ uptake in skeletal muscle but the mechanism responsible for this Ca2+ entry is not known. In this study a novel high-resolution fluorescence microscopy approach allowed the detection of an action potential-induced Ca2+ flux across the tubular (t-) system of rat extensor digitorum longus muscle fibres that appears to be responsible for the net uptake of Ca2+ in working muscle. Action potentials were triggered in the t-system of mechanically skinned fibres from rat by brief field stimulation and t-system [Ca2+] ([Ca2+]t-sys) and cytoplasmic [Ca2+] ([Ca2+]cyto) were simultaneously resolved on a confocal microscope. When initial [Ca2+]t-sys was ≥ 0.2 mm a Ca2+ flux from t-system to the cytoplasm was observed following a single action potential. The action potential-induced Ca2+ flux and associated t-system Ca2+ permeability decayed exponentially and displayed inactivation characteristics such that further Ca2+ entry across the t-system could not be observed after 2–3 action potentials at 10 Hz stimulation rate. When [Ca2+]t-sys was closer to 0.1 mm, a transient rise in [Ca2+]t-sys was observed almost concurrently with the increase in [Ca2+]cyto following the action potential. The change in direction of Ca2+ flux was consistent with changes in the direction of the driving force for Ca2+. This is the first demonstration of a rapid t-system Ca2+ flux associated with a single action potential in mammalian skeletal muscle. The properties of this channel are inconsistent with a flux through the L-type Ca2+ channel suggesting that an as yet unidentified t-system protein is conducting this current. This action potential-activated Ca2+ flux provides an explanation for the previously described Ca2+ entry and accumulation observed with prolonged, intermittent muscle activity. PMID:19332499

29 CFR 1990.147 - Final action.

Code of Federal Regulations, 2014 CFR

2014-07-01

...) IDENTIFICATION, CLASSIFICATION, AND REGULATION OF POTENTIAL OCCUPATIONAL CARCINOGENS Regulation of Potential Occupational Carcinogens § 1990.147 Final action. (a) Within one hundred twenty (120) days from the last day of... is classified as a Category I Potential Carcinogen or as a Category II Potential Carcinogen. If the...

29 CFR 1990.147 - Final action.

Code of Federal Regulations, 2012 CFR

2012-07-01

...) IDENTIFICATION, CLASSIFICATION, AND REGULATION OF POTENTIAL OCCUPATIONAL CARCINOGENS Regulation of Potential Occupational Carcinogens § 1990.147 Final action. (a) Within one hundred twenty (120) days from the last day of... is classified as a Category I Potential Carcinogen or as a Category II Potential Carcinogen. If the...

29 CFR 1990.147 - Final action.

Code of Federal Regulations, 2013 CFR

2013-07-01

...) IDENTIFICATION, CLASSIFICATION, AND REGULATION OF POTENTIAL OCCUPATIONAL CARCINOGENS Regulation of Potential Occupational Carcinogens § 1990.147 Final action. (a) Within one hundred twenty (120) days from the last day of... is classified as a Category I Potential Carcinogen or as a Category II Potential Carcinogen. If the...

29 CFR 1990.147 - Final action.

Code of Federal Regulations, 2011 CFR

2011-07-01

...) IDENTIFICATION, CLASSIFICATION, AND REGULATION OF POTENTIAL OCCUPATIONAL CARCINOGENS Regulation of Potential Occupational Carcinogens § 1990.147 Final action. (a) Within one hundred twenty (120) days from the last day of... is classified as a Category I Potential Carcinogen or as a Category II Potential Carcinogen. If the...

Effect of Exogenous Extracellular Nicotinamide Adenine Dinucleotide (NAD⁺) on Bioelectric Activity of the Pacemaker and Conduction System of the Heart.

PubMed

Pustovit, K B; Kuz'min, V S; Sukhova, G S

2015-06-01

In rat sinoatrial node, NAD(+) (10 μM) reduced the rate of spontaneous action potentials, duration of action potentials, and the velocity of slow diastolic depolarization, but the rate of action potential front propagation increases. In passed rabbit Purkinje fibers, NAD(+) (10 μM) reduced the duration of action potentials. Under conditions of spontaneous activity of Purkinje fibers, NAD(+) reduced the fi ring rate and the rate of slow diastolic depolarization. The effects of extracellular NAD(+) on bioelectric activity of the pacemaker (sinoatrial node) and conduction system of the heart (Purkinje fibers) are probably related to activation of P1 and P2 purinoceptors.

22 CFR 161.8 - General description of the Department's NEPA process.

Code of Federal Regulations, 2010 CFR

2010-04-01

... § 161.8 General description of the Department's NEPA process. In reviewing proposed actions for potential environmental effects in the United States responsible action officers will follow the procedural... review the action to determine if it may cause potential significant environmental effects on the...

Prolonged action potential duration in cardiac ablation of PDK1 mice.

PubMed

Han, Zhonglin; Jiang, Yu; Yang, Zhongzhou; Cao, Kejiang; Wang, Dao W

2015-01-01

The involvement of the AGC protein kinase family in regulating arrhythmia has drawn considerable attention, but the underlying mechanisms are still not clear. The aim of this study is to explore the role of 3-phosphoinositide-dependent protein kinase-1 (PDK1), one of upstream protein kinases of the AGC protein kinase family, in the pathogenesis of dysregulated electrophysiological basis. PDK1(F/F) αMHC-Cre mice and PDK1(F/F) mice were divided into experiment group and control group. Using patch clamping technology, we explored action potential duration in both groups, and investigated the functions of transient outward potassium channel and L-type Ca(2+) channel to explain the abnormal action potential duration. Significant prolongation action potential duration was found in mice with PDK1 deletion. Further, the peak current of transient outward potassium current and L-type Ca(2+) current were decreased by 84% and 49% respectively. In addition, dysregulation of channel kinetics lead to action potential duration prolongation further. In conclusion, we have demonstrated that PDK1 participates in action potential prolongation in cardiac ablation of PDK1 mice. This effect is likely to be mediated largely through downregulation of transient outward potassium current. These findings indicate the modulation of the PDK1 pathway could provide a new mechanism for abnormal electrophysiological basis.

A phantom axon setup for validating models of action potential recordings.

PubMed

Rossel, Olivier; Soulier, Fabien; Bernard, Serge; Guiraud, David; Cathébras, Guy

2016-08-01

Electrode designs and strategies for electroneurogram recordings are often tested first by computer simulations and then by animal models, but they are rarely implanted for long-term evaluation in humans. The models show that the amplitude of the potential at the surface of an axon is higher in front of the nodes of Ranvier than at the internodes; however, this has not been investigated through in vivo measurements. An original experimental method is presented to emulate a single fiber action potential in an infinite conductive volume, allowing the potential of an axon to be recorded at both the nodes of Ranvier and the internodes, for a wide range of electrode-to-fiber radial distances. The paper particularly investigates the differences in the action potential amplitude along the longitudinal axis of an axon. At a short radial distance, the action potential amplitude measured in front of a node of Ranvier is two times larger than in the middle of two nodes. Moreover, farther from the phantom axon, the measured action potential amplitude is almost constant along the longitudinal axis. The results of this new method confirm the computer simulations, with a correlation of 97.6 %.

Action potentials in retinal ganglion cells are initiated at the site of maximal curvature of the extracellular potential.

PubMed

Eickenscheidt, Max; Zeck, Günther

2014-06-01

The initiation of an action potential by extracellular stimulation occurs after local depolarization of the neuronal membrane above threshold. Although the technique shows remarkable clinical success, the site of action and the relevant stimulation parameters are not completely understood. Here we identify the site of action potential initiation in rabbit retinal ganglion cells (RGCs) interfaced to an array of extracellular capacitive stimulation electrodes. We determine which feature of the extracellular potential governs action potential initiation by simultaneous stimulation and recording RGCs interfaced in epiretinal configuration. Stimulation electrodes were combined to areas of different size and were presented at different positions with respect to the RGC. Based on stimulation by electrodes beneath the RGC soma and simultaneous sub-millisecond latency measurement we infer axonal initiation at the site of maximal curvature of the extracellular potential. Stimulation by electrodes at different positions along the axon reveals a nearly constant threshold current density except for a narrow region close to the cell soma. These findings are explained by the concept of the activating function modified to consider a region of lower excitability close to the cell soma. We present a framework how to estimate the site of action potential initiation and the stimulus required to cross threshold in neurons tightly interfaced to capacitive stimulation electrodes. Our results underscore the necessity of rigorous electrical characterization of the stimulation electrodes and of the interfaced neural tissue.

Autonomous initiation and propagation of action potentials in neurons of the subthalamic nucleus.

PubMed

Atherton, Jeremy F; Wokosin, David L; Ramanathan, Sankari; Bevan, Mark D

2008-12-01

The activity of the subthalamic nucleus (STN) is intimately related to movement and is generated, in part, by voltage-dependent Na(+) (Na(v)) channels that drive autonomous firing. In order to determine the principles underlying the initiation and propagation of action potentials in STN neurons, 2-photon laser scanning microscopy was used to guide tight-seal whole-cell somatic and loose-seal cell-attached axonal/dendritic patch-clamp recordings and compartment-selective ion channel manipulation in rat brain slices. Action potentials were first detected in a region that corresponded most closely to the unmyelinated axon initial segment, as defined by Golgi and ankyrin G labelling. Following initiation, action potentials propagated reliably into axonal and somatodendritic compartments with conduction velocities of approximately 5 m s(-1) and approximately 0.7 m s(-1), respectively. Action potentials generated by neurons with axons truncated within or beyond the axon initial segment were not significantly different. However, axon initial segment and somatic but not dendritic or more distal axonal application of low [Na(+)] ACSF or the selective Na(v) channel blocker tetrodotoxin consistently depolarized action potential threshold. Finally, somatodendritic but not axonal application of GABA evoked large, rapid inhibitory currents in concordance with electron microscopic analyses, which revealed that the somatodendritic compartment was the principal target of putative inhibitory inputs. Together the data are consistent with the conclusions that in STN neurons the axon initial segment and soma express an excess of Na(v) channels for the generation of autonomous activity, while synaptic activation of somatodendritic GABA(A) receptors regulates the axonal initiation of action potentials.

Autonomous initiation and propagation of action potentials in neurons of the subthalamic nucleus

PubMed Central

Atherton, Jeremy F; Wokosin, David L; Ramanathan, Sankari; Bevan, Mark D

2008-01-01

The activity of the subthalamic nucleus (STN) is intimately related to movement and is generated, in part, by voltage-dependent Na+ (Nav) channels that drive autonomous firing. In order to determine the principles underlying the initiation and propagation of action potentials in STN neurons, 2-photon laser scanning microscopy was used to guide tight-seal whole-cell somatic and loose-seal cell-attached axonal/dendritic patch-clamp recordings and compartment-selective ion channel manipulation in rat brain slices. Action potentials were first detected in a region that corresponded most closely to the unmyelinated axon initial segment, as defined by Golgi and ankyrin G labelling. Following initiation, action potentials propagated reliably into axonal and somatodendritic compartments with conduction velocities of ∼5 m s−1 and ∼0.7 m s−1, respectively. Action potentials generated by neurons with axons truncated within or beyond the axon initial segment were not significantly different. However, axon initial segment and somatic but not dendritic or more distal axonal application of low [Na+] ACSF or the selective Nav channel blocker tetrodotoxin consistently depolarized action potential threshold. Finally, somatodendritic but not axonal application of GABA evoked large, rapid inhibitory currents in concordance with electron microscopic analyses, which revealed that the somatodendritic compartment was the principal target of putative inhibitory inputs. Together the data are consistent with the conclusions that in STN neurons the axon initial segment and soma express an excess of Nav channels for the generation of autonomous activity, while synaptic activation of somatodendritic GABAA receptors regulates the axonal initiation of action potentials. PMID:18832425

Production of a solvent, detergent, and thermotolerant lipase by a newly isolated Acinetobacter sp. in submerged and solid-state fermentations.

PubMed

Khoramnia, Anahita; Ebrahimpour, Afshin; Beh, Boon Kee; Lai, Oi Ming

2011-01-01

The lipase production ability of a newly isolated Acinetobacter sp. in submerged (SmF) and solid-state (SSF) fermentations was evaluated. The results demonstrated this strain as one of the rare bacterium, which is able to grow and produce lipase in SSF even more than SmF. Coconut oil cake as a cheap agroindustrial residue was employed as the solid substrate. The lipase production was optimized in both media using artificial neural network. Multilayer normal and full feed forward backpropagation networks were selected to build predictive models to optimize the culture parameters for lipase production in SmF and SSF systems, respectively. The produced models for both systems showed high predictive accuracy where the obtained conditions were close together. The produced enzyme was characterized as a thermotolerant lipase, although the organism was mesophile. The optimum temperature for the enzyme activity was 45°C where 63% of its activity remained at 70°C after 2 h. This lipase remained active after 24 h in a broad range of pH (6-11). The lipase demonstrated strong solvent and detergent tolerance potentials. Therefore, this inexpensive lipase production for such a potent and industrially valuable lipase is promising and of considerable commercial interest for biotechnological applications.

A Neutral Network based Early Eathquake Warning model in California region

NASA Astrophysics Data System (ADS)

Xiao, H.; MacAyeal, D. R.

2016-12-01

Early Earthquake Warning systems could reduce loss of lives and other economic impact resulted from natural disaster or man-made calamity. Current systems could be further enhanced by neutral network method. A 3 layer neural network model combined with onsite method was deployed in this paper to improve the recognition time and detection time for large scale earthquakes.The 3 layer neutral network early earthquake warning model adopted the vector feature design for sample events happened within 150 km radius of the epicenters. Dataset used in this paper contained both destructive events and small scale events. All the data was extracted from IRIS database to properly train the model. In the training process, backpropagation algorithm was used to adjust the weight matrices and bias matrices during each iteration. The information in all three channels of the seismometers served as the source in this model. Through designed tests, it was indicated that this model could identify approximately 90 percent of the events' scale correctly. And the early detection could provide informative evidence for public authorities to make further decisions. This indicated that neutral network model could have the potential to strengthen current early warning system, since the onsite method may greatly reduce the responding time and save more lives in such disasters.

Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins

PubMed Central

Li, Bian; Mendenhall, Jeffrey; Nguyen, Elizabeth Dong; Weiner, Brian E.; Fischer, Axel W.; Meiler, Jens

2017-01-01

Prediction of the three-dimensional (3D) structures of proteins by computational methods is acknowledged as an unsolved problem. Accurate prediction of important structural characteristics such as contact number is expected to accelerate the otherwise slow progress being made in the prediction of 3D structure of proteins. Here, we present a dropout neural network-based method, TMH-Expo, for predicting the contact number of transmembrane helix (TMH) residues from sequence. Neuronal dropout is a strategy where certain neurons of the network are excluded from back-propagation to prevent co-adaptation of hidden-layer neurons. By using neuronal dropout, overfitting was significantly reduced and performance was noticeably improved. For multi-spanning helical membrane proteins, TMH-Expo achieved a remarkable Pearson correlation coefficient of 0.69 between predicted and experimental values and a mean absolute error of only 1.68. In addition, among those membrane protein–membrane protein interface residues, 76.8% were correctly predicted. Mapping of predicted contact numbers onto structures indicates that contact numbers predicted by TMH-Expo reflect the exposure patterns of TMHs and reveal membrane protein–membrane protein interfaces, reinforcing the potential of predicted contact numbers to be used as restraints for 3D structure prediction and protein–protein docking. TMH-Expo can be accessed via a Web server at www.meilerlab.org. PMID:26804342

Ling's Adsorption Theory as a Mechanism of Membrane Potential Generation Observed in Both Living and Nonliving Systems.

PubMed

Tamagawa, Hirohisa; Funatani, Makoto; Ikeda, Kota

2016-01-26

The potential between two electrolytic solutions separated by a membrane impermeable to ions was measured and the generation mechanism of potential measured was investigated. From the physiological point of view, a nonzero membrane potential or action potential cannot be observed across the impermeable membrane. However, a nonzero membrane potential including action potential-like potential was clearly observed. Those observations gave rise to a doubt concerning the validity of currently accepted generation mechanism of membrane potential and action potential of cell. As an alternative theory, we found that the long-forgotten Ling's adsorption theory was the most plausible theory. Ling's adsorption theory suggests that the membrane potential and action potential of a living cell is due to the adsorption of mobile ions onto the adsorption site of cell, and this theory is applicable even to nonliving (or non-biological) system as well as living system. Through this paper, the authors emphasize that it is necessary to reconsider the validity of current membrane theory and also would like to urge the readers to pay keen attention to the Ling's adsorption theory which has for long years been forgotten in the history of physiology.

Effects of acetylcholine and noradrenalin on action potentials of isolated rabbit sinoatrial and atrial myocytes.

PubMed

Verkerk, Arie O; Geuzebroek, Guillaume S C; Veldkamp, Marieke W; Wilders, Ronald

2012-01-01

The autonomic nervous system controls heart rate and contractility through sympathetic and parasympathetic inputs to the cardiac tissue, with acetylcholine (ACh) and noradrenalin (NA) as the chemical transmitters. In recent years, it has become clear that specific Regulators of G protein Signaling proteins (RGS proteins) suppress muscarinic sensitivity and parasympathetic tone, identifying RGS proteins as intriguing potential therapeutic targets. In the present study, we have identified the effects of 1 μM ACh and 1 μM NA on the intrinsic action potentials of sinoatrial (SA) nodal and atrial myocytes. Single cells were enzymatically isolated from the SA node or from the left atrium of rabbit hearts. Action potentials were recorded using the amphotericin-perforated patch-clamp technique in the absence and presence of ACh, NA, or a combination of both. In SA nodal myocytes, ACh increased cycle length and decreased diastolic depolarization rate, whereas NA decreased cycle length and increased diastolic depolarization rate. Both ACh and NA increased maximum upstroke velocity. Furthermore, ACh hyperpolarized the maximum diastolic potential. In atrial myocytes stimulated at 2 Hz, both ACh and NA hyperpolarized the maximum diastolic potential, increased the action potential amplitude, and increased the maximum upstroke velocity. Action potential duration at 50 and 90% repolarization was decreased by ACh, but increased by NA. The effects of both ACh and NA on action potential duration showed a dose dependence in the range of 1-1000 nM, while a clear-cut frequency dependence in the range of 1-4 Hz was absent. Intermediate results were obtained in the combined presence of ACh and NA in both SA nodal and atrial myocytes. Our data uncover the extent to which SA nodal and atrial action potentials are intrinsically dependent on ACh, NA, or a combination of both and may thus guide further experiments with RGS proteins.

Effects of Acetylcholine and Noradrenalin on Action Potentials of Isolated Rabbit Sinoatrial and Atrial Myocytes

PubMed Central

Verkerk, Arie O.; Geuzebroek, Guillaume S. C.; Veldkamp, Marieke W.; Wilders, Ronald

2012-01-01

The autonomic nervous system controls heart rate and contractility through sympathetic and parasympathetic inputs to the cardiac tissue, with acetylcholine (ACh) and noradrenalin (NA) as the chemical transmitters. In recent years, it has become clear that specific Regulators of G protein Signaling proteins (RGS proteins) suppress muscarinic sensitivity and parasympathetic tone, identifying RGS proteins as intriguing potential therapeutic targets. In the present study, we have identified the effects of 1 μM ACh and 1 μM NA on the intrinsic action potentials of sinoatrial (SA) nodal and atrial myocytes. Single cells were enzymatically isolated from the SA node or from the left atrium of rabbit hearts. Action potentials were recorded using the amphotericin-perforated patch-clamp technique in the absence and presence of ACh, NA, or a combination of both. In SA nodal myocytes, ACh increased cycle length and decreased diastolic depolarization rate, whereas NA decreased cycle length and increased diastolic depolarization rate. Both ACh and NA increased maximum upstroke velocity. Furthermore, ACh hyperpolarized the maximum diastolic potential. In atrial myocytes stimulated at 2 Hz, both ACh and NA hyperpolarized the maximum diastolic potential, increased the action potential amplitude, and increased the maximum upstroke velocity. Action potential duration at 50 and 90% repolarization was decreased by ACh, but increased by NA. The effects of both ACh and NA on action potential duration showed a dose dependence in the range of 1–1000 nM, while a clear-cut frequency dependence in the range of 1–4 Hz was absent. Intermediate results were obtained in the combined presence of ACh and NA in both SA nodal and atrial myocytes. Our data uncover the extent to which SA nodal and atrial action potentials are intrinsically dependent on ACh, NA, or a combination of both and may thus guide further experiments with RGS proteins. PMID:22754533

The effects of gentamicin and penicillin/streptomycin on the electrophysiology of human induced pluripotent stem cell-derived cardiomyocytes in manual patch clamp and multi-electrode array system.

PubMed

Hyun, Soo-Wang; Kim, Bo-Ram; Lin, Dan; Hyun, Sung-Ae; Yoon, Seong Shoon; Seo, Joung-Wook

Cell culture media usually contains antibiotics including gentamicin or penicillin/streptomycin (PS) to protect cells from bacterial contamination. However, little is known about the effects of antibiotics on action potential and field potential parameters in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). The present study examined the effects of gentamicin (10, 25, and 50μg/ml) and PS (50, 100, and 200U/μg/ml) on electrophysiological activity in spontaneously beating hiPSC-CMs using manual patch clamp and multi-electrode array. We also measured mRNA expression of cardiac ion channels in hiPSC-CMs grown in media with or without gentamicin (25μg/ml) using reverse transcription-polymerase chain reaction. We recorded action potential and field potential of hiPSC-CMs grown in the presence or absence of gentamicin or PS. We also observed action potential parameters in hiPSC-CMs after short-term treatment with these antibiotics. Changes in action potential and field potential parameters were observed in hiPSC-CMs grown in media containing gentamicin or PS. Treatment with PS also affected action potential parameters in hiPSC-CMs. In addition, the mRNA expression of cardiac sodium and potassium ion channels was significantly attenuated in hiPSC-CMs grown in the presence of gentamicin (25μg/ml). The present findings suggested that gentamicin should not be used in the culture media of hiPSC-CMs used for the measurement of electrophysiological parameters. Our findings also suggest that 100U/100μg/ml of PS are the maximum appropriate concentrations of these antibiotics for recording action potential waveform, because they did not influence action potential parameters in these cells. Copyright © 2017. Published by Elsevier Inc.

Beta Function Quintessence Cosmological Parameters and Fundamental Constants I: Power and Inverse Power Law Dark Energy Potentials

NASA Astrophysics Data System (ADS)

Thompson, Rodger I.

2018-04-01

This investigation explores using the beta function formalism to calculate analytic solutions for the observable parameters in rolling scalar field cosmologies. The beta function in this case is the derivative of the scalar ϕ with respect to the natural log of the scale factor a, β (φ )=d φ /d ln (a). Once the beta function is specified, modulo a boundary condition, the evolution of the scalar ϕ as a function of the scale factor is completely determined. A rolling scalar field cosmology is defined by its action which can contain a range of physically motivated dark energy potentials. The beta function is chosen so that the associated "beta potential" is an accurate, but not exact, representation of the appropriate dark energy model potential. The basic concept is that the action with the beta potential is so similar to the action with the model potential that solutions using the beta action are accurate representations of solutions using the model action. The beta function provides an extra equation to calculate analytic functions of the cosmologies parameters as a function of the scale factor that are that are not calculable using only the model action. As an example this investigation uses a quintessence cosmology to demonstrate the method for power and inverse power law dark energy potentials. An interesting result of the investigation is that the Hubble parameter H is almost completely insensitive to the power of the potentials and that ΛCDM is part of the family of quintessence cosmology power law potentials with a power of zero.

Beta function quintessence cosmological parameters and fundamental constants - I. Power and inverse power law dark energy potentials

NASA Astrophysics Data System (ADS)

Thompson, Rodger I.

2018-07-01

This investigation explores using the beta function formalism to calculate analytic solutions for the observable parameters in rolling scalar field cosmologies. The beta function in this case is the derivative of the scalar φ with respect to the natural log of the scale factor a, β (φ)=d φ/d ln (a). Once the beta function is specified, modulo a boundary condition, the evolution of the scalar φ as a function of the scale factor is completely determined. A rolling scalar field cosmology is defined by its action which can contain a range of physically motivated dark energy potentials. The beta function is chosen so that the associated `beta potential' is an accurate, but not exact, representation of the appropriate dark energy model potential. The basic concept is that the action with the beta potential is so similar to the action with the model potential that solutions using the beta action are accurate representations of solutions using the model action. The beta function provides an extra equation to calculate analytic functions of the cosmologies parameters as a function of the scale factor that are not calculable using only the model action. As an example, this investigation uses a quintessence cosmology to demonstrate the method for power and inverse power law dark energy potentials. An interesting result of the investigation is that the Hubble parameter H is almost completely insensitive to the power of the potentials and that Λ cold dark matter is part of the family of quintessence cosmology power-law potentials with a power of zero.

Pulsed magnetic stimulation modifies amplitude of action potentials in vitro via ionic channels-dependent mechanism.

PubMed

Ahmed, Zaghloul; Wieraszko, Andrzej

2015-07-01

This paper investigates the influence of pulsed magnetic fields (PMFs) on amplitude of evoked, compound action potential (CAP) recorded from the segments of sciatic nerve in vitro. PMFs were applied for 30 min at frequency of 0.16 Hz and intensity of 15 mT. In confirmation of our previous reports, PMF exposure enhanced amplitude of CAPs. The effect persisted beyond PMF activation period. As expected, CAP amplitude was attenuated by antagonists of sodium channel, lidocaine, and tetrodotoxin. Depression of the potential by sodium channels antagonists was reversed by subsequent exposure to PMFs. The effect of elevated potassium concentration and veratridine on the action potential was modified by exposure to PMFs as well. Neither inhibitors of protein kinase C and protein kinase A, nor known free radicals scavengers had any effects on PMF action. Possible mechanisms of PMF action are discussed. © 2015 Wiley Periodicals, Inc.

Na and Ca components of action potentials in amphioxus muscle cells

PubMed Central

Hagiwara, S.; Kidokoro, Y.

1971-01-01

1. The ionic mechanism of the action potential produced in lamella-like muscle cells of amphioxus, Branchiostoma californiense, was investigated with intracellular recording and polarization techniques. 2. The resting potential and action potential overshoot in normal saline are -53±5 mV (S.D.) and +29±10 mV (S.D.) respectively. 3. The action potential is eliminated by tetrodotoxin (3 μM) and by replacing NaCl in the saline with Tris-chloride but maintained by replacing Na with Li. 4. After elimination of the normal action potential by tetrodotoxin or replacing Na with Tris, the addition of procaine (7·3 mM) to the external saline makes the membrane capable of producing a regenerative potential change. 5. The peak potential of the regenerative response depends on external Ca concentration in a manner predicted by the Nernst equation with Ca concentrations close to normal. 6. The Ca dependent response is reversibly suppressed by Co or La ions. 7. Similar regenerative responses are obtained when Ca is substituted with Sr or Ba. 8. It is concluded that two independent mechanisms of ionic permeability increase occur in the membrane of amphioxus muscle cell, one to Na and the other to Ca. PMID:5158595

The Global Spike: Conserved Dendritic Properties Enable Unique Ca2+ Spike Generation in Low-Threshold Spiking Neurons.

PubMed

Connelly, William M; Crunelli, Vincenzo; Errington, Adam C

2015-11-25

Low-threshold Ca(2+) spikes (LTS) are an indispensible signaling mechanism for neurons in areas including the cortex, cerebellum, basal ganglia, and thalamus. They have critical physiological roles and have been strongly associated with disorders including epilepsy, Parkinson's disease, and schizophrenia. However, although dendritic T-type Ca(2+) channels have been implicated in LTS generation, because the properties of low-threshold spiking neuron dendrites are unknown, the precise mechanism has remained elusive. Here, combining data from fluorescence-targeted dendritic recordings and Ca(2+) imaging from low-threshold spiking cells in rat brain slices with computational modeling, the cellular mechanism responsible for LTS generation is established. Our data demonstrate that key somatodendritic electrical conduction properties are highly conserved between glutamatergic thalamocortical neurons and GABAergic thalamic reticular nucleus neurons and that these properties are critical for LTS generation. In particular, the efficiency of soma to dendrite voltage transfer is highly asymmetric in low-threshold spiking cells, and in the somatofugal direction, these neurons are particularly electrotonically compact. Our data demonstrate that LTS have remarkably similar amplitudes and occur synchronously throughout the dendritic tree. In fact, these Ca(2+) spikes cannot occur locally in any part of the cell, and hence we reveal that LTS are generated by a unique whole-cell mechanism that means they always occur as spatially global spikes. This all-or-none, global electrical and biochemical signaling mechanism clearly distinguishes LTS from other signals, including backpropagating action potentials and dendritic Ca(2+)/NMDA spikes, and has important consequences for dendritic function in low-threshold spiking neurons. Low-threshold Ca(2+) spikes (LTS) are critical for important physiological processes, including generation of sleep-related oscillations, and are implicated in disorders including epilepsy, Parkinson's disease, and schizophrenia. However, the mechanism underlying LTS generation in neurons, which is thought to involve dendritic T-type Ca(2+) channels, has remained elusive due to a lack of knowledge of the dendritic properties of low-threshold spiking cells. Combining dendritic recordings, two-photon Ca(2+) imaging, and computational modeling, this study reveals that dendritic properties are highly conserved between two prominent low-threshold spiking neurons and that these properties underpin a whole-cell somatodendritic spike generation mechanism that makes the LTS a unique global electrical and biochemical signal in neurons. Copyright © 2015 Connelly et al.

7 CFR 1945.19 - Reporting potential natural disasters and initial actions.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 13 2012-01-01 2012-01-01 false Reporting potential natural disasters and initial... Assistance-General § 1945.19 Reporting potential natural disasters and initial actions. (a) Purpose. The purpose of reporting potential natural disasters is to provide a systematic procedure for rapid reporting...

7 CFR 1945.19 - Reporting potential natural disasters and initial actions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 13 2011-01-01 2009-01-01 true Reporting potential natural disasters and initial... Assistance-General § 1945.19 Reporting potential natural disasters and initial actions. (a) Purpose. The purpose of reporting potential natural disasters is to provide a systematic procedure for rapid reporting...

7 CFR 1945.19 - Reporting potential natural disasters and initial actions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 13 2010-01-01 2009-01-01 true Reporting potential natural disasters and initial... Assistance-General § 1945.19 Reporting potential natural disasters and initial actions. (a) Purpose. The purpose of reporting potential natural disasters is to provide a systematic procedure for rapid reporting...

Action potentials recorded from bundles of very thin, gray matter axons in rat cerebellar slices using a grease-gap method.

PubMed

Palani, Damodharan; Pekala, Dobromila; Baginskas, Armantas; Szkudlarek, Hanna; Raastad, Morten

2012-07-15

We investigated the ability of a grease-gap method to record fast and slow changes of the membrane potential from bundles of gray matter axons. Their membrane potentials are of particular interest because these axons are different from most axons that have been investigated using intra-axonal or gap techniques. One of the main differences is that gray matter axons typically have closely spaced presynaptic specializations, called boutons or varicosities, distributed along their entire paths. In response to electrical activation of bundles of parallel fiber axons we were able to record small (128-416μV) but stable signals that we show most likely represented a fraction of the trans-membrane action potentials. A less-than 100% fraction prevents measurements of absolute values for membrane potentials, but the good signal-to-noise ratio (typically 10-16) allows detection of changes in resting membrane potential, action potentials and their after-potentials. Because very little is known about the shape of action potentials and after-potentials in these axons we used several independent methods to make it likely that the grease-gap signal was of intra-axonal origin. We demonstrate the utility of the method by showing that the action potentials in cerebellar parallel fibers and hippocampal Schaffer collaterals had a slowly decaying, depolarized after-potential. The method is ideal for pharmacological tests, which we demonstrate by showing that the slow after-potential was sensitive to 4-AP, and that the membrane potential was reduced by 200μM Ba(2+). Copyright © 2012 Elsevier B.V. All rights reserved.

A device for emulating cuff recordings of action potentials propagating along peripheral nerves.

PubMed

Rieger, Robert; Schuettler, Martin; Chuang, Sheng-Chih

2014-09-01

This paper describes a device that emulates propagation of action potentials along a peripheral nerve, suitable for reproducible testing of bio-potential recording systems using nerve cuff electrodes. The system is a microcontroller-based stand-alone instrument which uses established nerve and electrode models to represent neural activity of real nerves recorded with a nerve cuff interface, taking into consideration electrode impedance, voltages picked up by the electrodes, and action potential propagation characteristics. The system emulates different scenarios including compound action potentials with selectable propagation velocities and naturally occurring nerve traffic from different velocity fiber populations. Measured results from a prototype implementation are reported and compared with in vitro recordings from Xenopus Laevis frog sciatic nerve, demonstrating that the electrophysiological setting is represented to a satisfactory degree, useful for the development, optimization and characterization of future recording systems.

Nerve Impulses in Plants

ERIC Educational Resources Information Center

Blatt, F. J.

1974-01-01

Summarizes research done on the resting and action potential of nerve impulses, electrical excitation of nerve cells, electrical properties of Nitella, and temperature effects on action potential. (GS)

Central Nervous System-Toxic Lidocaine Concentrations Unmask L-Type Ca²⁺ Current-Mediated Action Potentials in Rat Thalamocortical Neurons: An In Vitro Mechanism of Action Study.

PubMed

Putrenko, Igor; Ghavanini, Amer A; Meyer Schöniger, Katrin S; Schwarz, Stephan K W

2016-05-01

High systemic lidocaine concentrations exert well-known toxic effects on the central nervous system (CNS), including seizures, coma, and death. The underlying mechanisms are still largely obscure, and the actions of lidocaine on supraspinal neurons have received comparatively little study. We recently found that lidocaine at clinically neurotoxic concentrations increases excitability mediated by Na-independent, high-threshold (HT) action potential spikes in rat thalamocortical neurons. Our goal in this study was to characterize these spikes and test the hypothesis that they are generated by HT Ca currents, previously implicated in neurotoxicity. We also sought to identify and isolate the specific underlying subtype of Ca current. We investigated the actions of lidocaine in the CNS-toxic concentration range (100 μM-1 mM) on ventrobasal thalamocortical neurons in rat brain slices in vitro, using whole-cell patch-clamp recordings aided by differential interference contrast infrared videomicroscopy. Drugs were bath applied; action potentials were generated using current clamp protocols, and underlying currents were identified and isolated with ion channel blockers and electrolyte substitution. Lidocaine (100 μM-1 mM) abolished Na-dependent tonic firing in all neurons tested (n = 46). However, in 39 of 46 (85%) neurons, lidocaine unmasked evoked HT action potentials with lower amplitudes and rates of de-/repolarization compared with control. These HT action potentials remained during the application of tetrodotoxin (600 nM), were blocked by Cd (50 μM), and disappeared after superfusion with an extracellular solution deprived of Ca. These features implied that the unmasked potentials were generated by high-voltage-activated Ca channels and not by Na channels. Application of the L-type Ca channel blocker, nifedipine (5 μM), completely blocked the HT potentials, whereas the N-type Ca channel blocker, ω-conotoxin GVIA (1 μM), had little effect. At clinically CNS-toxic concentrations, lidocaine unmasked in thalamocortical neurons evoked HT action potentials mediated by the L-type Ca current while substantially suppressing Na-dependent excitability. On the basis of the known role of an increase in intracellular Ca in the pathogenesis of local anesthetic neurotoxicity, this novel action represents a plausible contributing candidate mechanism for lidocaine's CNS toxicity in vivo.

An intracellular analysis of the visual responses of neurones in cat visual cortex.

PubMed Central

Douglas, R J; Martin, K A; Whitteridge, D

1991-01-01

1. Extracellular and intracellular recordings were made from neurones in the visual cortex of the cat in order to compare the subthreshold membrane potentials, reflecting the input to the neurone, with the output from the neurone seen as action potentials. 2. Moving bars and edges, generated under computer control, were used to stimulate the neurones. The membrane potential was digitized and averaged for a number of trials after stripping the action potentials. Comparison of extracellular and intracellular discharge patterns indicated that the intracellular impalement did not alter the neurones' properties. Input resistance of the neurone altered little during stable intracellular recordings (30 min-2 h 50 min). 3. Intracellular recordings showed two distinct patterns of membrane potential changes during optimal visual stimulation. The patterns corresponded closely to the division of S-type (simple) and C-type (complex) receptive fields. Simple cells had a complex pattern of membrane potential fluctuations, involving depolarizations alternating with hyperpolarizations. Complex cells had a simple single sustained plateau of depolarization that was often followed but not preceded by a hyperpolarization. In both simple and complex cells the depolarizations led to action potential discharges. The hyperpolarizations were associated with inhibition of action potential discharge. 4. Stimulating simple cells with non-optimal directions of motion produced little or no hyperpolarization of the membrane in most cases, despite a lack of action potential output. Directional complex cells always produced a single plateau of depolarization leading to action potential discharge in both the optimal and non-optimal directions of motion. The directionality could not be predicted on the basis of the position of the hyperpolarizing inhibitory potentials found in the optimal direction. 5. Stimulation of simple cells with non-optimal orientations occasionally produced slight hyperpolarizations and inhibition of action potential discharge. Complex cells, which had broader orientation tuning than simple cells, could show marked hyperpolarization for non-optimal orientations, but this was not generally the case. 6. The data do not support models of directionality and orientation that rely solely on strong inhibitory mechanisms to produce stimulus selectivity. PMID:1804981

Staff Handbook on Natural Gas.

ERIC Educational Resources Information Center

Gorges, H. A., Ed.; Raine, L. P., Ed.

The Department of Commerce created a Natural Gas Action Group early in the fall of 1975 to assist industrial firms and the communities they serve to cope with the effects of potentially severe and crippling curtailment situations. This action group was trained to assess a specific local situation, review the potential for remedial action and…

75 FR 43072 - Trichoderma Hamatum Isolate 382; Exemption from the Requirement of a Tolerance

Federal Register 2010, 2011, 2012, 2013, 2014

2010-07-23

... Information A. Does this Action Apply to Me? You may be potentially affected by this action if you are an agricultural producer, food manufacturer, or pesticide manufacturer. Potentially affected entities may include... exhaustive, but rather provides a guide for readers regarding entities likely to be affected by this action...

Remodelling of action potential and intracellular calcium cycling dynamics during subacute myocardial infarction promotes ventricular arrhythmias in Langendorff-perfused rabbit hearts

PubMed Central

Chou, Chung-Chuan; Zhou, Shengmei; Hayashi, Hideki; Nihei, Motoki; Liu, Yen-Bin; Wen, Ming-Shien; Yeh, San-Jou; Fishbein, Michael C; Weiss, James N; Lin, Shien-Fong; Wu, Delon; Chen, Peng-Sheng

2007-01-01

We hypothesize that remodelling of action potential and intracellular calcium (Cai) dynamics in the peri-infarct zone contributes to ventricular arrhythmogenesis in the postmyocardial infarction setting. To test this hypothesis, we performed simultaneous optical mapping of Cai and membrane potential (Vm) in the left ventricle in 15 rabbit hearts with myocardial infarction for 1 week. Ventricular premature beats frequently originated from the peri-infarct zone, and 37% showed elevation of Cai prior to Vm depolarization, suggesting reverse excitation–contraction coupling as their aetiology. During electrically induced ventricular fibrillation, the highest dominant frequency was in the peri-infarct zone in 61 of 70 episodes. The site of highest dominant frequency had steeper action potential duration restitution and was more susceptible to pacing-induced Cai alternans than sites remote from infarct. Wavebreaks during ventricular fibrillation tended to occur at sites of persistently elevated Cai. Infusion of propranolol flattened action potential duration restitution, reduced wavebreaks and converted ventricular fibrillation to ventricular tachycardia. We conclude that in the subacute phase of myocardial infarction, the peri-infarct zone exhibits regions with steep action potential duration restitution slope and unstable Cai dynamics. These changes may promote ventricular extrasystoles and increase the incidence of wavebreaks during ventricular fibrillation. Whereas increased tissue heterogeneity after subacute myocardial infarction creates a highly arrhythmogenic substrate, dynamic action potential and Cai cycling remodelling also contribute to the initiation and maintenance of ventricular fibrillation in this setting. PMID:17272354

Components of action potential repolarization in cerebellar parallel fibres.

PubMed

Pekala, Dobromila; Baginskas, Armantas; Szkudlarek, Hanna J; Raastad, Morten

2014-11-15

Repolarization of the presynaptic action potential is essential for transmitter release, excitability and energy expenditure. Little is known about repolarization in thin, unmyelinated axons forming en passant synapses, which represent the most common type of axons in the mammalian brain's grey matter.We used rat cerebellar parallel fibres, an example of typical grey matter axons, to investigate the effects of K(+) channel blockers on repolarization. We show that repolarization is composed of a fast tetraethylammonium (TEA)-sensitive component, determining the width and amplitude of the spike, and a slow margatoxin (MgTX)-sensitive depolarized after-potential (DAP). These two components could be recorded at the granule cell soma as antidromic action potentials and from the axons with a newly developed miniaturized grease-gap method. A considerable proportion of fast repolarization remained in the presence of TEA, MgTX, or both. This residual was abolished by the addition of quinine. The importance of proper control of fast repolarization was demonstrated by somatic recordings of antidromic action potentials. In these experiments, the relatively broad K(+) channel blocker 4-aminopyridine reduced the fast repolarization, resulting in bursts of action potentials forming on top of the DAP. We conclude that repolarization of the action potential in parallel fibres is supported by at least three groups of K(+) channels. Differences in their temporal profiles allow relatively independent control of the spike and the DAP, whereas overlap of their temporal profiles provides robust control of axonal bursting properties.

Effects of pioglitazone on cardiac ion currents and action potential morphology in canine ventricular myocytes.

PubMed

Kistamás, Kornél; Szentandrássy, Norbert; Hegyi, Bence; Ruzsnavszky, Ferenc; Váczi, Krisztina; Bárándi, László; Horváth, Balázs; Szebeni, Andrea; Magyar, János; Bányász, Tamás; Kecskeméti, Valéria; Nánási, Péter P

2013-06-15

Despite its widespread therapeutical use there is little information on the cellular cardiac effects of the antidiabetic drug pioglitazone in larger mammals. In the present study, therefore, the concentration-dependent effects of pioglitazone on ion currents and action potential configuration were studied in isolated canine ventricular myocytes using standard microelectrode, conventional whole cell patch clamp, and action potential voltage clamp techniques. Pioglitazone decreased the maximum velocity of depolarization and the amplitude of phase-1 repolarization at concentrations ≥3 μM. Action potentials were shortened by pioglitazone at concentrations ≥10 μM, which effect was accompanied with significant reduction of beat-to-beat variability of action potential duration. Several transmembrane ion currents, including the transient outward K(+) current (Ito), the L-type Ca(2+) current (ICa), the rapid and slow components of the delayed rectifier K(+) current (IKr and IKs, respectively), and the inward rectifier K(+) current (IK1) were inhibited by pioglitazone under conventional voltage clamp conditions. Ito was blocked significantly at concentrations ≥3 μM, ICa, IKr, IKs at concentrations ≥10 μM, while IK1 at concentrations ≥30 μM. Suppression of Ito, ICa, IKr, and IK1 has been confirmed also under action potential voltage clamp conditions. ATP-sensitive K(+) current, when activated by lemakalim, was effectively blocked by pioglitazone. Accordingly, action potentials were prolonged by 10 μM pioglitazone when the drug was applied in the presence of lemakalim. All these effects developed rapidly and were readily reversible upon washout. In conclusion, pioglitazone seems to be a harmless agent at usual therapeutic concentrations. Copyright © 2013 Elsevier B.V. All rights reserved.

Activation of cannabinoid CB1 receptors modulates evoked action potentials in rat retinal ganglion cells.

PubMed

Jiang, Shu-Xia; Li, Qian; Wang, Xiao-Han; Li, Fang; Wang, Zhong-Feng

2013-08-25

Activation of cannabinoid CB1 receptors (CB1Rs) regulates a variety of physiological functions in the vertebrate retina through modulating various types of ion channels. The aim of the present study was to investigate the effects of this receptor on cell excitability of rat retinal ganglion cells (RGCs) in retinal slices using whole-cell patch-clamp techniques. The results showed that under current-clamped condition perfusing WIN55212-2 (WIN, 5 μmol/L), a CB1R agonist, did not significantly change the spontaneous firing frequency and resting membrane potential of RGCs. In the presence of cocktail synaptic blockers, including excitatory postsynaptic receptor blockers CNQX and D-APV, and inhibitory receptor blockers bicuculline and strychnine, perfusion of WIN (5 μmol/L) hardly changed the frequencies of evoked action potentials by a series of positive current injection (from +10 to +100 pA). Phase-plane plot analysis showed that both average threshold voltage for triggering action potential and delay time to reach threshold voltage were not affected by WIN. However, WIN significantly decreased +dV/dtmax and -dV/dtmax of action potentials, suggestive of reduced rising and descending velocities of action potentials. The effects of WIN were reversed by co-application of SR141716, a CB1R selective antagonist. Moreover, WIN did not influence resting membrane potential of RGCs with synaptic inputs being blocked. These results suggest that activation of CB1Rs may regulate intrinsic excitability of rat RGCs through modulating evoked action potentials.

Strategies for improving neural signal detection using a neural-electronic interface.

PubMed

Szlavik, Robert B

2003-03-01

There have been various theoretical and experimental studies presented in the literature that focus on interfacing neurons with discrete electronic devices, such as transistors. From both a theoretical and experimental perspective, these studies have emphasized the variability in the characteristics of the detected action potential from the nerve cell. The demonstrated lack of reproducible fidelity of the nerve cell action potential at the device junction would make it impractical to implement these devices in any neural prosthetic application where reliable detection of the action potential was a prerequisite. In this study, the effects of several different physical parameters on the fidelity of the detected action potential at the device junction are investigated and discussed. The impact of variations in the extracellular resistivity, which directly affects the junction seal resistance, is studied along with the impact of variable nerve cell membrane capacitance and variations in the injected charge. These parameters are discussed in the context of their suitability to design manipulation for the purpose of improving the fidelity of the detected neural action potential. In addition to investigating the effects of variations in these parameters, the applicability of the linear equivalent circuit approach to calculating the junction potential is investigated.

Simulation of action potentials from metabolically impaired cardiac myocytes. Role of ATP-sensitive K+ current.

PubMed

Ferrero, J M; Sáiz, J; Ferrero, J M; Thakor, N V

1996-08-01

The role of the ATP-sensitive K+ current (IK-ATP) and its contribution to electrophysiological changes that occur during metabolic impairment in cardiac ventricular myocytes is still being discussed. The aim of this work was to quantitatively study this issue by using computer modeling. A model of IK-ATP is formulated and incorporated into the Luo-Rudy ionic model of the ventricular action potential. Action potentials under different degrees of activation of IK-ATP are simulated. Our results show that in normal ionic concentrations, only approximately 0.6% of the KATP channels, when open, should account for a 50% reduction in action potential duration. However, increased levels of intracellular Mg2+ counteract this shortening. Under conditions of high [K+]0, such as those found in early ischemia, the activation of only approximately 0.4% of the KATP channels could account for a 50% reduction in action potential duration. Thus, our results suggest that opening of IK-ATP channels should play a significant role in action potential shortening during hypoxic/ischemic episodes, with the fraction of open channels involved being very low ( < 1%). However, the results of the model suggest that activation of IK-ATP alone does not quantitatively account for the observed K+ efflux in metabolically impaired cardiac myocytes. Mechanisms other than KATP channel activation should be responsible for a significant part of the K+ efflux measured in hypoxic/ischemic situations.

Peripheral nerve recruitment curve using near-infrared stimulation

NASA Astrophysics Data System (ADS)

Dautrebande, Marie; Doguet, Pascal; Gorza, Simon-Pierre; Delbeke, Jean; Nonclercq, Antoine

2018-02-01

In the context of near-infrared neurostimulation, we report on an experimental hybrid electrode allowing for simultaneous photonic or electrical neurostimulation and for electrical recording of evoked action potentials. The electrode includes three contacts and one optrode. The optrode is an opening in the cuff through which the tip of an optical fibre is held close to the epineurium. Two contacts provide action potential recording. The remaining contact, together with a remote subcutaneous electrode, is used for electric stimulation which allows periodical assessment of the viability of the nerve during the experiment. A 1470 nm light source was used to stimulate a mouse sciatic nerve. Neural action potentials were not successfully recorded because of the electrical noise so muscular activity was used to reflect the motor fibres stimulation. A recruitment curve was obtained by stimulating with photonic pulses of same power and increasing duration and recording the evoked muscular action potentials. Motor fibres can be recruited with radiant exposures between 0.05 and 0.23 J/cm2 for pulses in the 100 to 500 μs range. Successful stimulation at short duration and at a commercial wavelength is encouraging in the prospect of miniaturisation and practical applications. Motor fibres recruitment curve is a first step in an ongoing research work. Neural action potential acquisition will be improved, with aim to shed light on the mechanism of action potential initiation under photonic stimulation.

Action Learning: Avoiding Conflict or Enabling Action

ERIC Educational Resources Information Center

Corley, Aileen; Thorne, Ann

2006-01-01

Action learning is based on the premise that action and learning are inextricably entwined and it is this potential, to enable action, which has contributed to the growth of action learning within education and management development programmes. However has this growth in action learning lead to an evolution or a dilution of Revan's classical…

Action Learning: Potential for Inner City Youth

ERIC Educational Resources Information Center

Epps, Edgar G.

1974-01-01

Working class and minority participation in action-learning poses potential problems likely to be overlooked by program planners. This presentation reveals the trouble spots and offers constructive suggestions. (Editor)

Onset dynamics of action potentials in rat neocortical neurons and identified snail neurons: quantification of the difference.

PubMed

Volgushev, Maxim; Malyshev, Aleksey; Balaban, Pavel; Chistiakova, Marina; Volgushev, Stanislav; Wolf, Fred

2008-04-09

The generation of action potentials (APs) is a key process in the operation of nerve cells and the communication between neurons. Action potentials in mammalian central neurons are characterized by an exceptionally fast onset dynamics, which differs from the typically slow and gradual onset dynamics seen in identified snail neurons. Here we describe a novel method of analysis which provides a quantitative measure of the onset dynamics of action potentials. This method captures the difference between the fast, step-like onset of APs in rat neocortical neurons and the gradual, exponential-like AP onset in identified snail neurons. The quantitative measure of the AP onset dynamics, provided by the method, allows us to perform quantitative analyses of factors influencing the dynamics.

Onset Dynamics of Action Potentials in Rat Neocortical Neurons and Identified Snail Neurons: Quantification of the Difference

PubMed Central

Volgushev, Maxim; Malyshev, Aleksey; Balaban, Pavel; Chistiakova, Marina; Volgushev, Stanislav; Wolf, Fred

2008-01-01

The generation of action potentials (APs) is a key process in the operation of nerve cells and the communication between neurons. Action potentials in mammalian central neurons are characterized by an exceptionally fast onset dynamics, which differs from the typically slow and gradual onset dynamics seen in identified snail neurons. Here we describe a novel method of analysis which provides a quantitative measure of the onset dynamics of action potentials. This method captures the difference between the fast, step-like onset of APs in rat neocortical neurons and the gradual, exponential-like AP onset in identified snail neurons. The quantitative measure of the AP onset dynamics, provided by the method, allows us to perform quantitative analyses of factors influencing the dynamics. PMID:18398478

Effect of Detergent on Electrical Properties of Squid Axon Membrane

PubMed Central

Kishimoto, Uichiro; Adelman, William J.

1964-01-01

The effects of detergents on squid giant axon action and resting potentials as well as membrane conductances in the voltage clamp have been studied. Anionic detergents (sodium lauryl sulfate, 0.1 to 1.0 mM; dimethyl benzene sulfonate, 1 to 20 mM, pH 7.6) cause a temporary increase and a later decrease of action potential height and the value of the resting potential. Cationic detergent (cetyl trimethyl ammonium chloride, 6 x 10-5 M or more, pH 7.6) generally brings about immediate and irreversible decreases in the action and resting potentials. Non-ionic detergent (tween 80, 0.1 M, pH 7.6) causes a slight reversible reduction of action potential height without affecting the value of the resting potential. Both anionic and cationic detergents generally decrease the sodium and potassium conductances irreversibly. The effect of non-ionic detergent is to decrease the sodium conductance reversibly, leaving the potassium conductance almost unchanged. PMID:14158665

The influence of action observation on action execution: Dissociating the contribution of action on perception, perception on action, and resolving conflict.

PubMed

Deschrijver, Eliane; Wiersema, Jan R; Brass, Marcel

2017-04-01

For more than 15 years, motor interference paradigms have been used to investigate the influence of action observation on action execution. Most research on so-called automatic imitation has focused on variables that play a modulating role or investigated potential confounding factors. Interestingly, furthermore, a number of functional magnetic resonance imaging (fMRI) studies have tried to shed light on the functional mechanisms and neural correlates involved in imitation inhibition. However, these fMRI studies, presumably due to poor temporal resolution, have primarily focused on high-level processes and have neglected the potential role of low-level motor and perceptual processes. In the current EEG study, we therefore aimed to disentangle the influence of low-level perceptual and motoric mechanisms from high-level cognitive mechanisms. We focused on potential congruency differences in the visual N190 - a component related to the processing of biological motion, the Readiness Potential - a component related to motor preparation, and the high-level P3 component. Interestingly, we detected congruency effects in each of these components, suggesting that the interference effect in an automatic imitation paradigm is not only related to high-level processes such as self-other distinction but also to more low-level influences of perception on action and action on perception. Moreover, we documented relationships of the neural effects with (autistic) behavior.

Electronic nose for the identification of pig feeding and ripening time in Iberian hams.

PubMed

Santos, J P; García, M; Aleixandre, M; Horrillo, M C; Gutiérrez, J; Sayago, I; Fernández, M J; Arés, L

2004-03-01

An electronic nose system to control the processing of dry-cured Iberian ham is presented. The sensors involved are tin oxide semiconductors thin films. They were prepared by RF sputtering. Some of the sensors were doped with metal catalysts as Pt and Pd, in order to improve the selectivity of the sensors. The multisensor with 16 semiconductor sensors, gave different responses from two types of dry-cured Iberian hams which differ in the feeding and curing time. The data has been analysed using the PCA (principal component analysis) and backpropagation and probabilistic neural networks. The analysis shows that different types of Iberian ham can be discriminated and identified successfully.

Deep learning

NASA Astrophysics Data System (ADS)

Lecun, Yann; Bengio, Yoshua; Hinton, Geoffrey

2015-05-01

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

Aircraft Aerodynamic Parameter Detection Using Micro Hot-Film Flow Sensor Array and BP Neural Network Identification

PubMed Central

Que, Ruiyi; Zhu, Rong

2012-01-01

Air speed, angle of sideslip and angle of attack are fundamental aerodynamic parameters for controlling most aircraft. For small aircraft for which conventional detecting devices are too bulky and heavy to be utilized, a novel and practical methodology by which the aerodynamic parameters are inferred using a micro hot-film flow sensor array mounted on the surface of the wing is proposed. A back-propagation neural network is used to model the coupling relationship between readings of the sensor array and aerodynamic parameters. Two different sensor arrangements are tested in wind tunnel experiments and dependence of the system performance on the sensor arrangement is analyzed. PMID:23112638

Aircraft aerodynamic parameter detection using micro hot-film flow sensor array and BP neural network identification.

PubMed

Que, Ruiyi; Zhu, Rong

2012-01-01

Air speed, angle of sideslip and angle of attack are fundamental aerodynamic parameters for controlling most aircraft. For small aircraft for which conventional detecting devices are too bulky and heavy to be utilized, a novel and practical methodology by which the aerodynamic parameters are inferred using a micro hot-film flow sensor array mounted on the surface of the wing is proposed. A back-propagation neural network is used to model the coupling relationship between readings of the sensor array and aerodynamic parameters. Two different sensor arrangements are tested in wind tunnel experiments and dependence of the system performance on the sensor arrangement is analyzed.

Static and transient performance prediction for CFB boilers using a Bayesian-Gaussian Neural Network

NASA Astrophysics Data System (ADS)

Ye, Haiwen; Ni, Weidou

1997-06-01

A Bayesian-Gaussian Neural Network (BGNN) is put forward in this paper to predict the static and transient performance of Circulating Fluidized Bed (CFB) boilers. The advantages of this network over Back-Propagation Neural Networks (BPNNs), easier determination of topology, simpler and time saving in training process as well as self-organizing ability, make this network more practical in on-line performance prediction for complicated processes. Simulation shows that this network is comparable to the BPNNs in predicting the performance of CFB boilers. Good and practical on-line performance predictions are essential for operation guide and model predictive control of CFB boilers, which are under research by the authors.

Neural networks predict tomato maturity stage

NASA Astrophysics Data System (ADS)

Hahn, Federico

1999-03-01

Almost 40% of the total horticultural produce exported from Mexico the USA is tomato, and quality is fundamental for maintaining the market. Many fruits packed at the green-mature stage do not mature towards a red color as they were harvested before achieving its physiological maturity. Tomato gassed for advancing maturation does not respond on those fruits, and repacking is necessary at terminal markets, causing losses to the producer. Tomato spectral signatures are different on each maturity stage and tomato size was poorly correlated against peak wavelengths. A back-propagation neural network was used to predict tomato maturity using reflectance ratios as inputs. Higher success rates were achieved on tomato maturity stage recognition with neural networks than with discriminant analysis.

Neurocontrol and neurobiology - New developments and connections

NASA Technical Reports Server (NTRS)

Werbos, Paul J.; Pellionisz, Andras J.

1992-01-01

At McDonnell-Douglas, controllers which combine adaptive critic networks with the use of backpropagation in real time have solved difficult control problems crucial to the feasibility of building the National Aerospace Plane (NASP) able to reach earth orbit. As details emerged, parallels to neurobiology have grown stronger and have begun to lead to empirical possibilities of importance to neuroscience. This has led to thoughts of institutional collaboration facilitating what could become a Newtonian revolution in neuroscience, with cognitive implications as well. The authors elaborate on each of these points. The topics discussed are recent progress in neurocontrol; progress in optimization and reinforcement learning; implications for neurobiology and science policy; and a new view of the brain.

Design and evaluation of a robust dynamic neurocontroller for a multivariable aircraft control problem

NASA Technical Reports Server (NTRS)

Troudet, T.; Garg, S.; Merrill, W.

1992-01-01

The design of a dynamic neurocontroller with good robustness properties is presented for a multivariable aircraft control problem. The internal dynamics of the neurocontroller are synthesized by a state estimator feedback loop. The neurocontrol is generated by a multilayer feedforward neural network which is trained through backpropagation to minimize an objective function that is a weighted sum of tracking errors, and control input commands and rates. The neurocontroller exhibits good robustness through stability margins in phase and vehicle output gains. By maintaining performance and stability in the presence of sensor failures in the error loops, the structure of the neurocontroller is also consistent with the classical approach of flight control design.

Deep learning.

PubMed

LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey

2015-05-28

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

Associative, Bidirectional Changes in Neural Signaling Utilizing NMDA Receptor- and Endocannabinoid-Dependent Mechanisms

ERIC Educational Resources Information Center

Li, Qin; Burrell, Brian D.

2011-01-01

Persistent, bidirectional changes in synaptic signaling (that is, potentiation and depression of the synapse) can be induced by the precise timing of individual pre- and postsynaptic action potentials. However, far less attention has been paid to the ability of paired trains of action potentials to elicit persistent potentiation or depression. We…

Final Environmental Assessment: Addressing An Army and Air Force Exchange Service (AAFES) Lifestyle Center at Eglin Air Force Base, Florida

DTIC Science & Technology

2008-11-01

the proposed site has the potential for adverse effects on surface water bodies in the event of a spill or uncontrolled erosion. Implementation of...inclusion of a No Action Alternative against which potential effects can be compared. While the No Action Alternative would not satisfy the purpose... potential effects on project site and adjacent land uses. The foremost factor affecting a proposed action in terms of land use is its compliance

Localization of effective actions in open superstring field theory

NASA Astrophysics Data System (ADS)

Maccaferri, Carlo; Merlano, Alberto

2018-03-01

We consider the construction of the algebraic part of D-branes tree-level effective action from Berkovits open superstring field theory. Applying this construction to the quartic potential of massless fields carrying a specific worldsheet charge, we show that the full contribution to the potential localizes at the boundary of moduli space, reducing to elementary two-point functions. As examples of this general mechanism, we show how the Yang-Mills quartic potential and the instanton effective action of a Dp/D( p - 4) system are reproduced.

77 FR 45535 - Aldicarb; Proposed Tolerance Actions

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-01

... Aldicarb; Proposed Tolerance Actions AGENCY: Environmental Protection Agency (EPA). ACTION: Proposed rule... Information A. Does this action apply to me? You may be potentially affected by this action if you are an... exhaustive, but rather provides a guide for readers regarding entities likely to be affected by this action...

Neural plasticity and behavior - sixty years of conceptual advances.

PubMed

Sweatt, J David

2016-10-01

This brief review summarizes 60 years of conceptual advances that have demonstrated a role for active changes in neuronal connectivity as a controller of behavior and behavioral change. Seminal studies in the first phase of the six-decade span of this review firmly established the cellular basis of behavior - a concept that we take for granted now, but which was an open question at the time. Hebbian plasticity, including long-term potentiation and long-term depression, was then discovered as being important for local circuit refinement in the context of memory formation and behavioral change and stabilization in the mammalian central nervous system. Direct demonstration of plasticity of neuronal circuit function in vivo, for example, hippocampal neurons forming place cell firing patterns, extended this concept. However, additional neurophysiologic and computational studies demonstrated that circuit development and stabilization additionally relies on non-Hebbian, homoeostatic, forms of plasticity, such as synaptic scaling and control of membrane intrinsic properties. Activity-dependent neurodevelopment was found to be associated with cell-wide adjustments in post-synaptic receptor density, and found to occur in conjunction with synaptic pruning. Pioneering cellular neurophysiologic studies demonstrated the critical roles of transmembrane signal transduction, NMDA receptor regulation, regulation of neural membrane biophysical properties, and back-propagating action potential in critical time-dependent coincidence detection in behavior-modifying circuits. Concerning the molecular mechanisms underlying these processes, regulation of gene transcription was found to serve as a bridge between experience and behavioral change, closing the 'nature versus nurture' divide. Both active DNA (de)methylation and regulation of chromatin structure have been validated as crucial regulators of gene transcription during learning. The discovery of protein synthesis dependence on the acquisition of behavioral change was an influential discovery in the neurochemistry of behavioral modification. Higher order cognitive functions such as decision making and spatial and language learning were also discovered to hinge on neural plasticity mechanisms. The role of disruption of these processes in intellectual disabilities, memory disorders, and drug addiction has recently been clarified based on modern genetic techniques, including in the human. The area of neural plasticity and behavior has seen tremendous advances over the last six decades, with many of those advances being specifically in the neurochemistry domain. This review provides an overview of the progress in the area of neuroplasticity and behavior over the life-span of the Journal of Neurochemistry. To organize the broad literature base, the review collates progress into fifteen broad categories identified as 'conceptual advances', as viewed by the author. The fifteen areas are delineated in the figure above. This article is part of the 60th Anniversary special issue. © 2016 International Society for Neurochemistry.

Sodium and potassium conductance changes during a membrane action potential.

PubMed

Bezanilla, F; Rojas, E; Taylor, R E

1970-12-01

1. A method for turning a membrane potential control system on and off in less than 10 musec is described. This method was used to record membrane currents in perfused giant axons from Dosidicus gigas and Loligo forbesi after turning on the voltage clamp system at various times during the course of a membrane action potential.2. The membrane current measured just after the capacity charging transient was found to have an almost linear relation to the controlled membrane potential.3. The total membrane conductance taken from these current-voltage curves was found to have a time course during the action potential similar to that found by Cole & Curtis (1939).4. The instantaneous current voltage curves were linear enough to make it possible to obtain a good estimate of the individual sodium and potassium channel conductances, either algebraically or by clamping to the sodium, or potassium, reversal potentials. Good general agreement was obtained with the predictions of the Hodgkin-Huxley equations.5. We consider these results to constitute the first direct experimental demonstration of the conductance changes to sodium and potassium during the course of an action potential.

Calcium-Induced Calcium Release during Action Potential Firing in Developing Inner Hair Cells

PubMed Central

Iosub, Radu; Avitabile, Daniele; Grant, Lisa; Tsaneva-Atanasova, Krasimira; Kennedy, Helen J.

2015-01-01

In the mature auditory system, inner hair cells (IHCs) convert sound-induced vibrations into electrical signals that are relayed to the central nervous system via auditory afferents. Before the cochlea can respond to normal sound levels, developing IHCs fire calcium-based action potentials that disappear close to the onset of hearing. Action potential firing triggers transmitter release from the immature IHC that in turn generates experience-independent firing in auditory neurons. These early signaling events are thought to be essential for the organization and development of the auditory system and hair cells. A critical component of the action potential is the rise in intracellular calcium that activates both small conductance potassium channels essential during membrane repolarization, and triggers transmitter release from the cell. Whether this calcium signal is generated by calcium influx or requires calcium-induced calcium release (CICR) is not yet known. IHCs can generate CICR, but to date its physiological role has remained unclear. Here, we used high and low concentrations of ryanodine to block or enhance CICR to determine whether calcium release from intracellular stores affected action potential waveform, interspike interval, or changes in membrane capacitance during development of mouse IHCs. Blocking CICR resulted in mixed action potential waveforms with both brief and prolonged oscillations in membrane potential and intracellular calcium. This mixed behavior is captured well by our mathematical model of IHC electrical activity. We perform two-parameter bifurcation analysis of the model that predicts the dependence of IHCs firing patterns on the level of activation of two parameters, the SK2 channels activation and CICR rate. Our data show that CICR forms an important component of the calcium signal that shapes action potentials and regulates firing patterns, but is not involved directly in triggering exocytosis. These data provide important insights into the calcium signaling mechanisms involved in early developmental processes. PMID:25762313

Calcium-Induced calcium release during action potential firing in developing inner hair cells.

PubMed

Iosub, Radu; Avitabile, Daniele; Grant, Lisa; Tsaneva-Atanasova, Krasimira; Kennedy, Helen J

2015-03-10

In the mature auditory system, inner hair cells (IHCs) convert sound-induced vibrations into electrical signals that are relayed to the central nervous system via auditory afferents. Before the cochlea can respond to normal sound levels, developing IHCs fire calcium-based action potentials that disappear close to the onset of hearing. Action potential firing triggers transmitter release from the immature IHC that in turn generates experience-independent firing in auditory neurons. These early signaling events are thought to be essential for the organization and development of the auditory system and hair cells. A critical component of the action potential is the rise in intracellular calcium that activates both small conductance potassium channels essential during membrane repolarization, and triggers transmitter release from the cell. Whether this calcium signal is generated by calcium influx or requires calcium-induced calcium release (CICR) is not yet known. IHCs can generate CICR, but to date its physiological role has remained unclear. Here, we used high and low concentrations of ryanodine to block or enhance CICR to determine whether calcium release from intracellular stores affected action potential waveform, interspike interval, or changes in membrane capacitance during development of mouse IHCs. Blocking CICR resulted in mixed action potential waveforms with both brief and prolonged oscillations in membrane potential and intracellular calcium. This mixed behavior is captured well by our mathematical model of IHC electrical activity. We perform two-parameter bifurcation analysis of the model that predicts the dependence of IHCs firing patterns on the level of activation of two parameters, the SK2 channels activation and CICR rate. Our data show that CICR forms an important component of the calcium signal that shapes action potentials and regulates firing patterns, but is not involved directly in triggering exocytosis. These data provide important insights into the calcium signaling mechanisms involved in early developmental processes. Copyright © 2015 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Power-Law Dynamics of Membrane Conductances Increase Spiking Diversity in a Hodgkin-Huxley Model.

PubMed

Teka, Wondimu; Stockton, David; Santamaria, Fidel

2016-03-01

We studied the effects of non-Markovian power-law voltage dependent conductances on the generation of action potentials and spiking patterns in a Hodgkin-Huxley model. To implement slow-adapting power-law dynamics of the gating variables of the potassium, n, and sodium, m and h, conductances we used fractional derivatives of order η≤1. The fractional derivatives were used to solve the kinetic equations of each gate. We systematically classified the properties of each gate as a function of η. We then tested if the full model could generate action potentials with the different power-law behaving gates. Finally, we studied the patterns of action potential that emerged in each case. Our results show the model produces a wide range of action potential shapes and spiking patterns in response to constant current stimulation as a function of η. In comparison with the classical model, the action potential shapes for power-law behaving potassium conductance (n gate) showed a longer peak and shallow hyperpolarization; for power-law activation of the sodium conductance (m gate), the action potentials had a sharp rise time; and for power-law inactivation of the sodium conductance (h gate) the spikes had wider peak that for low values of η replicated pituitary- and cardiac-type action potentials. With all physiological parameters fixed a wide range of spiking patterns emerged as a function of the value of the constant input current and η, such as square wave bursting, mixed mode oscillations, and pseudo-plateau potentials. Our analyses show that the intrinsic memory trace of the fractional derivative provides a negative feedback mechanism between the voltage trace and the activity of the power-law behaving gate variable. As a consequence, power-law behaving conductances result in an increase in the number of spiking patterns a neuron can generate and, we propose, expand the computational capacity of the neuron.

Imaging a Time-variant Earthquake Focal Region along an Interplate Boundary

NASA Astrophysics Data System (ADS)

Tsuruga, K.; Kasahara, J.; Hasada, Y.; Fujii, N.

2010-12-01

We show a preliminary result of a trial for detecting a time-variant earthquake focal region along an interplate boundary by means of a new imaging method through a numerical simulation. Remarkable seismic reflections from the interplate boundaries of a subducting oceanic plate have been observed in Japan Trench (Mochizuki et al, 2005) and in Nankai Trough (Iidaka et al., 2003). Those strong seismic reflection existing in the current aseismic zones suggest the existence of fluid along the subduction boundary, and it is considered that they closely relate to a future huge earthquake. Seismic ACROSS has a potential to monitor some changes of transfer function along the propagating ray paths, by using an accurately-controlled transmission and receiving of the steady continuous signals repeatedly (Kumazawa et al., 2000). If the physical state in a focal region along the interplate would be changed enough in the time and space, for instance, by increasing or decreasing of fluid flow, we could detect some differences of the amplitude and/or travel-time of the particular reflection phases from the time-variant target region. In this study, we first investigated the seismic characteristics of seismograms and their differences before and after the change of a target region through a numerical simulation. Then, as one of the trials, we attempted to make an image of such time-variant target region by applying a finite-difference back-propagation technique in the time and space to the differences of waveforms (after Kasahara et al., 2010). We here used a 2-D seismic velocity model in the central Japan (Tsuruga et al., 2005), assuming a time-variant target region with a 200-m thickness along a subducting Philippine Sea plate at 30 km in depth. Seismograms were calculated at a 500-m interval for 260 km long by using FDM software (Larsen, 2000), in the case that P- and S-wave velocities (Vp amd Vs) in the target region decreased about 30 % before to after the change (e.g., Vp=3.5 km/s to 2.5 km/s). After applying the new imaging method to the differences between both seismograms at each receiver, it is clear that the remarkable signals related with the target change were focused around the target region during a particular back-propagation time. As a preliminary result, it is not still easy to exactly identify the geometry and shape of the target region. However, we can conclude that it is almost possible to decide the location of the target region by means of an optimized receiver array together with the seismic source which can transmit the accurate and steady signals repeatedly as like as ACROSS even if a single source.

Recursive least-squares learning algorithms for neural networks

NASA Astrophysics Data System (ADS)

Lewis, Paul S.; Hwang, Jenq N.

1990-11-01

This paper presents the development of a pair of recursive least squares (ItLS) algorithms for online training of multilayer perceptrons which are a class of feedforward artificial neural networks. These algorithms incorporate second order information about the training error surface in order to achieve faster learning rates than are possible using first order gradient descent algorithms such as the generalized delta rule. A least squares formulation is derived from a linearization of the training error function. Individual training pattern errors are linearized about the network parameters that were in effect when the pattern was presented. This permits the recursive solution of the least squares approximation either via conventional RLS recursions or by recursive QR decomposition-based techniques. The computational complexity of the update is 0(N2) where N is the number of network parameters. This is due to the estimation of the N x N inverse Hessian matrix. Less computationally intensive approximations of the ilLS algorithms can be easily derived by using only block diagonal elements of this matrix thereby partitioning the learning into independent sets. A simulation example is presented in which a neural network is trained to approximate a two dimensional Gaussian bump. In this example RLS training required an order of magnitude fewer iterations on average (527) than did training with the generalized delta rule (6 1 BACKGROUND Artificial neural networks (ANNs) offer an interesting and potentially useful paradigm for signal processing and pattern recognition. The majority of ANN applications employ the feed-forward multilayer perceptron (MLP) network architecture in which network parameters are " trained" by a supervised learning algorithm employing the generalized delta rule (GDIt) [1 2]. The GDR algorithm approximates a fixed step steepest descent algorithm using derivatives computed by error backpropagatiori. The GDII algorithm is sometimes referred to as the backpropagation algorithm. However in this paper we will use the term backpropagation to refer only to the process of computing error derivatives. While multilayer perceptrons provide a very powerful nonlinear modeling capability GDR training can be very slow and inefficient. In linear adaptive filtering the analog of the GDR algorithm is the leastmean- squares (LMS) algorithm. Steepest descent-based algorithms such as GDR or LMS are first order because they use only first derivative or gradient information about the training error to be minimized. To speed up the training process second order algorithms may be employed that take advantage of second derivative or Hessian matrix information. Second order information can be incorporated into MLP training in different ways. In many applications especially in the area of pattern recognition the training set is finite. In these cases block learning can be applied using standard nonlinear optimization techniques [3 4 5].

Improved outcomes in auditory brainstem implantation with the use of near-field electrical compound action potentials.

PubMed

Mandalà, Marco; Colletti, Liliana; Colletti, Giacomo; Colletti, Vittorio

2014-12-01

To compare the outcomes (auditory threshold and open-set speech perception at 48-month follow-up) of a new near-field monitoring procedure, electrical compound action potential, on positioning the auditory brainstem implant electrode array on the surface of the cochlear nuclei versus the traditional far-field electrical auditory brainstem response. Retrospective study. Tertiary referral center. Among the 202 patients with auditory brainstem implants fitted and monitored with electrical auditory brainstem response during implant fitting, 9 also underwent electrical compound action potential recording. These subjects were matched retrospectively with a control group of 9 patients in whom only the electrical auditory brainstem response was recorded. Electrical compound action potentials were obtained using a cotton-wick recording electrode located near the surface of the cochlear nuclei and on several cranial nerves. Significantly lower potential thresholds were observed with the recording electrode located on the cochlear nuclei surface compared with the electrical auditory brainstem response (104.4 ± 32.5 vs 158.9 ± 24.2, P = .0030). Electrical brainstem response and compound action potentials identified effects on the neighboring cranial nerves on 3.2 ± 2.4 and 7.8 ± 3.2 electrodes, respectively (P = .0034). Open-set speech perception outcomes at 48-month follow-up had improved significantly in the near- versus far-field recording groups (78.9% versus 56.7%; P = .0051). Electrical compound action potentials during auditory brainstem implantation significantly improved the definition of the potential threshold and the number of auditory and extra-auditory waves generated. It led to the best coupling between the electrode array and cochlear nuclei, significantly improving the overall open-set speech perception. © American Academy of Otolaryngology—Head and Neck Surgery Foundation 2014.

Dofetilide promotes repolarization abnormalities in perfused Guinea-pig heart.

PubMed

Osadchii, Oleg E

2012-12-01

Dofetilide is class III antiarrhythmic agent which prolongs cardiac action potential duration because of selective inhibition of I (Kr), the rapid component of the delayed rectifier K(+) current. Although clinical studies reported on proarrhythmic risk associated with dofetilide treatment, the contributing electrophysiological mechanisms remain poorly understood. This study was designed to determine if dofetilide-induced proarrhythmia may be attributed to abnormalities in ventricular repolarization and refractoriness. The monophasic action potential duration and effective refractory periods (ERP) were assessed at distinct epicardial and endocardial sites along with volume-conducted ECG recordings in isolated, perfused guinea-pig heart preparations. Dofetilide was found to produce the reverse rate-dependent prolongation of ventricular repolarization, increased the steepness of action potential duration rate adaptation, and amplified transepicardial variability in electrical restitution kinetics. Dofetilide also prolonged the T peak-to-end interval on ECG, and elicited a greater prolongation of endocardial than epicardial ERP, thereby increasing transmural dispersion of refractoriness. At epicardium, dofetilide prolonged action potential duration to a greater extent than ERP, thus extending the critical interval for ventricular re-excitation. This change was associated with triangulation of epicardial action potential because of greater dofetilide-induced prolonging effect at 90 % than 30 % repolarization. Premature ectopic beats and spontaneous short-lasting episodes of monomorphic ventricular tachycardia were observed in 44 % of dofetilide-treated heart preparations. Proarrhythmic potential of dofetilide in guinea-pig heart is attributed to steepened electrical restitution, increased transepicardial variability in electrical restitution kinetics, amplified transmural dispersion of refractoriness, increased critical interval for ventricular re-excitation, and triangulation of epicardial action potential.

DBI potential, DBI inflation action and general Lagrangian relative to phantom, K-essence and quintessence

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

Zhang, Qing; Huang, Yong-Chang, E-mail: ychuang@bjut.edu.cn

We derive a Dirac-Born-Infeld (DBI) potential and DBI inflation action by rescaling the metric. The determinant of the induced metric naturally includes the kinetic energy and the potential energy. In particular, the potential energy and kinetic energy can convert into each other in any order, which is in agreement with the limit of classical physics. This is quite different from the usual DBI action. We show that the Taylor expansion of the DBI action can be reduced into the form in the non-linear classical physics. These investigations are the support for the statement that the results of string theory aremore » consistent with quantum mechanics and classical physics. We deduce the Phantom, K-essence, Quintessence and Generalized Klein-Gordon Equation from the DBI model.« less

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

PubMed

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

2017-03-01

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

Action Potential Dynamics in Fine Axons Probed with an Axonally Targeted Optical Voltage Sensor.

PubMed

Ma, Yihe; Bayguinov, Peter O; Jackson, Meyer B

2017-01-01

The complex and malleable conduction properties of axons determine how action potentials propagate through extensive axonal arbors to reach synaptic terminals. The excitability of axonal membranes plays a major role in neural circuit function, but because most axons are too thin for conventional electrical recording, their properties remain largely unexplored. To overcome this obstacle, we used a genetically encoded hybrid voltage sensor (hVOS) harboring an axonal targeting motif. Expressing this probe in transgenic mice enabled us to monitor voltage changes optically in two populations of axons in hippocampal slices, the large axons of dentate granule cells (mossy fibers) in the stratum lucidum of the CA3 region and the much finer axons of hilar mossy cells in the inner molecular layer of the dentate gyrus. Action potentials propagated with distinct velocities in each type of axon. Repetitive firing broadened action potentials in both populations, but at an intermediate frequency the degree of broadening differed. Repetitive firing also attenuated action potential amplitudes in both mossy cell and granule cell axons. These results indicate that the features of use-dependent action potential broadening, and possible failure, observed previously in large nerve terminals also appear in much finer unmyelinated axons. Subtle differences in the frequency dependences could influence the propagation of activity through different pathways to excite different populations of neurons. The axonally targeted hVOS probe used here opens up the diverse repertoire of neuronal processes to detailed biophysical study.

The marketing potential of corporate social responsibility activities: the case of the alcohol industry in Latin America and the Caribbean.

PubMed

Pantani, Daniela; Peltzer, Raquel; Cremonte, Mariana; Robaina, Katherine; Babor, Thomas; Pinsky, Ilana

2017-01-01

The aims were to: (1) identify, monitor and analyse the Corporate Social Responsibility (CSR) practices of the alcohol industry in Latin America and the Caribbean (LAC) and (2) examine whether the alcohol industry is using these actions to market their products and brands. Nine health experts from Argentina, Brazil and Uruguay conducted a content analysis of 218 CSR activities using a standardized protocol. A content rating procedure was used to evaluate the marketing potential of CSR activities as well as their probable population reach and effectiveness. The LEAD procedure (longitudinal, expert and all data) was applied to verify the accuracy of industry-reported descriptions. A total of 55.8% of the actions were found to have a marketing potential, based on evidence that they are likely to promote brands and products. Actions with marketing potential were more likely to reach a larger audience than actions classified with no marketing potential. Most actions did not fit into any category recommended by the World Health Organization; 50% of the actions involving classroom and college education for young people were found to have marketing potential; 62.3% were classified as meeting the definition of risk management CSR. Alcohol industry Corporate Social Responsibility activities in Latin America and the Caribbean appear to have a strategic marketing role beyond their stated philanthropic and public health purpose. © 2016 Society for the Study of Addiction.

76 FR 40811 - Maneb; Tolerance Actions

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-12

...; Tolerance Actions AGENCY: Environmental Protection Agency (EPA). ACTION: Final rule. SUMMARY: EPA is... established a docket for this action under docket identification (ID) number EPA-HQ-OPP-2010-0327. All... . SUPPLEMENTARY INFORMATION: I. General Information A. Does this action apply to me? You may be potentially...

Through a Feminist Poststructuralist Lens: Embodied Subjectivites and Participatory Action Research

ERIC Educational Resources Information Center

Chesnay, Catherine T.

2016-01-01

An emerging literature has been building bridges between poststructuralism and participatory action research, highlighting the latter's potential for transformative action. Using examples from participative action research projects with incarcerated or previously incarcerated women, this article discusses how participatory action research is a…

77 FR 18748 - Dicloran and Formetanate; Proposed Tolerance Actions

Federal Register 2010, 2011, 2012, 2013, 2014

2012-03-28

... Dicloran and Formetanate; Proposed Tolerance Actions AGENCY: Environmental Protection Agency (EPA). ACTION... . SUPPLEMENTARY INFORMATION: I. General Information A. Does this action apply to me? You may be potentially affected by this action if you are an agricultural producer, food manufacturer, or pesticide manufacturer...

Differential regulation of GnRH secretion in the preoptic area (POA) and the median eminence (ME) in male mice.

PubMed

Glanowska, Katarzyna M; Moenter, Suzanne M

2015-01-01

GnRH release in the median eminence (ME) is the central output for control of reproduction. GnRH processes in the preoptic area (POA) also release GnRH. We examined region-specific regulation of GnRH secretion using fast-scan cyclic voltammetry to detect GnRH release in brain slices from adult male mice. Blocking endoplasmic reticulum calcium reuptake to elevate intracellular calcium evokes GnRH release in both the ME and POA. This release is action potential dependent in the ME but not the POA. Locally applied kisspeptin induced GnRH secretion in both the ME and POA. Local blockade of inositol triphospate-mediated calcium release inhibited kisspeptin-induced GnRH release in the ME, but broad blockade was required in the POA. In contrast, kisspeptin-evoked secretion in the POA was blocked by local gonadotropin-inhibitory hormone, but broad gonadotropin-inhibitory hormone application was required in the ME. Although action potentials are required for GnRH release induced by pharmacologically-increased intracellular calcium in the ME and kisspeptin-evoked release requires inositol triphosphate-mediated calcium release, blocking action potentials did not inhibit kisspeptin-induced GnRH release in the ME. Kisspeptin-induced GnRH release was suppressed after blocking both action potentials and plasma membrane Ca(2+) channels. This suggests that kisspeptin action in the ME requires both increased intracellular calcium and influx from the outside of the cell but not action potentials. Local interactions among kisspeptin and GnRH processes in the ME could thus stimulate GnRH release without involving perisomatic regions of GnRH neurons. Coupling between action potential generation and hormone release in GnRH neurons is thus likely physiologically labile and may vary with region.

Amphetamine elevates nucleus accumbens dopamine via an action potential-dependent mechanism that is modulated by endocannabinoids

PubMed Central

Covey, Dan P.; Bunner, Kendra D.; Schuweiler, Douglas R.; Cheer, Joseph F.; Garris, Paul A.

2018-01-01

The reinforcing effects of abused drugs are mediated by their ability to elevate nucleus accumbens dopamine. Amphetamine (AMPH) was historically thought to increase dopamine by an action potential-independent, non-exocytotic type of release called efflux, involving reversal of dopamine transporter function and driven by vesicular dopamine depletion. Growing evidence suggests that AMPH also acts by an action potential-dependent mechanism. Indeed, fast-scan cyclic voltammetry demonstrates that AMPH activates dopamine transients, reward-related phasic signals generated by burst firing of dopamine neurons and dependent on intact vesicular dopamine. Not established for AMPH but indicating a shared mechanism, endocannabinoids facilitate this activation of dopamine transients by broad classes of abused drugs. Here, using fast-scan cyclic voltammetry coupled to pharmacological manipulations in awake rats, we investigated the action potential and endocannabinoid dependence of AMPH-induced elevations in nucleus accumbens dopamine. AMPH increased the frequency, amplitude and duration of transients, which were observed riding on top of slower dopamine increases. Surprisingly, silencing dopamine neuron firing abolished all AMPH-induced dopamine elevations, identifying an action potential-dependent origin. Blocking cannabinoid type 1 receptors prevented AMPH from increasing transient frequency, similar to reported effects on other abused drugs, but not from increasing transient duration and inhibiting dopamine uptake. Thus, AMPH elevates nucleus accumbens dopamine by eliciting transients via cannabinoid type 1 receptors and promoting the summation of temporally coincident transients, made more numerous, larger and wider by AMPH. Collectively, these findings are inconsistent with AMPH eliciting action potential-independent dopamine efflux and vesicular dopamine depletion, and support endocannabinoids facilitating phasic dopamine signalling as a common action in drug reinforcement. PMID:27038339

Amphetamine elevates nucleus accumbens dopamine via an action potential-dependent mechanism that is modulated by endocannabinoids.

PubMed

Covey, Dan P; Bunner, Kendra D; Schuweiler, Douglas R; Cheer, Joseph F; Garris, Paul A

2016-06-01

The reinforcing effects of abused drugs are mediated by their ability to elevate nucleus accumbens dopamine. Amphetamine (AMPH) was historically thought to increase dopamine by an action potential-independent, non-exocytotic type of release called efflux, involving reversal of dopamine transporter function and driven by vesicular dopamine depletion. Growing evidence suggests that AMPH also acts by an action potential-dependent mechanism. Indeed, fast-scan cyclic voltammetry demonstrates that AMPH activates dopamine transients, reward-related phasic signals generated by burst firing of dopamine neurons and dependent on intact vesicular dopamine. Not established for AMPH but indicating a shared mechanism, endocannabinoids facilitate this activation of dopamine transients by broad classes of abused drugs. Here, using fast-scan cyclic voltammetry coupled to pharmacological manipulations in awake rats, we investigated the action potential and endocannabinoid dependence of AMPH-induced elevations in nucleus accumbens dopamine. AMPH increased the frequency, amplitude and duration of transients, which were observed riding on top of slower dopamine increases. Surprisingly, silencing dopamine neuron firing abolished all AMPH-induced dopamine elevations, identifying an action potential-dependent origin. Blocking cannabinoid type 1 receptors prevented AMPH from increasing transient frequency, similar to reported effects on other abused drugs, but not from increasing transient duration and inhibiting dopamine uptake. Thus, AMPH elevates nucleus accumbens dopamine by eliciting transients via cannabinoid type 1 receptors and promoting the summation of temporally coincident transients, made more numerous, larger and wider by AMPH. Collectively, these findings are inconsistent with AMPH eliciting action potential-independent dopamine efflux and vesicular dopamine depletion, and support endocannabinoids facilitating phasic dopamine signalling as a common action in drug reinforcement. © 2016 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Naturalistic stimulation changes the dynamic response of action potential encoding in a mechanoreceptor

PubMed Central

Pfeiffer, Keram; French, Andrew S.

2015-01-01

Naturalistic signals were created from vibrations made by locusts walking on a Sansevieria plant. Both naturalistic and Gaussian noise signals were used to mechanically stimulate VS-3 slit-sense mechanoreceptor neurons of the spider, Cupiennius salei, with stimulus amplitudes adjusted to give similar firing rates for either stimulus. Intracellular microelectrodes recorded action potentials, receptor potential, and receptor current, using current clamp and voltage clamp. Frequency response analysis showed that naturalistic stimulation contained relatively more power at low frequencies, and caused increased neuronal sensitivity to higher frequencies. In contrast, varying the amplitude of Gaussian stimulation did not change neuronal dynamics. Naturalistic stimulation contained less entropy than Gaussian, but signal entropy was higher than stimulus in the resultant receptor current, indicating addition of uncorrelated noise during transduction. The presence of added noise was supported by measuring linear information capacity in the receptor current. Total entropy and information capacity in action potentials produced by either stimulus were much lower than in earlier stages, and limited to the maximum entropy of binary signals. We conclude that the dynamics of action potential encoding in VS-3 neurons are sensitive to the form of stimulation, but entropy and information capacity of action potentials are limited by firing rate. PMID:26578975

Noise Enhances Action Potential Generation in Mouse Sensory Neurons via Stochastic Resonance.

PubMed

Onorato, Irene; D'Alessandro, Giuseppina; Di Castro, Maria Amalia; Renzi, Massimiliano; Dobrowolny, Gabriella; Musarò, Antonio; Salvetti, Marco; Limatola, Cristina; Crisanti, Andrea; Grassi, Francesca

2016-01-01

Noise can enhance perception of tactile and proprioceptive stimuli by stochastic resonance processes. However, the mechanisms underlying this general phenomenon remain to be characterized. Here we studied how externally applied noise influences action potential firing in mouse primary sensory neurons of dorsal root ganglia, modelling a basic process in sensory perception. Since noisy mechanical stimuli may cause stochastic fluctuations in receptor potential, we examined the effects of sub-threshold depolarizing current steps with superimposed random fluctuations. We performed whole cell patch clamp recordings in cultured neurons of mouse dorsal root ganglia. Noise was added either before and during the step, or during the depolarizing step only, to focus onto the specific effects of external noise on action potential generation. In both cases, step + noise stimuli triggered significantly more action potentials than steps alone. The normalized power norm had a clear peak at intermediate noise levels, demonstrating that the phenomenon is driven by stochastic resonance. Spikes evoked in step + noise trials occur earlier and show faster rise time as compared to the occasional ones elicited by steps alone. These data suggest that external noise enhances, via stochastic resonance, the recruitment of transient voltage-gated Na channels, responsible for action potential firing in response to rapid step-wise depolarizing currents.

Noise Enhances Action Potential Generation in Mouse Sensory Neurons via Stochastic Resonance

PubMed Central

Onorato, Irene; D'Alessandro, Giuseppina; Di Castro, Maria Amalia; Renzi, Massimiliano; Dobrowolny, Gabriella; Musarò, Antonio; Salvetti, Marco; Limatola, Cristina; Crisanti, Andrea; Grassi, Francesca

2016-01-01

Noise can enhance perception of tactile and proprioceptive stimuli by stochastic resonance processes. However, the mechanisms underlying this general phenomenon remain to be characterized. Here we studied how externally applied noise influences action potential firing in mouse primary sensory neurons of dorsal root ganglia, modelling a basic process in sensory perception. Since noisy mechanical stimuli may cause stochastic fluctuations in receptor potential, we examined the effects of sub-threshold depolarizing current steps with superimposed random fluctuations. We performed whole cell patch clamp recordings in cultured neurons of mouse dorsal root ganglia. Noise was added either before and during the step, or during the depolarizing step only, to focus onto the specific effects of external noise on action potential generation. In both cases, step + noise stimuli triggered significantly more action potentials than steps alone. The normalized power norm had a clear peak at intermediate noise levels, demonstrating that the phenomenon is driven by stochastic resonance. Spikes evoked in step + noise trials occur earlier and show faster rise time as compared to the occasional ones elicited by steps alone. These data suggest that external noise enhances, via stochastic resonance, the recruitment of transient voltage-gated Na channels, responsible for action potential firing in response to rapid step-wise depolarizing currents. PMID:27525414

Generation of action potentials in a mathematical model of corticotrophs.

PubMed Central

LeBeau, A P; Robson, A B; McKinnon, A E; Donald, R A; Sneyd, J

1997-01-01

Corticotropin-releasing hormone (CRH) is an important regulator of adrenocorticotropin (ACTH) secretion from pituitary corticotroph cells. The intracellular signaling system that underlies this process involves modulation of voltage-sensitive Ca2+ channel activity, which leads to the generation of Ca2+ action potentials and influx of Ca2+. However, the mechanisms by which Ca2+ channel activity is modulated in corticotrophs are not currently known. We investigated this process in a Hodgkin-Huxley-type mathematical model of corticotroph plasma membrane electrical responses. We found that an increase in the L-type Ca2+ current was sufficient to generate action potentials from a previously resting state of the model. The increase in the L-type current could be elicited by either a shift in the voltage dependence of the current toward more negative potentials, or by an increase in the conductance of the current. Although either of these mechanisms is potentially responsible for the generation of action potentials, previous experimental evidence favors the former mechanism, with the magnitude of the shift required being consistent with the experimental findings. The model also shows that the T-type Ca2+ current plays a role in setting the excitability of the plasma membrane, but does not appear to contribute in a dynamic manner to action potential generation. Inhibition of a K+ conductance that is active at rest also affects the excitability of the plasma membrane. PMID:9284294

High-fidelity optical reporting of neuronal electrical activity with an ultrafast fluorescent voltage sensor

PubMed Central

St-Pierre, François; Marshall, Jesse D; Yang, Ying; Gong, Yiyang; Schnitzer, Mark J; Lin, Michael Z

2015-01-01

Accurate optical reporting of electrical activity in genetically defined neuronal populations is a long-standing goal in neuroscience. Here we describe Accelerated Sensor of Action Potentials 1 (ASAP1), a novel voltage sensor design in which a circularly permuted green fluorescent protein is inserted within an extracellular loop of a voltage-sensing domain, rendering fluorescence responsive to membrane potential. ASAP1 demonstrates on- and off- kinetics of 2.1 and 2.0 ms, reliably detects single action potentials and subthreshold potential changes, and tracks trains of action potential waveforms up to 200 Hz in single trials. With a favorable combination of brightness, dynamic range, and speed, ASAP1 enables continuous monitoring of membrane potential in neurons at KHz frame rates using standard epifluorescence microscopy. PMID:24755780

Stimulus waveform determines the characteristics of sensory nerve action potentials.

PubMed

Pereira, Pedro; Leote, João; Cabib, Christopher; Casanova-Molla, Jordi; Valls-Sole, Josep

2016-03-01

In routine nerve conduction studies supramaximal electrical stimuli generate sensory nerve action potentials by depolarization of nerve fibers under the cathode. However, stimuli of submaximal intensity may give rise to action potentials generated under the anode. We tested if this phenomenon depends on the characteristics of stimulus ending. We added a circuit to our stimulation device that allowed us to modify the end of the stimulus by increasing the time constant of the decay phase. Increasing the fall time caused a reduction of anode action potential (anAP) amplitude, and eventually abolished it, in all tested subjects. We subsequently examined the stimulus waveform in a series of available electromyographs stimulators and found that the anAP could only be obtained with stimulators that issued stimuli ending sharply. Our results prove that the anAP is generated at stimulus end, and depends on the sharpness of current shut down. Electromyographs produce stimuli of varying characteristics, which limits the reproducibility of anAP results by interested researchers. The study of anodal action potentials might be a useful tool to have a quick appraisal of distal human sensory nerve excitability. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Effects of Muscle Atrophy on Motor Control: Cage-size Effects

NASA Technical Reports Server (NTRS)

Stuart, D. G.

1985-01-01

Two populations of male Sprague-Dawley rats were raised either in conventional minimum-specification cages or in a larger cage. When the animals were mature (125 to 150 d), the physiological status of the soleus (SOL) and extensor digitorum longus (EDL) muscles of the small- and large-cage animals were compared. Analysis of whole-muscle properties including the performance of the test muscle during a standardized fatigue test in which the nerve to the test muscle was subjected to supramaximal intermittent stimulation shows: (1) the amplitude, area, mean amplitude, and peak-to-peak rate of the compound muscle action potential decreased per the course of the fatigue test; (2) cage size did not affect the profile of changes for any of the action-potential measurements; (3) changes exhibited in the compound muscle action potential by SOL and EDL were substantially different; and (4) except for SOL of the large-cage rats, there was a high correlation between all four measures of the compound muscle action potential and the peak tetanic force during the fatigue test; i.e., either the electrical activity largely etermines the force profile during the fatigue test or else contractile-related activity substantially affects the compound muscle action potential.

Electromyography (image)

MedlinePlus

... inserted through the skin into the muscle. Each muscle fiber that contracts will produce an action potential. The presence, size, and shape of the wave form of the action potential ... the ability of the muscle to respond to nervous stimulation.

Uniformly stable backpropagation algorithm to train a feedforward neural network.

PubMed

Rubio, José de Jesús; Angelov, Plamen; Pacheco, Jaime

2011-03-01

Neural networks (NNs) have numerous applications to online processes, but the problem of stability is rarely discussed. This is an extremely important issue because, if the stability of a solution is not guaranteed, the equipment that is being used can be damaged, which can also cause serious accidents. It is true that in some research papers this problem has been considered, but this concerns continuous-time NN only. At the same time, there are many systems that are better described in the discrete time domain such as population of animals, the annual expenses in an industry, the interest earned by a bank, or the prediction of the distribution of loads stored every hour in a warehouse. Therefore, it is of paramount importance to consider the stability of the discrete-time NN. This paper makes several important contributions. 1) A theorem is stated and proven which guarantees uniform stability of a general discrete-time system. 2) It is proven that the backpropagation (BP) algorithm with a new time-varying rate is uniformly stable for online identification and the identification error converges to a small zone bounded by the uncertainty. 3) It is proven that the weights' error is bounded by the initial weights' error, i.e., overfitting is eliminated in the proposed algorithm. 4) The BP algorithm is applied to predict the distribution of loads that a transelevator receives from a trailer and places in the deposits in a warehouse every hour, so that the deposits in the warehouse are reserved in advance using the prediction results. 5) The BP algorithm is compared with the recursive least square (RLS) algorithm and with the Takagi-Sugeno type fuzzy inference system in the problem of predicting the distribution of loads in a warehouse, giving that the first and the second are stable and the third is unstable. 6) The BP algorithm is compared with the RLS algorithm and with the Kalman filter algorithm in a synthetic example.

Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements

NASA Technical Reports Server (NTRS)

Lure, Y. M. Fleming; Grody, Norman C.; Chiou, Y. S. Peter; Yeh, H. Y. Michael

1993-01-01

A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular Neural Ring (MNR).

A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network

PubMed Central

Salari, Nader; Shohaimi, Shamarina; Najafi, Farid; Nallappan, Meenakshii; Karishnarajah, Isthrinayagy

2014-01-01

Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. The purpose is benefitting from the synergies obtained from combining these technologies for the development of classification models. Such a combination creates an opportunity to invest in the strength of each algorithm, and is an approach to make up for their deficiencies. To develop proposed model, with the aim of obtaining the best array of features, first, feature ranking techniques such as the Fisher's discriminant ratio and class separability criteria were used to prioritize features. Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. The performance of the proposed model was compared with thirteen well-known classification models based on seven datasets. Furthermore, the statistical analysis was performed using the Friedman test followed by post-hoc tests. The experimental findings indicated that the novel proposed hybrid model resulted in significantly better classification performance compared with all 13 classification methods. Finally, the performance results of the proposed model was benchmarked against the best ones reported as the state-of-the-art classifiers in terms of classification accuracy for the same data sets. The substantial findings of the comprehensive comparative study revealed that performance of the proposed model in terms of classification accuracy is desirable, promising, and competitive to the existing state-of-the-art classification models. PMID:25419659

Scheduling the blended solution as industrial CO2 absorber in separation process by back-propagation artificial neural networks.

PubMed

Abdollahi, Yadollah; Sairi, Nor Asrina; Said, Suhana Binti Mohd; Abouzari-lotf, Ebrahim; Zakaria, Azmi; Sabri, Mohd Faizul Bin Mohd; Islam, Aminul; Alias, Yatimah

2015-11-05

It is believe that 80% industrial of carbon dioxide can be controlled by separation and storage technologies which use the blended ionic liquids absorber. Among the blended absorbers, the mixture of water, N-methyldiethanolamine (MDEA) and guanidinium trifluoromethane sulfonate (gua) has presented the superior stripping qualities. However, the blended solution has illustrated high viscosity that affects the cost of separation process. In this work, the blended fabrication was scheduled with is the process arranging, controlling and optimizing. Therefore, the blend's components and operating temperature were modeled and optimized as input effective variables to minimize its viscosity as the final output by using back-propagation artificial neural network (ANN). The modeling was carried out by four mathematical algorithms with individual experimental design to obtain the optimum topology using root mean squared error (RMSE), R-squared (R(2)) and absolute average deviation (AAD). As a result, the final model (QP-4-8-1) with minimum RMSE and AAD as well as the highest R(2) was selected to navigate the fabrication of the blended solution. Therefore, the model was applied to obtain the optimum initial level of the input variables which were included temperature 303-323 K, x[gua], 0-0.033, x[MDAE], 0.3-0.4, and x[H2O], 0.7-1.0. Moreover, the model has obtained the relative importance ordered of the variables which included x[gua]>temperature>x[MDEA]>x[H2O]. Therefore, none of the variables was negligible in the fabrication. Furthermore, the model predicted the optimum points of the variables to minimize the viscosity which was validated by further experiments. The validated results confirmed the model schedulability. Accordingly, ANN succeeds to model the initial components of the blended solutions as absorber of CO2 capture in separation technologies that is able to industries scale up. Copyright © 2015 Elsevier B.V. All rights reserved.

Modeling the action-potential-sensitive nonlinear-optical response of myelinated nerve fibers and short-term memory

NASA Astrophysics Data System (ADS)

Shneider, M. N.; Voronin, A. A.; Zheltikov, A. M.

2011-11-01

The Goldman-Albus treatment of the action-potential dynamics is combined with a phenomenological description of molecular hyperpolarizabilities into a closed-form model of the action-potential-sensitive second-harmonic response of myelinated nerve fibers with nodes of Ranvier. This response is shown to be sensitive to nerve demyelination, thus enabling an optical diagnosis of various demyelinating diseases, including multiple sclerosis. The model is applied to examine the nonlinear-optical response of a three-neuron reverberating circuit—the basic element of short-term memory.

Position-dependent patterning of spontaneous action potentials in immature cochlear inner hair cells

PubMed Central

Johnson, Stuart L.; Eckrich, Tobias; Kuhn, Stephanie; Zampini, Valeria; Franz, Christoph; Ranatunga, Kishani M.; Roberts, Terri P.; Masetto, Sergio; Knipper, Marlies; Kros, Corné J.; Marcotti, Walter

2011-01-01

Spontaneous action potential activity is crucial for mammalian sensory system development. In the auditory system, patterned firing activity has been observed in immature spiral ganglion cells and brain-stem neurons and is likely to depend on cochlear inner hair cell (IHC) action potentials. It remains uncertain whether spiking activity is intrinsic to developing IHCs and whether it shows patterning. We found that action potentials are intrinsically generated by immature IHCs of altricial rodents and that apical IHCs exhibit bursting activity as opposed to more sustained firing in basal cells. We show that the efferent neurotransmitter ACh, by fine-tuning the IHC’s resting membrane potential (Vm), is crucial for the bursting pattern in apical cells. Endogenous extracellular ATP also contributes to the Vm of apical and basal IHCs by activating SK2 channels. We hypothesize that the difference in firing pattern along the cochlea instructs the tonotopic differentiation of IHCs and auditory pathway. PMID:21572434