To understand the working of brain large scale multi electrode systems were built for recording neuralsignals ,. Such systems helped in conducting various experiments and proved that prediction of limb movements by recording neuralsignals from multiple sites simultaneously and analyzing ,. As an example a paralyzed person can move wheel chair or a computer cursor using thoughts and to perform such tasks brain machine interfaces are built. These interfaces not only help in monitoring brain activities but also help immovable and physically handicapped people.
A low noise low micro power amplifier to record neuralsignals is presented. To keep the input referred noise of the amplifier near to the theoretical limit of the input differential pair low noise design techniques were employed. As per the measurements it appears that the amplifier had lowest power and energy efficient. Either action potentials or Local field potentials can be recorded by suitably configuring the amplifier’s band width. This can be used in many almost all the systems used for recording and processing brain signals.
One crucial aspect of performing online motor decod- ing is the training and re-calibration of the decoding model. Although the neural features for similar move- ments are relatively stable within a few days , the neural tuning curve may start to change when the sub- ject is learning to perform a new task . It is also very difficult to track the same neuron for an extended period of time [106, 107], due to the micro-movement of electrodes and fluctuations of other noise sources. Fur- thermore, training data are often acquired in an open-loop fashion, meaning that no feedback is provided by the decoder during training. However, in actual decoding ses- sion, feedback is provided and the subject may attempt to change his motor imagery in order to “learn” the decoder. This may lead to changes in the underlying neural fea- tures . Therefore, re-calibration of the trained model is often necessary and will be ideal if it can be performed online. A successful re-calibration method is the ReFIT- KF algorithm proposed by Gilja et al . ReFIT-KF assumes the subject’s true intention is to move towards the target, so it can generate a pseudo-ground truth from the decoded result automatically even though the prediction of the current model may be wrong. It can then calibrate the model using the estimate ground truth to adapt for the instability of the neuralsignals. It is able to produce better results than Kalman filter alone [92, 93, 109].
(unexpected ⬎ expected faces). This would result in an interaction between stimulus and expectation factors whereby FFA responses to face and house stimuli should be similar under high face expectation, because both of these conditions would be associated with activity related to face expectation but no activity related to face surprise. FFA responses to faces and houses should be max- imally differentiated under low face expectation, because faces would here be associated with activity related to face surprise while houses would not. By contrast, the feature-detection model would predict only a main effect of stimulus type (faces ⬎ houses). The neural data displayed a stimulus by expectation interaction effect (Figure 2a), a pattern of results that, qualitatively, matches the hypotheses of the predictive coding account. However, even though this FFA data pattern provides a descriptive match to the type of result anticipated on the basis of the predictive coding hypothesis, it is not certain whether these data could be explained quantitatively by the assumed underlying mechanisms of learned cue-stimulus associations (and violations thereof). Here, we ap- plied such a stringent, quantitative test of the predictive coding account: If the predictive coding view were accurate, it should be possible to explain the neural FFA responses via formal associa- tive learning variables derived from trial-by-trial estimation of cue-stimulus probability distributions in individual participants, analogous to the way that neural responses in the striatum can be modeled by reinforcement learning variables encoding reward prediction and reward prediction error signals (O’Doherty et al., 2004). Thus, we here tested whether the FFA data could be fit by variables of a formal, mathematical neural network model of associative learning, as represented by the Schmajuk, Lam and Gray (SLG) model (Schmajuk, 2010; Schmajuk et al., 1996), which has previously proved effective in explaining fMRI data patterns related to fear learning (Dunsmoor & Schmajuk, 2009). In the context of the SLG model, we assumed that the FFA signal reflects two variables: (1) the conditioned response (CR), which is proportional to the prediction of a face based on the association between Frame Color (considered a conditioned stimulus, CS) and Face (considered the unconditioned stimulus, US), and (2) the error of that prediction, given by the occurrence of a Face minus the prediction of a face by the Frame Color (Figure 1). In the model, the CR on each trial depends on the attention paid to the CS and its prediction of the US. We thus used the CR as an estimate of
Another suggestion is the potential for the application of EEG-based an- notation that does not require the physical motion of an annotator to press a keyboard or mouse. The observation of the EEG signals is that it is feasible to annotate images at a rate of 5 per second using an EEG-based approach. In experimentation, we have found that such an annotation task can last for approximately 30 minutes (with rest periods) before the average annotator gets too fatigued. However, the application of EEG sensing can easily be done in- correctly such as failing to acquire clean signals or failing to remove confound- ing/detrimental artefacts from the recording in preprocessing stages such as applying ICA (Independent Component Analysis) or band-pass filtering. These confounding sources of noise due to eye blinks for instance can provide discrim- inative information in a prediction model but are not of neural origin so are not considered in our work.
The results reported in Table 3 address this question, at least partially. However, the table does not include all brain regions. More specifically, the following five regions do not feature in the table; (1) mPFC, (2) middle cingulate cortex (MCC), (3) posterior cingulate cortex (PCC), (4) left insula, and (5) right insula. These regions are included in the forward-inference map obtained from Neurosynth, but not in the reverse-inference map (see Methods). In other words, the five regions are consistently activated by reward, but activation in each region is not selective to reward (thus not informative to our main research question). For the sake of completeness, we ran MVPA using neuralsignals in each region. Neuralsignals in the mPFC
Long-Term Potentiation(LTP) and Long-Term Depression(LTD) are two major forms of synaptic plasticity, which are also two well-know functional and unit activities involved in high advanced central neural system(CNS) activities, like memory. But we still know little about how the advanced CNS activities are organized in the brain and in the level of organism. Based on the current understanding and experimental evidence of neurology, we propose the term “Information Circulation” to summarize the current understandings for advanced CNS activities, and we define it as separately input neuralsignals finally converge in different levels of CNS and interact with each other, then neural information are circulated and processed in different levels of CNS to give out orders for next body actions. This review provides a detailed description for the functional organizations of advanced CNS activities in the term of Information Circulation. This article outlines the receiving of outside stimulation and transmission of neural information, especially transmission and procession of visual bioelectrical signals, then we described neural circuits of Information Circulation in advanced CNS activities, the corresponding specificity and dynamic properties of neural circuits, different sensation linkages, and neural synchronization for information circulation to produce consciousness in CNS. In conclusion, Information Circulation is defined as an important signature involved in advanced CNS activities.
Reduce the value of artificial neural networks Neural network speech recognition scheme implies a number equal to the number of classes of recognition. Each entry gives a value to indicate the probability of belonging to a given class, or a measure of closeness of this fragment to this speech resolves to sound. For simplicity we confine ourselves to describing a class of pattern recognition. Our reasoning without losses can be transferred to a more general case.
This dissertation was focused on improving human gait using a powered Ankle-Foot Orthosis (AFO). An AFO can provide assistance to individuals with lower limb muscle impairments . The knowledge of gait plays an important role for assisting an individual by powered AFO during walking. Generally, the gait cycle begins from the time of initial contact of the heel with the floor (0% of gait cycle) and ends at the point of the next heel strike (100% of gait cycle) of the same foot. If gait state, which can be represented by the percentage of gait cycle (% GC), can be estimated successfully, the control of a powered AFO can be more effective. Active lower limb assistive devices (prosthetic and orthotic devices) have been developed over the last few years to improve the locomotion of impaired populations . However, powered AFOs, described in different literature , –, had limitation in terms of controls. To explore the control strategy, we developed the portable powered ankle foot orthosis (PPAFO) in our Human Dynamics and Control Laboratory (HCDL). Three aspects of effective control for a powered AFO were proposed in this dissertation. In Study-01, improved methods for the estimation of the gait state during walking with the PPAFO were addressed. Two new methods were proposed in this study: Modified Fractional Time (MFT) and Artificial Neural Network (ANN). In Study 2, a machine learning based algorithm was proposed to find the proper plantarflexor actuation timing during walking with the PPAFO. Finally in Study 3, recognition of different gait modes (level ground, ascent, and descent) was addressed. An artificial neural network based algorithm was proposed to detect different gait modes.
The nature of this fast and modality-independent process was addressed in Experiment 2. We considered three competing hypotheses (see Introduction). If the common activation to direct gaze and infant-directed speech reflects increased attention (or other non-specific mechanism) induced by these ostensive stimuli, one would expect that the combination of these signals produces even higher activation than a unimodal stimulus. We did not find evidence for such an additive mechanism. Alternatively, if the stimuli from the two modalities are integrated into a single signal, one may expect that the non-ostensive nature of one component (e.g., averted gaze) would cancel the interpretation of the other stimulus (e.g., infant-directed speech) as an ostensive signal (’She may speak to another infant’). We did not find evidence for such a mature integration of multimodal stimuli either. Rather, the combined stimuli elicited the same activation as either of them, confirming the hypothesis that the neural activation to these signals represent a rigid and obligatory response. (This conclusion is also strengthened by the early latency of the response.) The most plausible interpretation of this response is that it manifests the fast and rudimentary interpretation of the eliciting stimuli as ostensive signals, i.e., as indicating the presence of a communicative intention targeting the infant .
ANNs have been prevalent in most machine learning applications. The ‘popular’ multilayer perceptron (MLP) suffers from difficulties such as the determination of the optimal architecture and the values of the optimal weights. These parameters are important in the performance of the neural networks. Furthermore, the MLP is affected by some well-known learning problems, such as over-fitting [17-19]. This means that the neural network can perfectly perform the mapping between the inputs and outputs in the training data, however it will not be able to sufficiently generalize this performance to an unseen data set. There is a number of studies, which investigated possible methods to improve the generalization ability of feed-forward neural networks and automatically select the best number of hidden units and their weights. Widyanto et al.  proposed a new technique using a self-organized hidden layer inspired by the immune algorithm (SONIA). SONIA was used to predict temperature-based food quality, demonstrating an improvement of 18% when compared to MLPs .
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In many female vertebrates, estradiol modulates sexual behavior by influencing sensory, motor and motivational brain regions such that sexual behavior occurs when gonadal activity is high. A principal way that estradiol affects sexual behavior is by regulating neural responses in the basal forebrain, which serves to integrate hormonal and sensory information and, in turn, influences motor circuits underlying sexual behavior. For example, the preoptic area in female rats is estradiol sensitive and enhances lordosis by reducing the excitatory neural impulses that project from the preoptic area to the ventral tegmental area (Sakuma, 2015). Estradiol can also impact sensory systems directly. For example, estradiol shapes auditory tuning in midshipmen fish so that, during the breeding season, females are more sensitive to the harmonics in the calls of courting males, which enhances the adaptive coupling of the sender and the receiver for reproductive success (Sisneros, 2009a,b,c; Sisneros et al., 2004). Similar mechanisms of estradiol-dependent
There are at least three hypothetical scenarios that address the questions posed above (Figure 1B-D). First, repulsive signals from the roof plate or dorsal neural tube may act to repel and/or polarize neural crest cells towards the medioventral direction (Figure 1B). Second, tracking of endothelial cells through their intersomitic journey has revealed the exciting possibility that trunk neural crest cells use endothelial cells as a scaffold and are perhaps directed by endothelial cell signaling to move along the medioventral pathway (Figure 1C). Third, a local secreted or membrane bound factor within the tissue near the dorsal neural tube or somitic mesoderm may attract neural crest cells to move along a medioventral pathway (Figure 1D). Here, we discuss data that support and identify limitations of these three scenarios and develop and simulate a computational model based on a chemotactic model of trunk neural crest cell migration.
Transmission line faults are the most common faults, triggered by falling trees across lines, lightning strikes or insulator strings to flash over. Economic and reliable operation of a power system requires fast fault location and fast fault clearing. Concepts of availability, efficiency and quality have an increasing importance nowadays due to the new marketing policies which can be directly interpreted as a cost reduction or a profit increasing. Conventional methods use Fundamental components of voltage and current. Fundamental component during pre-fault and fault are used in these methods to estimate the apparent impedance viewed from the measurement point and then fault location. However, the estimation of fundamental components of voltage and current signals requires application of robust algorithms against the undesired effects of generated transient components after occurrence of the fault. This is the essential problem which limits the operation speed of conventional techniques for identification of the faults [1-3] .Traveling wave algorithms are based on the fact that an abrupt change of voltage and current at the fault point results in transient waves which propagate along the transmission line in both directions away from the fault point close to the light velocity. These high frequency waves carry useful information associated to the relevant fault. [4-5].
Pancreatic islet cells and neurons share common functions and similar ontogenies, but originate in different germ layers. To determine whether ectoderm-derived cells contribute instructive signals to the developing endoderm-derived pancreas, we defined the chronology of migration and differentiation of neural crest cells in the pancreas, and tested their role in the development of the islets. The homeodomain transcription factor Phox2b marks the neural precursors from the neural crest that colonize the gut to form the enteric nervous system. In the embryonic mouse pancreas, we found Phox2b expressed briefly together with Sox10 along the epithelial-mesenchymal border at E12.5 in cells derived from the neural crest. Downregulation of Phox2b shortly thereafter was dependent upon Nkx2.2 expressed in the adjacent pancreatic epithelium. In Phox2b –/– embryos, neurons and glia did not develop in the pancreas, and Nkx2.2 expression was markedly upregulated in the epithelium. In addition, the number and replication rate of insulin-expressing beta-cells increased in the Phox2b –/– mice. We conclude that, during pancreatic development, Phox2b and Nkx2.2 form a non-cell-autonomous feedback loop that links the neural crest with the pancreatic epithelium, regulates the size of the beta-cell population, and thereby impacts insulin-secretory capacity and energy homeostasis.
Electromyography (EMG) signal is the muscle electrical activity. Electromyography is a technique for detecting and recording the electrical potential generated by muscle cells. This EMG signals are used in medical professionals to determine specific disorders. This paper basically deals with the analysis of different electromyography signals (NOR & MYO). In this paper, new method for classification of myopathy patient’s and healthy subjects with the help of EMG signal by using back propagation neural network classifier are proposed. This methodology provided 96.75 % accuracy in classification of Myopathy and normal EMG signals.
In both schemes, a portion of the ECG signal samples is used for training the MLP until the goal is reached. The weights and biases of the trained neural network along with the network setup information are sent to the receiving end for identical network setup. The first p samples are also sent to the receiving end for prediction, where p is the order of prediction. Prediction is done using the trained neural network at the transmitting and receiving ends simultane- ously. The residues are generated at the transmitting end, by subtracting the predicted sample values from the target values. In the single-stage scheme, the generated residues are rounded o ﬀ and sent to the receiving end, where the reconstruction of original samples is done by adding the rounded residues with the predicted samples. In the two- stage schemes, the rounded residues are further encoded with Hu ﬀ man/arithmetic/runlength encoders in the second stage. The binary-coded residue sequence generated in the second stage is transmitted to the receiving end, where it is decoded in a lossless manner using the corresponding entropy de- coder.
A critical task for question answering is the final answer selection stage, which has to combine multiple signals available about each answer candidate. This paper proposes EviNets: a novel neural network architecture for factoid question answer- ing. EviNets scores candidate answer enti- ties by combining the available supporting evidence, e.g., structured knowledge bases and unstructured text documents. EviNets represents each piece of evidence with a dense embeddings vector, scores their rel- evance to the question, and aggregates the support for each candidate to predict their final scores. Each of the components is generic and allows plugging in a variety of models for semantic similarity scoring and information aggregation. We demon- strate the effectiveness of EviNets in ex- periments on the existing TREC QA and WikiMovies benchmarks, and on the new Yahoo! Answers dataset introduced in this paper. EviNets can be extended to other information types and could facilitate fu- ture work on combining evidence signals for joint reasoning in question answering. 1 Introduction
Abstract—National Aeronautics and Space Administration (NASA) is investigating cognitive technologies for their future communication architecture. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase- shift keying (PSK), amplitude and phase-shift keying (APSK), and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite - Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.