Right hemisphere contributions to imitation tasks

Humans imitate biological movements faster than non-biological movements. The faster response has been attributed to an activation of the human mirror neuron system, which is thought to match observation and execution of actions. However, it is unclear which cortical areas are responsible for this behavioural advantage. Also, little is known about the timing of activations. Using whole-head magnetoencephalography we recorded neuronal responses to single biological finger movements and non-biological dot movements while the subjects were required to perform an imitation task or an observation task, respectively. Previous imaging studies on the human mirror neurone system suggested that activation in response to biological movements would be stronger in ventral premotor, parietal and superior temporal regions. In accordance with previous studies, reaction times to biological movements were faster than those to dot movements in all subjects. The analysis of evoked magnetic fields revealed that the reaction time benefit was paralleled by stronger and earlier activation of the left temporo-occipital cortex, right superior temporal area and right ventral motor/premotor area. The activity patterns suggest that the latter areas mediate the observed behavioural advantage of biological movements and indicate a predominant contribution of the right temporo-frontal hemisphere to action observation–execution matching processes in intransitive movements, which has not been reported previously.

Observing repetitive finger movements modulates response times of auditorily cued finger movements

Our motor and perceptual representations of actions seem to be intimately linked and the human mirror neuron system (MNS) has been proposed as the mediator. In two experiments, we presented biological or non-biological movement stimuli that were either congruent or incongruent to a required response prompted by a tone. When the tone occurred with the onset of the last movement in a series, i.e., it was perceived during the movement presentation, congruent biological stimuli resulted in faster reaction times than congruent non-biological stimuli. The opposite was observed for incongruent stimuli. When the tone was presented after visual movement stimulation, however, no such interaction was present. This implies that biological movement stimuli only affect motor behaviour during visual processing but not thereafter. These data suggest that the MNS is an “online” system; longstanding repetitive visual stimulation (Experiment 1) has no benefit in comparison to only one or two repetitions (Experiment 2).

Synchronized oscillations at α and θ frequencies in the lateral geniculate nucleus

In relaxed wakefulness, the EEG exhibits robust rhythms in the alpha band (8-13 Hz), which decelerate to theta (approximately 2-7 Hz) frequencies during early sleep. In animal models, these rhythms occur coherently with synchronized activity in the thalamus. However, the mechanisms of this thalamic activity are unknown. Here we show that, in slices of the lateral geniculate nucleus maintained in vitro, activation of the metabotropic glutamate receptor (mGluR) mGluR1a induces synchronized oscillations at alpha and theta frequencies that share similarities with thalamic alpha and theta rhythms recorded in vivo. These in vitro oscillations are driven by an unusual form of burst firing that is present in a subset of thalamocortical neurons and are synchronized by gap junctions. We propose that mGluR1a-induced oscillations are a potential mechanism whereby the thalamus promotes EEG alpha and theta rhythms in the intact brain.

Novel neuronal and astrocytic mechanisms in thalamocortical loop dynamics

In this review, we summarize three sets of findings that have recently been observed in thalamic astrocytes and neurons, and discuss their significance for thalamocortical loop dynamics. (i) A physiologically relevant ‘window’ component of the low–voltage–activated, T–type Ca2+ current (ITwindow) plays an essential part in the slow (less than 1 Hz) sleep oscillation in adult thalamocortical (TC) neurons, indicating that the expression of this fundamental sleep rhythm in these neurons is not a simple reflection of cortical network activity. It is also likely that ITwindow underlies one of the cellular mechanisms enabling TC neurons to produce burst firing in response to novel sensory stimuli. (ii) Both electrophysiological and dye–injection experiments support the existence of gap junction–mediated coupling among young and adult TC neurons. This finding indicates that electrical coupling–mediated synchronization might be implicated in the high and low frequency oscillatory activities expressed by this type of thalamic neuron. (iii) Spontaneous intracellular Ca2+ ([Ca2+]i) waves propagating among thalamic astrocytes are able to elicit large and long–lasting N–methyl–D–aspartate–mediated currents in TC neurons. The peculiar developmental profile within the first two postnatal weeks of these astrocytic [Ca2+]i transients and the selective activation of these glutamate receptors point to a role for this astrocyte–to–neuron signalling mechanism in the topographic wiring of the thalamocortical loop. As some of these novel cellular and intracellular properties are not restricted to thalamic astrocytes and neurons, their significance may well apply to (patho)physiological functions of glial and neuronal elements in other brain areas.

Generalization by Computation Through Memory

Usually, generalization is considered as a function of learning from a set of examples. In present work on the basis of recent neural network assembly memory model (NNAMM), a biologically plausible 'grandmother' model for vision, where each separate memory unit itself can generalize, has been proposed. For such a generalization by computation through memory, analytical formulae and numerical procedure are found to calculate exactly the perfectly learned memory unit's generalization ability. The model's memory has complex hierarchical structure, can be learned from one example by a one-step process, and may be considered as a semi-representational one. A simple binary neural network for bell-shaped tuning is described.

Synthesis Methods of Multiple-valued Structures of Language Systems

The basic construction concepts of many-valued intellectual systems, which are adequate to primal problems of person activity and using hybrid tools with many-valued of coding are considered. The many-valued intellectual systems being two-place, but simulating neuron processes of space toting which are different on a level of actions, inertial and threshold of properties of neurons diaphragms, and also modification of frequency of following of the transmitted messages are created. All enumerated properties and functions in point of fact are essential not only are discrete on time, but also many-valued.

Fourier Neural Networks: An Approach with Sinusoidal Activation Functions

* Supported by INTAS 2000-626, INTAS YSF 03-55-1969, INTAS INNO 182, and TIC 2003-09319-c03-03.

Sensitivity and Bias within the Binary Signal Detection Theory, BSDT

Similar to classic Signal Detection Theory (SDT), recent optimal Binary Signal Detection Theory (BSDT) and based on it Neural Network Assembly Memory Model (NNAMM) can successfully reproduce Receiver Operating Characteristic (ROC) curves although BSDT/NNAMM parameters (intensity of cue and neuron threshold) and classic SDT parameters (perception distance and response bias) are essentially different. In present work BSDT/NNAMM optimal likelihood and posterior probabilities are analytically analyzed and used to generate ROCs and modified (posterior) mROCs, optimal overall likelihood and posterior. It is shown that for the description of basic discrimination experiments in psychophysics within the BSDT a ‘neural space’ can be introduced where sensory stimuli as neural codes are represented and decision processes are defined, the BSDT’s isobias curves can simultaneously be interpreted as universal psychometric functions satisfying the Neyman-Pearson objective, the just noticeable difference (jnd) can be defined and interpreted as an atom of experience, and near-neutral values of biases are observers’ natural choice. The uniformity or no-priming hypotheses, concerning the ‘in-mind’ distribution of false-alarm probabilities during ROC or overall probability estimations, is introduced. The BSDT’s and classic SDT’s sensitivity, bias, their ROC and decision spaces are compared.

The Cascade Orthogonal Neural Network

In the paper new non-conventional growing neural network is proposed. It coincides with the Cascade- Correlation Learning Architecture structurally, but uses ortho-neurons as basic structure units, which can be adjusted using linear tuning procedures. As compared with conventional approximating neural networks proposed approach allows significantly to reduce time required for weight coefficients adjustment and the training dataset size.

Multiple-Valued Structures of Intellectual Systems

The basic construction concepts of many-valued intellectual systems, which are adequate to primal problems of person activity and using hybrid tools with many-valued intellectual systems being two-place, but simulating neuron processes of space toting which are different on a level of actions, inertial and threshold of properties of neuron diaphragms, and also frequency modification of the following transmitted messages are created. All enumerated properties and functions in point of fact are essential not only are discrete on time, but also many-valued.

Self-Learning Fuzzy Spiking Neural Network as a Nonlinear Pulse-Position Threshold Detection Dynamic System Based on Second-Order Critically Damped Response Units

Architecture and learning algorithm of self-learning spiking neural network in fuzzy clustering task are outlined. Fuzzy receptive neurons for pulse-position transformation of input data are considered. It is proposed to treat a spiking neural network in terms of classical automatic control theory apparatus based on the Laplace transform. It is shown that synapse functioning can be easily modeled by a second order damped response unit. Spiking neuron soma is presented as a threshold detection unit. Thus, the proposed fuzzy spiking neural network is an analog-digital nonlinear pulse-position dynamic system. It is demonstrated how fuzzy probabilistic and possibilistic clustering approaches can be implemented on the base of the presented spiking neural network.

Performance Comparison of MATLAB and Neuro Solution Software on Estimation of Fuel Economy by Using Artificial Neural Network

In the world, scientific studies increase day by day and computer programs facilitate the human’s life. Scientists examine the human’s brain’s neural structure and they try to be model in the computer and they give the name of artificial neural network. For this reason, they think to develop more complex problem’s solution. The purpose of this study is to estimate fuel economy of an automobile engine by using artificial neural network (ANN) algorithm. Engine characteristics were simulated by using “Neuro Solution” software. The same data is used in MATLAB to compare the performance of MATLAB is such a problem and show its validity. The cylinder, displacement, power, weight, acceleration and vehicle production year are used as input data and miles per gallon (MPG) are used as target data. An Artificial Neural Network model was developed and 70% of data were used as training data, 15% of data were used as testing data and 15% of data is used as validation data. In creating our model, proper neuron number is carefully selected to increase the speed of the network. Since the problem has a nonlinear structure, multi layer are used in our model.

The Cascade Neo-Fuzzy Architecture and its Online Learning Algorithm

In the paper learning algorithm for adjusting weight coefficients of the Cascade Neo-Fuzzy Neural Network (CNFNN) in sequential mode is introduced. Concerned architecture has the similar structure with the Cascade-Correlation Learning Architecture proposed by S.E. Fahlman and C. Lebiere, but differs from it in type of artificial neurons. CNFNN consists of neo-fuzzy neurons, which can be adjusted using high-speed linear learning procedures. Proposed CNFNN is characterized by high learning rate, low size of learning sample and its operations can be described by fuzzy linguistic “if-then” rules providing “transparency” of received results, as compared with conventional neural networks. Using of online learning algorithm allows to process input data sequentially in real time mode.

Modeling Data for Complications in Diabetics Using Logistic Regression

2010 Mathematics Subject Classification: 62P10.

Stem cell-derived astrocytes:are they physiologically credible?

Astrocytes are now increasingly acknowledged as having fundamental and sophisticated roles in brain function and dysfunction. Unravelling the complex mechanisms that underlie human brain astrocyte-neuron interactions is therefore an essential step on the way to understanding how the brain operates. Insights into astrocyte function to date, have almost exclusively been derived from studies conducted using murine or rodent models. Whilst these have led to significant discoveries, preliminary work with human astrocytes has revealed a hitherto unknown range of astrocyte types with potentially greater functional complexity and increased neuronal interaction with respect to animal astrocytes. It is becoming apparent, therefore, that many important functions of astrocytes will only be discovered by direct physiological interrogation of human astrocytes. Recent advancements in the field of stem cell biology have provided a source of human based models. These will provide a platform to facilitate our understanding of normal astrocyte functions as well as their role in CNS pathology. A number of recent studies have demonstrated that stem cell derived astrocytes exhibit a range of properties, suggesting that they may be functionally equivalent to their in vivo counterparts. Further validation against in vivo models will ultimately confirm the future utility of these stem-cell based approaches in fulfilling the need for human- based cellular models for basic and clinical research. In this review we discuss the roles of astrocytes in the brain and highlight the extent to which human stem cell derived astrocytes have demonstrated functional activities that are equivalent to that observed in vivo.