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At least 145 records · Page 8Linked to original sources
PubMed2005

Dynamic neural-based buffer management for Queuing systems with self-similar characteristics.

Buffer management in queuing systems plays an important role in addressing the tradeoff between efficiency measured in terms of overall packet loss and fairness measured in terms of individual source packet loss. Complete partitioning (CP) of a buffer with the best fairness characteristic and complete sharing (CS) of a buffer with the best efficiency characteristic are at the opposite ends of the spectrum of buffer management techniques. Dynamic partitioning buffer management techniques aim at addressing the tradeoff between efficiency and fairness. Ease of implementation is the key issue when determining the practicality of a dynamic buffer management technique. In this paper, two novel dynamic buffer management techniques for queuing systems accommodating self-similar traffic patterns are introduced. The techniques take advantage of the adaptive learning power of perceptron neural networks when applied to arriving traffic patterns of queuing systems. Relying on the water-filling approach, our proposed techniques are capable of coping with the tradeoff between packet loss and fairness issues. Computer simulations reveal that both of the proposed techniques enjoy great efficiency and fairness characteristics as well as ease of implementation.

Algorithms↗
PubMed2002

Prediction of movement following noxious stimulation during 1 minimum alveolar anesthetic concentration isoflurane/nitrous oxide anesthesia by means of middle latency auditory evoked responses.

This paper investigates the applicability of generalized dynamic neural networks for the design of a two-valued anesthetic depth indicator during isoflurane/nitrous oxide anesthesia. The indicator construction is based on the processing of middle latency auditory evoked responses (MLAER) in combination with the observation of the patient's movement reaction to skin incision. The framework of generalized dynamic neural networks does not require any data preprocessing, visual data inspection or subjective feature extraction. The study is based on a data set of 106 patients scheduled for elective surgery under isoflurane/nitrous oxide anesthesia. The processing of the measured MLAER is performed by a recurrent neural network that transforms the MLAER signals into signals having a very uncomplex structure. The evaluation of these signals is self-evident, and yields to a simple threshold classifier. Using only evoked potentials before the pain stimulus, the patient's reaction could be predicted with a probability of 81.5%. The MLAER is closely associated to the patient's reaction to skin incision following noxious stimulation during 1 minimum alveolar anesthetic concentration isoflurane/nitrous oxide anesthesia. In combination with other parameters, MLAER could contribute to an objective and trustworthy movement prediction to noxious stimulation.

Anesthetics, Inhalation↗
PubMed1979

Structure and dynamics of neural network oscillators.

Techniques are given to represent oscillating neural networks by asynchronous logical switching networks, and to analyze the oscillating networks using a directed graph called a state transition diagram. Consideration is restricted to network oscillators containing no rhythm determining pacemaker neurons, and no neurons exhibiting self-limiting properties such as post-inhibitory rebound or accumulating refractoriness. In the state transition diagrams, stable oscillations are associated with a particular geometric configuration called a cyclic attractor (the heavy cycle in Fig. 2). We show that given the network connectivity it is possible to predict autonomous dynamic behaviour, as well as behaviour following hyperpolarizing or depolarizing inputs to neurons of the network. Conversely, given information about patterns of firing activity during cycles and transients of neural networks, the network connectivity can be predicted. The theoretical techniques can be used to generate a census of network structures capable of generating stable oscillations. Several representative network oscillators are discussed in the context of previous theoretical and experimental studies of the structure of neural network oscillators. Although the number of theoretically possile network oscillators capable of generating sustained oscillations is very large, the techniques which are given should be useful in the design of experiments capable of distinguishing between equally plausible hypotheses.

Action Potentials↗
PubMedDate not supplied

Effect of renal perfusion pressure on responses of intrarenal blood flow to renal nerve stimulation in rabbits.

1. We investigated how sympathetic nerve activity and renal perfusion pressure (RPP) interact in controlling renal haemodynamics in pentobarbitone-anaesthetized rabbits. 2. Renal blood flow (RBF) was reduced by electrical renal nerve stimulation (0.5-8 Hz), with RPP set using an extracorporeal circuit to 65, 100 and 135 mmHg. 3. Responses of RBF and cortical laser Doppler flux to renal nerve stimulation were blunted by increased RPP. For example, 4 Hz stimulation reduced RBF by 68 +/- 7% with baseline perfusion pressure approximately 65 mmHg, but only by 22 +/- 3% at approximately 135 mmHg. Medullary laser Doppler flux was less responsive than cortical laser Doppler flux to renal nerve stimulation and its response was not dependent on perfusion pressure. 4. When perfusion pressure was clamped at its baseline level during renal nerve stimulation, responses of RBF and cortical laser Doppler flux, but not medullary laser Doppler flux, were still blunted with increased baseline perfusion pressure. 5. A frequency rich stimulus was applied to assess the effects of perfusion pressure on dynamic neural control of RBF. Renal blood flow responded similarly at each level of perfusion pressure, as a low-pass filter with a pure time delay. 6. Our results suggest that, in the rabbit extracorporeal circuit model, increased RPP blunts the ability of steady state renal nerve stimulation to reduce cortical, but not medullary perfusion. However, in this model the level of RPP appears to have little impact on dynamic neural control of RBF.

Animals↗
PubMed2002

Temporal-to-spatial dynamic mapping, flexible recognition, and temporal correlations in an olfactory cortex model.

This paper proposes temporal-to-spatial dynamic mapping inspired by neural dynamics of the olfactory cortex. In our model the temporal structure of olfactory-bulb patterns is mapped to the spatial dynamics of the ensemble of cortical neurons. This mapping is based on the following biological mechanism: while anterior part of piriform cortex can be excited by the afferent input alone, the posterior areas require both afferent and association signals, which are temporally correlated in a specific way. One of the functional types of the neurons in our model corresponds to the cortical spatial dynamics and encodes odor components, and another represents temporal activity of association-fiber signals, which, we suggest, may be relevant to the encoding of odor concentrations. The temporal-to-spatial mapping and distributed representation of the model enable simultaneous rough cluster classification and fine recognition of patterns within a cluster as parts of the same dynamic process. The model is able to extract and segment the components of complex odor patterns which are spatiotemporal sequences of neural activity.

Animals↗
PubMed2003

Transient increases of synchronized neural activity during movement preparation: influence of cognitive constraints.

The ability to prepare movement is an essential requirement for the control of goal-directed actions. It allows us to respond in an adaptable and swift manner to environmental conditions. In the present study, we manipulate cognitive context, by means of response probability, to modify the degree of movement preparation in a delayed cueing task performed with the right hand, and evaluate the neural dynamics (EEG coherence) and behavioural output (reaction time). Task-related coherence was stronger over the contralateral hemisphere. In particular, coherence between the left sensorimotor area and frontal (C3-F3, C3-FC3) and parietal (C3-P3) regions was increased during right-hand movement preparation as compared to rest in the alpha frequency band (8-12 Hz). Reducing response probability diminished the degree of functional coupling between C3-F3 and C3-FC3, and was associated with a prolonged reaction time. These findings suggest an association between neural dynamics and behavioural performance and emphasize that response predictability biases information processing in goal-oriented behaviour.

Adult↗
PubMed2003

Spatiotemporal dynamics of neural language processing: an MEG study using minimum-norm current estimates.

The inferior frontal and superior temporal areas in the left hemisphere are well-known to be crucial for language processing in most right-handed individuals. This has been established by classical neurological investigations and neuropsychological studies along with metabolic brain imaging have recently revealed converging evidence. Here, we use fast neurophysiological brain imaging, magnetoencephalography (MEG), and L1 Minimum-Norm Current Estimates to investigate the time course of cortical activation underlying the magnetic Mismatch Negativity elicited by a spoken word. Left superior-temporal areas became active 136 ms after the information in the acoustic input was sufficient for identifying the word, and activation of the left inferior-frontal cortex followed after an additional delay of 22 ms. By providing answers to the where- and when-questions of cortical activation, MEG recordings paired with current estimates of the underlying cortical sources may advance our understanding of the spatiotemporal dynamics of distributed neuronal networks involved in cognitive processing in the human brain.

Adult↗
PubMed2001

A biologically inspired neural network for dynamic programming.

An artificial neural network with a two-layer feedback topology and generalized recurrent neurons, for solving nonlinear discrete dynamic optimization problems, is developed. A direct method to assign the weights of neural networks is presented. The method is based on Bellmann's Optimality Principle and on the interchange of information which occurs during the synaptic chemical processing among neurons. The neural network based algorithm is an advantageous approach for dynamic programming due to the inherent parallelism of the neural networks; further it reduces the severity of computational problems that can occur in methods like conventional methods. Some illustrative application examples are presented to show how this approach works out including the shortest path and fuzzy decision making problems.

Algorithms↗
PubMed2001

On the reduction of spontaneous and glutamate-driven spinocerebellar and spinoreticular tract neuronal activity during active sleep.

The present study was performed to provide evidence that dynamic neural processes underlie the reduction in dorsal spinocerebellar tract and spinoreticular tract neuron activity that occurs during active sleep. To ascertain the effect of local inhibition on the spontaneous and glutamate-evoked spike discharge of sensory tract neurons, preliminary control tests were performed during the state of quiet wakefulness, where GABA or glycine was co-administered in a sustained fashion during pulsatile release of glutamate to dorsal spinocerebellar tract (n=3) or spinoreticular tract (n=2) neurons. Co-administration of GABA or glycine also resulted in a significant marked suppression of spontaneous spike activity and glutamate-evoked responses of these cells. Extracellular recording experiments combined with juxtacellular application of glutamate were then performed on 20 antidromically identified dorsal spinocerebellar tract and spinoreticular tract neurons in the chronic intact cat as a function of sleep and wakefulness. The glutamate-evoked activity of a group of 10 sensory tract neurons (seven dorsal spinocerebellar tract, three spinoreticular tract), which exhibited a significant decrease in their spontaneous spike activity during active sleep, was examined. Glutamate-evoked activity in these cells was significantly attenuated during active sleep compared with wakefulness. In contrast, the glutamate-evoked activity of a second group of eight sensory tract neurons (four dorsal spinocerebellar tract, four spinoreticular tract), which exhibited a significant increase in their spontaneous spike activity during active sleep, was not significantly altered in a state-dependent manner. These data indicate that, during natural active sleep, a dynamic neural process is engaged onto certain dorsal spinocerebellar tract and spinoreticular tract neurons, which in turn dampens sensory throughput to higher brain centers.

Action Potentials↗
PubMed1980

Characterization of clinical CSF dynamics and neural axis compliance using the pressure-volume index: I. The normal pressure-volume index.

The pressure-volume index (PVI) technique was used to measure neural axis compliance, cerebrospinal fluid (CSF) formation (If), and CSF absorption (Ro) in 23 children and 7 adults, all free from intracranial masses, who were undergoing diagnostic or therapeutic procedures. Using bolus manipulation of CSF, If was 0.36 +/- 0.08 ml/min and Ro was 2.8 +/- 0.8 mm Hg/ml/min in both adults and children. PVI, as a measure of neural axis compliance or volume buffering capacity, was 25.9 +/- 3.7 ml for the adults. Measured PVI in the children varied from 8.2 to 30.1 ml but correlated well (r = 0.93) with predicted PVI based on estimates of intracranial and spinal volumes obtained from external measurements. This study provides normal reference data in humans for assessing CSF hydrodynamics and neural axis compliance using the PVI technique in pathological settings.

Adolescent↗
PubMed1992

Time-frequency dynamics in neurally mediated syncope.

In this study, the responses during syncope were determined by noninvasive beat-to-beat analysis during passive orthostasis. Twenty patients with recurrent unexplained syncope (13 men and seven women) and ten healthy aged-matched control subjects were studied during 80 degrees head-up tilt for 25 min. Time-frequency mapping of R-R intervals, systolic and diastolic pressures and respiration was used to determine the responses to tilt. The spectral estimation was based on a modified Wigner distribution and the frequency content was evaluated on a beat-to-beat basis. Ten patients developed syncope (tilt-positive group) during tilt, while the remaining ten were asymptomatic (tilt-negative group). Control subjects reacted to tilt by the immediate shortening of R-R intervals to a plateau with an accompanying moderate increase in diastolic pressure. In the tilt-negative group the responses to tilt were similar, but of greater amplitude. In contrast, in the tilt-positive group, R-R intervals gradually and continuously decreased with tilt while systolic and diastolic pressures increased until shortly before syncope, when an abrupt fall in blood pressure followed by R-R intervals lengthening occurred. Furthermore, the R-R intervals fluctuations at both respiratory and nonrespiratory frequencies were the highest at rest as well as during tilt in the tilt-positive group. Nonrespiratory fluctuations in blood pressure increased more during tilt in both tilt-positive and negative groups compared to the control group. The nonrespiratory fluctuations in R-R intervals and blood pressure reached a maximum at syncope, simultaneously with hypotension and bradycardia. Time-frequency mapping has demonstrated that an elevated parasympathetic tone at rest which persists during orthostasis identifies patients prone to vasodepressor syncope. The counteracting sympathetic activation is not sustained and results in hypotension followed by cardioinhibition and loss of consciousness.

Adolescent↗
PubMed1997

Emergence of clusters in the hidden layer of a dynamic recurrent neural network.

The neural integrator of the oculomotor system is a privileged field for artificial neural network simulation. In this paper, we were interested in an improvement of the biologically plausible features of the Arnold-Robinson network. This improvement was done by fixing the sign of the connection weights in the network (in order to respect the biological Dale's Law). We also introduced a notion of distance in the network in the form of transmission delays between its units. These modifications necessitated the introduction of a general supervisor in order to train the network to act as a leaky integrator. When examining the lateral connection weights of the hidden layer, the distribution of the weights values was found to exhibit a conspicuous structure: the high-value weights were grouped in what we call clusters. Other zones are quite flat and characterized by low-value weights. Clusters are defined as particular groups of adjoining neurons which have strong and privileged connections with another neighborhood of neurons. The clusters of the trained network are reminiscent of the small clusters or patches that have been found experimentally in the nucleus prepositus hypoglossi, where the neural integrator is located. A study was conducted to determine the conditions of emergence of these clusters in our network: they include the fixation of the weight sign, the introduction of a distance, and a convergence of the information from the hidden layer to the motoneurons. We conclude that this spontaneous emergence of clusters in artificial neural networks; performing a temporal integration, is due to computational constraints, with a restricted space of solutions. Thus, information processing could induce the emergence of iterated patterns in biological neural networks.

Animals↗
PubMed1994

Structural dynamics of neural plasticity in the supraoptic nucleus of the rat hypothalamus during dehydration and rehydration.

It has been known that magnocellular neuroendocrine cells (MNCs) of mammalian hypothalamus show structural plasticity in response to chronic osmotic stimulation. In this study, we investigated the relationships among plasma osmolarity and several structural changes such as alterations of soma size, juxtaposition, and synapses of the supraoptic nucleus (SON) in the rat hypothalamus during dehydration and rehydration. Male rats were osmotically stimulated by supplying with 2% NaCl solution instead of tap water for 10 days, and then they were rehydrated with tap water. Plasma osmolarity was gradually elevated with progress of salt loading and returned to control level on the seventh day of rehydration. Both the percentage of membrane contact (juxtaposition) and the soma size of MNCs were increased in response to the rise of plasma osmolarity, and decreased to control level on the seventh day of rehydration. The number of synapses including both single synapses and multiple synapses per 100 microns soma membrane was lower than control on the fifth day of dehydration, but it was not different from controls on the tenth day of dehydration, and on the seventh and fourteenth day of rehydration. The total number of synapses per 100 microns soma membrane, the synaptic density, was maintained relatively constant, although soma size was progressively changed during dehydration or rehydration. This synaptic reorganization seems to be mainly regulated by synaptic sprouting during dehydration and by degradation of synapses during rehydration.(ABSTRACT TRUNCATED AT 250 WORDS)

Animals↗
PubMed1990

Toward modeling a dynamic biological neural network.

Mammalian macular endorgans are linear bioaccelerometers located in the vestibular membranous labyrinth of the inner ear. In this paper, the organization of the endorgan is interpreted on physical and engineering principles. This is a necessary prerequisite to mathematical and symbolic modeling of information processing by the macular neural network. Mathematical notations that describe the functioning system were used to produce a novel, symbolic model. The model is six-tiered and is constructed to mimic the neural system. Initial simulations show that the network functions best when some of the detecting elements (type I hair cells) are excitatory and others (type II hair cells) are weakly inhibitory. The simulations also illustrate the importance of disinhibition of receptors located in the third tier in shaping nerve discharge patterns at the sixth tier in the model system.

Acceleration↗
PubMed2004

Dynamics of neural coding in the accumbens during extinction and reinstatement of rewarded behavior.

Neural correlates of reward-seeking behavior are observed in the nucleus accumbens (NAC). The dependence of these correlates upon the presence of a reward was studied by comparing the behavioral correlates observed when the presence of the reward was manipulated within a single behavioral session. Rats were well-trained on a continuous reinforcement instrumental task reinforced by 0.1 ml drops of 5% sucrose. Extracellular single-unit neural activity was recorded from electrode arrays implanted into the NAC when instrumental behavior was and then was not reinforced with sucrose (within-session extinction). A variable delay between the instrumental response and the sucrose delivery allowed for separation of neural activity related to these task events. A spike activity increase around the time of the instrumental response was the most common behavioral correlate, while a decrease in spike activity upon sucrose delivery was the second most common behavioral correlate. Following removal of the reinforcer, subjects continued to perform the instrumental response, allowing for the examination of response-related spike activity under extinction conditions in which the response was no longer reinforced by sucrose. A majority of the response-related neural activity patterns were lost when sucrose was no longer available. New neural responses also were detected during this period. For some subjects, the reinforcer was again made available during the same session. Encoding of the primary behavioral events during this period of reinstated reinforcer was similar, but not identical, to that observed during the first period of reinforced responding. These findings reveal that instrumental task-associated spike activity within the NAC is partially dependent upon the presence of the reinforcer, and that encoding across the population is distinct under reinforced and extinction conditions.

Animals↗
PubMed2004

Dynamics of neural crest-derived cell migration in the embryonic mouse gut.

Neural crest-derived cells that form the enteric nervous system undergo an extensive migration from the caudal hindbrain to colonize the entire gastrointestinal tract. Mice in which the expression of GFP is under the control of the Ret promoter were used to visualize neural crest-derived cell migration in the embryonic mouse gut in organ culture. Time-lapse imaging revealed that GFP(+) crest-derived cells formed chains that displayed complicated patterns of migration, with sudden and frequent changes in migratory speed and trajectories. Some of the leading cells and their processes formed a scaffold along which later cells migrated. To examine the effect of population size on migratory behavior, a small number of the most caudal GFP(+) cells were isolated from the remainder of the population. The isolated cells migrated slower than cells in large control populations, suggesting that migratory behavior is influenced by cell number and cell-cell contact. Previous studies have shown that neurons differentiate among the migrating cell population, but it is unclear whether they migrate. The phenotype of migrating cells was examined. Migrating cells expressed the neural crest cell marker, Sox10, but not neuronal markers, indicating that the majority of migratory cells observed did not have a neuronal phenotype.

Animals↗
PubMed1994

Non-linear dynamics in neural networks.

A general framework for the analysis of neurons as stochastic, three-dimensionally complex and non-linear units with a range of temporal properties is outlined, and a class of problems delineated. Some general mathematical properties of the resulting network are deduced, together with information-theoretic questions to be pursued. In particular examples of the relevance of the nonlinear, temporal and stochastic properties of neurons in effective information processing are briefly outlined.

Animals↗