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At least 127 records · Page 7Linked to original sources

PubMed2006

Recognition of the physiological actions of the triphasic EMG pattern by a dynamic recurrent neural network.

Triphasic electromyographic (EMG) patterns with a sequence of activity in agonist (AG1), antagonist (ANT) and again in agonist (AG2) muscles are characteristic of ballistic movements. They have been studied in terms of rectangular pulse-width or pulse-height modulation. In order to take into account the complexity of the EMG signal within the bursts, we used a dynamic recurrent neural network (DRNN) for the identification of this pattern in subjects performing fast elbow flexion movements. Biceps and triceps EMGs were fed to all 35 fully-connected hidden units of the DRNN for mapping onto elbow angular acceleration signals. DRNN training was supervised, involving learning rule adaptations of synaptic weights and time constants of each unit. We demonstrated that the DRNN is able to perfectly reproduce the acceleration profile of the ballistic movements. Then we tested the physiological plausibility of all the networks that reached an error level below 0.001 by selectively increasing the amplitude of each burst of the triphasic pattern and evaluating the effects on the simulated accelerating profile. Nineteen percent of these simulations reproduced the physiological action classically attributed to the 3 EMG bursts: AG1 increase showed an increase of the first accelerating pulse, ANT an increase of the braking pulse and AG2 an increase of the clamping pulse. These networks also recognized the physiological function of the time interval between AG1 and ANT, reproducing the linear relationship between time interval and movement amplitude. This task-dynamics recognition has implications for the development of DRNN as diagnostic tools and prosthetic controllers.

Action Potentials↗

PubMed1978

Models of the dynamics of neural populations.

Three requirements are posed for constructing models to simulate EEG dynamics. The element of the model should be an interactive ensemble of neurons and not single neurons. The observations must be statistical, such as EEG waves and averages of unit activity over time and over local neighborhoods containing neural subsets. The state variables and operations of the model must be clearly related to behavioral functions such as sensory reception and perception. A model is presented that exemplifies these requirements. Its key feature is the dependence of its levels of interaction on the level of its input, so that with each burst of input the model switches from an equilibrium state to a limit cycle state. A mechanism is described for coding sensory input into the spatial modulation of the limit cycle activity viewed as a carrier. It is suggested that sensory recepts and percepts exist at different hierarchical levels in the brain, recepts at the level of single neurones, and percepts at the level of neural ensembles, the latter being possibly manifested in the EEG.

Animals↗

PubMed2005

Application of a neural observer to phenols ozonation in water: simulation and kinetic parameters identification.

Presented in this study, a dynamic neural network (DNN) is employed to estimate the states dynamics of the phenols-ozone-water system. A new technique based on the dynamic neural network observer (DNNO) with relay (signum) term is applied to estimate the decomposition dynamics of phenols and to identify their kinetic parameters without any mathematical model usage. The decomposition of phenols (phenol (PH), 4-chlorophenol (4-CPH) and 2,4-dichlorophenol (2,4-DCPH)) and their mixture by ozone, realized in a semi-batch reactor, is considered as a process with uncertain model ("black-box"). Only one parameter monitoring, namely, the ozone concentration in gas phase in the reactor outlet, is measured during ozonation. The variation of this variable is used to obtain the summary characteristic curve for the phenols ozonation. Then, using the experimental decomposition dynamics of phenols and of their mixture, obtained by HPLC method, the proposed DNNO is applied to estimate the ozonation constants of phenols at the different pH 2-12. A good correspondence between the decomposition dynamics and the estimated ones by DNNO is obtained.

Computer Simulation↗

PubMed2004

Dynamics of everyday life: rigorous modular modeling in neurobiology based on Bloch's dynamical theorem.

Natural, everyday sensorimotor behaviors, such as rising from sitting, typically have an intrinsic organization of several levels of analysis. Taking this intrinsic organization as key to understanding neural dynamics is neither a top-down nor a bottom-up approach, but rather a meshing of multiple centers and levels of analysis. Motor control requires body dynamics that are consistent with physical dynamics, besides the more microscopic levels of neural dynamics. The dynamics of separate movements have been investigated as if the ends can be capped off, separated from the rest of the individual's life. Is this dynamically correct? Even chaotic behavior is deterministic. However, the mathematics of nonlinear oscillations is not all of dynamics. This paper relates Bloch's dynamical theorem to the modular, conditional approach to sensorimotor and other neural functioning. Bloch's dynamical theorem lays a foundation for the piecewise study of structurally accurate dynamics in theoretical neurobiology. Piecewise studies can be used as a modeling option complementary to the methods of nonlinear oscillator dynamics. By applying Bloch's theorem, dynamics of movements analyzed piecewise can be extended into a smooth flow on any manifold, either as a whole or conditionally. Conditional dynamics makes dynamical modeling options explicit, often depending on what variables the organism can control, and allows one to take different modeling options at different junctures in analyzing the same phenomenon. For example, this approach allows the study of complex motor control problems to be reduced to modular constructions using singularities and flow lines. Dynamical contingencies are expressed using the mathematics of ordered structures. This paper presents Bloch's dynamical theorem and its relevance to model construction in theoretical neurobiology. Specific examples, integrated into physiological and behavioral context, are cited from the literature.

Models, Neurological↗

PubMed1997

Training Dynamics and Neural Network Performance.

We use an analysis of a simple model of recurrent network dynamics to gain qualitative insights into the training dynamics of feedforward multilayer perceptrons (MLPs) used for classification. These insights suggest changes to the training methods used for MLPs that improve network performance significantly. In previous work, the probabilistic neural network (PNN) was shown to provide better zero-reject error performance on character and fingerprint classification problems than radial basis function and MLP-based neural network methods. We will show that performance equal to or better than PNN can be achieved with a single three-layer MLP by making fundamental changes in the network optimization strategy. These changes are: 1) use of neuron activation functions, which reduce the probability of singular Jacobians; 2) use of successive regularization to constrain the volume of the minimized weight space; 3) use of Boltzmann pruning to constrain the dimension of the weight space; 4) use of Prior class probabilities to normalize all error calculations, so that statistically significant samples of rare but important classes can be included without distorting the error surface. All four of these changes are made in the inner loop of a conjugate gradient optimization iteration and are intended to simplify the training dynamics of the optimization. On handprinted digits and fingerprint classification problems these modifications improve error-reject performance by factors between 2 and 4, and reduce network size by 40-60%. Copyright 1997 Elsevier Science Ltd.

Journal Article↗

PubMed1998

Structural change and development in real and artificial neural networks.

Two related but different fields are reviewed. Initially some basic facts about developing real brains are set out and then work on dynamic neural networks is described. A dynamic neural network is defined as any artificial neural network that automatically changes its structure through exposure to input stimuli. Various models are described and evaluated and the functional correlates of both regressive and progressive structural changes are discussed. The paper concludes that, if future modelling work is to be set within a more neurally-plausible framework, then it would be fruitful to examine networks in which the connectivity between extant units is progressively embellished.

Journal Article↗

PubMed1999

Quantum dissipation and neural net dynamics.

Inspired by the dissipative quantum model of brain, we model the states of neural nets in terms of collective modes by the help of the formalism of Quantum Field Theory. We exhibit an explicit neural net model which allows to memorize a sequence of several informations without reciprocal destructive interference, namely we solve the overprinting problem in such a way last registered information does not destroy the ones previously registered. Moreover, the net is able to recall not only the last registered information in the sequence, but also anyone of those previously registered.

Nerve Net↗

PubMed2002

A minor subspace algorithm based on neural Stiefel dynamics.

In the present paper we investigate iterative minor subspace analysis computation by describing a neural approach based on weight flow on Stiefel manifold and by discussing four neural algorithms and a purely algebraic algorithm known from the scientific literature. A comparison of numerical experimental results and computational complexity estimates confirms the effectiveness and efficiency of the proposed approach.

Algorithms↗

PubMed2003

The time-organized map algorithm: extending the self-organizing map to spatiotemporal signals.

The new time-organized map (TOM) is presented for a better understanding of the self-organization and geometric structure of cortical signal representations. The algorithm extends the common self-organizing map (SOM) from the processing of purely spatial signals to the processing of spatiotemporal signals. The main additional idea of the TOM compared with the SOM is the functionally reasonable transfer of temporal signal distances into spatial signal distances in topographic neural representations. This is achieved by neural dynamics of propagating waves, allowing current and former signals to interact spatiotemporally in the neural network. Within a biologically plausible framework, the TOM algorithm (1) reveals how dynamic neural networks can self-organize to embed spatial signals in temporal context in order to realize functional meaningful invariances, (2) predicts time-organized representational structures in cortical areas representing signals with systematic temporal relation, and (3) suggests that the strength with which signals interact in the cortex determines the type of signal topology realized in topographic maps (e.g., spatially or temporally defined signal topology). Moreover, the TOM algorithm supports the explanation of topographic reorganizations based on time-to-space transformations (Wiemer, Spengler, Joublin, Stagge, & Wacquant, 2000).

Algorithms↗

PubMed2004

Temporal dynamics of neural adaptation effect in the human visual ventral stream.

When the same visual stimulus is repeatedly presented with a brief interval, the brain responses to that stimulus are attenuated relative to those at first presentation [neural adaptation (NA)]. Although this effect has been widely observed in various regions of human brain, its temporal dynamics as a neuronal population has been mostly unclear. In the present study, we used a magnetoencephalography (MEG) and conducted a macrolevel investigation of the temporal profiles of the NA occurring in the human visual ventral stream. The combination of MEG with our previous random dot blinking method isolated the neural responses in the higher visual cortex relating to shape perception. We dissociated three dimensions of the NA: activation strength, peak latency, and temporal duration of neural response. The results revealed that visual responses to the repeated compared with novel stimulus showed a significant reduction in both activation strength and peak latency but not in the duration of neural processing. Furthermore, this acceleration of peak latency showed a significant correlation with reaction time of the subjects, whereas no correlation was found between the reaction time and the temporal duration of neural responses. These results indicate that (1) the NA involves the brain response changes in the temporal domain as well as the response attenuation reported previously, and (2) this temporal change is primarily observed as a rapid rising of "what" responses, rather than a temporal shortening of neural response curves within the visual ventral stream as considered previously.

Adult↗

PubMed2005

Causal connectivity of evolved neural networks during behavior.

To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method, called 'causal connectivity analysis' is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics.

Aging↗

PubMed2001

The brain basis of a "consciousness monitor": scientific and medical significance.

Surgical patients under anesthesia can wake up unpredictably and be exposed to intense, traumatic pain. Current medical techniques cannot maintain depth of anesthesia at a perfectly stable and safe level; the depth of unconsciousness may change from moment to moment. Without an effective consciousness monitor anesthesiologists may not be able to adjust dosages in time to protect patients from pain. An estimated 40,000 to 200,000 midoperative awakenings may occur in the United States annually. E. R. John and coauthors present the scientific basis of a practical "consciousness monitor" in two articles. One article is empirical and shows widespread and consistent electrical field changes across subjects and anesthetic agents as soon as consciousness is lost; these changes reverse when consciousness is regained afterward. These findings form the basis of a surgical consciousness monitor that recently received approval from the U.S. Food and Drug Administration. This may be the first practical application of research on the brain basis of consciousness. The other John article suggests theoretical explanations at three levels, a neurophysiological account of anesthesia, a neural dynamic account of conscious and unconscious states, and an integrative field theory. Of these, the neurophysiology is the best understood. Neural dynamics is evolving rapidly, with several alternative points of view. The field theory sketched here is the most novel and controversial.

Anesthesia, General↗

PubMed2005

A learning rule for the emergence of stable dynamics and timing in recurrent networks.

Neural dynamics within recurrent cortical networks is an important component of neural processing. However, the learning rules that allow networks composed of hundreds or thousands of recurrently connected neurons to develop stable dynamical states are poorly understood. Here I use a neural network model to examine the emergence of stable dynamical states within recurrent networks. I describe a learning rule that can account both for the development of stable dynamics and guide networks to states that have been observed experimentally, specifically, states that instantiate a sparse code for time. Across trials, each neuron fires during a specific time window; by connecting the neurons to a hypothetical set of output units, it is possible to generate arbitrary spatial-temporal output patterns. Intertrial jitter of the spike time of a given neuron increases as a direct function of the delay at which it fires. These results establish a learning rule by which cortical networks can potentially process temporal information in a self-organizing manner, in the absence of specialized timing mechanisms.

Action Potentials↗

PubMed2006

Dynamics of neural populations: stability and synchrony.

A population formulation of neuronal activity is employed to study an excitatory network of (spiking) neurons receiving external input as well as recurrent feedback. At relatively low levels of feedback, the network exhibits time stationary asynchronous behavior. A stability analysis of this time stationary state leads to an analytical criterion for the critical gain at which time asynchronous behavior becomes unstable. At instability the dynamics can undergo a supercritical Hopf bifurcation and the population passes to a synchronous state. Under different conditions it can pass to synchrony through a subcritical Hopf bifurcation. And at high gain a network can reach a runaway state, in finite time, after which the network no longer supports bounded solutions. The introduction of time delayed feedback leads to a rich range of phenomena. For example, for a given external input, increasing gain produces transition from asynchrony, to synchrony, to asynchrony and finally can lead to divergence. Time delay is also shown to strongly mollify the amplitude of synchronous oscillations. Perhaps, of general importance, is the result that synchronous behavior can exist only for a narrow range of time delays, which range is an order of magnitude smaller than periods of oscillation.

Action Potentials↗

PubMed2005

The dynamic wave expansion neural network model for robot motion planning in time-varying environments.

We introduce a new type of neural network--the dynamic wave expansion neural network (DWENN)--for path generation in a dynamic environment for both mobile robots and robotic manipulators. Our model is parameter-free, computationally efficient, and its complexity does not explicitly depend on the dimensionality of the configuration space. We give a review of existing neural networks for trajectory generation in a time-varying domain, which are compared to the presented model. We demonstrate several representative simulative comparisons as well as the results of long-run comparisons in a number of randomly-generated scenes, which reveal that the proposed model yields dominantly shorter paths, especially in highly-dynamic environments.

Central Nervous System↗

PubMed2004

A neural circuit basis for spatial working memory.

The maintenance of a mental image in memory over a time scale of seconds is mediated by the persistent discharges of neurons in a distributed brain network. The representation of the spatial location of a remembered visual stimulus has been studied most extensively and provides the best-understood model of how mnemonic information is encoded in the brain. Neural correlates of spatial working memory are manifested in multiple brain areas, including the prefrontal and parietal association cortices. Spatial working memory ability is severely compromised in schizophrenia, a condition that has been linked to prefrontal cortical malfunction. Recent computational modeling work, in interplay with physiological studies of behaving monkeys, has begun to identify microcircuit properties and neural dynamics that are sufficient to generate memory-related persistent activity in a recurrent network of excitatory and inhibitory neurons during spatial working memory. This review summarizes recent results and discusses issues of current debate. It is argued that understanding collective neural dynamics in a recurrent microcircuit provides a key step in bridging the gap between network memory function and its underlying cellular mechanisms. Progress in this direction will shed fundamental insights into the neural basis of spatial working memory impairment associated with mental disorders.

Animals↗

PubMed2005

Neural pattern dynamics in an oscillator model of the thalamo-reticular system.

One of development issues for information processing with synchronous oscillations in the brain is how new information is coded and how a comparison with already existing information is performed. In the present work we study a simple neural network model of the thalamo-reticular system based on the Wilson-Cowan model of neuronal oscillatory behavior. Our results show that both cortical control over the thalamus and external sensory input are essential in coordinating and generating spatio-temporal patterns of synchronous activity. A main finding of the numerical simulations is that the network connectivity and the intrinsic oscillatory properties of the neurons result in distinct collective behaviors within the network. By varying the connectivity schemes comparable with lesionated or damaged brain regions our results are in good agreement with in vivo experimental results. Suppressing the sensory input results in temporal oscillatory activity in the beta and gamma range and a strong spatial dependence of the network activity.

Algorithms↗

PubMed1999

Dynamics of neural networks with a central element.

A neural network is considered which is designed as a system of phase oscillators and contains a central oscillator that interacts with a number of peripheral oscillators. Analytical and simulation methods are used to study the dynamics of the system that is conditioned by the interaction parameters and natural frequencies of the oscillators. The boundaries of parameter regions are found that correspond to the synchronization of the whole network or to partial synchronization between the central oscillator and a group of peripheral oscillators. For a system with two peripheral oscillators the bifurcation analysis is applied to describe the changes of synchronization modes. The implications of the results for attention modeling are discussed.

Journal Article↗