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At least 73 records · Page 4Linked to original sources
PubMed2006

Neural dynamics for facial threat processing as revealed by gamma band synchronization using MEG.

Facial threat conveys important information about imminent environmental danger. The rapid detection of this information is critical for survival and social interaction. However, due to technical and methodological difficulties, the spatiotemporal profile for facial threat processing is unknown. By utilizing magnetoencephalography (MEG), a brain-imaging technique with superb temporal resolution and fairly good spatial resolution, Synthetic Aperture Magnetometry (SAM), a recently developed source analysis technique, and a sliding window analysis, we identified the spatiotemporal development of facial threat processing in the gamma frequency band. We also tested the dual-route hypothesis by LeDoux who proposed, based on animal research, that there are two routes to the amygdala: a quick subcortical route and a slower and cortical route. Direct evidence with humans supporting this model has been lacking. Moreover, it has been unclear whether the subcortical route responds specifically to fearful expressions or to threatening expressions in general. We found early event-related synchronizations (ERS) in response to fearful faces in the hypothalamus/thalamus area (10-20 ms) and then the amygdala (20-30 ms). This was even earlier than the ERS response seen to fearful faces in visual cortex (40-50 ms). These data support LeDoux's suggestion of a quick, subcortical thamalo-amygdala route. Moreover, this route was specific for fear expressions; the ERS response in the amygdala to angry expressions had a late onset (150-160 ms). The ERS onset in prefrontal cortex followed that seen within the amygdala (around 160-210 ms). This is consistent with its role in higher-level emotional/cognitive processing.

Adult↗
PubMed1995

Saccadic motor planning by integrating visual information and pre-information on neural dynamic fields.

A functional model of target selection in the saccadic system is presented, incorporating elements of visual processing, motor planning, and motor control. We address the integration of visual information with pre-information, which is provided by manipulating the probability that a target appears at a certain location. This integration is achieved within a dynamic representation of planned eye movement which is modeled through distributions of activation on a topographic field. Visual input evokes activation, which is also constrained by lateral interaction within the field and by preshaping input representing pre-information. The model describes target selection observable in paradigms in which visual goals are presented at more than one location. Specifically, we model the transition from averaging, where endpoints of first saccades fall between two visual target locations, to decision making, where endpoints of first saccades fall accurately onto one of two simultaneously presented visual targets. We make predictions about how metrical biases of first saccades are induced by pre-information about target locations acquired by learning. When coupled to a motor control stage, activation dynamics on the planning level contribute to stabilizing gaze under fixation conditions. The neurophysiological relevance of our functional model is discussed.

Computer Simulation↗
PubMed2005

Neural dynamics of cross-modal and cross-temporal associations.

We have studied a neurodynamic model of cross-modal and cross-temporal associations. We show that a network of integrate-and-fire neurons can generate spiking activity with realistic dynamics during the delay period of a paired associates task. In particular, the activity of the model resembles reported data from single-cell recordings in the prefrontal cortex.

Acoustic Stimulation↗
PubMed1997

First passage statistics of periodically driven models for neural dynamics.

The dynamics of two simple neuron models subjected to periodic stimuli is studied at the level of the first passage time probability density. The results from analytic treatments of these models are presented, and a discussion of the important properties displayed by these models is given. Specifically, both models possess resonans phenomena in the first passage probability distribution, arising from of the interplay of characteristic time-scales in the system.

Models, Biological↗
PubMed2005

Neural dynamics of error processing in medial frontal cortex.

Adaptive behavior requires an organism to evaluate the outcome of its actions, such that future behavior can be adjusted accordingly and the appropriate response selected. During associative learning, the time at which such evaluative information is available changes as learning progresses, from the delivery of performance feedback early in learning to the execution of the response itself during learned performance. Here, we report a learning-dependent shift in the timing of activation in the rostral cingulate zone of the anterior cingulate cortex from external error feedback to internal error detection. This pattern of activity is seen only in the anterior cingulate, not in the pre-supplementary motor area. The dynamics of these reciprocal changes are consistent with the claim that the rostral cingulate zone is involved in response selection on the basis of the expected outcome of an action. Specifically, these data illustrate how the anterior cingulate receives evaluative information, indicating that an action has not produced the desired result.

Adaptation, Psychological↗
PubMed1998

Nonstationarity in epileptic EEG and implications for neural dynamics.

In this paper, we use a recently developed method to analyze the nonstationarity in time series from intracranial depth and subdural recordings of patients with temporal lobe epilepsy. We show that the nonstationarity in the signal can be accounted for by the variation of a single parameter. We then show that the various dominant nonlinear waveforms observed in different electrodes can be explained by a simple stochastic model in which the mesoscopic collection of neurons, whose potential the electrodes measure, can be on one of two states. The nonstationarity observed in our analysis is a consequence of a time-dependent transition probability between these two states. In general, this transition probability increases as a seizure is approached. The model that we propose incorporates this bistability. We find good agreement between real data and simulated data generated by our model. We understand that this mesoscopic bistability may be associated with the existence of excitation waves traversing the brain in these patients.

Electroencephalography↗
PubMed1999

Neural dynamics of binocular brightness perception.

How does the visual cortex combine information from both eyes to generate perceptual representations of object surfaces? Important clues about this process may be derived from data about the perceived brightness of surface regions under binocular viewing conditions, including data about binocular brightness summation in response to Ganzfelds, the U-shaped data of Fechner's paradox that violates binocular brightness summation, and the effects of different combinations of monocular and binocular contours and surface luminance differences on threshold sensitivity to monocular flashes of light. How to reconcile these apparently contradictory data properties has been a severe challenge to previous models, and none has explained them all. The present article quantitatively simulates them all by further developing the FACADE vision model. Key model processes discount the illuminant and compute image contrasts in each monocular channel using shunting on-center off-surround networks; binocularly fuse these discounted monocular signals using shunting on-center off-surround networks with nonlinear excitatory and inhibitory signals; and use these binocularly fused activities to trigger filling-in of a binocular surface representation that represents perceived surface brightness. Previous models that have suggested explanations of subsets of these data are discussed.

Contrast Sensitivity↗
PubMed1997

Dynamical Neural Networks that Ensure Exponential Identification Error Convergence.

Classical adaptive and robust adaptive schemes, are unable to ensure convergence of the identification error to zero, in the case of modeling errors. Therefore, the usage of such schemes to "black-box" identification of nonlinear systems ensures-in the best case-bounded identification error. In this paper, new learning (adaptive) laws are proposed which when applied to recurrent high order neural networks (RHONN) ensure that the identification error converges to zero exponentially fast, and even more, in the case where the identification error is initially zero, it remains equal to zero during the whole identification process. The parameter convergence properties of the proposed scheme, that is, their capability of converging to the optimal neural network model, is also examined; it is shown to be similar to that of classical adaptive and parameter estimation schemes. Finally, it is mentioned that the proposed learning laws are not locally implementable, as they make use of global knowledge of signals and parameters. Copyright 1997 Elsevier Science Ltd. All Rights Reserved.

Journal Article↗
PubMed2003

Principles of pleasure prediction: specifying the neural dynamics of human reward learning.

Accumulating evidence from nonhuman primates suggests that midbrain dopamine cells code reward prediction errors and that this signal subserves reward learning in dopamine-receiving brain structures. In this issue of Neuron, McClure et al. and O'Doherty et al. use event-related fMRI to provide some of the strongest evidence to date that the reward prediction error model of dopamine system activity applies equally well to human reward learning.

Animals↗
PubMedDate not supplied

Neural dynamics in a model of the thalamocortical system. I. Layers, loops and the emergence of fast synchronous rhythms.

A large-scale computer model was constructed to gain insight into the structural basis for the generation of fast synchronous rhythms (20-60 Hz) in the thalamocortical system. The model consisted of 65,000 spiking neurons organized topographically to represent sectors of a primary and secondary area of mammalian visual cortex, and two associated regions of the dorsal thalamus and the thalamic reticular nucleus. Cortical neurons, both excitatory and inhibitory, were organized in supragranular layers, infraganular layers and layer IV. Reciprocal intra- and interlaminar, interareal, thalamocortical, corticothalamic and thalamoreticular connections were set up based on known anatomical constraints. Simulations of neuronal responses to visual input revealed sporadic epochs of synchronous oscillations involving all levels of the model, similar to the fast rhythms recorded in vivo. By systematically modifying physiological and structural parameters in the model, specific network properties were found to play a major role in the generation of this rhythmic activity. For example, fast synchronous rhythms could be sustained autonomously by lateral and interlaminar interactions within and among local cortical circuits. In addition, these oscillations were propagated to the thalamus and amplified by corticothalamocortical loops, including the thalamic reticular complex. Finally, synchronous oscillations were differentially affected by lesioning forward and backward interareal connections.

Animals↗
PubMed2006

Application of generalized dynamic neural networks to biomedical data.

This paper reviews the application of continuous recurrent neural networks with time-varying weights to pattern recognition tasks in medicine. A general learning algorithm based on Pontryagin's maximum principle is recapitulated, and possibilities of improving the generalization capabilities of these networks are given. The effectiveness of the methods is demonstrated by three different real-world examples taken from the fields of anesthesiology, orthopedics, and radiology.

Algorithms↗