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PubMed1993

A dynamical neural network model for motor cortical activity during movement: population coding of movement trajectories.

As a dynamical model for motor cortical activity during hand movement we consider an artificial neural network that consists of extensively interconnected neuron-like units and performs the neuronal population vector operations. Local geometrical parameters of a desired curve are introduced into the network as an external input. The output of the model is a time-dependent direction and length of the neuronal population vector which is calculated as a sum of the activity of directionally tuned neurons in the ensemble. The main feature of the model is that dynamical behavior of the neuronal population vector is the result of connections between directionally tuned neurons rather than being imposed externally. The dynamics is governed by a system of coupled nonlinear differential equations. Connections between neurons are assigned in the simplest and most common way so as to fulfill basic requirements stemming from experimental findings concerning the directional tuning of individual neurons and the stabilization of the neuronal population vector, as well as from previous theoretical studies. The dynamical behavior of the model reveals a close similarity with the experimentally observed dynamics of the neuronal population vector. Specifically, in the framework of the model it is possible to describe a geometrical curve in terms of the time series of the population vector. A correlation between the dynamical behavior of the direction and the length of the population vector entails a dependence of the "neural velocity" on the curvature of the tracing trajectory that corresponds well to the experimentally measured covariation between tangential velocity and curvature in drawing tasks.

Animals↗

PubMed2004

Foetal ECG recovery using dynamic neural networks.

Non-invasive electrocardiography has proven to be a very interesting method for obtaining information about the foetus state and thus to assure its well-being during pregnancy. One of the main applications in this field is foetal electrocardiogram (ECG) recovery by means of automatic methods. Evident problems found in the literature are the limited number of available registers, the lack of performance indicators, and the limited use of non-linear adaptive methods. In order to circumvent these problems, we first introduce the generation of synthetic registers and discuss the influence of different kinds of noise to the modelling. Second, a method which is based on numerical (correlation coefficient) and statistical (analysis of variance, ANOVA) measures allows us to select the best recovery model. Finally, finite impulse response (FIR) and gamma neural networks are included in the adaptive noise cancellation (ANC) scheme in order to provide highly non-linear, dynamic capabilities to the recovery model. Neural networks are benchmarked with classical adaptive methods such as the least mean squares (LMS) and the normalized LMS (NLMS) algorithms in simulated and real registers and some conclusions are drawn. For synthetic registers, the most determinant factor in the identification of the models is the foetal-maternal signal-to-noise ratio (SNR). In addition, as the electromyogram contribution becomes more relevant, neural networks clearly outperform the LMS-based algorithm. From the ANOVA test, we found statistical differences between LMS-based models and neural models when complex situations (high foetal-maternal and foetal-noise SNRs) were present. These conclusions were confirmed after doing robustness tests on synthetic registers, visual inspection of the recovered signals and calculation of the recognition rates of foetal R-peaks for real situations. Finally, the best compromise between model complexity and outcomes was provided by the FIR neural network. Both the methodology for selecting a model and the introduction of advanced neural models are the main contributions of this paper.

Electrocardiography↗

PubMed1998

Neural dynamics of short and medium-term motor control effects of levodopa therapy in Parkinson's disease.

A neural network model of movement control in normal and Parkinson's disease (PD) conditions is proposed to simulate the time-varying dose-response relationship underlying the effects of levodopa on movement amplitude and movement duration in PD patients. Short and long-term dynamics of cell activations and neurotransmitter mechanisms underlying the differential expression of neuropeptide messenger RNA within the basal ganglia striatum are modeled to provide a mechanistic account for the effects of levodopa medication on motor performance (e.g. the pharmacodynamics). Experimental and neural network simulation data suggest that levodopa therapy in Parkinson's disease has differential effects on cell activities, striatal neuropeptides, and motor behavior. In particular, it is shown how dopamine depletion in the striatum may modulate differentially the level of substance P and enkephalin messenger RNA in the direct and indirect basal ganglia pathways. This dissociation in the magnitude and timing of peptide expression causes an imbalance in the opponently organized basal ganglia pathways which results in Parkinsonian motor deficits. The model is validated with experimental data obtained from handwriting movements performed by PD subjects before and after medication intake. The results suggest that fine motor control analysis and network modeling of the effects of dopamine in motor control are useful tools in drug development and in the optimization of pharmacological therapy in PD patients.

Aged↗

PubMed2001

Radical embodiment: neural dynamics and consciousness.

We propose a new approach to the neuroscience of consciousness, growing out of the 'enactive' viewpoint in cognitive science. This approach aims to map the neural substrates of consciousness at the level of large-scale, emergent and transient dynamical patterns of brain activity (rather than at the level of particular circuits or classes of neurons), and it suggests that the processes crucial for consciousness cut across the brain-body-world divisions, rather than being brain-bound neural events. Whereas standard approaches to the neural correlates of consciousness have assumed a one-way causal-explanatory relationship between internal neural representational systems and the contents of consciousness, our approach allows for theories and hypotheses about the two-way or reciprocal relationship between embodied conscious states and local neuronal activity.

Journal Article↗

PubMed1999

Neural dynamics of perceptual order and context effects for variable-rate speech syllables.

How does the brain extract invariant properties of variable-rate speech? A neural model, called PHONET, is developed to explain aspects of this process and, along the way, data about perceptual context effects. For example, in consonant-vowel (CV) syllables, such as /ba/ and /wa/, an increase in the duration of the vowel can cause a switch in the percept of the preceding consonant from /w/ to /b/ (J.L. Miller & Liberman, 1979). The frequency extent of the initial formant transitions of fixed duration also influences the percept (Schwab, Sawusch, & Nusbaum, 1981). PHONET quantitatively simulates over 98% of the variance in these data, using a single set of parameters. The model also qualitatively explains many data about other perceptual context effects. In the model, C and V inputs are filtered by parallel auditory streams that respond preferentially to the transient and sustained properties of the acoustic signal before being stored in parallel working memories. A lateral inhibitory network of onset- and rate-sensitive cells in the transient channel extracts measures of frequency transition rate and extent. Greater activation of the transient stream can increase the processing rate in the sustained stream via a cross-stream automatic gain control interaction. The stored activities across these gain-controlled working memories provide a basis for rate-invariant perception, since the transient-to-sustained gain control tends to preserve the relative activities across the transient and sustained working memories as speech rate changes. Comparisons with alternative models tested suggest that the fit cannot be attributed to the simplicity of the data. Brain analogues of model cell types are described.

Attention↗

PubMed1998

A dynamic neural network with temporal coding and functional connectivity.

A neural network model capable of altering its pattern classifying properties by program input is proposed. Here the "program input" is another source of input besides the pattern input. Unlike most neural network models, this model runs as a deterministic point process of spikes in continuous time; connections among neurons have finite delays, which are set randomly according to a normal distribution. Furthermore, this model utilizes functional connectivity which is dynamic connectivity among neurons peculiar to temporal-coding neural networks with short neuronal decay time constants. Computer simulation of the proposed network has been performed, and the results are considered in light of experimental results shown recently for correlated firings of neurons.

Computer Simulation↗

PubMed2006

Neural dynamics of autistic behaviors: cognitive, emotional, and timing substrates.

What brain mechanisms underlie autism, and how do they give rise to autistic behavioral symptoms? This article describes a neural model, called the Imbalanced Spectrally Timed Adaptive Resonance Theory (iSTART) model, that proposes how cognitive, emotional, timing, and motor processes that involve brain regions such as the prefrontal and temporal cortex, amygdala, hippocampus, and cerebellum may interact to create and perpetuate autistic symptoms. These model processes were originally developed to explain data concerning how the brain controls normal behaviors. The iSTART model shows how autistic behavioral symptoms may arise from prescribed breakdowns in these brain processes, notably a combination of underaroused emotional depression in the amygdala and related affective brain regions, learning of hyperspecific recognition categories in the temporal and prefrontal cortices, and breakdowns of adaptively timed attentional and motor circuits in the hippocampal system and cerebellum. The model clarifies how malfunctions in a subset of these mechanisms can, through a systemwide vicious circle of environmentally mediated feedback, cause and maintain problems with them all.

Affect↗

PubMed1998

Effect of environmental sound familiarity on dynamic neural activation/inhibition patterns: an ERD mapping study.

The aim of this study was to analyze the timing and topography of brain activity in relation to the cognitive processing of different types of auditory information. We specifically investigated the effects of familiarity on environmental sound identification, an issue which has been little studied with respect to cognitive processes, neural substrates, and time course of brain activity. To address this issue, we implemented and applied an electroencephalographic mapping method named event-related desynchronization, which allows one to assess the dynamics of neuronal activity with high temporal resolution (here, 125 ms); we used 19 recording electrodes with standard positioning. We designed an activation paradigm in which healthy subjects were asked to discriminate binaurally heard sounds belonging to one of two distinct categories, "familiar" (i.e., natural environmental sounds) or "unfamiliar" (i.e., altered environmental sounds). The sounds were selected according to strict preexperimental tests so that the former should engage greater semantic, and the latter greater structural, analysis, which we predicted to preferentially implicate left posterior and right brain regions, respectively. During the stimulations, significant desynchronizations (thought to reflect neuronal activations) were recorded over left hemisphere regions for familiar sounds and right temporofrontal regions for unfamiliar sounds, but with only few significant differences between the two sound categories and a common bilateral activation in the frontal regions. However, strongly significant differences between familiar and unfamiliar sounds occurred near the end of and following the stimulations, due to synchronizations (though to reflect deactivations) which appeared over the left posterior regions, as well as the vertex and bilateral frontal cortex, only after unfamiliar sounds. These unexpected synchronizations after the unfamiliar stimuli may reflect an awareness of the unfamiliarity of such sounds, which may have induced an inhibition of semantic and episodic representations because the latter could not be associated with meaningless sounds.

Arousal↗

PubMed2006

A machine learning approach to the analysis of time-frequency maps, and its application to neural dynamics.

The statistical analysis of experimentally recorded brain activity patterns may require comparisons between large sets of complex signals in order to find meaningful similarities and differences between signals with large variability. High-level representations such as time-frequency maps convey a wealth of useful information, but they involve a large number of parameters that make statistical investigations of many signals difficult at present. In this paper, we describe a method that performs drastic reduction in the complexity of time-frequency representations through a modelling of the maps by elementary functions. The method is validated on artificial signals and subsequently applied to electrophysiological brain signals (local field potential) recorded from the olfactory bulb of rats while they are trained to recognize odours. From hundreds of experimental recordings, reproducible time-frequency events are detected, and relevant features are extracted, which allow further information processing, such as automatic classification.

Algorithms↗

PubMed2004

Improving generalization capabilities of dynamic neural networks.

This work addresses the problem of improving the generalization capabilities of continuous recurrent neural networks. The learning task is transformed into an optimal control framework in which the weights and the initial network state are treated as unknown controls. A new learning algorithm based on a variational formulation of Pontrayagin's maximum principle is proposed. Under reasonable assumptions, its convergence is discussed. Numerical examples are given that demonstrate an essential improvement of generalization capabilities after the learning process of a dynamic network.

Algorithms↗

PubMed2001

Neural dynamics in cortical networks--precision of joint-spiking events.

Electrophysiological studies of cortical function on the basis of multiple single-neuron recordings reveal neuronal interactions which depend on stimulus context and behavioural events. These interactions exhibit dynamics on different time scales, with time constants down to the millisecond range. Mechanisms underlying such dynamic organization of the cortical network were investigated by experimental and theoretical approaches. We review some recent results from these studies, concentrating on the occurrence of precise joint-spiking events in cortical activity, both in physiological and in model neural networks. These findings suggest that a combinatorial neural code, based on rapid associations of groups of neurons co-ordinating their activity at the single spike level, is biologically feasible.

Action Potentials↗

PubMed2004

Neural dynamics and the fundamental mechanisms of event-related brain potentials.

Event-related potentials (ERPs) provide a critical link between the hemodynamic response, as measured by functional magnetic resonance imaging, and the dynamics of the underlying neuronal activity. Single-trial ERP recordings capture the oscillatory activity that are hypothesized to underlie both communication between brain regions and amplified processing of behaviorally relevant stimuli. However, precise interpretations of ERPs are precluded by uncertainty about their neural mechanisms. One influential theory holds that averaged sensory ERPs are generated by partial phase resetting of ongoing electroencephalographic oscillations, while another states that ERPs result from stimulus-evoked neural responses. We formulated critical predictions of each theory and tested these using direct, intracortical analyses of neural activity in monkeys. Our findings support a predominant role for stimulus-evoked activity in sensory ERP generation, and they outline both logic and methodology necessary for differentiating evoked and phase resetting contributions to cognitive and motor ERPs in future studies.

Animals↗

PubMed1996

Neural dynamics in cortex-striatum co-cultures--I. anatomy and electrophysiology of neuronal cell types.

An in vitro system was established to analyse corticostriatal processing. Cortical and striatal slices taken at postnatal days 0-2 were co-cultured for three to six weeks. The anatomy of the organotypic co-cultures was determined using immunohistochemistry. In the cortex parvalbumin-positive and calbindin-positive cells, which resembled those seen in vivo, had laminar distributions. In the striatum, strongly stained parvalbumin-positive cells resembling striatal GABAergic interneurons and cholinergic interneurons were scattered throughout the tissue. The soma area of these interneuron classes was larger than the average striatal soma area, thus enabling visual selection of cells by class before recording. Cortical neurons with projections to the striatum showed similar morphological features to corticostriatal projection neurons in vivo. No projections from the striatum to the cortex were found. Intracellular recordings were obtained from 94 neurons. These were first classified on the basis of electrophysiological characteristics and the morphologies of cells in each class were reconstructed. Two types of striatal secondary neurons with unique electrophysiological dynamics were identified: GABAergic interneurons (n = 17) and large aspiny, probably cholinergic, interneurons (n = 15). The electrophysiological and morphological characteristics of cortical pyramidal cells (n = 27), cortical interneurons (n = 1), as well as striatal principal neurons (n = 34), were identical to those reported for similar ages in vivo. Organotypic cortex-striatum co-cultures are therefore suitable as an in vitro system in which to analyse corticostriatal processing. The network dynamics, which developed spontaneously in that system, are examined in the companion paper.

Animals↗

PubMed2004

Evidence for complex system integration and dynamic neural regulation of skeletal muscle recruitment during exercise in humans.

A model is proposed in which the development of physical exhaustion is a relative rather than an absolute event and the sensation of fatigue is the sensory representation of the underlying neural integrative processes. Furthermore, activity is controlled as part of a pacing strategy involving active neural calculations in a "governor" region of the brain, which integrates internal sensory signals and information from the environment to produce a homoeostatically acceptable exercise intensity. The end point of the exercise bout is the controlling variable. This is an example of a complex, non-linear, dynamic system in which physiological systems interact to regulate activity before, during, and after the exercise bout.

Exercise↗

PubMed1986

Neural dynamics of speech and language coding: developmental programs, perceptual grouping, and competition for short-term memory.

A computational theory of how an observer parses a speech stream into context-sensitive language representations is described. It is shown how temporal lists of events can be chunked into unitized representations, how perceptual groupings of past item sublists can be reorganized due to information carried by newly occurring items, and how item information and temporal order information are bound together into context-sensitive codes. These language units are emergent properties due to intercellular interactions among large numbers of nerve cells. The controlling neural networks can arise through simple rules of neuronal development: random growth of connections along spatial gradients, activity-dependent self-similar cell growth, and competition for conserved synaptic sites. Within these networks, a spatial frequency analysis of temporally evolving activity patterns leads to competitive masking of inappropriate list encodings in short term memory. The neurons obey membrane equations undergoing shunting recurrent on-center off-surround interactions. Several design principles are embodied by the networks, such as the sequence masking principle, the long-term memory invariance principle, and the principle of self-similar growth.

Auditory Pathways↗

PubMed1995

Saccadic reaction times in gap/overlap paradigms: a model based on integration of intentional and visual information on neural, dynamic fields.

The systematic variations of regular saccadic reaction times induced in gap/overlap paradigms are addressed by a quantitative model. Intentional and visual information are integrated on a retinotopic representation of visual space, on which activity dynamics is related to movement initiation. Using a specific conception of "motor preparation", known effects of general warnings and fixation point on- and offsets are reproduced. Results of new experiments are predicted and the extent to which fixation point offsets are specific to ocular responses is analyzed in the light of the exposed model architecture. Relations of the theoretical framework to neurophysiological findings are discussed.

Fixation, Ocular↗

PubMed2006

Developmental instability and the neural dynamics of the speed-intelligence relationship.

Two of the most securely established findings in the biology of intelligence are the relationship between reaction time (RT) and intelligence, and the heritability of intelligence. To investigate why RT may related to intelligence, researchers have used a variety of techniques to subdivide RT into cognitive and motor components. In the current study, magnetoencephalographic (MEG) dipole latencies were used to examine the speed and timing of specific brain processing stages engaged during visually cued simple and choice reaction time tasks. Simple and choice reaction time and timing of MEG sources were considered in relation to fluid intelligence (as measured by the Raven's Advanced Progressive Matrices, RAPM). To address heritability of intelligence, developmental instability (DI) was assessed, measured here as fluctuating asymmetry. DI represents the degree to which an organism is susceptible to developmental stress arising from both environmental and genomic sources. Analyses showed that choice, but not simple reaction time was negatively correlated with RAPM score. MEG revealed a set of complex relationships between the timing of regional brain activations and psychometric intelligence. The neural component associated with integration of sensory and motor information was most associated with RAPM compared to other components. Higher values of fluctuating asymmetry predicted reduced psychometric intelligence, a result suggesting that some part of the variance of the heritability of intelligence reflects DI. Fluctuating asymmetry was significantly and negatively correlated with timing during all components of task completion. These observations suggest that fluid intelligence is primarily related to speed during processing associated with decision time, while fluctuating asymmetry predicted slower processing across all stages of information processing.

Adolescent↗

PubMed2000

Neural dynamics of 3-D surface perception: figure-ground separation and lightness perception.

This article develops the FACADE theory of three-dimensional (3-D) vision to simulate data concerning how two-dimensional pictures give rise to 3-D percepts of occluded and occluding surfaces. The theory suggests how geometrical and contrastive properties of an image can either cooperate or compete when forming the boundary and surface representations that subserve conscious visual percepts. Spatially long-range cooperation and short-range competition work together to separate boundaries of occluding figures from their occluded neighbors, thereby providing sensitivity to T-junctions without the need to assume that T-junction "detectors" exist. Both boundary and surface representations of occluded objects may be amodally completed, whereas the surface representations of unoccluded objects become visible through modal processes. Computer simulations include Bregman-Kanizsa figure-ground separation, Kanizsa stratification, and various lightness percepts, including the Münker-White, Benary cross, and checkerboard percepts.

Attention↗