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PubMed1993

Quantification and statistical verification of neuronal stimulus responses from noisy spike train data.

Usually neuronal responses to short-lasting stimuli are displayed as peri-stimulus time histogram. The function estimated by such a histogram allows to obtain informations about stimulus-induced postsynaptic events as long as the interpretation is restricted to the first response component after the stimulus. The interpretation of secondary response components is much more difficult, as they may be either due to stimulus effects or represent an "echo" of the primary response. In the present paper two output functions are developed that do not show such an echoing of responses. The first one, the interspike interval change function, represents an ideal way to quantify a neuronal stimulus response as its amplitude was found to be almost independent of the stimulation strategy used during acquisition of the spike train data. The other function, the displaced impulses function, allows to verify the statistical significance of an observed response component. Both functions may be estimated from stimulus-correlated spike train data, even if the neuron under investigation shows considerable interspike-interval variability in the absence of stimulation. The concepts underlying these neuronal output functions are developed on simulated responses of a Hodgkin-Huxley-type model for a mammalian neuron at body temperature that is exposed to a transient excitatory conductance increase. Additionally, estimation of these output functions is also demonstrated on responses of human soleus motoneurons that were exposed to electrical stimuli of the tibial nerve in the popliteal fossa.

Action Potentials↗
PubMed1993

A neurophysiologically-based mathematical model of flash visual evoked potentials.

Evidence is presented that a neurophysiologically-inspired mathematical model, originally developed for the generation of spontaneous EEG (electroencephalogram) activity, can produce VEP (visual evoked potential)-like waveforms when pulse-like signals serve as input. It was found that the simulated VEP activity was mainly due to intracortical excitatory connections rather than direct thalamic input. Also, the model-generated VEPs exhibited similar relationships between prestimulus EEG characteristics and subsequent VEP morphology, as seen in human data. Specifically, the large correlation between the N1 amplitude and the prestimulus alpha phase angle, and the insensitivity of P2 to the latter feature, as observed in actual VEPs to low intensity flashes, was also found in the model-generated data. These findings provide support for the hypothesis that the spontaneous EEG and the VEP are generated by some of the same neural structures and that the VEP is due to distributed activity, rather than dipolar sources.

Cerebral Cortex↗
PubMed1993

A neural network model for texture discrimination.

A model of texture discrimination in visual cortex was built using a feedforward network with lateral interactions among relatively realistic spiking neural elements. The elements have various membrane currents, equilibrium potentials and time constants, with action potentials and synapses. The model is derived from the modified programs of MacGregor (1987). Gabor-like filters are applied to overlapping regions in the original image; the neural network with lateral excitatory and inhibitory interactions then compares and adjusts the Gabor amplitudes in order to produce the actual texture discrimination. Finally, a combination layer selects and groups various representations in the output of the network to form the final transformed image material. We show that both texture segmentation and detection of texture boundaries can be represented in the firing activity of such a network for a wide variety of synthetic to natural images. Performance details depend most strongly on the global balance of strengths of the excitatory and inhibitory lateral interconnections. The spatial distribution of lateral connective strengths has relatively little effect. Detailed temporal firing activities of single elements in the lateral connected network were examined under various stimulus conditions. Results show (as in area 17 of cortex) that a single element's response to image features local to its receptive field can be altered by changes in the global context.

Cybernetics↗
PubMed1993

Topography and ocular dominance: a model exploring positive correlations.

The map from eye to brain in vertebrates is topographic, i.e., neighbouring points in the eye map to neighbouring points in the brain. In addition, when two eyes innervate the same target structure, the two sets of fibres segregate to form ocular dominance stripes. Experimental evidence from the frog and goldfish suggests that these two phenomena may be subserved by the same mechanisms. We present a computational model that addresses the formation of both topography and ocular dominance. The model is based on a form of competitive learning with subtractive enforcement of a weight normalization rule. Inputs to the model are distributed patterns of activity presented simultaneously in both eyes. An important aspect of this model is that ocular dominance segregation can occur when the two eyes are positively correlated, whereas previous models have tended to assume zero or negative correlations between the eyes. This allows investigation of the dependence of the pattern of stripes on the degree of correlation between the eyes: we find that increasing correlation leads to narrower stripes. Experiments are suggested to test this prediction.

Animals↗
PubMed1993

Modelling velocity profiles of rapid movements: a comparative study.

In this paper we compare 23 different models that can be used to describe the asymmetric bell-shaped velocity profiles of rapid-aimed movements. The comparison is performed with the help of an analysis-by-synthesis experiment over a database of 1052 straight lines produced by nine human subjects. For each line and for each model, a set of parameters is extracted that minimizes the error between the original and the reconstructed data. Performance analysis on the basis of the mean-square-error clearly reflects the superiority of the support-bounded lognormal model to globally describe the velocity profile characterizing rapid movements.

Biomechanical Phenomena↗
PubMed1993

Deterministic chaos and the first positive Lyapunov exponent: a nonlinear analysis of the human electroencephalogram during sleep.

Under selected conditions, nonlinear dynamical systems, which can be described by deterministic models, are able to generate so-called deterministic chaos. In this case the dynamics show a sensitive dependence on initial conditions, which means that different states of a system, being arbitrarily close initially, will become macroscopically separated for sufficiently long times. In this sense, the unpredictability of the EEG might be a basic phenomenon of its chaotic character. Recent investigations of the dimensionality of EEG attractors in phase space have led to the assumption that the EEG can be regarded as a deterministic process which should not be mistaken for simple noise. The calculation of dimensionality estimates the degrees of freedom of a signal. Nevertheless, it is difficult to decide from this kind of analysis whether a process is quasiperiodic or chaotic. Therefore, we performed a new analysis by calculating the first positive Lyapunov exponent L1 from sleep EEG data. Lyapunov exponents measure the mean exponential expansion or contraction of a flow in phase space. L1 is zero for periodic as well as quasiperiodic processes, but positive in the case of chaotic processes expressing the sensitive dependence on initial conditions. We calculated L1 for sleep EEG segments of 15 healthy men corresponding to the sleep stages I, II, III, IV, and REM (according to Rechtschaffen and Kales). Our investigations support the assumption that EEG signals are neither quasiperiodic waves nor a simple noise. Moreover, we found statistically significant differences between the values of L1 for different sleep stages.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗
PubMed1993

Spatial and temporal coding in single neurons.

Convergence between cells which differ in both spatial and temporal properties create higher order neurons with response properties that are distinctly different from those of the input neurons. The spatial properties of target neurons are not necessarily "cosine-tuned". In addition, unlike the independence between spatial and temporal properties in cosine-tuned afferent neurons, higher-order target cells generally exhibit a dependence of temporal dynamics on spatial properties. The response properties of target neurons receiving spatio-temporal convergence (STC) from tonic and phasic-tonic or phasic afferents is investigated here by considering a general case where the dynamic input is represented by a fractional, leaky, derivative transfer function. It is shown that, at frequencies below the corner frequency of the dynamic input, the temporal properties of target neurons can be described by leaky differentiators having time constants that are a function of spatial direction. Thus, STC target neurons exhibit tonic temporal response properties during stimulation along some spatial directions (having small time constants) and phasic properties along other directions (having large time constants). Specifically, target neurons encode the complete derivative of the stimulus along certain spatial directions. Thus, STC acts as a directionally specific high-pass filter and produces complete derivatives from fractional, leaky derivative afferent signals. In addition, spatio-temporal transformations can generate novel temporal dynamics in the central nervous system. These observations suggest that spatio-temporal computations might constitute an alternative to parallel, independent spatial and temporal channels.

Afferent Pathways↗
PubMed1993

Stochastical aspects of neuronal dynamics: Fokker-Planck approach.

The stochastical aspects of noise-perturbed neuronal dynamics are studied via the Fokker-Planck equation by considering the Langevin-type relaxational, nonlinear process associated with neuronal states. On the basis of a canonical, stochastically driven, dichotomous state modeling, the equilibrium conditions in the neuronal assembly are analyzed. The markovian structure of the random occurrence of action potentials due to the disturbances (noise) in the neuronal state is considered, and the corresponding solutions relevant to the colored noise spectrum of the disturbance effects are addressed. Stochastical instability (Lyapunov) considerations in solving discrete optimization problems via neural networks are discussed. The bounded estimate(s) of the stochastical variates involved are presented, and the noise-induced perturbations on the saturated-state neuronal population are elucidated.

Action Potentials↗
PubMed1993

A model for neural representation of binocular disparity in striate cortex: distributed representation and veto mechanism.

A model in striate cortex is proposed for a distributed neural representation of binocular disparity with a simple cell. In the model, disparity is represented by "far", "near" and "tuned inhibitory" simple cells. However, the representation will be vetoed by model cells where disparity is excessively large. The veto mechanism consists of a neural network of the model cell which received output from simple cells and which interacts with neighbors. The mechanism is necessary, the model cell responds like a simple cell, and the network is physiologically plausible in the brain. Computer simulation on the neural network model with random dot stereography indicates reasonable performance.

Computer Simulation↗
PubMed1993

Coincidence detectors and two-pulse visual temporal integration: new theoretical results and comparison with data.

Exact predictions for two-pulse visual temporal integration data are derived from the Bouman-van der Velden quantum coincidence model for threshold vision. The predictions of the model start with complete summation for superposed pulses, then pass to a transition zone of partial integration, and finally reach the level of probability summation for pulses presented with large interstimulus intervals. From our results we can clearly reject the assumption of constant integration times with the basic model. We thus generalize the coincidence model to allow for variable integration times, derive the corresponding predictions for two-pulse integration data, and compare these predictions to published data currently available. It is shown that detectors of low order of coincidence generally underestimate the actual reduction of threshold intensity (or equivalently the corresponding increase of the detection probability) for two pulses as compared to the simple-pulse performance.

Cybernetics↗
PubMed1992

Coordination of the legs of a slow-walking cat.

On the basis of behavioural studies the influences that coordinate the movement of the legs of a slowly walking cat have been investigated. The recording method applied here allows for the measurement of forward and backward movement of the legs which are called swing and stance movements, respectively. Influences between contralateral legs, i.e. both front legs or both hind legs, are stronger than those occurring between ipsilateral legs, i.e. front and hind leg of the same side. Influences which coordinate the front legs seem to be of the same kind as those for the hind legs. These influences are symmetrical, which means that the same type of influence acts from right to left leg and in the reverse direction. Two types of influences are described for contralateral legs: 1. When the influencing leg performs a swing movement, the influenced leg is prevented from starting a swing movement. 2. When the influencing leg performs a stance movement, the probability that the influenced leg starts a swing movement increases as the influencing leg moves backwards during its stance movement. In contrast to contralateral coupling, the ipsilateral influences are symmetric, i.e. a different influence acts from front to hind leg than does in the reverse direction. The front leg is influenced to start a swing when both legs have approached each other to a given value. The hind leg is influenced to start a stance movement after the front leg has begun its swing.

Animals↗
PubMed1996

Evaluating the maneuverability of a control stick using electromyography.

This paper proposes maneuverability indexes for a control stick that are based on considerations of muscle characteristics. The indexes are defined on the basis of muscle forces: stiffness of the musculoskeletal system and torque for accelerating the stick. Measuring muscle forces with electromyography, we examine the effectiveness of the proposed indexes experimentally. In a slow tracking operation, the index based on stiffness conforms to the subjective evaluation, but the index based on the torque is inadequate as an index of maneuverability; we therefore use the index based on stiffness as the maneuverability index. In experiments on a step operation, we divided the operation into two phases: moving and holding. The index in the holding operation is in good agreement with the subjective evaluation. In both tracking and step operations the characteristics of the index are appropriate whether or not the controlled object has a first-order lag element. As a result, it is shown that the index can indicate the maneuverability in the low-frequency tracking operation of the stick.

Arm↗
PubMed1996

A model of the cerebellum in adaptive control of saccadic gain. I. The model and its biological substrate.

We review data showing that the cerebellum is required for adaptation of saccadic gain to repeated presentations of dual-step visual targets and thus, presumably, for providing adaptive corrections for the brainstem saccade generator in response to any error created by the open-loop saccadic system. We model the adaptability of the system in terms of plasticity of synapses from parallel fibers to Purkinje cells in cerebellar cortex, stressing the integration of cerebellar cortex and nuclei in microzones as the units for correction of motor pattern generators. We propose a model of the inferior olive as an error detector, and use a 'window of eligibility' to insure that error signals that elicit a corrective movement are used to adjust the original movement, not the secondary movement. In a companion paper we simulate this large, realistic network of neural-like units to study the complex spatiotemporal behavior of neuronal subpopulations implicated in the control and adaptation of saccades.

Adaptation, Physiological↗
PubMed1996

A model of the cerebellum in adaptive control of saccadic gain. II. Simulation results.

A large, realistic cerebellar neural network has been incorporated into a previously developed saccade model. Using this model, in the present paper, we simulate the complex spatiotemporal behavior of the neuronal subpopulations implicated in adaptive saccadic control. Our simulation results are in good agreement with neurophysiological and behavioral data. Furthermore, we suggest several new experiments to test the validity of our predictions on adaptive saccadic control.

Adaptation, Physiological↗
PubMed1996

Breakthrough to awareness: a preliminary neural network model of conscious and unconscious perception in word processing.

A neural network model is constructed to mimic the processing involved in semantic and working memory when subliminal effects are involved. These effects involve modifications of reaction time to later inputs, according to whether or not there has been conscious or unconscious processing of the earlier input. The model is constructed of two separate modules: one (a semantic memory module) allowing for processing at a semantic, but unconscious, level, and the other (a working memory module) for conscious experience. The latter module, although a replica of the earlier one, has different lateral connectivity and output function from the former. The model is shown to give a good fit to Marcel's data on the processing of polysemous words. Further tests are suggested for the model, and a possible cortical implementation suggested. The relevance of the model to recent approaches to consciousness is also explored.

Consciousness↗
PubMed1996

Learning combined feedback and feedforward control of a musculoskeletal system.

The goal of this paper is the learning of neuromuscular control, given the following necessary conditions: (1) time delays in the control loop, (2) non-linear muscle characteristics, (3) learning of feedforward and feedback control, (4) possibility of feedback gain modulation during a task. A control system and learning methodology that satisfy those conditions is given. The control system contains a neural network, comprising both feedforward and feedback control. The learning method is backpropagation through time with an explicit sensitivity model. Results will be given for a one degree of freedom arm with two muscles. Good control results are achieved which compare well with experimental data. Analysis of the controller shows that significant differences in controller characteristics are found if the loop delays are neglected. During a control task the system shows feedback gain modulation, similar to experimentally found reflex gain modulation during rapid voluntary contraction. If only limited feedback information is available to the controller the system learns to co-contract the antagonistic muscle pair. In this way joint stiffness increases and stable control is more easily maintained.

Cybernetics↗
PubMed1996

Surrogate data analysis of sleep electroencephalograms reveals evidence for nonlinearity.

We tested the hypothesis of whether sleep electroencephalographic (EEG) signals of different time windows (164 s, 82 s, 41 s and 20.5 s) are in accordance with linear stochastic models. For this purpose we analyzed the all-night sleep electroencephalogram of a healthy subject and corresponding Gaussian-rescaled phase randomized surrogates with a battery of five non-linear measures. The following nonlinear measures were implemented: largest Lyapunov exponent L1, correlation dimension D2, and the Green-Savit measures delta 2, delta 4 and delta 6. The hypothesis of linear stochastic data was rejected with high statistical significance. L1 and D2 yielded the most pronounced effects, while the Green-Savit measures were only partially successful in differentiating EEG epochs from the phase randomized surrogates. For L1 and D2 the efficiency of distinguishing EEG signals from linear stochastic data decreased with shortening of the time window. Altogether, our results indicate that EEG signals exhibit nonlinear elements and cannot completely be described by linear stochastic models.

Adult↗
PubMed1994

Analysis of bursting in a thalamic neuron model.

We extend a quantitative model for low-voltage, slow-wave excitability based on the T-type calcium current (Wang et al. 1991) by juxtaposing it with a Hodgkin-Huxley-like model for fast sodium spiking in the high voltage regime to account for the distinct firing modes of thalamic neurons. We employ bifurcation analysis to illustrate the stimulus-response behavior of the full model under both voltage regimes. The model neuron shows continuous sodium spiking when depolarized sufficiently from rest. Depending on the parameters of calcium current inactivation, there are two types of low-voltage responses to a hyperpolarizing current step: a single rebound low threshold spike (LTS) upon release of the step and periodic LTSs. Bursting is seen as sodium spikes ride the LTS crest. In both cases, we analyze the LTS burst response by projecting its trajectory into a fast/slow phase plane. We also use phase plane methods to show that a potassium A-current shifts the threshold for sodium spikes, reducing the number of fast sodium spikes in an LTS burst. It can also annihilate periodic bursting. We extend the previous work of Rose and Hindmarsh (1989a-c) for a thalamic neuron and propose a simpler model for thalamic activity. We consider burst modulation by using a neuromodulator-dependent potassium leakage conductance as a control parameter. These results correspond with experiments showing that the application of certain neurotransmitters can switch firing modes.

Action Potentials↗