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At least 1,423 records · Page 79Linked to original sources
PubMed1977

Cardio-respiratory synchronisms: synchrony with artificial circulation.

Rabbits with an artificial cerebral circulation can synchronize their respiratory rhythm with the pump stroke. There is evidence that the responsible kybernetic system is of a very elementary nature. The efficacy of the system is considerable and the experimental set-up as a whole offers itself as a model to search for drugs to increase the synchronizing potentialities.

Animals↗
PubMed1984

A comparison of LISP and MUMPS as implementation languages for knowledge-based systems.

Major components of knowledge-based systems are summarized, along with the programming language features generally useful in their implementation. LISP and MUMPS are briefly described and compared as vehicles for building knowledge-based systems. The paper concludes with suggestions for extensions to MUMPS that might increase its usefulness in artificial intelligence applications without affecting the essential nature of the language.

Computers↗
PubMed1990

Optimal task performance of antagonistic muscles.

Movements against a variety of loads are relatively invariant in form. These movements are controlled in general by antagonistic groups of muscles. In this paper optimal control strategies are computed for coupling antagonistic muscles so as to minimize deviations from a desired trajectory. Simulations are presented for linear and nonlinear "decision functions" linking control of the two muscles for a variety of movements in a way that may be compared with experimental observations.

Animals↗
PubMed1990

Self-organization of associative memory and pattern classification: recurrent signal processing on topological feature maps.

We extend the neural concepts of topological feature maps towards self-organization of auto-associative memory and hierarchical pattern classification. As is well-known, topological maps for statistical data sets store information on the associated probability densities. To extract that information we introduce a recurrent dynamics of signal processing. We show that the dynamics converts a topological map into an auto-associative memory for real-valued feature vectors which is capable to perform a cluster analysis. The neural network scheme thus developed represents a generalization of non-linear matrix-type associative memories. The results naturally lead to the concept of a feature atlas and an associated scheme of self-organized, hierarchical pattern classification.

Algorithms↗
PubMed1990

Perception of size and lightness of human observers: two criteria for holistic and analytic processing show no correlation in individuals.

Results of a triad-classification task and a multidimensional-scaling (MDS) experiment are compared for individual observers. Both paradigms are designed to reveal whether stimuli are perceived in a holistic or analytic manner (Garner 1974). Subjects differed substantially and consistently in their triad classification pattern. The majority of subjects selected stimuli according to dimensional criteria; this classification type is thought to indicate an analytic stimulus processing. Approximately one third of subjects, however, used a classification according to overall similarity (indicating holistic processing). Except for the very first session, virtually no intermediate classification occurred. This clear separation into two classification types suggests that there actually exist two strongly preferred processing modes. Intraindividual variability between sessions in general was small. In one case, however, a spontaneous switching from a purely dimensional classification to a purely similarity classification occurred. This indicates that the observers have different processing options at their disposal, and are not forced to use a particular processing mode by the stimulus type--as has been supposed in the original concept of integrality/separability of stimuli (Garner 1974). In the MDS experiment also substantial interindividual differences in the "best-fitting" Minkowski metric were found, indicating different processing types. However, for individuals participating in both experiments, there was no correlation between the results of the two experimental paradigms. This is interpreted as a result of the subject's ability to choose between a few perceptual-processing options.

Central Nervous System↗
PubMed1990

A vector-sum process produces curved aiming paths under rotated visual-motor mappings.

Under a 90 degrees rotation of motor space relative to visual space, human two-dimensional aiming movements frequently take the form of smooth arcs such as spirals and semi-circles. A time-independent differential equation explains this tendency in terms of a rotation-induced vector field made up, at each point in the two-dimensional space, of two input vectors. One vector represents a visual error signal and the other represents a motor error signal. A trajectory's instantaneous direction of movement at each point can be described as the resultant of the two vectors. This mathematical formulation incorporates plausible visual-motor mechanisms and, when expressed in polar coordinates, leads to a new method for analyzing the spatial properties of movements (i.e., movement paths). Plots of the angle between the resultant and the target vector (phi) against distance from the target (r, in the polar representation) summarize the arc-shaped movement paths as a simple relation that can be analyzed statistically with respect to properties such as monotonicity. The polar representation is a plausible representation of visually-guided movements, with the visual error vector functioning as an objective function relative to which behavior is optimized. We extend the model and the r, phi movement path analysis to non-90 degrees rotations, and we find that the model predicts an observed qualitative shift in behavior for rotations greater than 90 degrees. It also predicts qualitatively different path shapes observed under visual-motor reflections.

Cybernetics↗
PubMed1990

Conduction along myelinated and demyelinated nerve fibres with a reorganized axonal membrane during the recovery cycle: model investigations.

The changes in the excitability of the reorganized axonal membrane in myelinated and demyelinated nerve fibres as well as the causes conditioning such changes have been investigated by paired stimulation during the first 30 ms of the recovery cycle. The variations of the action potential parameters (amplitude and velocity) are traced also. The simulation of the conduction along the normal fiber is based on the Frankenhaeuser and Huxley (1964) and Goldman and Albus (1968) equations, while the demyelination is considered to be an elongation of the nodes of Ranvier. The axonal membrane reorganization is achieved by means of potassium channel blocking and increase of the sodium-channel permeability. It is shown that potassium channels block decreases membrane excitability for the myelinated and demyelinated fibres in the cases of initial and paired stimulation. With increasing sodium-channel permeability on the background of the blocked potassium channels, the membrane excitability is increased. For the fibres with a reorganized membrane, a supernormality of the membrane excitability is obtained, the latter remaining unrecovered during the 30 ms cycle under investigation. The supernormality of the excitability grows from the demyelinated fibre without reorganized membrane to the demyelinated fibre with reorganized one. For short interstimulus intervals, the second action potential propagates along the fibres with a reduced velocity and a decreased amplitude. No supernormality of the potential parameters (amplitude, velocity) is observed during the cycle up to 30 ms.(ABSTRACT TRUNCATED AT 250 WORDS)

Action Potentials↗
PubMed1990

Two types of neuronal synchrony in monkey striate cortex.

Peaks in more than 5000 spike train correlograms, obtained from monkey striate cortex, were measured. Earlier work had shown qualitatively that there are frequent prominent peaks having widths in a range around 50 ms, and narrower peaks less than about 7 ms wide. Here we demonstrate that the distribution of peak widths shows a dichotomy.

Animals↗
PubMed1990

An analysis of Kohonen's self-organizing maps using a system of energy functions.

In this paper a new method for analyzing Kohonen's self-organizing feature maps is presented. The method makes use of a system of energy functions, one energy function for each processing unit. It is shown that the training process is equivalent to minimizing each energy function subject to constraints. The analysis is used to prove the formation of topologically correct maps when the inherent dimensionality of the input patterns matches that of the network. The energy equations can be used to compute the steady-state weight values of the network. In addition, the analysis allows bounds on the training parameters to be determined. Finally, examples of energy landscapes are presented to graphically show the behavior of the network.

Algorithms↗
PubMed1990

Forming sparse representations by local anti-Hebbian learning.

How does the brain form a useful representation of its environment? It is shown here that a layer of simple Hebbian units connected by modifiable anti-Hebbian feed-back connections can learn to code a set of patterns in such a way that statistical dependency between the elements of the representation is reduced, while information is preserved. The resulting code is sparse, which is favourable if it is to be used as input to a subsequent supervised associative layer. The operation of the network is demonstrated on two simple problems.

Brain↗
PubMed1990

Anti-Hebbian learning in a non-linear neural network.

The Hebbian rule (Hebb 1949), coupled with an appropriate mechanism to limit the growth of synaptic weights, allows a neuron to learn to respond to the first principal component of the distribution of its input signals (Oja 1982). Rubner and Schulten (1990) have recently suggested the use of an "anti-Hebbian" rule in a network with hierarchical lateral connections. When applied to neurons with linear response functions, this model allows additional neurons to learn to respond to additional principal components (Rubner and Tavan 1989). Here we apply the model to neurons with non-linear response functions characterized by a threshold and a transition width. We propose local, unsupervised learning rules for the threshold and the transition width, and illustrate the operation of these rules with some simple examples. A network using these rules sorts the input patterns into classes, which it identifies by a binary code, with the coarser structure coded by the earlier neurons in the hierarchy.

Brain↗