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T B Schillen

Publications and source records attributed to T B Schillen.

5 recordsLinked to original sources
PubMed1994

Binding by temporal structure in multiple feature domains of an oscillatory neuronal network.

An important step in visual processing is the segregation of objects in a visual scene from one another and from the embedding background. According to current theories of visual neuroscience, the different features of a particular object are represented by cells which are spatially distributed across multiple visual areas in the brain. The segregation of an object therefore requires the unique identification and integration of the pertaining cells which have to be "bound" into one assembly coding for the object in question. Several authors have suggested that such a binding of cells could be achieved by the selective synchronization of temporally structured responses of the neurons activated by features of the same stimulus. This concept has recently gained support by the observation of stimulus-dependent oscillatory activity in the visual system of the cat, pigeon and monkey. Furthermore, experimental evidence has been found for the formation and segregation of synchronously active cell assemblies representing different stimuli in the visual field. In this study, we investigate temporally structured activity in networks with single and multiple feature domains. As a first step, we examine the formation and segregation of cell assemblies by synchronizing and desynchronizing connections within a single feature module. We then demonstrate that distributed assemblies can be appropriately bound in a network comprising three modules selective for stimulus disparity, orientation and colour, respectively. In this context, we address the principal problem of segregating assemblies representing spatially overlapping stimuli in a distributed architecture. Using synchronizing as well as desynchronizing mechanisms, our simulations demonstrate that the binding problem can be solved by temporally correlated responses of cells which are distributed across multiple feature modules.

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PubMed1992

Temporal coding in the visual cortex: new vistas on integration in the nervous system.

Although our knowledge of the cellular components of the cortex is accumulating rapidly, we are still largely ignorant about how distributed neuronal activity can be integrated to contribute to unified perception and behaviour. In the visual system, it is still unresolved how responses of feature-detecting neurons can be bound into representations of perceptual objects. Recent crosscorrelation studies show that visual cortical neurons synchronize their responses depending on how coherent features are in the visual field. These results support the hypothesis that temporal correlation of neuronal discharges may serve to bind distributed neuronal activity into unique representations. Furthermore, these studies indicate that neuronal responses with an oscillatory temporal structure may be particularly advantageous as carrier signals for such a temporal coding mechanism. Based on these recent findings, it is suggested here that binding of neuronal activity by a temporal code may provide a solution to the problem of integration in distributed neuronal networks.

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PubMed1991

Designing a neural network simulator--the MENS modelling environment for network systems: I.

During recent years, the field of neural network research has increasingly attracted the interest of workers from a large number of different disciplines. Current research topics include aspects as different as detailed simulations in brain physiology, predictions of protein structure in biochemistry, database organization in computer science, or various technical applications. The common scheme behind these different approaches is the use of distributed networks of simple computational elements that communicate with each other by means of weighted links. Computer simulations of neural networks require an appropriate software environment. Due to the computational similarities of many classes of such networks, simulation software can be structured into modular components that, to a large degree, are independent of specific applications. The aim of this and the following paper is to discuss some of the design considerations concerning software for neural network simulations. The aspects presented are interesting for both the development of new simulation software and the efficient use and modification of existing programs. Therefore, the general user as well as the software designer may hopefully benefit from this material. This paper briefly introduces some of the basic principles of neural networks. After a short discussion of different approaches to software design, two simple example applications are presented in order to demonstrate a conceptual framework common to many network simulations. The transfer of these considerations to the design of simulation software is then shown by example of the MENS network simulator developed in the Max-Planck-Institute for Brain Research. The paper gives a general introduction to the layout of data structures and different software components. Using the two introductory examples some aspects of network analysis are demonstrated. The following paper then considers further details of the design of a neural network simulator with respect to performance, implementation, and testing.

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PubMed1991

Designing a neural network simulator--the MENS modelling environment for network systems: II.

During recent years, neural network research has been extended to a large number of different fields, increasingly attracting the interest of workers from various disciplines. The computer simulations carried out with this research require an appropriate software environment. The computational similarities of many kinds of simulations allow the design of software components that are largely independent of the specific application. These considerations are reflected, for example, by the general layout of the MENS network simulator, as described in the accompanying first paper. This paper presents the design considerations for the simulator's different software components in more detail. In particular, design and implementation are discussed with respect to computational and memory efficiency. The discussion includes, for example, the representation of a network by the simulator's data structure, the file-driven configuration and initialization of a network, the simulator's stimulus and monitor system, and the simulator's control structures. In addition, the separation and interaction of application-specific and application-independent software components are addressed. Particular performance aspects comprise the implementation of synaptic delays, the dynamic deletion of synaptic links in network learning, and the preprocessing of stimulus films. In addition, some general aspects of simulator performance and testing are considered. The material presented in this paper concerns both the development of new simulation software and the efficient use of existing programs. Therefore, both the general user as well as the software designer may hopefully benefit from this presentation.

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