Thomas B. Schillen

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.

Stimulus-dependent assembly formation of oscillatory responses: II. desynchronization

Current concepts in neurobiology of vision assume that local object features are represented by distributed neuronal populations in the brain. Such representations can lead to ambiguities if several distinct objects are simultaneously present in the visual field. Temporal characteristics of the neuronal activity have been proposed as a possible solution to this problem and have been found in various cortical areas. In this paper we introduce a delayed nonlinear oscillator to investigate temporal coding in neuronal networks. We show synchronization within two-dimensional layers consisting of oscillatory elements coupled by excitatory delay connections. The observed correlation length is large compared to coupling length. Following the experimental situation, we then demonstrate the response of such layers to two short stimulus bars of varying gap distance. Coherency of stimuli is reflected by the temporal correlation of the responses, which closely resembles the experimental observations.

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.

Alternating oscillatory and stochastic states in a network of spiking neurons

We focus on a phenomenon observed in cat visual cortex, namely the alternation of oscillatory and irregular neuronal activity. This aspect of the dynamics has been neglected in brain modelling, but it may be essential for the dynamic binding of different neuronal assemblies. The authors present a simple, but physiologically plausible model network which exhibits such a behaviour in spite of its simplicity—e.g. dendritic dynamics is neglected—as an emergent network property. It comprises a number of spiking neurons which are interconnected in a mutually excitatory way. Each neuron is stimulated by several stochastic spike trains. The resulting large input variance is shown to be important for the response properties of the network, which they characterize in terms of two parameters of the autocorrelation function: the frequency and the modulation amplitude. They calculate these parameters as functions of the internal coupling strength, the external input strength and several input connectivity schemes and ...

Stimulus-dependent assembly formation of oscillatory responses: Iii. learning

A temporal structure of neuronal activity has been suggested as a potential mechanism for defining cell assemblies in the brain. This concept has recently gained support by the observation of stimulus-dependent oscillatory activity in the visual cortex of the cat. Furthermore, experimental evidence has been found showing the formation and segregation of synchronously oscillating cell assemblies in response to various stimulus conditions. In previous work, we have demonstrated that a network of neuronal oscillators coupled by synchronizing and desynchronizing delay connections can exhibit a temporal structure of responses, which closely resembles experimental observations. In this paper, we investigate the self-organization of synchronizing and desynchronizing coupling connections by local learning rules. Based on recent experimental observations, we modify synchronizing connections according to a two-threshold learning rule, involving synaptic potentiation and depression. This rule is generalized to its functional inverse for weight changes of desynchronizing connections. We show that after training, the resulting network exhibits stimulus-dependent formation and segregation of oscillatory assemblies in agreement with the experimental data. These results indicate that local learning rules during ontogenesis can suffice to develop a connectivity pattern in support of the observed temporal structure of stimulus responses in cat visual cortex.

Coherency detection and response segregation by synchronizing and desynchronizing delay connections in a neuronal oscillator model

Nonlinear units with delayed coupling are used as a representation of elementary neuronal oscillators. These basic elements are then coupled through delay connections to form an oscillatory network. Two classes of synchronizing and desynchronizing coupling connections within networks of oscillatory units are studied. With these types of connections, stimulus coherency leads to synchronization of the activated neuronal oscillators. A contiguous stimulus is shown to be coded by an assembly of oscillators active with zero phase lag, as required by experimental data. Furthermore, activities resulting from different stimuli lead to the formation of distinct oscillating assemblies. Two different, but overlapping, stimuli become represented by two different, coherently oscillating cell assemblies whose repetitive activities are no longer synchronized. This again agrees well with experimental observations

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.

STOCHASTIC AND OSCILLATORY BURST ACTIVITIES IN A MODEL OF SPIKING NEURONS

Switching between oscillatory and stochastic states in single electrode signals from cat visual cortex is an experimental phenomenon which had not been included in recent models on cortical oscillations. Here, we present a model network of spiking neurons which exhibits such alternating responses as an emergent network property. Simulations of this model reveal a detailed agreement between numerical results and experimental observations.

Temporal Structure Can Solve the Binding Problem for Multiple Feature Domains

We investigate a solution to the binding problem in visual processing using temporal structure in a neuronal network. We, first, demonstrate binding and segregation of assemblies by synchronizing and desynchronizing connections for a single feature representation. Then, we extend this model to multiple feature domains where each member of an distributed assembly is specific for its single particular stimulus feature only. This avoids the combinatorial explosion of multispecific cardinal cells. Besides binding by synchronizing the temporal structure of distributed neuronal responses, our simulations demonstrate that synchronization has to be complemented by a means for stimulus-dependent desynchronization.

Assembly Formation and Segregation by a Self-Organizing Neuronal Oscillator Model

Experimental evidence demonstrates the stimulus-dependent formation and segregation of neuronal assemblies defined by coherent oscillatory response patterns. In this paper, we investigate whether the self-organization of synchronizing and desynchronizing connections can establish a corresponding temporal response structure using local learning rules. Motivated by recent experimental observations, synchronizing connections are modified according to a two-threshold Hebb-like learning rule, while we generalize this rule to analogous Anti-Hebb-like weight changes for the desynchronizing connections. We show that the resulting network exhibits synchronization and segregation of oscillatory activity in agreement with the experiment.