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In this paper we consider some classical control theoretic properties of a nonlinear neural network proposed by Oğuztöreli (1979) to represent the activities of constituent neurones in terms of the input signals and coupling (associative) properties. By breaking the network into linear and nonlinear components we have been able to localize the nonlinearities in the individual neural response latencies through the system.
Sensory segmentation is an outstanding unsolved problem of theoretical, practical and technical importance. The basic idea of a solution is described in the form of a model. The response of "neurons" within the sensory field is temporally unstable. Segmentation is expressed by synchronization within segments and desynchronization between segments. Correlations are generated by an autonomous pattern formation process. Neuronal coupling is the result both of peripheral evidence (similarity of local quality) and of central evidence (common membership in a stored pattern). The model is consistent with known anatomy and physiology. However, a new physiological function, synaptic modulation, has to be postulated. The present paper restricts explicit treatment to the peripheral evidence represented by amplitude modulations globally present in all components of a sound spectrum. Generalization to arbitrary sensory qualities will be the subject of a later paper. The model is an application and illustration of the Correlation Theory of brain function.
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In this paper the visual masking effect is interpreted on the basic of the transient characteristic in two dimensional neuronal networks. The study investigates the suitability of the effect for use as a measurement method. It is shown that the stimulus distribution in space can be scanned at different points in time and that various dynamic characteristic values of the system can be measured.
The model is based on the concept that non-linear lateral interaction at the inner plexiform layer accounts for most of the specialization and marked non-linearities in cat's retinal ganglion cell responses. The inputs to the lateral interaction processes are a spatio-temporal signal and its retarded, as suggested by the behaviour of simple ganglion cells. Lateral interaction in the model consists of lateral linear inhibition followed by local half wave rectification. The resulting signals are weighted and summated by the ganglion cell thereafter. A transparent and general expression is obtained for the response of the cell model which, albeit its simplicity, leads to most of known types of non-linear responses, including the rarely encountered specialized cells in cat's, retina, except colour coding units. For negligible lateral interaction, the model reduces to spatio-temporal linear models under the two paths hypothesis. A discussion of the possible role of anatomical units in these retinal processes in presented, where a general interpretation for visual processing in cat's retina evolves from.
In the present work we investigate the neuronal activities in a vertebrate retina by modelling and simulations using the results of (Oğuztöreli, 1979). The basic retinal network considered here consists of interconnected five neurons: a receptor cell (rod or cone), a horizontal cell, a bipolar cell, an amacrine cell, and a retinal ganglion cell. The mathematical model for the basic network is a system of nonlinear ordinary integral differential difference equations. A number of simulations describing the dynamics of the neural activities in the basic network under different conditions are presented, actual and steady-state solutions are discussed. An algorithm is proposed for the determination of the system parameters experimentally.
Specific changes occur in the cells of the uppers layers of the cat's superior colliculus when a two dimensional noise (background) is superimposed onto a deterministic signal (spot of light). Some of the measurements can be interpreted as meaning that some cells only react to certain relative movements of object (spot) and background (noise). The movement of the visual background is interpreted as environmental movement occurring due to the animal's own movement. The results of the measurements provide all the necessary presuppositions for a distinction between the animal's own velocity and that of the object (Part I). The experimental results can be interpreted with a model. The essential factor for the interpretation is the direction specific behavior of the cells which is bound up with an asymmetrical spatial coupling of the neurons with each other. The decisive advantage of asymmetrical systems for the pattern recognition of moving objects is that they can work without distortion and spatial displacement over large ranges of velocity (Part II).
A template matching model for pattern recognition is proposed. By following a previously-proposed algorithm for synpatic modification (Hirai, 1980), the template of a stimulus pattern is self-organized as a spatial distribution pattern of matured synapses on the cells receiving modifiable synapses. Template matching is perfomed by the disinhibitory neural network cascaded beyond the neural layer composed of the cells receiving the modifiable synapses. The performance of the model has been simulated on a digital computer. After repetitive presentations of a stimulus pattern, a cell receiving the modifiable synapses comes to have the template of that pattern. And the cell in the latter layer of the disinhibitory neural network that receives the disinhibitory input from that cell becomes selectively sensitive to that pattern. Learning patterns are not restricted by previously learned ones. They can be subset or superset patterns of the ones previously learned. If an unknown pattern is presented to the model, no cell beyond the disinhibitory neural network will respond. However, if previously learned patterns are embedded in that pattern, the cells which have the templates of those patterns respond and are assumed to transmit the information to higher center. The computer simulation also show that the model can organize a clean template under a noisy environment.
The statistical properties of background spike train activity recorded from a molluscan neurone are used to identify lengths of discharge which are produced by endogeneous pacemaker mechanisms. Such pacemaker discharge has an infinitely divisible interspike interval probability density function.
A model is described to account for damped oscillatory activity of two interacting neural populations, pyramidal cells and interneurons. This network in the hippocampus is treated as a lumped system with time delays between elements. The physiological mechanism underlying the oscillatory activity appears to involve neural population interaction and cannot be described in terms of a network composed of but two neurons, a single pyramidal cell and a single interneuron. An unusual aspect of the model is the explicit incorporation of an ongoing background input to raise the mean level of activity of the pyrammidal cell population. This model has evolved from a series of studies previously performed on cats. To test the model experiments were performed on rabbits. The data showing oscillatory activity following fornix stimulation in the rabbit indicate that the model can be applied not only to the cat but also to the rabbit. In additions, for commissural stimulation oscillatory potentials of neural populations and individual pyramidal cells were evoked as predicted by the model.
In the setting of a kinetic approach to neural systems a more refined model is proposed. The subsequent kinetic equations are subjected to numerical investigation. The results show that the model retains the most evident properties of transmission of information of the natural neural systems.
Dorsal spinocerebellar tract (DSCT) neurones adequately activated by primary afferents from the muscle spindles in one muscle in the hindleg of the cat, is regularly inhibited by primary afferents from other muscles. The inhibitory input causes a constant reduction in firing frequency independent of the excitatory drive of the cell. In a simple model the effects of presynaptic inhibition and of postsynaptic inhibition with different time course of the inhibitory action, have been explored. Within the scope of this model, only postsynaptic inhibition with a very long time-constant could explain the experimental results. It is suggested that the inhibitory action is transmitted to the DSCT-cell through a number of synapses distributed over the dendrites.
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Samples of the olfactory bulb and cerebellar cortex from rats were impregnated in zinc-iodide-osmium (ZIO) solution. The fine structural localization of ZIO impregnation product was studied and, in addition, the percentage distribution of ZIO-positive synaptic vesicles was ascertained in certain synapses of known function. Spherical vesicles, particularly in aldehyde-prefixed material, were found to be more resistant to ZIO impregnation than ovoid vesicles, both in dendro-dendritic and in axo-dendritic synapses. The different ZIO reactivity of synaptic vesicles in excitatory and inhibitory synapses, apart from reflecting an important difference in the chemical composition of synaptic vesicles, may offer a new method for characterizing synapses in relation to certain functions.
This paper develops a concordance of methods for designating laminae in the primate striate cortex. The classic notation schemes of Ramón y Cajal, Brodmann, and von Bonin and four recent modifications are described in words and photomicrographs. The recent trend toward adopting a lamination scheme that effects a division of cells into recognizable layers and at the same time reflects their connections is considered. Since a single, consistent plan for lamination of the visual cortex would facilitate communication among investigators, it is suggested that Brodmann's original scheme be followed as the most generally useful and applicable.