Evoked response correlates of psychophysical magnitude estimates for tactile stimulation in man.
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Cerebral Cortex↗
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Results for “Models, Neurological”
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A hypothetical synaptic-neural-behavioral model of operant learning.
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Computers↗
A stochastic model for the membrane potential of a stimulated neuron.
We present a simple model describing the transition between the prefiring, firing and postfiring phases of a single neuron in a large neural net. Using typical values for the physiological parameters that enter the model, we find average interspike times that are close to those reported in experimental measurements.
Animals↗
The usefulness of mathematical modeling in hydrocephalus research.
A mathematical model of the regulation of ventricular volume, which emphasizes the importance of the intrinsic properties (turgor) of the brain for the understanding of hydrocephalus, has been developed. How the model was generated is described. The use of the model for understanding the various forms of hydrocephalus is discussed. Finally, the usefulness of the model in solving difficult clinical problems, such as diffuse pediatric head injury and progressive ventriculomegaly with low intracranial pressure, is described.
Adult↗
Solid perception mechanism by a shading pattern: spatial frequency components in a corrugated wave pattern.
Illumination allows solid object perception to be obtained and depicted by a shading pattern produced by lighting. The shading cue, as one of solid perception cues (Gibson 1979), was investigated in regard to a white corrugated wave shape, using computer graphic device: Tospix-2. The reason the corrugated wave was chosen, is that an alternately bright and dark pattern, produced by shading, can be conveniently analyzed into contained spatial frequencies. This paper reports spatial frequency properties contained in the shading pattern. The shading patterns, input into the computer graphic device, are analyzed by Fourier Transformation by the same device. After the filtration by various spatial frequency low and high pass filters, Inverse Fourier Transformation is carried out for the residual components. The result of the analysis indicates that the third through higher harmonics components are important in regard to presenting a solid reality feeling in solid perception. Sakata (1983) also reported that an edged pattern, superimposed onto a lower sinusoidal pattern, was important in solid perception. The third through higher harmonics components express the changing position of luminance on the pattern, and a slanted plane relating to the light direction. Detection of a solid shape, constructed with flat planes, is assumed to be on the bottom of the perfect curved solid perception mechanism. Apparent evidence for this assumption, in difficult visual conditions, is that a flat paneled solid is seen before the curved solid. This mechanism is explained by two spatial frequency neural network systems, assumed as having correspondence with higher spatial frequency detection and lower spatial frequency detection.
Computer Graphics↗
Disjunctive models of Boolean category learning.
Four connectionistic/neural models which are capable of learning arbitrary Boolean functions are presented. Three are probably convergent, but of differing generalization power. The fourth is not necessarily convergent, but its empirical behavior is quite good. The time and space characteristics of the four models are compared over a diverse range of functions and testing conditions. These include the ability to learn specific instances, to effectively generalize, and to deal with irrelevant or redundant information. Trade-offs between time and space are demonstrated by the various approaches.
Brain↗
Coordinates transformation and learning control for visually-guided voluntary movement with iteration: a Newton-like method in a function space.
In order to control visually-guided voluntary movements, the central nervous system (CNS) must solve the following three computational problems at different levels: (1) determination of a desired trajectory in the visual coordinates, (2) transformation of the coordinates of the desired trajectory to the body coordinates and (3) generation of motor command. In this paper, the second and the third problems are treated at computational, representational and hardware levels of Marr. We first study the problems at the computational level, and then propose an iterative learning scheme as a possible algorithm. This is a trial and error type learning such as repetitive training of golf swing. The amount of motor command needed to coordinate activities of many muscles is not determined at once, but in a step-wise, trial and error fashion in the course of a set of repetitions. Actually, the motor command in the (n + 1)-th iteration is a sum of the motor command in the n-th iteration plus two modification terms which are, respectively, proportional to acceleration and speed errors between the desired trajectory and the realized trajectory in the n-th iteration. We mathematically formulate this iterative learning control as a Newton-like method in functional spaces and prove its convergence under appropriate mathematical conditions with use of dynamical system theory and functional analysis. Computer simulations of this iterative learning control of a robotic manipulator in the body or visual coordinates are shown. Finally, we propose that areas 2, 5, and 7 of the sensory association cortex are possible sites of this learning control. Further we propose neural network model which acquires transformation matrices from acceleration or velocity to motor command, which are used in these schemes.
Central Nervous System↗
Maximum likelihood analysis of spike trains of interacting nerve cells.
Suppose that a neuron is firing spontaneously or that it is firing under the influence of other neurons. Suppose that the data available are the firing times of the neurons present. An "integrate several inputs and fire" model is developed and studied empirically. For the model a neuron's firing occurs when an internal state variable crosses a random threshold. This conceptual model leads to maximum likelihood estimates of internal quantities, such as the postsynaptic potentials of the measured influencing neurons, the membrane potential, the absolute threshold and also estimates of derived quantities such as the strength-duration curve and the recovery process of the threshold. The model's validity is examined via an estimate of the conditional firing probability. The approach appears useful for estimating biologically meaningful parameters, for examining hypotheses re these parameters, for understanding the connections present in neural networks and for aiding description and classification of neurons and synapses. Analyses are presented for a number of data sets collected for the sea hare, Aplysia californica, by J. P. Segundo. Both excitatory and inhibitory examples are provided. The computations were carried out via the Glim statistical package. An example of a Glim program realizing the work is presented in the Appendix.
Animals↗
A model of neural network for spatiotemporal pattern recognition.
A model of neural network to recognize spatiotemporal patterns is presented. The network consists of two kinds of neural cells: P-cells and B-cells. A P-cell generates an impulse responding to more than one impulse and embodies two special functions: short term storage (STS) and heterosynaptic facilitation (HSF). A B-cell generates several impulses with high frequency as soon as it receives an impulse. In recognizing process, an impulse generated by a P-cell represents a recognition of stimulus pattern, and triggers the generation of impulses of a B-cell. Inhibitory impulses with high frequency generated by a B-cell reset the activities of all P-cells in the network. Two examples of spatiotemporal pattern recognition are presented. They are achieved by giving different values to the parameters of the network. In one example, the network recognizes both directional and non-directional patterns. The selectivities to directional and non-directional patterns are realized by only adjusting excitatory synaptic weights of P-cells. In the other example, the network recognizes time series of spatial patterns, where the lengths of the series are not necessarily the same and the transitional speeds of spatial patterns are not always the same. In both examples, the HSF signal controls the total activity of the network, which contributes to exact recognition and error recovery. In the latter example, it plays a role to trigger and execute the recognizing process. Finally, we discuss the correspondence between the model and physiological findings.
Brain↗
A neural network model for cognitive activity.
A consideration of the storage of information as an energized neuronal state leads to the development of a new type of neural network model which is capable of pattern recognition, concept formation and recognition of patterns of events in time. The network consists of several layers of cells, each cell representing by connections from the lower levels some combination of features or concepts. Information travels toward higher layers by such connections during an association phase, and then reverses during a recognition phase, where higher-order concepts can redirect the flow to more appropriate elements, revising the perception of the environment. This permits a more efficient method of distinguishing closely-related patterns and also permits the formation of negative associations, which is a likely requirement for formation of "abstract" concepts.
Association Learning↗
A model of binocular brightness and binaural loudness perception in humans with general applications to nonlinear summation of sensory inputs.
A single neural model is proposed to account for how responses of the two eyes and two ears combine to form the perception of binocular brightness and binaural loudness respectively. It involves nonlinear reciprocal feedback inhibition between left and right channels, followed by linear summation between the channels. Local circuit synaptic interactions are an important source of nonlinearity. The model combines inputs in a manner that approximates vector magnitude models in general. This suggests that the model can be applied to a variety of circumstances beyond the visual and auditory data discussed here.
Auditory Perception↗
A mathematical model of the primary visual cortex and hypercolumn.
A mathematical model of the primary visual cortex is presented. Basically, the model comprises two features. Firstly, in analogy with the principle of the computerized tomography (CT), it assumes that simple cells in each hypercolumn are not merely detecting line segments in images as features, but rather that they are as a whole representing the local image with a certain representation. Secondly, it assumes that each hypercolumn is performing spatial frequency analyses of local images using that representation, and that the resultant spectra are represented by complex cells. The model is analyzed using numerical simulations and its advantages are discussed from the viewpoint of visual information processing. It is shown that 1) the proposed processing is tolerant to shifts in position of input images, and that 2) spatial frequency filtering operations can be easily performed in the model.
Depth Perception↗
Evidence, information, and surprise.
A numerical measure for "evidence" is defined in a probabilistic framework. The established mathematical concept of information or entropy (as defined in ergodic theory) can be obtained from this definition in a special case, although in general information in a special case, although in general information is greater than evidence. In another, somewhat complementary, special case a numerical measure for "surprise" is derived from the definition of evidence. Some applications of the new concept of evidence are discussed, concerning statistics in general and the special kind of statistics performed by neurophysiologists, when they analyze the "response" of neurons, and perhaps by the neurons themselves.
Axons↗
Solving a combinatorial problem via self-organizing process: an application of the Kohonen algorithm to the traveling salesman problem.
We present an application of the Kohonen algorithm to the traveling salesman problem: Using only this algorithm, without energy function nor any parameter chosen "ad hoc", we found good suboptimal tours. We give a neural model version of this algorithm, closer to classical neural networks. This is illustrated with various numerical examples.
Algorithms↗
A Boolean complete neural model of adaptive behavior.
A multi-layered neural assembly is developed which has the capability of learning arbitrary Boolean functions. Though the model neuron is more powerful than those previously considered, assemblies of neurons are needed to detect non-linearly separable patterns. Algorithms for learning at the neuron and assembly level are described. The model permits multiple output systems to share a common memory. Learned evaluation allows sequences of actions to be organized. Computer simulations demonstrate the capabilities of the model.
Adaptation, Psychological↗
A unified model for the combined temporal and spatial Broca-Sulzer effect.
A briefly pulsed light is brighter than a pulse of longer duration. For a brief flash of fixed duration and small area, enlarging a target results initially in an increase in brightness. Beyond some critical area, the target dims with further increases in size. These two phenomena are the temporal and the spatial Broca-Sulzer effect respectively. Each effect can be modeled using a generalization of the Hartline-Ratliff equation. The resulting analysis produces a mathematically unified treatment of both effects.
Humans↗
A neuro-synaptic model of the auditory masking and unmasking process.
A model of bilateral information processing in the auditory system was presented on the basis of the interaction of postsynaptic potentials intra- and inter-nuclei in order to analyze the mechanism of binaural unmasking as well as monaural masking. The system was composed of a bilateral pair of auditory nuclei, which were organized in two parallel afferent systems as well as an efferent system. In the model, bilateral inputs were processed in three stages, i.e., the detection of interaural differences by the first afferent system, the equalization of relative neural timing by the efferent system, and the cancellation of specific spectral components by the second afferent system. Assuming the masking process to be forward and backward inhibitions on the auditory memory of signal by the ones of noise, the unmasking process could be explained as a result of disinhibition by the cancellation of bilateral masker inputs.
Acoustic Stimulation↗
Identification of figural elements in a visual domain.
Identification of figural elements is based upon comparison of one of several formal pattern descriptions with subjective similarity judgements. The best description appears to contain figural elements which are heavily dependent on the visual domain chosen for the experiment. Generally those figural elements seem to be involved which distinguish optimally between the reference patterns used to mark off the visual domain. An algorithm is proposed for searching figural elements which are building stones of optimal descriptions. The present study indicates that grammars generating structures based upon features, higher order figures and rules of composition may be tools for visual research.
Cybernetics↗