This work is concerned with the question of how a population of neurons responds to tonic and transient synaptic input from other similar populations. Because of the methodological problems involved in measuring and m...
This work is concerned with the question of how a population of neurons responds to tonic and transient synaptic input from other similar populations. Because of the methodological problems involved in measuring and manipulating the firing properties of a large set of real neurons simultaneously, another strategy is used here: the experiments are made as a series of simulations using a population of realistic model neurons. The steady state response of this particular model neuron is found to be similar to that used in abstract nonspiking models. The transient response, however, reveals that even though each individual neuron simply changes its frequency moderately, the population can respond quickly and with damped oscillations. These oscillations are due to spike synchronization caused by systematic phase shifts induced by synchronous changes in the input.
We describe a robot vision system that achieves complex object recognition with two layers of behaviors, performing the tasks of planning and object recognition, respectively. The recognition layer is a pipeline in wh...
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The problems and algorithms in question are applied to the parameter design of automatic control systems. The generality of (1-1) permits the definition of a wide range of constraints in the time and frequency domains...
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This report describes a three-dimensional computer model of the olfactory cortex developed for the study of cortical oscillations and their biological significance. The model was designed with the intention of investi...
This report describes a three-dimensional computer model of the olfactory cortex developed for the study of cortical oscillations and their biological significance. The model was designed with the intention of investigating the relative role of network circuitry and network unit properties, resulting in a model complexity between simple Hopfield nets and detailed realistic simulations. Network connections are essentially the same as in a detailed simulation of the olfactory (piriform) cortex by Wilson and Bower (1989), but the network units are here modeled with continuous output functions and single compartments. It is shown that the present model is capable of reproducing all major results of the more complex model, corresponding to spatiotemporal patterns found in the actual cortex (Freeman 1975). This indicates that action potentials and the geometry of cells are not needed per se for explaining certain cortical activities. In contrast, connections between units, in particular feedforward and feedback inhibitory loops and long-range, excitatory-excitatory connections, are found to be crucial for the dynamical behavior of this system. The model describes intrinsic oscillatory properties of olfactory cortex and reproduces response patterns associated with a continuous random-input signal and with a shock pulse given to the cortex. In the latter case, waves of activity move across the model cortex in a way similar to the detailed simulations by Wilson and Bower, and consistent with corresponding global dynamic behavior of the functioning cortex. For a constant random input, the network is able to oscillate with two separate frequencies simultaneously, purely as a result of its intrinsic network properties. A delicate balance between inhibition and excitation, in terms of connection strength and timing of events, is necessary for coherent frequency and phase of the oscillating neural units. The analytical equations used in this model seem an adequate representation
A probabilistic artificial neural network is presented. It is of a one-layer, feedback-coupled type with graded units. The learning rule is derived from Bayes's rule. Learning is regarded as collecting statistics ...
A probabilistic artificial neural network is presented. It is of a one-layer, feedback-coupled type with graded units. The learning rule is derived from Bayes's rule. Learning is regarded as collecting statistics and recall as a statistical inference process. Units correspond to events and connections come out as compatibility coefficients in a logarithmic combination rule. The input to a unit via connections from other active units affects the a posteriori belief in the event in question. The new model is compared to an earlier binary model with respect to storage capacity, noise tolerance, etc. in a content addressable memory (CAM) task. The new model is a real time network and some results on the reaction time for associative recall are given. The scaling of learning and relaxation operations is considered together with issues related to representation of information in one-layer artificial neural networks. An extension with complex units is discussed.
A technique for estimating and iteratively correct for the smooth errors of discretization algorithms is presented. The theoretical foundation is given as a number of theorems. Some problems for ordinary differential ...
A technique for estimating and iteratively correct for the smooth errors of discretization algorithms is presented. The theoretical foundation is given as a number of theorems. Some problems for ordinary differential equations are used as illustrative examples.
This Festschrift, published on the occasion of the sixtieth birthday of Yutaka - mamoto (‘YY’ as he is occasionally casually referred to), contains a collection of articles by friends, colleagues, and former Ph.D. s...
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ISBN:
(数字)9783540939184
ISBN:
(纸本)9783540939177
This Festschrift, published on the occasion of the sixtieth birthday of Yutaka - mamoto (‘YY’ as he is occasionally casually referred to), contains a collection of articles by friends, colleagues, and former Ph.D. students of YY. They are a tribute to his friendship and his scienti?c vision and oeuvre, which has been a source of inspiration to the authors. Yutaka Yamamoto was born in Kyoto, Japan, on March 29, 1950. He studied applied mathematics and general engineering science at the Department of Applied Mathematics and Physics of Kyoto University, obtaining the B.S. and ***. degrees in 1972 and 1974. His ***. work was done under the supervision of Professor Yoshikazu Sawaragi. In 1974, he went to the Center for Mathematical System T- ory of the University of Florida in Gainesville. He obtained the ***. and Ph.D. degrees, both in Mathematics, in 1976 and 1978, under the direction of Professor Rudolf Kalman.
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