MIN PB is the class of minimization problems whose objective functions are bounded by a polynomial in the size of the input. We show that there exist several problems which are MIN PB-complete with respect to an appro...
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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.
The dynamic behavior of cortical structures can change significantly in character by different types of neuromodulators. We simulate such effects in a neural network model of the olfactory cortex and analyze the resul...
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The dynamic behavior of cortical structures can change significantly in character by different types of neuromodulators. We simulate such effects in a neural network model of the olfactory cortex and analyze the resulting nonlinear dynamics of this system. The model uses simple network units and a network connectivity which closely resembles that of the real cortex. The input-output relation of populations of neurons is represented as a sigmoid function, with a single parameter determining threshold, slope and amplitude of the curve. This parameter is taken to correspond to the level of neuromodulator and correlated with the level of arousal of an animal. By varying this "gain parameter" we show that the model can give point attractor, limit cycle attractor and strange chaotic or nonchaotic attractor behavior. We also display "transient chaos", which begins with chaos-like behavior but eventually goes to a limit cycle. We show how this complex dynamics is related to learning and associative memory in our system and discuss the biological significance of this.
Probabilistic neural networks can approximate class conditional densities in optimal (Bayesian) pattern classifiers. In natural pattern recognition applications, the size of the training set is always limited, making ...
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Probabilistic neural networks can approximate class conditional densities in optimal (Bayesian) pattern classifiers. In natural pattern recognition applications, the size of the training set is always limited, making the approximation task difficult. Invariance constraints can significantly simplify the task of density approximation. A technique is presented for learning invariant representations, based on a statistical approach to ground invariance. An iterative method is developed formally for computing the maximum likelihood estimate to the parameters of an invariant mixture model. The method can be interpreted as a competitive training strategy for a radial basis function (RBF) network. It can be used for self-organizing formation of both invariant templates and features.< >
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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We present a multi-scale representation of grey-level shape, called the scale-space primal sketch, that makes explicit features in scale-space as well as the relations between features at different levels of scale. Th...
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We present a multi-scale representation of grey-level shape, called the scale-space primal sketch, that makes explicit features in scale-space as well as the relations between features at different levels of scale. The representation gives a qualitative description of the image structure that allows for extraction of significant image structure - stable scales and regions of interest - in a solely bottom-up data-driven manner. Hence, it can be seen as preceding further processing, which can then be properly tuned. Experiments on real imagery demonstrate that the proposed theory gives intuitively reasonable results.
Some versions of the maximum common subgraph problem are studied and approximation algorithms are given. The maximum bounded common induced subgraph problem is shown to be Max SNP-hard and the maximum unbounded common...
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Elementary techniques from real analysis, and singularity theory are applied to derive analytical results for the behaviour in scale-space of critical points and related entities. The main results of the treatment com...
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Elementary techniques from real analysis, and singularity theory are applied to derive analytical results for the behaviour in scale-space of critical points and related entities. The main results of the treatment comprise
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