We propose a model for early visual processing in primates. The model consists of a population of linear spatial filters which interact through non-linear excitatory and inhibitory pooling. Statistical estimation theo...
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ISBN:
(纸本)0262100762
We propose a model for early visual processing in primates. The model consists of a population of linear spatial filters which interact through non-linear excitatory and inhibitory pooling. Statistical estimation theory is then used to derive human psychophysical thresholds from the responses of the entire population of units. The model is able to reproduce human thresholds for contrast and ori-entation discrimination tasks, and to predict contrast thresholds in the presence of masks of varying orientation and spatial frequency.
Parametric feedback control of chaos relies on detailed knowledge of the locations of unstable periodic orbits. We show that unstable periodic orbits of dynamical systems with unknown locations but known periodicity ...
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Parametric feedback control of chaos relies on detailed knowledge of the locations of unstable periodic orbits. We show that unstable periodic orbits of dynamical systems with unknown locations but known periodicity τ can be stabilized by an oscillating feedback term proportional to ɛt (x→t−x→t−τ), where x→t is the location of the trajectory at time t and ɛt is periodic in time. Periodic feedback overcomes the limitations of Giona’s theorem [Nonlinearity 4, 911 (1991)], which states that constant feedback (i.e., a time-independent ɛ) can stabilize an unstable periodic orbit only if the stability matrix has no positive eigenvalues greater than unity. As an application of oscillating feedback, we use it to stabilize the memory patterns in an associative memory (Hopfield [Proc. Natl. Acad. Sci. USA 79, 2554 (1982); 81, 3088 (1984)]) network, thereby enhancing the total capacity of the memory device. We extend our method to high-dimensional systems described by differential equations; in this framework, it is possible to stabilize the spatiotemporal chaos generated by the Kuramoto-Sivashinsky equation [G. J. Sivashinsky and D. M. Michelson, Prog. Theor. Phys. 63, 2122 (1980)].
An integrated circuit that computes the velocity vector of a visual stimulus in one dimension is presented. The circuit combines optical sensors and associated electronics on a single silicon chip, processed with stan...
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An integrated circuit that computes the velocity vector of a visual stimulus in one dimension is presented. The circuit combines optical sensors and associated electronics on a single silicon chip, processed with standard CMOS technology. The velocity is inferred from the time delay of the appearance of an image feature at two fixed locations on the chip. The circuit operates quite robustly for high-contrast stimuli over considerable irradiance and velocity ranges. With lower-contrast stimuli the output signal for a given velocity tends to decrease, while the direction selectivity is still maintained. The individual motion-sensing cells are compact, and they are therefore suited for use in dense 1D or 2D imaging arrays.
Despite the fact that mental arithmetic is based on only a few hundred basic facts and some simple algorithms, humans have a difficult time mastering the subject, and even experienced individuals make mistakes. Associ...
ISBN:
(纸本)0262112450
Despite the fact that mental arithmetic is based on only a few hundred basic facts and some simple algorithms, humans have a difficult time mastering the subject, and even experienced individuals make mistakes. Associative multiplication, the process of doing multiplication by memory without the use of rules or algorithms, is especially problematic. Humans exhibit certain characteristic phenomena in performing associative multiplications, both in the type of error and in the error frequency. We propose a model for the process of associative multiplication, and compare its performance in both these phenomena with data from normal humans and from the model proposed by Anderson et al (1994).
Feature detection and tracking is a fundamental problem in computer vision research. By detecting and tracking features in an image sequence it is possible to recover information about both the motion of the viewer an...
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Sensing visual motion gives a creature valuable information about its interactions with the environment. Flies in particular use visual motion information to navigate through turbulent air, avoid obstacles, and land s...
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Sensing visual motion gives a creature valuable information about its interactions with the environment. Flies in particular use visual motion information to navigate through turbulent air, avoid obstacles, and land safely. Mobile robots are ideal candidates for using this sensory modality to enhance their performance, but so far have been limited by the computational expense of processing video. Also, the complex structure of natural visual scenes poses an algorithmic challenge for extracting useful information in a robust manner. We address both issues by creating a small, low-power visual sensor with integrated analog parallel processing to extract motion in real-time. Because our architecture is based on biological motion detectors, we gain the advantages of this highly evolved system: A design that robustly and continuously extracts relevant information from its visual environment. We show that this sensor is suitable for use in the real world, and demonstrate its ability to compensate for an imperfect motor system in the control of an autonomous robot. The sensor attenuates open-loop rotation by a factor of 31 with less than 1 mW power dissipation.
We show that the dynamical behavior of a coupled map lattice where the individual maps are Bernoulli shift maps can be solved analytically for integer couplings. We calculate the invariant density of the system and sh...
We show that the dynamical behavior of a coupled map lattice where the individual maps are Bernoulli shift maps can be solved analytically for integer couplings. We calculate the invariant density of the system and show that it displays a nontrivial spatial behavior. We also introduce and calculate a generalized spatiotemporal correlation function.
Linear control theory is used to develop an improved localized control scheme for spatially extended chaotic systems, which is applied to a coupled map lattice as an example. The optimal arrangement of the control sit...
Linear control theory is used to develop an improved localized control scheme for spatially extended chaotic systems, which is applied to a coupled map lattice as an example. The optimal arrangement of the control sites is shown to depend on the symmetry properties of the system, while their minimal density depends on the strength of noise in the system. The method is shown to work in any region of parameter space and requires a significantly smaller number of controllers compared to the method proposed earlier by Hu and Qu [Phys. Rev. Lett. 72, 68 (1994)]. A nonlinear generalization of the method for a 1D lattice is also presented.
We present a novel method for recovering articulator movements from speech acoustics based on a constrained form [9] of a hidden Markov model. The model attempts to explain sequences of high dimensional data using smo...
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