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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(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.
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.
The complexity of analog VLSI systems is often limited by the number of pins on a chip rather than by the die area. Currently, many analog parameters and biases are stored off chip. Moving parameter storage on chip co...
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The complexity of analog VLSI systems is often limited by the number of pins on a chip rather than by the die area. Currently, many analog parameters and biases are stored off chip. Moving parameter storage on chip could save pins and allow us to create complex programmable analog systems. In this paper, we present a design for an on-chip non-volatile analog memory cell that can be configured in addressable arrays and programmed easily. We use floating-gate MOS transistors to store charge, and we use the processes of tunneling and hot-electron injection to program values. We achieve greater than 13-bit precision with no crosstalk between memory cells.
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)].
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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Several encouraging developments towards identifying the neuronal correlate of visual awareness have emerged recently. Increasingly sophisticated behavioral paradigms permit the study of visual awareness in humans as ...
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Several encouraging developments towards identifying the neuronal correlate of visual awareness have emerged recently. Increasingly sophisticated behavioral paradigms permit the study of visual awareness in humans as well as in non-human primates. In patients with anatomically restricted lesions in striate and extrastriate cortex, highly informative deficits of visual awareness are observed. Similar deficits can be obtained in normal observers with a novel class of psychophysical displays. Taken together, these results suggest that the contents of visual awareness reflect neuronal activity in certain extrastriate, but not in striate, visual cortical areas.
Input noise, defined as the root mean square of the fluctuations in the input, typically limits the performance of any system in engineering or biology. We show that three different performance measures scale identica...
Input noise, defined as the root mean square of the fluctuations in the input, typically limits the performance of any system in engineering or biology. We show that three different performance measures scale identically as a function of the noise in a simple model of neuronal spiking that has both a voltage and current threshold. These performance measures are: the probability of correctly detecting a constant input in a limited time, the signal-to-noise ratio in response to sinusoidal input, and the mutual information between an arbitrarily varying input and the output spike train of the model neuron. Of these, detecting a constant signal is the simplest and most fundamental quantity. For subthreshold signals, the model exhibits stochastic resonance, a non-zero noise amplitude that optimally enhances signal detection. in this case, noise paradoxically does not limit but instead improves performance. This resonance arises through the conjunction of two competing mechanisms: the noise-induced linearization ('dithering') of the model's firing rate and the increase in the variability of the number of spikes in the output. Even though the noise amplitude dwarfs the signal, detection of a weak constant signal using stochastic resonance is still possible when the signal elicits on average only one additional spike. Stochastic resonance could thus play a role in neurobiological sensory systems, where speed is of the utmost importance and averaging over many individual spikes is not possible.
Intermediate and higher vision processes require selection of a subset of the available sensory information before further processing. Usually, this selection is implemented in the form of a spatially circumscribed re...
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Both vertebrate and invertebrate retinas are highly efficient in extracting contrast independent of the background intensity over five or more decades. This efficiency has been rendered possible by the adaptation of t...
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