The disparity energy model can interpret a variety of physiological properties of binocular neurons in the early visual cortex quantitatively. Therefore, many physiologically plausible models for binocular stereopsis ...
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The disparity energy model can interpret a variety of physiological properties of binocular neurons in the early visual cortex quantitatively. Therefore, many physiologically plausible models for binocular stereopsis employed the disparity energy model as a model neuron. These models can explain a variety of psychological findings concerning stereo perception. However, most of the models cannot handle with stereo transparency. Here, we develop a simple stereo model for transparency perception with the hybrid-type disparity energy model, and examine the ability to detect overlapping disparities. Computer simulations showed that the model properties of transparency detection are consistent with many psychophysical findings concerning stereo transparency. (C) 2008 Elsevier B.V. All rights reserved.
Bayesian statistics is has been very successful in describing behavioural data on decision making and cue integration under noisy circumstances. However, it is still an open question how the human brain actually incor...
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ISBN:
(纸本)9783540875581
Bayesian statistics is has been very successful in describing behavioural data on decision making and cue integration under noisy circumstances. However, it is still an open question how the human brain actually incorporates this functionality. Here we compare three ways in which Bayes rule can be implemented using neural fields. The result is a truly dynamic framework that can easily be extended by non-Bayesian mechanisms such as learning and memory.
The disparity energy model can interpret a variety of physiological properties of binocular neurons in the early visual cortex quantitatively. Therefore, many physiologically plausible models for binocular stereopsis ...
详细信息
ISBN:
(纸本)3540464794
The disparity energy model can interpret a variety of physiological properties of binocular neurons in the early visual cortex quantitatively. Therefore, many physiologically plausible models for binocular stereopsis employed the disparity energy model as a model neuron. These models can explain a variety of psychological findings concerning stereo perception. However, most of the models cannot handle with stereo transparency. Here, we develop a simple stereo model for transparency perception with the hybrid-type disparity energy model, and examine the ability to detect overlapping disparities. Computer simulations showed that the model properties of transparency detection are consistent with many psychophysical findings concerning stereo transparency. (C) 2008 Elsevier B.V. All rights reserved.
We examine the question of how a population of independently noisy sensory neurons should be configured to optimize the encoding of a random stimulus into sequences of neural action potentials. For the case where firi...
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ISBN:
(纸本)9780819467393
We examine the question of how a population of independently noisy sensory neurons should be configured to optimize the encoding of a random stimulus into sequences of neural action potentials. For the case where firing rates are the same in all neurons, we consider the problem of optimizing the noise distribution for a known stimulus distribution, and the converse problem of optimizing the stimulus for a given noise distribution. This work is related to suprathreshold stochastic resonance (SSR). It is shown that, for a large number of neurons, the SSR model is equivalent to a single rate-coding neuron with multiplicative output noise.
The existence of spectro-temporal receptive fields and evidence for population coding in auditory cortex motivate the development of such models, that explicitly operate in the time-frequency domain and are based on a...
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Elasmobranchs can detect a little amount of electric fields and they have characteristic approach strategies to find an electric dipole source generated by prey or conspecifics. They appear to align the body at a cons...
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Elasmobranchs can detect a little amount of electric fields and they have characteristic approach strategies to find an electric dipole source generated by prey or conspecifics. They appear to align the body at a constant angle with the current flow line of the electric field while swimming towards prey. However, it has not been studied how they process the perception of electric fields for the approach behaviour or what kind of neural mechanism is used. We use a model of electrosensory perception with electrodynamics and explore a possible approach mechanism based on the sensory landscape distributed on electroreceptors. This paper presents that elasmobranchs can estimate the direction of the electric field by swaying their head, which will be a basis information for their particular approach behaviour. A velocity profile of voltage gradients and intensity difference among the ampullary clusters can be another cues to detect a prey source. (c) 2006 Elsevier Ireland Ltd. All rights reserved.
Physiological studies suggest that decision networks read from the neural representation in the middle temporal area to determine the perceived direction of visual motion, whereas psychophysical studies tend to charac...
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Physiological studies suggest that decision networks read from the neural representation in the middle temporal area to determine the perceived direction of visual motion, whereas psychophysical studies tend to characterize motion perception in terms of the statistical properties of stimuli. To reconcile these different approaches, we examined whether estimating the central tendency of the physical direction of global motion was a better indicator of perceived direction than algorithms (e.g., maximum likelihood) that read from directionally tuned mechanisms near the end of the motion pathway. The task of human observers was to discriminate the global direction of random dot kinematograms composed of asymmetrical distributions of local directions with distinct measures of central tendency. None of the statistical measures of image direction central tendency provided consistently accurate predictions of perceived global motion direction. However, regardless of the local composition of motion directions, a maximum-likelihood decoder produced global motion estimates commensurate with the psychophysical data. Our results suggest that mechanism-based, read-out algorithms offer a more accurate and robust guide to human motion perception than any stimulus-based, statistical estimate of central tendency.
By frame of reference transformations, an input variable in one coordinate system is transformed into an output variable in a different coordinate system depending on another input variable. If the variables are repre...
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By frame of reference transformations, an input variable in one coordinate system is transformed into an output variable in a different coordinate system depending on another input variable. If the variables are represented as neural population codes, then a sigma-pi network is a natural way of coding this transformation. By multiplying two inputs it detects coactivations of input units, and by summing over the multiplied inputs, one output unit can respond invariantly to different combinations of coactivated input units. Here, we present a sigma-pi network and a learning algorithm by which the output representation self-organizes to form a topographic map. This network solves the frame of reference transformation problem by unsupervised learning. (C) 2006 Elsevier B.V. All rights reserved.
Pooling networks are composed of noisy independent neurons that all noisily process the same information in parallel. The output of each neuron is summed into a single output by a fusion center. In this paper we study...
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ISBN:
(纸本)9780819467393
Pooling networks are composed of noisy independent neurons that all noisily process the same information in parallel. The output of each neuron is summed into a single output by a fusion center. In this paper we study such a network in a detection or discrimination task. It is shown that if the network is not properly matched to the symmetries of the detection problem, the internal noise may restore at least partially some kind of optimality. This is shown for both (i) noisy threshold model neurons, as well as (ii) Poisson neuron models. We also study an optimized version of the network, mimicking the notion of excitation/inhibition. We show that, when properly tuned, the network may reach optimality in a very robust way. Furthermore, we find in this optimization that some neurons remain inactive. The pattern of inactivity is organized in a strange branching structure, the meaning of which remains to be elucidated.
We examine the optimal threshold distribution in populations of noisy threshold devices. When the noise on each threshold is independent, and sufficiently large. the optimal thresholds are realized by the suprathresho...
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We examine the optimal threshold distribution in populations of noisy threshold devices. When the noise on each threshold is independent, and sufficiently large. the optimal thresholds are realized by the suprathreshold stochastic resonance effect, in which case all threshold devices are identical. This result has relevance for neural population coding, as such noisy threshold devices model the key dynamics of nerve fibres. It is also relevant to quantization and lossy source coding theory, since the model provides a form of stochastic signal quantization. Furthermore, it is shown that a bifurcation pattern appears in the optimal threshold distribution as the noise intensity increases. Fisher information is used to demonstrate that the optimal threshold distribution remains in the suprathreshold stochastic resonance configuration as the population size approaches infinity. (c) 2005 Elsevier B.V. All rights reserved.
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