It is shown that Suprathreshold Stochastic Resonance (SSR) is effectively a way of using noise to perform quantization or lossy signal compression with a population of identical threshold-based devices. Quantization o...
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ISBN:
(纸本)0819458368
It is shown that Suprathreshold Stochastic Resonance (SSR) is effectively a way of using noise to perform quantization or lossy signal compression with a population of identical threshold-based devices. Quantization of an analog signal is a fundamental requirement for its efficient storage or compression in a digital system. This process will always result in a loss of quality, known as distortion, in a reproduction of the original signal. The distortion can be decreased by increasing the number of states available for encoding the signal (measured by the rate, or mutual information). Hence, designing a quantizer requires a tradeoff between distortion and rate. Quantization theory has recently been applied to the analysis of neural coding and here we examine the possibility that SSR is a possible mechanism used by populations of sensory neurons to quantize signals. In particular, we analyze the rate-distortion performance of SSR for a range of input SNR's and show that both the optimal distortion and optimal rate occurs for an input SNR of about 0 dB, which is a biologically plausible situation. Furthermore, we relax the constraint that all thresholds are identical, and find the optimal threshold values for a range of input SNRs. We find that for sufficiently small input SNRs, the optimal quantizer is one in which all thresholds are identical, that is, the SSR situation is optimal in this case.
We propose a quantum information scheme that builds on the interference properties of entangled ion states that are transiently confined by local potentials within the permeation path of voltage-gated, ion-conducting ...
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ISBN:
(纸本)0819458368
We propose a quantum information scheme that builds on the interference properties of entangled ion states that are transiently confined by local potentials within the permeation path of voltage-gated, ion-conducting membrane proteins. We show, that the sub-molecular organization of parts of the protein, as revealed by the recent progress in high-resolution atomic-level spectroscopy and accompaning molecular dynamics simulations, carries a logical coding potency that goes beyond the pure catalytic function of the channel, subserving the transmembrane crossing of an electrodiffusive barrier. As we argue that 'within channel states' can become super-correlated with the environment, this also sheds new light on the role of noise in controlling the access of ions to voltage-gated ion channels ('channel noise').
We create a framework based on Fisher information for determining the most effective population coding scheme for representing a continuous-valued stimulus attribute over its entire range. Using this scheme, we derive...
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We create a framework based on Fisher information for determining the most effective population coding scheme for representing a continuous-valued stimulus attribute over its entire range. Using this scheme, we derive optimal single- and multi-neuron rate codes for homogeneous populations using several statistical models frequently used to describe neural data. We show that each neuron's discharge rate should increase quadratically with the stimulus and that statistically independent neural outputs provides optimal coding. Only cooperative populations can achieve this condition in an informationally effective way.
The output of the cat retina is conveyed to the brain by the axons of similar to170,000 retinal ganglion cells that together constitute an optic nerve. Ganglion cells come in a number of varieties with the result that...
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ISBN:
(纸本)0780376129
The output of the cat retina is conveyed to the brain by the axons of similar to170,000 retinal ganglion cells that together constitute an optic nerve. Ganglion cells come in a number of varieties with the result that the message conveyed along the nerve to the brain is partitioned into a number of components. In this paper some key discoveries about neural coding in the cat retina are reported and used to illustrate how efficient the retina is at encoding visual information.
Cracking the neural code has long been a central issue in neuroscience. However, it has been proved difficult because there logically exist an infinite number of other models and interpretations that could account for...
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Cracking the neural code has long been a central issue in neuroscience. However, it has been proved difficult because there logically exist an infinite number of other models and interpretations that could account for the same data and phenomena (i.e. the problem of underdetermination). Therefore, I suggest that applying biologically realistic multiple constraints from ion-channel level to system level (e.g. cognitive neuroscience and human brain disorders) can only solve the problem of underdetermination. Here I have explored whether the noise shaping/predictive neural coding hypothesis can provide a unified view on following realistic multiple constraints: (1) cortical gain control mechanisms in vivo;(2) the relationships between acetylcholine, nicotine, dopamine, calcium-activated potassium ion-channel, and cognitive functions;(3) oscillations and synchrony;(4) why should spontaneous activity be irregular;(5) whether the cortical neurons in vivo are coincidence detectors or integrators;and (6) the causal relationship between theta oscillation, gamma band fluctuation, and P3 (or P300) ERP responses. Finally, recent experimental results supporting the unified view shall be discussed. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.
There have been three main ideas about the basic law of psychophysics. In 1860, Fechner used Weber’s law to infer that the subjective sense of intensity is related to the physical intensity of a stimulus by a logarit...
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There have been three main ideas about the basic law of psychophysics. In 1860, Fechner used Weber’s law to infer that the subjective sense of intensity is related to the physical intensity of a stimulus by a logarithmic function (the Weber-Fechner law). A hundred years later, Stevens refuted Fechner’s law by showing that direct reports of subjective intensity are related to the physical intensity of stimuli by a power law. MacKay soon showed, however, that the logarithmic and power laws are indistinguishable without examining the underlying neural mechanisms. Mountcastle and his colleagues did so, and, on the basis of transducer functions obeying power laws, inferred that subjective intensity must be related linearly to the neural coding measure on which it is based. In this review, we discuss these issues and we review a series of studies aimed at the neural mechanisms of a very complex form of subjective experience—the experience of roughness produced by a textured surface. The results, which are independent of any assumptions about the form of the psychophysical law, support the idea that the basic law of psychophysics is linearity between subjective experience and the neural activity on which it is based.
We consider the dependence of information transfer by neurons on the Type I vs. Type II classification of their dynamics. Our computational study is based on Type I and II implementations of the Morris-Lecar model. It...
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We consider the dependence of information transfer by neurons on the Type I vs. Type II classification of their dynamics. Our computational study is based on Type I and II implementations of the Morris-Lecar model. It mainly concerns neurons, such as those in the auditory or electrosensory system, which encode band-limited amplitude modulations of a periodic carrier signal, and which fire at random cycles yet preferred phases of this carrier. We first show that the Morris-Lecar model with additive broadband noise ("synaptic noise") can exhibit such firing patterns with either Type I or II dynamics, with or without amplitude modulations of the carrier. We then compare the encoding of band-limited random amplitude modulations for both dynamical types. The comparison relies on a parameter calibration that closely matches firing rates for both models across a range of parameters. In the absence of synaptic noise, Type I performs slightly better than Type II, and its performance is optimal for perithreshold signals. However, Type II performs well over a slightly larger range of inputs, and this range lies mostly in the subthreshold region. Further, Type II performs marginally better than Type I when synaptic noise, which yields more realistic baseline firing patterns, is present in both models. These results are discussed in terms of the tuning and phase locking properties of the models with deterministic and stochastic inputs.
When sensory stimuli are encoded in a lossy fashion for efficient transmission, there are necessarily tradeoffs between the represented fidelity of various aspects of the stimuli. In the model of attention presented h...
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When sensory stimuli are encoded in a lossy fashion for efficient transmission, there are necessarily tradeoffs between the represented fidelity of various aspects of the stimuli. In the model of attention presented here, a top-down signal informs the encoder of these tradeoffs. Given the stimulus ensemble and tradeoff requirements, our system learns an optimal encoder. This general model is instantiated in a simple network: an autoencoder with a bottleneck, innervated by a top-down attentional signal, and trained using backpropagation. The modulation of neural activity learned by this model qualitatively matches that measured in animals during visual attention tasks. (C) 2003 Published by Elsevier B.V.
A powerful approach to studying how information is transmitted in basic neural systems is based on finding stimulus-response classes that optimize the mutual information shared between the classes. The problem can be ...
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A powerful approach to studying how information is transmitted in basic neural systems is based on finding stimulus-response classes that optimize the mutual information shared between the classes. The problem can be formally described in terms of finding a optimal quantization (A, B) of a large discrete joint (X, Y) distribution and various algorithms have been developed for this purpose. Recently, it has been proved that finding the optimal such quantization is NP-complete (optimal mutual information quantization is NP-complete, neural information coding indicating that exact solutions may be computationally infeasible to find in some circumstances. We have developed a new randomized algorithm to solve the joint quantization problem. Under assumptions about the underlying (X, Y) distribution, we prove that this algorithm converges to the "true" optimal quantization with high probability that can be increased by performing additional random trials. (C) 2004 Elsevier B.V. All rights reserved.
Shaped by evolutionary processes, sensory systems often represent behaviorally relevant stimuli with higher fidelity than other stimuli. The stimulus dependence of neural reliability could therefore provide an importa...
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Shaped by evolutionary processes, sensory systems often represent behaviorally relevant stimuli with higher fidelity than other stimuli. The stimulus dependence of neural reliability could therefore provide an important clue in a search for relevant sensory signals. We explore this relation and introduce a novel iterative algorithm that allows one to find stimuli that are reliably represented by the sensory system under study. To assess the quality of a neural representation, we use stimulus reconstruction methods. The algorithm starts with the presentation of an initial stimulus (e.g. white noise). The evoked spike train is recorded and used to reconstruct the stimulus online. Within a closed-loop setup, this reconstruction is then played back to the sensory system. Iterating this procedure, the newly generated stimuli can be better and better reconstructed. We demonstrate the feasibility of this method by applying it to auditory receptor neurons in locusts. Our data show that the optimal stimuli often exhibit pronounced sub-threshold periods that are interrupted by short, yet intense pulses. Similar results are obtained for simple model neurons and suggest that these stimuli are encoded with high reliability by a large class of neurons.
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