A receive-combining-aided MIMO system, in which multiple combiners are added before the decoder (or detector) for noise suppression, is proposed to improve the effective SNR and the symbol error rate. We apply the con...
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A receive-combining-aided MIMO system, in which multiple combiners are added before the decoder (or detector) for noise suppression, is proposed to improve the effective SNR and the symbol error rate. We apply the concept of the receive combining to STBC to establish receive-combining-aided STBC (RC-STBC) and derive the effective SNR for this scheme. Simulation results show that RC-STBC has larger effective SNR than STBC, also, they show that RC-STBC can have lower symbol error rate than STBC at low input SNR or in the high-order modulation scheme. (C) 2013 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
The agranular architecture of motor cortex lacks a functional interpretation. Here, we consider a 'predictive coding' account of this unique feature based on asymmetries in hierarchical cortical connections. I...
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The agranular architecture of motor cortex lacks a functional interpretation. Here, we consider a 'predictive coding' account of this unique feature based on asymmetries in hierarchical cortical connections. In sensory cortex, layer 4 (the granular layer) is the target of ascending pathways. We theorise that the operation of predictive coding in the motor system (a process termed 'active inference') provides a principled rationale for the apparent recession of the ascending pathway in motor cortex. The extension of this theory to interlaminar circuitry also accounts for a sub-class of 'mirror neuron' in motor cortex whose activity is suppressed when observing an action explaining how predictive coding can gate hierarchical processing to switch between perception and action.
For a Noetherian local ring (R, m), the first two Hilbert coefficients, e(0) and e(1), of the I-adic filtration of an m-primary ideal I are known to code for properties of R, of the blowup of Spec(R) along V (I), and ...
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For a Noetherian local ring (R, m), the first two Hilbert coefficients, e(0) and e(1), of the I-adic filtration of an m-primary ideal I are known to code for properties of R, of the blowup of Spec(R) along V (I), and even of their normalizations. We give estimations for these coefficients when I is enlarged (in the case of e(1) in the same integral closure class) for general Noetherian local rings.
The study of lacunarity and statistical independence of functional series is continued in the context of the theory of uniformly distributed point sets in the unit cube.
The study of lacunarity and statistical independence of functional series is continued in the context of the theory of uniformly distributed point sets in the unit cube.
Clark appears to be moving toward epistemic internalism, which he once rightly rejected. This results from a double over-interpretation of predictive coding's significance. First, Clark argues that predictive codi...
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Clark appears to be moving toward epistemic internalism, which he once rightly rejected. This results from a double over-interpretation of predictive coding's significance. First, Clark argues that predictive coding offers a Grand Unified theory (GUT) of brain function. Second, he over-reads its epistemic import, perhaps even conflating causal and epistemic mediators. We argue instead for a plurality of neurofunctional principles.
We identify the different styles of texting in Filipino short message service (SMS) texts and analyze the change in unigram and bigram frequencies due to these styles. Style preference vectors for sample texts were ca...
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We identify the different styles of texting in Filipino short message service (SMS) texts and analyze the change in unigram and bigram frequencies due to these styles. Style preference vectors for sample texts were calculated and used to identify the style combination used by an average individual. The change in Shannon entropy of the SMS text is explained in light of a coding process.
Although the existence of correlated spiking between neurons in a population is well known, the role such correlations play in encoding stimuli is not. We address this question by constructing pattern-based encoding m...
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Although the existence of correlated spiking between neurons in a population is well known, the role such correlations play in encoding stimuli is not. We address this question by constructing pattern-based encoding models that describe how time-varying stimulus drive modulates the expression probabilities of population-wide spike patterns. The challenge is that large populations may express an astronomical number of unique patterns, and so fitting a unique encoding model for each individual pattern is not feasible. We avoid this combinatorial problem using a dimensionality-reduction approach based on regression trees. Using the insight that some patterns may, from the perspective of encoding, be statistically indistinguishable, the tree divisively clusters the observed patterns into groups whose member patterns possess similar encoding properties. These groups, corresponding to the leaves of the tree, are much smaller in number than the original patterns, and the tree itself constitutes a tractable encoding model for each pattern. Our formalism can detect an extremely weak stimulus-driven pattern structure and is based on maximizing the data likelihood, not making a priori assumptions as to how patterns should be grouped. Most important, by comparing pattern encodings with independent neuron encodings, one can determine if neurons in the population are driven independently or collectively. We demonstrate this method using multiple unit recordings from area 17 of anesthetized cat in response to a sinusoidal grating and show that pattern-based encodings are superior to those of independent neuron models. The agnostic nature of our clustering approach allows us to investigate encoding by the collective statistics that are actually present rather than those (such as pairwise) that might be presumed.
An animal confronts numerous challenges when constructing an optimal navigational route. Spatial representations used for path optimization are likely constrained by critical environmental factors that dictate which n...
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An animal confronts numerous challenges when constructing an optimal navigational route. Spatial representations used for path optimization are likely constrained by critical environmental factors that dictate which neural systems control navigation. Multiple coding schemes depend upon their ecological relevance for a particular species, particularly when dealing with the third, or vertical, dimension of space.
Although the bicoded map constitutes an interesting candidate representation, proposing it as the predominant representation for three-dimensional space is too restrictive. We present and argue for scalable spatial re...
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Although the bicoded map constitutes an interesting candidate representation, proposing it as the predominant representation for three-dimensional space is too restrictive. We present and argue for scalable spatial representation structures as a more comprehensive alternative account that includes the bicoded map as a special case.
We consider a computational model comparing the possible roles of "association" and "simulation" in phonetic decoding, demonstrating that these two routes can contain similar information in some &q...
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We consider a computational model comparing the possible roles of "association" and "simulation" in phonetic decoding, demonstrating that these two routes can contain similar information in some "perfect" communication situations and highlighting situations where their decoding performance differs. We conclude that optimal decoding should involve some sort of fusion of association and simulation in the human brain.
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