933 resultados para neurotrophic signals


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This research is concerned with block coding for a feedback communication system in which the forward and feedback channels are independently disturbed by additive white Gaussian noise and average power constrained. Two coding schemes are proposed in which the messages to be coded for transmission over the forward channel are realized as a set of orthogonal waveforms. A finite number of forward and feedback transmissions (iterations) per message is made. Information received over the feedback channel is used to modify the waveform transmitted on successive forward iterations in such a way that the expected value of forward signal energy is zero on all iterations after the first. Similarly, information is sent over the feedback channel in such a way that the expected value of feedback signal energy is also zero on all iterations after the first. These schemes are shown to achieve a lower probability of error than the best one-way coding scheme at all rates up to the forward channel capacity, provided only that the feedback channel capacity be greater than the forward channel capacity. These schemes make more efficient use of the available feedback power than existing feedback coding schemes, and therefore require less feedback power to achieve a given error performance.

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The problem of global optimization of M phase-incoherent signals in N complex dimensions is formulated. Then, by using the geometric approach of Landau and Slepian, conditions for optimality are established for N = 2 and the optimal signal sets are determined for M = 2, 3, 4, 6, and 12.

The method is the following: The signals are assumed to be equally probable and to have equal energy, and thus are represented by points ṡi, i = 1, 2, …, M, on the unit sphere S1 in CN. If Wik is the halfspace determined by ṡi and ṡk and containing ṡi, i.e. Wik = {ṙϵCN:| ≥ | ˂ṙ, ṡk˃|}, then the Ʀi = ∩/k≠i Wik, i = 1, 2, …, M, the maximum likelihood decision regions, partition S1. For additive complex Gaussian noise ṅ and a received signal ṙ = ṡie + ṅ, where ϴ is uniformly distributed over [0, 2π], the probability of correct decoding is PC = 1/πN ∞/ʃ/0 r2N-1e-(r2+1)U(r)dr, where U(r) = 1/M M/Ʃ/i=1 Ʀi ʃ/∩ S1 I0(2r | ˂ṡ, ṡi˃|)dσ(ṡ), and r = ǁṙǁ.

For N = 2, it is proved that U(r) ≤ ʃ/Cα I0(2r|˂ṡ, ṡi˃|)dσ(ṡ) – 2K/M. h(1/2K [Mσ(Cα)-σ(S1)]), where Cα = {ṡϵS1:|˂ṡ, ṡi˃| ≥ α}, K is the total number of boundaries of the net on S1 determined by the decision regions, and h is the strictly increasing strictly convex function of σ(Cα∩W), (where W is a halfspace not containing ṡi), given by h = ʃ/Cα∩W I0 (2r|˂ṡ, ṡi˃|)dσ(ṡ). Conditions for equality are established and these give rise to the globally optimal signal sets for M = 2, 3, 4, 6, and 12.

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A central question in Neuroscience is that of how the nervous system generates the spatiotemporal commands needed to realize complex gestures, such as handwriting. A key postulate is that the central nervous system (CNS) builds up complex movements from a set of simpler motor primitives or control modules. In this study we examined the control modules underlying the generation of muscle activations when performing different types of movement: discrete, point-to-point movements in eight different directions and continuous figure-eight movements in both the normal, upright orientation and rotated 90 degrees. To test for the effects of biomechanical constraints, movements were performed in the frontal-parallel or sagittal planes, corresponding to two different nominal flexion/abduction postures of the shoulder. In all cases we measured limb kinematics and surface electromyographic activity (EMB) signals for seven different muscles acting around the shoulder. We first performed principal component analysis (PCA) of the EMG signals on a movement-by-movement basis. We found a surprisingly consistent pattern of muscle groupings across movement types and movement planes, although we could detect systematic differences between the PCs derived from movements performed in each sholder posture and between the principal components associated with the different orientations of the figure. Unexpectedly we found no systematic differences between the figute eights and the point-to-point movements. The first three principal components could be associated with a general co-contraction of all seven muscles plus two patterns of reciprocal activatoin. From these results, we surmise that both "discrete-rhythmic movements" such as the figure eight, and discrete point-to-point movement may be constructed from three different fundamental modules, one regulating the impedance of the limb over the time span of the movement and two others operating to generate movement, one aligned with the vertical and the other aligned with the horizontal.