An Enquiry Concerning Charmless Semileptonic Decays of Bottom Mesons

The branching fractions for the decays B {yields} P{ell}{nu}{sub {ell}}, where P are the pseudoscalar charmless mesons {pi}{sup {+-}}, {pi}{sup 0}, {eta} and {eta}{prime} and {ell} is an electron or muon, are measured with B{sup 0} and B{sup {+-}} mesons found in the recoil of a second B meson decaying as B {yields} D{ell}{nu}{sub {ell}} or B {yields} D*{ell}{nu}{sub {ell}}. The measurements are based on a data set of 348 fb{sup -1} of e{sup +}e{sup -} collisions at {radical}s = 10.58 GeV recorded with the BABAR detector. Assuming isospin symmetry, measured pionic branching fractions are combined into {Beta}(B{sup 0} {yields} {pi}{sup -}{ell}{sup +}{nu}{sub {ell}}) = (1.54 {+-} 0.17{sub (stat)} {+-} 0.09{sub (syst)}) x 10{sup -4}. First evidence of the B{sup +} {yields} {eta}{ell}{sup +}{nu}{sub {ell}} decay is seen; its branching fraction is measured to be {Beta}(B{sup +} {yields} {eta}{ell}{sup +}{nu}{sub {ell}}) = (0.64 {+-} 0.20{sub (stat)} {+-} 0.03{sub (syst)}) x 10{sup -4}. It is determined that {Beta}(B{sup +} {yields} {eta}{prime}{ell}{sup +}{nu}{sub {ell}}) < 0.47 x 10{sup -4} to 90% confidence. Partial branching fractions for the pionic decays in ranges of the momentum transfer and various published calculations of the B {yields} {pi} hadronic form factor are used to obtain values of the magnitude of the Cabibbo-Kobayashi-Maskawa matrix element V{sub ub} between 3.61 and 4.07 x 10{sup -3}

Motor Variability Arises from a Slow Random Walk in Neural State

Even well practiced movements cannot be repeated without variability. This variability is thought to reflect "noise" in movement preparation or execution. However, we show that, for both professional baseball pitchers and macaque monkeys making reaching movements, motor variability can be decomposed into two statistical components, a slowly drifting mean and fast trial-by-trial fluctuations about the mean. The preparatory activity of dorsal premotor cortex/primary motor cortex neurons in monkey exhibits similar statistics. Although the neural and behavioral drifts appear to be correlated, neural activity does not account for trial-by-trial fluctuations in movement, which must arise elsewhere, likely downstream. The statistics of this drift are well modeled by a double-exponential autocorrelation function, with time constants similar across the neural and behavioral drifts in two monkeys, as well as the drifts observed in baseball pitching. These time constants can be explained by an error-corrective learning processes and agree with learning rates measured directly in previous experiments. Together, these results suggest that the central contributions to movement variability are not simply trial-by-trial fluctuations but are rather the result of longer-timescale processes that may arise from motor learning

Sensory Population Decoding for Visually Guided Movements

We have used a new approach to study the neural decoding function that converts the population response in extrastriate area MT into estimates of target motion to drive smooth pursuit eye movement. Experiments reveal significant trial-by-trial correlations between the responses of MT neurons and the initiation of pursuit. The preponderance of significant correlations and the relatively low reduction in noise between MT and the behavioral output support the hypothesis of a sensory origin for at least some of the trial-by-trial variation in pursuit initiation. The finding of mainly positive MT-pursuit correlations, whether the target speed is faster or slower than the neuron's preferred speed, places strong constraints on the neural decoding computation. We propose that decoding is based on normalizing a weighted population vector of opponent motion responses; normalization comes from neurons uncorrelated with those used to compute the weighted population vector

Neural Representation and Causal Models in Motor Cortex

Dorsal premotor (PMd) and primary motor (M1) cortices play a central role in mapping sensation to movement. Many studies of these areas have focused on correlation-based tuning curves relating neural activity to task or movement parameters, but the link between tuning and movement generation is unclear. We recorded motor preparatory activity from populations of neurons in PMd/M1 as macaque monkeys performed a visually guided reaching task and show that tuning curves for sensory inputs (reach target direction) and motor outputs (initial movement direction) are not typically aligned. We then used a simple, causal model to determine the expected relationship between sensory and motor tuning. The model shows that movement variability is minimized when output neurons (those that directly drive movement) have target and movement tuning that are linearly related across targets and cells. In contrast, for neurons that only affect movement via projections to output neurons, the relationship between target and movement tuning is determined by the pattern of projections to output neurons and may even be uncorrelated, as was observed for the PMd/M1 population as a whole. We therefore determined the relationship between target and movement tuning for subpopulations of cells defined by the temporal duration of their spike waveforms, which may distinguish cell types. We found a strong correlation between target and movement tuning for only a subpopulation of neurons with intermediate spike durations (trough-to-peak ∼350 μs after high-pass filtering), suggesting that these cells have the most direct role in driving motor output.SIGNIFICANCE STATEMENT This study focuses on how macaque premotor and primary motor cortices transform sensory inputs into motor outputs. We develop empirical and theoretical links between causal models of this transformation and more traditional, correlation-based "tuning curve" analyses. Contrary to common assumptions, we show that sensory and motor tuning curves for premovement preparatory activity do not generally align. Using a simple causal model, we show that tuning-curve alignment is only expected for output neurons that drive movement. Finally, we identify a physiologically defined subpopulation of neurons with strong tuning-curve alignment, suggesting that it contains a high concentration of output cells. This study demonstrates how analysis of movement variability, combined with simple causal models, can uncover the circuit structure of sensorimotor transformations

Neural Representation and Causal Models in Motor Cortex

Dorsal premotor (PMd) and primary motor (M1) cortices play a central role in mapping sensation to movement. Many studies of these areas have focused on correlation-based tuning curves relating neural activity to task or movement parameters, but the link between tuning and movement generation is unclear. We recorded motor preparatory activity from populations of neurons in PMd/M1 as macaque monkeys performed a visually guided reaching task and show that tuning curves for sensory inputs (reach target direction) and motor outputs (initial movement direction) are not typically aligned. We then used a simple, causal model to determine the expected relationship between sensory and motor tuning. The model shows that movement variability is minimized when output neurons (those that directly drive movement) have target and movement tuning that are linearly related across targets and cells. In contrast, for neurons that only affect movement via projections to output neurons, the relationship between target and movement tuning is determined by the pattern of projections to output neurons and may even be uncorrelated, as was observed for the PMd/M1 population as a whole. We therefore determined the relationship between target and movement tuning for subpopulations of cells defined by the temporal duration of their spike waveforms, which may distinguish cell types. We found a strong correlation between target and movement tuning for only a subpopulation of neurons with intermediate spike durations (trough-to-peak ∼350 μs after high-pass filtering), suggesting that these cells have the most direct role in driving motor output. SIGNIFICANCE STATEMENT This study focuses on how macaque premotor and primary motor cortices transform sensory inputs into motor outputs. We develop empirical and theoretical links between causal models of this transformation and more traditional, correlation-based “tuning curve” analyses. Contrary to common assumptions, we show that sensory and motor tuning curves for premovement preparatory activity do not generally align. Using a simple causal model, we show that tuning-curve alignment is only expected for output neurons that drive movement. Finally, we identify a physiologically defined subpopulation of neurons with strong tuning-curve alignment, suggesting that it contains a high concentration of output cells. This study demonstrates how analysis of movement variability, combined with simple causal models, can uncover the circuit structure of sensorimotor transformations

The Neural Code for Motor Control in the Cerebellum and Oculomotor Brainstem

Spike trains are rich in information that can be extracted to guide behaviors at millisecond time resolution or across longer time intervals. In sensory systems, the information usually is defined with respect to the stimulus. Especially in motor systems, however, it is equally critical to understand how spike trains predict behavior. Thus, our goal was to compare systematically spike trains in the oculomotor system with eye movement behavior on single movements. We analyzed the discharge of Purkinje cells in the floccular complex of the cerebellum, floccular target neurons in the brainstem, other vestibular neurons, and abducens neurons. We find that an extra spike in a brief analysis window predicts a substantial fraction of the trial-by-trial variation in the initiation of smooth pursuit eye movements. For Purkinje cells, a single extra spike in a 40 ms analysis window predicts, on average, 0.5 SDs of the variation in behavior. An optimal linear estimator predicts behavioral variation slightly better than do spike counts in brief windows. Simulations reveal that the ability of single spikes to predict a fraction of behavior also emerges from model spike trains that have the same statistics as the real spike trains, as long as they are driven by shared sensory inputs. We think that the shared sensory estimates in their inputs create correlations in neural spiking across time and across each population. As a result, one or a small number of spikes in a brief time interval can predict a substantial fraction of behavioral variation