Modern Machine Learning as a Benchmark for Fitting Neural Responses.

Neuroscience has long focused on finding encoding models that effectively ask "what predicts neural spiking?" and generalized linear models (GLMs) are a typical approach. It is often unknown how much of explainable neural activity is captured, or missed, when fitting a model. Here we compared the predictive performance of simple models to three leading machine learning methods: feedforward neural networks, gradient boosted trees (using XGBoost), and stacked ensembles that combine the predictions of several methods. We predicted spike counts in macaque motor (M1) and somatosensory (S1) cortices from standard representations of reaching kinematics, and in rat hippocampal cells from open field location and orientation. Of these methods, XGBoost and the ensemble consistently produced more accurate spike rate predictions and were less sensitive to the preprocessing of features. These methods can thus be applied quickly to detect if feature sets relate to neural activity in a manner not captured by simpler methods. Encoding models built with a machine learning approach accurately predict spike rates and can offer meaningful benchmarks for simpler models.

Efficacy Analysis of Capsaicin 8% Patch in Neuropathic Peripheral Pain Treatment.

BACKGROUND/AIMS: Several guidelines for neuropathic pain management and various effective drugs are available; however, neuropathic pain remains undertreated. This retrospective study aimed to evaluate the efficacy of topical capsaicin 8% in peripheral neuropathic pain in a routine clinical setting. METHODS: Therapeutic efficacy was evaluated through pain intensity, using numerical pain rating scale at baseline and 7-14 days after each treatment, and using pain treatment area (PTA) assessed immediately before each treatment. RESULTS: A total of 43 patients with either post-herpetic neuralgia or post-traumatic/post-surgical neuropathic pain were enrolled. The median percentage reduction in numerical pain rating scale score and in PTA was -40.0 (-50.0 to -33.3; 95% CI, bootstrap) and -35.1 (-50.9 to 3.4; 95% CI, bootstrap), respectively. Pain intensity and PTA were equally improved and reduced in both treated conditions. CONCLUSION: This study suggests that topical capsaicin 8% reduces peripheral neuropathic pain as well as treatment pain area.

Quantifying Mesoscale Neuroanatomy Using X-Ray Microtomography.

Methods for resolving the three-dimensional (3D) microstructure of the brain typically start by thinly slicing and staining the brain, followed by imaging numerous individual sections with visible light photons or electrons. In contrast, X-rays can be used to image thick samples, providing a rapid approach for producing large 3D brain maps without sectioning. Here we demonstrate the use of synchrotron X-ray microtomography (µCT) for producing mesoscale (∼1 µm 3 resolution) brain maps from millimeter-scale volumes of mouse brain. We introduce a pipeline for µCT-based brain mapping that develops and integrates methods for sample preparation, imaging, and automated segmentation of cells, blood vessels, and myelinated axons, in addition to statistical analyses of these brain structures. Our results demonstrate that X-ray tomography achieves rapid quantification of large brain volumes, complementing other brain mapping and connectomics efforts.

A cryptography-based approach for movement decoding.

Brain decoders use neural recordings to infer the activity or intent of a user. To train a decoder, one generally needs to infer the measured variables of interest (covariates) from simultaneously measured neural activity. However, there are cases for which obtaining supervised data is difficult or impossible. Here, we describe an approach for movement decoding that does not require access to simultaneously measured neural activity and motor outputs. We use the statistics of movement-much like cryptographers use the statistics of language-to find a mapping between neural activity and motor variables, and then align the distribution of decoder outputs with the typical distribution of motor outputs by minimizing their Kullback-Leibler divergence. By using datasets collected from the motor cortex of three non-human primates performing either a reaching task or an isometric force-production task, we show that the performance of such a distribution-alignment decoding algorithm is comparable to the performance of supervised approaches. Distribution-alignment decoding promises to broaden the set of potential applications of brain decoding.

Using psychophysics to ask if the brain samples or maximizes.

The two-alternative forced-choice (2AFC) task is the workhorse of psychophysics and is used to measure the just-noticeable difference, generally assumed to accurately quantify sensory precision. However, this assumption is not true for all mechanisms of decision making. Here we derive the behavioral predictions for two popular mechanisms, sampling and maximum a posteriori, and examine how they affect the outcome of the 2AFC task. These predictions are used in a combined visual 2AFC and estimation experiment. Our results strongly suggest that subjects use a maximum a posteriori mechanism. Further, our derivations and experimental paradigm establish the already standard 2AFC task as a behavioral tool for measuring how humans make decisions under uncertainty.

The generalization of prior uncertainty during reaching.

Bayesian statistics defines how new information, given by a likelihood, should be combined with previously acquired information, given by a prior distribution. Many experiments have shown that humans make use of such priors in cognitive, perceptual, and motor tasks, but where do priors come from? As people never experience the same situation twice, they can only construct priors by generalizing from similar past experiences. Here we examine the generalization of priors over stochastic visuomotor perturbations in reaching experiments. In particular, we look into how the first two moments of the prior--the mean and variance (uncertainty)--generalize. We find that uncertainty appears to generalize differently from the mean of the prior, and an interesting asymmetry arises when the mean and the uncertainty are manipulated simultaneously.

Saliency and saccade encoding in the frontal eye field during natural scene search.

The frontal eye field (FEF) plays a central role in saccade selection and execution. Using artificial stimuli, many studies have shown that the activity of neurons in the FEF is affected by both visually salient stimuli in a neuron's receptive field and upcoming saccades in a certain direction. However, the extent to which visual and motor information is represented in the FEF in the context of the cluttered natural scenes we encounter during everyday life has not been explored. Here, we model the activities of neurons in the FEF, recorded while monkeys were searching natural scenes, using both visual and saccade information. We compare the contribution of bottom-up visual saliency (based on low-level features such as brightness, orientation, and color) and saccade direction. We find that, while saliency is correlated with the activities of some neurons, this relationship is ultimately driven by activities related to movement. Although bottom-up visual saliency contributes to the choice of saccade targets, it does not appear that FEF neurons actively encode the kind of saliency posited by popular saliency map theories. Instead, our results emphasize the FEF's role in the stages of saccade planning directly related to movement generation.

Primary motor cortical discharge during force field adaptation reflects muscle-like dynamics.

We often make reaching movements having similar trajectories within very different mechanical environments, for example, with and without an added load in the hand. Under these varying conditions, our kinematic intentions must be transformed into muscle commands that move the limbs. Primary motor cortex (M1) has been implicated in the neural mechanism that mediates this adaptation to new movement dynamics, but our recent experiments suggest otherwise. We have recorded from electrode arrays that were chronically implanted in M1 as monkeys made reaching movements under two different dynamic conditions: the movements were opposed by either a clockwise or counterclockwise velocity-dependent force field acting at the hand. Under these conditions, the preferred direction (PD) of neural discharge for nearly all neurons rotated in the direction of the applied field, as did those of proximal limb electromyograms (EMGs), although the median neural rotation was significantly smaller than that of muscles. For a given neuron, the rotation angle was very consistent, even across multiple sessions. Within the limits of measurement uncertainty, both the neural and EMG changes occurred nearly instantaneously, reaching a steady state despite ongoing behavioral adaptation. Our results suggest that M1 is not directly involved in the adaptive changes that occurred within an experimental session. Rather, most M1 neurons are directly related to the dynamics of muscle activation that themselves reflect the external load. It appears as though gain modulation, the differential recruitment of M1 neurons by higher motor areas, can account for the load and behavioral adaptation-related changes in M1 discharge.

Generalization of stochastic visuomotor rotations.

Generalization studies examine the influence of perturbations imposed on one movement onto other movements. The strength of generalization is traditionally interpreted as a reflection of the similarity of the underlying neural representations. Uncertainty fundamentally affects both sensory integration and learning and is at the heart of many theories of neural representation. However, little is known about how uncertainty, resulting from variability in the environment, affects generalization curves. Here we extend standard movement generalization experiments to ask how uncertainty affects the generalization of visuomotor rotations. We find that although uncertainty affects how fast subjects learn, the perturbation generalizes independently of uncertainty.

Differential representations of prior and likelihood uncertainty in the human brain.

BACKGROUND: Uncertainty shapes our perception of the world and the decisions we make. Two aspects of uncertainty are commonly distinguished: uncertainty in previously acquired knowledge (prior) and uncertainty in current sensory information (likelihood). Previous studies have established that humans can take both types of uncertainty into account, often in a way predicted by Bayesian statistics. However, the neural representations underlying these parameters remain poorly understood. RESULTS: By varying prior and likelihood uncertainty in a decision-making task while performing neuroimaging in humans, we found that prior and likelihood uncertainty had quite distinct representations. Whereas likelihood uncertainty activated brain regions along the early stages of the visuomotor pathway, representations of prior uncertainty were identified in specialized brain areas outside this pathway, including putamen, amygdala, insula, and orbitofrontal cortex. Furthermore, the magnitude of brain activity in the putamen predicted individuals' personal tendencies to rely more on either prior or current information. CONCLUSIONS: Our results suggest different pathways by which prior and likelihood uncertainty map onto the human brain and provide a potential neural correlate for higher reliance on current or prior knowledge. Overall, these findings offer insights into the neural pathways that may allow humans to make decisions close to the optimal defined by a Bayesian statistical framework.

In praise of "false" models and rich data.

The authors argue that "true" models that aim at faithfully mimicking or reproducing every property of the sensorimotor system cannot be compact as they need many free parameters. Consequently, most scientists in motor control use what are called "false" models--models that derive from well-defined approximations. The authors conceptualize these models as a priori limited in scope and approximate. As such, they argue that a quantitative characterization of the deviations between the system and the model, more than the mere act of falsifying, allows scientists to make progress in understanding the sensorimotor system. Ultimately, this process should result in models that explain as much data variance as possible. The authors conclude by arguing that progress in that direction could strongly benefit from databases of experimental results and collections of models.

Bayesian integration and non-linear feedback control in a full-body motor task.

A large number of experiments have asked to what degree human reaching movements can be understood as being close to optimal in a statistical sense. However, little is known about whether these principles are relevant for other classes of movements. Here we analyzed movement in a task that is similar to surfing or snowboarding. Human subjects stand on a force plate that measures their center of pressure. This center of pressure affects the acceleration of a cursor that is displayed in a noisy fashion (as a cloud of dots) on a projection screen while the subject is incentivized to keep the cursor close to a fixed position. We find that salient aspects of observed behavior are well-described by optimal control models where a Bayesian estimation model (Kalman filter) is combined with an optimal controller (either a Linear-Quadratic-Regulator or Bang-bang controller). We find evidence that subjects integrate information over time taking into account uncertainty. However, behavior in this continuous steering task appears to be a highly non-linear function of the visual feedback. While the nervous system appears to implement Bayes-like mechanisms for a full-body, dynamic task, it may additionally take into account the specific costs and constraints of the task.