Learning Priors for Bayesian Computations in the Nervous System

Our nervous system continuously combines new information from our senses with information it has acquired throughout life. Numerous studies have found that human subjects manage this by integrating their observations with their previous experience (priors) in a way that is close to the statistical optimum. However, little is known about the way the nervous system acquires or learns priors. Here we present results from experiments where the underlying distribution of target locations in an estimation task was switched, manipulating the prior subjects should use. Our experimental design allowed us to measure a subject's evolving prior while they learned. We confirm that through extensive practice subjects learn the correct prior for the task. We found that subjects can rapidly learn the mean of a new prior while the variance is learned more slowly and with a variable learning rate. In addition, we found that a Bayesian inference model could predict the time course of the observed learning while offering an intuitive explanation for the findings. The evidence suggests the nervous system continuously updates its priors to enable efficient behavior

Estimating the Relevance of World Disturbances to Explain Savings, Interference and Long-Term Motor Adaptation Effects

Recent studies suggest that motor adaptation is the result of multiple, perhaps linear processes each with distinct time scales. While these models are consistent with some motor phenomena, they can neither explain the relatively fast re-adaptation after a long washout period, nor savings on a subsequent day. Here we examined if these effects can be explained if we assume that the CNS stores and retrieves movement parameters based on their possible relevance. We formalize this idea with a model that infers not only the sources of potential motor errors, but also their relevance to the current motor circumstances. In our model adaptation is the process of re-estimating parameters that represent the body and the world. The likelihood of a world parameter being relevant is then based on the mismatch between an observed movement and that predicted when not compensating for the estimated world disturbance. As such, adapting to large motor errors in a laboratory setting should alert subjects that disturbances are being imposed on them, even after motor performance has returned to baseline. Estimates of this external disturbance should be relevant both now and in future laboratory settings. Estimated properties of our bodies on the other hand should always be relevant. Our model demonstrates savings, interference, spontaneous rebound and differences between adaptation to sudden and gradual disturbances. We suggest that many issues concerning savings and interference can be understood when adaptation is conditioned on the relevance of parameters

A Bayesian Model of Sensory Adaptation

Recent studies reported two opposite types of adaptation in temporal perception. Here, we propose a Bayesian model of sensory adaptation that exhibits both types of adaptation. We regard adaptation as the adaptive updating of estimations of time-evolving variables, which determine the mean value of the likelihood function and that of the prior distribution in a Bayesian model of temporal perception. On the basis of certain assumptions, we can analytically determine the mean behavior in our model and identify the parameters that determine the type of adaptation that actually occurs. The results of our model suggest that we can control the type of adaptation by controlling the statistical properties of the stimuli presented

Bloodlines: mammals, leeches, and conservation in southern Asia

Southern Asia is a biodiversity hotspot both for terrestrial mammals and for leeches. Many small-mammal groups are under-studied in this region, while other mammals are of known conservation concern. In addition to standard methods for surveying mammals, it has recently been demonstrated that residual bloodmeals within leeches can be sequenced to find mammals in a given area. While these invertebrate-parasite-derived DNA (iDNA) methods are promising, most of the leech species utilized for this type of survey remain unevaluated, notwithstanding that their diversity varies substantially. Here we examine approximately 750 individual leech specimens in the genus Haemadipsa across a large range in southern Asia (Bangladesh, Cambodia, and China), specifically reviewing the diversity of mammals they feed on and their own genetic structuring. Leeches were found to feed on a considerable variety of mammals, corroborating prior studies. Additionally, leeches were found to have fed both on bats and on birds, neither of which has previously been recorded with this method. The genetic structuring of the leeches themselves revealed 15 distinct clades of which only two precisely corresponded to previously characterized species, indicating that much work is needed to finalize classifications in this genus. Most importantly, with regards to mammal conservation, leeches in these clades appear to feed on a broad range of mammals

Adaptive tuning functions arise from visual observation of past movement

Visual observation of movement plays a key role in action. For example, tennis players have little time to react to the ball, but still need to prepare the appropriate stroke. Therefore, it might be useful to use visual information about the ball trajectory to recall a specific motor memory. Past visual observation of movement (as well as passive and active arm movement) affects the learning and recall of motor memories. Moreover, when passive or active, these past contextual movements exhibit generalization (or tuning) across movement directions. Here we extend this work, examining whether visual motion also exhibits similar generalization across movement directions and whether such generalization functions can explain patterns of interference. Both the adaptation movement and contextual movement exhibited generalization beyond the training direction, with the visual contextual motion exhibiting much broader tuning. A second experiment demonstrated that this pattern was consistent with the results of an interference experiment where opposing force fields were associated with two separate visual movements. Overall, our study shows that visual contextual motion exhibits much broader (and shallower) tuning functions than previously seen for either passive or active movements, demonstrating that the tuning characteristics of past motion are highly dependent on their sensory modality

Measuring Generalization of Visuomotor Perturbations in Wrist Movements Using Mobile Phones

Recent studies in motor control have shown that visuomotor rotations for reaching have narrow generalization functions: what we learn during movements in one direction only affects subsequent movements into close directions. Here we wanted to measure the generalization functions for wrist movement. To do so we had 7 subjects performing an experiment holding a mobile phone in their dominant hand. The mobile phone's built in acceleration sensor provided a convenient way to measure wrist movements and to run the behavioral protocol. Subjects moved a cursor on the screen by tilting the phone. Movements on the screen toward the training target were rotated and we then measured how learning of the rotation in the training direction affected subsequent movements in other directions. We find that generalization is local and similar to generalization patterns of visuomotor rotation for reaching

Evidence for sparse synergies in grasping actions

Converging evidence shows that hand-actions are controlled at the level of synergies and not single muscles. One intriguing aspect of synergy-based action-representation is that it may be intrinsically sparse and the same synergies can be shared across several distinct types of hand-actions. Here, adopting a normative angle, we consider three hypotheses for hand-action optimal-control: sparse-combination hypothesis (SC) – sparsity in the mapping between synergies and actions - i.e., actions implemented using a sparse combination of synergies; sparse-elements hypothesis (SE) – sparsity in synergy representation – i.e., the mapping between degrees-of-freedom (DoF) and synergies is sparse; double-sparsity hypothesis (DS) – a novel view combining both SC and SE – i.e., both the mapping between DoF and synergies and between synergies and actions are sparse, each action implementing a sparse combination of synergies (as in SC), each using a limited set of DoFs (as in SE). We evaluate these hypotheses using hand kinematic data from six human subjects performing nine different types of reach-to-grasp actions. Our results support DS, suggesting that the best action representation is based on a relatively large set of synergies, each involving a reduced number of degrees-of-freedom, and that distinct sets of synergies may be involved in distinct tasks

A novel computational framework for deducing muscle synergies from experimental joint moments

Prior experimental studies have hypothesized the existence of a “muscle synergy” based control scheme for producing limb movements and locomotion in vertebrates. Such synergies have been suggested to consist of fixed muscle grouping schemes with the co-activation of all muscles in a synergy resulting in limb movement. Quantitative representations of these groupings (termed muscle weightings) and their control signals (termed synergy controls) have traditionally been derived by the factorization of experimentally measured EMG. This study presents a novel approach for deducing these weightings and controls from inverse dynamic joint moments that are computed from an alternative set of experimental measurements—movement kinematics and kinetics. This technique was applied to joint moments for healthy human walking at 0.7 and 1.7 m/s, and two sets of “simulated” synergies were computed based on two different criteria (1) synergies were required to minimize errors between experimental and simulated joint moments in a musculoskeletal model (pure-synergy solution) (2) along with minimizing joint moment errors, synergies also minimized muscle activation levels (optimal-synergy solution). On comparing the two solutions, it was observed that the introduction of optimality requirements (optimal-synergy) to a control strategy solely aimed at reproducing the joint moments (pure-synergy) did not necessitate major changes in the muscle grouping within synergies or the temporal profiles of synergy control signals. Synergies from both the simulated solutions exhibited many similarities to EMG derived synergies from a previously published study, thus implying that the analysis of the two different types of experimental data reveals similar, underlying synergy structures

How does our motor system determine its learning rate?

Motor learning is driven by movement errors. The speed of learning can be quantified by the learning rate, which is the proportion of an error that is corrected for in the planning of the next movement. Previous studies have shown that the learning rate depends on the reliability of the error signal and on the uncertainty of the motor system’s own state. These dependences are in agreement with the predictions of the Kalman filter, which is a state estimator that can be used to determine the optimal learning rate for each movement such that the expected movement error is minimized. Here we test whether not only the average behaviour is optimal, as the previous studies showed, but if the learning rate is chosen optimally in every individual movement. Subjects made repeated movements to visual targets with their unseen hand. They received visual feedback about their endpoint error immediately after each movement. The reliability of these error-signals was varied across three conditions. The results are inconsistent with the predictions of the Kalman filter because correction for large errors in the beginning of a series of movements to a fixed target was not as fast as predicted and the learning rates for the extent and the direction of the movements did not differ in the way predicted by the Kalman filter. Instead, a simpler model that uses the same learning rate for all movements with the same error-signal reliability can explain the data. We conclude that our brain does not apply state estimation to determine the optimal planning correction for every individual movement, but it employs a simpler strategy of using a fixed learning rate for all movements with the same level of error-signal reliability

Challenges and New Approaches to Proving the Existence of Muscle Synergies of Neural Origin

Muscle coordination studies repeatedly show low-dimensionality of muscle activations for a wide variety of motor tasks. The basis vectors of this low-dimensional subspace, termed muscle synergies, are hypothesized to reflect neurally-established functional muscle groupings that simplify body control. However, the muscle synergy hypothesis has been notoriously difficult to prove or falsify. We use cadaveric experiments and computational models to perform a crucial thought experiment and develop an alternative explanation of how muscle synergies could be observed without the nervous system having controlled muscles in groups. We first show that the biomechanics of the limb constrains musculotendon length changes to a low-dimensional subspace across all possible movement directions. We then show that a modest assumption—that each muscle is independently instructed to resist length change—leads to the result that electromyographic (EMG) synergies will arise without the need to conclude that they are a product of neural coupling among muscles. Finally, we show that there are dimensionality-reducing constraints in the isometric production of force in a variety of directions, but that these constraints are more easily controlled for, suggesting new experimental directions. These counter-examples to current thinking clearly show how experimenters could adequately control for the constraints described here when designing experiments to test for muscle synergies—but, to the best of our knowledge, this has not yet been done