A Neural Circuit for Coordinating Reaching with Grasping: Autocompensating Variable Initial Apertures, Perturbations to Target Size, and Perturbations to Target Orientation

A neural network model is presented, that extends principles of the VITE (vector integration to end-point) model [1, 2, 3, 4] of primate reaching to the more complex case of reach-grasp coordination. The main new planning problem addressed by the model is how to simulate human data on temporal coordination between reaching and grasping, while at the same time remaining stable and compensating for altered initial apertures and perturbations of object size and object location/ orientation. Simulations of the model replicate key features of four different experimental protocols with a single set of parameters. The proposed circuit computes reaching to grasp trajectories in real-time, by continuously updating vector positioning commands, and with no precomputation of total or component movement times. The model consists of three generator channels: transport, which generates a reaching trajectory; aperture, which controls distance between thumb and index finger; and orientation, which controls hand orientation vis-a-vis target's orientation.CONACYT of Mexico; Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409

A Neural Circuit for Coordinating Reaching with Grasping: Autocompensating Variable Initial Apertures, Perturbations to Target Size, and Perturbations to Target Orientation

A neural network model is presented, that extends principles of the VITE (vector integration to end-point) model [1, 2, 3, 4] of primate reaching to the more complex case of reach-grasp coordination. The main new planning problem addressed by the model is how to simulate human data on temporal coordination between reaching and grasping, while at the same time remaining stable and compensating for altered initial apertures and perturbations of object size and object location/ orientation. Simulations of the model replicate key features of four different experimental protocols with a single set of parameters. The proposed circuit computes reaching to grasp trajectories in real-time, by continuously updating vector positioning commands, and with no precomputation of total or component movement times. The model consists of three generator channels: transport, which generates a reaching trajectory; aperture, which controls distance between thumb and index finger; and orientation, which controls hand orientation vis-a-vis target's orientation.CONACYT of Mexico; Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409

Muscle synergies in neuroscience and robotics: from input-space to task-space perspectives

In this paper we review the works related to muscle synergies that have been carried-out in neuroscience and control engineering. In particular, we refer to the hypothesis that the central nervous system (CNS) generates desired muscle contractions by combining a small number of predefined modules, called muscle synergies. We provide an overview of the methods that have been employed to test the validity of this scheme, and we show how the concept of muscle synergy has been generalized for the control of artificial agents. The comparison between these two lines of research, in particular their different goals and approaches, is instrumental to explain the computational implications of the hypothesized modular organization. Moreover, it clarifies the importance of assessing the functional role of muscle synergies: although these basic modules are defined at the level of muscle activations (input-space), they should result in the effective accomplishment of the desired task. This requirement is not always explicitly considered in experimental neuroscience, as muscle synergies are often estimated solely by analyzing recorded muscle activities. We suggest that synergy extraction methods should explicitly take into account task execution variables, thus moving from a perspective purely based on input-space to one grounded on task-space as well

Predictive Coding for Dynamic Visual Processing: Development of Functional Hierarchy in a Multiple Spatio-Temporal Scales RNN Model

The current paper proposes a novel predictive coding type neural network model, the predictive multiple spatio-temporal scales recurrent neural network (P-MSTRNN). The P-MSTRNN learns to predict visually perceived human whole-body cyclic movement patterns by exploiting multiscale spatio-temporal constraints imposed on network dynamics by using differently sized receptive fields as well as different time constant values for each layer. After learning, the network becomes able to proactively imitate target movement patterns by inferring or recognizing corresponding intentions by means of the regression of prediction error. Results show that the network can develop a functional hierarchy by developing a different type of dynamic structure at each layer. The paper examines how model performance during pattern generation as well as predictive imitation varies depending on the stage of learning. The number of limit cycle attractors corresponding to target movement patterns increases as learning proceeds. And, transient dynamics developing early in the learning process successfully perform pattern generation and predictive imitation tasks. The paper concludes that exploitation of transient dynamics facilitates successful task performance during early learning periods.Comment: Accepted in Neural Computation (MIT press

Human premotor areas parse sequences into their spatial and temporal features.

Skilled performance is characterized by precise and flexible control of movement sequences in space and time. Recent theories suggest that integrated spatio-temporal trajectories are generated by intrinsic dynamics of motor and premotor networks. This contrasts with behavioural advantages that emerge when a trained spatial or temporal feature of sequences is transferred to a new spatio-temporal combination arguing for independent neural representations of these sequence features. We used a new fMRI pattern classification approach to identify brain regions with independent vs integrated representations. A distinct regional dissociation within motor areas was revealed: whereas only the contralateral primary motor cortex exhibited unique patterns for each spatio-temporal sequence combination, bilateral premotor areas represented spatial and temporal features independently of each other. These findings advocate a unique function of higher motor areas for flexible recombination and efficient encoding of complex motor behaviours

Attentive Learning of Sequential Handwriting Movements: A Neural Network Model

Defense Advanced research Projects Agency and the Office of Naval Research (N00014-95-1-0409, N00014-92-J-1309); National Science Foundation (IRI-97-20333); National Institutes of Health (I-R29-DC02952-01)

Going Deeper into Action Recognition: A Survey

Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader