Expression of emotional states during locomotion based on canonical parameters

n/

Origins of vocal-entangled gesture

Gestures during speaking are typically understood in a representational framework: they represent absent or distal states of affairs by means of pointing, resemblance, or symbolic replacement. However, humans also gesture along with the rhythm of speaking, which is amenable to a non-representational perspective. Such a perspective centers on the phenomenon of vocal-entangled gestures and builds on evidence showing that when an upper limb with a certain mass decelerates/accelerates sufficiently, it yields impulses on the body that cascade in various ways into the respiratory–vocal system. It entails a physical entanglement between body motions, respiration, and vocal activities. It is shown that vocal-entangled gestures are realized in infant vocal–motor babbling before any representational use of gesture develops. Similarly, an overview is given of vocal-entangled processes in non-human animals. They can frequently be found in rats, bats, birds, and a range of other species that developed even earlier in the phylogenetic tree. Thus, the origins of human gesture lie in biomechanics, emerging early in ontogeny and running deep in phylogeny

The Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book

Probabilistic Models of Motor Production

N. Bernstein defined the ability of the central neural system (CNS) to control many degrees of freedom of a physical body with all its redundancy and flexibility as the main problem in motor control. He pointed at that man-made mechanisms usually have one, sometimes two degrees of freedom (DOF); when the number of DOF increases further, it becomes prohibitively hard to control them. The brain, however, seems to perform such control effortlessly. He suggested the way the brain might deal with it: when a motor skill is being acquired, the brain artificially limits the degrees of freedoms, leaving only one or two. As the skill level increases, the brain gradually "frees" the previously fixed DOF, applying control when needed and in directions which have to be corrected, eventually arriving to the control scheme where all the DOF are "free". This approach of reducing the dimensionality of motor control remains relevant even today. One the possibles solutions of the Bernstetin's problem is the hypothesis of motor primitives (MPs) - small building blocks that constitute complex movements and facilitite motor learnirng and task completion. Just like in the visual system, having a homogenious hierarchical architecture built of similar computational elements may be beneficial. Studying such a complicated object as brain, it is important to define at which level of details one works and which questions one aims to answer. David Marr suggested three levels of analysis: 1. computational, analysing which problem the system solves; 2. algorithmic, questioning which representation the system uses and which computations it performs; 3. implementational, finding how such computations are performed by neurons in the brain. In this thesis we stay at the first two levels, seeking for the basic representation of motor output. In this work we present a new model of motor primitives that comprises multiple interacting latent dynamical systems, and give it a full Bayesian treatment. Modelling within the Bayesian framework, in my opinion, must become the new standard in hypothesis testing in neuroscience. Only the Bayesian framework gives us guarantees when dealing with the inevitable plethora of hidden variables and uncertainty. The special type of coupling of dynamical systems we proposed, based on the Product of Experts, has many natural interpretations in the Bayesian framework. If the dynamical systems run in parallel, it yields Bayesian cue integration. If they are organized hierarchically due to serial coupling, we get hierarchical priors over the dynamics. If one of the dynamical systems represents sensory state, we arrive to the sensory-motor primitives. The compact representation that follows from the variational treatment allows learning of a motor primitives library. Learned separately, combined motion can be represented as a matrix of coupling values. We performed a set of experiments to compare different models of motor primitives. In a series of 2-alternative forced choice (2AFC) experiments participants were discriminating natural and synthesised movements, thus running a graphics Turing test. When available, Bayesian model score predicted the naturalness of the perceived movements. For simple movements, like walking, Bayesian model comparison and psychophysics tests indicate that one dynamical system is sufficient to describe the data. For more complex movements, like walking and waving, motion can be better represented as a set of coupled dynamical systems. We also experimentally confirmed that Bayesian treatment of model learning on motion data is superior to the simple point estimate of latent parameters. Experiments with non-periodic movements show that they do not benefit from more complex latent dynamics, despite having high kinematic complexity. By having a fully Bayesian models, we could quantitatively disentangle the influence of motion dynamics and pose on the perception of naturalness. We confirmed that rich and correct dynamics is more important than the kinematic representation. There are numerous further directions of research. In the models we devised, for multiple parts, even though the latent dynamics was factorized on a set of interacting systems, the kinematic parts were completely independent. Thus, interaction between the kinematic parts could be mediated only by the latent dynamics interactions. A more flexible model would allow a dense interaction on the kinematic level too. Another important problem relates to the representation of time in Markov chains. Discrete time Markov chains form an approximation to continuous dynamics. As time step is assumed to be fixed, we face with the problem of time step selection. Time is also not a explicit parameter in Markov chains. This also prohibits explicit optimization of time as parameter and reasoning (inference) about it. For example, in optimal control boundary conditions are usually set at exact time points, which is not an ecological scenario, where time is usually a parameter of optimization. Making time an explicit parameter in dynamics may alleviate this

A roadmap to integrate astrocytes into Systems Neuroscience.

Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

Behaviour of cattle: molecular biological background and relationship to milk performance

In a genome-wide association study 41 markers were identified for behaviour traits and BTA29 emerged to be an interesting genomic target region. Mutual markers for agitated behaviour and milk production showed competitive genotype effects. Gene expression profiles of the adrenal cortex revealed differentially expressed genes between cows with distinct temperament types. “Fearful/neophobic-alert” cows could be clearly discriminated from others by transcripts involved in the physiological stress response. Moreover, cows that were more fearful had lower milk yields in comparison to the others

HYPOTHALAMIC CIRCUITS IN THE CONTROL OF FEEDING AND EMOTIONAL BEHAVIORS

Feeding results from the integration of both nutritional and affective states, and is guided by complex neural circuitry in the brain. The hypothalamus is a critical center controlling feeding and motivated behaviors. We found that targeted photostimulation of projections from the lateral hypothalamus (LH) to the paraventricular hypothalamus (PVH) in mice elicited voracious feeding and repetitive self-grooming behavior. GABA neurotransmission in the LH-\u3ePVH circuit mediated the evoked feeding behavior, and elicited behavioral approach, whereas glutamate release promoted repetitive self-grooming, which was stress-related in nature. Optogenetic inhibition of LHGABA -\u3ePVH circuit reduced feeding after fasting, whereas photostimulation abruptly stopped ongoing self-grooming and immediately elicited feeding. Oppositely, optogenetic inhibition of LHGlutamate-\u3ePVH circuit reduced repetitive self-grooming, whereas photostimulation suppressed fast-refeeding in exchange for repetitive self-grooming. Optogenetically activating and silencing PVH neurons directly recapitulated these findings, and demonstrated the necessity of glutamatergic PVH neurons in mediating the competition between self-grooming and feeding. Together, these results provided evidence that the mutually exclusive nature of feeding and self-grooming behaviors are in part mediated by distinct components in the LH-\u3ePVH circuit. Interestingly, photostimulating PVH neurons with greater intensity promoted transitions from grooming to frantic escape-jumping, suggesting scalability of stress-related behaviors mediated by PVH neural activity. Because evoked jumping resembled attempts to escape, we posited PVH neurons mediate defensive responses. Validating this, photostimulating PVH neurons induced avoidance and increased locomotion, two classic behavioral indicators of active defense strategies. Anterograde tracing showed that PVH neurons densely projected to the midbrain region in and surrounding the ventral tegmental area (VTA), a brain region well-known for its roles in motivated behaviors. Indeed, photostimulation of PVH-\u3emidbrain projections produced escape behaviors and conditioned place aversion. Combined optogenetic and chemogenetic experiments showed that glutamatergic-midbrain neurons were required for escape behaviors. Further, glutamatergic-midbrain neurons displayed increased neural population activity in vivo during a fear-provoking situation, validating a role for this population in processing threat. Taken together, our work reveals novel hypothalamic circuits in the control of feeding, emotional valence, and behaviors related to stress and defense. These findings shed light on possible neural mechanisms underlying complex disease states characterized by feeding abnormalities, anxiety and fear