Adaptive motion synthesis and motor invariant theory.

Generating natural-looking motion for virtual characters is a challenging research topic. It becomes even harder when adapting synthesized motion to interact with the environment. Current methods are tedious to use, computationally expensive and fail to capture natural looking features. These difficulties seem to suggest that artificial control techniques are inferior to their natural counterparts. Recent advances in biology research point to a new motor control principle: utilizing the natural dynamics. The interaction of body and environment forms some patterns, which work as primary elements for the motion repertoire: Motion Primitives. These elements serve as templates, tweaked by the neural system to satisfy environmental constraints or motion purposes. Complex motions are synthesized by connecting motion primitives together, just like connecting alphabets to form sentences. Based on such ideas, this thesis proposes a new dynamic motion synthesis method. A key contribution is the insight into dynamic reason behind motion primitives: template motions are stable and energy efficient. When synthesizing motions from templates, valuable properties like stability and efficiency should be perfectly preserved. The mathematical formalization of this idea is the Motor Invariant Theory and the preserved properties are motor invariant In the process of conceptualization, newmathematical tools are introduced to the research topic. The Invariant Theory, especially mathematical concepts of equivalence and symmetry, plays a crucial role. Motion adaptation is mathematically modelled as topological conjugacy: a transformation which maintains the topology and results in an analogous system. The Neural Oscillator and Symmetry Preserving Transformations are proposed for their computational efficiency. Even without reference motion data, this approach produces natural looking motion in real-time. Also the new motor invariant theory might shed light on the long time perception problem in biological research

Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review

Seeing things

This paper is concerned with the problem of attaching meaningful symbols to aspects of the visible environment in machine and biological vision. It begins with a review of some of the arguments commonly used to support either the 'symbolic' or the 'behaviourist' approach to vision. Having explored these avenues without arriving at a satisfactory conclusion, we then present a novel argument, which starts from the question : given a functional description of a vision system, when could it be said to support a symbolic interpretation? We argue that to attach symbols to a system, its behaviour must exhibit certain well defined regularities in its response to its visual input and these are best described in terms of invariance and equivariance to transformations which act in the world and induce corresponding changes of the vision system state. This approach is illustrated with a brief exploration of the problem of identifying and acquiring visual representations having these symmetry properties, which also highlights the advantages of using an 'active' model of vision

Mirror Adaptation in Sensory-Motor Simultaneity

Background: When one watches a sports game, one may feel her/his own muscles moving in synchrony with the player's. Such parallels between observed actions of others and one's own has been well supported in the latest progress in neuroscience, and coined “mirror system.” It is likely that due to such phenomena, we are able to learn motor skills just by observing an expert's performance. Yet it is unknown whether such indirect learning occurs only at higher cognitive levels, or also at basic sensorimotor levels where sensorimotor delay is compensated and the timing of sensory feedback is constantly calibrated. Methodology/Principal Findings: Here, we show that the subject's passive observation of an actor manipulating a computer mouse with delayed auditory feedback led to shifts in subjective simultaneity of self mouse manipulation and auditory stimulus in the observing subjects. Likewise, self adaptation to the delayed feedback modulated the simultaneity judgment of the other subjects manipulating a mouse and an auditory stimulus. Meanwhile, subjective simultaneity of a simple visual disc and the auditory stimulus (flash test) was not affected by observation of an actor nor self-adaptation. Conclusions/Significance: The lack of shift in the flash test for both conditions indicates that the recalibration transfer is specific to the action domain, and is not due to a general sensory adaptation. This points to the involvement of a system for the temporal monitoring of actions, one that processes both one's own actions and those of others

Action and behavior: a free-energy formulation

We have previously tried to explain perceptual inference and learning under a free-energy principle that pursues Helmholtz’s agenda to understand the brain in terms of energy minimization. It is fairly easy to show that making inferences about the causes of sensory data can be cast as the minimization of a free-energy bound on the likelihood of sensory inputs, given an internal model of how they were caused. In this article, we consider what would happen if the data themselves were sampled to minimize this bound. It transpires that the ensuing active sampling or inference is mandated by ergodic arguments based on the very existence of adaptive agents. Furthermore, it accounts for many aspects of motor behavior; from retinal stabilization to goal-seeking. In particular, it suggests that motor control can be understood as fulfilling prior expectations about proprioceptive sensations. This formulation can explain why adaptive behavior emerges in biological agents and suggests a simple alternative to optimal control theory. We illustrate these points using simulations of oculomotor control and then apply to same principles to cued and goal-directed movements. In short, the free-energy formulation may provide an alternative perspective on the motor control that places it in an intimate relationship with perception