How Spinal Neural Networks Reduce Discrepancies between Motor Intention and Motor Realization

This paper attempts a rational, step-by-step reconstruction of many aspects of the mammalian neural circuitry known to be involved in the spinal cord's regulation of opposing muscles acting on skeletal segments. Mathematical analyses and local circuit simulations based on neural membrane equations are used to clarify the behavioral function of five fundamental cell types, their complex connectivities, and their physiological actions. These cell types are: α-MNs, γ-MNs, IaINs, IbINs, and Renshaw cells. It is shown that many of the complexities of spinal circuitry are necessary to ensure near invariant realization of motor intentions when descending signals of two basic types independently vary over large ranges of magnitude and rate of change. Because these two types of signal afford independent control, or Factorization, of muscle LEngth and muscle TEnsion, our construction was named the FLETE model (Bullock and Grossberg, 1988b, 1989). The present paper significantly extends the range of experimental data encompassed by this evolving model.National Science Foundation (IRI-87-16960, IRI-90-24877); Instituto Tecnológico y de Estudios Superiores de Monterre

Separable Convex Optimization with Nested Lower and Upper Constraints

We study a convex resource allocation problem in which lower and upper bounds are imposed on partial sums of allocations. This model is linked to a large range of applications, including production planning, speed optimization, stratified sampling, support vector machines, portfolio management, and telecommunications. We propose an efficient gradient-free divide-and-conquer algorithm, which uses monotonicity arguments to generate valid bounds from the recursive calls, and eliminate linking constraints based on the information from sub-problems. This algorithm does not need strict convexity or differentiability. It produces an $\epsilon$-approximate solution for the continuous problem in $\mathcal{O}(n \log m \log \frac{n B}{\epsilon})$ time and an integer solution in $\mathcal{O}(n \log m \log B)$ time, where $n$ is the number of decision variables, $m$ is the number of constraints, and $B$ is the resource bound. A complexity of $\mathcal{O}(n \log m)$ is also achieved for the linear and quadratic cases. These are the best complexities known to date for this important problem class. Our experimental analyses confirm the good performance of the method, which produces optimal solutions for problems with up to 1,000,000 variables in a few seconds. Promising applications to the support vector ordinal regression problem are also investigated

Approximate transformations and robust manipulation of bipartite pure state entanglement

We analyze approximate transformations of pure entangled quantum states by local operations and classical communication, finding explicit conversion strategies which optimize the fidelity of transformation. These results allow us to determine the most faithful teleportation strategy via an initially shared partially entangled pure state. They also show that procedures for entanglement manipulation such as entanglement catalysis [Jonathan and Plenio, Phys. Rev. Lett. 83, 3566 (1999)] are robust against perturbation of the states involved, and motivate the notion of non-local fidelity, which quantifies the difference in the entangled properties of two quantum states.Comment: 11 pages, 4 figure

Cerebellar Learning in an Opponent Motor Controller for Adaptive Load Compensation and Synergy Formation

This paper shows how a minimal neural network model of the cerebellum may be embedded within a sensory-neuro-muscular control system that mimics known anatomy and physiology. With this embedding, cerebellar learning promotes load compensation while also allowing both coactivation and reciprocal inhibition of sets of antagonist muscles. In particular, we show how synaptic long term depression guided by feedback from muscle stretch receptors can lead to trans-cerebellar gain changes that are load-compensating. It is argued that the same processes help to adaptively discover multi-joint synergies. Simulations of rapid single joint rotations under load illustrates design feasibility and stability.National Science Foundation (IRI-90-24877, IRI-87-16960); Office of Naval Research (N00014-92-J-1309); Consejo Nacional de Ciencia y Technología (63462); Air Force Office of Scientific Research (F49620-92-J-0499); Defense Advanced Research Projects Agency (AFOSR 90-0083, ONR N00014-92-J-4015

Inertial Load Compensation by a Model Spinal Circuit During Single Joint Movement

Office of Naval Research (N00014-92-J-1309); CONACYT (Mexico) (63462

Equilibria and Dynamics of a Neural Network Model for Opponent Muscle Control

One of the advantages of biological skeleto-motor systems is the opponent muscle design, which in principle makes it possible to achieve facile independent control of joint angle and joint stiffness. Prior analysis of equilibrium states of a biologically-based neural network for opponent muscle control, the FLETE model, revealed that such independent control requires specialized interneuronal circuitry to efficiently coordinate the opponent force generators. In this chapter, we refine the FLETE circuit variables specification and update the equilibrium analysis. We also incorporate additional neuronal circuitry that ensures efficient opponent force generation and velocity regulation during movement.National Science Foundation (IRI-90-24877); Consejo Nacional de Ciencia y Tecnologia, Méxic

Daniel Ramón Vidal: "Als científics, se'ns veu com a freaks"

Daniel Ramon és director científic i conseller delegat de l'empresa biotecnològica Biópolis S.L., i també conseller delegat en una altra, Lifesequencing S.L., dedicada a la seqüenciació genòmica massiva, dues iniciatives empresarials situades al Parc Científic de la Universitat de València. Però abans d'estar en la primera línia empresarial, Ramón va fer carrera acadèmica com a catedràtic de Tecnologia dels Aliments de la Universitat de València, on prèviament s'havia llicenciat i doctorat en Ciències Biològiques. Ha exercit també de professor d'investigació a l'Institut d'Agroquímica i Tecnologia d'Aliments (IATA). Aquest científic, d'itinerari professional singular, és, a més, membre del Comitè Científic de l'Agència Espanyola de Seguretat Alimentària i Nutrició, i del Consell Rector del Consell Superior d'Investigacions Científiques. El seu treball Els gens que mengem, publicat el 1997, que va rebre el premi europeu de divulgació científica Estudi General, és un assaig sobre la biotecnologia dels aliments. El 2007, el Ministeri de Ciència li va atorgar el Premi Nacional d'Investigació Juan de la Cierva

Triple resonant four-wavemixing boosts the yield of continuous coherent VUV generation

Continuous-wave coherent radiation in the vacuum ultraviolet (VUV)wavelength region at 121 nm will be essential for future laser-cooling of trapped antihydrogen [1]. Cold antihydrogen will enable both tests of the fundamental symmetry between matter and antimatter at unprecedented experimental precision [2] and also experiments in antimatter gravity [3]. Another fascinating application of narrowband continuous laser radiation in the VUV is quantum information processing using single trapped ions in Rydberg-states [4, 5]. Here we describe highly efficient continuous four-wave mixing in the VUV by using three different fundamental wavelengths with a sophisticated choice of detunings to resonances of the nonlinear medium. Up to 6 microwatts of vacuum ultraviolet radiation at 121 nm can be generated which corresponds to an increase of three orders of magnitude in efficiency.Comment: 11 pages, 3 figure