25 research outputs found
Tracking Control for FES-Cycling based on Force Direction Efficiency with Antagonistic Bi-Articular Muscles
A functional electrical stimulation (FES)-based tracking controller is
developed to enable cycling based on a strategy to yield force direction
efficiency by exploiting antagonistic bi-articular muscles. Given the input
redundancy naturally occurring among multiple muscle groups, the force
direction at the pedal is explicitly determined as a means to improve the
efficiency of cycling. A model of a stationary cycle and rider is developed as
a closed-chain mechanism. A strategy is then developed to switch between muscle
groups for improved efficiency based on the force direction of each muscle
group. Stability of the developed controller is analyzed through Lyapunov-based
methods.Comment: 8 pages, 4 figures, submitted to ACC201
Stationary Cycling Induced by Switched Functional Electrical Stimulation Control
Functional electrical stimulation (FES) is used to activate the dysfunctional
lower limb muscles of individuals with neuromuscular disorders to produce
cycling as a means of exercise and rehabilitation. However, FES-cycling is
still metabolically inefficient and yields low power output at the cycle crank
compared to able-bodied cycling. Previous literature suggests that these
problems are symptomatic of poor muscle control and non-physiological muscle
fiber recruitment. The latter is a known problem with FES in general, and the
former motivates investigation of better control methods for FES-cycling.In
this paper, a stimulation pattern for quadriceps femoris-only FES-cycling is
derived based on the effectiveness of knee joint torque in producing forward
pedaling. In addition, a switched sliding-mode controller is designed for the
uncertain, nonlinear cycle-rider system with autonomous state-dependent
switching. The switched controller yields ultimately bounded tracking of a
desired trajectory in the presence of an unknown, time-varying, bounded
disturbance, provided a reverse dwell-time condition is satisfied by
appropriate choice of the control gains and a sufficient desired cadence.
Stability is derived through Lyapunov methods for switched systems, and
experimental results demonstrate the performance of the switched control system
under typical cycling conditions.Comment: 8 pages, 3 figures, submitted to ACC 201
A reduced complexity numerical method for optimal gate synthesis
Although quantum computers have the potential to efficiently solve certain
problems considered difficult by known classical approaches, the design of a
quantum circuit remains computationally difficult. It is known that the optimal
gate design problem is equivalent to the solution of an associated optimal
control problem, the solution to which is also computationally intensive.
Hence, in this article, we introduce the application of a class of numerical
methods (termed the max-plus curse of dimensionality free techniques) that
determine the optimal control thereby synthesizing the desired unitary gate.
The application of this technique to quantum systems has a growth in complexity
that depends on the cardinality of the control set approximation rather than
the much larger growth with respect to spatial dimensions in approaches based
on gridding of the space, used in previous literature. This technique is
demonstrated by obtaining an approximate solution for the gate synthesis on
- a problem that is computationally intractable by grid based
approaches.Comment: 8 pages, 4 figure
Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States
UP and DOWN states, the periodic fluctuations between increased and decreased spiking activity of a neuronal population, are a fundamental feature of cortical circuits. Understanding UP-DOWN state dynamics is important for understanding how these circuits represent and transmit information in the brain. To date, limited work has been done on characterizing the stochastic properties of UP-DOWN state dynamics. We present a set of Markov and semi-Markov discrete- and continuous-time probability models for estimating UP and DOWN states from multiunit neural spiking activity. We model multiunit neural spiking activity as a stochastic point process, modulated by the hidden (UP and DOWN) states and the ensemble spiking history. We estimate jointly the hidden states and the model parameters by maximum likelihood using an expectation-maximization (EM) algorithm and a Monte Carlo EM algorithm that uses reversible-jump Markov chain Monte Carlo sampling in the E-step. We apply our models and algorithms in the analysis of both simulated multiunit spiking activity and actual multi- unit spiking activity recorded from primary somatosensory cortex in a behaving rat during slow-wave sleep. Our approach provides a statistical characterization of UP-DOWN state dynamics that can serve as a basis for verifying and refining mechanistic descriptions of this process.National Institutes of Health (U.S.) (Grant R01-DA015644)National Institutes of Health (U.S.) (Director Pioneer Award DP1- OD003646)National Institutes of Health (U.S.) (NIH/NHLBI grant R01-HL084502)National Institutes of Health (U.S.) (NIH institutional NRSA grant T32 HL07901
Solving the conundrum of intra-specific variation in metabolic rate: A multidisciplinary conceptual and methodological toolkit
Researchers from diverse disciplines, including organismal and cellular physiology, sports science, human nutrition, evolution and ecology, have sought to understand the causes and consequences of the surprising variation in metabolic rate found among and within individual animals of the same species. Research in this area has been hampered by differences in approach, terminology and methodology, and the context in which measurements are made. Recent advances provide important opportunities to identify and address the key questions in the field. By bringing together researchers from different areas of biology and biomedicine, we describe and evaluate these developments and the insights they could yield, highlighting the need for more standardisation across disciplines. We conclude with a list of important questions that can now be addressed by developing a common conceptual and methodological toolkit for studies on metabolic variation in animals
Uncovering spatial topology represented by rat hippocampal population neuronal codes
Hippocampal population codes play an important role in representation of spatial environment and spatial navigation. Uncovering the internal representation of hippocampal population codes will help understand neural mechanisms of the hippocampus. For instance, uncovering the patterns represented by rat hippocampus (CA1) pyramidal cells during periods of either navigation or sleep has been an active research topic over the past decades. However, previous approaches to analyze or decode firing patterns of population neurons all assume the knowledge of the place fields, which are estimated from training data a priori. The question still remains unclear how can we extract information from population neuronal responses either without a priori knowledge or in the presence of finite sampling constraint. Finding the answer to this question would leverage our ability to examine the population neuronal codes under different experimental conditions. Using rat hippocampus as a model system, we attempt to uncover the hidden “spatial topology” represented by the hippocampal population codes. We develop a hidden Markov model (HMM) and a variational Bayesian (VB) inference algorithm to achieve this computational goal, and we apply the analysis to extensive simulation and experimental data. Our empirical results show promising direction for discovering structural patterns of ensemble spike activity during periods of active navigation. This study would also provide useful insights for future exploratory data analysis of population neuronal codes during periods of sleep.National Institutes of Health (U.S.) (NIH Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant MH061976