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Details of Madhusudhan Venkadesan
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Postdoctoral Associate
Department of Mathematics
411 Malott Hall
Cornell University
Ithaca, NY 14853
Phone: +1(607)339-6653
Fax: +1(607)255-1222
Email: mv72 AT cornell DOT edu The core of my research interest is to develop experimental and theoretical capability to understand "how" animals coordinate their body using muscles to stably and robustly interact with the world. To this end, I combine experimental, numerical and theoretical approaches as appropriate to the natural phenomenon under investigation. My debut in this line of research has been with investigating macroscopic neuromuscular behavior in humans. How do the "brain" and "body" interact with the world to yield the quintessential robustness and versatility of animal behavior? This question dates back to at least 2300 years as seen from Aristotle's arguments in De partibus animalium (ca. 340 BCE). Attempts to answer this question have resulted in remarkably detailed knowledge of the constituent sensorimotor elements in animals ranging from relatively simple nematodes to extraordinarily complex humans. However, we remain unable to predict or mimic the overall behavioral dynamics of most animals under most circumstances, with added difficulties arising from "noise" and "time-delays" that are ubiquitous at almost every level in biological systems. For my doctoral dissertation (Download: Please send me an email for the PDF version), I developed an experimental-mathematical paradigm to quantify the complex, nonlinear and dynamical behavior of dexterous manipulation in humans. I used a simple data-driven model to explain a very common observation: we normally handle objects without looking at our hands, but if the sensation from our fingerpads is degraded (due to coldness, anaesthesia, disease, etc), we rely heavily on our vision rather than other sensors (e.g., mechanoreceptors in our hand muscles) to compensate for this loss. I found that this contextual use of vision arises as a natural consequence of combining multiple redundant sensations in a task-optimal fashion, i.e., in a manner that maximizes their functional utility. I used this experimental setup to develop a clinical outcome measure of sensorimotor integration during dynamic manipulation. This outcome measure could quantify hand disability and the effect of treatment more sensitively than existing measures of hand function. This work is part of an ongoing study at the Hospital for Special Surgery in New York, NY. Currently, my work (jointly with John Guckenheimer) is to design experimental methods and data analysis techniques for creating data-driven low order dynamical models. Most dynamical systems encountered in biology or engineering are often too high-dimensional to model from first-principles. By being able to generate data-driven low-dimensional models of their behaviour, we will create the capability to mimic, predict, or control these otherwise intractably complex systems. Professional areas of interest - Dexterous manipulation using the hand: Neuromuscular control and sensorimotor integration for dynamic (time-varying) manipulation of objects.
- Optimality in sensorimotor control, sensory feedback, multisensory integration, and optimality in biological systems in general.
- Nonlinear dynamical systems, bifurcation theory and bifurcation detection in noisy dynamical systems. Also, applications of stochastic dynamical systems and delay differential equations in life sciences.
- Multi-body dynamics and hybrid dynamical systems (such as your finger when tapping on a table).
- Multiscale computer modeling of skeletal muscle (from cross-bridges to whole muscle) and parameter estimation in musculoskeletal mechanisms.
- Cortical control of the musculoskeletal systems: Experimental measurements using electromyography (EMG) and functional magnetic resonance imaging (fMRI).
- Clinical measurement and evaluation of hand function.
- Using mechanics, physics and mathematics to understand the functioning of any biological system.
Selected Publications Doctoral dissertation Journal articles - Venkadesan M, Guckenheimer J, Valero-Cuevas FJ. Manipulating the edge of instability. Journal of Biomechanics, 2007:40(8):p. 1653-1661. doi:10.1016/j.jbiomech.2007.01.022. PDF (1.27 MB)
- In this paper, we studied the dynamics and sensorimotor integration when subjects used their thumbpad to compress a slender helical spring. The dynamics of the slender spring (object) + hand + nervous system at the edge of instability is well represented by a 1D dynamical system. This 1D differential equation arises naturally by identifying the bifurcation that occurs when the spring becomes unstable. As expected, vision played a contextual role depending on the presence or absence of thumbpad sensation. Using the simple 1D phenomenological model, we found that the contextual use of vision arises as an automatic consequence of maximizing performance using multiple sensory modalities.
- This manuscript was an invited submission as part of the 2006 Journal of Biomechanics Award awarded for an abstract presented at the 30th Annual Meeting of the American Society of Biomechanics.
- Valero-Cuevas FJ, Smaby N, Venkadesan M, Peterson M, Wright T. The Strength-Dexterity test as a measure of dynamic pinch performance. Journal of Biomechanics, 2003:36(2): p. 265-270. Preprint PDF (224 KB)
Manuscripts in review - Venkadesan M§, Valero-Cuevas FJ§. Tapping with your finger, the precise neural control of contact transitions. §These authors contributed equally.
- Todorov E, Venkadesan M, Valero-Cuevas FJ. Structure in the variability of muscle activations. Manuscript under review, suggestions welcome.
- Mosier K, Lau C, Wang Y, Venkadesan M, Valero-Cuevas FJ. Cortical networks for the control of hand dexterity.
Invited symposia - Venkadesan M, Valero-Cuevas FJ, Guckenheimer JM. The boundary of instability as a powerful experimental paradigm for understanding complex dynamical sensorimotor behavior: Dexterous manipulation as an example. In Advances in Computational Motor Control II Symposium at the 33rd Annual Meeting of the Society for Neuroscience. New Orleans, LA, 2003.
Peer reviewed conference abstracts - Venkadesan M, Guckenheimer J, Valero-Cuevas FJ. Dynamic multisensory integration at the edge of instability is explained by a simple data-based model. Proceedings of the 30th Annual Meeting of the American Society of Biomechanics, Blacksburg, VA: Abstract no. 189. 2006.
- Valero-Cuevas FJ, McNamara III RV, Santos VJ, Venkadesan M, Song S, Grace-Martin K. The nervous system transitions rapidly between incompatible control strategies by predictively exploiting the margins of error of the task. Proceedings of the World Congress of Biomechanics, Munich, Germany. 2006.
- Venkadesan M, Srinivasan M, Guckenheimer J, Valero-Cuevas FJ. Computational time-delays due to sensory processing affect multisensory integration strategies. Proceedings of the Neural Control of Movement Annual Meeting, Key Biscayne, FL. 2006.
- Venkadesan M, Backus S, Mandl LA, Swigart A, Peterson M, Lyman S, Ariola L, Hotchkiss RN, Valero-Cuevas FJ. The strength-dexterity test is a novel and clinically informative measure of treatment outcome in thumb osteoarthritis. Arthritis and Rheumatism, 2005:52(9):S516, 1362 Suppl. S.
Education - B. Tech., 2000, Mechanical Engineering, Indian Institute of Technology, Madras.
- M.S., 2003, Mechanical Engineering, Cornell University.
- Ph.D., 2007, Major: Mechanical Engineering, Minor: Applied Mathematics, Cornell University.
Employment 
Category: General Engineering
Type: Scientist & Engineers
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