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Bidirectional Neuroprostheses


As neuroengineers, we design prosthetic devices that interface with the brain and spinal cord. While the first generation of neuroprostheses focused on replacing lost sensory functions on the periphery of the nervous system, the next generation will engage in bidirectional communication with intrinsic neural circuits. This new paradigm demands a more detailed understanding of the underlying networks of neurons, as well as sophisticated microelectronic design techniques to create small, powerful, and efficient signal-processing devices. Success in this field will require highly interdisciplinary work and expertise in both electrical engineering and neuroscience.

Motivation and Background Information


Bidirectional neuroprostheses are a novel class of devices capable of receiving input from, and sending output to, neurons in the central nervous system (CNS). In the case of trauma to the CNS, a future neuroprosthesis might communicate with intact cells and emulate the properties of the damaged region, thereby restoring function. Traditionally, sophisticated signal-processing algorithms are used to interpret neural activity, but these methods require significant computational power and impose a rigid mathematical structure to explain the dynamic operation of thousands of neurons. A potentially more appropriate solution for neuroprosthetic applications is to generate computational models that mimic the operation of a network of cells, and then reconstruct the neural circuits in silico using neuromorphic very-large scale integration (VLSI) design techniques.

Neuromorphic circuits resemble their biological counterparts in both structure and function. By utilizing the intrinsic properties of silicon transistors in low-power operation, small circuit elements can exhibit current-voltage relationships similar to those observed in real neurons. Thus, the complex calculations performed by individual cells come “for free” in this paradigm, enabling a designer to concentrate on the emergent signal-processing capabilities of the network. In addition, neuromorphic circuits can operate in parallel and function in real-time. All of these properties make neuromorphic VLSI technology an ideal candidate for implementing implantable neuroprostheses.

Research Plan

The long-term goal of this project is to create a neuroprosthetic device that will re-enable locomotion in paraplegic individuals. It is well known that the spinal cord of vertebrates contains local networks of neurons that are hardwired to execute various motor “programs”. These circuits, called central pattern generators (CPG), facilitate the sequential activation of muscle groups required to produce complex behaviors such as breathing, chewing, locomoting, and other rhythmic movements. Spinal injury disrupts the descending pathways used by the brain to activate CPGs, but leaves most local circuits intact. Therefore, it may be possible to restore motor function after spinal injury by designing a neuroprosthesis capable of interfacing with and controlling intrinsic CPG networks.


Publications

K.A. Mazurek, B.J. Holinski, D.G.Everaert, R.B. Stein, R. Etienne-Cummings, and V.K. Mushahwar, "Feed forward and feedback control for over-ground locomotion in anaesthetized cats," Journal of Neural Engineering, Vol. 9, No. 2, 2012

R. J. Vogelstein, F. Tenore, L. Guevremont, R. Etienne-Cummings, and V. K. Mushahwar. A Silicon Central Pattern Generator Controls Locomotion in vivo. IEEE T. Biomedical Circuits and Systems, Vol. 2, No. 3, pp 212-222 Sept 2008 Best Paper Award 2012

L. Guevremont, J. A. Norton, and V. K. Mushahwar. Physiologically based controller for generating overground locomotion using functional electrical stimulation. Journal of Neurophysiology, 97(3):2499–2510, 2007.

R. J. Vogelstein, R. Etienne-Cummings, N. V. Thakor, and A. H. Cohen.  Dynamic Control of the Central Pattern Generator for Locomotion. Biological Cybernetics, 95(6):555–566, 2006.

L. Guevremont, C. G. Renzi, J. A. Norton, J. Kowalczewski, R. Saigal, and V. K. Mushahwar. Locomotor-related networks in the lumbosacral enlargement of the adult spinal cat: activation through intraspinal microstimulation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(3):266–272, 2006.

R. J. Vogelstein, R. Etienne-Cummings, N. V. Thakor, and A. H. Cohen. Phase-Dependent Effects of Spinal Cord Stimulation on Locomotor Activity. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(3):257–265, 2006.

R. J. Vogelstein, R. Etienne-Cummings, N. V. Thakor, and A. H. Cohen. Dynamic Control of Spinal Locomotion Circuits. Proceedings of the 2006 IEEE International Symposium on Circuits and Systems, Kos, Greece, May 2006.

F. Tenore, R. J. Vogelstein, R. Etienne-Cummings, G. Cauwenberghs, and P. Hasler. A Floating-Gate Programmable Array of Silicon Neurons for Central Pattern Generating Networks. Proceedings of the 2006 IEEE International Symposium on Circuits and Systems, Kos, Greece, May 2006.

F. Tenore, R. J. Vogelstein, R. Etienne-Cummings, M. A. Lewis, and P. Hasler. A Spiking Silicon Central Pattern Generator with Floating Gate Synapses. Proceedings of the 2005 IEEE International Symposium on Circuits and Systems, Kobe, Japan, May 2005.

F. Tenore, R. Etienne-Cummings, and M. A. Lewis. A Programmable Array of Silicon Neurons for the Control of Legged Locomotion. Proceedings of the 2004 IEEE International Symposium on Circuits and Systems, Vancouver, Canada, May 2004.

R. Saigal, C. Renzi, and V. K. Mushahwar. Intraspinal microstimulation generates functional movements after spinal-cord injury. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 12(4):430–440, 2004.

References

F. Delcomyn. Neural basis of rhythmic behavior in animals. Science, 210(4469):492–498, 1980.

S. P. DeWeerth, G. N. Patel, M. F. Simoni, D. E. Schimmel, and R. L. Calabrese. A VLSI architecture for modeling intersegmental coordination. In R. Brown and A. Ishii, editors, 17th Conference on Advanced Research in VLSI, pages 182–200, Ann Arbor, Michigan, September 1997. IEEE Computer Society.

S. Grillner, T. Deliagina, O. Ekeberg, A. El Manira, R. Hill, A. Lansner, G. Orlovsky, and P. Wallen. Neural networks that coordinate locomotion and body orientation in lamprey. Trends in Neurosciences, 18:270–279, 1995.

R. Jung, E. J. Brauer, and J. J. Abbas. Real-time interaction between a neuromorphic electronic circuit and the spinal cord. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 9(3):319–326, 2001.

R. Jung, T. Kiemel, and A. H. Cohen. Dynamic behavior of a neural network model of locomotor control in the lamprey. Journal of Neurophysiology, 75(3):1074–1086, 1996.

M. A. Lewis, M. J. Hartmann, R. Etienne-Cummings, and A. H. Cohen. Control of a robot leg with an adaptive aVLSI CPG chip. Neurocomputing, 38-40:1409–1421, 2001.

A. D. McClellan and W. Jang. Mechanosensory inputs to the central pattern generators for locomotion in the lamprey spinal cord: Resetting, entrainment, and computer modeling. Journal of Neurophysiology, 70(6):2442–2454, 1993.

C. Mead. Analog VLSI and Neural Systems. Addison-Wesley, Reading, Massachusetts, 1989.