(Excerpted from my NSF Graduate Fellowship application)

Bidirectional Neuroprostheses

Introduction

As a neuroengineer, I want to design prosthetic devices that interface with the brain. While the first generation of neuroprostheses have 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. Here I propose a research plan that begins to explore one application of bidirectional neuroprostheses.

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 [8].

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

Goals

My long-term goal is to create a prosthetic device that will re-enable locomotion in paraplegic individuals. It is well known that the spinal cord (SC) 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 [1]. 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 [4]. I believe that my educational background combined with my advisors’ expertise in these areas makes me uniquely suited to conduct this research.

As a graduate student, I will work with an animal model of spinal injury. Specifically, I plan to investigate CPG networks in the lamprey, a well-studied primitive vertebrate. The lamprey CPG consists of a chain of approximately 100 coupled oscillators called unit pattern generators (uPG), each located within a single segment of the spinal cord. In an intact specimen, all of the oscillators work in tandem to produce a traveling wave of activity that alternately excites the motor neurons on opposite sides of the body, producing a sinusoidal pattern of muscle contraction that propels the animal through the water [3]. In the case of spinal injury, one or more spinal segments caudal to the lesion site become isolated from the rest of the SC and no longer receive activation or synchronization signals. To restore function in this situation, it is necessary to first initiate and then coordinate the oscillations between the regions rostral and caudal to the location of trauma. Because naturally-occurring uPG circuits perform these duties efficiently and effectively, I intend to employ a neuromorphic microchip to execute these tasks.

Specific Aims

My proposed graduate research would make significant contributions toward understanding and controlling spinal CPG networks. As I work to create a neuroprosthesis capable of restoring motor function after chronic spinal injury, I have three short-term aims. First, I hope to improve on existing neural models of CPGs [5] by incorporating biologically accurate feedback into the cellular network. This will be an essential component of my work, as real-time signals from recording electrodes will be used extensively to modulate the activity of the neuroprosthesis, improving the coordination between the silicon hardware and biological “wetware”.

Second, I intend to determine the appropriate parameters for effective electrical stimulation of spinal CPG networks so that I can generate a phase-response curve (PRC) for the lamprey SC. A PRC is a mathematical description of the effects of perturbing an oscillating circuit at various times during its activity cycle [7]. Once I am able to characterize the lamprey CPG in this way, I can employ a number of analytical tools to evaluate potential control strategies.

Third, I wish to create a neuroprosthetic device that enables the functional coupling of two halves of a severed lamprey SC in an isolated (in vitro) spinal cord preparation. This neuromorphic microchip will translate the sophisticated CPG model described in my first aim into compact, low-power, silicon VLSI hardware. My circuits will improve on previous designs of neuromorphic CPGs [6, 2] by including adaptive capabilities, accepting biological inputs, and providing appropriate outputs for directly stimulating nervous tissue.

Discussion

Much of my work on the spinal injury model in lampreys is likely to be applicable to understanding motor control and restoring motor function in humans. For example, knowledge about the fundamental organization of CPG circuits in primitive vertebrates will facilitate the identification of their counterparts in higher organisms. Additionally, by mathematically characterizing the effects of stimulation on the lamprey CPG, we will become better equipped to interact with such networks and emulate their function. Integrating sensory feedback into the CPG control mechanism is also important for future experiments: when we work with freely-moving animals in unpredictable external conditions, our microchips must be able to detect deviations from the expected behavior and respond rapidly. Most importantly, the experience I gain through this project should perfectly prepare me for a career devoted to designing and implementing bidirectional neuroprostheses. The outcome of this work will have ramifications for both basic science and people suffering from spinal cord injuries.

References

[1] F. Delcomyn. Neural basis of rhythmic behavior in animals. Science, 210(4469):492–498, 1980. {back}
[2] 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. {back}
[3] 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. {back}
[4] 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. {back}
[5] 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. {back}
[6] 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. {back}
[7] 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. {back}
[8] C. Mead. Analog VLSI and Neural Systems. Addison-Wesley, Reading, Massachusetts, 1989. {back}