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.
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
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