Low Power Circuits and Systems for Wireless Neural Stimulation and Arbitrary-Scale Neural Recording

Lecture / Panel
For NYU Community

Speaker:  Professor Scott Arfin

Host Faculty: Professor Jon Viventi


Electrical recording of neurons and stimulation of tissues are increasingly useful as tools for both studying the brain and treating a variety of disorders, with applications including brain-machine interfaces, cochlear implants, visual prostheses, deep brain stimulators, and spinal cord stimulators. Brain implants for paralysis treatments rely on both neural recording for decoding actions, and increasingly are providing sensory feedback via neural stimulation. In implantable stimulators, power consumption is often the limiting factor in determining battery or power coil size, cost, and level of tissue heating, with stimulation circuitry typically dominating the power budget of the entire implant. Thus, there is strong motivation to improve the energy efficiency of implantable electrical stimulators. In neural recording devices, the relatively few numbers of channels that are currently available to neuroscientists, as compared to the number of neurons in the brain, is dauntingly small. Thus, there is also strong motivation for developing architectures for enabling the recording of arbitrary numbers of neurons, eventually approaching the entire brain. Our hope is to empower a collaborative network of neuroscientists to use arbitrary-scale neural recording and stimulation devices to solve fundamental questions about how the brain works, and to support a new generation of brain-machine interfaces.

In this talk, I present two examples of low-power tissue stimulators. The first type is a wireless, low-power neural stimulation system for use in freely behaving animals. I show design, measurements, and in-vivo results from testing the device in zebra finches. The second stimulator is a novel, energy-efficient electrode stimulator with feedback current regulation. This stimulator uses a dynamic power supply to drive an electrode in an adiabatic fashion such that energy consumption is minimized. Since there are no explicit current sources or current limiters, wasteful energy dissipation across such elements is naturally avoided. The stimulator monitors and regulates the current through the electrode via feedback, thus enabling flexible and safe stimulation. The dynamic power supply allows efficient transfer of energy both to and from the electrode, and is based on a DC-DC converter topology that is used in a bidirectional fashion. In an exemplary electrode implementation, I show how the stimulator combines the efficiency of voltage control and the safety and accuracy of current control in a single low-power integrated-circuit. In its current proof-of-concept implementation, this stimulator achieves a 2x-3x reduction in energy consumption as compared to a conventional current-source-based stimulator operating from a fixed power supply.

I will also discuss the principles of designing amplifiers and probes for arbitrary-scale neural recording systems, focusing on enabling strategies such as multiplexing circuitry that reduces connectivity challenges, 3-D probe designs that enable the recording of neurons throughout large brain volumes with micron scale precision, and systems design strategies that can handle thousands to millions of channels of electronic data. For example, the mouse brain contains perhaps 100 million neurons, but current technologies scale to recording about a few hundred channels of neural activity. Accordingly, we propose to develop a fundamentally new probe architecture, amplifier architecture, and signal processing architecture, aimed at enabling the recording of arbitrary numbers of neurons, eventually approaching the entire brain..

About the Speaker:

Scott Arfin received the B.S. degree (summa cum laude) in electrical engineering from Columbia University, New York, in 2004 and the S.M. and Ph.D. degrees in electrical engineering from the Massachusetts Institute of Technology (MIT), Cambridge, in 2006 and 2011, respectively. He was as a member of the Analog VLSI and Biological Systems Group at MIT while working towards the S.M and Ph.D degrees. His work there focused on low-power and wireless neural stimulators. Currently, he is a Postdoctoral Associate with the Synthetic Neurobiology Group at MIT working on arbitrary-scale neural recording systems.