[Picture courtesy of K. Diesseroth, Stanford]

An Exciting Time for Neuroscience

Brain-Machine Interfaces (BMI) Making News

Listening to the voices inside your head “Neuroscientists may one day be able to hear the imagined speech of a patient unable to speak due to stroke or paralysis, according to University of California, Berkeley researchers.” [Pasley at al, PLOS12]

Some Interesting Numbers

Human Brain: 80 billion neurons, 100 trillion synapses 1200 cm3, 1300 gram 15-20 W, 1017 floating point ops/sec

Mouse Brain: 75 million neurons, 100 billion synapses 420 mm3, 0.5 gram 15 mW

Sampling every neuron in a mouse brain at 1 bit resolution at 1 kHz: 75 Gbit/sec

Instrumenting the Brain

[Courtesy: Marblestone at el, 2013]

Imaging the Brain for BMI

[Schwartz et al. Neuron, 2006]

BMI Interface Challenges: Acquire and Transmit Neural Signals Reliably and Consistently Over Long Lifespans (> 10 Years)

Weak signals, Low SNR, High Offset, Mixture of various signals (field and action potentials)

ADC LNA

electrodes

DSP

memory

Tx

regulator

clock

1 2 3 4 5−200

−150

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Time (s)

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Full WaveformDelta Band

ECoG

The Berkeley Brain-Machine Interface (BMI) - Combining “Neural Dust” and μECoG

“An implanted neural interface that can provide imaging (and possibly stimulation) of neural activity at multiple scales of resolution using arrays of patterned and free-floating sensors”

A collaborative effort between BWRC, UCB Engineering, UCB Neuroscience, and UCSF NeuroSurgery (as part of the CNEP Center)

Key requirement: reliability and longevity   Tons of selectable channels   Wireless powering and data

communications   Compliant, flexible and

microscopic (!)

Physical Interface Platforms across Scale and Modality

μECoG+BMIPeter Ledochowitsch / Aaron Koralek

Carmena / Maharbiz

Example: High-density Nanotrodes

•  Optical excitation, electrical readout

•  Ultra-compliant parylene cables allow truly floating electrodes

•  Scalable and manufacturable

•  Ultra-high density electrical recording (64-128 channels)

•  16 integrated 3 µm thick optical waveguides on each probe

A Library of Acquisition, Communication and Power Delivery Components!

2.4mm

64 channel remotely powered wireless uECoG [Muller, Le, Ledoschowicz, Li]

Free-floating wireless AP acquisition electrodes [Biederman, Yeager, VLSI12]

Register Bank

Memory Feature Extraction

Preamble Buffer

Spike Detection

Spike Alignment

Asynchronous 250 nW/channel spike-sorting [Liu, VLSI12]

The Neural Sensor Node: Size, Power/Energy and Bandwidth

Wireless powering only realistic option But limited by safety concerns

Bandwidth requirements set by # channels and information resolution

Reference: AP Acquisition Channel

[R. Muller et al, ISSCC 2011]

0.013mm2, 5uW DC Coupled Neural Signal Acquisition IC with 0.5V Supply (65 nm CMOS)   Digital transistors are

cheap in advanced CMOS processes

  Avoid off-chip components by dc-coupling and filtering in digital domain

  No high-precision analog components

Getting to the Point – Some System Examples

  Wireless μECoG may provide up to 1000 channels with pitch as low as 200 μm.

  Antenna + electrodes printed on parylene substrate using semiconductor-like process

  Providing unprecedented resolution and offering huge potential for BMI (ALS, Epilepsy).

[Courtesy: P. Ledocowich, R. Muller, M. Maharbiz, J. Rabaey]

Circuit elements similar to AP sensor nodes

A Global View with μECoG

64 channel array

Loop Antenna for Link Optimization

0 200 400 600 800-20-19-18-17-16-15

f [MHz]

MA

G [

dB

]

0 200 400 600 8000

100200300400

f [MHz]

Ptx

[m

W]

0 200 400 600 8000123456

f [MHz]

Prx

[m

W]

[Courtesy: T. Bjorninen, R. Muller]

Power availability limits functionality

[Courtesy: R. Muller, W. Li, H. Le, S. Gambini]]

65 nm CMOS 2.4 uW/channel @ 0.5V 0.025 mm2/channel 1 Mbits/sec data rate

Confidential – pre-publication A 240 uW Self-Contained Wireless uECoG System

Neural Dust v1 – Free-floating Electrodes

μ

 

 

 

[Biederman, Yeager, VLSI12]

Measured Power

Layout Area (um2)

4 Neural Amplifiers

8 uW 100 x 450

Total System: 10 uW 220 x 450

Node Architecture

4x Φ Sample

10b ADC LNA VGA DAC

Verification of Wireless Power Transfer

Slide 20

0

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h Lo

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)

Transmission Distance (mm)

TX Power in Air (Meas)

Path loss in Air (Sim)

  Edge-edge channel loss was verified using a micromanipulator in air and compared to simulation

  Simulated channel loss in brain tissue is ~6db higher

Matching Network

IC

SMA

TX Coil 3mm x 3mm

Multi-Node Communication   Programmable Miller subcarrier

  1-10 MHz   6 simultaneous sensors

Neural Dust v2 – The Network Emerges

Skull

Brain

Interrogator

Power / Comm. Transcranial Link

Radio

Utilize “chiplet” designs to create dynamic system building blocks for a scalable & reconfigurable system

FlexSubs

Power & Data Controller

Can record or stimulate from each electrode & monitor electrode impedance

Scalable power output and data aggregation

Amp + ADC chiplet

Tether free data link to signal processing unit

Compliant Tether Improved scalability & power

transfer efficiency

Neural Dust v∞ - Scaling to Thousands

  Electro-magnetic data communication and powering becomes extremely inefficient when nodes become very small (< 100 μm)

  Yet optimal node size is around 10 μm

The ultra-sound opportunity   Wavelength of 1MHz Ultrasound wave in brain = 1.562mm   Wavelength of 1.5GHz RF wave in brain = 20 mm

Better resolution, better power-transfer efficiency

[DJ Seo et al, White Paper, June 2103]

The Data Deluge

Data link throughput limited by bandwidth and power constraints   Passive RFID-like radio: 1-2 Mbit/sec @ 10 pJ/bit   Active radio could do considerably more (≥ 10

Mbit/sec), but consumes more power/bit (> 100 pJ/bit)

  Acquisition power/channel = 2-5 μW (amplification, conversion)

Action Potential Channel ECoG Channel 20 kHz x 10 bits = 200 kBit/sec 1 KHz x 12 bits = 12 kBit/sec

The challenge: How to scale to larger channel counts (1000’s or millions?)

Trade-off in Rate-Limited Systems

Low Channel Count High Channel Count

Full Information

Little Information

Current Systems

Our Goal

Goals and expectations differ: •  Neuroscience requires full waveforms •  Brain-machine interfaces operate from firing rates

(AP) or energy binning (ECoG, LFP)

Action Potentials

[Karkare, JSSC’11]

Straightforward compression yields little reduction (< 2) Better approach:

exploit sparseness – spike detection and clustering

Data Rates (including overhead) Raw 213 kBit/sec 5 channels Spike timing + epochs 51 kBit/sec 20 channels Firing rates 160 Bit/sec 6000 channels

(assuming 100 Hz avg. firing rate)*

Spike Detection and Feature Extraction

Example: NEO + Max & Epochs

[Courtesy: D. Markovic, UCLA; N. Narevsky, UCB]

The Energy Cost

Register Bank

Memory Feature Extraction

Preamble Buffer

Spike Detection

Spike Alignment

•  Fully asynchronous to minimize leakage

•  250 nW/channel @ 0.25V •  0.03mm2 in 65nm CMOS

[Liu, VLSI 2012]

Spike detection + feature extraction

V. Karkare et al., A-

2 uW and 0.07 mm2 / channel [Karkare, Markovic, 2009]

Energy cost equal or smaller then acquisition

Clustering and Sorting   One electrode may observe 1-10 neurons (or even more)

Commercial spike sorting software

V. Karkare et al.,

On-line spike sorting [Karkare, 2011]

Clustering algorithms

[Courtesy: D. Markovic, UCLA]

If lossy data compression is allowed - possible to transmit 1000’s of channels over single data link

The challenge resides now in the power domain: How to simplify acquisition front-end? (information extraction in the analog domain, compressive sampling, )

DC Offset ±50mV

ECoG 1Hz-500Hz 1µV-100µV

volta

ge

time

1 2

1 4 7 15 30 65 250

EEG EcOG Only

Anesthesia Sleep

Arousal Drowsiness Relaxed

Eye Closing

Alert/ Active Activity

Sensorimotor

ECoG (LFP, EEG)

[Heldman, Tran. Neur. Sys. 2006]

•  Little information to be gained from time domain

•  Most relevant: deviation from baseline in frequency bins

•  Again: straightforward compression not effective

There is Hope … Compressed sampling

•  Compression between 2 and 24 •  Feature extraction in compressed

domain •  But still needs full acquisition

[Shoaib, Verma, 2012]

Compressed Sampling in the Analog Domain?

Recovered spectrum [Courtesy: F. Maksimovic]

Exploit Spatial Correlation and/or Activity Sparseness?

0 0.02 0.04 0.06 0.08 0.1 0.12-0.1

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8Image plot of all electrode voltages at a single time

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But time-domain image is not sparse

Plenty of Room for Creativity in the Million Neuron March

  Innovation in wireless transceivers and

  Neural swarm networking

  Beam-forming   Adaptation   Learning

Final Reflections ….   Brain-Machine Interfaces the ultimate in immersive

technologies -  The potential is huge - Societal impact first, human advancement next

  ULP circuit and systems design in concert with innovative technologies to provide “cellular electronics”

-  Requires careful balance between energy availability, data bandwidth and information contents

  Signal processing, interpretation in conjunction with control to play an important role

  A new breed of integrated mixed

bio/electronics devices emerging -  BioCyber (biotic-abiotic) systems a

formidable bet for EECS!

Acknowledgements: The many contributions of Elad Alon, Jose Carmena, Edward Chang, Bob Knight, Michel Maharbiz, K. Ganguly, Leena Ukkonen, Bruno Olshausen, Dejan Markovic, Simone Gambini, Rikky Muller, Michael Mark, David Chen, Will Biederman, Dan Yeager, Peter Ledochowitsch, Toni Bjorninen, Wen Li, Ping_chen Huang and Tsung-Te Liu to this presentation are gratefully acknowledged.

Research performed as part of the Berkeley-UCSF Center for Neural Engineering and Prosthetics. The support of the California Discovery program, the FCRP MuSyC and SONIC centers, and the member companies of BWRC is greatly appreciated.