IEEE Circuits and Systems Magazine - Q4 2020 - 44

Yes
No
1

No wireless demonstration, 2 Time amplitude based, 3 Amplitude based, 4 Simple trigger on AP detection, 5 LFP energy & AP occurrence, 6 w/o stimulation

No
No
No
No
No
Freely moving trials

1.13
1.95
>10
-
-
-
-
Syst. volume [cm3]

1.8

3.0
11.3
18
-
Wired
Wired
Wired
Syst. weight [g]

1.9 (Wired)

216
8
8
-
-
-
-
Syst. power [mW]

3.2
2.5
1.5
-
2.5
IRN [ÂµVRMS]

2.5

3.12

4.57

Sorting +
Wind.
AP Detect.4
AP Detect.4
Firing rate
Firing rate
Sorting
CL feedback control

AP Detect.4

PID5

Yes
No
No
No
No
Yes
Yes
AP sorting

Yes
No
No
No
No
No
No
Signal compression

Yes3
Yes (analog)2
Yes
Yes
Yes
AP detection

Yes

Yes2

Yes

AP
AP
AP/LFP
AP
AP
AP
AP
Targeted signals

General
brain study
General
brain study
General
brain study
Repairing damaged
neural pathways
Epilepsy
seizure study
Firing rate
control
General
brain study
Application

General
brain study

Wireless
system
Wired system1
Wired/PC
Wired/PC
Wired/PC
Type

Wired/PC

IC only

Wireless
system

This
Work

CL IC or Systems for Optogenetics

Front. Neurosci.'18
[33]
TBioCAS'17
[35]

CL IC or Systems for Elect. Stim.

JSSC'11
[32]
TBioCAS'18
[34]
Nat. Protocols'13
[58]

PC-Based CL Optogenetics

eLife'15
[14]
JoNe'14
[13]

Table I.
Comparison with other systems for CL optogenetics, optogenetics and recording or CL elect. stimulation.
44â

IEEE CIRCUITS AND SYSTEMS MAGAZINE

IX. Conclusion
We covered the design of advanced microelectronic systems
for performing CL optogenetics with live neural circuits. We
presented an overview of the
important paradigm of CL optogenetics and its associated
implementation challenges. We
showed the critical importance
of neural electrophysiology, optogenetics, digital neural signal
processing and wireless systems
are critically important for applying this paradigm to small laboratory mice. After reviewing the
existing solutions, we presented
the design of a complete wireless system for performing CL
optogenetics, and we covered
the design of all modules in detail. In particular, we presented
the design of advanced digital
building blocks for implementing a real-time CL controller with
very short latency. We can reliably close the loop between the
recording module and the photosimulator by performing detection, compression and classification of the neural waveforms
in real-time over many recording channels using low-power
mixed-mode and digital circuits.
Finally, we presented experimental results obtained in vivo with
an electro-optic prototype utilized in CL with a freely moving
mouse virally expressing ChR2mCherry in inhibitory neurons
of the prelimbic cortex.
Acknowledgments
The authors would like to thank
the CERVO Brain Research Center for testing the systems in
vivo and CMC Microsystems for
providing the design and testing tools. We would also like to
thank Iason Keramidis of the
CERVO Brain Research Center for helping with the in vivo
validations. We also thank the
FOURTH QUARTER 2020

IEEE Circuits and Systems Magazine - Q4 2020

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