In this webinar, Dr. Michael Long from the NYU Grossman School of Medicine and Dr. Kari Hoffman from Vanderbilt University present their work investigating the neural mechanisms of learning, memory, and behavior using high-density silicon probes from Diagnostic Biochips in small and large animals. The ability to record network activity using emerging high-density electrophysiological arrays has revolutionized understanding of the link between brain function and behavior. In the first portion of the webinar, Dr. Long discusses how his laboratory has used custom-built (and now standard) probes to investigate the neural mechanisms of vocal production in two model systems: the zebra finch (a songbird) and a newly characterized Costa Rican singing mouse. In ongoing work, they have begun to apply these approaches to the study of human speech. His team has collaborated with Diagnostic Biochips and the University of Iowa Department of Neurosurgery to develop a recording electrode for measuring population activity in the human brain. Through these combined efforts, they have advanced understanding of the neural mechanisms of vocal production that can inform therapeutic approaches intended to combat a range of communication disorders. In the second portion of the webinar, Dr. Hoffman introduces work in her lab investigating the neural mechanisms of learning and memory in freely-behaving macaques. This includes recording neural signatures from animals in a rich environment to examine how experiences shape new learning. Using spatially resolved units, distinct waveforms, and interactions with local currents and fields, they hope to identify the role of functional cell types in network states and network plasticity. Dr. Hoffman also describes the features and current limitations of the probe, and presents preliminary wireless recording results from her lab. She concludes with a discussion of factors that may make it more or less suitable for other users, and best practices for generating high quality data.

1. Using High-Density Electrophysiological Recordings to
Investigate Neural Mechanisms in Small and Large Animals
Michael Long, PhD
Neuroscience Institute
NYU Grossman School of Medicine
Professor
Kari Hoffman, PhD
Psychological Sciences
Vanderbilt University
Associate Professor

2. Using High-Density Electrophysiological Recordings to
Investigate Neural Mechanisms in Small and Large Animals
Dr. Michael Long from the NYU Grossman School of Medicine and Dr. Kari
Hoffman from Vanderbilt University present their work investigating the
neural mechanisms of learning, memory, and behavior using high-density
silicon probes from Diagnostic Biochips in small and large animals.

3. Neural mechanisms of interactive communication
Michael Long, PhD
Neuroscience Institute
NYU Grossman School of Medicine
Professor
Copyright 2022 M. Long, InsideScientific and Diagnostic Biochips. All rights reserved.

4. Michael A. Long
NYU School of Medicine
scientist.com
June 28, 2022
Neural
mechanisms of
interactive
communication
(Artwork: Julia Kuhl)

6. Human Speech: A complex behavior
(Jens Frahm / Max-Planck-Institut für biophysikalische Chemie)
Words have rapidly changing
acoustic structure (~20 ms).
(Giraud & Poeppel, 2012)
Each phoneme requires
coordination of multiple
articulators…
(Guenther, 2016)
…involving approximately 100
different muscles.
(Darley et al., 1975; Duffy, 1995)

7. 2500
0
-2 -1 1
0
2
Frequency
Time between speakers (seconds)
3
~200 ms!
(Levinson, 2016)
(‘Nerdist’ podcast, 10/12/2016)
Coordinated vocal exchanges across individuals
2 sec

8. What neural mechanisms underlie the
planning processes required for
interactive vocal communication?
Gregg Castellucci
U Iowa Neurosurgery
Taylor Abel
Haiming Chen
David Christianson
Matt Howard
Hiroto Kawasaki
Chris Kovach
Kirill Nourski
Hiroyuki Oya
Jeremy Greenlee
U Iowa

9. Experimental design: Electrocorticography
and interactive speech
Experimenter: Participant:
Time (sec)
Count
8
-6
Interaction
#
61
1
0
0
15
Records local field potentials directly
from the surface of the cortex
Spatial resolution: < 10 mm
Temporal resolution: < 10 ms
Patient volunteers are undergoing
neurosurgical treatment for medically
intractable epilepsy or brain tumors
(Castellucci et al., 2022)

10. Single CRITICAL word can lead to the answer,
initiating response planning.
The opposite of SOFT is what frequent word?
The opposite of NICE is which common word?
The opposite of SAD is what familiar word?
The opposite of HOT is what common word?
How many FINGERS does a healthy person have?
How many TOES does the average human have?
How many ARMS does a human being have?
How many LEGS does a healthy person have?
What animals, who MOO, are often found either on farms or in zoos?
Which animals, who OINK, are often found either on farms or in zoos?
What animals, who QUACK, are often found either on farms or in zoos?
What animals, who MEOW, are often seen either on farms or in zoos?
BODY PARTS ANTONYMS
ANIMAL SOUNDS
The ‘Critical Information’ Task
(60 questions across 3 categories)
Task developed by:
Sara Bögels
Stephen Levinson

11. Linking motor and planning responses to anatomy
Auditory cortex -0.5
Response
(z-score)
0
1.5
SENSORY (VOCAL PARTNER)
PLANNING
Broca’s/Speech MCtx
RECORDING LOCATIONS
Auditory Ctx
After interaction
MOTOR (SPOKEN RESPONSE)
Auditory cortex -0.5
Response
(z-score)
0
1.5
SENSORY (VOCAL PARTNER)
PLANNING
Broca’s/Speech MCtx
RECORDING LOCATIONS
Auditory Ctx
During speech
MOTOR (SPOKEN RESPONSE)
Auditory cortex -0.5
Response
(z-score)
0
1.5
SENSORY (VOCAL PARTNER)
PLANNING
Broca’s/Speech MCtx
RECORDING LOCATIONS
Auditory Ctx
Premotor (before speech)
MOTOR (SPOKEN RESPONSE)
Auditory cortex -0.5
Response
(z-score)
0
1.5
SENSORY (VOCAL PARTNER)
PLANNING
Broca’s/Speech MCtx
RECORDING LOCATIONS
Auditory Ctx
Planning continues
MOTOR (SPOKEN RESPONSE)
Start of planning
Auditory cortex -0.5
Response
(z-score)
0
1.5
SENSORY (VOCAL PARTNER)
PLANNING
Broca’s/Speech MCtx
RECORDING LOCATIONS
Auditory Ctx
MOTOR (SPOKEN RESPONSE)
Auditory cortex -0.5
Response
(z-score)
0
1.5
SENSORY (VOCAL PARTNER)
PLANNING
Broca’s/Speech MCtx
RECORDING LOCATIONS
Auditory Ctx
Start of partner’s speech
MOTOR (SPOKEN RESPONSE)
Auditory cortex -0.5
Response
(z-score)
0
1.5
MOTOR (SPOKEN RESPONSE)
Before interaction
SENSORY (VOCAL PARTNER)
PLANNING
Broca’s/Speech MCtx
RECORDING LOCATIONS
Auditory Ctx
Critical Info
Speech sensorimotor cortex
Broca’s region
(Castellucci et al., 2022)

12. Linking motor and planning responses to anatomy
Auditory cortex -0.5
Response
(z-score)
0
1.5
SENSORY (VOCAL PARTNER)
PLANNING
Broca’s/Speech MCtx
RECORDING LOCATIONS
Auditory Ctx
After interaction
MOTOR (SPOKEN RESPONSE)
Auditory cortex -0.5
Response
(z-score)
0
1.5
SENSORY (VOCAL PARTNER)
PLANNING
Broca’s/Speech MCtx
RECORDING LOCATIONS
Auditory Ctx
During speech
MOTOR (SPOKEN RESPONSE)
Auditory cortex -0.5
Response
(z-score)
0
1.5
SENSORY (VOCAL PARTNER)
PLANNING
Broca’s/Speech MCtx
RECORDING LOCATIONS
Auditory Ctx
Premotor (before speech)
MOTOR (SPOKEN RESPONSE)
Auditory cortex -0.5
Response
(z-score)
0
1.5
SENSORY (VOCAL PARTNER)
PLANNING
Broca’s/Speech MCtx
RECORDING LOCATIONS
Auditory Ctx
Planning continues
MOTOR (SPOKEN RESPONSE)
Start of planning
Auditory cortex -0.5
Response
(z-score)
0
1.5
SENSORY (VOCAL PARTNER)
PLANNING
Broca’s/Speech MCtx
RECORDING LOCATIONS
Auditory Ctx
MOTOR (SPOKEN RESPONSE)
Auditory cortex -0.5
Response
(z-score)
0
1.5
SENSORY (VOCAL PARTNER)
PLANNING
Broca’s/Speech MCtx
RECORDING LOCATIONS
Auditory Ctx
Start of partner’s speech
MOTOR (SPOKEN RESPONSE)
Auditory cortex -0.5
Response
(z-score)
0
1.5
MOTOR (SPOKEN RESPONSE)
Before interaction
SENSORY (VOCAL PARTNER)
PLANNING
Broca’s/Speech MCtx
RECORDING LOCATIONS
Auditory Ctx
Critical Info
Speech sensorimotor cortex
Broca’s region
(Castellucci et al., 2022)

13. Primary motor and
somatosensory
cortex
Inferior frontal
gyrus?
Premotor cortex?
Inferior parietal
cortex?
Anterior temporal
cortex?
n = 8 participants (left hemisphere)
Superior and
middle temporal
gyri
Cortical representations of interactive speech
SENSORY MOTOR PLANNING
(Castellucci et al., 2022)

14. Are the planning signals relevant for unstructured
vocal interactions (i.e., conversation)?

15. Cortical activity during natural conversation
Experimenter:
Participant: 10 sec
Speech
Motor Cortex
Broca’s
Region
1 sec
4
0
-2
‘Were you born
in Mexico?’
‘New Mexico’
‘Have you been
to Mexico?’
‘No’
‘Never?’
‘Never’
ECoG
(z-score)
Experimenter:
Participant:
(Castellucci et al., unpublished)

16. Are the planning signals necessary for
normal vocal interactions?

17. Disruption of planning activity
slows response time
*
CI
Control
Response Time (s)
0 2
Relative
Count
-6 2
Time from Question Offset (s)
0
Participant
Experimenter
-6 2
0
Stimulated
Stimulated
p < 0.0001
(Rank Sum Test)
(Castellucci et al., in prep)
Electrode #212
(cIFG/Broca’s)

18. ERROR RATE:
Control: 2.1%
Stimulation: 19.2%
‘Say the plural of MAN clearly into the microphone.’
‘Man’
(Correct: ‘Men’)
Example #1
1 sec
‘Which word is the opposite of FAST?’
‘Rapid’ ‘The opposite, the opposite would be slow then’
Example #2
Stimulation
(Castellucci et al., in prep)
Disruption of planning activity
results in linguistic errors
*
Electrode #212
(cIFG/Broca’s)

19. Investigating human cortical ensembles
(Christianson, Wu, Elmaleh, Greenlee, Windolf, Paninski, Greenlee, Long)

20. (1) Speech perception, planning, and production activity are separately
represented in the human neocortex.
(2) A language-planning network necessary for interactive speech includes
the middle frontal gyrus and Broca’s region.
(3) Consistent cortical responses occur during scripted tasks and natural
conversation.
(4) Perturbation of activity in the language planning network leads to
slowed interactions and linguistic errors.
Neural mechanisms of interactive communication

21. Embrace the diversity of the animal kingdom
1 Foundation for Biomedical Research
2 2019 Society for Neuroscience meeting planner
Nobel prize
0 0.1 0.2
sheep
cat
crab
bird
dog
chimpanzee
fruit fly
chicken
hamster
cow
horse
fish
mouse
frog
newt
guinea pig
rabbit
monkey
nematode
pig
sea slug
rat
Proportion
SfN Abstracts
0 0.2 0.4 0.6
Proportion
sheep
cat
crab
bird
dog
chimpanzee
fruit fly
chicken
hamster
cow
horse
fish
mouse
frog
newt
guinea pig
rabbit
monkey
nematode
pig
sea slug
rat
Elena Gracheva:
Hibernation and homeostasis
Gul Dolen:
Comparative mechanisms of
complex behaviors
Lauren O’Connel:
Evolution of
parental behavior
Dmitriy Aronov:
Episodic memory
Daniel Huber:
Active sensation in
visual cortex

22. The singing mouse (Scotinomys teguina)
(Photo: Christopher Auger-Dominguez)
Steve Phelps
U Texas
Arka Banerjee
Daniel Okobi Yuki Fujishima
Shaul Druckmann
Stanford
Feng Chen
Stanford

23. Countersinging in S. teguina
(Movie: Arka Banerjee)

24. S. Teguina song structure
1 sec
Freq
(kHz)
0
Note
duration
(ms)
250
100
0
(Okobi*, Banerjee* et al., 2019)

25. S. teguina
#2
S. teguina
#1
S. teguina #2
S. teguina #1
Countersinging in S. teguina
(Okobi*, Banerjee* et al., 2019)

26. (Okobi*, Banerjee* et al., 2019; Takahashi and Ghazanfar, unpublished data, Levinson, 2016)
Rapid and reliable vocal exchanges
-30 0 30
25 25
-30 0 30
Time (seconds)
Time (seconds)
1
Trial
#
1
Trial
#
Singing mouse Marmoset
0.1
Relative
proportion
Time (seconds)
0 10
0
Temporal
Comparison
Singing mouse
Human speech
Marmoset

27. Orofacial motor cortex (OMC)
Orofacial motor
cortex (OMC)
Intracortical microstimulation
in S. teguina
Photo: Steve Phelps
(Okobi*, Banerjee* et al., 2019)

28. 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30 1.35 1.40 1.45 1.50 1.55 1.60 1.65 1.70 1.75 1.80 1.85 1.90 1.95 2.00 2.05 2.10 2.15 2.20 2.25 2.30 2.35 2.40 2.45 2.50 2.55 2.60 2.65 2.70 2.75 2.80 2.85 2.90 2.95 3.00 3.05 3.10 3.15 3.20 3.25 3.30 3.35 3.40 3.45 3.50 3.55 3.60 3.65 3.70 3.75 3.80 3.85 3.90 3.95 4.00 4.05 4.10 4.15 4.20 4.25 4.30 4.35 4.40 4.45 4.50 4.55 4.60 4.65 4.70 4.75 4.80 4.85 4.90 4.95 5.00 5.05 5.10 5.15 5.20 5.25 5.30 5.35 5.40 5.45 5.50 5.55 5.60 5.65 5.70 5.75 5.80 5.85 5.90 5.95 6.00 6.05 6.10 6.15 s
25
50
75
100
kHz
28
1
250 ms
Cell
#
Population activity in OMC of S. teguina
During singing
Trial #1 Trial #2 Trial #3 Trial #4
28
1
Cell
#
28
1
Cell
#
20 Hz
0 Hz
Trial #1 Trial #2 Trial #3 Trial #4
Outside of song
(Banerjee*, Chen* et al., in prep)

29. 5 sec
Trial
#
1
29
8
0
Freq
(Hz)
5 sec
Trial
#
1
29
Social modulation of single neuron activity
Song Production Song Production
5 sec
Trial
#
1
15
5 sec
Trial
#
1
14
Partner’s song
Alone Countersinging
(Banerjee*, Chen* et al., in prep)

30. OMC inactivation decreases countersinging
500 s
Control (saline)
OMC inactivated (muscimol)
Playback times
Time
5 s
Playback from
loudspeaker
S. teguina
responds
(Okobi*, Banerjee* et al., 2019)

31. OMC inactivation eliminates fast
vocal exchanges in S. teguina
0.1
Relative
proportion
Time (seconds)
0 10
0
Control
OMC inactivated
-30 0 30
20 20
-30 0 30
Time (seconds)
Time (seconds)
1
Trial
#
1
Trial
#
Control OMC inactivated
(Okobi*, Banerjee* et al., 2019)

32. Motor
Response
Sensory
Trigger
Planning/
Motor Preparation
partner’s song
(specific features?)
partner’s speech
(critical information)
orofacial
motor cortex
interactive
language hub
countersinging
fast spoken
exchanges
pharmacological
inactivation
focal
stim disruption
slowed interactions/
linguistic errors
countersinging
abolished

33. NSF • NIH • Simons Global Brain
Arka Banerjee
Shaul Druckmann (Stanford)
Feng Chen (Stanford)
Yuki Fujishima
Daniel Okobi
Steve Phelps (UT Austin)
Kalman Katlowitz
Gregg Castellucci
David Christianson (Iowa)
Jeremy Greenlee (Iowa)
Matthew Howard (Iowa)
Jelena Krivokapic (Mich)
Chris Kovach (Iowa)
Frank Guenther (BU)
Lyn Ackert-Smith
Ariadna Corredera Asensio
Sam Benezra
Rachel Clary
Margot Elmaleh
Ellie Hozhabri
Dezhe Jin (Penn St)
(Artwork: Julia Kuhl)
Devorah Kranz
Georg Kosche
Jörgen Kornfeld (MPI)
Abby Paulson
Matt Phillips
Michel Picardo
Daniela Vallentin

34. Kari Hoffman, PhD
Psychological Sciences
Vanderbilt University
Associate Professor
Deep Probes to study circuit mechanisms
of learning and memory in macaques
Copyright 2022 K. Hoffman, InsideScientific and Diagnostic Biochips. All rights reserved.

35. Kari L. Hoffman
@perpl_lab
Whitehall Foundation
Saman Abbaspoor
Ken Rahman
@perpl_lab
Deep Probes to study circuit mechanisms
of learning and memory in macaques

36. • Microcircuit computation
• Spatial input specificity
• Ensemble unit activity
• Functional cell types
• Local and long-range computations
• Resolution tradeoff
• Moving-animal (chronic) implants
• Naturalistic, external validity
• Complexity matters
• Immersive, embodied cognition
@perpl_lab
motivation for high-density, linear, deep probes

37. We need probes that reach!
@perpl_lab
motivation for high-density, linear, deep probes

38. what we did before
Single-channel high density
“warp drive” 576-ch recordings
(Grey matter v0)
Hoffman and McNaughton, Science, 2002
Tetrode drives, spun and TRec
Tested 16, 32, 64-ch linear: polymer, silicon
Chronic implants >20 years
@perpl_lab

39. what we’re doing now
DBC deep probes
• N=2 animals (1 retrofit)
• 64, 128 channel, linear, bilinear, 3-D
• 20-45mm length
• 30-90um pitch
• Up to ~5mm coverage
• N=1st still implanted (~1.5 years)
• Using drives, no limit yet for yields
~1 week highest-quality unit yields as a
lower estimate, YMMV for a few wks/mos
@perpl_lab

40. microcircuit computation
via input specificity in space (time, frequency)
@perpl_lab
SR
PYR
SLM

41. ensemble responses
@perpl_lab

42. functional cell types
• Waveshape
• Spatial (3-D) waveshape
• Firing characteristics
• Local interactions
@perpl_lab

43. Wireless: treehouse task apparatus
• Neuralynx freelynx/Cube wireless recordings, up to 256-ch
• Sequence of 4 screens in “Z” shape
• trials per corner either RECENTly or REMOTEly learned
• Remote = 3-4 weeks old
• Epochs: REST – REM – REC – REM – REC –REST – sleep
@perpl_lab

45. population vectors -> state space
proof-of-principle of true (not pseudo) population code
varies across behavioral state
Cell
ID
PV
@perpl_lab

46. state-space similarity analysis
REMOTE
RECENT
proof-of-principle of true (not pseudo) population code
varies across behavioral state
@perpl_lab

47. Saman
Abbaspoor
Q and A (and thanks)
Whitehall Foundation @perpl_lab
Ken
Rahman

48. Behavioral correlates of hippocampal oscillations differ across species
(but macaques’ and humans’ are in register)
Hippocampal theta oscillations are not characteristic of memory-guided search
Leonard et al., JNeurosci 2015;
Curr.Biol. 2017
Abbaspoor, Hussin,
Hoffman, bioRxiv 2021
Human review: Herweg et al., TiCS 2020; Sleep: Tamura et al., Takeuchi et al., 2015; Uchida et al. 2001;
Cox et al., 2019 (but Cantero’03, Bodizs’01), Wireless: Mao et al., Neuron 2021; Courellis et al., 2020
𝜃 𝛾
Talakoub et al., bioRxiv, 2019

49. image registration and rendering: Wolf Zinke
• N=2 female adult macaques
• Chronic implant
• 16-channel linear probes:
HPC, RSC, mPFC, +
• Digital Lynx DAQ (Neuralynx) fs
@32 kHz
[Retrieval of old memories in macaques]
Hussin Abbaspoor and Hoffman, bioRxiv 2020

50. Thank you for participating!
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