michale fee mcgovern institute for brain research department of brain and cognitive sciences mit...
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![Page 1: Michale Fee McGovern Institute for Brain Research Department of Brain and Cognitive Sciences MIT Jerusalem in Motion Workshop Jerusalem, Israel December](https://reader035.vdocument.in/reader035/viewer/2022070416/56649d425503460f94a1da65/html5/thumbnails/1.jpg)
Michale Fee
McGovern Institute for Brain Research
Department of Brain and Cognitive Sciences
MIT
Jerusalem in Motion Workshop
Jerusalem, Israel
December 18, 2003
Vocal control in the songbird: Neural mechanisms of sequence
generation
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A
B
C
D
EF
G
H
I
J
A-B-C-D-E-F-G-H-I-J
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1 2 3
Abeles, Hertz, ‘80s and ‘90s
Synchronous Firing Chain
Neural Circuits for Sequence Generation
1 2 3 4
1
23
4
Metastable AttractorsSompolinsky, Kleinfeld, Platt, 1980s
fast
slow
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Neural Circuits for Sequence Generation
• Train a specified sequence of neural states,
• Sequence of states must be nearly orthogonal• A-B-C-A-D is not allowed
• Interference between sequence and dynamics
• Timescale is set by synaptic/biophysical time constants
Wij = SitSj
t+1
Sit
t
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Overview
• Songbird as a model system
• Technological challenges
• Mechanisms of sequence generation in the songbird
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Zebra Finches
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0 kHz
10 kHz
Zebra Finch Song Structure
1s
Fre
quen
cy
Motif Motif Motif
Syllable
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Songbird Vocalizations are Highly Stereotyped
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Songbirds Can Generate Output Over a Wide Range of Timescales
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Biological systems can:
• Learn and reliably generate low-dimensional sequential behavior– not a specified sequence of neural states
• Generate an arbitrary sequence– not constrained by orthogonality between output
states
• Operate over a wide range of timescales
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Circuits for Vocal Production and Learning
H V C
R A
U VA
XDLM
LM AN
nX IIts
S yrinx
N If
Motor Circuit
Learning Circuit
(7)
1000
7000
20,000
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Technical Difficulties
• Songbirds will only sing while unconstrained
• Zebra finch weighs only 12-15 grams
• Singing is suppressed by handling
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• 3 independently controlled electrodes
• Motorized for remote control
• 1.5 gram total weight
Motorized Miniature Microdrive
Fee and Leonardo, 2000
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Premotor Activity During Singing
Bou
tM
otif
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Instantaneous Firing Rate
0.0 0.4 0.6 0.80.2
1
6
12N
euro
n #
Time [s]
Firing R
ate [1 kHz/D
iv]
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How Are the Burst Sequences in RA Generated?
• Internal dynamics within RA?
- OR -
• Imposed from HVC?
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Models of Pattern Generation in HVC and RA
Fee
d-fo
rwar
dIn
trin
sic
HVC
RA
HVC
RA
~10ms
~10ms
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Singing Related Firing Patterns in Nucleus HVC
Yu and Margoliash, 1996
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Antidromic Identification of HVC Neurons
X
Stim
StimHV c
RA
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What do RA-Projecting HVC neurons do during singing?
Hahnloser, Kozhevnikov, and Fee, Nature (2002)
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Hahnloser, Kozhevnikov and Fee, Nature (2002)
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Simple Sequence Generation Circuit
Sparse representation of time
Fixed synaptic weights
Plastic synaptic weights
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Downstream effect of RA activity
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Simple Sequence Generation Circuit
Sparse representation of time
Fixed synaptic weights
Plastic synaptic weights
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Model of Vocal LearningH
VC
100
110
120
Initi
al o
utpu
tF
inal
ou
tpu
t
0 50 100 150Tim e (m s)
with Sebastian Seung and Ila Fiete
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A Sparse Representation in HVC Speeds Learning
0 5 10 15 20 25 30
10-2
10-1
100
Sq
uare
d e
rror
Learn ing iterations
1
248
with Sebastian Seung and Ila Fiete
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Simple Sequence Generation Circuit:Emergent RA activity
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Emergent Activity in RA Neurons
with Sebastian Seung and Ila Fiete
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Emergent Activity in RA Neurons
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• Each model RA neuron has a unique pattern of bursts
• A different ensemble of active RA neurons at each time in the sequence
• The ensemble of active RA neurons evolves to an uncorrelated ensemble every ~10 ms, even during constant output
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RA ensembles are uniquely related to a temporal position in the output
– not to motor output
How is this possible?
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High Degree of Convergence From RA to Motor Output
• ~7000 RA projection neurons
• ~1000 motor neurons
• 7 muscles
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Many Different Ensembles of Active RA Neurons Can Produce the Same Motor Output
Model RA outputs form a highly degenerate code for motor signals
RA
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Instantaneous Firing Rate
0.0 0.4 0.6 0.80.2
1
6
12N
euro
n #
Time [s]
Firing R
ate [1 kHz/D
iv]
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Tim
e t 2
020
040
060
01 25
Neuron #
Time t1
0 200 400 600
1
25
Neu
ron
#
Time t1
Tim
e t 2
0 200 400 600
020
040
060
0
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How are the Timescales of Neural and Motor Activity Related?
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Neural and Song Correlation Matrices
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Neural and Song Correlation Width
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Circuits for Vocal Production and Learning
H V C
R A
U VA
XDLM
LM AN
nX IIts
S yrinx
N If
Motor Circuit
Learning Circuit
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• Each RA neuron has a unique pattern of bursts
• A different ensemble of active RA neurons at each time in the song motif
• The ensemble of active RA neurons evolves to an uncorrelated ensemble every ~10 ms, even during parts of the song with constant acoustic output
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Our proposed network can:
• Learn and reliably generate low-dimensional sequential behavior– not a specified sequence of neural states
• Generate an arbitrary sequence– not constrained by orthogonality between output
states
• Operate over a wide range of timescales
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Design Principles and Implications
• Separate the temporal dynamics and the mapping to motor output – Changes in learned output do not affect temporal
structure
• Sparse coding of temporal order in HVC– Fast learning?
– No single neuron tuning in RA?
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Future Directions• When during development does the sparse
representation of time in HVC arise?
• Where do sparse sequences in HVC originate? Intrinsic dynamics within HVC, or driven from NIf?
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Circuits for Vocal Production and Learning
H V C
R A
U VA
XDLM
LM AN
nX IIts
S yrinx
N If
Motor Circuit
Learning Circuit
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1 2 3
Where and how is ‘time’ generated?
1 2 3 4
1
23
4
fast
slow
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Collaborators
• Richard Hahnloser– Bell Laboratories
• Alexay Kozhevnikov– Bell Laboratories
• Anthony Leonardo– Bell Laboratories
• Ila Fiete, Sebastian Seung
– Brain and cognitive sciences department – MIT
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Simple Models of Neural Circuits
1 2
1
21
1
A
B
• stable states - fast, symmetric connections
1
2
1 2
• dynamic states - slow or asymmetric connections