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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
A
B
C
D
EF
G
H
I
J
A-B-C-D-E-F-G-H-I-J
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
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
Overview
• Songbird as a model system
• Technological challenges
• Mechanisms of sequence generation in the songbird
Zebra Finches
0 kHz
10 kHz
Zebra Finch Song Structure
1s
Fre
quen
cy
Motif Motif Motif
Syllable
Songbird Vocalizations are Highly Stereotyped
Songbirds Can Generate Output Over a Wide Range of Timescales
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
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
Technical Difficulties
• Songbirds will only sing while unconstrained
• Zebra finch weighs only 12-15 grams
• Singing is suppressed by handling
• 3 independently controlled electrodes
• Motorized for remote control
• 1.5 gram total weight
Motorized Miniature Microdrive
Fee and Leonardo, 2000
Premotor Activity During Singing
Bou
tM
otif
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]
How Are the Burst Sequences in RA Generated?
• Internal dynamics within RA?
- OR -
• Imposed from HVC?
Models of Pattern Generation in HVC and RA
Fee
d-fo
rwar
dIn
trin
sic
HVC
RA
HVC
RA
~10ms
~10ms
Singing Related Firing Patterns in Nucleus HVC
Yu and Margoliash, 1996
Antidromic Identification of HVC Neurons
X
Stim
StimHV c
RA
What do RA-Projecting HVC neurons do during singing?
Hahnloser, Kozhevnikov, and Fee, Nature (2002)
Hahnloser, Kozhevnikov and Fee, Nature (2002)
Simple Sequence Generation Circuit
Sparse representation of time
Fixed synaptic weights
Plastic synaptic weights
Downstream effect of RA activity
Simple Sequence Generation Circuit
Sparse representation of time
Fixed synaptic weights
Plastic synaptic weights
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
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
Simple Sequence Generation Circuit:Emergent RA activity
Emergent Activity in RA Neurons
with Sebastian Seung and Ila Fiete
Emergent Activity in RA Neurons
• 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
RA ensembles are uniquely related to a temporal position in the output
– not to motor output
How is this possible?
High Degree of Convergence From RA to Motor Output
• ~7000 RA projection neurons
• ~1000 motor neurons
• 7 muscles
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
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]
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
How are the Timescales of Neural and Motor Activity Related?
Neural and Song Correlation Matrices
Neural and Song Correlation Width
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
• 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
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
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?
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?
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
1 2 3
Where and how is ‘time’ generated?
1 2 3 4
1
23
4
fast
slow
Collaborators
• Richard Hahnloser– Bell Laboratories
• Alexay Kozhevnikov– Bell Laboratories
• Anthony Leonardo– Bell Laboratories
• Ila Fiete, Sebastian Seung
– Brain and cognitive sciences department – MIT
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