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HAL Id: hal-01963115https://hal.inria.fr/hal-01963115
Submitted on 21 Dec 2018
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Learning an inverse model for vocal production: towarda bio-inspired model
Silvia Pagliarini, Xavier Hinaut, Arthur Leblois
To cite this version:Silvia Pagliarini, Xavier Hinaut, Arthur Leblois. Learning an inverse model for vocal production: to-ward a bio-inspired model. European Birdsong Meeting, Apr 2018, Odense, Denmark. �hal-01963115�
Learning an inverse model for vocal production: toward a bio-inspired model
6th European Birdsong Meeting, April 12-13, 2018, Odense, Denmark
Silvia Pagliarini (with Xavier Hinaut and Arthur Leblois)INRIA Bordeaux Sud-Ouest, Institut des Maladies Neurodégénératives, Université de Bordeaux, FR
WHAT IS SENSORIMOTOR LEARNING?
Control problem which maps a sensory input into a motor output
Basic components:
● Input: sensory stimulus
● Output: reproduction of the stimulus
Da Cunha et al., 2010
LEARNING BY IMITATION AND INVERSE MODEL
Imitation: learning from a tutor using a feedback guided error
Sensory area Motor area
Motor production
LEARNING BY IMITATION AND INVERSE MODEL
Imitation: learning from a tutor using a feedback guided error
Inverse model: the aim is to transform a sensory stimulus into the corresponding motor command
Sensory area Motor area
Inverse model
Motor production
A BIOLOGICAL EXAMPLE: SONG LEARNING IN BIRDS
Sensory Subsong (Babbling) Plastic song Crystallization
● Comparable learning mechanisms and behavior
Brainard and Doupe, 2002
A BIOLOGICAL EXAMPLE: SONG LEARNING IN BIRDS
● Comparable learning mechanisms and behavior
Sensory Subsong (Babbling) Plastic song Crystallization
Brainard and Doupe, 2002
A BIOLOGICAL EXAMPLE: SONG LEARNING IN BIRDS
● Comparable learning mechanisms and behavior
Sensory Subsong (Babbling) Plastic song Crystallization
Brainard and Doupe, 2002
LEARNING AN INVERSE MODEL
Synaptic weights initially weak
Sensory Area
Motor Area
…
…
LEARNING AN INVERSE MODEL
Synaptic weights initially weak
At each time :
●
Sensory Area
Motor Area
…
…
LEARNING AN INVERSE MODEL
Synaptic weights initially weak
At each time :
●
●
: learning rate
Sensory Area
Motor Area
…
…
Hebbian learning rule
LEARNING AN INVERSE MODEL
Synaptic weights initially weak
At each time :
●
●
●
: learning rate
Sensory Area
Motor Area
…
…
Hebbian learning rule
HAHNLOSER-GANGULI THEORETICAL MODEL
Time (in number of time steps)
Ave
rage
dis
tanc
e ov
er 5
0 s
imul
atio
ns
NONLINEAR MODEL INTRODUCTION
: target motor pattern
: tuning selectivity width
represents the distance between the target and the random exploration
GANGULI-HAHNLOSER MODEL
Time (in number of time steps)
Ave
rage
dis
tanc
e ov
er 5
0 s
imul
atio
ns
NORMALIZATION
Synaptic weights have a maximal value, related to the number of synaptic receptors one neuron is able to produce.
NORMALIZATION
Synaptic weights have a maximal value, related to the number of synaptic receptors one neuron is able to produce.
● Maximal weights normalization
● Supremum weights normalization
NORMALIZATION
Synaptic weights have a maximal value, related to the number of synaptic receptors one neuron is able to produce.
● Maximal weights normalization
● Supremum weights normalization
● Decreasing factor normalization
NORMALIZED INVERSE MODEL
Normalization applied over the auditory neurons
Maximum weights
Supremum weights
Decreasing factor
Time (in number of time steps)
Evol
utio
n of
the
dist
ance
NORMALIZED INVERSE MODEL
Norm
Time (in number of time steps)
Evol
utio
n of
the
dist
ance
Mean
AUDITORY SELECTIVITY EFFECTC
onve
rgen
ce ti
me
(in n
umbe
r of t
ime
step
s)
Tuning selectivity width
Dis
tanc
e fr
om th
e ta
rget
VARYING INPUT/OUTPUT DIMENSION
Distance from the motor target at convergence
Dis
tanc
e fr
om th
e ta
rget
Number of neurons in the network
VARYING INPUT/OUTPUT DIMENSION
Convergence time
Con
verg
ence
tim
e (in
num
ber o
f tim
e st
eps)
Number of neurons in the network
SUMMARY
● Simple normalization schema are successful in the nonlinear model
● Decreasing tuning selectivity width: ○ convergence time explosion ○ accuracy of learning increases
● Auditory VS motor dimension
WHAT’S NEXT?
● Duration of syllable and feedback delay
WHAT’S NEXT?
● Duration of syllable and feedback delay
● Production of sound
WHAT’S NEXT?
● Duration of syllable and feedback delay
● Production of sound Enjoy the poster from Xavier Hinaut
WHAT’S NEXT?
● Duration of syllable and feedback delay
● Production of sound
● Make prediction on experimental data
Enjoy the poster from Xavier Hinaut
Thanks for the attention.