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A bio-inspired model towards vocal gesture learningin songbird
Silvia Pagliarini1,2,3, Xavier Hinaut1,2,3, Arthur Leblois3
1) Mnemosyne, Inria Bordeaux Sud-Ouest2) LaBRI, UMR 5800, CNRS
3) IMN,UMR 5293, CNRSUniversité de Bordeaux, France.
WHAT’S NEXT?
NORMALIZED INVERSE MODELINTRODUCTION
Sensorimotor learning: control problem which maps a sensory input into a motor output.
RESULTS
Imitation: learning from a tutor using a feedback guided error.
Da Cunha et al., 2010
Inverse model: the aim is to transform a sensory stimulus into the corresponding motor command.
VARYING INPUT/OUTPUT DIMENSION
Dis
tanc
e fr
om th
e ta
rget
Number of neurons in the network Number of neurons in the network
Conv
erge
nce
time
(in n
umbe
r of t
ime
step
s)Learning accuracy Learning speed
Motor dimension influences learning making it slower and slower as it increases.
LEARNING AN INVERSE MODEL
SYNAPSIS WEIGHTS LIMITATION
Synaptic weights have a maximal value, related to the number of synaptic receptors one neuron is able to produce.
LINEAR (R. H. Hahnloser & S. Ganguli [4] ) vs NONLINEAR MODEL
● Exploration of motor dimension, dividing it in term of behavioral output and motor commands.● Implement a realistic vocal production system (motor control model).
Time (in number of time steps)
Dis
tanc
e fr
om th
e ta
rget
● Maximal weights normalization:
● Supremum weights normalization:
● Decreasing factor normalization:
normmean
AIM: implementation of an inverse model which describes the sensorimotor phase of learning in birdsong.
● Sensorimotor phase is characterized by babbling.
● Dedicated vocal circuit:
Learning by imitation
Brainard and Doupe, 2002
Motor pathwayAuditory pathwayLearning pathway
a. Adult zebra finch song (TUTOR).b. Juvenile zebra finch song at an early stage of learning.c. Song close to crystallization.
SONG LEARNING IN BIRDS
KEYWORDS
SUMMARY● Normalization gives better performance when applied over auditory neurons.● Low selectivity tuning width makes learning slower but more accurate.● Motor dimension influences learning making it exponentially increasing.
BIBLIOGRAPHY
DISTANCE FROM THE TARGET AND CONVERGENCE
Distance at each time step: Convergence is reached when the distance reaches a plateau.
(1) Brainard, M. S. and Doupe, A. J., What songbirds teach us about learning, Nature,2002(2) Mooney R., Neural mechanisms for learned birdsong, Cold Spring Harbor Laboratory Press, 2009
(3) Wolpert D. M., Diedrichsen J. and Flanagan J. R., Principles of sensorimotor learning, Nature Reviews, 2011(4) Hahnloser R. H. R. and Ganguli S., Vocal learning with inverse models, Principles of Neural Coding, 2013, CRC Press Boca Raton
Number of motor neurons
Mean of synaptic weights columns
Ideal auditory activity
NORMALIZED INVERSE MODEL
AUDITORY SELECTIVITY ANALYSIS
Selectivity tuning width
Conv
erge
nce
time
(in n
umbe
r of t
ime
step
s)
Dis
tanc
e fr
om th
e ta
rget
High selectivity - which means low tuning width -
makes learning slower but more accurate.
For simulations the decreasing factor
normalization has been applied.
● Linear auditory response:
● Nonlinear auditory response:
Two populations: auditory and motor neurons
selectivity tuning width which drives auditory
selectivity.
At each time step :
● Auditory feedback: ● ● Update weights:
Hebbian learning rule
: learning rate
Motor random exploration
Target motor pattern
Dis
tanc
e fr
om th
e ta
rget
Time (in number of time steps)
Nonlinear model does not converge applying this learning rule.
Dis
tanc
e fr
om th
e ta
rget
Sensory area
Motor area
- Synaptic weights
Selective sensory responseSensory response
Three normalizations
meannorm
Normalization applied over auditory neurons gives better performance.
Auditory neurons
Time (in number of time steps) Time (in number of time steps)
Evol
utio
n of
te w
eigh
ts
Motor neurons
Motor VS AuditoryEvolution of weights
Decreasing factor normalization gives better performance.
Normalization applied over auditory neurons
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