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SUMMARY:BMI Progress Reports 2021 // Prof. A. Mathis's Lab\, Axel Bisi "Us
 ing task-driven deep neural networks to investigate the proprioceptive pat
 hway"
DTSTART:20210203T121500
DTEND:20210203T130000
DTSTAMP:20260407T043519Z
UID:dc533c94e30c772a9650e879ba6c11374b0e1187d47bdd67ba2f46d6
CATEGORIES:Conferences - Seminars
DESCRIPTION:Axel Bisi\nBiological motor control is adaptive\, efficient an
 d is in part enabled by proprioception. How proprioception provides feedba
 ck to the sensorimotor system is not fully understood. We pursue a task-dr
 iven modeling approach that has provided important insights into other sen
 sory systems. In order to do so\, we first generated a large-scale dataset
  of human arm trajectories as the hand is tracing the alphabet in 3D space
 \, then using a musculoskeletal model derived the muscle spindle firing ra
 tes during these movements. For this action recognition task\, artificial 
 neural networks were trained to classify the character identity from this 
 proprioceptive stimuli. Network units showed directional tuning akin to ne
 urons in the primate somatosensory cortex\, and task learning yielded inva
 riant tuning across a 3D workspace. We then used these task-driven models 
 were to predict neural activity in primates. While not being explicitly tr
 ained to predict neural data\, the models’ internal representations pred
 icted neuron responses with high fidelity. Overall\, our work links tuning
  properties in the proprioceptive system to the behavioral level\, and exp
 lores neural coding with task-driven models in the proprioceptive pathway.
 \n 
LOCATION:Online
STATUS:CONFIRMED
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