Learning sensorimotor transformations
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Learning sensorimotor transformations. Maurice J. Chacron. The principle of sensory reafference:. Von Holst and Mittelstaedt, 1950. Movements can lead to sensory reafference (e.g. body movements) An efference copy and the reafferent stimulus are combined and give rise to the

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Learning sensorimotor transformations

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Learning sensorimotor transformations

Learning sensorimotor transformations

Maurice J. Chacron


The principle of sensory reafference

The principle of sensory reafference:

Von Holst

and Mittelstaedt, 1950


Learning sensorimotor transformations

  • Movementscan lead to sensory reafference (e.g. body movements)

  • An efference copy and the reafferent stimulus are combined and give rise to the

    perceived stimulus.

  • Question: how is the efference copy combined with the reafferent stimulus to give rise to the perceived stimulus?


Mechanical tickling experiment

Mechanical tickling experiment:

Blakemore, Frith, and Wolpert, J. Cogn. Neurosci. (1999)


Learning sensorimotor transformations

  • Motor command  arm movement

  • Reafference  tactile stimulus

  • Perceived stimulus  tickling sensation


Learning sensorimotor transformations

Wolpert and

Flanagan, 2001


Learning sensorimotor transformations

  • The predicted sensory stimulus(efference copy)is compared to the actual stimulus

  • If there is a discrepancy, then the subject perceives the stimulus as causing a tickling sensation.

  • The efference copycontainsboth temporal and spatial information about the reafferent stimulus.


Adaptive cancellation of sensory reafference

Adaptive cancellation of sensory reafference


Motor learning

Motor learning:

Martin et al. 1996


Learning sensorimotor transformations

  • Sensorimotor coordinationdoes not require the cerebellum.

  • Adaptation to novel conditionsdoes require cerebellar function.

  • Adaptation is an error driven process.


Cerebellar plasticity

Cerebellar Plasticity:


Co activation of parallel and climbing fiber input gives rise to ltd

Co-activation of parallel and climbing fiber input gives rise toLTD


Learning sensorimotor transformations

  • How does cerebellar LTD help achieve cancellation of expected stimuli?


Weakly electric fish

Weakly electric Fish

  • Electric fish emit electric fields through

    an electric organ in their tail.


Anatomy

Anatomy

Trout

Electric Fish


Learning sensorimotor transformations

  • The cerebellum of electric fish is very developed.

  • Cerebellar anatomy is conserved across vertebrates.

  • Electric fish have “simple” anatomy and behaviors.

  • Electric fish are a good model system to study cancellation of reafferent input.


Electrolocation

Electrolocation


Learning sensorimotor transformations

  • Electric fishuseperturbations of their self-generated electric field to interact with their environment.

  • Pulses generated by the animal can activate their own electrosensory system.

  • Are there mechanisms by which sensory neurons can “ignore” these reafferent stimuli?


Cerebellar like anatomy

Cerebellar-like anatomy:

Bell, 2001


Learning sensorimotor transformations

Bell, 2001


Learning sensorimotor transformations

  • Changes in the reafferent stimulus

    causechanges in the efference copy

  • What mechanisms underlie these changes?


Plasticity experiment

Plasticity experiment:

granule cell

Parallel fiber

sensory input


Anti hebbian stdp

Anti-Hebbian STDP:

presynaptic

postsynaptic


Learning sensorimotor transformations

  • Cancellation of unwanted stimuli requires precise timing.

  • Anti-Hebbian STDPunderlies the adaptive cancellation of reafferent input.


Learning sensorimotor transformations

How?


Adaptive cancellation of tail bends

Adaptive cancellation of tail bends


Cerebellar like anatomy1

Cerebellar-like anatomy


Anatomy1

Anatomy


Burst firing in pyramidal cells

Burst firing in pyramidal cells

Burst-timing dependent plasticity


Model of adaptive cancellation in the electrosensory system

Model of adaptive cancellation in the electrosensory system


Model assumptions how to carve out a negative image

Model Assumptions: How to “carve out” a negative image

  • A subset of cerebellar granule cells fires at

    every phase of the stimulus

  • Probability to fire a burst is largest/smallest

    at a local stimulus maximum/minimum

  • Weights from synapses near the local maximum/

    minimum will be most/least depressed


Graphically

Graphically…

Synaptic weights

Most depression

Least depression

stimulus

π

0

Phase (rad)


Extra assumptions

Extra assumptions

  • Non-associative potentiation (in order to prevent the weights from going to zero).


Does the model work

Does the model work?


Bursting is frequency dependent

Bursting is frequency dependent


Bursts and isolated spikes code for different features of a stimulus

Bursts and isolated spikes code for different features of a stimulus

Oswald et al. 2004


Adaptive learning

Adaptive learning


Summary

Summary

  • Sensorimotor transformations require learning.

  • This learning must be adaptive (e.g. adapt to changes during development, etc…)

  • Anti-Hebbian plasticity provides a mechanism for adaptive cancellation of reafferent stimuli


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