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Submitted on 7 Jun 2019

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Anticipation in the retina and the primary visualcortex : towards an integrated retino-cortical model for

motion processingBruno Cessac, Selma Souihel, Matteo Di Volo, Frédéric Chavane, Alain

Destexhe, Sandrine Chemla, Olivier Marre

To cite this version:Bruno Cessac, Selma Souihel, Matteo Di Volo, Frédéric Chavane, Alain Destexhe, et al.. Anticipationin the retina and the primary visual cortex : towards an integrated retino-cortical model for motionprocessing. Workshop on visuo motor integration, Jun 2019, Paris, France. �hal-02150600�

Anticipation in the retina and the primary visual cortex :towards an integrated retino-cortical model for motion

processing

Bruno Cessac, Selma Souihel Biovision

Anticipation in the retina and the primary visual cortex :towards an integrated retino-cortical model for motion

processing

Bruno Cessac, Selma Souihel Biovision

Anticipation in the retina and the primary visual cortex :towards an integrated retino-cortical model for motion

processing

Bruno Cessac, Selma Souihel

In collaboration with :

Frédéric ChavaneSandrine Chemla

Olivier MarreMatteo Di VoloAlain Destexhe

Biovision

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The visual flow

Source : Wikipedia

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The visual flow

Source : Wikipedia

Source : Ryskampet al. 2014

Upcoming light

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The visual flow

Source : Wikipedia

Source : Ryskampet al. 2014

Upcoming light

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The visual flow

Source : Wikipedia

Source : Ryskampet al. 2014

Upcoming light

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The visual flow

Source : Wikipedia

Source : Ryskampet al. 2014

Upcoming light

Decoding spike trains

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The visual flow

Source : Wikipedia

Source : Ryskampet al. 2014

Upcoming light

Decoding spike trains

Encoding motion

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The visual flow

Source : Wikipedia

Source : Ryskampet al. 2014

Upcoming light

Decoding spike trains

« Analogic computing »Low energy consumpution

Dedicated circuitsSmall number of neurons

Specialized synapses

Encoding motion

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The visual flow

Source : Wikipedia

Source : Ryskampet al. 2014

Upcoming light

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The visual flow

Source : Wikipedia

Source : Ryskampet al. 2014

Upcoming light

Too slow !

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Visual Anticipation

Source : Benvenutti et al. 2015

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Visual Anticipation

Source : Benvenutti et al. 2015

Anticipation is carried out by the primary visual cortex (V1) through an activation wave

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Visual Anticipation

Source :Berry et al.1999

Anticipation also takes place in the retina

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Visual Anticipation

What are the respective :

➢Mechanisms underlying retinal and corticalanticipation?

➢Role of each part ?

TrajectoryTrajectory

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Visual Anticipation

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Visual Anticipation

No thalamus ...

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Visual Anticipation

Which animal ?No thalamus ...

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Visual Anticipation

No thalamus ... Which animal ?

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Visual Anticipation

No thalamus ... Which animal ?

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Visual Anticipation

No thalamus ... Which animal ?

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Visual Anticipation

No thalamus ... Which animal ?

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Visual Anticipation

No thalamus ... Which animal ?

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Visual Anticipation

Developping a retino-cortical model of anticipation soas to

understand / propose

possible mechanisms for anticipation in the retina and in the cortex.

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Anticipation in the retina

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The Hubel-Wiesel view of vision

Ganglion cells

Nobel prize 1981

Ganglion cells response is the convolution of the stimulus with a spatio-temporalreceptive field followed by a non linearity

Ganglion cells are independent encoders

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The Hubel-Wiesel view of vision

Source : Berry et al. 1999

Ganglion cells

Nobel prize 1981

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Building a 2D retina model for motionanticipation

Gain control (Chen et al. 2013)

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Building a 2D retina model for motionanticipation

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Building a 2D retina model for motionanticipation

Gain control (Chen et al. 2013)

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Building a 2D retina model for motionanticipation

Gain control (Chen et al. 2013)

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1D results : smooth motion anticipationwith gain control

Bipolar layer Ganglionlayer

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1D results : smooth motion anticipationwith gain control

Anticipation variability with stimulusparameters

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Building a 2D retina model for motionanticipation

Ganglion cells are independent encoders

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Building a 2D retina model for motionanticipation

Ganglion cells are not independent encoders

Gap junctions connectivity

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Building a 2D retina model for motionanticipation

Gap junctions connectivity

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Building a 2D retina model for motionanticipation

Gap junctions connectivity

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Building a 2D retina model for motionanticipation

Gap junctions connectivity

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Building a 2D retina model for motionanticipation

Diffusive wave of activity ahead of the motion

Gap junctions connectivity

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1D results : smooth motion anticipationwith gap junctions

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1D results : smooth motion anticipationwith gap junctions

Anticipation variability with stimulusparameters

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Building a 2D retina model for motionanticipation

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Building a 2D retina model for motionanticipation

Ganglion cells are not independent encoders

Amacrine cells connectivity

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Amacrine cells connectivity

● A class of RGCs are selective to differential motion

Building a 2D retina model for motionanticipation

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Amacrine cells connectivity

● The circuitry involves amacrine cells connectivity upstream of ganglion cells

Building a 2D retina model for motionanticipation

● A class of RGCs are selective to differential motion

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Connectivity pathways

Amacrine cells connectivity

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Connectivity pathways

Amacrine cells connectivity

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Connectivity pathways

Amacrine cells connectivity

Anti diffusive wave of activityahead of the bar

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1D results : smooth motion anticipationwith amacrine connectivity

Bipolar layer Ganglion layer

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1D results : smooth motion anticipationwith amacrine connectivity

Anticipation variability with stimulusparameters

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Comparing the performance of the three layers

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Suggesting new experiments : 2D results

1) Angular anticipation

Stimulus

t = 0 ms 100 200 ms 300 ms 400 ms 500 ms 600 ms 700 ms

Bipolar linearresponse

Bipolar gainresponse

Ganglion linearresponse

Ganglion gainresponse

A)

B) C)

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Suggesting new experiments : 2D results

1) Angular anticipation

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Anticipation in V1

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Anticipation in V1

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A mean field model to reproduce VSDIrecordings Zerlaut et al 2016

Chemla et al 2018

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A mean field model to reproduce VSDIrecordings Zerlaut et al 2016

Chemla et al 2018

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A mean field model to reproduce VSDIrecordings Zerlaut et al 2016

Chemla et al 2018

Affords a retino thalamic input

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A mean field model to reproduce VSDIrecordings Zerlaut et al 2016

Chemla et al 2018

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A mean field model to reproduce VSDIrecordings Zerlaut et al 2016

Chemla et al 2018

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A mean field model to reproduce VSDIrecordings Zerlaut et al 2016

Chemla et al 2018

Response of the cortical model to a LNretina drive

Response of the cortical model to a retinadrive with gain control

Anticipation in the cortex : VSDI dataanalysis (Data courtesy of F.

Chavane et S. Chemla)

Comparing simulation results to VSDIrecordings

Cortex experimentalrecordings

Simulation resultsResponse to an LNmodel of the retina

Simulation resultsResponse to a gaincontrol model of theretina

Conclusions

● We developped a 2D retina with three ganglion cell layers,implementing gain control and connectivity.

● We use the output of our model as an input to a mean field model ofV1, and were able to reproduce anticipation as observed in VSDI

Conclusions

● How to improve object identification ● 1) exploring the model's parameters and

● 2) using connectivity ?

● Is our model able to anticipate more complex trajectories, withaccelerations for instance ?

● How to calibrate connectivity using biology ?

● How does anticipation affect higher order correlations ?

● Would it be possible to design psycho-physical tests clearly showingthe role of the retina in visual anticipation ?

Thank you for your attention !