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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
4
The visual flow
Source : Wikipedia
5
The visual flow
Source : Wikipedia
Source : Ryskampet al. 2014
Upcoming light
6
The visual flow
Source : Wikipedia
Source : Ryskampet al. 2014
Upcoming light
7
The visual flow
Source : Wikipedia
Source : Ryskampet al. 2014
Upcoming light
8
The visual flow
Source : Wikipedia
Source : Ryskampet al. 2014
Upcoming light
Decoding spike trains
9
The visual flow
Source : Wikipedia
Source : Ryskampet al. 2014
Upcoming light
Decoding spike trains
Encoding motion
10
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
11
The visual flow
Source : Wikipedia
Source : Ryskampet al. 2014
Upcoming light
12
The visual flow
Source : Wikipedia
Source : Ryskampet al. 2014
Upcoming light
Too slow !
13
Visual Anticipation
Source : Benvenutti et al. 2015
14
Visual Anticipation
Source : Benvenutti et al. 2015
Anticipation is carried out by the primary visual cortex (V1) through an activation wave
15
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
18
Visual Anticipation
No thalamus ...
19
Visual Anticipation
Which animal ?No thalamus ...
20
Visual Anticipation
No thalamus ... Which animal ?
21
Visual Anticipation
No thalamus ... Which animal ?
22
Visual Anticipation
No thalamus ... Which animal ?
23
Visual Anticipation
No thalamus ... Which animal ?
24
Visual Anticipation
No thalamus ... Which animal ?
25
Visual Anticipation
Developping a retino-cortical model of anticipation soas to
understand / propose
possible mechanisms for anticipation in the retina and in the cortex.
26
Anticipation in the retina
27
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
28
The Hubel-Wiesel view of vision
Source : Berry et al. 1999
Ganglion cells
Nobel prize 1981
29
Building a 2D retina model for motionanticipation
Gain control (Chen et al. 2013)
30
Building a 2D retina model for motionanticipation
31
Building a 2D retina model for motionanticipation
Gain control (Chen et al. 2013)
32
Building a 2D retina model for motionanticipation
Gain control (Chen et al. 2013)
33
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
36
Building a 2D retina model for motionanticipation
Ganglion cells are not independent encoders
Gap junctions connectivity
37
Building a 2D retina model for motionanticipation
Gap junctions connectivity
38
Building a 2D retina model for motionanticipation
Gap junctions connectivity
39
Building a 2D retina model for motionanticipation
Gap junctions connectivity
40
Building a 2D retina model for motionanticipation
Diffusive wave of activity ahead of the motion
Gap junctions connectivity
41
1D results : smooth motion anticipationwith gap junctions
6
42
1D results : smooth motion anticipationwith gap junctions
Anticipation variability with stimulusparameters
43
Building a 2D retina model for motionanticipation
44
Building a 2D retina model for motionanticipation
Ganglion cells are not independent encoders
Amacrine cells connectivity
45
Amacrine cells connectivity
● A class of RGCs are selective to differential motion
Building a 2D retina model for motionanticipation
46
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
48
Connectivity pathways
Amacrine cells connectivity
49
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
52
Comparing the performance of the three layers
53
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)
54
Suggesting new experiments : 2D results
1) Angular anticipation
55
Anticipation in V1
56
Anticipation in V1
57
A mean field model to reproduce VSDIrecordings Zerlaut et al 2016
Chemla et al 2018
58
A mean field model to reproduce VSDIrecordings Zerlaut et al 2016
Chemla et al 2018
59
A mean field model to reproduce VSDIrecordings Zerlaut et al 2016
Chemla et al 2018
Affords a retino thalamic input
60
A mean field model to reproduce VSDIrecordings Zerlaut et al 2016
Chemla et al 2018
61
A mean field model to reproduce VSDIrecordings Zerlaut et al 2016
Chemla et al 2018
62
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 !
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