View
7
Download
0
Category
Preview:
Citation preview
HAL Id: hal-02150600https://hal.inria.fr/hal-02150600
Submitted on 7 Jun 2019
HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.
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
16
Visual Anticipation
What are the respective :
➢Mechanisms underlying retinal and corticalanticipation?
➢Role of each part ?
TrajectoryTrajectory
17
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
34
1D results : smooth motion anticipationwith gain control
Anticipation variability with stimulusparameters
35
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
47
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
50
1D results : smooth motion anticipationwith amacrine connectivity
Bipolar layer Ganglion layer
51
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 !
Recommended