Motion anticipation in the retina

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Submitted on 15 Oct 2019

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Motion anticipation in the retina

Bruno Cessac, Selma Souihel

To cite this version:

Bruno Cessac, Selma Souihel. Motion anticipation in the retina. NeuroSTIC 2019 - 7e édition des journées NeuroSTIC, Oct 2019, Sophia-Antipolis, France. �hal-02316888�

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Motion anticipation in the retina

Bruno Cessac, Selma Souihel

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Motion anticipation in the retina

Bruno Cessac, Selma Souihel

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Motion anticipation in the retina

Bruno Cessac, Selma Souihel

In collaboration with :

Frédéric Chavane

_{Olivier Marre}

Matteo Di Volo

(5)

4

The visual flow

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6

The visual flow

Source : Wikipedia

Decoding spike trains Encoding motion

(8)

The visual flow

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8

The visual flow

Source : Wikipedia

The retina is NOT a camera

• High transduction rate : 1 photon can

trigger a membrane voltage variation of ~1 mV

• Able to detect approaching motion • Able to detect differential motion • Sensitive to « surprise » in a visual

scene

(10)

The visual flow

Encoding motion

• Able to detect approaching motion • Able to detect differential motion

(11)

10

The visual flow

Source : Wikipedia

scene

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The visual flow

Encoding motion

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12

The visual flow

Source : Wikipedia

scene

(14)

The visual flow

Encoding motion

(15)

14

Visual Anticipation

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Visual Anticipation

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16

Visual Anticipation

Source : Berry et al. 1999

(18)

Visual Anticipation

What are the respective mechanisms underlying retinal

and cortical anticipation?

Trajectory

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18

The visual flow

Source : Wikipedia

scene

(20)

The visual flow

(21)

20

The visual flow

Source : Wikipedia

« Analogic computing » No spikes (except in GCs)

Low energy consumption Dedicated circuits Small number of neurons

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The visual flow

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22

The visual flow

Source : Wikipedia

(24)

The visual flow

Encoding motion

(25)

24

The visual flow

Source : Wikipedia

Horizontal cells Amacrine cells

(26)

The visual flow

(27)

26

The visual flow

Source : Wikipedia

(28)

The visual flow

Encoding motion

« Analogic computing » No spikes (except in GCs)

Low energy consumption Dedicated circuits Small number of neurons

(29)

28

Which generic computational paradigms are at

work in the retina ?

(30)

(31)

30

Visual Anticipation

(32)

Visual Anticipation

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32

Visual Anticipation

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Visual Anticipation

(35)

34

Visual Anticipation

(36)

Visual Anticipation

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36

Visual Anticipation

Developping a retino-cortical model of anticipation so

as to

understand / propose

possible generic mechanisms for anticipation in the

retina and in the cortex.

(38)

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38

The Hubel-Wiesel view of vision

Ganglion cells

Nobel prize 1981

Ganglion cells response is the convolution of the stimulus with a spatio-temporal

receptive field followed by a non linearity

Ganglion cells are independent encoders

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The Hubel-Wiesel view of vision

Ganglion cells

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40

Gain control (Berry et al, 1999, Chen et al. 2013)

Building a 2D retina model for motion

anticipation

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Building a 2D retina model for motion

anticipation

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42

Building a 2D retina model for motion

anticipation

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Building a 2D retina model for motion

anticipation

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44

1D results : smooth motion anticipation

with gain control

Bipolar layer Ganglion

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1D results : smooth motion anticipation

with gain control

Anticipation variability with stimulus parameters

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46

Building a 2D retina model for motion

anticipation

Ganglion cells are independent encoders

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Building a 2D retina model for motion

anticipation

Ganglion cells are not

independent encoders

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48

Building a 2D retina model for motion

anticipation

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Building a 2D retina model for motion

anticipation

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50

Building a 2D retina model for motion

anticipation

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Building a 2D retina model for motion

anticipation

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52

Smooth motion anticipation with gap

junctions

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6

Smooth motion anticipation with gap

junctions

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54 6

Low gap junction conductance High gap junction conductance

Smooth motion anticipation with gap

junctions

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6

Smooth motion anticipation with gap

junctions

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56

Smooth motion anticipation with gap

junctions

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Building a 2D retina model for motion

anticipation

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58

Building a 2D retina model for motion

anticipation

Ganglion cells are not

independent encoders

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Amacrine cells connectivity

Building a 2D retina model for motion

anticipation

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60

Amacrine cells connectivity

Building a 2D retina model for motion

anticipation

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Amacrine cells connectivity

Building a 2D retina model for motion

anticipation

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62

Amacrine cells connectivity

Building a 2D retina model for motion

anticipation

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Amacrine cells connectivity

Building a 2D retina model for motion

anticipation

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64

Amacrine cells connectivity

Diffusive wave of activity ahead of the bar

Building a 2D retina model for motion

anticipation

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Building a 2D retina model for motion

anticipation

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66

Building a 2D retina model for motion

anticipation

(68)

Building a 2D retina model for motion

anticipation

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68

Building a 2D retina model for motion

anticipation

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Building a 2D retina model for motion

anticipation

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70

Building a 2D retina model for motion

anticipation

Spatial profiles w=3

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Building a 2D retina model for motion

anticipation

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72

Building a 2D retina model for motion

anticipation

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1D results : smooth motion anticipation

with amacrine connectivity

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74

1D results : smooth motion anticipation

with amacrine connectivity

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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 linear response Bipolar gain response Ganglion linear response Ganglion gain response A) B) C)

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Suggesting new experiments : 2D results

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Conclusions

●

The retina is able to encode complex visual scene features, fast and reliably, with a

(80)

Conclusions

●

The retina is able to encode complex visual scene features, fast and reliably, with a

very low energy consumption, without spikes, except at the ganglion cells level.

(81)

Conclusions

●

The retina is able to encode complex visual scene features, fast and reliably, with a

very low energy consumption, without spikes, except at the ganglion cells level.

●

A large part of the « computation » is made by synapses.

●

Starting from the retina architecture one can extract circuits solving « tasks » such as

(82)

Conclusions

●

The retina is able to encode complex visual scene features, fast and reliably, with a

very low energy consumption, without spikes, except at the ganglion cells level.

●

A large part of the « computation » is made by synapses.

●

Starting from the retina architecture one can extract circuits solving « tasks » such as

motion anticipation.

●

The role of gain control and local synaptic balance between excitation-inhibition has

been experimentally shown to play a central rôle in anticipation

(Berry et al, 1999 ; Chen at al, 2013 ; Johnston-Lagando, 2015)

.

(83)

Conclusions

●

The retina is able to encode complex visual scene features, fast and reliably, with a

very low energy consumption, without spikes, except at the ganglion cells level.

●

A large part of the « computation » is made by synapses.

●

Starting from the retina architecture one can extract circuits solving « tasks » such as

motion anticipation.

●

The role of gain control and local synaptic balance between excitation-inhibition has

been experimentally shown to play a central rôle in anticipation

.

●

Here we propose that lateral connectivity also plays a role in motion anticipation

(84)

Conclusions

●

The retina is able to encode complex visual scene features, fast and reliably, with a

very low energy consumption, without spikes, except at the ganglion cells level.

●

A large part of the « computation » is made by synapses.

●

Starting from the retina architecture one can extract circuits solving « tasks » such as

motion anticipation.

●

The role of gain control and local synaptic balance between excitation-inhibition has

been experimentally shown to play a central rôle in anticipation

.

(85)

84

Anticipation in V1

(86)

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86

A mean field model to reproduce VSDI

recordings

Zerlaut et al 2016

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A mean field model to reproduce VSDI

recordings

Zerlaut et al 2016

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88

A mean field model to reproduce VSDI

recordings

Zerlaut et al 2016

Chemla et al 2018

(90)

A mean field model to reproduce VSDI

recordings

Zerlaut et al 2016

(91)

90

A mean field model to reproduce VSDI

recordings

Zerlaut et al 2016

(92)

A mean field model to reproduce VSDI

recordings

Zerlaut et al 2016

(93)

Response of the cortical model to a LN

retina drive

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Response of the cortical model to a retina

drive with gain control

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Anticipation in the cortex : VSDI data

analysis

(Data courtesy of F.

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Comparing simulation results to VSDI

recordings

Cortex experimental recordings

(97)

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 of

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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, with

accelerations for instance ?

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