Probing retinal function with a multi-layered simulator

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Probing retinal function with a multi-layered simulator

Evgenia Kartsaki, Bruno Cessac, Gerrit Hilgen, Evelyne Sernagor

To cite this version:

Evgenia Kartsaki, Bruno Cessac, Gerrit Hilgen, Evelyne Sernagor. Probing retinal function with a multi-layered simulator. NeuroMod 2019 - First meeting of the NeuroMod Institute, Jul 2019, Fréjus, France. �hal-02389086�

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Probing retinal function with

a multi-layered simulator

1_{Biovision team, Université Côte d’Azur, Inria, France} 2_{Institute of Neuroscience, University of Newcastle, UK}

Evgenia Kartsaki1,2_{, Bruno Cessac}1_{, Gerrit Hilgen}2_{, Evelyne Sernagor}2

NeuroMod Institute meeting

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source: Howard Hughes Medical Institute Visual cortex LGN Retina 2

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Source: ETH Zürich

Retinal visual flow

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Source: ETH Zürich

Retinal visual flow

(6)

Retinal neurons

inventory

(7)

Leverhulme funded project:

“A novel approach to functional classification of retinal ganglion cells”

☞ Understand their role in population encoding of complex visual scenes

☞ Achieve full characterisation of retinal ganglion cells sub-classes based on:

• pharmacogenetics

• large-scale retinal electrophysiology

• immunochemistry

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Yet another RGC classification…

Coombs et al., 2006 Baden, Berens, Franke et al., 2016 Martersteck, Hirokawa, Evarts et al., 2017

o Novel and interdisciplinary

o Combines shared gene expression with physiology and parts of anatomy

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o Identify RGCs sharing a common gene expression by combining

pharmacogenetics, large-scale high-density electrophysiology and immunohistochemistry

Aims

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o Isolate subgroups from that RGC pool based on responses to different stimuli

Aims

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o Unravel how RGCs in these subgroups respond and interact to basic and complex visual scenes

Aims

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o Unravel how RGCs in these subgroups respond and interact to basic and complex visual scenes

Modelling Numerical simulations

Aims

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Pharmacogenetics – DREADD technology

inhibitory DREADD excitatory DREADD

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Pharmacogenetics – DREADD technology

inhibitory DREADD excitatory DREADD

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Source: ETH Zürich & RavaAzeredo da Silveira et al., 2011

Macular

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Image motion

Differential motion

Global motion

movement of body, head, or eye of the observer motion of objects within the scene

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Global motion

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Differential motion

Static background – Object moving Background & Object moving

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Object region: Excitatory

input to the ganglion cell (G)

from many fast bipolar cells

(B).

Background region: Inhibitory

input to the ganglion cell (G)

from amacrine cells (A).

Retinal circuit

related to motion

(21)

DREADDs on :

• Amacrine cells (ACs) and/or

• Ganglion cells (RGCs)

CNO effect on the circuit

(22)

DREADDs on Ganglion cells

(RGCs)

- If excitatory, the neuronal

activity will increase

- If inhibitory, the neuronal

activity will decrease

CNO effect on the circuit

(23)

DREADDs on Amacrine cells

(ACs):

- If excitatory, the neuronal

activity of G will decrease

- If inhibitory, the neuronal

activity of G will increase

CNO effect on the circuit

(24)

DREADDs on : ACs AND RGCs

CNO effect on the circuit

(25)

Courtesy of Gerrit Hilgen

anticipated

24

Electrophysiological characterisation

of Scnn1a and Grik4 DREADD RGCs

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reality

DREADDs in ACs makes the interpretation of functional

results from DREADD-expressing RGCs more challenging.

Electrophysiological characterisation

of Scnn1a and Grik4 DREADD RGCs

(27)

reality

Solution: Study it numerically

?

Electrophysiological characterisation

of Scnn1a and Grik4 DREADD RGCs

(28)

Connectivity graph

General structure

Cell type 1 Cell type 2

Visual or Electrical Input

Inter-type connectivity

Intra-type

connectivity connectivityIntra-type

Visualisation

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Basic building blocks - cell

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Cell

ü A set of variables evolving in time and characterizing the cell’s evolution : e.g.

membrane potential, probability that a ionic channel of a given type is open etc.

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Cell

ü A set of parameters that constrain the cell’s

evolution : e.g. conductance, reversal potential, membrane capacitance, characteristic time of a channel’s activity

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Cell

ü A function controlling the cell’s evolution with a differential equation

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Cell

ü A function controlling the cell’s evolution

ü Isyn : A synaptic input corresponding to synaptic

connections with other cells

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Cell

ü A function controlling the cell’s evolution

ü Isyn : A synaptic input corresponding to synaptic

connections with other cells

ü Iext : An external input corresponding either to a

visual input or the electric current provided by

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Basic building blocks - synapse

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Synapse

ü Chemical or electrical (gap junction)

ü A set of variables that evolve in time : e.g. conductance

ü A set of parameters : e.g. synaptic weight

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Visual or Electrical Input

Basic building blocks – External Input

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External Input

ü Emulate the electric current provided by an electrode

Virtual Retina module

ü Emulate the outer plexiform layer (OPL) current

Retinal prosthesis

A.Wohrer et al., 2009

Visual Input

(39)

Connectivity graph

General structure

Intra-type

Visualisation

(40)

Connectivity graph

General structure

Intra-type

Visualisation

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Graph

ü Cell types ü Synapse types ü Cell coordinates ü Synapse indices 40

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Graph

☞ Design a local circuit with specific connectivity patterns ☞ Deploy it to the whole retina

Source: From Gabriel Luna, Steve Fisher and Geoff Lewis, retinal cell

biology group at UC Santa Barbara Neuroscience Research Institute

Connectivity graph

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Image input AC Bipolar cells BC Ganglion cells GC BC BC BC BC BC

Inspired from Baccus et al., 2008

BC BC BC AC Amacrine cells Excitatory synapse Inhibitory synapse Electrical synapse Background Object 42

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Chemical synapse Electrical synapse BC AC GC BC AC GC BC 43

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𝐾 𝑥, 𝑦, 𝑡 = 𝐾_' 𝑥, 𝑦 𝐾₍(𝑡) +(𝑆 ∗ K)(x,y,t) BC AC GC BC AC GC BC 44

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𝐾 𝑥, 𝑦, 𝑡 = 𝐾_' 𝑥, 𝑦 𝐾₍(𝑡) +(𝑆 ∗ K)(x,y,t) 𝐼₃₄₅ = −𝑔₃₄₅ 𝑉_9:3; − 𝐸₃₄₅ 𝐼_=>? = −𝑔_=>? 𝑉_9:3; − 𝑉_9@A BC AC GC BC AC GC BC Chemical synapse Electrical synapse 45

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𝐾 𝑥, 𝑦, 𝑡 = 𝐾_' 𝑥, 𝑦 𝐾₍(𝑡) +(𝑆 ∗ K)(x,y,t) 𝐼₃₄₅ = −𝑔₃₄₅ 𝑉_9:3; − 𝐸₃₄₅ 𝐼_=>? = −𝑔_=>? 𝑉_9:3; − 𝑉_9@A BC AC GC BC AC GC BC Chemical synapse Electrical synapse 𝐶 𝑑𝑉 𝑑𝑡 = −𝑔D 𝑉 − 𝑉D + 𝐼𝑠𝑦𝑛 + 𝐼𝑒𝑥𝑡 + 𝐼IJK 46

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𝐾 𝑥, 𝑦, 𝑡 = 𝐾_' 𝑥, 𝑦 𝐾₍(𝑡) +(𝑆 ∗ K)(x,y,t) 𝐼₃₄₅ = −𝑔₃₄₅ 𝑉_9:3; − 𝐸₃₄₅ 𝐼_=>? = −𝑔_=>? 𝑉_9:3; − 𝑉_9@A 𝑁₌ 𝑉 = 0, 𝑖𝑓 𝑉 ≤ 0 𝛼 𝑉 − 𝜃 , 𝑖𝑓(𝜃 ≤ 𝑉 ≤ 𝑁STU 𝛼 + 𝜃) 𝑁_STU, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

Chen et al., Journal of Neuroscience 2 January 2013

BC AC GC BC AC GC BC Chemical synapse Electrical synapse 𝐶 𝑑𝑉 𝑑𝑡 = −𝑔D 𝑉 − 𝑉D + 𝐼𝑠𝑦𝑛 + 𝐼𝑒𝑥𝑡 + 𝐼IJK 47

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+1 23 987 6554

[email protected] www.proseware.com

Probing retinal function with a multi-layered simulator

Probing retinal function with

a multi-layered simulator

Retinal visual flow

Retinal visual flow

Retinal neurons

inventory

Leverhulme funded project:

“A novel approach to functional classification of retinal ganglion cells”

Yet another RGC classification…

Aims

Aims

Aims

Aims

Pharmacogenetics – DREADD technology

Pharmacogenetics – DREADD technology

Image motion

Differential motion

Global motion

Global motion

Differential motion

Object region: Excitatory

input to the ganglion cell (G)

from many fast bipolar cells

(B).

Background region: Inhibitory

input to the ganglion cell (G)

from amacrine cells (A).

Retinal circuit

related to motion

DREADDs on :

•

Amacrine cells (ACs) and/or

•

Ganglion cells (RGCs)

CNO effect on the circuit

DREADDs on Ganglion cells

(RGCs)

- If excitatory, the neuronal

activity will increase

- If inhibitory, the neuronal

activity will decrease

CNO effect on the circuit

DREADDs on Amacrine cells

(ACs):

- If excitatory, the neuronal

activity of G will decrease

- If inhibitory, the neuronal

activity of G will increase

CNO effect on the circuit

DREADDs on : ACs AND RGCs

CNO effect on the circuit

Electrophysiological characterisation

of Scnn1a and Grik4 DREADD RGCs

DREADDs in ACs makes the interpretation of functional

results from DREADD-expressing RGCs more challenging.

Electrophysiological characterisation

of Scnn1a and Grik4 DREADD RGCs

Solution: Study it numerically

?

Electrophysiological characterisation

of Scnn1a and Grik4 DREADD RGCs

General structure

Basic building blocks - cell

Cell

Cell

Cell

Cell

Cell

Basic building blocks - synapse

Synapse

Basic building blocks – External Input

External Input

General structure

General structure

Graph

Graph

Thank you!

Questions?