Probing retinal function with a multi-layered simulator

HAL Id: hal-02389076

https://hal.archives-ouvertes.fr/hal-02389076 Submitted on 2 Dec 2019

HAL is a multi-disciplinary open access

archive for the deposit and dissemination of sci-entific research documents, whether they are

pub-L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, 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. The Rank Prize Funds - Symposium on The retinal processing of natural signals, Jun 2019, Grasmere, United Kingdom. �hal-02389076�

Probing retinal function with

a multi-layered simulator

1_{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

Electrophysiology & IHC Retinal Simulator

Connectivity graph

General structure

Cell type 1 Cell type 2

Visual or Electrical Input

Inter-type connectivity

Intra-type

connectivity connectivityIntra-type

Visual or Electrical Input

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.

Cell

characterizing the cell’s evolution : e.g.

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

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

Cell

characterizing the cell’s evolution : e.g.

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

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

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

Cell

characterizing the cell’s evolution : e.g.

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

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

ü A function controlling the cell’s evolution

ü Isyn : A synaptic input corresponding to synaptic

Cell

characterizing the cell’s evolution : e.g.

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

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

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 an electrode

Visual or Electrical Input

Synapse

ü Chemical or electrical (gap junction)

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

Visual or Electrical Input

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

Connectivity graph

General structure

Cell type 1 Cell type 2

Visual or Electrical Input

Inter-type connectivity

Intra-type

Connectivity graph

General structure

Cell type 1 Cell type 2

Visual or Electrical Input

Inter-type connectivity

Intra-type

connectivity connectivityIntra-type

Graph

ü Cell types

ü Synapse types ü Cell coordinates ü Synapse indices

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

BC

AC

BC

AC

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

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 BC AC BC Chemical synapse Electrical synapse

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

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

Acknowledgements

Prof. Evelyne Sernagor Dr. Gerrit Hilgen

Dr. Bruno Cessac AMDT team

+1 23 987 6554

april@www.proseware.com www.proseware.com

Thank you!

Questions?