Following stage II retinal waves during development with a biophysical model

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Following stage II retinal waves during development with a biophysical model

Dora Karvouniari, Lionel Gil, Olivier Marre, Serge Picaud, Bruno Cessac

To cite this version:

Dora Karvouniari, Lionel Gil, Olivier Marre, Serge Picaud, Bruno Cessac. Following stage II retinal waves during development with a biophysical model : A biophysical model for retinal waves . Bernstein Conference 2017, Sep 2017, Gottingen, Germany. pp.1. �hal-01638098�

Following stage II retinal waves during development with a

biophysical model

Dora Karvouniari,

1

Lionel Gil,

2

Olivier Marre,

3

Serge Picaud,

3

Bruno Cessac

1

(1) Biovision team, Universite Cote d’Azur, INRIA, France, (2) INLN, Sophia-Antipolis, France, (3) Vision Institute, Paris, France

theodora.karvouniari@inria.fr; bruno.cessac@inria.fr

Abstract

Retinal waves are bursts of activity occurring spontaneously in the developing retina of vertebrate species, contributing to the shaping of the visual system organization and disappear short after birth. Waves during development are a transient process which evolves dynamically exhibiting different features. Based on our previous mod-elling work [1,2], we now propose a classification of stage II retinal waves patterns as a function of acetylcholine coupling strength and a possible mechanism for waves gen-eration. Our model predicts that spatiotemporal patterns evolve upon maturation or pharmacological manipulation and that waves emerge from a complete homogeneous state without the need of the variability of a neural population.

Context & Motivation

SACs dynamically change their synaptic coupling upon maturation

Coupled cholinergic SACs [3]

Cholinergic current evolution

upon maturation [4]

A biophysical model for retinal waves

1. Bursting

2. Calcium Buffering

3. sAHP

Spontaneous Bursting Mechanism of SACs

-70 mV 60mV 0 0.8 88 nM 300 nM 0.2 0.4 0.4 0.2 Bursting sAHP

Increase of Calcium load during bursting

Calcium controls the sAHP phase. Voltage is low and Calcium

starts offloading Bursting starts again

when the Calcium load is low enough

Intracellular Calcium Concentration

Our biophysical model reproduces experimental observations for individual and

coupled SACs dynamics [3,4], where no previous computational model [5] has

been able to do so before.

Network Simulations

Simulated Retinal Waves (Voltage)

Weak Moderate Strong Simulated Calcium Waves

Weak Moderate Strong

• Simulated Calcium waves of 4096 neurons on a square lattice. Black and white colours correspond to low and high activity respectively. Weak coupling: localised bumps of activity. Strong coupling: complete synchrony standing waves. Moderate coupling: propagating patterns.

Following Retinal Waves Evolution

Average Population Bursting Rate

g (nS) A <FR >_G 0.01 0.02 0.03 0.3 0 0.1 0.2 0.4 0.5 A B C E D

• The average population bursting rate exhibits a sharp transition upon

increasing the cholinergic coupling

• Network simulations show patterns where waves do emerge (red) and where

they do not (blue).

A heat map of the average bursting period T , in

seconds, of the network illustrates this observation.

• Domaines are formed where the propagation of waves is restrained, as

ob-served experimentally in [7]. In contrast to what is shown in [7], our model

describes these domains without the need to add two neural population

species.

• Different types of spatiotemporal patterns are observed corresponding to

each value of cholinergic coupling strength

Evolution of the spatiotemporal patterns

Evolution of the spatiotemporal patterns when increasing the cholinergic

conduc-tance (from left to right) starting from a completely homogeneous state where all

cells are identical.

Conclusions and Perspectives

• Spatio-temporal patterns emerge out of a complete homogeneous state showing that variability in neurons is not the underlying drive of pattern formation. • The role of variability in neural populations can be addressed from a

compu-tational aspect more easily.

• Biophysical parameters (e.g. conductances) could vary upon maturation or pharmacological manipulations, affecting the characterstics of emerging waves. • A computational model able to follow the transitions of neural networks prop-erties could help us gain a lot of insight in understanding the underlying mech-anisms that drive them.

Acknowledgements

This work was supported by the French ministry of Research and EDSTIC. We warmly acknowledge Matthias Hennig and Evelyne Sernagor for their invaluable help.

References

1. D. Karvouniari, L. Gil, O. Marre, S. Picaud, B. Cessac, A biophysical model explains the spontaneous bursting behavior in the developing retina, March 2017, (under review)

2. D. Karvouniari, L. Gil, O. Marre, S. Picaud, B. Cessac, Mathematical and experimental studies on retinal waves, ICMNS, May 2016 (selected talk)

3. J. Zheng, S. Lee, Z.Jimmy Zhou, A transient network of intrinsically bursting starburst cells underlies the generation of retinal waves, Nat Neurosci, 9(3):363–371, 2006

4. J. Zheng, S. Lee, and Z.Jimmy Zhou, A Developmental Switch in the Excitability and Func-tion of the Starburst Network in the Mammalian Retina, Neuron, Vol. 44, 851ˆa ˘A¸S864, December 2, 2004

5. M. Hennig, C. Adams, D. Willshaw, E. Sernagor, Early-stage waves in the retinal network emerge close to a critical state transition between local and global functional connectivity, Journal of Neurosci, 2009.

6. M. Feller, D. Butts, H. Aaron, D. Rokhsar, C.hatz, Dynamic Processes Shape Spatiotempo-ral Properties of Retinal Waves, Neuron, Volume 19, Issue 2, p293-306, 1997