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Changes in V1 orientation tuning when blocking astrocytic glutamate transporters: models for extra- and intrasynaptic mechanisms

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Changes in V1 orientation tuning when blocking

astrocytic glutamate transporters: models

for extra- and intrasynaptic mechanisms

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Citation

Mergenthaler, Konstantin et al. “Changes in V1 Orientation Tuning

When Blocking Astrocytic Glutamate Transporters: Models for

Extra- and Intrasynaptic Mechanisms.” BMC Neuroscience 14, 1

(July 2013): P298 © 2013 Mergenthaler et al

As Published

http://dx.doi.org/10.1186/1471-2202-14-S1-P298

Publisher

Springer Nature

Version

Final published version

Citable link

http://hdl.handle.net/1721.1/116074

Terms of Use

Attribution 2.0 Generic (CC BY 2.0)

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POSTER PRESENTATION

Open Access

Changes in V1 orientation tuning when blocking

astrocytic glutamate transporters: models for

extra- and intrasynaptic mechanisms

Konstantin Mergenthaler

1*

, Dipanjan Roy

1

, Jeremy Petravicz

2

, Mriganka Sur

2

, Klaus Obermayer

1

From Twenty Second Annual Computational Neuroscience Meeting: CNS*2013

Paris, France. 13-18 July 2013

Several recent studies reveal a close interplay of neurons and astrocytes in information processing [1]. Astrocytes affect neural transmission by their control of glutamate concentrations by glutamate transporters [2] in direct vicinity to the synaptic cleft as well as extrasynaptically. An in-vivo study in primary visual cortex of ferrets [3] demonstrated the impact of astrocytic glutamate trans-port on neuronal response by blocking glutamate uptake pharmacologically with TBOA. A severe effect on neuro-nal orientation tuning curves to a full-field stationary grating, tilted every second by additional 10 degrees, was observed. While glutamate transporters were blocked tuning curves were broadened (HWHM increased from 28 deg. to 39 deg.) and response at preferred orientation was enhanced. However, it is unclear if the change in response originates from a prolongation of synaptic glutamate clearance or locally increased ambient gluta-mate concentration. Here we investigate in a computa-tional model of ferret V1 how the intra- and extrasynaptic mechanisms affect orientation tuning.

We implemented both mechanisms in a highly recurrent single layer 2d map model based on [4]. Neurons are placed on a 50 × 50 grid and connected to their neighbors by randomly drawn connections from a Gaussian. On every grid point an excitatory neuron is placed, addition-ally every third grid point is occupied by an inhibitory neuron. Peak conductivities for the four types of connec-tions are set to be in the recurrent regime, exceeding the conductivity of the additional afferent input. The afferent

input specific to each grid location induces a pinwheel-domain organization of neurons in the network. For the implementation of the synaptic mechanism the glutamate concentration follows a bi-exponential description with a decay time prolonged if glutamate uptake is reduced [2]. Synaptic glutamate then activates complex kinetic NMDA and AMPA-receptors [5]. The synaptic mechanism can however have a stronger influence on excitatory-to-excitatory or excitatory-to-excitatory-to-inhibitory connections, as the different synapse geometries also affects glutamate clear-ance. Extrasynaptically ambient glutamate provides a constant NMDA-receptor mediated somatic current [6]. Here different sensitivities (different NMDA-receptor densities) of excitatory and inhibitory neurons to ambient glutamate may affects tuning. For both mechanism possi-ble parameter combinations are assessed in a grid search. We observed that a selective increase in modalities towards inhibitory neurons even leads to a sharpening of orientation tuning. While selective enhancement in the modalities towards excitatory neurons leads first to a drop in orientation tuning and very fast for further increase to pathological firing rates. For both mechanisms the closest fit in orientation tuning (HWHM) to the experimental observation with TBOA was found for a stronger effect on excitatory neurons along with a simultaneous but weaker effect on the inhibitory population. While both models can explain the current data, they, however, provide differ-ent predictions for sub-threshold properties and for neu-rons close to pinwheels or domain centers.

Author details

1

Neural Information Processing, School of Computer Science and Electrical Engineering and Bernstein Center for Computational Neurocience, Technische Universität Berlin, Germany.2Department of Brain and Cognitive Sciences, Piclower Institut for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

* Correspondence: mergen@ni.tu-berlin.de

1Neural Information Processing, School of Computer Science and Electrical

Engineering and Bernstein Center for Computational Neurocience, Technische Universität Berlin, Germany

Full list of author information is available at the end of the article Mergenthaleret al. BMC Neuroscience 2013, 14(Suppl 1):P298 http://www.biomedcentral.com/1471-2202/14/S1/P298

© 2013 Mergenthaler et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Published: 8 July 2013 References

1. De Pittà M, Volman V, Berry H, Parpura V, Volterra A, Ben-Jacob E: Computational quest for understanding the role of astrocyte signaling in synaptic transmission and plasticity. Frontiers in Computational Neuroscience 2012, 6:1-25.

2. Diamond JS: Deriving the glutamate clearance time course from transporter currents in CA1 hippocampal astrocytes: transmitter uptake gets faster during development. Journal of neuroscience 2005, 25:2906-2916.

3. Schummers J, Yu H, Sur M: Tuned responses of astrocytes and their influence on hemodynamic signals in the visual cortex. Science (New York, N.Y.) 2008, 320:1638-1643.

4. Stimberg M, Wimmer K, Martin R, Schwabe L, Mariño J, Schummers J, Lyon D, Sur M, Obermayer K: The Operating Regime of Local Computations in Primary Visual Cortex. Cerebral Cortex 2009, 19:2166-2180.

5. Destexhe A, Mainen ZF, Sejnowski TJ: Kinetic models of synaptic transmission. In Methods in neuronal modeling. MIT Press;Koch C, Segev I 1998:1-25.

6. Bentzen NCK, Zhabotinsky AM, Laugesen JL: Modeling of glutamate-induced dynamical patterns. International journal of neural systems 2009, 19:395-407.

doi:10.1186/1471-2202-14-S1-P298

Cite this article as: Mergenthaler et al.: Changes in V1 orientation tuning when blocking astrocytic glutamate transporters: models for extra- and intrasynaptic mechanisms. BMC Neuroscience 2013 14(Suppl 1):P298.

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Mergenthaleret al. BMC Neuroscience 2013, 14(Suppl 1):P298 http://www.biomedcentral.com/1471-2202/14/S1/P298

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