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How to keep cool in hot situations: temperature compensation in grasshopper auditory neurons Susanne Schreiber Humboldt-Universität and Bernstein Center.

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Presentation on theme: "How to keep cool in hot situations: temperature compensation in grasshopper auditory neurons Susanne Schreiber Humboldt-Universität and Bernstein Center."— Presentation transcript:

1 How to keep cool in hot situations: temperature compensation in grasshopper auditory neurons Susanne Schreiber Humboldt-Universität and Bernstein Center Berlin Tübingen, July 7 th 2012

2 Acoustic communication in grasshoppers Susanne Schreiber, BCCN Berlin

3 Reliable mate recognition... in warm and cold environments.

4 The grasshopper auditory periphery Susanne Schreiber, BCCN Berlin The auditory periphery consists of a simple feed-forward network:

5 Susanne Schreiber, BCCN Berlin Temperature-dependence in the receiver Ion-channel dynamics depend on temperature. Neuronal activity is hence likely to depend on temperature too.

6 Susanne Schreiber, BCCN Berlin Quantifying temperature-dependence Relative firing-rate change: (RMS)

7 Experimental findings (receptor neurons): Susanne Schreiber, BCCN Berlin Receptor neurons are surprisingly temperature invariant. Given the feedforward structure of the network, invariance must arise from cell-intrinsic properties. Monika Eberhard Relative change in firing rate: relative change (spike rate) cell count Q10-value (spike rate)

8 Can temperature invariance be cell-intrinsic?

9 Susanne Schreiber, BCCN Berlin Study of single-neuron models Connor-Stevens model with 9 temperature-dependent parameters (peak conductances and rates).

10 Model analysis Introduce temperature dependence for peak conductances and transition rates. Simulate parameter combinations in the physiological range. Question: Can temperature invariance of the firing rate arise? Susanne Schreiber, BCCN Berlin

11 Results of the model analysis Distribution of firing rate changes across all models: Temperature invariance as observed experimentally (about 30%) is possible. But what are the mechanisms? relative change (spike rate) model count Frederic Römschied Susanne Schreiber, BCCN Berlin

12 Relative firing-rate change as a function of all parameters Visualization: Dimensional stacking. Different parameters are represented on different scales of the image. Susanne Schreiber, BCCN Berlin Impact of parameters: relative change (spike rate)

13 Is temperature invariance metabolically expensive?

14 Susanne Schreiber, BCCN Berlin Quantification of energy-efficiency 2. Overlap between Na and K currents (separability). 1. Total Na current (total energy consumption).

15 Susanne Schreiber, BCCN Berlin Energy-efficiency is possible Distribution of changes in energy consumption across: firing-rate invariant models: (relative change < 40%) not firing-rate invariant models: (relative change > 40%) relative consumption count relative consumption count

16 Sodium channel temperature- dependence has a large influence on neural energy- efficiency. Parameters influencing energy consumption Susanne Schreiber, BCCN Berlin relative energy consumption

17 Susanne Schreiber, BCCN Berlin Two examples... but different energy efficiency. Two models with similar temperature invariance...

18 Key players for temperature invariance and energy efficiency are not the same Largely different parameters determine temperature invariance and energy efficiency. Temperature-invariant models can be energy efficient! Susanne Schreiber, BCCN Berlin

19 Grasshopper receptor neurons are surprisingly invariant to changes in temperature. This temperature invariance must be cell-intrinsic (no network input). Some ion channels are particularly suited to mediate temperature invariance (potassium channels). Energy-efficiency and temperature invariance of spike rate are not incompatible (mechanisms are largely independent). Susanne Schreiber, BCCN Berlin Summary

20 The computational neurophysiology group

21 Collaborators: Bernhard Ronacher (Humboldt-University) Monika Eberhard (Humboldt-University) Dietmar Schmitz (Charite Berlin) Richard Kempter (Humboldt-University) Ines Samengo (Bariloche, Argentina) Andreas Herz (LMU Munich), Irina Erchova (University of Edinburgh, UK), Tania Engel (Stanford University) Thanks to BMBF: Bernstein Center for Computational Neuroscience Berlin, BPCN, BFNL DFG: SFB 618, GK1589 The lab: Sven Blankenburg Katharina Glomb, Janina Hesse, Eric Reifenstein, Michiel Remme, Frederic Roemschied, Fabian Santi, Katharina Wilmes, Wei Wu, Dmitry Zarubin, Ekaterina Zhuchkova

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24 Further improvement by mechanotransduction Susanne Schreiber, BCCN Berlin +

25 Other projects in the group Entorhinal cortex: Insects: population coding in the auditory periphery of the grasshopper: summed population versus labeled line insect cellular morphology Heart: subthreshold resonance: - spatial dependence, - information transfer phase precession in grid cells ion channel cooperativity

26 Susanne Schreiber, BCCN Berlin Temperature affects grasshopper communication

27 Susanne Schreiber, BCCN Berlin Receptor neurons are most temperature-invariant Given the feedforward structure of the network, temperature robustness in receptor neurons must arise from cell- intrinsic properties.

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