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How to keep cool in hot situations: temperature

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Presentation on theme: "How to keep cool in hot situations: temperature"— 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 7th 2012

2 Acoustic communication in grasshoppers
Susanne Schreiber, BCCN Berlin

3 Reliable mate recognition
... in warm and cold environments. Susanne Schreiber, BCCN Berlin

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

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

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

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

8 Can temperature invariance be cell-intrinsic?

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

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

13 Is temperature invariance metabolically expensive?

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

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

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

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

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 Summary 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

20 The computational neurophysiology group

21 Thanks to The lab: Sven Blankenburg Katharina Glomb, Janina Hesse,
Eric Reifenstein, Michiel Remme, Frederic Roemschied, Fabian Santi, Katharina Wilmes, Wei Wu, Dmitry Zarubin, Ekaterina Zhuchkova 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) BMBF: Bernstein Center for Computational Neuroscience Berlin, BPCN, BFNL DFG: SFB 618, GK1589



24 Further improvement by mechanotransduction
+ Susanne Schreiber, BCCN Berlin

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

26 Temperature affects grasshopper communication
Susanne Schreiber, BCCN Berlin

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


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