How to keep cool in hot situations: temperature compensation in grasshopper auditory neurons

Susanne SchreiberHumboldt-Universität and Bernstein Center Berlin

Tübingen, July 7th 2012

Acoustic communication in grasshoppers

Susanne Schreiber, BCCN Berlin

Susanne Schreiber, BCCN Berlin

Reliable mate recognition

... in warm ... ... and cold environments.

The grasshopper auditory periphery

Susanne Schreiber, BCCN Berlin

The auditory periphery consists of a simple feed-forward network:

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.

Susanne Schreiber, BCCN Berlin

Quantifying temperature-dependence

• Relative firing-rate change: (RMS)

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

coun

t

cell

coun

t

Q10-value (spike rate)

Can temperature invariance be cell-intrinsic?

Susanne Schreiber, BCCN Berlin

Study of single-neuron models

• Connor-Stevens model with 9 temperature-dependent parameters (peak conductances and rates).

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

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)

mod

el c

ount

Frederic Römschied

Susanne Schreiber, BCCN Berlin

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:re

lativ

e ch

ange

(sp

ike

rate

)

Is temperature invariance metabolically expensive?

Susanne Schreiber, BCCN Berlin

Quantification of energy-efficiency

2. Overlap between Na and K currents (separability).

1. Total Na current (total energy consumption).

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

coun

t

relative consumption

coun

t

Sodium channel temperature-dependence has a large influence on neural energy-efficiency.

Parameters influencing energy consumption

Susanne Schreiber, BCCN Berlin

rela

tive

ene

rgy

cons

umpt

ion

Susanne Schreiber, BCCN Berlin

Two examples

... but different energy efficiency.

Two models with similar temperature invariance ...

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

• 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

The computational neurophysiology group

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, BFNLDFG: SFB 618, GK1589

The lab:Sven BlankenburgKatharina Glomb,Janina Hesse,Eric Reifenstein,Michiel Remme,Frederic Roemschied,Fabian Santi,Katharina Wilmes,Wei Wu,Dmitry Zarubin,Ekaterina Zhuchkova

Further improvement by mechanotransduction

Susanne Schreiber, BCCN Berlin

+

Susanne Schreiber, BCCN Berlin

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

Susanne Schreiber, BCCN Berlin

Temperature affects grasshopper communication

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.