Frequency preference and reliability of signal integration [Elektronische Ressource] / von Susanne Schreiber

Sujets

• Biologie

Informations

• Publié par: humboldt-universitat_zu_berlin
• Publié le: 01 janvier 2004
• Nombre de lectures: 24
• Langue: English
• Poids de l'ouvrage: 5 Mo

Extrait

Frequency preference and reliability of signal integration:
the role of intrinsic conductances
D I S S E R T A T I O N
zur Erlangung des akademischen Grades
doctor rerum naturalium
(dr. rer. nat.)
im Fach Biophysik
eingereicht an der
Mathematisch-Naturwissenschaftlichen Fakult¨at I
Humboldt-Universit¨at zu Berlin
von
Frau Dipl.-Biophys. Susanne Schreiber
geboren am 26.02.1976 in K¨onigs Wusterhausen
Pr¨asident der Humboldt-Universit¨at zu Berlin:
Prof. Dr. Jurgen¨ Mlynek
Dekan der Mathematisch-Naturwissenschaftlichen Fakult¨at I:
Prof. Thomas Buckhout, PhD
Gutachter:
1. Prof. Dr. Andreas V. M. Herz
2. Prof. Dr. Uwe Heinemann
3. Prof. Dr. Thomas H¨ofer
eingereicht am: 14. Mai 2004
Tag der mun¨ dlichen Pru¨fung: 07. Juli 2004iiAcknowledgments
Many people have influenced my thinking during my PhD, the more so, because the work was
done in two different places, the Institute of Theoretical Biology at the Humboldt-Universit¨at
zu Berlin (ITB) and The Salk Institute of Biological Studies in La Jolla, USA.
Foremost, I would like to warmly thank my advisor Andreas Herz for his continuous support
and encouragement, as well as the the opportunity to combine the work in his group with an
extended experience abroad. I am grateful that he was always open to my research interests
and I have truly enjoyed the exciting research environment that he created at the institute.
Iam alsogreatlyindebtedtoTerry Sejnowskiat theSalkInstitute. He letme joinhisgroup
and provided an inspiring research environment, where I could profit from the rich scientific
life and the expertise in temporal coding in particular.
Thevalueofatheoreticalworkmultiplies, iftheresultscanbeconfirmedinexperiments. It
has been a pleasure to work with Irina Erchova at the ITB and Jean-Marc Fellous at The Salk
Institute. They performed the experimental work presented in this thesis, but also discussed
many other aspects of the thesis with me.
I am extremely happy that I was given the chance to work with In´es Samengo (ITB) and
Paul Tiesinga (The Salk Institute), with whom I had many stimulating discussions about the
theoretical part of this work. With In´es, in particular, I enjoyed a lively scientific exchange
in a warm and personal working atmosphere. Tim Gollisch did not only contribute to this
atmosphere, but he also proved a steady source of knowledge, from which I benefited many
times.
I have been inspired by many other people at the ITB and I would especially like to thank
Roberto Fern´andez Gal´an, Richard Kempter, Christian Leibold, Hartmut Schu¨tze, and Martin
Stemmler who always had time for my questions and interesting discussions. Jan Benda and
Uwe Heinemann provided fruitful feedback on the subthreshold resonance project.
Of the people at the Salk Institute, I will gladly remember Tom Albright and Margaret
Mitchell (the good spirit of the Sloan center), who supported me throughout my stay, as well
as Jutta Kretzberg, Martina Wicklein, Rachel Kalmar, and Alex Koulakov with whom I shared
many scientific discussions and many happy hours. I am also grateful to Arthur Houweling
who helped me with the programming in NEURON and who shared his views on spike timing
reliability with me.
I have enjoyed working with two students, Wiebke Krambeck (ITB) and Diane Whitmer
(The Salk Institute), whose projects have inspired my work. Raphael Ritz I would like to thank
for helpful comments on the thesis, but also for his constant readiness to transport my luggage
across the Atlantic (in both directions!). My special thanks go to Arndt Telschow and Sabine
Becker-Weimann for relaxing tea times as well as to all the other people who helped create
iiiiv
the friendly and welcoming atmosphere in both institutes!
Last but not least, I am grateful to the Daimler-Benz Foundation, the Luftbruc¨ ken-
gedenkfond, the Sloan-Swartz Center for Theoretical Neurobiology, and the German National
Academic Foundation who supported me during various phases of my PhD.
Susanne Schreiber
Berlin, May 2004”Men ought to know that from the human brain
and from the brain only arise our pleasures,
joys, laughter, and jests as well as our sorrows,
pains, griefs and tears ... With it we think and
understand, see and hear and we discriminate
between the ugly and the beautiful, between what
is pleasant and what is unpleasant and between
good and evil.”
Hippocrates (460 – 377 B.C.)Contents
1 Introduction 1
1.1 Overture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 From the rhythms in the brain ... . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.3 ... to reliability and frequency selectivity . . . . . . . . . . . . . . . . . . . . . 2
1.4 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 General concepts 5
2.1 Ion channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1 A multitude of ion channels . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.2 The principles underlying conductance-based neuron models . . . . . . 6
2.2 Subthreshold resonance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2.1 Importance of subthreshold resonance . . . . . . . . . . . . . . . . . . 9
2.2.2 Measurement of subthreshold resonance . . . . . . . . . . . . . . . . . 9
2.2.3 Fitting of impedance profiles . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.4 How ion channels determine the resonance . . . . . . . . . . . . . . . . 11
2.2.5 Slow potassium and H channels are major players . . . . . . . . . . . . 12
2.2.6 Neuron type and subthreshold resonance . . . . . . . . . . . . . . . . . 14
2.3 Spike timing reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3.1 Firing rate versus the timing of spikes . . . . . . . . . . . . . . . . . . 15
2.3.2 The correlation measure of spike timing reliability . . . . . . . . . . . . 16
2.3.3 Stimulus statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3 The integration of subthreshold signals 21
3.1 The questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2 The noise stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3 Experimental characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.3.1 General cell parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.3.2 Impedance profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.4 Noise signals in the subthreshold regime . . . . . . . . . . . . . . . . . . . . . 25
3.4.1 Prediction of the response power . . . . . . . . . . . . . . . . . . . . . 25
3.4.2 Experimentally observed response power . . . . . . . . . . . . . . . . . 25
3.4.3 Theoretical predictions and model cells . . . . . . . . . . . . . . . . . . 26
3.4.4 Dependence on the DC . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.4.5 Impedance functions calculated on the basis of nonperiodic stimuli . . . 27
3.5 Noise signals in the spiking regime . . . . . . . . . . . . . . . . . . . . . . . . 28
viiviii CONTENTS
3.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.6.1 Subthreshold responses . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.6.2 Spiking responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4 Spike timing: the subthreshold regime 33
4.1 The questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.2 The periodic stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.3 Two different regimes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.4 Dependence of spike timing reliability on the stimulus . . . . . . . . . . . . . . 34
4.5 Spike timing reliability and subthreshold resonance . . . . . . . . . . . . . . . 36
4.5.1 Two example cells with resonance . . . . . . . . . . . . . . . . . . . . 36
4.5.2 An example cell without resonance . . . . . . . . . . . . . . . . . . . . 38
4.6 Amplitude dependence of subthreshold resonance . . . . . . . . . . . . . . . . 40
4.7 A more precise estimate of preferred frequency . . . . . . . . . . . . . . . . . . 43
4.7.1 The three model cells revisited . . . . . . . . . . . . . . . . . . . . . . 44
4.7.2 Morris-Lecar neurons . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.8 Spike timing reliability at the harmonics . . . . . . . . . . . . . . . . . . . . . 46
5 Spike timing: the suprathreshold regime 49
5.1 A suprathreshold resonance effect . . . . . . . . . . . . . . . . . . . . . . . . . 49
5.1.1 Dependence of spike timing reliability on the stimulus . . . . . . . . . . 49
5.1.2 The DC component is fixed . . . . . . . . . . . . . . . . . . . . . . . . 51
5.1.3 DC firing rate determines the preferred frequency . . . . . . . . . . . . 51
5.1.4 Spike timing reliability at the harmonics . . . . . . . . . . . . . . . . . 52
5.2 Influence of ion channels on the preferred frequency . . . . . . . . . . . . . . . 52
5.2.1 Dynamic regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.2.2 Influence of individual ion channels . . . . . . . . . . . . . . . . . . . . 54
5.2.3 Influence of slow potassium channels . . . . . . . . . . . . . . . . . . . 56
5.2.4 Firing rate anal