The contribution of spike-frequency adaptation to the variability of spike responses in a sensory neuron [Elektronische Ressource] / Karin Fisch. Betreuer: Andreas Herz

Karin Fisch

Sujets

• Biologie

Informations

• Publié par: ludwig-maximilians-universitat_munchen
• Publié le: 01 janvier 2011
• Nombre de lectures: 35
• Langue: English
• Poids de l'ouvrage: 4 Mo

Extrait

The contribution of spike-frequency
adaptation to the variability of spike
responses in a sensory neuron
Karin Fisch
Dissertation
an der Fakultät für Biologie
der Ludwig-Maximilians-Universität
München
vorgelegt von
Karin Fisch
München, Juli 2011Erstgutachter: Prof. Dr. Andreas Herz
Zweitgutachter: PD Dr. Thomas Wachtler
Tag der mündlichen Prüfung: 23.09.2011Contents
Summary xiii
Zusammenfassung xvii
I Introduction 1
1 Spike-response variability 3
2 Sources of spike-response variability 5
2.1 Channel noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3 Role of noise in the nervous system 11
3.1 Noise in sensory systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4 Spike-frequency adaptation in sensory systems 15
5 The auditory system ofLocustamigratoria 17
II Material & methods 21
6 Intracellular recordings 23
6.1 Electrophysiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
6.2 Stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
6.3 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
6.3.1 Interspike-interval statistics . . . . . . . . . . . . . . . . . . . . . . . 24
6.3.2 Spike-count statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
6.3.3 Effective time constants of adaptation . . . . . . . . . . . . . . . . . 27
7 Model of the locust auditory transduction cascade 29
7.1 Spike generator with spike-frequency adaptation . . . . . . . . . . . . . . . 29
7.2 Model of the mechanosensory transduction process . . . . . . . . . . . . . 31
7.3 Kinetic schemes for the stochastic ion channel models . . . . . . . . . . . . 32
7.4 Simulation of stochastic opening ion channels . . . . . . . . . . . . . . . . . 34vi CONTENTS
8 Effect of spike-frequency adaptation on the interspike-interval statistics 35
8.1 Hodgkin-Huxley-type model with adaptation . . . . . . . . . . . . . . . . . 35
9 Effect of adaptation on the spike-count statistics 37
9.1 PIF model with colored noise or stochastic adaptation . . . . . . . . . . . . 37
9.2 model with two currents . . . . . . . . . 39
III Results 41
10 What are the sources of spike-response variability? 43
10.1 Spike-response variability in locust auditory receptor neurons . . . . . . . 44
10.1.1 Interspike-interval distributions . . . . . . . . . . . . . . . . . . . . . 46
10.1.2 correlations . . . . . . . . . . . . . . . . . . . . . 48
10.2 Locust auditory transduction model with ion channel noise . . . . . . . . . 51
10.2.1 Mechanosensitive channel gating . . . . . . . . . . . . . . . . . . . . 52
10.2.2 Single-current stochasticity . . . . . . . . . . . . . . . . . . . . . . . 55
11 How does adaptation contribute to the interspike-interval variability? 61
11.1 How noisy adaptation of neurons shapes ISI histograms and correlations . 62
11.2 ISI statistics of a Hodgkin-Huxley-type model with stochastic adaptation . 66
11.2.1 Interspike-interval distributions . . . . . . . . . . . . . . . . . . . . . 66
11.2.2 correlations . . . . . . . . . . . . . . . . . . . . . 68
11.2.3 Mixed-case model with fast and slow noise sources . . . . . . . . . 69
11.3 Locust auditory transduction model with mixed channel noise sources . . 71
12 How does adaptation contribute to the spike-count variability? 75
12.1 Spike-frequency adaptation with two time constants . . . . . . . . . . . . . 75
12.2 Spike-count variability in locust auditory receptors . . . . . . . . . . . . . . 76
12.3 Locust auditory transduction model with two stochastic adaptation currents 80
13 How do two time scales of adaptation shape the spike-count variability? 85
13.1 Spike-count variability in models with two adaptation currents . . . . . . 86
13.1.1 PIF model driven by colored noise . . . . . . . . . . . . . . . . . . . 86
13.1.2 PIF with adaptation currents . . . . . . . . . . . . . . . . . . 89
13.1.3 Interaction of two currents . . . . . . . . . . . . . . . . . 91
13.2 Firing-rate models with two adaptation currents . . . . . . . . . . . . . . . 93
13.2.1 Linear adaptation model . . . . . . . . . . . . . . . . . . . . . . . . . 96
13.2.2 Non-linear model . . . . . . . . . . . . . . . . . . . . . . 100
IV Discussion 105
14 Spike-response variability in locust auditory receptor neurons 107CONTENTS vii
15 Different noise sources and how they contribute to the ISI variability 109
16 Effect of multiple time scales of adaptation on the spike-count variability 115
17 Functional role of channel noise 119
References 123
Danksagung/Acknowledgments 137List of Figures
1.1 Spike-response variability . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1 Conductance variance of a population of ion channels . . . . . . . . . . . 7
2.2 Variability in the number of open ion channels . . . . . . . . . . . . . . . 8
2.3 The simulation of an action potential with deterministic and stochastic
voltage-dependent currents . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.1 A potential beneficial effect of a high noise level . . . . . . . . . . . . . . 13
4.1 Spike-frequency adaptation in an auditory receptor neurons . . . . . . . 16
5.1 Müller’s organ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
5.2 Auditory transduction cascade of locusts . . . . . . . . . . . . . . . . . . . 20
10.1 Interspike-interval variability in auditory receptor neurons . . . . . . . . 45
10.2 Response characteristics of auditory receptor neurons . . . . . . . . . . . 46
10.3 Comparison of interspike-interval histograms with the colored- and
white-noise ISI distributions . . . . . . . . . . . . . . . . . . . . . . . . . . 47
10.4 Shape of the histograms . . . . . . . . . . . . . . . . . 49
10.5 Correlations between successive interspike intervals . . . . . . . . . . . . 50
10.6 Auditory signal processing . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
10.7 Influence of the mechanosensitive channel gating on the response prop-
erties of the auditory receptor cells . . . . . . . . . . . . . . . . . . . . . . 54
10.8 Low-pass filter properties of the mechanosensory receptor channels . . . 55
10.9 Comparison of the interspike-interval variability resulting from different
channel-noise sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
10.10 of the diffusion coefficient resulting from different channel-
noise sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
10.11 Interspike-interval distributions resulting from fast and slow channel noise 58
10.12 correlations r from fast and slow noise 59
10.13 and correlations caused by stochastic
potassium channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
11.1 Integrate-and-fire dynamics with adaptation channels . . . . . . . . . . . 63
11.2 ISI histograms of the Traub-Miles model – deterministic vs. stochastic
adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67x LIST OF FIGURES
11.3 Shape parameters of the ISIH for deterministic and stochastic adaptation 68
11.4 of the ISIH as a function of the time scale separation . 69
11.5 Serial correlation coefficient at lag 1 as a function of the time scale sepa-
ration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
11.6 Serial correlation coefficient as a function of the lag between ISIs . . . . . 70
11.7 ISI statistics of the Traub-Miles model in the presence of both stochastic
adaptation and white noise . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
11.8 ISI statistics of the mixed stochastic channel models . . . . . . . . . . . . 72
11.9 Comparison of the model with stochastic receptor/sodium and adapta-
tion channels with experimental data . . . . . . . . . . . . . . . . . . . . . 73
12.1 Two processes mediating spike-frequency adaptation in locust auditory
receptor neurons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
12.2 Effective time constants of two adaptation processes . . . . . . . . . . . . 77
12.3 Fano factor analysis of the spike trains of an auditory receptor neuron
for different sound intensities . . . . . . . . . . . . . . . . . . . . . . . . . 78
12.4 Fano factor curves of the locust auditory transduction model with one
and two stochastic adaptation currents . . . . . . . . . . . . . . . . . . . . 81
12.5 Fano factor curves of the locust auditory transduction model with one
fast stochastic M-type and one slow stochastic calcium-dependent potas-
sium currents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
13.1 The exponentk for different correlation time constants . . . . . . . . . . . 86
13.2 Fano factor curves of a PIF model driven by Ornstein-Uhlenbeck noise . 88
13.3 The exponentk for different correlation time constants of one vs. two
Ornstein-Uhlenbeck noise sources . . . . . . . . . . . . . . . . . . . . . . . 89
13.4 Fano factor curves of a PIF model with stochastic adaptation currents . . 90
13.5 Effect of adaptation strength, noise intensity and time constant on the
Fano factor curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
13.6 Two adaptation currents counteract and affect the ISI and spike-count
variability . . . . . . . . . . . . . . . . . . . . . . .