Computer simulation of chopper neurons [Elektronische Ressource] : intrinsic oscillations and temporal processing in the auditory system / von Andreas Bahmer

Sujets

• Psychologie

Informations

• Publié par: technischen_universitat_darmstadt
• Publié le: 01 janvier 2007
• Nombre de lectures: 21
• Langue: English
• Poids de l'ouvrage: 4 Mo

Extrait

Computer simulation of chopper neurons:
intrinsic oscillations and temporal
processing in the auditory system
Vom Fachbereich Biologie
der Technischen Universit˜at Darmstadt
zur Erlangung des Grades
eines Doktors der Naturwissenschaften
(Dr. rer. nat.)
genehmigte
DISSERTATION
von
Dipl.-Phys. cand.med. Andreas Bahmer
aus Aschafienburg
Darmstadt 2007
D 17Referent: Prof. Dr. G. Langner
Korreferent: Prof. Dr. R. Galuske
Tag der Einreichung: 28.2.2007
Tag der mundlic˜ hen Prufung:˜ 20.4.2007Contents
1 Motivation and Introduction 1
1.1 Neural oscillations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Intention and structure of the thesis . . . . . . . . . . . . . . . . . . . . 4
2 General Introduction 5
2.1 Oscillations in the auditory system . . . . . . . . . . . . . . . . . . . . 5
2.1.1 The auditory system . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.2 Cochlear nucleus (CN) . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.3 Neurons in the CN . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1.4 Oscillators in the CN: Chopper Neurons . . . . . . . . . . . . . 15
2.1.5 Chopper Neurons in the Periodicity Model according to Langner
(1981) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2 Neuron models for the simulation of oscillations . . . . . . . . . . . . . 19
2.2.1 Leaky integrate-and-flre (LIF) neuron model . . . . . . . . . . . 20
2.2.2 LIF network of the Periodicity Model . . . . . . . . . . . . . . . 21
2.2.3 Hodgkin-Huxley (HH) model. . . . . . . . . . . . . . . . . . . . 23
2.2.4 HH model from Rothman and Manis (2003c) . . . . . . . . . . . 25
2.2.5 Compartment neuron model . . . . . . . . . . . . . . . . . . . . 29
2.3 Simulation environments . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3 Oscillating neurons in the cochlear nucleus: I. Experimental basis of
a simulation paradigm 33
3.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.3 Neuroanatomy and physiology of chopper neurons in the cochlear nucleus 35
iiiContents
3.4 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.5 Review of experiments on pitch perception and on the physiology of
temporal coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.5.1 Evidence for preferred intervals of intrinsic oscillations . . . . . 37
3.5.2 Dynamic features of auditory nerve, onset-, and chopper neurons 40
3.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.6.1 Rationaleforasynapticdelayof0.4msasabasisforanauditory
time constant . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.6.2 Rationale for the integration of an input from onset neuron to
chopper neurons. . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.7.1 Physiological and psychoacoustical data . . . . . . . . . . . . . 47
3.7.2 Simulation paradigm . . . . . . . . . . . . . . . . . . . . . . . . 47
4 Oscillating neurons in the cochlear nucleus: II. Simulation results 48
4.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.3 Neuronal modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.3.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.3.2 Simulation paradigms: \Circular oscillator" and \Multi-oscillator" 51
4.3.3 Modelling of inner ear, hair cell, and auditory nerve flbers . . . 52
4.3.4 Theory of the implemented neuronal models . . . . . . . . . . . 52
4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.4.1 Simulation of auditory nerve flbers . . . . . . . . . . . . . . . . 58
4.4.2 Simulation of onset neurons . . . . . . . . . . . . . . . . . . . . 58
4.4.3 Circuit of auditory nerve, onset- , and chopper neurons . . . . . 63
4.4.4 Comparisonofthesimulationresultswithpropertiesofrealchop-
per neurons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.5.1 Comparison of the model with previous computer models . . . . 67
4.5.2 Regularity and periodicity analysis . . . . . . . . . . . . . . . . 70
4.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
ivContents
5 Spectral integration in a simulation of onset and chopper neurons in
the cochlear nucleus 73
5.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.4.1 Tuning and nonlinear efiects of the simulated chopper neurons . 75
5.4.2 Periodicity representation of the simulated chopper neurons . . 76
5.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.5.1 Tuning of the simulated chopper neurons . . . . . . . . . . . . . 79
5.5.2 Periodicity representation of the simulated chopper neurons . . 79
5.5.3 Explanation for a dichotomy of pitch perception? . . . . . . . . 81
5.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6 NetworksofHodgkin-Huxley-likeneuronmodelsforthesimulationof
oscillating neurons in the cochlear nucleus 82
6.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
6.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
6.2.1 Special features of chopper neurons in the VCN . . . . . . . . . 83
6.2.2 VCN neuron models . . . . . . . . . . . . . . . . . . . . . . . . 83
6.2.3 The HH-like \Rothman chopper" . . . . . . . . . . . . . . . . . 84
6.2.4 Substituting LIF neurons by HH-like neurons
in the Multi-oscillator . . . . . . . . . . . . . . . . . . . . . . . 89
6.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
6.3.1 NEURON and Matlab . . . . . . . . . . . . . . . . . . . . . . . 90
6.3.2 Neuronal modeling . . . . . . . . . . . . . . . . . . . . . . . . . 90
6.3.3 Genetic algorithm in Matlab . . . . . . . . . . . . . . . . . . . . 92
6.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
6.4.1 Patch clamp simulations of a single \Rothman chopper" . . . . 93
6.4.2 Patch clamp sim of a network of two \Rothman choppers" 93
6.4.3 \Acceleration" of the \Rothman chopper" using genetic algo-
rithms (GA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
6.4.4 The \fast Rothman chopper" in the Multi-oscillator . . . . . . . 97
vContents
6.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
6.5.1 Simulations of single chopper neurons and networks of chopper
neurons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
6.5.2 The optimization of the model using Genetic Algorithms . . . . 103
6.5.3 The \fast Rothman chopper" in the Multi-oscillator . . . . . . . 105
6.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
7 Particular Issues 106
7.1 Input to chopper neurons from onset units:
Octopus or D-stellate cells? . . . . . . . . . . . . . . . . . . . . . . . . 106
7.2 Functional role of inhibition of D-stellate cells . . . . . . . . . . . . . . 107
7.3 Is the nerve input necessary for the
Multi-oscillator? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
7.4 Comparison of particular simulation results with experimental results . 108
8 General Discussion and Outlook 111
8.1 General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
8.1.1 Biological background for the Circular Oscillator and the Multi-
oscillator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
8.1.2 Regularity and periodicity analysis . . . . . . . . . . . . . . . . 112
8.1.3 Efiects of changing the width of the frequency channel integration 113
8.1.4 Periodicity coding despite saturation of nerve flbers? . . . . . . 114
8.1.5 Experimental basis for the time constants in VCN neurons . . . 115
8.1.6 The \fast Rothman chopper" in the Multi-oscillator . . . . . . . 115
8.2 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
8.2.1 Implementation of inhibition . . . . . . . . . . . . . . . . . . . . 116
8.2.2 Large networks containing the Multi-oscillator . . . . . . . . . . 116
8.2.3 Pure tone and AM processing . . . . . . . . . . . . . . . . . . . 117
8.2.4 Integration of the results in the periodicity model from Langner 117
9 Summary 118
Zusammenfassung in deutscher Sprache 120
10 Appendix 122
viContents
10.1 Leaky integrate-and-flre neuron and synapses as Matlab function-flle. . 122
10.1.1 Synapse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
10.1.2 Soma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
10.2 Hodgkin Huxley Equations of the model of Rothman and Manis (2003c) 124