New biophysical methods for the characterization of signal transfer in neurons [Elektronische Ressource] / presented by Arnd Roth

Sujets

• Biologie

Informations

• Publié par: ruprecht-karls-universitat_heidelberg
• Publié le: 01 janvier 2004
• Nombre de lectures: 18
• Langue: Deutsch
• Poids de l'ouvrage: 13 Mo

Extrait

Dissertation
submitted to the
Combined Faculties for the Natural Sciences and for Mathematics
of the Ruperto-Carola University of Heidelberg, Germany
for the degree of
Doctor of Natural Sciences
presented by
Dipl.-Phys. Arnd Roth
born in Heilbronn, Germany
Oral examination: 21st April 2004New biophysical methods for the characterization of
signal transfer in neurons
Referees: Prof. Dr. Bert Sakmann
Prof. Dr. Winfried DenkNeue biophysikalische Methoden zur Charakterisierung der Signalübertragung in
Nervenzellen
Viele Nervenzellen besitzen ausgedehnte Dendritenbäume, und die somatische
Spannungsklemme dendritischer Synapsen führt daher zu Verzerrungen und Abschwächungen
der gemessenen synaptischen Ströme. Eine neue Methode wird vorgestellt, die die genaue
Bestimmung der Abfallszeitkonstante der synaptischen Leitfähigkeit, unabhängig von der
Geometrie der Zelle und dem elektrotonischen Ort der Synapse, erlaubt. In allen getesteten
Geometrien wurde der Zeitverlauf der Leitfähigkeit auch bei hohen
Serienwiderständen, geringen Membranwiderständen und distalen, verteilten Synapsen mit hoher
Genauigkeit rekonstruiert. Die Methode liefert auch den Zeitverlauf der Spannungsantwort am Ort
der Synapse auf einen somatischen Spannungssprung, und ist daher nützlich bei der Konstruktion
von Kompartimentmodellen von Nervenzellen und zur Abschätzung des relativen elektrotonischen
Abstands von Synapsen.
Die Ausbreitung dendritischer Aktionspotentiale verbindet die Informationsverarbeitung in
verschiedenen Regionen des Dendritenbaums. Trotz identischer Vorschriften für die Platzierung
von spannungsgesteuerten Kanälen verursachten verschiedene dendritische Verzweigungsmuster
in Kompartimentmodellen von Nervenzellen eine Verteilung verschieden starker
Aktionspotentialausbreitung, wie sie auch experimentell beobachtet wird. Das
Verzweigungsmuster bestimmt auch, zu welchem Grad die Modulation von Kanaldichten die
Aktionspotentialausbreitung verändern kann. Die Vorwärtsausbreitung dendritisch initiierter
Aktionspotentiale wird von der Geometrie auf ähnliche Weise beeinflusst. Indem sie das räumliche
Muster der bestimmt, trägt die dendritische Geometrie maßgeblich
dazu bei, die Größe und Wechselwirkung zwischen funktionellen Kompartimenten in einer
Nervenzelle zu bestimmen.
New biophysical methods for the characterization of signal transfer in neurons
Many neurons have extensive dendritic trees, and therefore somatic voltage clamp of dendritic
synapses is often associated with substantial distortion and attenuation of the synaptic currents. A
new method is presented which permits faithful extraction of the decay time constant of the
synaptic conductance independent of dendritic geometry and the electrotonic location of the
synapse. The decay time course of the synaptic conductance was recovered with high accuracy in
all the tested geometries, even with high series resistances, low membrane resistances, and
electrotonically remote, distributed synapses. The method also provides the time course of the
voltage change at the synapse in response to a somatic voltage clamp step, and thus will be useful
for constraining compartmental models and estimating the relative electrotonic distance of
synapses.
Action potential propagation in dendrites links information processing in different regions of the
dendritic tree. In simulations using compartmental models with identical complements of voltage-
gated channels, different dendritic branching patterns caused a range of backpropagation
efficacies, similar to that observed experimentally. Dendritic geometry also determines the extent
to which modulation of channel densities can affect propagation. Forward propagation of
dendritically initiated action potentials is influenced by geometry in a similar manner. By
determining the spatial pattern of action potential signalling, dendritic geometry thus helps to define
the size and interdependence of functional compartments in the neuron.Contents
1 Introduction .............................................................................................................. 1
1.1 The cable equation................................................................................................ 7
1.1.1 Main assumptions...................................................................................... 7
1.1.2 The linear cable equation........................................................................... 8
1.1.3 Steady-state solution in an infinite cable.................................................. 10
1.2 Compartmental models ....................................................................................... 10
1.2.1 Introduction.............................................................................................. 10
1.2.2 The Hodgkin–Huxley model..................................................................... 11
1.2.3 The NEURON simulation environment .................................................... 13
2 Estimating the time course of the synaptic conductance under conditions of
inadequate space clamp ........................................................................................... 15
2.1 Introduction ......................................................................................................... 15
2.2 Methods .............................................................................................................. 17
2.2.1 Equivalent cylinder model........................................................................ 17
2.2.2 CA3 pyramidal cell model ........................................................................ 17
2.2.3 Neocortical pyramidal cell model ............................................................. 18
2.3 Results ................................................................................................................ 19
2.3.1 Attenuation and filtering of synaptic currents under poor space-clamp
conditions.......................................................................................................... 19
2.3.2 Measuring charge recovery ..................................................................... 20
2.3.3 Charge recovery after the onset of the synaptic conductance is
determined by the conductance time course .................................................... 22
2.3.4 Charge recovery before the onset of the synaptic conductance is
determined by the electrotonic distance of the synapse ................................... 25
2.3.5 A simple analytical function describes the charge recovery curve ........... 25
2.3.6 The voltage jump method also works in current-clamp mode.................. 29
2.3.7 Effect of voltage escape at the synapse .................................................. 29
2.3.8 Application to realistic neuronal geometries: CA3 pyramidal cell............. 31
2.3.9 Application to realistic neuronal geometries: Neocortical pyramidal cell.. 35
iContents
2.3.10 Estimating synaptic conductance time course with a train of brief
voltage jumps ................................................................................................... 37
2.3.11 Estimating the attenuation of synaptic charge....................................... 39
2.4 Discussion .......................................................................................................... 43
2.4.1 Comparison with previous approaches ................................................... 43
2.4.2 Sources of error....................................................................................... 44
2.4.3 Application to neocortical pyramidal cells................................................ 46
2.4.4 Application to Bergmann glia cells........................................................... 49
2.4.5 Application to hippocampal interneurons................................................. 50
2.4.6 Application in cerebellar granule cells ..................................................... 51
2.4.7 Future applications of the voltage jump method...................................... 51
3 Propagation of action potentials in dendrites depends on dendritic geometry ....... 53
3.1 Introduction......................................................................................................... 53
3.2 Methods.............................................................................................................. 55
3.2.1 Dendritic geometries ............................................................................... 55
3.2.2 Compartmental models ........................................................................... 55
3.2.3 Measurements......................................................................................... 57
3.3 Results................................................................................................................ 58
3.3.1 Action potential backpropagation depends on dendritic geometry .......... 58
3.3.2 Sensitivity of backpropagation to modulation of channel densities in
different dendritic geometries ........................................................................... 60
3.3.3 Morphological determinants of backpropagation..................................... 63
3.3.4 Forward propagation of dendritic APs depends on dendritic geometry ... 68
3.4 Discussion ......................