Information Processing Analysis in Neural Networks

by Chenfei Zhang
Doctoral thesis
Date of Examination:2018-06-07
Date of issue:2019-06-06
Advisor:Prof. Dr. Fred Wolf
Referee:Prof. Dr. Alexander Gail
Referee:Prof. Dr. Siegrid Loewel
Referee:Dr. Marion Silies
Persistent Address: http://dx.doi.org/10.53846/goediss-7498

 

 

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Abstract

English

Populations of cortical neurons in the fluctuation-driven regime exhibit an ultrafast population response. Some theoretical studies suggest that passive, morphological features of the axon and dendrites determine the population response, while others emphasize the importance of ion channel properties at the neuron’s axon initial segment (AIS), the site of action potential initiation. One hypothesis posits that the electrotonic separation of the site of action potential initiation from the soma is a sufficient condition for the emergence of the ultrafast population response. Another hypothesis proposes that the cooperative gating of sodium channels can reproduce sharp action potential waveform and ultrafast population response. In a more recent study, a big dendrite was proposed to be crucial for high bandwidth encoding in cortical neurons. Here I use multiple neuron models to explore how various passive and active axonal biophysical parameters and morphological structures impact the response dynamics of neuron populations. I will show that features of the action potential waveform that are experimentally associated with the ultrafast response do not guarantee its emergence. Depending on the active biophysical properties of the AIS, models differ profoundly in the spike generation dynamics, population response bandwidth and the amount by which slow background current fluctuations can boost high frequency gain. These results corroborate that the response dynamics of cortical population critically depends on the active biophysical properties of the AIS. They highlight that structure-function studies of AIS bio-molecular organization can gain sensitivity and probe the impact of cellular physiology on neuronal population codes by the method of dynamic gain analysis.
Keywords: dynamic gain; population encoding; action potential generation dynamics
 
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