Mechanisms of signal encoding and information transmission in cortical neurons

As its fundamental function, the brain processes and transmits information using populations of interconnected nerve cells alias neurons. The communication between these neurons occurs via discrete electric impulses call
As its fundamental function, the brain processes and transmits information using populations of interconnected nerve cells alias neurons. The communication between these neurons occurs via discrete electric impulses called spikes. A core challenge in neuroscience has been to quantify how much information about relevant stimuli or signals a neuron transports in its spike sequences, or spike trains. The recently introduced correlation method allows to determine this so-called mutual information in terms of a neuron’s temporal spike correlations under certain stationarity assumptions. Based on the correlation method, I address several open questions regarding neural information encoding in the cortex.
In the first part (chapter 2), I investigate the role of temporal spike correlations for neural information transmission. Temporal correlations in neuronal spike trains diminish independence in the information that is transmitted by the different spikes and hence introduce redundancy to stimulus encoding. However, exact methods to describe how such spike correlations impact information transmission quantitatively have been lacking. Here, I provide a general measure for the information carried by spike trains of neurons with correlated rate modulations only, neglecting other spike correlations, and use it to investigate the effect of rate correlations on encoding redundancy. I derive it analytically by calculating the mutual information between a time correlated, rate-modulating signal and the resulting spikes of Poisson neurons. Whereas this information is determined by spike autocorrelations only, the redundancy in information encoding due to rate correlations depends on both the distribution and the autocorrelation of the rate histogram. I further demonstrate that, at very small signal strengths, the information carried by rate correlated spikes becomes identical to that of independent spikes, in effect measuring the rate modulation depth. In contrast, a vanishing signal correlation time maximizes information transmission but does not generally yield the information of independent spikes. 
In the second part (chapter 3), I analyze the information transmission capabilities of two particular schemes of encoding stimuli in the synaptic inputs using integrate-and-fire neuron models. Specifically, I calculate the exact information contained in spike trains about signals which modulate either the mean or the variance of the somatic currents in neurons, as is observed experimentally. I show that the information content about mean modulating signals is generally substantially larger than about variance modulating signals for biological parameters. This result provides evidence, by means of exact calculations of the mutual information, against the potential benefit of variance encoding that had been suggested previously. 
Another analysis reveals that higher information transmission is generally associated with a larger proportion of nonlinear signal encoding. Moreover, I show that a combination of signal-dependent mean and variance modulations of the input current can synergistically benefit information transmission through a nonlinear coupling of both channels. On a more general level, I identify what was previously considered an upper bound as the exact, full mutual information. Furthermore, by analyzing the statistics of the spike train Fourier coefficients, I identify the means of the Fourier coefficients as information-carrying features. 
Overall, this work contributes answers to central questions of theoretical neuroscience concerning the neural code and neural information transmission. It sheds light on the role of signal-induced temporal correlations for neural coding by providing insight into how signal features shape redundancy and by establishing mathematical links between existing methods and providing new insights into the spike train statistics in stationary situations. Moreover, I determine what fraction of the mutual information is linearly decodable for two specific signal encoding schemes.
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Author:Tim Herfurth
Place of publication:Frankfurt am Main
Referee:Tatjana Tchumatchenko, Achilleas Frangakis
Document Type:Doctoral Thesis
Date of Publication (online):2020/02/22
Year of first Publication:2019
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Granting Institution:Johann Wolfgang Goethe-Universität
Date of final exam:2020/02/14
Release Date:2020/02/27
HeBIS PPN:460284142
Dewey Decimal Classification:530 Physik
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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