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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.
Neurons are cells with a highly complex morphology; their dendritic arbor spans up to thousands of micrometers. This extended arbor poses a challenge for the logistics of neuronal processes: mRNA, proteins, and organelles have to be transported to dendrites, hundreds of micrometers away from the soma. This thesis aims to calculate the minimum number of proteins needed to populate the dendritic trees for different scenarios.
In chapter 2, I analyzed the ability of different mechanisms to populate the dendritic arbor. I started from the solution of the diffusion equation in Sec. 2.1, then I included the contribution of active transport in Sec. 2.2 and showed how it could have either the effect of increasing the effective diffusion coefficient or of introducing a bias in the diffusion process. In Sec. 2.3 I studied the spatial distribution of locally synthesized protein, accordingly with actively and passively transported mRNA. In Sec. 2.5, I derived the boundary condition for branches showing a qualitatively different behavior of surface and cytoplasmic proteins induced by the medium’s dimensionality in which they diffuse.
In chapter 3, I introduced the concept of protein requirement, defined as the minimum number of proteins that the neuron needs to produce to provide at least one protein to each micrometer of the dendritic arbor. In Sec. 3.1, I derived the protein requirement for diffusive proteins for somatic translation and constant translation in the dendritic arbor. In Sec. 3.2, I analyzed numerically the protein requirement in the case of actively transported protein synthesized in the soma, and, in Sec. 3.3, in the case of actively transported proteins synthesized in the dendritic arbor. In Sec. 3.4, I analyzed the protein requirement of protein synthesized in the dendrite accordingly with the distribution of mRNA described in Sec. 3.3 and 3.2. In Sec. 3.5, I derived the protein requirement for a single branch and purely diffusive proteins.
In chapter 4, I analyzed the relation between the radii of the three afferent dendrites in a branch, their length, and the diffusion length of a protein. In Sec. 4.1 I derived the optimal ratio between the radii of the daughter dendrites that minimizes the protein requirement. In Sec. 4.3 I introduced the 3/2− Rall Rule and in Sec. 4.5 its generalization. Finally, I used those rules to estimate the fraction of proteins diffusing away from and toward the soma.
In chapter 5, I analyzed the radii distribution for three categories of neurons: cultured hippocampal neurons in Sec. 5.1, stomatogastric ganglia neuron in Sec. 5.2, and 3DEM reconstructed prefrontal pyramidal neurons in Sec. 5.3. For each of these three classes, I analyzed the distribution of radii, Rall exponents, and the probability ratio. For most of them, I found that the probability of a protein diffusing away from the soma is higher for surface proteins than for cytoplasmic ones. I quantified this with a parameter called surface bias.
In Chapter 6, I analyzed the fluorescent ratio imaged by our collaborators Anne-Sophie Hafner, for a surface protein, GFP::Nlg, and a soluble one, GFP, in cultured hippocampal neurons, and I compared the fluorescent ratio with the probability ratio obtained in 5.1, finding that they are in good agreement.
In chapter 7, I compared the real dendritic morphologies imaged by one of our collaborators Ali Karimi with the optimal branching rule obtained in Sec. 4.1 and I calculated the cost for not having optimal branching radii.
Finally, in Chapter 8, I used the knowledge of the branching statistics gathered in 5.3 to simulate the protein profile on three different classes of neurons: pyramidal neurons, granule neuron, and Purkinje neurons. I compared the protein profile for surface and cytoplasmic neurons for each morphology for two different values of the diffusion length: λ = 109µm and λ = 473µm, both for optimized radii and symmetrical radii. I showed how the radii optimization reduces the protein requirement of a factor 10 4 for pyramidal neurons.