Abstract
Neuronal signal integration and information processing in cortical neuronal networks critically depend on the organization of synaptic connectivity. Because of the challenges involved in measuring a large number of neurons, synaptic connectivity is difficult to determine experimentally. Current computational methods for estimating connectivity typically rely on the juxtaposition of experimentally available neurons and applying mathematical techniques to compute estimates of neural connectivity. However, since the number of available neurons is very limited, these connectivity estimates may be subject to large uncertainties. We use a morpho-density field approach applied to a vast ensemble of model-generated neurons. A morpho-density field (MDF) describes the distribution of neural mass in the space around the neural soma. The estimated axonal and dendritic MDFs are derived from 100,000 model neurons that are generated by a stochastic phenomenological model of neurite outgrowth. These MDFs are then used to estimate the connectivity between pairs of neurons as a function of their inter-soma displacement. Compared with other density-field methods, our approach to estimating synaptic connectivity uses fewer restricting assumptions and produces connectivity estimates with a lower standard deviation. An important requirement is that the model-generated neurons reflect accurately the morphology and variation in morphology of the experimental neurons used for optimizing the model parameters. As such, the method remains subject to the uncertainties caused by the limited number of neurons in the experimental data set and by the quality of the model and the assumptions used in creating the MDFs and in calculating estimating connectivity. In summary, MDFs are a powerful tool for visualizing the spatial distribution of axonal and dendritic densities, for estimating the number of potential synapses between neurons with low standard deviation, and for obtaining a greater understanding of the relationship between neural morphology and network connectivity.
Highlights
The dynamics of neuronal network activity, which underlies all brain functions, depend crucially on the pattern and strengths of synaptic connections between neurons
Since this yields a considerable computational burden we investigate the influence of this third assumption on the estimated connectivity values. In both [2] and [4] the methods were applied to limited data of experimentally reconstructed neurons. We demonstrate that this sparsity of experimental data leads to a large variation in the connectivity estimates, whereas connectivity values based on morpho-density field (MDF) calculated from a vast ensemble of modelgenerated neurons considerably reduce this variability
Estimated morpho-density fields Figure 2 displays the value of the estimated MDF M^ d for the dendritic arbors near the neural soma as a function of the radial distance rd and height zd, based on an ensemble of 100,000 L2/3 pyramidal NETMORPH-generated neurons
Summary
The dynamics of neuronal network activity, which underlies all brain functions, depend crucially on the pattern and strengths of synaptic connections between neurons. The formation of synaptic connections between neurons requires physical contact between axonal segments of one neuron and dendritic segments of another neuron. We estimate the MDFs using a vast ensemble of model-generated neurons that have been shown to be realistic representations of their biological counterparts based on many statistical properties [5]. These MDFs are used for estimating synaptic connectivity between neurons. We thereby test the influence of sparsity of morphological data and the impact of assumptions involved in the generation of MDFs. Lastly, we use our MDF approach to generate neural networks and investigate the efficiency of their connectivity patterns
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