Pycabnn: Efficient and Extensible Software to Construct an Anatomical Basis for a Physiologically Realistic Neural Network Model

Physiologically detailed models of neural networks are an important tool for studying how biophysical mechanisms impact neural information processing. An important, fundamental step in constructing such a model is determining where neurons are placed and how they connect to each other, based on known anatomical properties and constraints given by experimental data. Here we present an open-source software tool, pycabnn, that is dedicated to generating an anatomical model, which serves as the basis of a full network model. In pycabnn, we implemented efficient algorithms for generating physiologically realistic cell positions and for determining connectivity based on extended geometrical structures such as axonal and dendritic morphology. We demonstrate the capabilities and performance of pycabnn by using an example, a network model of the cerebellar granular layer, which requires generating more than half a million cells and computing their mutual connectivity. We show that pycabnn is efficient enough to carry out all the required tasks on a laptop computer within reasonable runtime, although it can also run in a parallel computing environment. Written purely in Python with limited external dependencies, pycabnn is easy to use and extend, and it can be a useful tool for computational neural network studies in the future.

Systems Properties and Spatiotemporal Regulation of Cell Position Variability during Embryogenesis

An intriguing question in developmental biology is how do developmental processes achieve high reproducibility among individuals? An in-depth analysis of information contained in phenotypic variability provides an important perspective to address this question. In this work, we present a quantitative and functional analysis of cell position variability during Caenorhabditis elegans embryogenesis. We find that cell position variability is highly deterministic and regulated by intrinsic and extrinsic mechanisms. Positional variability is determined by cell lineage identity and is coupled to diverse developmental properties of cells, including embryonic localization, cell contact, and left-right symmetry. Temporal dynamics of cell position variability are highly concordant, and fate specification contributes to a systems-wide reduction of variability that could provide a buffering strategy. Positional variability is stringently regulated throughout embryogenesis and cell-cell junctions function to restrict variability. Our results provide insight into systems properties and spatiotemporal control of cellular variability during development.

Histogenetic processes leading to the laminated neocortex: migration is only a part of the story

The principal events of neocortical histogenesis were anticipated by work published prior to the 20th century. These were neuronal proliferation and migration and the complex events of cortical pattern formation leading to a laminated architecture where each layer is dominated by a principal neuronal class. Work that has followed has extended the knowledge of the workings of the proliferative epithelium, cellular mechanisms of migration and events through which cells are winnowed and then differentiate once their postmigratory positions are established. Work yet ahead will emphasize mechanisms that coordinate the molecular events that integrate proliferation and cell class specification in relation to the final neocortical neural system map.