Abstract
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.
Highlights
Realistic neural network simulations are becoming increasingly important in neurobiology studies as they allow investigating experimentally identified biophysical features of a system (Einevoll et al, 2019)
This model is composed of two cell types, excitatory granular cells (GC) and inhibitory Golgi cells (GoC)
Both types of neurons receive external inputs from mossy fibers (MF) that enter from the bottom of the granular layer, and branch to form glomeruli (Glo), distinctive intertwinings between MF presynaptic terminals, granule cells (GC) dendrites, and GoC axons
Summary
Realistic neural network simulations are becoming increasingly important in neurobiology studies as they allow investigating experimentally identified biophysical features of a system (Einevoll et al, 2019). A large number of network models rely on random anatomical configurations, such as a random positioning of cells in space, and/or random connectivity between them, even when physiological realism is pursued. Those models contradict a growing number of experimental discoveries that reveal non-random anatomical features in diverse neural systems. The probability of electric and synaptic connections between two cells depends on their mutual distance (Dugué et al, 2009; Rieubland et al, 2014).
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