Parametric Anatomical Modeling (PAM) Computational Neuroscience


Here is a short introduction into PAM.

With Parametric Anatomical Modeling (PAM), we propose a technique and a Python implementation to create artificial neural networks that meet connectivity patterns and connection lengths of large scale neural networks.

The basic idea of PAM is to trace neural, synaptic and intermediate layers from anatomical data and relate those layers to each other. With a set of mapping techniques, complex relationships between those layers can be defined to determine how axonal and dendritic projections traverse through space and where synapses are formed.


The manuscript refers to a video that explains figure 7a in more detail. This video can be found here.

More videos about PAM can be found here.


PAM is available as an Addon for Blender and can be downloaded from a repository on Github. An importer for the neural network simulator NEST is available in a separate repository.

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science.

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