A connectome manipulation framework for the systematic and reproducible study of structure–function relationships through simulations DOI Creative Commons
Christoph Pokorny, Omar Awile, James B. Isbister

и другие.

Network Neuroscience, Год журнала: 2024, Номер 9(1), С. 207 - 236

Опубликована: Дек. 2, 2024

Synaptic connectivity at the neuronal level is characterized by highly nonrandom features. Hypotheses about their role can be developed correlating structural metrics to functional But, prove causation, manipulations of would have studied. However, fine-grained scale which trends are expressed makes this approach challenging pursue experimentally. Simulations networks provide an alternative route study arbitrarily complex in morphologically and biophysically detailed models. Here, we present Connectome-Manipulator, a Python framework for rapid connectome large-scale network models Scalable Open Network Architecture TemplAte (SONATA) format. In addition creating or manipulating model, it provides tools fit parameters stochastic against existing connectomes. This enables replacement any with equivalent connectomes different levels complexity, transplantation features from one another, systematic study. We employed model rat somatosensory cortex two exemplary use cases: transplanting interneuron electron microscopy data simplified excitatory connectivity. ran series simulations found diverse shifts activity individual neuron populations causally linked these manipulations.

Язык: Английский

A connectome manipulation framework for the systematic and reproducible study of structure–function relationships through simulations DOI Creative Commons
Christoph Pokorny, Omar Awile, James B. Isbister

и другие.

Network Neuroscience, Год журнала: 2024, Номер 9(1), С. 207 - 236

Опубликована: Дек. 2, 2024

Synaptic connectivity at the neuronal level is characterized by highly nonrandom features. Hypotheses about their role can be developed correlating structural metrics to functional But, prove causation, manipulations of would have studied. However, fine-grained scale which trends are expressed makes this approach challenging pursue experimentally. Simulations networks provide an alternative route study arbitrarily complex in morphologically and biophysically detailed models. Here, we present Connectome-Manipulator, a Python framework for rapid connectome large-scale network models Scalable Open Network Architecture TemplAte (SONATA) format. In addition creating or manipulating model, it provides tools fit parameters stochastic against existing connectomes. This enables replacement any with equivalent connectomes different levels complexity, transplantation features from one another, systematic study. We employed model rat somatosensory cortex two exemplary use cases: transplanting interneuron electron microscopy data simplified excitatory connectivity. ran series simulations found diverse shifts activity individual neuron populations causally linked these manipulations.

Язык: Английский

Процитировано

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