Macaque Cortex Connectome Model with Mean-Field Dynamics

Comprising simulation results from a multi-scale network model of vision-related areas in macaque cortex, employing mean-field reduction to incorporate activity constraints. The model was developed by Jannis Schuecker and published in 2020, exploring connectivity refinements to achieve realistic layer- and area-specific neural activity.

Use Cases

• Analyze the relationship between connectivity refinements and layer-specific activity patterns in the macaque cortex model.
• Investigate the role of system instability in shaping the collective dynamics of the simulated neural network.
• Validate mean-field theory predictions against simulation results for networks with known neuron gain functions.
• Identify critical network components for stable activity using constraints prohibiting quiescence and requiring global stability.

Strengths

• Model is grounded in physiological observations of macaque cortex.
• Applies mean-field reduction to bridge structural connectivity and network activity.
• Dataset is openly licensed under CC0-1.0, facilitating reuse.

Limitations

• The dataset is derived from a simulation model, not direct experimental measurements.
• Specific data volume, format, and structural details like row and column counts are unknown.
• Model focuses on vision-related areas of macaque cortex, limiting generalizability to other brain regions or species.

Provenance

Source

Jannis Schuecker via Dryad.

Collection Method

Computational simulation of a multi-scale neural network model using mean-field theory.

Freshness

Last updated in June 2020.

Geography

Model based on macaque cortex neuroanatomy.