Connectivity inference from neural recording data: Challenges, mathematical bases and research directions

Area of Science:

• Neuroscience
• Computational Biology
• Data Science

Background:

• Neural connectivity is crucial for understanding brain function.
• Inferring connectivity from neural activity data presents significant biophysical and technical challenges.
• Existing methods vary in their mathematical foundations and ability to address these challenges.

Purpose of the Study:

• To review and categorize computational methods for neural connectivity inference.
• To identify and discuss challenges in the neural data processing pipeline.
• To explore current research and future directions in the field.

Main Methods:

• Review of computational methods for connectivity inference.
• Categorization of methods into descriptive model-free and generative model-based approaches.
• Analysis of representative studies within each category.

Main Results:

• Identification of biophysical and technical challenges in connectivity inference.
• Clarification of which methods address specific challenges.
• Overview of the strengths and limitations of different approaches.

Conclusions:

• Connectivity inference remains a complex challenge in neuroscience.
• Both model-free and model-based approaches offer valuable tools.
• Further research is needed to overcome existing limitations and advance the field.