Compressive Sensing Inference of Neuronal Network Connectivity in Balanced Neuronal Dynamics

Area of Science:

• Neuroscience
• Applied Mathematics
• Network Science
• Computational Neuroscience

Background:

• Understanding neuronal network structure is crucial for deciphering brain function.
• Reconstructing neuronal connectivity is a significant theoretical and experimental challenge.
• Neuronal networks often operate in a balanced state with balanced excitatory and inhibitory inputs.

Purpose of the Study:

• To develop a theoretical framework for efficient reconstruction of neuronal network connectivity.
• To investigate the role of network dynamical regimes, particularly the balanced state, in connectivity reconstruction.
• To leverage compressive sensing principles for mapping sparse neuronal connections.

Main Methods:

• Developed a compressive sensing theoretical framework tailored for neuronal networks.
• Utilized model neurons operating in a balanced dynamical regime.
• Measured individual neuronal activity in response to a small set of random stimuli over a short timescale.

Main Results:

• The compressive sensing framework enables efficient reconstruction of network connections.
• Highest fidelity reconstructions were achieved when neuronal networks operated in the balanced state.
• The method is effective even with sparse connectivity and limited measurements.

Conclusions:

• The balanced state in neuronal networks may be an evolutionary adaptation for optimal information encoding.
• The developed compressive sensing methodology provides an efficient tool for mapping neuronal connectivity.
• The framework is potentially generalizable to other model networks and experimental paradigms.