Multiple models to capture the variability in biological neurons and networks

Area of Science:

• Computational Neuroscience
• Systems Neuroscience
• Biophysics

Background:

• Synaptic and intrinsic neuronal properties exhibit significant variability across different species and identified neurons.
• This biological variability complicates the development of accurate computational models of neural function.
• Understanding how complex system dynamics emerge from component interactions is a key challenge.

Purpose of the Study:

• To review the challenges in building computational models of neurons and circuits.
• To propose an alternative modeling approach using a population of models.
• To highlight the benefits of this approach for uncovering compensatory mechanisms.

Main Methods:

• Review of existing experimental data on neuronal property variability.
• Discussion of limitations in single, generic computational modeling approaches.
• Proposal for constructing a population of models mirroring experimental data populations.

Main Results:

• A single generic model cannot capture the full spectrum of neuronal and circuit behavior observed experimentally.
• A population of models, each with different parameters but similar overall behavior, can better represent experimental data.
• This population-based modeling approach facilitates the discovery of novel compensatory mechanisms.

Conclusions:

• Modeling neuronal and circuit function requires embracing biological variability.
• A population of computational models is a more effective strategy than a single generic model.
• Studying model populations reveals compensatory strategies essential for neural function and robustness.