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Statistical data assimilation for estimating electrophysiology simultaneously with connectivity within a biological
1Department of Physics, New York Institute of Technology, New York, New York 10023, USA and Department of Astrophysics, American Museum of Natural History, New York, New York 10024, USA.
Physical Review. E
|February 20, 2020
Summary
Data assimilation (DA) estimates neuron electrical properties and synaptic connections. This method successfully recovers network activity modes using chaotic currents and membrane voltage measurements.
Area of Science:
- Computational Neuroscience
- Systems Neuroscience
- Biophysics
Background:
- Estimating electrophysiological parameters and synaptic connectivity in neural networks is crucial for understanding brain function.
- Existing methods often struggle with the complexity and non-convexity of neural system models.
Purpose of the Study:
- To develop and validate a data assimilation (DA) method for simultaneous estimation of neuronal electrophysiological parameters and synaptic connectivity.
- To demonstrate the method's ability to identify distinct functional modes of network activity.
Main Methods:
- Data assimilation framed as an optimization problem with a combined measurement and model error cost function.
- Iterative reweighting technique to navigate non-convex cost function landscapes and find local minima.
- Application to a small model biological network with two distinct activity modes.
Main Results:
- The DA procedure successfully recovered two functional modes: simultaneous neuronal firing and sequential pattern generation.
- Effective recovery required chaotic electrical current stimulation and measurement of all neuronal membrane voltages.
- The method demonstrated model order reduction, identifying the essential dimensionality for reproducing measurements.
Conclusions:
- Data assimilation offers a powerful approach for characterizing electrophysiological parameters and synaptic connectivity in neural circuits.
- This proof-of-concept supports the potential of DA to guide experimental designs for studying small, isolatable neural circuits.
- The findings highlight the importance of specific stimulation and measurement strategies for successful parameter estimation.
