Simultaneous Bayesian Estimation of Excitatory and Inhibitory Synaptic Conductances by Exploiting Multiple Recorded...

Area of Science:

• Computational Neuroscience
• Systems Neuroscience
• Statistical Modeling

Background:

• Accurate inference of excitatory and inhibitory synaptic conductances (SCs) is crucial for understanding neural circuit function.
• Conventional single-trial Bayesian methods often yield suboptimal SC estimates when multiple trials are available, as they ignore shared synaptic input statistics across trials.

Purpose of the Study:

• To develop an advanced statistical method that leverages multiple recorded trials for improved inference of excitatory and inhibitory SCs.
• To enhance the estimation of neural circuit dynamics by extracting common synaptic input statistics across trials.

Main Methods:

• Developed a novel expectation maximization (EM) algorithm integrated with parallel Kalman filters (MtKF) or particle filters (MtPF).
• The algorithm iteratively updates common synaptic input statistics across multiple trials.
• These updated statistics are then used for individual trial SC inference.

Main Results:

• Demonstrated superior performance of multi-trial Kalman filtering (MtKF) and particle filtering (MtPF) compared to single-trial methods.
• Showed that both excitatory and inhibitory SCs can be reliably inferred using an optimal level of current injection.
• Validated the robustness and applicability of the technique through simulations and application to *in vivo* rat barrel cortex data.

Conclusions:

• The proposed multi-trial approach significantly improves the accuracy of synaptic conductance inference.
• This method offers a more robust and comprehensive understanding of neural information processing by utilizing available trial data effectively.
• The validated technique holds promise for analyzing complex neural recordings in various experimental settings.