Inferring presynaptic population spiking from single-trial membrane potential recordings

Area of Science:

• Neuroscience
• Computational Neuroscience
• Electrophysiology

Background:

• Cortical neuron membrane potential reflects network activity.
• Separating excitatory and inhibitory synaptic inputs from single trials is crucial but challenging.
• Existing methods often require multiple trials or have limitations in accuracy and temporal resolution.

Purpose of the Study:

• To develop and validate a novel method for extracting excitatory and inhibitory synaptic input time courses from single-trial membrane potential recordings.
• To overcome limitations of existing methods that rely on trial averaging or have restricted applicability.

Main Methods:

• The proposed method leverages differences in time constants and reversal potentials of excitatory and inhibitory synaptic currents.
• It allows for the untangling of distinct conductance types from membrane potential data.
• The technique is designed for single-trial analysis without averaging.

Main Results:

• The method demonstrated high-quality estimation of excitatory synaptic conductance changes and presynaptic population spikes in a model neuron.
• Accurate estimation of excitatory synaptic conductance time course was achieved in real cortical neurons.
• Complex network activity was revealed in recordings from intact brain cortical pyramidal neurons.

Conclusions:

• An efficient method for estimating excitatory and inhibitory synaptic conductances from single-trial membrane potential recordings has been developed and validated.
• The method's simplicity is expected to facilitate its widespread adoption in neuroscience research.
• This technique offers a powerful tool for analyzing neural network dynamics at the single-trial level.