Synaptic input statistics tune the variability and reproducibility of neuronal responses

Area of Science:

• Neuroscience
• Computational Neuroscience
• Computational Biology

Background:

• Neurons integrate excitatory and inhibitory synaptic inputs.
• Neuronal output variability and reproducibility are key characteristics of neural function.

Purpose of the Study:

• To differentiate neuronal variability from reproducibility.
• To investigate how synaptic input characteristics influence these neuronal output measures.

Main Methods:

• Simulating synaptic waveforms using presynaptic Poisson trains.
• Presenting these waveforms to both living and computational neurons.
• Analyzing neuronal firing rate, variability, and reproducibility.

Main Results:

• Neuronal average output (firing rate) is determined by the difference between excitatory and inhibitory event rates.
• Neuronal variability is determined by the sum of excitatory and inhibitory event rates.
• For ideal synapses, reproducibility is equivalent to variability; for realistic synapses, it is distinct and depends on input synchrony.

Conclusions:

• Neuronal variability and reproducibility are distinct concepts, though related.
• Synaptic input synchrony is a critical factor for reproducibility in conductance-based neuronal models.
• Understanding these principles is crucial for modeling neural circuits and function.