Noise suppression and surplus synchrony by coincidence detection

Area of Science:

• Computational neuroscience
• Neural dynamics

Background:

• The role of neuronal correlations in brain function is debated.
• Existing analytical methods struggle to model spike synchrony.

Purpose of the Study:

• Investigate contributions of common synaptic afferents and synchronized inputs to correlated neuronal spiking.
• Analyze how neuronal networks transmit correlated signals.

Main Methods:

• Direct simulation of neuronal networks.
• Extended diffusion approximation to pulse-coupling models.
• Analysis of integrate-and-fire neurons receiving correlated inputs.

Main Results:

• Identified two regimes of correlation transmission.
• Low correlation regime: transmission coefficient is <1 but increases with input synchrony.
• High correlation regime: output correlation can exceed input correlation due to non-linear processing.

Conclusions:

• Common inputs and synchronized inputs have distinct effects on neuronal correlation.
• Non-linear neural processing amplifies synchrony-coded signals.
• Findings elucidate mechanisms of information processing in cortical networks.