Cross-correlation between neurons: a source of information about the nervous system

Area of Science:

• Neuroscience
• Computational Neuroscience
• Neural Coding

Background:

• Spiking neuron activity generates impulse trains, which are inherently stochastic.
• Impulse trains from related neurons often exhibit statistical dependence, indicating neural communication.
• This dependence can arise from shared inputs or direct synaptic interactions between neurons.

Purpose of the Study:

• To investigate the statistical properties of impulse trains from related neurons.
• To differentiate between common input and direct synaptic influence as sources of neural correlation.
• To infer how correlating influences interact with cellular variability.

Main Methods:

• Analysis of interval distributions preceding and following coincident firings.
• Comparison of these distributions with overall inter-impulse intervals within each train.
• Application to two distinct neural systems: common input and presynaptic-postsynaptic relationships.

Main Results:

• The analysis successfully inferred restrictions on how correlating influences interact with variability.
• Distinct patterns in interval distributions were observed for common input versus direct synaptic influence.
• The methods provided insights into the mechanisms underlying neural synchrony.

Conclusions:

• Statistical analysis of inter-spike intervals can elucidate the nature of neural interactions.
• Understanding these interactions is crucial for deciphering neural coding and network dynamics.
• The study provides a framework for analyzing correlated neuronal firing in different biological contexts.