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Coding properties of spiking neurons: reverse and cross-correlations
1Swiss Federal Institute of Technology Lausanne, Laboratory of Computational Neuroscience. wulfram.gerstner@epfl.ch
Summary
This study analytically calculates spike-triggered and cross-correlations for a spiking neuron model with escape noise. It reveals how neuronal parameters influence these correlations and their role in Hebbian plasticity.
Area of Science:
- Computational Neuroscience
- Theoretical Neuroscience
- Neural Coding
Background:
- Spike-triggered averaging (reverse correlations) reveals typical inputs preceding a neuron's spike.
- Cross-correlations quantify the probability of an output spike following a presynaptic input spike.
Purpose of the Study:
- To analytically calculate reverse and cross-correlations for a spiking neuron model incorporating escape noise.
- To investigate the impact of neuronal parameters on correlation function forms.
- To explore the role of cross-correlations in spike-time dependent Hebbian plasticity.
Main Methods:
- Analytical calculation of reverse and cross-correlations.
- Utilizing a spiking neuron model with escape noise.
- Framework of population theory and discussion of population density methods.
Main Results:
- Derived analytical expressions for reverse and cross-correlations.
- Illustrated the influence of membrane time constant, noise level, and mean firing rate on correlation functions.
- Demonstrated the connection between population activity equations and population density methods.
Conclusions:
- Neuronal parameters significantly shape spike-triggered and cross-correlation functions.
- Cross-correlations are integral to understanding spike-time dependent Hebbian plasticity.
- The study provides a theoretical framework for analyzing neural responses and plasticity.