Information-geometric method for multiple neuronal spike data analysis

This dissertation explores a novel statistical technique—information geometric method for theory and its application in analysis of multiple neuronal spike data. The previous studies have indicated that information-geometric method provides a powerful tool of estimating neuronal interactions from observed spiking data. However, these studies were conducted based on simplified neural network structure, which has limitations in the real brain. We systematically extended the previous studies by using intensive mathematical analysis and numerical simulations of realistic and complex neural network. The studies show that information geometric approach provide robust estimation for the sum of the connection weights between neuronal pairs in a complex recurrent network, providing a way of investigating the underlying network structures from neuronal spike data.

Author

• Nie, Yimin

• Information geometry
• neural network
• neuroscience
• spike data

Identifier

hdl:10133/3528

OAI identifier oai:identifier

oai:opus.uleth.ca:10133/3528