Talk by Dr. Ryota Kobayashi
Principles of Informatics Research Division, National Institute of Informatics, Tokyo, Japan
- 22.Jul.2013 15:00
- 22.Jul.2013 16:30
- INM-6, Bldg. 15.22, Seminar Room 3009, 1. OG
Inferring synaptic connections from multiple spike train data
Significant correlations in neuronal activity are defined as“functional connections” between pairs of neurons. Characteristics of the functional connectivity in the brain have illustrated how neurons transmit information cooperatively. On the other hand, it is still unclear how the derived functional connectivity is related to underlying synaptic connectivity. Here, we developed a coupled escape rate model (CERM) to infer synaptic connections from multiple neural spike train data. We applied this method as well as the functional connectivity methods, i.e., transfer entropy and cross-correlation, to simulated multi-neuronal activities generated by a cortical network model, which consists of thousands of biophysically detailed neurons (Kitano & Fukai, 2007). We also applied these methods to the spike data generated by the cortical network model with different topologies of synaptic connectivity (regular, small-world and random). Our results indicate that all the methods perform better for highly clustered (regular and small-world) networks than for random networks. CERM performs best especially in non-regular networks. Overall, CERM method is most suitable to infer synaptic connections from multi-neuronal spike activities although it involves high computational cost.