Our understanding of neural population coding has been limited by a lack of analysis methods to characterize spiking data from large populations. The biggest challenge comes from the fact that the number of possible network activity patterns scales exponentially with the number of neurons recorded (~2Neurons). Here we introduce a new statistical method for characterizing neural population activity that requires semiindependent fitting of only as many parameters as the square of the number of neurons, requiring drastically smaller data sets and minimal computation time. The model works by matching the population rate.
ASJC Scopus subject areas
- Arts and Humanities (miscellaneous)
- Cognitive Neuroscience