Probing the organization of interactions within and across neuronal populations is a promising approach to understanding the principles of brain processing. The rapidly advancing technical capabilities to record from hundreds of neurons in parallel open up new possibilities to disentangle the correlative structure within neuronal networks. However, the complexity of these massive data streams calls for novel, tractable analysis tools that exploit the parallel aspect of the data. Due to the fundamental computational and theoretical difficulties in describing interactions within a large set of neurons scientists are in search for the optimal models and mathematical tools to tackle this challenge. This workshop was held in Stockholm, Sweden, as part of the 2013 Neuroinformatics Conference, and showcased a few of these different approaches and analysis tools to exploit electrophysiological data.
Analysis and Interpretation of Massively Parallel Electrophysiological Data
This lesson covers simultaneously recorded neurons in non-human primates coordinate their spiking activity in a sequential manner that mirrors the dominant wave propagation directions of the local field potentials.
This talk covers statistical analysis of spike train data, the modeling approach GLM, and the problem of assessing neural synchrony.
This talk covers statistical methods for characterizing neural population responses and extracting structure from high-dimensional neural data.
This presentation discusses research aimed at understanding the activity of single neurons and populations of neurons in the visual system.