Analysis and interpretation of massively parallel electrophysiological data

Analysis and interpretation of massively parallel electrophysiological data

By
INCF
Category
Neuroinformatics
Level
Intermediate

This workshop showcases a few different approaches and analysis tools to exploit electrophysiological data.

Workshop at Neuroinformatics 2013 in Stockholm, Sweden
Workshop title: Analysis and Interpretation of Massively Parallel Electrophysiological Data

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 should showcase a few of these different approaches.

Course Features
Large-scale spatio-temporal patterns in motor cortex involved in motor control
Statistical analysis of spike train data
The modeling approach GLM
The problem of assessing neural synchrony
Statistical methods for characterizing neural population
Understand the activity of single neurons and populations of neurons in the visual system.
Lessons of this Course
1
1
Large-scale spatio-temporal patterns in motor cortex involved in motor control
Duration:
26:54
Speaker:
Nicholas Hatsopoulos

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.

2
2
Point process regression models of neural synchrony
Duration:
25:17
Speaker:
Rob Kass

This talk covers statistical analysis of spike train data, the modeling approach GLM, and the problem of assessing neural synchrony.

3
3
Scalable nonparametric models for large-scale neural datasets
Duration:
26:15
Speaker:
Jonathan Pillow

This talk covers statistical methods for characterizing neural population responses and extracting structure from high-dimensional neural data.

4
4
Looking for canonical neural computations in the visual system
Duration:
28:23
Speaker:
Matteo Carandini

This presentation covers research to understand the activity of single neurons and populations of neurons in the visual system.

Recent courses