Estimation and Visualization of Neuronal Functional Connectivity in Motor Tasks. Li, L., Seth, S., Park, I., Sanchez, J. C., & Príncipe, J. C. 2009. doi abstract bibtex In brain-machine interface (BMI) modeling, the firing patterns of hundreds of neurons are used to reconstruct a variety of kinematic variables. The large number of neurons produces an explosion in the number of free parameters, which affects model generalization. This paper proposes a model-free measure of pairwise neural dependence to rank the importance of neurons in neural to motor mapping. Compared to a model-dependent approach such as sensitivity analysis, sixty percent of the neurons with the strongest dependence coincide with the top 10 most sensitive neurons trained through the model. Using this data-driven approach that operates on the input data alone, it is possible to perform neuron selection in a more efficient way that is not subject to assumptions about decoding models. To further understand the functional dependencies that influence neural to motor mapping, we use an open source available graph visualization toolkit called Prefuse to visualize the neural dependency graph and quantify the functional connectivity in motor cortex. This tool when adapted to the analysis of neuronal recordings has the potential to easily display the relationships in data of large dimension.
@CONFERENCE{Li2009,
author = {Lin Li and Sohan Seth and Il Park and Justin C. Sanchez and Jos\'e
C. Pr\'incipe},
title = {Estimation and Visualization of Neuronal Functional Connectivity
in Motor Tasks},
booktitle = {Annual International Conference of the IEEE Engineering in Medicine
and Biology Society (EMBS)},
year = {2009},
abstract = {In brain-machine interface (BMI) modeling, the firing patterns of
hundreds of neurons are used to reconstruct a variety of kinematic
variables. The large number of neurons produces an explosion in the
number of free parameters, which affects model generalization. This
paper proposes a model-free measure of pairwise neural dependence
to rank the importance of neurons in neural to motor mapping. Compared
to a model-dependent approach such as sensitivity analysis, sixty
percent of the neurons with the strongest dependence coincide with
the top 10 most sensitive neurons trained through the model. Using
this data-driven approach that operates on the input data alone,
it is possible to perform neuron selection in a more efficient way
that is not subject to assumptions about decoding models. To further
understand the functional dependencies that influence neural to motor
mapping, we use an open source available graph visualization toolkit
called Prefuse to visualize the neural dependency graph and quantify
the functional connectivity in motor cortex. This tool when adapted
to the analysis of neuronal recordings has the potential to easily
display the relationships in data of large dimension.},
doi = {10.1109/IEMBS.2009.5333991},
owner = {memming},
timestamp = {2009.12.15}
}
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