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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 |
| Pages | 2926-2929 |
| Number of pages | 4 |
| DOIs | |
| State | Published - Dec 1 2009 |
| Externally published | Yes |
| Event | 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 - Minneapolis, MN, United States Duration: Sep 2 2009 → Sep 6 2009 |
Other
| Other | 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 |
|---|---|
| Country | United States |
| City | Minneapolis, MN |
| Period | 9/2/09 → 9/6/09 |
Fingerprint
ASJC Scopus subject areas
- Cell Biology
- Developmental Biology
- Biomedical Engineering
- Medicine(all)
Cite this
Estimation and visualization of neuronal functional connectivity in motor tasks. / Li, N.; Seth, Sohan; Park, Il; Sanchez, Justin C.; Principe, Jose C.
Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009. 2009. p. 2926-2929 5333991.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Estimation and visualization of neuronal functional connectivity in motor tasks
AU - Li, N.
AU - Seth, Sohan
AU - Park, Il
AU - Sanchez, Justin C.
AU - Principe, Jose C.
PY - 2009/12/1
Y1 - 2009/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77951008497&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77951008497&partnerID=8YFLogxK
U2 - 10.1109/IEMBS.2009.5333991
DO - 10.1109/IEMBS.2009.5333991
M3 - Conference contribution
C2 - 19964602
AN - SCOPUS:77951008497
SN - 9781424432967
SP - 2926
EP - 2929
BT - Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009
ER -