Abstract
Visualizing the collective modulation of multiple neurons during a known behavioral task is useful for exploratory analysis, but handling the large dimensionality of neural recordings is challenging. We further investigate using static dimensionality reduction techniques on neural firing rate data during an arm movement task. This lower-dimensional representation of the data is able to capture the neural states corresponding to different portions of the behavior task. A simulation using a dynamical model lends credence to the ability of the technique to generate a representation that preserves underlying dynamics of the model. This technique is a straightforward way to extract a useful visualization for neural recordings during brain-machine interface tasks. Meaningful visualization confirms underlying structure in data, which can be captured with parametric modeling.
| Original language | English |
|---|---|
| Title of host publication | 2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011 |
| Pages | 144-147 |
| Number of pages | 4 |
| DOIs | |
| State | Published - Jul 20 2011 |
| Event | 2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011 - Cancun, Mexico Duration: Apr 27 2011 → May 1 2011 |
Other
| Other | 2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011 |
|---|---|
| Country | Mexico |
| City | Cancun |
| Period | 4/27/11 → 5/1/11 |
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ASJC Scopus subject areas
- Neuroscience(all)
Cite this
Latent state visualization of neural firing rates. / Brockmeier, Austin J.; Kriminger, Evan G.; Sanchez, Justin C.; Príncipe, José C.
2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011. 2011. p. 144-147 5910509.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Latent state visualization of neural firing rates
AU - Brockmeier, Austin J.
AU - Kriminger, Evan G.
AU - Sanchez, Justin C.
AU - Príncipe, José C.
PY - 2011/7/20
Y1 - 2011/7/20
N2 - Visualizing the collective modulation of multiple neurons during a known behavioral task is useful for exploratory analysis, but handling the large dimensionality of neural recordings is challenging. We further investigate using static dimensionality reduction techniques on neural firing rate data during an arm movement task. This lower-dimensional representation of the data is able to capture the neural states corresponding to different portions of the behavior task. A simulation using a dynamical model lends credence to the ability of the technique to generate a representation that preserves underlying dynamics of the model. This technique is a straightforward way to extract a useful visualization for neural recordings during brain-machine interface tasks. Meaningful visualization confirms underlying structure in data, which can be captured with parametric modeling.
AB - Visualizing the collective modulation of multiple neurons during a known behavioral task is useful for exploratory analysis, but handling the large dimensionality of neural recordings is challenging. We further investigate using static dimensionality reduction techniques on neural firing rate data during an arm movement task. This lower-dimensional representation of the data is able to capture the neural states corresponding to different portions of the behavior task. A simulation using a dynamical model lends credence to the ability of the technique to generate a representation that preserves underlying dynamics of the model. This technique is a straightforward way to extract a useful visualization for neural recordings during brain-machine interface tasks. Meaningful visualization confirms underlying structure in data, which can be captured with parametric modeling.
UR - http://www.scopus.com/inward/record.url?scp=79960360336&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79960360336&partnerID=8YFLogxK
U2 - 10.1109/NER.2011.5910509
DO - 10.1109/NER.2011.5910509
M3 - Conference contribution
AN - SCOPUS:79960360336
SN - 9781424441402
SP - 144
EP - 147
BT - 2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011
ER -