Name
Abstract | ||
|---|---|---|
In this paper the use of topological clustering for decoding population neuronal responses and reducing stimulus features is described. The discrete spike trains, recorded in rat somatosensory cortex in response to sinusoidal vibrissal stimulations characterised by different frequencies and amplitudes, are first interpreted to continuous temporal activities by convolving with a decaying exponential filter. Then the self-organising map is utilised to cluster the continuous responses. The result is a topologically ordered clustering of the responses with respect to the stimuli. The clustering is formed mainly along the product of amplitude and frequency of the stimuli. Such grouping agrees with the energy coding result obtained previously based on spike counts and mutual information. To further investigate how the clustering preserves information, the mutual information between resulting stimulus grouping and responses has been calculated. The cumulative mutual information of the clustering resembles closely that of the energy grouping. It suggests that topological clustering can naturally find underlying stimulus-response patterns and preserve information among the clusters. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-87559-8_57 | ICANN (2) |
Keywords | Field | DocType |
continuous response,discrete spike train,energy grouping,stimulus grouping,mutual information,spike count,continuous temporal activity,topological clustering,stimulus feature,cumulative mutual information,decoding population neuronal responses,cumulant,clustering,somatosensory cortex | Population,Coding (social sciences),Barrel cortex,Artificial intelligence,Stimulus (physiology),Cluster analysis,Topology,Exponential function,Pattern recognition,Mutual information,Decoding methods,Machine learning,Mathematics | Conference |
Volume | ISSN | Citations |
5164 | 0302-9743 | 3 |
PageRank | References | Authors |
0.41 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hujun Yin | 1 | 384 | 30.13 |
| Stefano Panzeri | 2 | 404 | 62.09 |
| Zareen Mehboob | 3 | 15 | 2.98 |
| Mathew E. Diamond | 4 | 32 | 5.86 |