Paper Info
Title
Topological Clustering Of Synchronous Spike Trains
Abstract
This paper describes a topological clustering of synchronous spike trains recorded in rat somatosensory cortex in response to sinusoidal vibrissal stimulations characterized by different frequencies and amplitudes. Discrete spike trains are first interpreted as continuous synchronous activities by a smoothing filter such as causal exponential function. Then clustering is performed using the self-organizing map, which yields topologically ordered clusters of responses with respect to the stimuli. The grouping is formed mainly along the product of amplitude and frequency of the stimuli. This result coincides with the result obtained previously using mutual Information analysis on the same data set That is, the response is proportional in logarithm to the energy of the vibration. It suggests that such clustering can naturally find underlying stimulus-response patterns and it also seems to associate the spike-count based mutual information decoding with temporal patterns of the neuronal activities. The study also shows that causal decaying exponential kernel is better than noncausal Gaussian kernel in interpreting the discrete spike trains into continues ones and produces better clusters.
Year
DOI
Venue
2008
10.1109/IJCNN.2008.4634357
2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8
Keywords
Field
DocType
mutual information,somatosensory cortex,neuronal activity,exponential function,neural networks,artificial neural networks,gaussian kernel
Computer science,Artificial intelligence,Logarithm,Cluster analysis,Artificial neural network,Gaussian function,Amplitude,Kernel (linear algebra),Topology,Exponential function,Pattern recognition,Mutual information,Machine learning
Conference
ISSN
Citations 
PageRank 
2161-4393
2
0.45
References 
Authors
1
4
Name
Order
Citations
PageRank
Zareen Mehboob1152.98
Stefano Panzeri240462.09
Mathew E. Diamond3325.86
Hujun Yin41577149.88