Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
A unified framework for model-based clustering
The Journal of Machine Learning Research
Adaptive mixtures of local experts
Neural Computation
Discovering clusters in motion time-series data
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Uncovering spatial topology represented by rat hippocampal population neuronal codes
Journal of Computational Neuroscience
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This paper builds upon the previous Brain Machine Interface (BMI) signal processing models that require apriori knowledge about the patient's arm kinematics. Specifically, we propose an unsupervised hierarchical clustering model that attempts to discover both the interdependencies between neural channels and the self-organized clusters represented in the spatial-temporal neural data. Results from both synthetic data generated with a realistic neural model and real BMI data are used to quantify the performance of the proposed methodology. Since BMIs must work with disabled patients who lack arm kinematic information, the clustering work described within this paper is relevant for future BMIs.