Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Vector quantization and signal compression
Vector quantization and signal compression
Introduction to data compression
Introduction to data compression
Winner-Relaxing Self-Organizing Maps
Neural Computation
Data Compression: The Complete Reference
Data Compression: The Complete Reference
Quantifying the neighborhood preservation of self-organizing feature maps
IEEE Transactions on Neural Networks
A sequential algorithm for training the SOM prototypes based on higher-order recursive equations
Advances in Artificial Neural Systems
Speedup of color palette indexing in self-organization of Kohonen feature map
Expert Systems with Applications: An International Journal
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Wireless Brain Machine Interface (BMI) communication protocols are faced with the challenge of transmitting the activity of hundreds of neurons which requires large bandwidth. Previously a data compression scheme for neural activity was introduced based on Self Organizing Maps (SOM). In this paper we propose a dynamic learning rule for improved training of the SOM on signals with sparse events which allows for more representative prototype vectors to be found, and consequently better signal reconstruction. This work was developed with BMI applications in mind and therefore our examples are geared towards this type of signals. The simulation results show that the proposed strategy outperforms conventional vector quantization methods for spike reconstruction.