title: Self-organizing maps with dynamic learning for signal reconstruction.
abstract: 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.
authors.affiliation: Computational NeuroEngineering Lab., University of Florida, Gainesville, FL 32611, United States. jeongho@cnel.ufl.edu
authors.forename: Jeongho
authors.initials: J
authors.lastname: Cho
authors.forename: António R C
authors.initials: AR
authors.lastname: Paiva
authors.forename: Sung-Phil
authors.initials: SP
authors.lastname: Kim
authors.forename: Justin C
authors.initials: JC
authors.lastname: Sanchez
authors.forename: José C
authors.initials: JC
authors.lastname: Príncipe
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D000818:Animals; D001921:Brain / Q000166:cytology / Q000502:physiology*; D006801:Humans; D016247:Information Storage and Retrieval; D007858:Learning; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D051381:Rats