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In this demonstration we present a spiking neural network architecture for audio samples classification using SpiNNaker. The network consists of different leaky integrate-and-fire neuron layers. The connections between them are trained using firing rate based algorithms. Tests use sets of pure tones with frequencies that range from 130.813 to 1396.91 Hz. Audio signals coming from the computer are...
Several studies have focused on classifying behavioral patterns in wildlife and captive species to monitor their activities and so to understanding the interactions of animals and control their welfare, for biological research or commercial purposes. The use of pattern recognition techniques, statistical methods and Overall Dynamic Body Acceleration (ODBA) are well known for animal behavior recognition...
The study and monitoring of wildlife has always been a subject of great interest. Studying the behavior of wild animals is a difficult task due to the difficulties of tracking and classifying their actions. Nowadays, technology allows designing low-cost systems that make these tasks easier to carry out, and some of these systems produce good results; however, none of them obtains a high-accuracy classification...
Neuromorphic systems are engineering solutions that take inspiration from biological neural systems. They use spike-or event-based representation and codification of the information. This codification allows performing complex computations, filters, classifications and learning in a pseudo-simultaneous way. Small incremental processing is done per event, which shows useful results with very low latencies...
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