Exploring the classification of biomedical signals using plasticity in recurrent networks of ...
Continuous monitoring of physiological biomarkers could help to detect and diagnose several health conditions at their early stages, such as cardiovascular and neurological diseases and as a result real-time processing of biomedical signals in a low-power fashion is becoming increasingly important. Recurrent neural networks (RNNs) are capable of learning features and long term dependencies from sequentiality and time-series data. Implementing RNNs on neuromorphic platforms can be a good solution to this problem. Starting from a simple RNN, we plan to investigate the spiking learning algorithms which can be implemented on a hardware platform. The final goal of the workgroup is to be able to classify EMG signals using BRIAN2 simulator.
|Tue, 24.04.2018||19:00 - 20:00||Sala Panorama|
|Wed, 25.04.2018||19:00 - 20:00||Sala panorama|
|Thu, 26.04.2018||11:00 - 12:00||outside|
|Fri, 27.04.2018||21:00 - 00:00||Sala panorama|
|Sat, 28.04.2018||15:00 - 16:00||Sala Panorama|
|Mon, 30.04.2018||14:00 - 16:00||Sala Panorama|
|Wed, 02.05.2018||15:00 - 16:00||Panorama|