Neuromorphic Information Processing for Intelligent and Cognitive Data Processing (Prof. Bogdan)
Real neuromorphic information processing requires models closer to the neurological dynamical system. In this context, so-called Spiking Neural Networks (SNN), which are based on Integrate-and-Fire (IaF) neurons, already outperform Deep Learning, e.g. in recognition of handwritten patterns. However, SNN still lacks reliable and universal training algorithms and dynamic changes in a trained network are not yet possible. This is due to the fact that the synaptic plasticity required here is still not implemented in IaF neurons. With our research we envision dynamic synapses based on the Modified Stochastic Synaptic Model (MSSM) (K. Ellaithy and M.Bogdan 2017).