R. K. Standish’s scientific contributions

... Maass has demonstrated that spiking neurons are more computationally powerful than threshold-based neuron models[24]and that SNNs possess similar and often more computation ability compared to second generation multi-layer perceptrons[25]. Other works[26][27][28]have investigated SNN hardware implementations and have found that computation in the temporal domain can be performed more efficiently in hardware compared to employing complex non-linear sigmoidal neural models. These findings, and an increasing interest in efficient temporal computation have encouraged interest in SNNs and their application to classification and control tasks. ...

Reference:

Spiking Neural Networks for Breast Cancer Classification in a Dielectrically Heterogeneous Breast