Effective multispike learning in a spiking neural network with a new temporal feedback backpropagation for breast cancer detection

This paper presents an effective learning multi-spike deep spiking neural network with temporal feedback backpropagation for breast cancer detection using contrast-enhanced MRI images. The learning in the spiking network is a new universal temporal feedback called Temporal_Feedback_SpikeProp (TF_SpikeProp) and Temporal_Feedback_ReSuMe (TF_ReSuMe). Thus, it can be implemented on all kinds of algorithms such as MultiSpikeProp and MultiReSuMe algorithms and is compatible with all temporal codings. The presented spiking network is a functional network with high accuracy and convergence in low epochs. The new algorithm explores the influence of all presynaptic neurons on the output error and reduces the role of inactive neurons. Therefore, the error propagation of the spiking network is such that it affects not only the spike time of each neuron in the output spike time, but also the refractory time and the presynaptic spikes of the neuron during the spiking time of the output neuron. The spiking network can correct and adjust the weights, spike delay, spike threshold, and also the time constant of the spike collection kernel. In order to diagnose cancer tissue, time-frequency features such as STFT and packet wavelet transform (WPT) have been used along with texture recognition features such as cooccurrence matrix. The presented network achieved an accuracy of 98.3% in 23 epochs in SpikeProp algorithm, 97.4% in ReSuMe with Duke MRI dataset, 98.64% on MNIST, and 96.1% on Iris. The results have shown that the training algorithm used in this study can achieve high accuracy in low epochs compared to traditional algorithms, while solving the challenge of getting stuck in local minima and poor convergence, as well as reducing the problem of time confusion of output spikes to an acceptable level and providing a fully functional network in practice and reality.