Abstract Earlier research by our team have focussed the multilayer SAM spiking neural network using training algorithms for implementing FPGA. In the current work we have outlined the utilization of SAM-based network for developing function approximation. We have deployed the spike coding model for the work. In the testing we have proved that the “interpolated XOR” and 3-polynominal function approximation of... Earlier research by our team have focussed the multilayer SAM spiking neural network using training algorithms for implementing FPGA. In the current work we have outlined the utilization of SAM-based network for developing function approximation. We have deployed the spike coding model for the work. In the testing we have proved that the “interpolated XOR” and 3-polynominal function approximation of this SAM network. We found that the SAM network has the ability to perform these function approximations to high accuracy. Read More Read Less

Abstract Speech recognition is one of the most important research fields nowadays because of its necessity in our daily lives and to raise the fields of security to the highest level, It’s a task of speech processing, and our main scope in this paper is on speaker verification, which is to identify persons from their voices where the process depends on... Speech recognition is one of the most important research fields nowadays because of its necessity in our daily lives and to raise the fields of security to the highest level, It’s a task of speech processing, and our main scope in this paper is on speaker verification, which is to identify persons from their voices where the process depends on digitizing the sound waves into a form that allows the system to deal with it. The verification process is based on the characteristics of the speaker's voice (voice biometrics) and sends it to a further process to extract the features of that voice using the feature extraction method and using AI techniques to perform the task of identification. MFCC is used for the task of features extraction and obtains the spectrogram of a given voice signal where it represents a bank of information about the voice and sends it to the CNN model for further processing for training the model on that signal to verify if the voice belongs to a user in the system or it’s a new enrollment. Read More Read Less

Abstract We have previously reported progress in developing a multilayer SAM spiking neural network and a training algorithm, suitable for implementation on an FPGA with “On- Chip Learning”. Here we report on utilization of a SAM -based network for continuous function approximation, which to date has proved difficult to achieve on a LIF type spiking neural network, by using a spike... We have previously reported progress in developing a multilayer SAM spiking neural network and a training algorithm, suitable for implementation on an FPGA with “On- Chip Learning”. Here we report on utilization of a SAM -based network for continuous function approximation, which to date has proved difficult to achieve on a LIF type spiking neural network, by using a spike coding approach called ‘NFR-coding’. We demonstrate “interpolated XOR” and 3-polynominal function approximation of this SAM network in computational experiments. It is demonstrated that the SAM network has the capability to perform these function approximations to high accuracy. Read More Read Less