Abstract: Human like visual and auditory sensory devices became very popular in recent years through the work of deep learning models that incorporate aspects of brain processing such as edge and line detectors found in the visual cortex. However, very little work has been done on the human memory, and thus our aim is to model human long-term declarative episodic memory storage using deep learning methods. An innovative way of deep neural network was created on supervised feature learning dataset such as MNIST to achieve high accuracy as well as storing the models hidden layers for future extraction. Convolutional Neural Network (CNN) learning models with transfer learning models were trained to imitate the long-term declarative episodic memory storage of human. A Recurrent Neural Network (RNN) in the form of Long Short Term Memory (LSTM) model was assembled in layers and then trained and evaluated. A Variational Autoencoder was also used for training and evaluation to mimic the human memory model. Frameworks were constructed using TensorFlow for training and testing the deep learning models.

Abu Kamruzzaman and Charles C. Tappert, “Developing Deep Learning Models to Simulate Human Declarative Episodic Memory Storage” International Journal of Advanced Computer Science and Applications(IJACSA), 10(3), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100302

@article{Kamruzzaman2019,
title = {Developing Deep Learning Models to Simulate Human Declarative Episodic Memory Storage},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100302},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100302},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {3},
author = {Abu Kamruzzaman and Charles C. Tappert}
}