# Improvement of Heterogeneous Transfer Learning Efficiency by Using Hebbian Learning Principle
## 1 Introduction
To increase the ease of transferring knowledge is the purpose of this method. On the contrary, in absence of such learning, the traditional deep learning techniques use millions and millions of samples of data to learn from existing labeled datasets. Such supervised learning neural network techniques are only possible when we have plenty of labeled data available, and lots of good computer hardware to do all the millions and billions of calculations. However, using the transfer learning with even heterogeneous datasets, the training becomes easier and faster, and needs less cost of computations. There are many significant applied domains of transfer learning, such as pediatric pneumonia diagnosis, medical imaging, cancer classification using deep neural networks, digital mammographic tumor classification, object classification, and visual categorization [18; 19; 20; 21; 22; 23]. Transfer learning in deep convolutional neural networks (DCNNs) and unsupervised transfer learning via multi-scale convolutional sparse coding for biomedical applications is an important step in its application to medical imaging tasks. In a non-medical domain, Field Programmable Gate Arrays (FPGAs) can be used for transferring the knowledge from learned neural networks to computer hardware or microcontrollers--hardware, such as FPGAs, where companies are trying to put computer vision on hardware chips is an example of futuristic application. Thus, learned knowledge can be used in small and mobile objects, such as robots, cars, and other vehicles, for example. Transfer learning of deep neural networks in automatic speech recognition systems is also an interesting domain. The cited pioneer, of transfer learning work in the field of machine learning, is attributed to [PERSON], who contributed to the discriminability-based transfer (DBT) algorithm in 1993. Cross-domain transfer learning is a well-proven technique and has been used earlier as well [24]. However, the mathematical approaches for learning in such neural network models are considerably away from what happens in a real animal brain. Neuroscience suggests that the gradient descent optimization processes are different from the real brain processes [25]. According to neuroscientists, the biologically inspired rules, such as spike-timing-dependent plasticity (STDP) or the Hebbian learning rules [26; 27], are more relevant to actual animal brains processes. About half a century ago, the development and plasticity of the brain was studied by scientists. They investigated/inquired the neuronal response behavior, in general, called plasticity [28]. The Hebbian learning principle presents how adjacent neurons firing together strengthens the corresponding connection in an animal brain [29]. This characteristic of neural connection is called the plasticity [28]. Recent work in neural networks, such as [30], has demonstrated the implementation of powerful principles of Hebbian plasticity with backpropagation in neural network training. The article discusses the derived analytical expressions for calculating the gradient in neural networks with Hebbian plastic connections and its backpropagation. As the neural network model is trained using large amount of data, the model parameters are fixed and used to predict outputs of new instances of the same task. If one tries to apply the model to a different task, the parameters must be re-trained using large number of new training instances. Animals and human beings, however, learn new similar task quickly and efficiently with small amount of data (experience). Learning from experience has also been studied in learning to learn domain [31; 32]. An intelligent way of learning is to extract the knowledge from one or more source tasks, and apply the knowledge to a target task [33].