Sharif Digital Repository

In this thesis, we first introduce the required biological preparations and popular models for modeling single neuron, synapse and cable. Then by introduction of limit cycle oscillators and the necessary prerequisites, investigations are limited to systems involving weakly coupled oscillators. As two examples of such models, famous Kuramoto and Wilson- Cowan models are described. In the following, we introduce some methods for reduction dimension of weakly coupled oscillators and finally we apply one of the expressed methods on the dynamics of cortical network  

The study of the religious experience is always one of the most important topics in researches related to the religion field. Religious and mystical experiences have always been one of the key religious concepts. The religious experience, like other human experiences, always comes with reactions in the human brain. Study of the religious experience from a neuroscience perspective can help us to answer questions and issues in the field of religious experience and cognitive credit of it. Assessment of what happens in the people’s brain during rituals like meditation or deep concentration provides a good window into understanding of what this means for people, being mystical or religious or... 

One of the tasks of philosophy of science is to study the pre-suppositions involved in the natural sciences, to study the concepts used in scientific researches and theorizing. Hence, neglecting philosophy and even the modality of philosophical views, can have devastating effects on any science, including psychology (if it can be called science). Wittgenstein believed that in psychology we are confronted with experimental methods and conceptual confusion, and that this confusion does not go away with giving the title of young knowledge to the psychology. The emergence and dominance of behaviorism can be attributed to the effects of positivism and the uncomprehensive interpretation of physics... 

Reinforcement learning is one of the most well-known learning paradigms in biological agents and one of the most used ones for solving plenty of problems. One of the reasons for this widespread use is the low demand for supervising signals. However, the sparsity of the reward signal causes increasing in sample complexity that needs for learning new tasks. This issue makes trouble in multi-task settings, specifically.One of the most promising approaches to learning new tasks by limited interaction with the environment is meta reinforcement learning. An approach in which fast adaption becomes possible by limiting hypothesis space and creating inductive biases by learning meta parameters.... 

With the advancement of technologies related to electroencephalography signals, brain and computer interfaces, the program has received much attention. This report deals with one of the new and important issues in this field, i.e. converting thought into text. In this research, the letters, words, and sentences that a person thinks or utters in his mind are decoded and converted into text based on electroencephalography signals. There is still no credible and credible information in neuroscience about whether the same patterns of neuronal activity occur in the brain when thinking about similar letters or words. However, the remarkable growth and development of deep neural networks has made... 

The relation of mind and brain has always been a fundamental and challenging problem for science. The study of this subject with the help of modern physics, considering the failure of classical physics and materialistic philosophy in explaining spiritual and mental phenomena, has attracted the attention of contemporary eminent physicist and biologists. This area is known as "quantum consciousness" and involves theories about mind and brain. Among these theories are: Eccles-Beck theory, Heisenberg-James theory (presented by Stapp) and Penrose-Hameroff theory. In dealing with the problem of mind-matter interaction and consciousness, modern physics, psychology and some philosophical schools are... 

Despite the remarkable success of deep learning algorithms in recent years, it still has a long way to reach the status of human natural intelligence and to acquire the expected self-autonomy. As a result, many researchers in this field have focused on the development of these algorithms while taking inspiration from human cognitive behaviors. One of the disadvantages of current algorithms is the lack of their ability to learn in a continual manner while deployed in the environment. More precisely, deep learning models are not able to gradually gather knowledge from the environment and if they are in a situation of limited access to data, they will suffer from catastrophic forgetting; a...