Mohammad Reza Daliri

Maryam Rahimi, Yadollah Yaghoobzadeh, Mohammad Reza Daliri

Large language models (LLMs) not only exhibit human-like performance but also share computational principles with the brain's language processing mechanisms. While prior research has focused on mapping LLMs' internal representations to neural activity, we propose a novel approach using explainable AI (XAI) to strengthen this link. Applying attribution methods, we quantify the influence of preceding words on LLMs' next-word predictions and use these explanations to predict fMRI data from participants listening to narratives. We find that attribution methods robustly predict brain activity across the language network, revealing a hierarchical pattern: explanations from early layers align with the brain's initial language processing stages, while later layers correspond to more advanced stages. Additionally, layers with greater influence on next-word prediction$\unicode{x2014}$reflected in higher attribution scores$\unicode{x2014}$demonstrate stronger brain alignment. These results underscore XAI's potential for exploring the neural basis of language and suggest brain alignment for assessing the biological plausibility of explanation methods.

Amirmasoud Ahmadi, Sepideh Farakhor Seghinsara, Mohammad Reza Daliri et al.

The brain stimulation and its widespread use is one of the most important subjects in studies of neurophysiology. In brain electrical stimulation methods, following the surgery and electrode implantation, electrodes send electrical impulses to the specific targets in the brain. The use of this stimulation method is provided therapeutic benefits for treatment chronic pain, essential tremor, Parkinsons disease, major depression, and neurological movement disorder syndrome (dystonia). One area in which advancements have been recently made is in controlling the movement and navigation of animals in a specific pathway. It is important to identify brain targets in order to stimulate appropriate brain regions for all the applications listed above. An animal navigation system based on brain electrical stimulation is used to develop new behavioral models for the aim of creating a platform for interacting with the animal nervous system in the spatial learning task. In the context of animal navigation the electrical stimulation has been used either as creating virtual sensation for movement guidance or virtual reward for movement motivation. In this paper, different approaches and techniques of brain electrical stimulation for this application has been reviewed. Keywords: Rat Robot, Brain Computer Interface, Electrical Stimulation, Cyborg Intelligence, Brain to Brain Interface

Amirmasoud Ahmadi, Mahsa Behroozi, Vahid Shalchyan et al.

Electroencephalogram, an influential equipment for analyzing humans activities and recognition of seizure attacks can play a crucial role in designing accurate systems which can distinguish ictal seizures from regular brain alertness, since it is the first step towards accomplishing a high accuracy computer aided diagnosis system (CAD). In this article a novel approach for classification of ictal signals with wavelet based cross frequency coupling (CFC) is suggested. After extracting features by wavelet based CFC, optimal features have been selected by t-test and quadratic discriminant analysis (QDA) have completed the Classification.