Cognitive Science

This study delves into the concept of consciousness in the Qur'an, comparing it with cognitive science. While cognitive science views consciousness as a function of the brain, the Qur'an employs various terms such as soul (al-<em>nafs</em>), heart (<em>al-qalb</em>, <em>al-ṣadr</em> and <em>al-af</em><em>ʾidah</em>), hearing (<em>al-samʿ</em>), sight (<em>al-baṣar</em>), etc. to describe tools of consciousness. In order to explore the Qur'anic perspective on consciousness and its compatibility with cognitive science, an in-depth analysis of relevant areas pertaining to human consciousness, as well as the capacities of the heart and brain, is essential. Employing an analytical-comparative method with an extra-religious perspective, we extract cognitive terminology associated with consciousness from the Qur'an. By employing graph-based tools to create and analyze a conceptual map of these terms, we find that soul, heart, hearing, and sight emerge as crucial tools for generating consciousness. Of these, the Qur'an highlights the human soul as the primary tool for acquiring and processing consciousness, distinct from the spirit (<em>al-rūḥ</em>). This soul is balanced in such a way that it can collaborate with the body and brain of the human to achieve consciousness.

Recent advancements in neurophysiological recording technologies have led to significant complexities in managing large-scale neural data, creating potential bottlenecks in the storage, sharing, and processing within the neuroscience community. To address these challenges, we developed the Big Neuronal Data Framework (BNDF), a distributed high-performance computing (HPC) solution. BNDF leverages open-source big data frameworks, Hadoop and Spark, to offer a flexible and scalable architecture. We tested BNDF on three large-scale electrophysiological datasets from nonhuman primate brains, demonstrating improved runtimes and scalability due to its distributed design. In comparative analyses against MATLAB, a widely used platform, BNDF showcased over five times faster performance in spike sorting, a common task in neuroscience. This significant speed advantage highlights BNDF’s potential to enhance the efficiency of neural data processing and analysis, making it a valuable tool for researchers navigating the complexities of modern neural datasets. Overall, BNDF represents a promising approach to streamline the handling of extensive neural data in the field of neuroscience.