Convergence of Neuroscience and AI

- Overview

Artificial intelligence (AI) is a field of computer science that involves using machines to simulate human intelligence so that machines can acquire problem-solving and decision-making abilities similar to those of the human brain. Neuroscience is the scientific study of brain structure and cognitive function. Neuroscience and AI are interrelated. These two fields promote each other and make progress together. 

Neuroscience theories have brought many unique improvisations to the field of AI. Biological neural networks implement complex deep neural network architectures and are used to develop a variety of applications, such as word processing, speech recognition, target detection, etc. Additionally, neuroscience helps validate existing AI-based models. 

Reinforcement learning in humans and animals has inspired computer scientists to develop reinforcement learning algorithms in artificial systems, enabling these systems to learn complex strategies without explicit instructions. This learning helps build complex applications such as robot-based surgeries, self-driving vehicles, gaming apps, and more. 

In turn, AI, with its ability to intelligently analyze complex data and extract hidden patterns, is perfect for analyzing very complex neuroscience data. 

Large-scale simulations based on AI can help neuroscientists test their hypotheses. By interfacing with the brain, AI-based systems can extract brain signals and generate commands based on the signals. These commands are fed into devices such as robotic arms to help paralyzed muscles or other body parts move. 

AI has multiple use cases in analyzing neuroimaging data and reducing the workload of radiologists. Neuroscience research contributes to the early detection and diagnosis of neurological diseases. Likewise, AI can be effectively applied to the prediction and detection of neurological diseases.