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.

- Computational Neuroscience: A Frontier of the 21st Century 

Neuroscience provides us with the means to understand brain function, and thereby insights into their implementation using AI algorithms. In contrast, AI is used in neuroscience research to analyze vast amounts of data related to brain functionality and pathology.

Computational neuroscience (CNS) serves to advance theory in basic brain research as well as psychiatry, and bridge from brains to machines.

CNS is a branch of neuroscience which employs mathematical models, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system. 

In theory, CNS would be a sub-field of theoretical neuroscience which employs computational simulations to validate and solve the mathematical models. However, since the biologically plausible mathematical models formulated in neuroscience are in most cases too complex to be solved analytically, the two terms are essentially synonyms and are used interchangeably. The term mathematical neuroscience is also used sometimes, to stress the quantitative nature of the field. 

CNS focuses on the description of biologically plausible neurons (and neural systems) and their physiology and dynamics, and it is therefore not directly concerned with biologically unrealistic models used in connectionism, control theory, cybernetics, quantitative psychology, machine learning, artificial neural networks, AI and computational learning theory; although mutual inspiration exists and sometimes there is no strict limit between fields, with model abstraction in CNS depending on research scope and the granularity at which biological entities are analyzed. 

Models in theoretical neuroscience are aimed at capturing the essential features of the biological system at multiple spatial-temporal scales, from membrane currents, and chemical coupling via network oscillations, columnar and topographic architecture, nuclei, all the way up to psychological faculties like memory, learning and behavior. These computational models frame hypotheses that can be directly tested by biological or psychological experiments.