Neural Engineering is an interdisciplinary field that combines principles from neuroscience, engineering, and computer science to develop technologies that interface with the nervous system. It aims to understand, repair, replace, enhance, or otherwise exploit the properties of neural systems, leading to advancements in medical treatments, brain-machine interfaces, and artificial intelligence.

Neural Engineering

An interdisciplinary field is like a big team where people from different areas, like science and art, work together to solve problems. They mix their ideas and skills to create new things and find answers that one person alone might not think of.

interdisciplinary field

principles from neuroscience

Engineering is the application of scientific and mathematical principles to design, build, and analyze structures, machines, and systems, aiming to solve real-world problems efficiently and sustainably. It encompasses a wide range of specialized fields that address various aspects of technology and innovation, driving progress in industries and improving quality of life.

Engineering

Computer science is the study of algorithmic processes, computational machines, and computation itself, which underpins the development of software and systems that drive modern technology. It encompasses a wide range of topics from theoretical foundations to practical applications, making it essential for innovation in various fields such as artificial intelligence, cybersecurity, and data science.

computer science

technologies that interface with the nervous system

Understand neural systems

Repair neural systems

Replace neural systems

Enhance neural systems

Exploit properties of neural systems

advancements in medical treatments

brain-machine interfaces

Artificial intelligence refers to the development of computer systems capable of performing tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. It encompasses a range of technologies and methodologies, including machine learning, neural networks, and natural language processing, to create systems that can learn, adapt, and improve over time.

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