Machine learning and artificial intelligence in neuroscience: A primer for researchers

Area of Science:

• Neuroscience
• Computer Science
• Data Science

Background:

• Artificial intelligence (AI) and machine learning (ML) are increasingly used in scientific research.
• ML methods can analyze complex, multi-dimensional data from sources like brain imaging and omics.
• Traditional statistical approaches may not identify patterns discoverable by ML.

Purpose of the Study:

• To introduce the principles and common methods of machine learning (ML) and artificial intelligence (AI).
• To discuss recent advancements, risks, and future directions in ML/AI for scientific applications.
• To illustrate the practical use and limitations of ML in neuroscience with examples.

Main Methods:

• Explanation of fundamental ML/AI principles.
• Overview of commonly used ML methods.
• Discussion of recent methodological advancements in the field.

Main Results:

• ML can identify complex patterns in scientific data, surpassing traditional methods.
• Significant limitations exist, including the need for external validation and the 'black-box' nature of algorithms.
• Potential for ethical issues in medical decision-making due to lack of interpretability.

Conclusions:

• ML/AI holds great promise for scientific discovery, particularly in data-rich fields like neuroscience.
• External validation and interpretability are critical considerations for responsible ML/AI implementation.
• Future directions involve addressing limitations and expanding applications while considering ethical implications.