Written by Christopher Kim
Introduction
The interdisciplinary study between Neuroscience and AI has a long history. Especially, over the last several years, the rapid development of AI and its processing skills helped us learn more about the overall structure of the brain. But how exactly,neuroscience and AI interact with each other? Moreover, what should we expect in understanding our brain by using artificial intelligence?
Use of AI in neurological diseases
As we all know, AI algorithms are heavily inspired by human’s brain. Since the 1950s, scientists have tried mimicking the information processings of neurons to make their artificial version. The AI we have today has observation, learning, and performing skills similar to the human brain.
AI can be divided into two categories based on its training methods: Deep Learning and Machine Learning. In this article, the focus should be in the area of deep learning as their algorithms are more inspired by the human brain, both in structural and functional way. The four different sections in deep learning are Deep Belief Network, Recurrent Neural Network, Generative Adversarial Network and Convolutional Neural Network. These deep learning methods help us not only to visualize the detailed components of the central nervous system and peripheral system, but to identify effective medical treatment of neurological diseases. Considering there are more than 600 neurological diseases that are known to be associated with the central nervous system, which can be fatal to human lives, there’s more space for AI to fit in.
Utilizing AI’s deep learning skills to analyze the specific cause and adequate treatment is certainly supporting both the field of neuroscience and healthcare. In the near future, scientists are hoping to find an effective cure for challenging diseases like Alzheimer and Parkinson’s disease.
Decoding the Brain through Machine-Learning AI
In 2023, researchers once again proved the practicality of using AI into figuring out the complex structure of the human brain. Performed in University of Texas at Austin, researchers collected the data of the participants’ brain activity while they were listening to certain words. Then, using Large Language Models, commonly known as LLMs, analyzed the data to train and understand the activities of the brain. Eventually, based on the data gained, LLM was able to predict the patterns of the brain when it is given certain types of word. Although its accuracy and commercialization is still distant, it is evident that assistance from AI technology helped us find patterns and predict brain activity which was once thought as a mystery galaxy.
Views on use of AI in field of Neuroscience
However, there is a negative opinion about neuroscience being excessively dependent on AI. Tsvi Achler, who received PhD in computational neuroscience and founder of Optimizing Mind, argues that the brain seldom uses trial-and-error learning algorithms like AI. Using Trial and error learning algorithms, AI reinforces the most successful options from all possible options given by their past experiences. However, according to Achelr, unlike AI, it’s evident that the brain controls its inputs and outputs in a different way. During a process of identification and regulatory feedback the brain forms the outputs and puts it back to inputs even when the inputs are considered to be the same. Therefore, he states that although use of algorithms of AI can be fairly informative about the human brain and its inherent algorithms, algorithms generated based on computers should not be a motivation for understanding neuroscience.
Conclusion
It is now an obvious fact that there will be more intervention of AI not only in neuroscience, but also in our daily lives. In neuroscientific view, the constant evolution of AI technology almost promises remarkable enhancements in curing neurological diseases, cognition, and more. Constant interdisciplinary research might potentially be the key to unveiling the secret of the human brain. Nevertheless, it is important to understand that the complexity of the brain cannot be clearly explained with AI. The ethical use of AI should also be taken into consideration. With the acknowledgement that AI and brain are different, AI will fasten development of neuroscience in a proper way.
References
Macpherson, T., Churchland, A. K., Sejnowski, T., DiCarlo, J. J., Kamitani, Y., Takahashi, H., & Hikida, T. (2021). Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research. Neural Networks, 144, 603–613. https://doi.org/10.1016/j.neunet.2021.09.018
Gopinath, N. (2023). Artificial intelligence and neuroscience: An update on fascinating relationships. Process Biochemistry, 125, 113–120. https://doi.org/10.1016/j.procbio.2022.12.011
Achler, T. (2023). What AI, Neuroscience, and Cognitive Science Can Learn from Each Other: An Embedded Perspective. Cognitive Computation. https://doi.org/10.1007/s12559-023-10194-9
Stix, G. (2023, December 27). 2023’s Mind-Bending Revelations in the Brain Sciences. Scientific American. https://www.scientificamerican.com/article/2023s-mind-bending-revelations-in-the-brain-sciences/
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