I am very honored to receive the Japan Neuroscience Society Young Investigator Award, particularly as the first foreigner in the history of the award. More importantly, I would like to take this opportunity to express my gratitude to the selection committee, to my mentors and many collaborators who have guided me and made my scientific journey so enjoyable.

As a child, I loved experimenting new things, and I was fascinated by the nature of our sensory experiences. The sensation of the sun on the skin in a warm spring afternoon, or the smell of flowers in a grass field, or the sharp pain of a wasp bite. As I grew up and studied biology, I wondered how can billions of neurons and electrical signals give rise to our awareness, thoughts and cognition? Thus, I began my journey to understand the inner workings of our mind.

From my PhD studies at NAIST, under the supervision of Profs. Kazushi Ikeda, Kaoru Amano (Cinet), Hakwan Lau (UCLA) and Mitsuo Kawato (ATR), I studied metacognition and decision-making in human subjects, using neuroimaging (fMRI) and machine learning. At the time, I sought to clarify an old debate in the literature: whether our feeling of confidence, when we make choices, is a simple measure of the information available, or whether it is a higher order ‘meta-cognitive’ process. I used a new technique, called ‘decoded neurofeedback’, to change a person’s confidence at the neural level, non-invasively with fMRI (Nature Commun 2016). Looking further ahead, the decoded neurofeedback approach seemed incredibly promising: if we can change mental representations non-invasively with fMRI, there may be powerful clinical innovations. For example, to treat phobia or PTSD by remapping fearful memories (Nature Hum Behav 2016, PNAS 2018).

But as I progressed, I also realized that decoded neurofeedback presented an incredible theoretical challenge. How does the brain learn to shape its own activity patterns in response to external teaching signals? This is particularly mysterious because in these experiments the feedback is based on fMRI rather than direct neural signals, and the brain does not know which area nor what ‘information’ are targeted. During my time as postdoctoral researcher I embarked on a series of studies to investigate the function of cognition in learning very complex problems from a small sample (i.e., without extensive experience). I first discovered that decision confidence accelerates reinforcement learning, and that it does so via the coupling between the dorsal part of the prefrontal cortex and the basal ganglia (Nature Commun. 2020).

Increasingly fascinated by the flexibility of biological learning and decision-making, I have continued to expand my research in computational neuroscience towards other cognitive dimensions (abstraction, attention, memory), and psychiatric disorders (PTSD, depression, anxiety). Since 2019, I have been able to develop a multidisciplinary team to clarify the nature of flexible and adaptive behaviors. From this point of view, I am particularly interested in exploring the intersection between neuroscience and artificial intelligence (AI). Computational models in neuroscience can be used to implement new algorithms in AI that are more efficient and ethical. At the same time, techniques or methods from these fields can provide new ways to analyze and understand neural data. We may also open new ways in which humans and AI can interact, with a positive impact on society.

Looking back, I would like to take this opportunity to express my deep gratitude to Professor Mitsuo Kawato for helping me grow as a researcher over the years, and giving me the freedom to explore many ideas. I wouldn't be here without the guidance of Professor Hakwan Lau and Professor Benedetto De Martino, and the encouragement of the many other senior scientists I have encountered and discussed with. Special thanks to Professor Yuji Ikegaya for his trust and giving me the opportunity to contribute innovative research at the intersection of neuroscience and AI in the ERATO Ikegaya Brain-AI Hybrid project. Finally, special mention to all the students I worked with and who devoted their enthusiasm to this research.

Aurelio Cortese (Computational Neuroscience Labs, ATR Institute International)