Where AI meets natural intelligence

In the age of artificial intelligence, natural human intelligence is underrated. We believe that if we want to develop AI tools that are intelligent, we have to try to figure out how our brain does it. And the other way around, too: if we want to understand how our brain works, it is informative to investigate how deep neural networks work and what they lack. Hence, we combine AI and deep learning with neuroscience, physics, and psychology.

Top 3

Vicente, R., Wibral, M., Lindner, M. et al. (2011). Transfer entropy—a model-free measure of effective connectivity for the neurosciences. J Comput Neurosci 30, 45–67.

Tampuu, A., Matiisen, T., Kodelja, D., Kuzovkin, I., Korjus, K., Aru, J., … & Vicente, R. (2017). Multiagent cooperation and competition with deep reinforcement learning. PloS one, 12(4), e0172395.

Aru, J., Suzuki, M., & Larkum, M. E. (2020). Cellular mechanisms of conscious processing. Trends in cognitive sciences, 24(10), 814-825.

Recent 3

Aru, J., Labash, A., Corcoll, O., & Vicente, R. (2023). Mind the gap: Challenges of deep learning approaches to Theory of Mind. Artificial Intelligence Review, 1-16.

Suzuki, M., Pennartz, C. M., & Aru, J. (2023). How deep is the brain? The shallow brain hypothesis. Nature Reviews Neuroscience, 1-14.

Aru, J., Larkum, M., & Shine, J. M. (2023). The feasibility of artificial consciousness through the lens of neuroscience. Trends in Neurosciences.

Cardiovascular Stress Impacts On Neuronal Function. Funder: Estonian Research Council

Transparent, Reliable and Unbiased Smart Tool for AI. Funder: European Commission

Contextual uncertainty and representation learning in machine perception. Funder: Estonian Research Council

The processing cycle of human intelligence on the behavioral, neural and algorithmic level. Funder: Estonian Research Council

ICT program. Funder: European Social Fund