Naoki Hiratani, PhD

Naoki Hiratani, PhD

Assistant Professor of Neuroscience

Hiratani Lab


My research goal is to uncover the fundamental principles underlying learning and computation in the brain. Currently, I am exploring the intersection between neuroscience and artificial intelligence (AI). While modern AI systems demonstrate remarkable performance in various tasks, such as image recognition and language processing, the brain surpasses them in terms of efficient learning from limited data and energy-efficient computation. My objective is to develop a theoretical understanding of how the intricate connectivity patterns at both micro and macroscopic levels, along with complex learning mechanisms, contribute to the brain’s exceptional efficiency.

To test theoretical hypotheses and interpret complex neuroscience data, I will leverage advanced data analysis algorithms from modern machine learning. These algorithms offer sophisticated tools for examining neural, behavioral, and structural data. Applying these techniques, I aim to gain valuable insights into brain functions and develop brain-inspired AI.


2011-2016: PhD, The University of Tokyo

2007-2011: BS, The University of Tokyo

Selected publications

  • Mastrogiuseppe F, Hiratani N, Latham PE. Evolution of neural activity in circuits bridging sensory and abstract knowledge. eLife. 2023 Mar 7; 12:e79908. doi: 10.7554/eLife.79908..
  • Hiratani N, Metha Y, Lillicrap TP, Latham PE. On the stability and scalability of node perturbation learning. Thirty-sixth Annual Conference on Neural Information Processing Systems (NeurIPS 2022). 2022. 31929-31941.
  • Hiratani N, Latham PE. Developmental and evolutionary constraints on olfactory circuit selection. Proceedings of the National Academy of Sciences. 2022 Mar 15; 119(11):e2100600119. doi: 10.1073/pnas.2100600119. Epub 2022 Mar 9.
  • Hiratani N, Latham PE. Rapid bayesian learning in the mammalian olfactory system. Nature Communications. 2020 Jul 31; 11(1):3845. doi: 10.1038/s41467-020-17490-0.
  • Hiratani N, Fukai T. Redundancy in synaptic connections enables neurons to learn optimally. Proceedings of the National Academy of Sciences. 2018 Jul 17; 115(29):E6871-E6879. doi: 10.1073/pnas.1803274115. Epub 2018 Jul 2.
  • Asabuki T, Hiratani N, Fukai T. Interactive reservoir computing for chunking information streams. PLoS Computational Biology. 2018 Oct 8; 14(10):e1006400. doi: 10.1371/journal.pcbi.1006400. eCollection 2018 Oct.
  • Hiratani N, Fukai T. (2017). Detailed dendritic excitatory/inhibitory balance through heterosynaptic spike-timing-dependent plasticity. Journal of Neuroscience. 2017 Dec 13; 37(50):12106-12122. doi: 10.1523/JNEUROSCI.0027-17.2017. Epub 2017 Oct 31.
  • Hiratani N, Fukai T. (2015). Mixed signal learning by spike correlation propagation in feedback inhibitory circuits. PLoS Computational Biology. 2015 Apr 24; 11(4):e1004227. doi: 10.1371/journal.pcbi.1004227. eCollection 2015 Apr.

Honors and awards

2020-2023 Swartz Foundation Postdoctoral Fellowship

2014-2016 Japan Science Promotion Society Doctoral Fellowship