Publications - Theses


  1. Hebbian attractor to model working memory in complex human behavior. Ravi Srinivasan. ETH (2023). PDF

  2. Bridging artificial and primate vision: the impact of visual angle, scene context, and IT-alignment. Sara Djambazovska. Ecole Polytechnique Federale de Laussane (EPFL) (2023). PDF

  3. A data-driven description of sleep using intracranial EEG recordings. Alexander Luster. Ecole Polytechnique Federale de Laussane (EPFL) (2023). PDF

  4. Unveiling Principles of Neural Computations: From Biological to Artificial Intelligence, and Back. Giorgia Dellaferrera. ETH (2023). PDF

  5. Synaptic failure is a flat minima optimizer. Deepak Singh. Harvard University (2023). PDF

  6. Less than reckless: assessing the role of consciousness in the moral appraisal of risky action. Ilai Gavish. Harvard University (2023). PDF

  7. 2022

  8. Seeing context: macaque ventral visual responses to diverse stimuli and during natural vision. Wu Xiao. Harvard University (2022). PDF

  9. Neural mechanisms underlying human cognitive control and working memory. Yuchen Xiao. Harvard University (2022). PDF

  10. On structured domain generation for generalization in reinforcement learning. Serena Bono. ETH (2022). PDF

  11. An intracranial EEG study on human short-term memory. Lopez Sanchez, Paula. Ecole Polytechnique Federale de Lausanne (EPFL) (2022). PDF

  12. Dynamically Decoding Human Physiological Behviors from Intracranial Field Potentials. Manana Hakobyan. Harvard University (2022). PDF

  13. Neuronal correlates of rapid learning in a human visual memory task. Camille Gollety. Ecole Polytechnique Federale de Lausanne (EPFL) (2022). PDF

  14. Biologically-inspired deep predictive learning for episodic memory event segmentation. Zergham Ahmed. Harvard University (2022). PDF

  15. Classification of Continuous Natural Human Behavior Using Intracranial Field Potential. Jay Chandra. Harvard University (2022). PDF

  16. Comparing neural responses between action execution and action perception. Yael Porte. Ecole Polytechnique Federale de Laussanne (EPFL) (2022). PDF 

  17. An integrated computational models of visual search combining eccentricity, bottom-up, and top-down cues. Shashi Kant Gupta. India Institute of Technology Kanpur (2021). PDF

  18. 2021

  19. Mesoscopic physiological interactions in the human brain reveal small-world properties and associations with behavior. Jiarui Wang. Harvard University (2021). PDF

  20. Efficient and insidious adversaries in deep reinforcement learning. Stephen Casper. Harvard University (2021). PDF

  21. Context-robust object recognition via object manipulation in a synthetic 3D environment. Dimitar Karev. Harvard University (2021). PDF

  22. Movies and Memory: How Film Editing Can Impact Episodic Memory Formation. Jake Schwencke. Harvard University (2021). PDF 

  23. Combining neurophysiology and computational modeling through VGG19. Leonardo Pollina. Ecole Polytechnique Federale de Lausanne (EPFL) (2021). PDF 

  24. 2020

  25. Recognition of minimal images in the human brain. Aurelie Cordier. Ecole Nationale Superieure de Physique, Electronique, Materieux (2020). PDF

  26. 2019

  27. Plasticity and Firing Rate Dynamics in Leaky Integrate-and-Fire Models of Cortical Circuits. Joseph Olson. Harvard University (2019). PDF

  28. Computational Models of Bottom-up and Top-down Attention. Mengmi Zhang. National University of Singapore (2019). PDF

  29. Turing's Child Machine: A Deep Learning Model of Neural Development. Duncan Stothers. Harvard University (2019). PDF

  30. Movement-Related Characteristics of Mirror Neuron Activity in Humans and Monkeys. Alice Motschi. Ecole Polytechnique Federale de Lausanne (EPFL) (2019). PDF

  31. Human vision versus computer vision to classify simple actions. Vincent Jacquot. Ecole Polytechnique Federale de Lausanne (EPFL) (2019). PDF

  32. 2018

  33. Neural circuits of visual pattern completion. Matthias Tsai. Ecole Polytechnique Federale de Lausanne (EPFL) (2018). PDF

  34. Learning Scene Gist to Improve Object Recognition in Convolutional Neural Networks. Kevin Wu. Department of Engineering and Applied Sciences, Harvard University, (2018). PDF

  35. Spike-field coherence reveals complex cortical interactions in human visual memory task. Stephan Grzelkowski. University of Amsterdam (2018). PDF

  36. Twenty-Four Hours in the Human Brain. Eleonora Iaselli. Ecole Polytechnique Federale de Lausanne (EPFL) (2018). PDF

  37. 2017

  38. Prediction as a Rule for Unsupervised Learning in Deep Neural Networks. William Lotter. Harvard University (2017). PDF

  39. Behavioral and computational study on the recognition of novel occluded objects. Charlotte Moerman. Ecole Polytechnique Federale de Lausanne (EPFL) (2017). PDF

  40. 2016

  41. The Volitional (In)significance of Neuroscience: What Libetian Investigations Can and Cannot Do for Free Will. Garrett Lam.Harvard University (2016). PDF

  42. Brain-inspired Recurrent Neural Algorithms for Advanced Object Recognition. Martin Schrimpf. Tehnische Universitat Munchen (2016). PDF

  43. Quantifying episodic memories from real-world experience. Alyssa Marconi. Emmanuel College, (2016). PDF

  44. 2015

  45. Role of recurrent computations in object completion. Hanlin Tang. Program in Biophysics, Harvard University (2015). PDF

  46. Predicting episodic memories for movie events. Sarah Dowcett. Emmanuel College, (2015). PDF

  47. 2014

  48. The functional neuroanatomy of speech perception. Philipp Kunhnke. University of Osnabruk, Germany, (2014). PDF

  49. 2001

  50. On the neuronal activity in the human brain during visual recognition, imagery and binocular rivalry. Gabriel Kreiman. Department of Biology, California Institute of Technology (2001). ABSTRACT PDF

  51. Neural coding and feature extraction of time varying signals. Gabriel Kreiman. Computation and Neural Systems Program, California Institute of Technology (2001). ABSTRACT PDF