KLAB Theses

Manana Hakobyan. Dynamically decoding human physiological behaviors from intracranial field potentials. Harvard University (2022). PDF

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

Jay Chandra. Classification of continuous natural human behavior using intracranial field potentials. Harvard University (2022). PDF

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

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

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

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

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

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

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

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

Aurélie Cordier. Recognition of minimal images in the human brain. Ecole Nationale Superieure de Physique, electronique, materieux (2020). PDF.

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

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

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

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

Vincent Jacquot. Human vision versus computer vision to classify simple actions. Ecole Polythechnique Federale de Laussanne (EPFL) (2019). PDF.

Matthias Tsai. Neural circuits of visual pattern completion. Ecole Polythechnique Federale de Laussanne (EPFL) (2018). PDF.

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

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

Eleonora Iaselli. Twenty-four Hours in the Human Brain. Ecole Polythechnique Federale de Lausanne (EPFL) (2018). PDF.

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

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

Garret Lam. Volitional (In)significance of Neuroscience: What Libetian Investigations Can and Cannot Do for Free Will. Harvard Universtiy (2016). PDF.

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

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

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

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

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

Gabriel Kreiman. Neural coding and feature extraction of time varying signals. California Institute of Technology (2001). PDF.