Publications – Theses

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Madan, S. (2024). Out-of-distribution generalization in biological and artificial intelligence [Harvard University]. https://drive.google.com/file/d/19_azuONFThFBpG4yxAr5KB_e0WBKUeug/view?usp=sharing

Bechard, D. (2024). The pen and the processor: A Turing-like test to gauge GPT-generated poetry [Thesis, Harvard University]. https://drive.google.com/file/d/18sq6VxwcYBVrMjMZB8UwgcGWD5D26zKD/view?usp=sharing

Alvandian, N. (2024). Testing the alignment of multimodal neural networks models to human brain areas [Thesis, EPFL]. https://drive.google.com/file/d/1IwBY0-ba90GnC36EYBhCnmkFHIqSCVSY/view?usp=sharing

Kaminsky, T. (2024). Towards characterizing curriculum reinforcement learning in sparse robotics tasks [Thesis, Harvard University]. https://drive.google.com/file/d/1HwuioL2OVAe7Gh1RZS7Rf4SfFOZQFi_9/view?usp=sharing

Narayanan, H. (2024). Classifying Ragams in carnatic music with machine learning models: a Shazam for south indican classical music [Thesis, Harvard University]. https://drive.google.com/file/d/12sMoWdpq0Eh6-hGilkLri0ufiuhVB-dt/view?usp=sharing

Le Lan, B. (2024). Robust convolutional neural networks as models of primate vision [Thesis, EPFL]. https://drive.google.com/file/d/1E95NM1cI3-i6HHrTWIMRf6O4IDiLWUyT/view?usp=sharing

Misra, P. (2024). Robust and multimodal signals for language in the brain [Thesis, Harvard University]. https://drive.google.com/file/d/1Clp6BlTJc07bsf8TsewjF71uOrcZqZbd/view?usp=sharing

Gillioz, V. (2024). Alignment of large language models and brain activity: exploring language processing through sEEG in a multimodal syntactic task [Thesis, ETH]. https://drive.google.com/file/d/1cHuOe_vkBAzm5M7yCkndvGRjeBZRvf0p/view?usp=sharing

Li, C. (2024). Adding to and building up very small nervous systems [Thesis, Harvard University]. https://drive.google.com/file/d/1ZgtfM2qy5ytbPIE-jjJQiz1VS8E5fkoW/view?usp=sharing

Dellaferrera, G. (2023). Unveiling principles of neural computations: from biological to artificial intelligence and back [Thesis, ETH]. https://drive.google.com/file/d/1zCwXy-1g67ILpvUwyq5Wpnp1xnSEUgDG/view?usp=sharing

Singh, D. (2023). Synaptic failure is a flat minima optimizer [Thesis, Harvard University]. https://drive.google.com/file/d/17nVlu_PLTfWp8mheLjSr_19FJIzRiXvm/view?usp=sharing

Gavish, I. (2023). Less than reckless: assessing the role of consciousness in the moral appraisal of risky action [Thesis, Harvard University]. https://drive.google.com/file/d/1AE2_9CcDWrmzrKo9S4-TFSmZjD0cKaS1/view?usp=sharing

Srinivasan, R. (2023). Hebbian attractor to model working memory in complex human behavior [Thesis, ETH]. https://drive.google.com/file/d/17fEMrGTLxzd8OCJTziXiNkUES7kKuRAL/view?usp=sharing

Djambazovska, S. (2023). Briidging artificial and primate vision: the impact of visual angle, scene context, and IT-alignment [Thesis, EPFL]. https://drive.google.com/file/d/1k3G_bm0b3zTlwmV2FqgqdKB5jaugJFR2/view?usp=sharing

Luster, A. (2023). A data-driven description of sleep using intracranial EEG recordings [Thesis, EPFL]. https://drive.google.com/file/d/1UDchpu_9Q0PjyhUUxmSvvXyE6gpgx-dp/view?usp=sharing

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Hakobyan, M. (2022). Dynamically decoding human physiological behaviors from intracranial field potentials [Thesis, Harvard University]. https://drive.google.com/file/d/1MMiEc35vVLKUc6vueTBEJxhnAnXNojFr/view?usp=sharing

Chandra, J. (2022). Classification of continuous natural human behavior using intracranial field potentials [Thesis, Harvard University]. https://drive.google.com/file/d/1cV3SFfvdHb8jKBBJb2T5XaSmnufp_Vka/view?usp=sharing

Xiao, W. (2022). Seeing context: macaque ventral visual responses to diverse stimuli and during natural vision [Thesis, Harvard University]. https://drive.google.com/file/d/1oIZaxs3M5gpE1ulNTTNvVCQQFTz3FT1R/view?usp=sharing

Bono, S. (2022). On structured domain generation for generalization in reinforcement learning [Thesis, ETH]. https://drive.google.com/file/d/1o7cLhPqZ62DblQBXM4jzBk0IooIZr2R-/view?usp=sharing

Gollety, C. (2022). Neuronal correlates of rapid learning in a human visual memory task [Thesis, EPFL]. https://drive.google.com/file/d/1F9A7WsOXEYEnfTXLsU0GkQgvmXnjpTbl/view?usp=sharing

Xiao, Y. (2022). Neural mechanisms underlying human cognitive control and working memory [Thesis, Harvard University]. https://drive.google.com/file/d/1Kz8Eez30u7gM07NCgKPJaFLLQ_5z2ADb/view?usp=sharing

Porte, Y. (2022). Comparing neural responses between action execution and action perception [Thesis, EPFL]. https://drive.google.com/file/d/1M_aZ1Kx-SwmN6wZDw3X6Go2WHVtAyd9E/view?usp=sharing

Zergham, A. (2022). Biologically-inspired deep predictive learning for episodic memory event segmentation [Thesis, Harvard University]. https://drive.google.com/file/d/1FAK2uyUTFCfVtAdTPqbUUapDYK6txUap/view?usp=sharing

Lopez Sanchez, P. (2022). An intracranial EEG study on human short-term memory [Thesis, EPFL]. https://drive.google.com/file/d/1F9ck_BC04eGD2o2BrHgYAttUpZuQlO8d/view?usp=sharing

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Schwencke, J. (2021). Movies and memory: how film editing can impact episodic memory formation [Thesis, Harvard University]. https://drive.google.com/file/d/1g8K70TRGGe02UX4b_Ca5UIFe7ALr5QmZ/view?usp=sharing

Wang, J. (2021). Mesoscopic physiological interactions in the human brain reveal small-world properties and associations with behavior [Thesis, Harvard University]. https://drive.google.com/file/d/1LCjnySm7Bvd5bB5UjnhjhsUr6IGPRyF0/view?usp=sharing

Casper, S. (2021). Efficient and insidious adversaries in deep reinforcement learning [Thesis, Harvard University]. https://drive.google.com/file/d/1bgd4vZsFvTXcwytwF9Mg7I2oTqorpGFT/view?usp=sharing

Karev, D. (2021). Context-robust object recognition via object manipulation in a synthetic 3D environment [Thesis, Harvard University]. https://drive.google.com/file/d/1aOwmfxi7Sm765xWJ_SzqFWSqFnsAt8G5/view?usp=sharing

Pollina, L. (2021). Combining neurophysiology and computational modeling through VGG19 [Thesis, EPFL]. https://drive.google.com/file/d/1Nxa730283z8aRMAVkZHF5GWfYJTgOEhh/view?usp=sharing

Gupta, S. K. (2021). An integrated computational model of visual search combining eccentricity, bottom-up, and top-down cues [Thesis, India Institute of Technology, Kanpur]. https://drive.google.com/file/d/1pKHearm0-S8aMPCrkpODSSGPIP-Lf2AX/view?usp=sharing

Cordier, A. (2020). Recognition of minimal images in the human brain [Thesis, EPFL]. https://drive.google.com/file/d/1iZRNtZkEREp6kRkYqDTJjOVjRjX-c-8l/view?usp=sharing

6543092 thesis2 1 apa 50 date 211 https://kreimanlab.com/wp-content/plugins/zotpress/

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Wu, K. (2018). Learning scene gist to Improve object recognition in convolutional neural networks [Thesis, Harvard University]. https://drive.google.com/file/d/1ZyaLe57AvvfM0iJ28YEt8utjsdi8OpNc/view?usp=sharing

Grzelkowski, S. (2018). Spike-field coherence reveals complex cortical interactions in human visual memory task [Thesis, University of Amsterdam]. https://drive.google.com/file/d/1EU1EqSLojUMI7a-kNyYwDt8dCVFBOjDX/view?usp=sharing

Tsai, M. (2018). Neural circuits of visual pattern completion [Thesis, EPFL]. https://drive.google.com/file/d/1F-nVJWOD5-GyU-CgtfmLhGNUBStlRUUP/view?usp=sharing

Iaselli, E. (2018). 24 hours in the human brain [Thesis, EPFL]. https://drive.google.com/file/d/1BlMQzuLgVggdXmNqKkuEi_zuAA0i7C_F/view?usp=sharing

Lotter, W. E. (2017). Prediction as a rule for unsupervised learning in deep neural networks [Thesis, Harvard University]. https://drive.google.com/file/d/1DICgaloMFhCFpOLgFQ8BltAONG77dLxK/view?usp=sharing

Moerman, C. (2017). Behavioral and computational study on the recognition of novel occluded objects [Thesis, EPFL]. https://drive.google.com/file/d/18wifYdcbumWV–5fmPsFS3c5l_r4PNir/view?usp=sharing

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Schrimpf, M. (2016). Brain-inspired recurrent neural algorithms for advanced object recognition [Thesis, Tehnische Universitat Munchen]. https://drive.google.com/file/d/1ofsTFIoSuzGnlHiC7akzFyHZNsAhNom_/view?usp=sharing

Lam, G. (2016). The volitional (in)significance of neuroscience: what libetian investigations can and cannot do for free will [Thesis, Harvard University]. https://drive.google.com/file/d/14XCOqOSSfk6rj-SJsEIpnzeSOWQ19Ad0/view?usp=sharing

Marconi, A. (2016). Quantifying episodic memories from real-world experience [Thesis, Emmanuel College]. https://drive.google.com/file/d/1opznJdODM3G95avFXdMQ66PlMn_n7Qj8/view?usp=sharing

Tang, H. (2015). Role of recurrent computations in object completion [Thesis, Harvard University]. https://drive.google.com/file/d/12KpgZVYCkt5zgiTYY06TbK6iGgtuRutT/view?usp=sharing

Dowcett, S. (2015). Predicting episodic memories of movie events [Thesis, Emmanuel College]. https://drive.google.com/file/d/1GLIzmyZdsoy229imLtFfDTEuzm6_UYKp/view?usp=sharing

Kuhnke, P. (2014). The functional neuroanatomy of speech perception [Thesis, University of Osnabrück]. https://drive.google.com/file/d/1OfiXjMaQ1W0I_qVpFKyOXopA7wXTh-Oq/view?usp=sharing

Meyers, E. M. (2010). Using neural population decoding to understand high level visual processing [Thesis, MIT]. https://drive.google.com/file/d/1FyQkhqHsTeFpHDYctVWRkCdCAzMmFZnh/view?usp=sharing

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Kreiman, G. (2002). On the neuronal activity in the human brain during visual recognition, imagery and binocular rivalry [Thesis, Caltech]. https://drive.google.com/file/d/16QwLybUdpryUq4sbGgDQ4nk_ZfXEe0gW/view?usp=sharing

Kreiman, G. (2002). Neural coding and feature extraction of time-varying signals [Thesis, Caltech]. https://drive.google.com/file/d/15Wk3A15OCd5ldCXi3xt-n0CheXnEivN7/view?usp=sharing