Steele, A. (2026). Using large language models to predict linguistic neurological intracranial signals for multimodal sentences. Harvard University. PDF
Talbot, M. (2026). Emulating and enhancing human visual perception and learning with image computable models. Harvard University. PDF
Otani, A. (2025). Mitigating catastrophic forgetting and mode collapse in text-to-image diffusion via latent replay. Harvard University. PDF
Krishnan, S. (2025). Beyond modalities: robust neural representation of language in the brain. Birla Insittute of Technology and Science. PDF
Bocini, E. (2025). Beyond anecdotal evidence: A systematic framework for evaluating neuron interpretability. Ecole Polytechnique Federale de Lausanne. PDF
Bricken, T. (2025). Sparse representations in artificial and biological neural networks. Harvard University. PDF
Narayanan, H. (2024). Classifying Ragams in carnatic music with machine learning models: a Shazam for south indican classical music. Thesis, Harvard University. PDF
Misra, P. (2024). Robust and multimodal signals for language in the brain. Thesis, Harvard University. PDF
Madan, S. (2024). Out-of-distribution generalization in biological and artificial intelligence. Harvard University. PDF
Li, C. (2024). Adding to and building up very small nervous systems. Thesis, Harvard University. PDF
Le Lan, B. (2024). Robust convolutional neural networks as models of primate vision. Thesis, EPFL. PDF
Kaminsky, T. (2024). Towards characterizing curriculum reinforcement learning in sparse robotics tasks. Thesis, Harvard University. PDF
Gillioz, V. (2024). Alignment of large language models and brain activity: exploring language processing through sEEG in a multimodal syntactic task. Thesis, ETH. PDF
Bechard, D. (2024). The pen and the processor: A Turing-like test to gauge GPT-generated poetry. Thesis, Harvard University. PDF
Alvandian, N. (2024). Testing the alignment of multimodal neural networks models to human brain areas. Thesis, EPFL. PDF
Srinivasan, R. (2023). Hebbian attractor to model working memory in complex human behavior. Thesis, ETH. PDF
Singh, D. (2023). Synaptic failure is a flat minima optimizer. Thesis, Harvard University. PDF
Luster, A. (2023). A data-driven description of sleep using intracranial EEG recordings. Thesis, EPFL. PDF
Gavish, I. (2023). Less than reckless: assessing the role of consciousness in the moral appraisal of risky action. Thesis, Harvard University. PDF
Djambazovska, S. (2023). Bridging artificial and primate vision: the impact of visual angle, scene context, and IT-alignment. Thesis, EPFL. PDF
Dellaferrera, G. (2023). Unveiling principles of neural computations: from biological to artificial intelligence and back. Thesis, ETH. PDF
Zergham, A. (2022). Biologically-inspired deep predictive learning for episodic memory event segmentation. Thesis, Harvard University. PDF
Xiao, W. (2022). Seeing context: macaque ventral visual responses to diverse stimuli and during natural vision. Thesis, Harvard University. PDF
Xiao, Y. (2022). Neural mechanisms underlying human cognitive control and working memory. Thesis, Harvard University. PDF
Porte, Y. (2022). Comparing neural responses between action execution and action perception. Thesis, EPFL. PDF
Lopez Sanchez, P. (2022). An intracranial EEG study on human short-term memory. Thesis, EPFL. PDF
Hakobyan, M. (2022). Dynamically decoding human physiological behaviors from intracranial field potentials. Thesis, Harvard University. PDF
Gollety, C. (2022). Neuronal correlates of rapid learning in a human visual memory task. Thesis, EPFL. PDF
Chandra, J. (2022). Classification of continuous natural human behavior using intracranial field potentials. Thesis, Harvard University. PDF
Bono, S. (2022). On structured domain generation for generalization in reinforcement learning. Thesis, ETH. PDF
Wang, J. (2021). Mesoscopic physiological interactions in the human brain reveal small-world properties and associations with behavior. Thesis, Harvard University. PDF
Schwencke, J. (2021). Movies and memory: how film editing can impact episodic memory formation. Thesis, Harvard University. PDF
Pollina, L. (2021). Combining neurophysiology and computational modeling through VGG19. Thesis, EPFL. PDF
Karev, D. (2021). Context-robust object recognition via object manipulation in a synthetic 3D environment. Thesis, Harvard University. PDF
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. PDF
Casper, S. (2021). Efficient and insidious adversaries in deep reinforcement learning. Thesis, Harvard University. PDF
Cordier, A. (2020). Recognition of minimal images in the human brain. Thesis, EPFL. PDF
Zhang, M. (2019). Computational models of bottom-up and top-down attention. Thesis, National University of Singapore. PDF
Stothers, D. (2019). Turing’s child machine: a deep learning model of neural development. Thesis, Harvard University. PDF
Olson, J. (2019). Plasticity and firing rate dynamics in leaky integrate-and-fire models of cortical circuits. Thesis, Harvard University. PDF
Motschi, A. R. (2019). Movement-related characteristics of mirror neuron activity in humans and monkeys. Thesis, EPFL. PDF
Jacquot, V. (2019). Human vision versus computer vision to classify simple actions. Thesis, EPFL. PDF
Wu, K. (2018). Learning scene gist to Improve object recognition in convolutional neural networks. Thesis, Harvard University. PDF
Tsai, M. (2018). Neural circuits of visual pattern completion. Thesis, EPFL. PDF
Iaselli, E. (2018). 24 hours in the human brain. Thesis, EPFL. PDF
Grzelkowski, S. (2018). Spike-field coherence reveals complex cortical interactions in human visual memory task. Thesis, University of Amsterdam. PDF
Moerman, C. (2017). Behavioral and computational study on the recognition of novel occluded objects. Thesis, EPFL. PDF
Lotter, W. E. (2017). Prediction as a rule for unsupervised learning in deep neural networks. Thesis, Harvard University. PDF
Schrimpf, M. (2016). Brain-inspired recurrent neural algorithms for advanced object recognition. Thesis, Tehnische Universitat Munchen. PDF
Marconi, A. (2016). Quantifying episodic memories from real-world experience. Thesis, Emmanuel College. PDF
Lam, G. (2016). The volitional (in)significance of neuroscience: what libetian investigations can and cannot do for free will. Thesis, Harvard University. PDF
Tang, H. (2015). Role of recurrent computations in object completion. Thesis, Harvard University. PDF
Dowcett, S. (2015). Predicting episodic memories of movie events. Thesis, Emmanuel College. PDF
Kuhnke, P. (2014). The functional neuroanatomy of speech perception. Thesis, University of Osnabrück. PDF
Meyers, E. M. (2010). Using neural population decoding to understand high level visual processing. Thesis, MIT. PDF
Kreiman, G. (2002). On the neuronal activity in the human brain during visual recognition, imagery and binocular rivalry. Thesis, Caltech. PDF
Kreiman, G. (2002). Neural coding and feature extraction of time-varying signals. Thesis, Caltech. PDF