Gabriel Kreiman, Ph.D. (He/ Him/ His)

Professor
Children's Hospital Boston, Harvard Medical School
Center for Brain Science, Harvard University Center for Minds, Brains and Machines

Investigating the neuronal circuits responsible for visual recognition and learning.

The Kreiman lab is interested in elucidating how neural circuits compute and building biologically-inspired Artificial Intelligence. To this end, we combine behavioral measurements, invasive neurophysiological recordings in the human brain and computational neuroscience models. The main topics of investigation center around visual recognition, learning, and memory. Within visual recognition, current projects include studying the mechanisms of pattern completion, visual search, context and task dependence, spatiotemporal integration and building machines that can see and interpret the world the way we do. Within learning and memory, current projects include studying real life memories, understanding how medial temporal lobe circuits lead to memory consolidation, and building biologically plausible models for episodic memory formation.

Current Lab Members:

8 Graduate Students

2 Postdoctoral Fellows

Selected Publications:

Tang H, Buia C, Madhavan R, Madsen J, Anderson W, Crone N, Kreiman G. (2014). Spatiotemporal dynamics underlying object completion in human ventral visual cortex. Neuron, 83:736-748.

Ponce C.R., Xiao W., Schade P.F., Hartmann T.S., Kreiman G., Livingstone M. (2019). Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences. Cell, 177:999-1009.

Lotter W, Kreiman G, Cox D. (2020) A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception. Nature Machine Intelligence, 2:210-219.

Vinken K, Boix X, Kreiman G (2020). Incorporating intrinsic suppression in deep neural networks captures dynamics of adaptation in neurophysiology and perception. Science Advances, 6: eabd4205.

Gupta SK, Zhang M, Wu CC, Wolfe JM, Kreiman G (2021). Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases. NeurIPS arXiv 2106.02953.

Zheng J, Schjetnan AGP, Yebra M, Mosher C, Kalia S, Valiante TA, Mamelak A, Kreiman G, Rutishauser U (2021). Cognitive boundary signals in the human medial temporal lobe shape episodic memory representation. Nature Neuroscience 25:358-368.