Neuro AI Papers
Guillaume Bellec, Franz Scherr, Anand Subramoney, Elias Hajek, Darjan Salaj, Robert Legenstein, Wolfgang Maass A solution to the learning dilemma for recurrent 2 networks of spiking neurons bioRxiv (2019)
Albert Gidon, Timothy Adam Zolnik, Pawel Fidzinski, Felix Bolduan, Athanasia Papoutsi, Panayiota Poirazi, Martin Holtkamp, Imre Vida, Matthew Evan Larkum Dendritic action potentials and computation in human layer 2/3 cortical neurons Science (2019)
Ben Sorscher, Gabriel C. Mel, Surya Ganguli, Samuel A. Ocko A unified theory for the origin of grid cells through the lens of pattern formation NeurIPS (2019)
Sara Hooker, Aaron Courville, Yann Dauphin, Andrea Frome Selective Brain Damage: Measuring the Disparate Impact of Model Pruning arXiv (2019)
Alessio Ansuini, Alessandro Laio, Jakob H. Macke, Davide Zoccolan Intrinsic dimension of data representations in deep neural networks arXiv (2019)
Josh Merel, Diego Aldarondo, Jesse Marshall, Yuval Tassa, Greg Wayne, Bence Ölveczky Deep neuroethology of a virtual rodent arXiv (2019)
Zhe Li, Wieland Brendel, Edgar Y. Walker, Erick Cobos, Taliah Muhammad, Jacob Reimer, Matthias Bethge, Fabian H. Sinz, Xaq Pitkow, Andreas S. Tolias Learning From Brains How to Regularize Machines arXiv (2019)
Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan, Lane McIntosh, Stephen Baccus, Surya Ganguli From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction NeurIPS (2019)
Stefano Recanatesi, Matthew Farrell ,Guillaume Lajoie, Sophie Deneve, Mattia Rigotti, and Eric Shea-Brown Predictive learning extracts latent space representations from sensory observations BiorXiv (2019)
Nasr, Khaled, Pooja Viswanathan, and Andreas Nieder. Number detectors spontaneously emerge in a deep neural network designed for visual object recognition. Science Advances (2019)
Bashivan, Pouya, Kohitij Kar, and James J. DiCarlo. Neural population control via deep image synthesis. Science (2019)
Ponce, Carlos R., Will Xiao, Peter F. Schade, Till S. Hartmann, Gabriel Kreiman, and Margaret S. Livingstone. Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences Cell (2019)
Kar, Kohitij, Jonas Kubilius, Kailyn M. Schmidt, Elias B. Issa, and James J. DiCarlo. Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior. Nature Neuroscience (2019)
Russin, Jake, Jason Jo, and Randall C. O'Reilly. Compositional generalization in a deep seq2seq model by separating syntax and semantics. arXiv (2019)
Rajalingham, Rishi, Elias B. Issa, Pouya Bashivan, Kohitij Kar, Kailyn Schmidt, and James J. DiCarlo. Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks. Journal of Neuroscience (2018)
Eslami, SM Ali, Danilo Jimenez Rezende, Frederic Besse, Fabio Viola, Ari S. Morcos, Marta Garnelo, Avraham Ruderman et al. Neural scene representation and rendering. Science (2018)
Banino, Andrea, Caswell Barry, Benigno Uria, Charles Blundell, Timothy Lillicrap, Piotr Mirowski, Alexander Pritzel et al. Vector-based navigation using grid-like representations in artificial agents. Nature (2018)
Guerguiev, Jordan, Timothy P. Lillicrap, and Blake A. Richards. Towards deep learning with segregated dendrites. ELife (2017).
Bengio, Yoshua, Dong-Hyun Lee, Jorg Bornschein, Thomas Mesnard, and Zhouhan Lin. Towards biologically plausible deep learning. arXiv (2015).
Güçlü, Umut, and Marcel AJ van Gerven. Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. Journal of Neuroscience (2015)
Cadieu, Charles F., Ha Hong, Daniel LK Yamins, Nicolas Pinto, Diego Ardila, Ethan A. Solomon, Najib J. Majaj, and James J. DiCarlo. Deep neural networks rival the representation of primate IT cortex for core visual object recognition. PLoS computational biology (2014)
Reviews
Richards, Blake A., Timothy P. Lillicrap, Philippe Beaudoin, Yoshua Bengio, Rafal Bogacz, Amelia Christensen, Claudia Clopath et al. A deep learning framework for neuroscience. Nature neuroscience (2019)
Hassabis, Demis, Dharshan Kumaran, Christopher Summerfield, and Matthew Botvinick. Neuroscience-inspired artificial intelligence. Neuron (2017)
Lake, Brenden M., Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman. Building machines that learn and think like people. Behavioral and brain sciences (2017).
Marblestone, Adam H., Greg Wayne, and Konrad P. Kording. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience (2016)