2. Computational Neuroscience

Journal Articles:

  • Kar, Kohitij., Kubilius Jonas., Schmidt, Kailyn., Issa, Elias., and DiCarlo, James. Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior.” Nature Neuroscience (2019).
  • Bashivan, Pouya*, Kar, Kohitij*, & DiCarlo, James. Neural Population Control via Deep Image Synthesis. Science (2019)
  • Rajalingham, R.*, Issa, E.*, Bashivan, P.,  Kar, K., Schmidt, K., and Dicarlo J: 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)

Machine Learning Conference Papers

  • Kubilius, J.*, Schrimpf, M.*, Kar, K., Hong, H., Majaj, N., Rajalingham, R., Issa, E., Bashivan, P., Prescott-Roy, J., Schmidt, K. and Nayebi, A., 2019. Brain-like object recognition with high-performing shallow recurrent anns. In Advances in Neural Information Processing Systems (pp. 12785-12796).
  • Nayebi A*, Bear D*, Kubilius J*, Kar K, Ganguli S, Sussillo D, DiCarlo JJ, Yamins DL. Task-Driven convolutional recurrent models of the visual system. InAdvances in Neural Information Processing Systems 2018 (pp. 5290-5301).

Preprints 

  • Schrimpf M, Kubilius J, Hong H, Majaj NJ, Rajalingham R, Issa EB, Kar K, Bashivan P, Prescott-Roy J, Schmidt K, Yamins DL. Brain-Score: which artificial neural network for object recognition is most brain-like?. BioRxiv. 2018 Jan 1:407007.

Short conference papers:

Conference abstracts:

  • Kar, K., Kubilius, J., Issa, E., Schmidt, K., amd DiCarlo, J: Does the primate ventral stream need cortical feedback to compute rapid online image-by-image object identity? Neuroscience 2017 Abstract (Nanosymposium; to be presented), Washington DC. Abstract
  • Kar, K., Kubilius, J., Issa, E., Schmidt, K., amd DiCarlo, J: Evidence that feedback is required for object identity inferences computed by the ventral stream. COSYNE 2017, Salt Lake City, Utah. AbstractPoster
  • Rajalingham, R., Issa, E., Schmidt, K., Kar, K., and Dicarlo J: Feedforward Deep Neural Networks Diverge from Humans and Monkeys on Core Visual Object Recognition Behavior. Cognitive Computational Neuroscience (CCN) 2017. Paper , Poster
  • Rajalingham, R., Issa, E., Kar, K., Schmidt, K., and Dicarlo J: Image-grain comparison of core object recognition behavior in humans, monkeys and machines. Neuroscience 2016 Abstracts. San Diego, CA: Society for Neuroscience,(2016). Poster
  • Kar, K., Moustafa, A., Myers, C., Gluck, M : “Using an animal learning model of the hippocampus to simulate human fMRI data,” Bioengineering Conference, Proceedings of the 2010 IEEE 36th Annual Northeast , vol., no., pp.1-2, 26-28 (March 2010)Short Paper