2. Computational Neuroscience

Journal Articles:

  • 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. BioRXiv (2018)

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