Publication

Found 332 results
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2014
Chen, X. et al. Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts. (2014).PDF icon CBMM-Memo-015.pdf (974.07 KB)
Isik, L., Meyers, E., Leibo, J. Z. & Poggio, T. The dynamics of invariant object recognition in the human visual system. (2014). doi:http://dx.doi.org/10.7910/DVN/KRUPXZ
Isik, L., Meyers, E., Leibo, J. Z. & Poggio, T. The dynamics of invariant object recognition in the human visual system. J Neurophysiol 111, 91-102 (2014).
Mottaghi, R., Fidler, S., Yuille, A., Urtasun, R. & Parikh, D. Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding. (2014).PDF icon CBMM-Memo-020.pdf (1.89 MB)
Bansal, A. et al. Neural Dynamics Underlying Target Detection in the Human Brain. Journal of Neuroscience 34, (2014).
Bansal, A. et al. Neural Dynamics Underlying Target Detection in the Human Brain. Journal of Neuroscience 34, (2014).
Miconi, T., Groomes, L. & Kreiman, G. A normalization model of visual search predicts single trial human fixations in an object search task. (2014).PDF icon CBMM-Memo-008.pdf (854.51 KB)
Yan, P., Magid, R. & Schulz, L. Preschoolers expect others to learn rationally from evidence. Annual Conference of the Cognitive Science Society (2014).PDF icon Yan, Magid, & Schulz_CogSci14_REVISED.pdf (302.4 KB)
Tang, H., Buia, C., Madsen, J., Anderson, W. S. & Kreiman, G. A role for recurrent processing in object completion: neurophysiological, psychophysical and computational evidence. (2014).PDF icon CBMM-Memo-009.pdf (4.21 MB)
Barbu, A. et al. Seeing is Worse than Believing: Reading People’s Minds Better than Computer-Vision Methods Recognize Actions. (2014).PDF icon CBMM Memo 012.pdf (678.95 KB)
Barbu, A. et al. Computer Vision – ECCV 2014, Lecture Notes in Computer Science 8693, 612–627 (Springer International Publishing, 2014).
Singer, J., Madsen, J., Anderson, W. S. & Kreiman, G. Sensitivity to Timing and Order in Human Visual Cortex. (2014).PDF icon CBMM-Memo-005.pdf (1.12 MB)
Mendoza-Halliday, D., Torres, S. & Martinez-Trujillo, J. Sharp emergence of feature-selective sustained activity along the dorsal visual pathway. Nature Neuroscience 7, (2014).
Mendoza-Halliday, D., Torres, S. & Martinez-Trujillo, J. Sharp emergence of feature-selective sustained activity along the dorsal visual pathway. Nature Neuroscience 7, (2014).
Tang, H. et al. Spatiotemporal Dynamics Underlying Object Completion in Human Ventral Visual Cortex. Neuron 83, 736 - 748 (2014).
Tang, H. et al. Spatiotemporal Dynamics Underlying Object Completion in Human Ventral Visual Cortex. Neuron 83, 736 - 748 (2014).
Anselmi, F. et al. Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning?. (2014).PDF icon CBMM Memo No. 001 (940.36 KB)
Mormann, F. et al. Single neuron studies of the human brain. Probing cognition (2014).
2013
Meyers, E. The Neural Decoding Toolbox. (2013). at <http://www.readout.info/>
Cadieu, C. F. et al. Neural Representation Benchmark [code]. (2013).
Anselmi, F. et al. Unsupervised Learning of Invariant Representations in Hierarchical Architectures. (2013).PDF icon 1311.4158v2.pdf (3.78 MB)
Ma, K. - T., Sim, T. & Kankanhalli, M. VIP: A unifying framework for eye-gaze research. (2013). at <http://mmas.comp.nus.edu.sg/VIP.html>
2012
Mutch, J. & Turaga, S. cnpkg: 3-D Convolutional Network Package for CNS. (2012).File cnpkg.tar (50 KB)
Mutch, J. HMAX Package for CNS. (2012).File hmax.tar (210 KB)
Peyrache, A. et al. Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep. Proceedings of the National Academy of Sciences (2012). doi:10.1073/pnas.1109895109PDF icon SpatiotemporalDynamic.pdf (2.56 MB)

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