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2014
Liao, Q., Leibo, J. Z. & Poggio, T. Unsupervised learning of clutter-resistant visual representations from natural videos. (2014).PDF icon 1409.3879v2.pdf (3.64 MB)
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).
Gao, T., Harari, D., Tenenbaum, J. B. & Ullman, S. When Computer Vision Gazes at Cognition. (2014).PDF icon CBMM-Memo-025.pdf (3.78 MB)
Voinea, S., Zhang, C., Evangelopoulos, G., Rosasco, L. & Poggio, T. Word-level Invariant Representations From Acoustic Waveforms. INTERSPEECH 2014 - 15th Annual Conf. of the International Speech Communication Association (International Speech Communication Association (ISCA), 2014). at <http://www.isca-speech.org/archive/interspeech_2014/i14_2385.html>
2013
Kreiman, G. Principles of neural coding (2013).
Dillon, M. R., Huang, Y. & Spelke, E. S. Core foundations of abstract geometry. Proceedings of National Academy of Sciences of the United States of America 110, (2013).
Villa, S. et al. Empirical Inference 59 - 69 (Springer Berlin Heidelberg, 2013). doi:10.1007/978-3-642-41136-610.1007/978-3-642-41136-6_7PDF icon Author's Version (147.25 KB)
Meyers, E. The Neural Decoding Toolbox. (2013). at <http://www.readout.info/>
Cadieu, C. F. et al. Neural Representation Benchmark [code]. (2013).
Lattman, E., Poggio, T. & Westervelt, R. NSF Science and Technology Centers – The Class of 2013. (2013).PDF icon NSFGender2013_poster.pdf (2.77 MB)
Rosasco, L. Object recognition data sets (iCub/IIT). (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
Dehghani, N. et al. Avalanche analysis from multielectrode ensemble recordings in cat, monkey, and human cerebral cortex during wakefulness and sleep. Frontiers in Physiology (2012). doi:10.3389/fphys.2012.00302PDF icon AvalancheDynamics.pdf (2.45 MB)
Mutch, J. & Turaga, S. cnpkg: 3-D Convolutional Network Package for CNS. (2012).File cnpkg.tar (50 KB)
Knoblich, U. hhpkg: Hodgkin-Huxley Package for CNS. (2012).File hhpkg.tar (380 KB)
Mutch, J. HMAX Package for CNS. (2012).File hmax.tar (210 KB)
Canas, G. D., Poggio, T. & Rosasco, L. Learning manifolds with k-means and k-flats. Advances in Neural Information Processing Systems 25 (NIPS 2012) (2012). at <https://papers.nips.cc/paper/2012/hash/b20bb95ab626d93fd976af958fbc61ba-Abstract.html>
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)
2011
Mendoza-Halliday, D., Schneiderman, M., Kaul, C. & Martinez-Trujillo, J. Combined effects of feature-based working memory and feature-based attention on the perception of visual motion direction. Journal of Vision 11, (2011).
Mendoza-Halliday, D., Schneiderman, M., Kaul, C. & Martinez-Trujillo, J. Combined effects of feature-based working memory and feature-based attention on the perception of visual motion direction. Journal of Vision 11, (2011).
Garrote, E., Jhuang, H., Huehne, H., Poggio, T. & Serre, T. A Large Video Database for Human Motion Recognition. (2011).PDF icon Kuehne_etal_ICCV2011.pdf (433.27 KB)
2010
Mutch, J., Knoblich, U. & Poggio, T. CNS (“Cortical Network Simulator”): a GPU-based framework for simulating cortically-organized networks. (2010).File cns.tar (1.46 MB)PDF icon MIT-CSAIL-TR-2010-013.pdf (389.38 KB)File (last version before switch to classdef syntax)  (1.05 MB)
Mutch, J. hmin: A Minimal HMAX Implementation. (2010).

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