Publication
] hmin: A Minimal HMAX Implementation. (2010).
cnpkg: 3-D Convolutional Network Package for CNS. (2012).
cnpkg.tar (50 KB)
cnpkg.tar (50 KB) Computational and Cognitive Neuroscience of Vision 85-104 (Springer, 2017).
CNS (“Cortical Network Simulator”): a GPU-based framework for simulating cortically-organized networks. (2010). cns.tar (1.46 MB)
MIT-CSAIL-TR-2010-013.pdf (389.38 KB) (last version before switch to classdef syntax) (1.05 MB)
cns.tar (1.46 MB) MIT-CSAIL-TR-2010-013.pdf (389.38 KB) (last version before switch to classdef syntax) (1.05 MB) HMAX Package for CNS. (2012). hmax.tar (210 KB)
hmax.tar (210 KB) To What Extent Does Global Shape Influence Category Representation in the Brain?. Journal of Neuroscience 36, 4149 - 4151 (2016).
Dynamics of 3D view invariance in monkey inferotemporal cortex. Journal of Neurophysiology 11319212373232821, 2180 - 2194 (2015).
Seeing a straight line on a curved surface: decoupling of patterns from surfaces by single IT neurons. Journal of Neurophysiology 11773, 104 - 116 (2017).
Effect of silhouetting and inversion on view invariance in the monkey inferotemporal cortex. Journal of Neurophysiology 11823, 353 - 362 (2017).
Computational models of category-selective brain regions enable high-throughput tests of selectivity. Nature Communications 12, (2021). s41467-021-25409-6.pdf (6.47 MB)
s41467-021-25409-6.pdf (6.47 MB) A Balanced Comparison of Object Invariances in Monkey IT Neurons. eneuro 4, ENEURO.0333-16.2017 (2017).
Brain-Observatory-Toolbox. (2018).
Beating SGD Saturation with Tail-Averaging and Minibatching. Neural Information Processing Systems (NeurIPS 2019) (2019). 9422-beating-sgd-saturation-with-tail-averaging-and-minibatching.pdf (389.35 KB)
9422-beating-sgd-saturation-with-tail-averaging-and-minibatching.pdf (389.35 KB) Learning with Group Invariant Features: A Kernel Perspective. NIPS 2015 (2015). at <https://papers.nips.cc/paper/5798-learning-with-group-invariant-features-a-kernel-perspective> LearningInvarianceKernel_NIPS2015.pdf (292.18 KB)
LearningInvarianceKernel_NIPS2015.pdf (292.18 KB) Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding. (2014). CBMM-Memo-020.pdf (1.89 MB)
CBMM-Memo-020.pdf (1.89 MB) Single neuron studies of the human brain. Probing cognition (2014).
Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval. arXiv.org (2016). at <https://arxiv.org/abs/1603.04595> 1603.04595.pdf (2.9 MB)
1603.04595.pdf (2.9 MB) Group Invariant Deep Representations for Image Instance Retrieval. (2016). CBMM-Memo-043.pdf (2.66 MB)
CBMM-Memo-043.pdf (2.66 MB) Learning to Answer Questions from Wikipedia Infoboxes. The 2016 Conference on Empirical Methods on Natural Language Processing (EMNLP 2016) (2016). Morales-EMNLP2016.pdf (197.28 KB)
Morales-EMNLP2016.pdf (197.28 KB) Scalable Causal Discovery with Score Matching. NeurIPS 2022 (2022). at <https://openreview.net/forum?id=v56PHv_W2A>
Assumption violations in causal discovery and the robustness of score matching. 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (2024). at <https://proceedings.neurips.cc/paper_files/paper/2023/file/93ed74938a54a73b5e4c52bbaf42ca8e-Paper-Conference.pdf>
Adaptive Coding for Dynamic Sensory Inference. eLife (2018).
Lossy Compression of Uninformative Stimuli in the Auditory System. Association for Otolaryngology Mid-Winter Meeting (2017).
Learning mid-level codes for natural sounds. Computational and Systems Neuroscience (Cosyne) 2016 (2016). at <http://www.cosyne.org/c/index.php?title=Cosyne2016_posters_2> Wiktor_COSYNE_2015_hierarchy_final.pdf (2.52 MB)
Wiktor_COSYNE_2015_hierarchy_final.pdf (2.52 MB) Learning Mid-Level Auditory Codes from Natural Sound Statistics. Neural Computation 30, 631-669 (2018).