| 2023 | Fei Y, Pitkow X (2013). Generalization of graph network inferences in higher-order probabilistic graphical models. Journal of Applied and Computational Topology. doi.org/10.1007/s41468-023-00147-4. [pdf] |  |
| 2023 | Li Z*, Ortega Caro J*, Rusak E, Brendel W, Bethge M, Anselmi F, Patel AB†, Tolias AS†, Pitkow X† (2023). Robust deep learning object recognition models rely on low frequency information in natural images. PLoS Computational Biology. doi.org/10.1371/journal.pcbi.1010932. |  |
| 2023 | Wang EY, Fahey PG, Ponder K, Ding Z, Change A, Muhammad T, Patel S, Ding Z, Tran DT, Fu J, Papadopoulos S, Franke K, Ecker AS, Reimer J, Pitkow X, Sinz FH, Tolias AS. Towards a Foundation Model of the Mouse Visual Cortex. BioRXiv 10.1101/2023.03.21.533548. |  |
| 2023 | Kunin AB, Guo J, Bassler KE, Pitkow X, Josić K (in press). Hierarchical modular structure of the Drosophila connectome. Journal of Neuroscience. BioRXiv. doi.org/10.1101/2022.11.23.517722. |  |
| 2023 | Forseth KJ, Pitkow X, Fischer-Baum S, Tandon N (2022). What The Brain Does As We Speak. BioRXiv doi.org/10.1101/2021.02.05.429841. |  |
| 2023 | Pitkow X (2023). Algorithmic similarity depends continuously on the input distribution, not categorically on how inputs are generated. Trends in Cognitive Sciences. doi.org/10.1016/j.tics.2022.11.002. [Commentary] |  |
| 2023 | Lakshminarasimhan K, Avila E, Pitkow X*, Angelaki DE* (2023). Dynamical Latent State Computation in the Posterior Parietal Cortex. Nature Communications, in press. BioRXiv 10.1101/2022.01.12.476065. |  |
| 2023 | Xiao J, Provenza NR, Asfouri J, Myers J, Mathura RK, Metzger B, Adkinson JA, Allawala AB, Pirtle V, Oswalt D, Shofty B, Robinson ME, Mathew SJ, Goodman WK, Pouratian N, Schrater PR, Patel AB, Tolias AS, Bijanki KR, Pitkow X, Sheth SA (2023). Decoding Depression Severity from Intracranial Neural Activity. Biological Psychiatry. |  |
| 2022 | Li Z, Tolias AS, Pitkow X (2022). Learning Dynamics and Structure of Complex Systems Using Graph Neural Networks. NeurIPS Workshop: A causal view on dynamical systems. |  |
| 2022 | Fei Y, Pitkow X (2022). Attention as inference with third-order interactions. NeurIPS Workshop on All Things Attention: Bridging Different Perspectives on Attention. [pdf] |  |
| 2022 | Alefantis P, Lakshminarasimhan KJ, Avila E, Pitkow X, Angelaki DE (2022). Sensory evidence accumulation using optic flow in a naturalistic navigation task. Journal of Neuroscience 42(27): 5451–5462. |  |
| 2022 | ’t Hart M, et al. Neuromatch Academy (2022). Neuromatch Academy: a 3-week, online summer school in computational neuroscience. Journal of Open Source Education 5(49):118. |  |
| 2022 | Boominathan L, Pitkow X (2022). Phase transitions in when feedback is useful. Advances in Neural Information Processing Systems. arXiv 2110.07873. [pdf] |  |
| 2022 | Kim J, Orhan E, Yoon K, Pitkow X (2022). Two-argument activation functions learn soft XOR operations like cortical neurons. IEEE Access 10: 58071-58080. doi: 10.1109/ACCESS.2022.3178951. arXiv:2110.06871. [pdf] |  |
| 2022 | Lange R, Benjamin A, Haefner RM, Pitkow X (2022). Interpolating between sampling and variational inference with infinite stochastic mixtures. Uncertainty in Artificial Intelligence. arXiv 2110.09618. [pdf] |  |
| 2021 | MICrONS Consortium (2021). Functional connectomics spanning multiple areas of mouse visual cortex. BioRXiv doi.org/10.1101/2021.07.28.454025. [pdf] |  |
| 2021 | Yang Q, Walker EY, Cotton RJ, Tolias AS, Pitkow X (2021). Revealing nonlinear decoding by analyzing choices. Nature Communications 12(1): 1-13. [pdf] |  |
| 2021 | Neuromatch Academy (2021). Neuromatch Academy: Teaching Computational Neuroscience with global accessibility. Trends in Cognitive Sciences doi.org/10.1016/j.tics.2021.03.018. [pdf] |  |
| 2020 | Ren M, Triantafillou E, Wang K-C, Lucas J, Snell J, Pitkow X, Tolias AS, Zemel R (2020). Flexible Few-Shot Learning with Contextual Similarity. arXiv 2012.05895. [pdf] |  |
| 2020 | Kwon M, Daptardar S, Schrater P, Pitkow X (2020). Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics. Advances in Neural Information Processing Systems. arXiv 2009.12576. [pdf][code] |  |
| 2020 | Wu Z, Kwon M, Daptardar S, Schrater P, Pitkow X (2020). Rational Thoughts in Neural Codes. PNAS. doi.org/10.1073/pnas.1912336117. [pdf][code][talk] |  |
| 2020 | Orhan AE, Pitkow X (2020). Improved memory in recurrent neural networks with sequential non-normal dynamics. arXiv:1905.13715. ICLR. [pdf] |  |
| 2020 | Lakshminarasimhan KJ*, Avila E*, Neyhart E, DeAngelis GC, Pitkow X, Angelaki DE (2020). Tracking the mind’s eye: Primate gaze behavior during virtual visuomotor navigation reflects belief dynamics. Neuron. doi.org/10.1016/j.neuron.2020.02.023.[pdf] |  |
| 2019 | Li Z, Brendel W, Walker EY, Cobos E, Muhammad T, Reimer J, Bethge M, Sinz FH, Pitkow X, Tolias AS (2019). Learning from brains how to regularize machines. Advances in Neural Information Processing Systems. ArXiv 1911.05072. [pdf] |  |
| 2019 | Sinz FH, Pitkow X, Reimer J, Bethge M, Tolias AS (2019). Engineering a Less Artificial Intelligence. Neuron 103(6). [pdf] |  |
| 2019 | Walker EY, Sinz FH, Froudarakis E, Fahey PG, Muhammad T, Ecker AS, Cobos E, Reimer J, Pitkow X, Tolias AS (2019). Inception loops discover what excites neurons most using deep predictive models. Nature Neuroscience. doi: 10.1038/s41593-019-0517-x [pdf] |  |
| 2019 | Giahi Saravani A, Forseth K, Tandon N, Pitkow X (2019). Dynamic brain interactions during picture naming. eNeuro. 0472-18.2019. [pdf] |  |
| 2019 | Kumar A, Wu Z, Pitkow X, Schrater P (2019). Belief dynamics extraction. Proceedings of the Cognitive Science Society. arXiv1902.00673. [pdf] |  |
| 2018 | Yoon K, Liao R, Xiong L, Zhang L, Fetaya E, Urtasun R, Zemel R, Pitkow X (2018). Inference in Probabilistic Graphical Models by Graph Neural Networks. 3rd Workshop of Tractable Probabilistic Modeling, ICML arXiv:1803.07710. [Best paper award] |  |
| 2018 | Wu Z, Schrater P, Pitkow X (2018). Inverse POMDP: Inferring what you think from what you do. NeurIPS Workshop, arXiv:1805.09864. [pdf][90-sec video] |  |
| 2018 | Sinz FH, Ecker AS, Fahey PG, Walker EY, Cobos E, Froudarakis E, Yatsenko D, Pitkow X, Reimer J, Tolias AS (2018). Stimulus domain transfer in recurrent models for large scale cortical population prediction on video. BioRXiv: 452672. Advances in Neural Information Processing 2018. [pdf] |  |
| 2018 | Subramaniyan M, Ecker AS, Patel SS, Cotton RJ, Bethge M, Pitkow X, Berens P, Tolias AS (2018). Faster processing of moving compared to flashed bars in awake macaque V1 provides a neural correlate of the flash lag illusion. Journal of Neurophysiology 120: 2430–2452. [pdf] |  |
| 2018 | Liao R, Xiong L, Zhang L, Fetaya E, Yoon K, Pitkow X, Urtasun R, Zemel R (2018). Revisiting Recurrent Backpropagation. Proceedings of the 35th International Conference on Machine Learning, in PMLR 80:3088-3097. [pdf] |  |
| 2018 | Lakshminarasimhan K, Petsalis M, Park H, DeAngelis G, Pitkow X, Angelaki D (2018). A Dynamic Bayesian Observer Model Reveals Origins of Bias in Visual Path Integration. Neuron 99(1): 194–206. [pdf] |  |
| 2018 | Orhan AE, Pitkow X (2018). Skip connections eliminate singularities. International Conference on Learning Representations. [pdf] |  |
| 2018 | Lakshminarasimhan K, Pouget A, DeAngelis GC, Angelaki DE*, Pitkow X* (2018). Inferring decoding strategies for multiple correlated neural populations. PLoS Computational Biology 14(9): e1006371. [pdf] |  |
| 2017 | Pitkow X, Angelaki D (2017). Inference in the brain: Statistics flowing in redundant population codes. Neuron. 94(5): 943–53. [pdf |  |
| 2016 | Pitkow X (2016). Probability by time. Neuron. 92(2): 275–277 [pdf]. Preview of Orbán G, Berkes P, Fiser J, Lengyel M (2016). Neural variability and sampling-based probabilistic representations in the visual cortex. Neuron. 92(2): 530–543. |  |
| 2016 | Kim HG, Pitkow X, Angelaki D, DeAngelis G (2016). A simple approach to ignoring irrelevant variables by population decoding based on multisensory neurons. Journal of Neurophysiology. 116(3): 1449–67. doi: 10.1152/jn.00005.2016. [link] |  |
| 2016 | Raju RV, Pitkow X (2016). Inference by reparameterization in neural population codes. Advances in Neural Information Processing Systems [pdf] |  |
| 2015 | Pitkow X, Liu S, Angelaki D, DeAngelis GD, Pouget A (2015). How can single sensory neurons predict behavior? Neuron 87(2): 411–423. doi:10.1016/j.neuron.2015.06.033 [pdf] |  |
| 2014 | Moreno-Bote R, Beck J, Kanitscheider I, Pitkow X, Latham P, Pouget A (2014). Information-limiting correlations. Nature Neuroscience 17: 1410–1417. doi:10.1038/nn.3807 [pdf] |  |
| 2012 | Beck J, Ma WJ, Pitkow X, Latham P, Pouget A (2012). Not noisy, just wrong: the role of suboptimal inference in behavioral variability. Neuron 74(1): 30–39. doi: 10.1016/j.neuron.2012.03.016 [pdf] |  |
| 2012 | Pitkow X, Meister M (2012). Decorrelation and efficient coding by retinal ganglion cells. Nature Neuroscience 15: 628–35. doi: 10.1038/nn.3064 [pdf] |  |
| 2012 | Pitkow X (2012). Compressive neural representation of sparse, high-dimensional probabilities. Advances in Neural Information Processing Systems. [pdf] [4-min video] |  |
| 2011 | Pitkow X, Ahmadian Y, Miller DK (2011). Learning unbelievable probabilities. Advances in Neural Information Processing Systems. [pdf] |  |
| 2010 | Pitkow X (2010). Exact feature probabilities in images with occlusion. Journal of Vision 10(14): 42. [pdf] |  |
| 2007 | Pitkow X, Sompolinsky H, Meister M (2007). A neural computation for visual acuity in the presence of eye movements. PLoS Biology 5(12): e331. [pdf] |  |
