Lee MJ, DiCarlo JJ. An empirical assay of view-invariant object learning in humans and comparison with baseline image-computable models. bioRxiv. 2023:2022.12.31.522402. doi:https://www.biorxiv.org/content/10.1101/2022.12.31.522402v1.

Lee MJ, DiCarlo JJ. How well do rudimentary plasticity rules predict adult visual object learning?. Kietzmann TChristian. PLOS Computational Biology. 2023;19(12):e1011713. doi:10.1371/journal.pcbi.1011713.

Lee MJ, DiCarlo JJ. How well do rudimentary plasticity rules predict adult visual object learning?. Kietzmann TChristian. PLOS Computational Biology. 2023;19(12):e1011713. doi:10.1371/journal.pcbi.1011713.

DiCarlo JJ, Yamins DLK, Ferguson ME, et al. Let's move forward: Image-computable models and a common model evaluation scheme are prerequisites for a scientific understanding of human visionAbstract. Behavioral and Brain Sciences. 2023;4634. doi:10.1017/S0140525X23001607.

Dapello J, Kar K, Schrimpf M, et al. Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness. bioRxiv. 2022. doi:https://doi.org/10.1101/2022.07.01.498495.

Rajalingham R, Sorenson M, Azadi R, Bohn S, DiCarlo JJ, Afraz A. Chronically implantable LED arrays for behavioral optogenetics in primates. Nature Methods. 2021;18(9):1112 - 1116. doi:10.1038/s41592-021-01238-9.

Murty NAR, Bashivan P, Abate A, DiCarlo JJ, Kanwisher N. Computational models of category-selective brain regions enable high-throughput tests of selectivity. Nature Communications. 2021;12(1). doi:10.1038/s41467-021-25409-6.

Kar K, DiCarlo JJ. Fast Recurrent Processing Via Ventral Prefrontal Cortex is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition. Neuron. 2021;109(1):164-167.e5. doi:https://doi.org/10.1016/j.neuron.2020.09.035.

Zhuang C, Yan S, Nayebi A, et al. Unsupervised neural network models of the ventral visual stream. Proceedings of the National Academy of Sciences. 2021;118(3):e2014196118. doi:10.1073/pnas.2014196118.

Rajalingham R, Sorenson M, Azadi R, Bohn S, DiCarlo JJ, Afraz A. Chronically implantable LED arrays for behavioral optogenetics in primates. bioRxiv. 2020. doi:10.1101/2020.09.10.291583.

Rajalingham R, Kar K, Sanghavi S, Dehaene S, DiCarlo JJ. The inferior temporal cortex is a potential cortical precursor of orthographic processing in untrained monkeys. Nature Communications. 2020;11(1). doi:10.1038/s41467-020-17714-3.

Schrimpf M, Kubilius J, Lee MJ, Murty NAR, Ajemian R, DiCarlo JJ. Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence. Neuron. 2020. doi:10.1016/j.neuron.2020.07.040.

Tremblay S, Acker L, Afraz A, et al. An Open Resource for Non-human Primate Optogenetics. Neuron. 2020. doi:10.1016/j.neuron.2020.09.027.

Gan C, Schwartz J, Alter S, et al. ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. arXiv. 2020. Available at: https://arxiv.org/abs/2007.04954.

Zhuang C, Yan S, Nayebi A, et al. Unsupervised Neural Network Models of the Ventral Visual Stream. bioRxiv. 2020. doi:10.1101/2020.06.16.155556.

Kar K, Kubilius J, Schmidt K, Issa EB, DiCarlo JJ. Evidence that recurrent circuits are critical to the ventral stream?s execution of core object recognition behavior. Nature Neuroscience. 2019;22(6):974 - 983. doi:10.1038/s41593-019-0392-5.

Bashivan P, Kar K, DiCarlo JJ. Neural population control via deep image synthesis. Science. 2019;364(6439):eaav9436. doi:10.1126/science.aav9436.

Rajalingham R, DiCarlo JJ. Reversible Inactivation of Different Millimeter-Scale Regions of Primate IT Results in Different Patterns of Core Object Recognition Deficits. Neuron. 2019;102(2):493 - 505.e5. doi:10.1016/j.neuron.2019.02.001.

Schrimpf M, Kubilius J, Hong H, et al. Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?. bioRxiv. 2018. doi:https://doi.org/10.1101/407007.

Kubilius J, Schrimpf M, Nayebi A, Bear D, Yamins DLK, DiCarlo JJ. CORnet: Modeling the Neural Mechanisms of Core Object Recognition. bioRxiv. 2018. doi:https://doi.org/10.1101/408385.

Batista AP, DiCarlo JJ. Deep learning reaches the motor system. Nature Methods. 2018;15(10):772 - 773. doi:10.1038/s41592-018-0152-6.

Kar K, Kubilius J, Schmidt K, Issa EB, DiCarlo JJ. Evidence that recurrent circuits are critical to the ventral stream's execution of core object recognition behavior. bioRxiv. 2018. doi:https://doi.org/10.1101/354753.

Rajalingham R, Issa EB, Bashivan P, Kar K, Schmidt K, DiCarlo JJ. Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks. The Journal of Neuroscience. 2018;38(33):7255 - 7269. doi:10.1523/JNEUROSCI.0388-18.2018.

Rajalingham R, Issa EB, Bashivan P, Kar K, Schmidt K, DiCarlo JJ. 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. doi:https://doi.org/10.1101/240614.

Ohayon S, Caravaca-Aguirre A, Piestun R, DiCarlo JJ. Minimally invasive multimode optical fiber microendoscope for deep brain fluorescence imaging. Biomedical Optics Express. 2018;9(4):1492-1509. doi:10.1364/BOE.9.001492.

Rajalingham R, DiCarlo JJ. Reversible inactivation of different millimeter-scale regions of primate IT results in different patterns of core object recognition deficits. bioRxiv. 2018. doi:https://doi.org/10.1101/390245.

Nayebi A, Bear D, Kubilius J, et al. Task-Driven Convolutional Recurrent Models of the Visual System. arXiv. 2018. doi:https://arxiv.org/abs/1807.00053.

Bashivan P, Tensen M, DiCarlo JJ. Teacher Guided Architecture Search. arXiv. 2018. doi:https://arxiv.org/abs/1808.01405.

Aparicio PL, Issa EB, DiCarlo JJ. Neurophysiological Organization of the Middle Face Patch in Macaque Inferior Temporal Cortex. The Journal of Neuroscience. 2016;36(50):12729 - 12745. doi:10.1523/JNEUROSCI.0237-16.2016.

Afraz A, Boyden ES, DiCarlo JJ. Optogenetic and pharmacological suppression of spatial clusters of face neurons reveal their causal role in face gender discrimination. Proceedings of the National Academy of Sciences. 2015:6730 - 6735. doi:10.1073/pnas.1423328112.

Cadieu CF, Hong H, Yamins DLK, et al. Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition. Bethge M. PLoS Computational Biology. 2014;10(12):e1003963. doi:10.1371/journal.pcbi.1003963.

Afraz A, Yamins DLK, DiCarlo JJ. Neural Mechanisms Underlying Visual Object Recognition. Cold Spring Harbor Symposia on Quantitative Biology. 2014;79:99 - 107. doi:10.1101/sqb.2014.79.024729.

Yamins DLK, Hong H, Cadieu CF, DiCarlo JJ. Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream. In:  Advances in Neural Information Processing Systems. Advances in Neural Information Processing Systems. Lake Tahoe, Nevada, United States.; 2013. doi:https://papers.nips.cc/paper/4991-hierarchical-modular-optimization-of-convolutional-networks-achieves-representations-similar-to-macaque-it-and-human-ventral-stream.

Baldassi C, Alemi-Neissi A, Pagan M, DiCarlo JJ, Zecchina R, Zoccolan D. Shape Similarity, Better than Semantic Membership, Accounts for the Structure of Visual Object Representations in a Population of Monkey Inferotemporal Neurons. PLoS Computational Biology. 2013;9(8):e1003167. doi:10.1371/journal.pcbi.1003167.

Majaj NJ, Hong H, Solomon EA, DiCarlo JJ. A unified neuronal population code fully explains human object recognition. In:  Computation and Systems Neuroscience (COSYNE). Computation and Systems Neuroscience (COSYNE). Salt Lake City, Utah, USA; 2012. doi:http://www.cosyne.org/c/index.php?title=Cosyne_12.

Pinto N, Barhomi Y, Cox DD, DiCarlo JJ. Comparing-State-of-the-Art Visual Features on Invariant Object Recognition Tasks. IEEE Workshop on Applications of Computer Vision (WACV). 2011:463-470. doi:10.1109/WACV.2011.5711540.

Pagan M, Neissi AA, Baldassi C, Zecchina R, DiCarlo JJ, Zoccolan D. From luminance to semantics: how natural objects are represented in monkey inferotemporal cortex. Computational and Systems Neuroscience (COSYNE). 2011. Available at: http://www.cosyne.org/c/index.php?title=Cosyne_11_posters/I-89.

DiCarlo JJ. Do we have a strategy for understanding how the visual system accomplishes object recognition?. In: Dickenson SJ, Leonardis A, Schiele B, Tarr MJ Object Categorization: Computer and Human Vision Perspectives. Object Categorization: Computer and Human Vision Perspectives. New York, NY, USA: Cambridge University Press; 2010.

Li N, DiCarlo JJ. Does the visual system use natural experience to construct size invariant object representations?. Computation and Systems Neuroscience (COSYNE). 2010. doi:10.3389/conf.fnins.2010.03.00326.

Pinto N, DiCarlo JJ, Cox DD. A High-Throughput Screening Approach to Biologically-Inspired Object Recognition. Learning Workshop-Computation and Systems Neuroscience (COSYNE). 2010.

Pinto N, Majaj NJ, Barhomi Y, Solomon EA, Cox DD, DiCarlo JJ. Human versus machine: comparing visual object recognition systems on a level playing field. Computation and Systems Neuroscience (COSYNE). 2010. doi:10.3389/conf.fnins.2010.03.00283.

Issa EB, Papanastassiou AM, Andken BB, DiCarlo JJ. Towards large-scale, high resolution maps of object selectivity in inferior temporal cortex. Computation and Systems Neuroscience (COSYNE). 2010. doi:10.3389/conf.fnins.2010.03.00154.

Li N, DiCarlo JJ. Unsupervised Natural Visual Experience Rapidly Reshapes Size-Invariant Object Representation in Inferior Temporal Cortex. Neuron. 2010;67(6):1062 - 1075. doi:10.1016/j.neuron.2010.08.029.

Aparicio PL, Issa EB, DiCarlo JJ. What is the middle face patch?. Society for Neuroscience. 2010;40:581.8. Available at: https://www.abstractsonline.com/Plan/ViewAbstract.aspx?sKey=e08f5ff4-1ba9-4faf-a459-5c9d4be0a1bf&cKey=36fa0d7d-3e83-4910-be75-57d361ae9e58&mKey=%7bE5D5C83F-CE2D-4D71-9DD6-FC7231E090FB%7d.

Pinto N, Doukhan D, DiCarlo JJ, Cox DD. A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation. Friston KJ. PLoS Computational Biology. 2009;5(11):e1000579. doi:10.1371/journal.pcbi.1000579.

Pinto N, DiCarlo JJ, Cox DD. How far can you get with a modern face recognition test set using only simple features?. In:  IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). Miami, FL: IEEE; 2009. doi:10.1109/CVPR.2009.5206605.

Li N, DiCarlo JJ. The size invariance of neuronal object representations can be reshaped by temporally contiguous visual experience. Society for Neuroscience. 2009:306.10. Available at: https://www.abstractsonline.com/Plan/ViewAbstract.aspx?sKey=8bb461de-0fd1-4f6a-9dfe-d62b65382083&cKey=507938c9-dc2c-4a47-a74f-601df562eddc&mKey=%7b081F7976-E4CD-4F3D-A0AF-E8387992A658%7d.

Papanastassiou AM, de Beeck HPOp, Andken BB, DiCarlo JJ. A systematic exploration of the relationship of fMRI signals and neuronal activity in the primate temporal lobe. Society for Neuroscience. 2009.

Pinto N, Cox DD, DiCarlo JJ. Unlocking Biologically-Inspired Computer Vision: a High-Throughput Approach. NVIDIA GPU Technology Conference. 2009.

Pinto N, Cox DD, DiCarlo JJ. Unlocking Brain-Inspired Computer Vision. GPU@BU. 2009.

Pinto N, Cox DD, DiCarlo JJ. The Visual Cortex and GPUs. GPU Computing for Biomedical Research. 2009.

Li N, Cox DD, Zoccolan D, DiCarlo JJ. What Response Properties Do Individual Neurons Need to Underlie Position and Clutter ?Invariant? Object Recognition?. Journal of Neurophysiology. 2009;102(1):360 - 376. doi:10.1152/jn.90745.2008.

Rust NC, DiCarlo JJ. Concurrent increases in selectivity and tolerance produce constant sparseness across the ventral visual stream. Computation and Systems Neuroscience (COSYNE). 2008. Available at: http://www.cosyne.org/c/images/8/8e/Cosyne_pf_new.pdf.

Pinto N, DiCarlo JJ, Cox DD. Establishing Good Benchmarks and Baselines for Face Recognition. In:  European Conference on Computer Vision-Faces in 'Real-Life' Images Workshop. European Conference on Computer Vision-Faces in 'Real-Life' Images Workshop. Marseille, France: EECV; 2008.

Cox DD, Papanastassiou AM, Oreper D, Andken BB, DiCarlo JJ. High-Resolution Three-Dimensional Microelectrode Brain Mapping Using Stereo Microfocal X-ray Imaging. Journal of Neurophysiology. 2008;100(5):2966 - 2976. doi:10.1152/jn.90672.2008.

Cox DD, Pinto N, Doukhan D, Corda B, DiCarlo JJ. A high-throughput screening approach to discovering good forms of visual representation. Computation and Systems Neuroscience (COSYNE). 2008.

Rust NC, DiCarlo JJ. Inferior temporal cortex robustly signals encounters with new objects, but is not an online representation of the visual world. Society for Neuroscience. 2008:316.6. Available at: https://www.abstractsonline.com/Plan/ViewAbstract.aspx?sKey=ee83e7f7-5aea-4ec8-a948-436658d20e37&cKey=fc64f0af-c81e-4b0e-b809-796349279531&mKey=%7bAFEA068D-D012-4520-8E42-10E4D1AF7944%7d.

Li N, DiCarlo JJ. Natural experience drives online learning of tolerant object representations in visual cortex. Computation and Systems Neuroscience (COSYNE). 2008. Available at: http://www.cosyne.org/c/images/8/8e/Cosyne_pf_new.pdf.

Zoccolan D, Cox DD, Oertelt N, Radwam B, Tsang S, DiCarlo JJ. Is the rodent a valuable model system for studying invariant object recognition?. Computation and Systems Neuroscience (COSYNE). 2008. Available at: http://www.cosyne.org/c/images/8/8e/Cosyne_pf_new.pdf.

Li N, DiCarlo JJ. Unsupervised Natural Experience Rapidly Alters Invariant Object Representation in Visual Cortex. Science. 2008;321:1502 - 1507. doi:10.1126/science.1160028.

Li N, DiCarlo JJ. Unsupervised natural experience rapidly alters invariant object representation in visual cortex. Society for Neuroscience. 2008:316.5. Available at: https://www.abstractsonline.com/Plan/ViewAbstract.aspx?sKey=ee83e7f7-5aea-4ec8-a948-436658d20e37&cKey=9a873eb3-f8d3-48b5-8df9-9f7b2ef0a3d9&mKey=%7bAFEA068D-D012-4520-8E42-10E4D1AF7944%7d.

Pinto N, Cox DD, Corda B, Doukhan D, DiCarlo JJ. Why is real-world object recognition hard?: Establishing honest benchmarks and baselines for object recognition. Computation and Systems Neuroscience (COSYNE). 2008.

de Beeck HPOp, Deutsch JA, Vanduffel W, Kanwisher NG, DiCarlo JJ. A Stable Topography of Selectivity for Unfamiliar Shape Classes in Monkey Inferior Temporal Cortex. Cerebral Cortex. 2007;18(7):1676 - 1694. doi:10.1093/cercor/bhm196.

DiCarlo JJ, Cox DD. Untangling invariant object recognition. Trends in Cognitive Sciences. 2007;11(8):333 - 341. doi:10.1016/j.tics.2007.06.010.

Li N, Cox DD, Zoccolan D, DiCarlo JJ. Flexible and robust object recognition in inferior temporal cortex supported by neurons with limited position and clutter tolerance. Society for Neuroscience. 2006.

de Beeck HPOp, Deutsch JA, Vanduffel W, Kanwisher N, DiCarlo JJ. A large-scale shape map in monkey inferior temporal cortex. Society for Neuroscience. 2006.

DiCarlo JJ. Making faces in the brain. Nature. 2006;442:644 - 644. doi:10.1038/nature05000.

Kreiman G, Hung CP, Kraskov A, Quiroga RQuian, Poggio T, DiCarlo JJ. Object Selectivity of Local Field Potentials and Spikes in the Macaque Inferior Temporal Cortex. Neuron. 2006;49(3):433 - 445. doi:10.1016/j.neuron.2005.12.019.

Zoccolan D, Kouh M, Poggio T, DiCarlo JJ. Trade-off between shape selectivity and tolerance to identity-preserving transformations in monkey inferotemporal cortex. Gordon Conference: Sensation and the Natural Environment. 2006.

Cox DD, DiCarlo JJ. Is the ?binding problem? a problem in inferiotemporal cortex?. Society for Neuroscience. 2005.

Hung CP, Kreiman G, Poggio T, DiCarlo JJ. Fast Readout of Object Identity from Macaque Inferior Temporal Cortex. Science. 2005;310:863 - 866. doi:10.1126/science.1117593.

Zoccolan D, Cox DD, DiCarlo JJ. Multiple Object Response Normalization in Monkey Inferotemporal Cortex. Journal of Neuroscience. 2005;25(36):8150 - 8164. doi:10.1523/JNEUROSCI.2058-05.2005.

Zoccolan D, Cox DD, DiCarlo JJ. Multiple object response normalization in monkey inferotemporal cortex. Society for Neuroscience. 2005.

Hung CP, Kreiman G, Poggio T, DiCarlo JJ. Ultra-fast object recognition from few spikes. Cambridge, MA: MIT; 2005:1-31. Available at: https://dspace.mit.edu/handle/1721.1/30556.

Hung CP, Kreiman G, Quiroga RQuian, Kraskov A, Poggio T, DiCarlo JJ. Using ?read-out? of object identity to understand object coding in the macaque anterior inferior temporal cortex. Computation and Systems Neuroscience (COSYNE). 2005.

Cox DD, DiCarlo JJ. The effect of visual experience on the position tolerance of primate object representations. Society for Neuroscience. 2004.

DiCarlo JJ, Maunsell JHR. Mapping functional neuronal processing chains underlying sensory-motor tasks in the primate. Gordon Conference: Sensory coding and the natural environment. 2004.

Kreiman G, Hung CP, Poggio T, DiCarlo JJ. Object recognition by selective spike and LFP data in macaque inferior temporal cortex. Society for Neuroscience. 2004.

Kreiman G, Hung CP, Poggio T, DiCarlo JJ. Selectivity of local field potentials in macaque inferior temporal cortex. Cambridge, M: MIT; 2004. Available at: https://dspace.mit.edu/handle/1721.1/30417.

DiCarlo JJ, Maunsell JHR. Using Neuronal Latency to Determine Sensory?Motor Processing Pathways in Reaction Time Tasks. Journal of Neurophysiology. 2004;93(5):2974 - 2986. doi:10.1152/jn.00508.2004.

DiCarlo JJ, Maunsell JHR. Anterior Inferotemporal Neurons of Monkeys Engaged in Object Recognition  Can be Highly Sensitive to Object Retinal Position. Journal of Neurophysiology. 2003;89(6):3264 - 3278. doi:10.1152/jn.00358.2002.

DiCarlo JJ, Maunsell JHR. Using reaction time tasks to map sensory-motor chains in the monkey. Society for Neuroscience. 2002.

DiCarlo JJ, Maunsell JHR. Form representation in monkey inferotemporal cortex is virtually unaltered by free viewing. Nature Neuroscience. 2000;3(8):814 - 821. doi:10.1038/77722.

DiCarlo JJ, Maunsell JHR. Inferotemporal representations underlying object recognition in the free viewing monkey. Society for Neuroscience. 2000.

DiCarlo JJ, Johnson KO. Spatial and Temporal Structure of Receptive Fields in Primate Somatosensory Area 3b: Effects of Stimulus Scanning Direction and Orientation. The Journal of Neuroscience. 2000;20(1):495 - 510. doi:10.1523/JNEUROSCI.20-01-00495.2000.

DiCarlo JJ, Johnson KO. Form processing in area 3b. International Symposium on Brain Mechanisms of Tactile Perception. 1999.

DiCarlo JJ, Johnson KO. Velocity Invariance of Receptive Field Structure in Somatosensory Cortical Area 3b of the Alert Monkey. The Journal of Neuroscience. 1999;19(1):401 - 419. doi:10.1523/JNEUROSCI.19-01-00401.1999.

DiCarlo JJ, Johnson KO, Hsiao SS. Structure of Receptive Fields in Area 3b of Primary Somatosensory Cortex in the Alert Monkey. The Journal of Neuroscience. 1998;18(7):2626 - 2645. doi:10.1523/JNEUROSCI.18-07-02626.1998.

DiCarlo JJ, Hsiao SS, Johnson KO. Spatial and temporal properties of neural receptive fields in area 3b of the awake monkey. Society for Neuroscience. 1997.

Hsiao SS, Johnson KO, Twombly IA, DiCarlo JJ. Form processing and attention effects in somatosensory cortex. In: Franzen O, Johansson R, Terenius L Somesthesis and the Neurobiology of the Somatosensory Cortex. Somesthesis and the Neurobiology of the Somatosensory Cortex. Switzerland: Birkhauser Basel; 1996.

DiCarlo JJ, Twombly IA, Hsiao SS, Johnson KO. Laminar differences in spatiotemporal receptive field structure of neurons in area 3b of the awake macaque. Society for Neuroscience. 1996.

Twombly IA, DiCarlo JJ, Hsiao SS, Johnson KO. Linear and non-linear processing of tactile spatial form in area 3b of the awake macaque. Society for Neuroscience. 1996.

DiCarlo JJ, Lane JW, Hsiao SS, Johnson KO. Marking microelectrode penetrations with fluorescent dyes. Journal of Neuroscience Methods. 1996;64(1):75 - 81. doi:10.1016/0165-0270(95)00113-1.

Hsiao SS, DiCarlo JJ, Johnson KO. Animals, Brain, Electrophysiology, Fluorescent Dyes, Macaca mulatta, Microelectrodes, Neurosciences. Biomedical Engineering Society. 1995.

DiCarlo JJ, Hsiao SS, Johnson KO. Transformation of tactile spatial form within a cortical column in area 3b of the macaque. Society for Neuroscience. 1994.

Schmajuk NA, DiCarlo JJ. Stimulus configuration, classical conditioning, and hippocampal function. Psychological Review. 1992;99(2):268 - 305. doi:10.1037/0033-295X.99.2.268.

Schmajuk NA, DiCarlo JJ. A hippocampal theory of schizophrenia. Behavioral and Brain Sciences. 1991;14:47-49. doi:10.1017/S0140525X00065353.

Schmajuk NA, DiCarlo JJ. Neural dynamics of hippocampal modulation of classical conditioning. In: Commons M, Grossberg S, Staddon JER Neural Network Models of Conditioning and Action. Neural Network Models of Conditioning and Action. Hillsdale, NJ: Lawrence Erlbaum Association; 1991.

Schmajuk NA, DiCarlo JJ. A neural network approach to hippocampal function in classical conditioning. Behavioral Neuroscience. 1991;105(1):82 - 110. doi:10.1037/0735-7044.105.1.82.

Schmajuk NA, DiCarlo JJ. The short-term memory regulation hypothesis of hippocampal function. Midwestern Psychology Association. 1990.

Schmajuk NA, DiCarlo JJ. Neural dynamics of hippocampal modulation of classical conditioning. 12th Symposium on Models of Behavior: Neural Network Models of Conditioning and Action. 1989.