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BIOLOGY, NEUROSCIENCE & PSYCHOLOGY
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Andreatta, M., Ehresmann, A., Guitart, R., & Mazzola, G. (2013). Towards a Categorical Theory of Creativity for Music, Discourse, and Cognition. In J. Yust, J. Wild, & J. A. Burgoyne (Eds.), Mathematics and Computation in Music (pp. 19–37). Berlin, Heidelberg: Springer. https://doi.org/10/ggdndz
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Brown, R., & Porter, T. (2008). Category Theory and Higher Dimensional Algebra: potential descriptive tools in neuroscience. ArXiv:Math/0306223. Retrieved from http://arxiv.org/abs/math/0306223
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Ehresmann, A. C. (2018). Applications of Categories to Biology and Cognition. https://doi.org/10/ggdf93
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Ehresmann, A. C. (2012). MENS, an Info-Computational Model for (Neuro-)cognitive Systems Capable of Creativity. Entropy, 14, 1703–1716. https://doi.org/10/ggdf9t
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Ehresmann, A. C., & Gomez-Ramirez, J. (2015). Conciliating neuroscience and phenomenology via category theory. Progress in Biophysics and Molecular Biology, 119(3), 347–359. https://doi.org/10/f75jzr
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Engeler, E. (2008). Neural Algebra and Consciousness: A Theory of Structural Functionality in Neural Nets. In K. Horimoto, G. Regensburger, M. Rosenkranz, & H. Yoshida (Eds.), Algebraic Biology (Vol. 5147, pp. 96–109). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-85101-1_8
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Gómez, J. (2009). Modeling cognitive systems with Category Theory Towards rigor in cognitive sciences.
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Hamrick, J. B. (2019). Analogues of mental simulation and imagination in deep learning. Current Opinion in Behavioral Sciences, 29, 8–16. https://doi.org/10.1016/j.cobeha.2018.12.011
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Healy, M. J. (2000). Category theory applied to neural modeling and graphical representations. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium (pp. 35–40 vol.3). Como, Italy: IEEE. https://doi.org/10/dr29pc
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Healy, M. J., & Caudell, T. P. (2004). Neural Networks, Knowledge and Cognition: A Mathematical Semantic Model Based upon Category Theory.
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Hess, K., Dotko, P., Levi, R., Nolte, M., Reimann, M., Scolamiero, M., … Markram, H. (2017). Topological analysis of the connectome of digital reconstructions of neural microcircuits. Frontiers in Computational Neuroscience, 11, 48. https://doi.org/10/gdjbfn
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Hess, K., Kanari, L., Dłotko, P., Scolamiero, M., Levi, R., Shillcock, J., & Markram, H. (2016). Quantifying topological invariants of neuronal morphologies. ArXiv:1603.08432 [Cs, Math, q-Bio]. Retrieved from http://arxiv.org/abs/1603.08432
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Hess, K., Reimann, M. W., Nolte, M., Scolamiero, M., Turner, K., Perin, R., … Markram, H. (2017). Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function. Frontiers in Computational Neuroscience, 11. https://doi.org/10/gdjbfn
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Philipona, D., O’Regan, J., & Nadal, J.-P. (2003). Is There Something Out There? Inferring Space from Sensorimotor Dependencies. Neural Computation, 15, 2029–2049. https://doi.org/10/frg7gs
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Sizemore, A., Giusti, C., Kahn, A., Betzel, R. F., & Bassett, D. S. (2016). Cliques and Cavities in the Human Connectome. ArXiv:1608.03520 [Math, q-Bio]. Retrieved from http://arxiv.org/abs/1608.03520
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