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BIOLOGY, NEUROSCIENCE & PSYCHOLOGY

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Wilkinson, D. J. (2006). Stochastic Modelling for Systems Biology. CRC Press.
Tuyéras, R. (2018). Category theory for genetics II: genotype, phenotype and haplotype. ArXiv:1805.07004 [Math]. Retrieved from http://arxiv.org/abs/1805.07004
Tuyéras, R. (2018). Category theory for genetics I: mutations and sequence alignments. ArXiv:1805.07002 [Math]. Retrieved from http://arxiv.org/abs/1805.07002
Tuyéras, R. (2018). Category Theory for Genetics. ArXiv:1708.05255 [Math]. Retrieved from http://arxiv.org/abs/1708.05255
Rosen, R. (1958). The representation of biological systems from the standpoint of the theory of categories. The Bulletin of Mathematical Biophysics, 20(4), 317–341. https://doi.org/10/fdgzxz
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
Mazzola, G. (2002). The Topos of Music: Geometric Logic of Concepts, Theory, and Performance. Birkhäuser Basel. https://doi.org/10.1007/978-3-0348-8141-8
Mascari, J.-F., Giacchero, D., & Sfakianakis, N. (2017). Symetries and asymetries of the immune system response: A categorification approach. In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 1451–1454). https://doi.org/10/ggdnd3
Lawvere, F. W. (1994). Tools for the Advancement of Objective Logic: Closed Categories and Toposes. In J. Macnamara & G. E. Reyes (Eds.), The Logical Foundations of Cognition (pp. 43–56). Oxford University Press USA.
Kato, G. C., & Struppa, D. C. (2002). Category Theory and Consciousness. https://doi.org/10/ggdf92
Heller, M. (2019). Homunculus’ Brain and Categorical Logic. ArXiv, abs/1903.03424.
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
Gromov, M. (n.d.). Structures, Learning and Ergosystems: Chapters, 159.
Gómez, J. (2009). Modeling cognitive systems with Category Theory Towards rigor in cognitive sciences.
Fages, F., Martinez, T., Rosenblueth, D. A., & Soliman, S. (2018). Influence Networks Compared with Reaction Networks: Semantics, Expressivity and Attractors. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 15(4), 1138–1151. https://doi.org/10/ggdf94
Fages, F., Calzone, L., Chabrier-Rivier, N., & Soliman, S. (2006). Machine Learning Biochemical Networks from Temporal Logic Properties. In C. Priami & G. Plotkin (Eds.), Transactions on Computational Systems Biology VI (pp. 68–94). Berlin, Heidelberg: Springer. https://doi.org/10/dd8
Fages, F. (2014). Cells as Machines: Towards Deciphering Biochemical Programs in the Cell. In R. Natarajan (Ed.), Distributed Computing and Internet Technology (pp. 50–67). Cham: Springer International Publishing. https://doi.org/10/ggdf96
Fages, F., Calzone, L., & Soliman, S. (2006). BIOCHAM: an environment for modeling biological systems and formalizing experimental knowledge. Bioinformatics, 22(14), 1805–1807. https://doi.org/10/dfv
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
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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