Intelligence, Self-Awareness, and Sentience Reading List

Questions to think about while reading:

• What qualifies as intelligence?
• What qualifies as self-awareness?
• What qualifies as sentience?
• Can an intelligence not be sentient, but still report back that it is?
• Can something be sentient, but not intelligent enough to report back that it is?
• Can something be neither intelligent or sentient, but be statistically likely to compose sentences like it was trained on that fool a human into thinking it it both intelligent and sentient?
• How harmful can an AI become that can fool someone into thinking it is a person?
• How do we handle a case where an AI is a completely different "personality" (for lack of a better word) from one interaction to another and from one person to another? How do we a handle a being that has no consistent self-identity from one moment to another?

Foundations for Understanding Intelligence & Consciousness

• Hofstadter, D. R. (2000). Godel, Escher, Bach (Penguin Press Science). United Kingdom: Penguin.
• Hofstadter, D. R., Dennett, D. C. (1982). The Mind's I: Fantasies and Reflections on Self and Soul. United Kingdom: Bantam Books.
• Chalmers, D. J. (1996). The conscious mind: In search of a fundamental theory. Oxford Paperbacks.
• Dennett, D. C. (2017). Consciousness Explained. United States: Little, Brown.
• Chalmers, D. J. (2002). Philosophy of mind: Classical and contemporary readings.
• Edelman, G. M. (2005). Wider Than the Sky: The Phenomenal Gift of Consciousness. United Kingdom: Yale University Press.
• Begley, S., Schwartz, J. M. (2009). The Mind and the Brain: Neuroplasticity and the Power of Mental Force. United States: HarperCollins.
• Aleksander, I. (2001). How to Build a Mind: Toward Machines with Imagination. New York: Columbia University Press.
• Brooks, R. A. (2003). Flesh and Machines: How Robots Will Change Us. United States: Vintage Books.
• LeDoux, J., LeDoux, J. (2003). Synaptic Self: How Our Brains Become Who We Are. United Kingdom: Penguin Publishing Group.
• Hawkins, J., Blakeslee, S. (2007). On Intelligence. United States: Henry Holt and Company.
• Taylor, J. B. (2008). My Stroke of Insight: A Brain Scientist's Personal Journey. United States: Penguin Publishing Group.
• Pinker, S. (2009). How the Mind Works. United Kingdom: W. W. Norton.
• McCorduck, P. (2004). Machines Who Think: A Personal Inquiry Into the History and Prospects of Artificial Intelligence. United Kingdom: Taylor & Francis.
• Chalmers, D. J. (2010). The character of consciousness. Oxford University Press.
• Damasio, A. (2011) The quest to understand consciousness. TED2011.
• Turkle, S. (2011). Alone Together: Why We Expect More from Technology and Less from Each Other. United States: Basic Books.
• Brockman, J. (2015). What to Think About Machines That Think: Today's Leading Thinkers on the Age of Machine Intelligence. United States: HarperCollins.
• Hutchins, E., Hutchins, E. (1995). Cognition in the wild. Cambridge: MIT Press.
• Clark, A. (2004). Natural-born Cyborgs: Minds, Technologies, and the Future of Human Intelligence. United Kingdom: Oxford University Press.
• Haraway, D. (2006). A cyborg manifesto: Science, technology, and socialist-feminism in the late 20th century. In The international handbook of virtual learning environments (pp. 117-158). Springer, Dordrecht.
• Lloyd, S. (2012). A Turing test for free will. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 370(1971), 3597-3610.
• Tononi, G., Edelman, G. M. (2008). A Universe Of Consciousness: How Matter Becomes Imagination. United States: Basic Books.
• Koch, C. (2012). Consciousness: Confessions of a Romantic Reductionist. United States: MIT Press.
• Jabr, F. (2012) Does Self-Awareness Require a Complex Brain? Scientific American. 22 Aug.
• Sacks, O., Sacks, O. (2017). The River of Consciousness. United States: Knopf Doubleday Publishing Group.
• Koch, C. (2019). The feeling of life itself: why consciousness is widespread but can't be computed. Mit Press.
• Graziano, M. S. A. (2019). Rethinking Consciousness: A Scientific Theory of Subjective Experience. United States: W. W. Norton.
• Andersen, R. (2019) Do Animals Have Feelings? The Atlantic.
• Christian, B. (2020). The alignment problem: Machine learning and human values. WW Norton & Company.
• De Waal, F. (2016). Are we smart enough to know how smart animals are?. WW Norton & Company.
• Fiske, A., Henningsen, P., & Buyx, A. (2019). Your robot therapist will see you now: ethical implications of embodied artificial intelligence in psychiatry, psychology, and psychotherapy. Journal of medical Internet research, 21(5), e13216.
• Vince, G. (2020). Transcendence: How Humans Evolved Through Fire, Language, Beauty, and Time. United States: Basic Books.
• Yonck, R. (2020). Future Minds: The Rise of Intelligence from the Big Bang to the End of the Universe. United States: Arcade.
• Schmidt, E., Huttenlocher, D., Kissinger, H. A. (2021). The Age of AI: And Our Human Future. United States: Little, Brown.
• Connock, A., Stephen, A. (2022) We taught an AI to impersonate Shakespeare and Oscar Wilde – here’s what it revealed about sentience. The Conversation. 14 June.
• OpenAI’s new language generator GPT-3 is shockingly good—and completely mindless
• Bratton, B. & Agüera y Arcas, B. (2022) The Model is the Message: Why We Need New Language For Artificial Intelligence. Noema. 12 July
• Psychophysicists: Your Brain Might Not Be as Conscious as You Think. The Byte.
• Ward, C. (2022) This robot teaches ants, then the ants teach their friends. SyFy Wire. 16 August.
• Ball, P. (2022) The study of nonhuman intelligence could be missing major insights. Big Think. 14 May.

Seeing things that might not be there

• Nayak, S. M., & Griffiths, R. R. (2022). A single belief-changing psychedelic experience is associated with increased attribution of consciousness to living and non-living entities. Frontiers in psychology, 1035.
• Liu, J., Li, J., Feng, L., Li, L., Tian, J., & Lee, K. (2014). Seeing Jesus in toast: neural and behavioral correlates of face pareidolia. Cortex, 53, 60-77.
• The ELIZA effect:
  • ELIZA effect
  • Episode 382: The ELIZA Effect. 99% Invisible.

Bias in AI

• Sham, A. H., Aktas, K., Rizhinashvili, D., Kuklianov, D., Alisinanoglu, F., Ofodile, I., … & Anbarjafari, G. (2022). Ethical AI in facial expression analysis: Racial bias. Signal, Image and Video Processing, 1-8.
• Boutin, C. (2022) There’s More to AI Bias Than Biased Data, NIST Report Highlights. NIST News. 16 March.
• Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., & Hall, P. (2022). Towards a Standard for Identifying and Managing Bias in Artificial Intelligence.
• https://blogs.ischool.berkeley.edu/w231/2021/06/18/ai-bias-where-does-it-come-from-and-what-can-we-do-about-it/
• Girish, D. (2020) ‘Coded Bias’ Review: When the Bots Are Racist. The New York Times. 11 Nov.
• Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M. E., … & Staab, S. (2020). Bias in data‐driven artificial intelligence systems—An introductory survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1356.
• Sharma, K. (2019) How to keep human bias out of AI. TEDxWarwick.

Information Theory

• Aftab, O., Cheung, P., Kim, A., Thakkar, S., & Yeddanapudi, N. (2001). Information theory: Information theory and the digital age. Final paper, Project History, Massachusetts Institute of Technology.
• Wikipedia contributors. (2022, June 14). Information theory. In Wikipedia, The Free Encyclopedia.
• Wikipedia contributors. (2022, May 19). Entropy (order and disorder). In Wikipedia, The Free Encyclopedia.
• DeDeo, S. (2018). Information theory for intelligent people. Santa Fe.
• Kak, S. (2020). Information theory and dimensionality of space. Scientific Reports, 10(1), 1-5.
• Maroney, Owen, "Information Processing and Thermodynamic Entropy", The Stanford Encyclopedia of Philosophy (Fall 2009 Edition), Edward N. Zalta (ed.).
• Wikipedia contributors. (2022, February 6). Principle of maximum entropy. In Wikipedia, The Free Encyclopedia.

Order from entropy

• Frenkel, D. (1993). Order through disorder: entropy strikes back. Physics world, 6(2), 24.
• Lorenz, R. D., & Kleidon, A. (Eds.). (2005). Non-equilibrium Thermodynamics and the Production of Entropy: Life, Earth, and Beyond. Springer-Verlag GmbH..
• Haji-Akbari, A., Engel, M., Keys, A. S., Zheng, X., Petschek, R. G., Palffy-Muhoray, P., & Glotzer, S. C. (2009). Disordered, quasicrystalline and crystalline phases of densely packed tetrahedra. Nature, 462(7274), 773-777.
• Ben-Naim, A. (2011). Entropy: Order or information. Journal of chemical education, 88(5), 594-596.
• Damasceno, P. F., Engel, M., & Glotzer, S. C. (2012). Predictive self-assembly of polyhedra into complex structures. Science, 337(6093), 453-457.
• Frenkel, D. (2015). Order through entropy. Nature materials, 14(1), 9-12.
• Saglam, H., Duzgun, A., Kargioti, A., Harle, N., Zhang, X., Bingham, N. S., … & Schiffer, P. (2022). Entropy-driven order in an array of nanomagnets. Nature Physics, 1-7.
• Alexander, S. & Almagro-Moreno, S. (2022) Is life the result of the laws of entropy? New Scientist. 11 June.

Combinatorial Models

• T-79.5204 Combinatorial Models and Stochastic Algorithms (6 cr) P

From Information Theory to Machine Learning & Neural Networks

• MacKay, D. J., & Mac Kay, D. J. (2003). Information theory, inference and learning algorithms. Cambridge university press.
• Tutorials. Kernel Machines.
• Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences, 79(8), 2554-2558.
• colah's blog
• Recurrent Neural Networks
• Tishby, N., Pereira, F. C., & Bialek, W. (2000). The information bottleneck method. arXiv preprint physics/0004057.
• Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013). Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602.
• Tishby, N., & Zaslavsky, N. (2015, April). Deep learning and the information bottleneck principle. In 2015 ieee information theory workshop (itw) (pp. 1-5). IEEE.
• Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332-1338.
• Whittington, J. C., & Bogacz, R. (2017). An approximation of the error backpropagation algorithm in a predictive coding network with local hebbian synaptic plasticity. Neural computation, 29(5), 1229-1262.
• Shwartz-Ziv, R., & Tishby, N. (2017). Opening the black box of deep neural networks via information. arXiv preprint arXiv:1703.00810.
• Alemi, A. A., Fischer, I., Dillon, J. V., & Murphy, K. (2016). Deep variational information bottleneck. arXiv preprint arXiv:1612.00410.
• Zhang, C., Ren, M., & Urtasun, R. (2018). Graph hypernetworks for neural architecture search. arXiv preprint arXiv:1810.05749.
• Li, Y., Gimeno, F., Kohli, P., & Vinyals, O. (2020). Strong generalization and efficiency in neural programs. arXiv preprint arXiv:2007.03629.
• Song, Y., Lukasiewicz, T., Xu, Z., & Bogacz, R. (2020). Can the Brain Do Backpropagation?–-Exact Implementation of Backpropagation in Predictive Coding Networks. Advances in neural information processing systems, 33, 22566-22579.
• Millidge, B., Tschantz, A., & Buckley, C. L. (2022). Predictive coding approximates backprop along arbitrary computation graphs. Neural Computation, 34(6), 1329-1368.
• Bubeck, S., & Sellke, M. (2021). A universal law of robustness via isoperimetry. Advances in Neural Information Processing Systems, 34, 28811-28822.
• Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., … & Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
• Rosenbaum, R. (2022). On the relationship between predictive coding and backpropagation. Plos one, 17(3), e0266102.

Ising Models

• Wikipedia contributors. (2022, May 12). Ising model. In Wikipedia, The Free Encyclopedia.
• Wikipedia contributors. (2022, May 23). Spin glass. In Wikipedia, The Free Encyclopedia.
• Schaap, H. G. (2005). Ising models and neural networks. University Library Groningen[Host].
• The Ising Model, Spin Glasses and Neural Networks
• Little, W. A. (1980). An ising model of a neural network. In Biological Growth and Spread (pp. 173-179). Springer, Berlin, Heidelberg.
• Amit, Daniel J., Hanoch Gutfreund, and Haim Sompolinsky. "Spin-glass models of neural networks." Physical Review A 32.2 (1985): 1007.
• van Hemmen, J. L. (1986). Spin-glass models of a neural network. Physical Review A, 34(4), 3435.
• Gardner, E., & Derrida, B. (1988). Optimal storage properties of neural network models. Journal of Physics A: Mathematical and general, 21(1), 271.
• Schneidman, E., Berry, M. J., Segev, R., & Bialek, W. (2006). Weak pairwise correlations imply strongly correlated network states in a neural population. Nature, 440(7087), 1007-1012.
• Roudi, Y., Tyrcha, J., & Hertz, J. (2009). Ising model for neural data: model quality and approximate methods for extracting functional connectivity. Physical Review E, 79(5), 051915.
• Yoshioka, M. (2009). Learning of spatiotemporal patterns in Ising-spin neural networks: analysis of storage capacity by path integral methods. Physical review letters, 102(15), 158102.
• Witoelar, A., & Roudi, Y. (2011). Neural network reconstruction using kinetic Ising models with memory. BMC Neuroscience, 12(1), 1-2.
• Mehta, P., & Schwab, D. J. (2014). An exact mapping between the variational renormalization group and deep learning. arXiv preprint arXiv:1410.3831.
• Pai, S. (2016) Convolutional Neural Networks Arise From Ising Models and Restricted Boltzmann Machines. Stanford University, APPPHYS 293 Term Paper. 7 June.
• Yamamoto, Y., Aihara, K., Leleu, T., Kawarabayashi, K. I., Kako, S., Fejer, M., … & Takesue, H. (2017). Coherent Ising machines—Optical neural networks operating at the quantum limit. npj Quantum Information, 3(1), 1-15.
• Morningstar, A., & Melko, R. G. (2018). Deep Learning the Ising Model Near Criticality. Journal of Machine Learning Research, 18(163), 1-17.
• Efthymiou, S., Beach, M. J., & Melko, R. G. (2019). Super-resolving the Ising model with convolutional neural networks. Physical Review B, 99(7), 075113.
• Talalaev, D. V. (2020, October). Hopfield neural network and anisotropic Ising model. In International Conference on Neuroinformatics (pp. 381-386). Springer, Cham.
• D'Angelo, F., & Böttcher, L. (2020). Learning the Ising model with generative neural networks. arXiv preprint arXiv:2001.05361.
• Aguilera, M., Moosavi, S. A., & Shimazaki, H. (2021). A unifying framework for mean-field theories of asymmetric kinetic Ising systems. Nature communications, 12(1), 1-12.
• Kara, O., Sehanobish, A., & Corzo, H. H. (2021). Fine-tuning Vision Transformers for the Prediction of State Variables in Ising Models. arXiv preprint arXiv:2109.13925.
• Zhang, Y. (2021). Ising spin configurations with the deep learning method. Journal of Physics Communications, 5(1), 015006.

Tensor Networks

• Gesteau, E., Marcolli, M., & Parikh, S. (2022). Holographic tensor networks from hyperbolic buildings. arXiv preprint arXiv:2202.01788.
• Park, C., Hwang, C. O., Cho, K., & Kim, S. J. (2022). Dual Geometry of Entanglement Entropy via Deep Learning. arXiv preprint arXiv:2205.04445.
• Howard, E. (2021). Holographic renormalization with machine learning. In Emerging Technologies in Data Mining and Information Security (pp. 253-261). Springer, Singapore.
• Kobayashi, M. (2021). Information geometry of hyperbolic-valued Boltzmann machines. Neurocomputing, 431, 163-168.
• Zhang, Q., Guo, B., Kong, W., Xi, X., Zhou, Y., & Gao, F. (2021). Tensor-based dynamic brain functional network for motor imagery classification. Biomedical Signal Processing and Control, 69, 102940.
• Ganchev, A. (2019, February). On bulk/boundary duality and deep networks. In AIP Conference Proceedings (Vol. 2075, No. 1, p. 100002). AIP Publishing LLC.
• Chirco, G., Oriti, D., & Zhang, M. (2018). Group field theory and tensor networks: towards a Ryu–Takayanagi formula in full quantum gravity. Classical and Quantum Gravity, 35(11), 115011.
• Gan, W. C., & Shu, F. W. (2017). Holography as deep learning. International Journal of Modern Physics D, 26(12), 1743020.
• Czech, B., Lamprou, L., McCandlish, S., & Sully, J. (2016). Tensor networks from kinematic space. Journal of High Energy Physics, 2016(7), 1-38.
• Czech, B., Lamprou, L., McCandlish, S., & Sully, J. (2015). Integral geometry and holography. Journal of High Energy Physics, 2015(10), 1-41.
  • Sully, J. (2015) Geometry from Compression. Perimeter Institute Recorded Seminar Archive. 3 Feb.
  • It would be interesting to see if what deep connections exist between this and information geometry.
• Evenbly, G., & Vidal, G. (2015). Tensor network renormalization. Physical review letters, 115(18), 180405.
• Phien, H. N., McCulloch, I. P., & Vidal, G. (2015). Fast convergence of imaginary time evolution tensor network algorithms by recycling the environment. Physical Review B, 91(11), 115137.
• Orús, R. (2014). Advances on tensor network theory: symmetries, fermions, entanglement, and holography. The European Physical Journal B, 87(11), 1-18.
• Orús, R. (2014). A practical introduction to tensor networks: Matrix product states and projected entangled pair states. Annals of physics, 349, 117-158.
• Socher, R., Chen, D., Manning, C. D., & Ng, A. (2013). Reasoning with neural tensor networks for knowledge base completion. Advances in neural information processing systems, 26.
• Lv, Z., Luo, S., Liu, Y., & Zheng, Y. (2006, August). Information geometry approach to the model selection of neural networks. In First International Conference on Innovative Computing, Information and Control-Volume I (ICICIC'06) (Vol. 3, pp. 419-422). IEEE.
• Pellionisz, A. (1989). Tensor Network Model of the Cerebellum and its Olivary System. The Olivo-Cerebellar System in Motor Control, 400-424.
• Pellionisz, A. (1989). Tensor geometry: A language of brains & neurocomputers. Generalized coordinates in neuroscience & robotics. In Neural Computers (pp. 381-391). Springer, Berlin, Heidelberg.
• Pellionisz, A. (1988). Tensorial aspects of the multidimensional massively parallel sensorimotor function of neuronal networks. Progress in brain research, 76, 341-354.
• Pellionisz, A. J. (1986). Tensor network theory of the central nervous system and sensorimotor modeling. In Brain theory (pp. 121-145). Springer, Berlin, Heidelberg.
• Pellionisz, A., & Llinas, R. (1985). Tensor network theory of the metaorganization of functional geometries in the central nervous system. Neuroscience, 16(2), 245-273.
• Pellionisz, A. J. (1985). Robotics connected to neurobiology by tensor theory of brain function. In IEEE Proceedings of the International Conference on Cybernetics and Society (pp. 411-414).
• Pellionisz, A., & Llinás, R. (1982). Tensor theory of brain function. The cerebellum as a space-time metric. In Competition and cooperation in neural nets (pp. 394-417). Springer, Berlin, Heidelberg.
• Pellionisz, A., & Llinás, R. (1980). Tensorial approach to the geometry of brain function: Cerebellar coordination via a metric tensor. Neuroscience, 5(7), 1125-1136.

Foundations for Complexity and Emergence

• Waldrop, M. M. (1993). Complexity: The emerging science at the edge of order and chaos. Simon and Schuster.
• Kauffman, S., & Kauffman, S. A. (1995). At home in the universe: The search for laws of self-organization and complexity. Oxford University Press, USA.
• Bak, P. (2013). How nature works: the science of self-organized criticality. Springer Science & Business Media.
• Gleick, J. (2008). Chaos: Making a new science. Penguin.
• Prigogine, I., & Stengers, I. (2018). Order out of chaos: Man's new dialogue with nature. Verso Books.
• Meadows, D. H. (2008). Thinking in systems: A primer. chelsea green publishing.
• Mitchell, M. (2009). Complexity: A guided tour. Oxford university press.
• Tuszynski, J. A. (Ed.). (2006). The emerging physics of consciousness. Springer Science & Business Media.
• Bedau, M. A., & Humphreys, P. E. (2008). Emergence: Contemporary readings in philosophy and science. MIT press.

Non-linear Dyanmics & Emergence

• Wikipedia contributors. (2022, April 25). Dissipative system. In Wikipedia, The Free Encyclopedia.
• Kondepudi, D., & Prigogine, I. (2014). Modern thermodynamics: from heat engines to dissipative structures. John Wiley & Sons.
• Ott, E. (2002). Chaos in dynamical systems. Cambridge university press.
• Strogatz, S. H. (2018). Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering. CRC press.
• Siegelmann, H. T. (2010). Complex systems science and brain dynamics. Frontiers in Computational Neuroscience, 4, 7.
• Hirsch, M. W., Smale, S., & Devaney, R. L. (2012). Differential equations, dynamical systems, and an introduction to chaos. Academic press.
• Holland, J. H. (2012). Signals and boundaries: Building blocks for complex adaptive systems. Mit Press.

Complexity

• Morowitz, H. J. (2018). The mind, the brain and complex adaptive systems. Routledge.
• Krakauer, D. C. (Ed.). (2019). Worlds Hidden in Plain Sight: The Evolving Idea of Complexity at the Santa Fe Institute, 1984-2019. Santa Fe, NM: SFI Press.
• Shayan, S. A., Shayan, S. A. (2019). Understanding Complex Adaptive Systems: A Macro and Holistic Approach. (n.p.): Independently Published.
• Computational Complexity by Christos H. Papadimitriou
• Sipser, M. (1996). Introduction to the Theory of Computation. ACM Sigact News, 27(1), 27-29.
• Arora, S., & Barak, B. (2009). Computational complexity: a modern approach. Cambridge University Press.
• Goldreich, O. (2008). Computational complexity: a conceptual perspective. ACM Sigact News, 39(3), 35-39.
• Li, M., & Vitányi, P. (2019). An introduction to Kolmogorov complexity and its applications. New York: Springer.
• Chaitin, G. J., Chaitin, G. J. (2012). Exploring RANDOMNESS. United Kingdom: Springer London.
• Piantadosi, S. T. (2018). One parameter is always enough. AIP Advances, 8(9), 095118.
• James P. Crutchfield's Research

Biology and Complexity

• Alon, U. (2006). An introduction to systems biology: design principles of biological circuits. Chapman and Hall/CRC.
• Borden, N. M., Stefan, C., & Forseen, S. E. (2015). Imaging Anatomy of the Human Brain: a Comprehensive Atlas Including Adjacent Structures. Springer Publishing Company.

In Forests

• Filotas, E., Parrott, L., Burton, P. J., Chazdon, R. L., Coates, K. D., Coll, L., … & Messier, C. (2014). Viewing forests through the lens of complex systems science. Ecosphere, 5(1), 1-23.

Modeling the Brain

• Boden, M. A. (1987). Artificial Intelligence And Natural Man. United Kingdom: Basic Books.
• Boden, M. A. (1988). Computer Models of Mind: Computational Approaches in Theoretical Psychology. Netherlands: Cambridge University Press.
• Lytton, W. W. (2007). From computer to brain: foundations of computational neuroscience. Springer Science & Business Media.
• Spitzer, M., Spitzer, M. (1999). The Mind Within the Net: Models of Learning, Thinking, and Acting. United Kingdom: MIT Press.
• The research of Lisa Feldman Barrett
• Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S., Hudspeth, A. J., & Mack, S. (Eds.). (2000). Principles of neural science (Vol. 4, pp. 1227-1246). New York: McGraw-hill.
• Rieke, F., Warland, D., Van Steveninck, R. D. R., & Bialek, W. (1999). Spikes: exploring the neural code. MIT press.
• Cohen, M. X. (2014). Analyzing neural time series data: theory and practice. MIT press.
• Bonelli, R. M., & Cummings, J. L. (2007). Frontal-subcortical circuitry and behavior. Dialogues Clin Neurosci, 9, 141-151.
• Bassett, D. S., & Gazzaniga, M. S. (2011). Understanding complexity in the human brain. Trends in cognitive sciences, 15(5), 200-209.
• Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). Neuronal dynamics: From single neurons to networks and models of cognition. Cambridge University Press.
• Lohse, C., Bassett, D. S., Lim, K. O., & Carlson, J. M. (2014). Resolving anatomical and functional structure in human brain organization: identifying mesoscale organization in weighted network representations. PLoS computational biology, 10(10), e1003712.
• Markram, H., Muller, E., Ramaswamy, S., Reimann, M. W., Abdellah, M., Sanchez, C. A., … & Schürmann, F. (2015). Reconstruction and simulation of neocortical microcircuitry. Cell, 163(2), 456-492.
• Lahav, N., Ksherim, B., Ben-Simon, E., Maron-Katz, A., Cohen, R., & Havlin, S. (2016). K-shell decomposition reveals hierarchical cortical organization of the human brain. New Journal of Physics, 18(8), 083013.
• Strausfeld, N. J., & Hirth, F. (2016). Introduction to ‘Homology and convergence in nervous system evolution’. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1685), 20150034.
• Gabbiani, F., & Cox, S. J. (2017). Mathematics for neuroscientists. Academic Press.
• Lamberti, M., Hess, M., Dias, I., van Putten, M., le Feber, J., & Marzen, S. (2022). Maximum entropy models provide functional connectivity estimates in neural networks. Scientific Reports, 12(1), 1-10.

Cognition and Self-hood in Animals

Christof Koch wants you to know that Consciousness Is Everywhere. Philip Sopher want you to know What Animals Teach Us About Measuring Intelligence. Also see Mirror test. In Wikipedia, The Free Encyclopedia..

Some kids put a fish tank on wheels and gave it the ability to control where it goes: Kent, A. (2017) Just Keep Swimming. Build18@CMU. YouTube.. Read more.

• Hubená, P., Horký, P., & Slavík, O. (2021). Fish self-awareness: limits of current knowledge and theoretical expectations. Animal Cognition, 1-15.
• Lenkei, R., Faragó, T., Zsilák, B., & Pongrácz, P. (2021). Dogs (Canis familiaris) recognize their own body as a physical obstacle. Scientific reports, 11(1), 1-8.
• Baragli, P., Scopa, C., Maglieri, V., & Palagi, E. (2021). If horses had toes: demonstrating mirror self recognition at group level in Equus caballus. Animal cognition, 24(5), 1099-1108.
• Stacho, M., Herold, C., Rook, N., Wagner, H., Axer, M., Amunts, K., & Güntürkün, O. (2020). A cortex-like canonical circuit in the avian forebrain. Science, 369(6511), eabc5534.
• Herculano-Houzel, S. (2020). Birds do have a brain cortex—and think. Science, 369(6511), 1567-1568.
• Nieder, A., Wagener, L., & Rinnert, P. (2020). A neural correlate of sensory consciousness in a corvid bird. Science, 369(6511), 1626-1629.
• Kohda, M., Hotta, T., Takeyama, T., Awata, S., Tanaka, H., Asai, J. Y., & Jordan, A. L. (2019). If a fish can pass the mark test, what are the implications for consciousness and self-awareness testing in animals?. PLoS biology, 17(2), e3000021.
• Klump, B. C., Cantat, M., & Rutz, C. (2019). Raw-material selectivity in hook-tool-crafting New Caledonian crows. Biology letters, 15(2), 20180836.
• Hartmann, K., Veit, L., & Nieder, A. (2018). Neurons in the crow nidopallium caudolaterale encode varying durations of visual working memory periods. Experimental Brain Research, 236(1), 215-226.
• Gallup Jr, G. G., & Anderson, J. R. (2018). The “olfactory mirror” and other recent attempts to demonstrate self-recognition in non-primate species. Behavioural Processes, 148, 16-19.
• Morrison R, Reiss D (2018) Precocious development of self-awareness in dolphins. PLoS ONE 13(1): e0189813.
• Boyle, A. (2018). Mirror Self‐Recognition and Self‐Identification. Philosophy and Phenomenological Research, 97(2), 284-303.
• Horowitz, A. (2017). Smelling themselves: Dogs investigate their own odours longer when modified in an “olfactory mirror” test. Behavioural processes, 143, 17-24.
• Thomas, N. (2016). Self-Awareness and Selfhood in Animals. In Animal Ethics and the Autonomous Animal Self (pp. 37-67). Palgrave Macmillan, London.
• Ari, C., & D’Agostino, D. P. (2016). Contingency checking and self-directed behaviors in giant manta rays: Do elasmobranchs have self-awareness?. Journal of Ethology, 34(2), 167-174.
• Redish, A. D. (2016). Vicarious trial and error. Nature Reviews Neuroscience, 17(3), 147-159.
• Hills, T. T., & Butterfill, S. (2015). From foraging to autonoetic consciousness: The primal self as a consequence of embodied prospective foraging. Current Zoology, 61(2), 368-381.
• Anderson, J. R., & Gallup, G. G. (2015). Mirror self-recognition: a review and critique of attempts to promote and engineer self-recognition in primates. Primates, 56(4), 317-326.
• Hockings, K. J., Bryson-Morrison, N., Carvalho, S., Fujisawa, M., Humle, T., McGrew, W. C., … & Matsuzawa, T. (2015). Tools to tipple: ethanol ingestion by wild chimpanzees using leaf-sponges. Royal Society open science, 2(6), 150150.
• Logan CJ, Jelbert SA, Breen AJ, Gray RD, Taylor AH (2014) Modifications to the Aesop's Fable Paradigm Change New Caledonian Crow Performances. PLoS ONE 9(7): e103049.
• Savanah, S. (2013). Mirror self-recognition and symbol-mindedness. Biology & Philosophy, 28(4), 657-673.
• Templer, V. L., & Hampton, R. R. (2013). Episodic memory in nonhuman animals. Current Biology, 23(17), R801-R806.
• DeGrazia, D. (2009). Self-awareness in animals (pp. 201-217). The philosophy of animal minds. Cambridge, England: Cambridge University Press.
• Prior, H., Schwarz, A., & Güntürkün, O. (2008). Mirror-induced behavior in the magpie (Pica pica): evidence of self-recognition. PLoS biology, 6(8), e202.
• Plotnik, J. M., De Waal, F. B., & Reiss, D. (2006). Self-recognition in an Asian elephant. Proceedings of the National Academy of Sciences, 103(45), 17053-17057.
• de Waal, F. B., Dindo, M., Freeman, C. A., & Hall, M. J. (2005). The monkey in the mirror: hardly a stranger. Proceedings of the National Academy of sciences, 102(32), 11140-11147.
• Bekoff, M., & Sherman, P. W. (2004). Reflections on animal selves. Trends in ecology & evolution, 19(4), 176-180.
• Bekoff, M. (2002). Awareness: animal reflections. Nature, 419(6904), 255-255.
• Gallup Jr, G. G., Anderson, J. R., & Shillito, D. J. (2002). The mirror test. The cognitive animal: Empirical and theoretical perspectives on animal cognition, 325-333.
• Delfour, F., & Marten, K. (2001). Mirror image processing in three marine mammal species: killer whales (Orcinus orca), false killer whales (Pseudorca crassidens) and California sea lions (Zalophus californianus). Behavioural processes, 53(3), 181-190.
• Reiss, D., & Marino, L. (2001). Mirror self-recognition in the bottlenose dolphin: A case of cognitive convergence. Proceedings of the National Academy of Sciences, 98(10), 5937-5942.
• Hauser, M. D., Miller, C. T., Liu, K., & Gupta, R. (2001). Cotton‐top tamarins (Saguinus oedipus) fail to show mirror‐guided self‐exploration. American Journal of Primatology: Official Journal of the American Society of Primatologists, 53(3), 131-137.
• Gallup Jr, G. G. (1998). Self-awareness and the evolution of social intelligence. Behavioural Processes, 42(2-3), 239-247.
• Westergaard, G. C., & Hyatt, C. W. (1994). The responses of bonobos (Pan paniscus) to their mirror images: evidence of selfrecognition. Human Evolution, 9(4), 273-279.
• Marten, K., & Psarakos, S. (1994). Evidence of self-awareness in the bottlenose dolphin (Tursiops truncatus).
• Parker, S. T. E., Mitchell, R. W., & Boccia, M. L. (1994). Self-awareness in animals and humans: Developmental perspectives. In Based on papers presented at a Conference on Self-Awareness in Monkeys, Apes, and Humans, Sonoma State U, Rohnert, CA, 1991.. Cambridge University Press.
• Patterson, F., & Gordon, W. (1993). The case for the personhood of gorillas.
• Povinelli, D. J. (1989). Failure to find self-recognition in Asian elephants (Elephas maximus) in contrast to their use of mirror cues to discover hidden food. Journal of Comparative Psychology, 103(2), 122.
• Robert, S. (1986). Ontogeny of mirror behavior in two species of great apes. American Journal of Primatology, 10(2), 109-117.
• Gallup Jr, G. G. Jr 1970. Chimpanzees: self-recognition. Science, 167, 86-87.
• Amsterdam, B. (1972). Mirror self‐image reactions before age two. Developmental Psychobiology: The Journal of the International Society for Developmental Psychobiology, 5(4), 297-305.
• Gallup, G. G., McClure, M. K., Hill, S. D., & Bundy, R. A. (1971). Capacity for self-recognition in differentially reared chimpanzees. The Psychological Record, 21(1), 69-74.

Animal Culture (Social Learning)

• Byholm, P., Beal, M., Isaksson, N., Lötberg, U., & Åkesson, S. (2022). Paternal transmission of migration knowledge in a long-distance bird migrant. Nature communications, 13(1), 1-7.
• Franks, N. R., Podesta, J. A., Jarvis, E. C., Worley, A., & Sendova-Franks, A. B. (2022). Robotic communication with ants. Journal of Experimental Biology, 225(15), jeb244106.
• Whiten, A. (2021). The psychological reach of culture in animals’ lives. Current Directions in Psychological Science, 30(3), 211-217.
• Lewis, M. A., Fagan, W. F., Auger-Methe, M., Frair, J., Fryxell, J. M., Gros, C., … & Merkle, J. A. (2021). Learning and animal movement. Frontiers in Ecology and Evolution.
• Safina, C. (2020) The secret call of the wild: how animals teach each other to survive. The Guardian. 9 April.
• Lachlan, R. F., & Whiten, A. (2020). Cultural evolution in non-human animals. Oxford University Press.
• Allen, J. A. (2019). Community through culture: From insects to whales: How social learning and culture manifest across diverse animal communities. BioEssays, 41(11), 1900060.
• Jesmer, B. R., Merkle, J. A., Goheen, J. R., Aikens, E. O., Beck, J. L., Courtemanch, A. B., … & Kauffman, M. J. (2018). Is ungulate migration culturally transmitted? Evidence of social learning from translocated animals. Science, 361(6406), 1023-1025.
• Langley, L. (2016) Schooled: Animals That Teach Their Young. National Geographic. 7 May.
• Mueller, T., O’Hara, R. B., Converse, S. J., Urbanek, R. P., & Fagan, W. F. (2013). Social learning of migratory performance. Science, 341(6149), 999-1002.
• Sawyer, H., Kauffman, M. J., Middleton, A. D., Morrison, T. A., Nielson, R. M., & Wyckoff, T. B. (2013). A framework for understanding semi‐permeable barrier effects on migratory ungulates. Journal of Applied Ecology, 50(1), 68-78.

Intelligence in Plants

Here's a fun project: Elowan: A plant-robot hybrid

• Karban, R., Grof-Tisza, P., & Couchoux, C. (2022). Consistent individual variation in plant communication: do plants have personalities?. Oecologia, 199(1), 129-137.
• Mallatt, J., Blatt, M. R., Draguhn, A., Robinson, D. G., & Taiz, L. (2021). Debunking a myth: plant consciousness. Protoplasma, 258(3), 459-476.
• Raja, V., Silva, P. L., Holghoomi, R., & Calvo, P. (2020). The dynamics of plant nutation. Scientific reports, 10(1), 1-13.
• Calvo, P., Gagliano, M., Souza, G. M., & Trewavas, A. (2020). Plants are intelligent, here’s how. Annals of Botany, 125(1), 11-28.
• Gagliano, M., Vyazovskiy, V. V., Borbély, A. A., Depczynski, M., & Radford, B. (2020). Comment on'Lack of evidence for associative learning in pea plants'. Elife, 9, e61141.
• Markel, K. (2020). Lack of evidence for associative learning in pea plants. Elife, 9.
• Taiz, L., Alkon, D., Draguhn, A., Murphy, A., Blatt, M., Hawes, C., … & Robinson, D. G. (2019). Plants neither possess nor require consciousness. Trends in Plant Science, 24(8), 677-687.
• Simard, S.W. (2018). Mycorrhizal Networks Facilitate Tree Communication, Learning, and Memory. In: Baluska, F., Gagliano, M., Witzany, G. (eds) Memory and Learning in Plants. Signaling and Communication in Plants. Springer, Cham.
• Affifi, R. (2018). Deweyan psychology in plant intelligence research: Transforming stimulus and response. In Memory and learning in plants (pp. 17-33). Springer, Cham.
• Gagliano, M., Abramson, C. I., & Depczynski, M. (2018). Plants learn and remember: lets get used to it. Oecologia, 186(1), 29-31.
• Biegler, R. (2018). Insufficient evidence for habituation in Mimosa pudica. Response to Gagliano et al.(2014). Oecologia, 186(1), 33-35.
• Chamovitz, D. A. (2018). Plants are intelligent; now what?. Nature Plants, 4(9), 622-623.
• Yokawa, K., Kagenishi, T., Pavlovič, A., Gall, S., Weiland, M., Mancuso, S., & Baluška, F. (2018). Anaesthetics stop diverse plant organ movements, affect endocytic vesicle recycling and ROS homeostasis, and block action potentials in Venus flytraps. Annals of Botany, 122(5), 747-756.
• Trewavas, A. (2017). The foundations of plant intelligence. Interface focus, 7(3), 20160098.
• Trewavas, T. (2016). Plant intelligence: an overview. BioScience, 66(7), 542-551.
• Gagliano, M., Vyazovskiy, V. V., Borbély, A. A., Grimonprez, M., & Depczynski, M. (2016). Learning by association in plants. Scientific reports, 6(1), 1-9.
• Abramson, C. I., & Chicas-Mosier, A. M. (2016). Learning in plants: lessons from Mimosa pudica. Frontiers in Psychology, 7, 417.
• Gagliano, M., Renton, M., Depczynski, M., & Mancuso, S. (2014). Experience teaches plants to learn faster and forget slower in environments where it matters. Oecologia, 175(1), 63-72.
• Cahill Jr, J. F., Bao, T., Maloney, M., & Kolenosky, C. (2013). Mechanical leaf damage causes localized, but not systemic, changes in leaf movement behavior of the Sensitive Plant, Mimosa pudica (Fabaceae) L. Botany, 91(1), 43-47.
• Marder, M. (2013). Plant intelligence and attention. Plant signaling & behavior, 8(5), e23902.
• Calvo Garzón, P., & Keijzer, F. (2011). Plants: Adaptive behavior, root-brains, and minimal cognition. Adaptive behavior, 19(3), 155-171.
• Fromm, J., & Lautner, S. (2007). Electrical signals and their physiological significance in plants. Plant, cell & environment, 30(3), 249-257.
• Trewavas, A. (2005). Plant intelligence. Naturwissenschaften, 92(9), 401-413.
• Trewavas, A. (2003). Aspects of plant intelligence. Annals of botany, 92(1), 1-20.
• Trewavas, A. (2002). Plant intelligence: Mindless mastery. Nature, 415(6874), 841-841.

Electrical signals in plants

• Luo, Y., Li, W., Lin, Q., Zhang, F., He, K., Yang, D., … & Chen, X. (2021). A Morphable Ionic Electrode Based on Thermogel for Non‐Invasive Hairy Plant Electrophysiology. Advanced Materials, 33(14), 2007848.
• Li, W., Matsuhisa, N., Liu, Z., Wang, M., Luo, Y., Cai, P., … & Chen, X. (2021). An on-demand plant-based actuator created using conformable electrodes. Nature Electronics, 4(2), 134-142.
• Volkov, A. G. (2021). A Venus-flytrap-based actuator. Nature Electronics, 4(2), 97-97.

Communication in Fungi

• Adamatzky, A. (2022). Language of fungi derived from their electrical spiking activity. Royal Society Open Science, 9(4), 211926.
• Gorzelak, M. A., Asay, A. K., Pickles, B. J., & Simard, S. W. (2015). Inter-plant communication through mycorrhizal networks mediates complex adaptive behaviour in plant communities. AoB plants, 7.

Decisions

• Kim, S. J., Aono, M., & Nameda, E. (2015). Efficient decision-making by volume-conserving physical object. New Journal of Physics, 17(8), 083023.
• Wang, Z., Solloway, T., Shiffrin, R. M., & Busemeyer, J. R. (2014). Context effects produced by question orders reveal quantum nature of human judgments. Proceedings of the National Academy of Sciences, 111(26), 9431-9436.
• Busemeyer, J. R., Jessup, R. K., Johnson, J. G., & Townsend, J. T. (2006). Building bridges between neural models and complex decision making behaviour. Neural Networks, 19(8), 1047-1058.
• Busemeyer, J. R., & Townsend, J. T. (1993). Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment. Psychological review, 100(3), 432.

Uniquely Human

Currently, what seems to be uniquely human is how we plan things out and imagine futures.

• Gaesser, B., Spreng, R. N., McLelland, V. C., Addis, D. R., & Schacter, D. L. (2013). Imagining the future: Evidence for a hippocampal contribution to constructive processing. Hippocampus, 23(12), 1150-1161.
• Schacter, D. L., & Addis, D. R. (2007). The cognitive neuroscience of constructive memory: remembering the past and imagining the future. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1481), 773-786.
• Hassabis, D., Kumaran, D., Vann, S. D., & Maguire, E. A. (2007). Patients with hippocampal amnesia cannot imagine new experiences. Proceedings of the National Academy of Sciences, 104(5), 1726-1731.
• Busby, J., & Suddendorf, T. (2005). Recalling yesterday and predicting tomorrow. Cognitive Development, 20(3), 362-372.

Understanding and Modeling Conciousness

• Kerskens, C. M., & Perez, D. L. (2022). Experimental indications of non-classical brain functions. Journal of Physics Communications.
• Graziano, M. S. (2022). A conceptual framework for consciousness. Proceedings of the National Academy of Sciences, 119(18), e2116933119.
• Lau, H., Michel, M., LeDoux, J. E., & Fleming, S. M. (2022). The mnemonic basis of subjective experience. Nature Reviews Psychology, 1-10.
• Zlomuzica, A., & Dere, E. (2022). Towards an animal model of consciousness based on the platform theory. Behavioural Brain Research, 419, 113695.
• Farisco, M., Pennartz, C., Annen, J., Cecconi, B., & Evers, K. (2022). Indicators and criteria of consciousness: ethical implications for the care of behaviourally unresponsive patients. BMC Medical Ethics, 23(1), 1-15.
• Liu, Y., Nour, M. M., Schuck, N. W., Behrens, T. E., & Dolan, R. J. (2022). Decoding cognition from spontaneous neural activity. Nature Reviews Neuroscience, 23(4), 204-214.
• Davis, K. M., de la Torre-Ortiz, C., & Ruotsalo, T. (2022). Brain-Supervised Image Editing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 18480-18489).
• Murray, E. A., & Fellows, L. K. (2022). Prefrontal cortex interactions with the amygdala in primates. Neuropsychopharmacology, 47(1), 163-179.
• Derakhshani, M., Diósi, L., Laubenstein, M., Piscicchia, K., & Curceanu, C. (2022). At the crossroad of the search for spontaneous radiation and the Orch OR consciousness theory. Physics of Life Reviews.
• Brogaard, B., Chomanski, B., & Gatzia, D. E. (2021). Consciousness and information integration. Synthese, 198(3), 763-792.
• Yaden, D. B., Johnson, M. W., Griffiths, R. R., Doss, M. K., Garcia-Romeu, A., Nayak, S., … & Barrett, F. S. (2021). Psychedelics and consciousness: Distinctions, demarcations, and opportunities. International Journal of Neuropsychopharmacology, 24(8), 615-623. — This is a great paper with a good overview of the different problems of consciousness.
• Varley, T. F., Carhart-Harris, R., Roseman, L., Menon, D. K., & Stamatakis, E. A. (2020). Serotonergic psychedelics LSD & psilocybin increase the fractal dimension of cortical brain activity in spatial and temporal domains. Neuroimage, 220, 117049.
• McFadden, J. (2020). Integrating information in the brain’s EM field: the cemi field theory of consciousness. Neuroscience of Consciousness, 2020(1), niaa016.
• Millière, R., Carhart-Harris, R. L., Roseman, L., Trautwein, F. M., & Berkovich-Ohana, A. (2018). Psychedelics, meditation, and self-consciousness. Frontiers in psychology, 9, 1475.
• Bayne, T., & Carter, O. (2018). Dimensions of consciousness and the psychedelic state. Neuroscience of consciousness, 2018(1), niy008.
• Woźniak, M. (2018). “I” and “Me”: the self in the context of consciousness. Frontiers in Psychology, 9, 1656.
• Keppler, J. (2018). The role of the brain in conscious processes: A new way of looking at the neural correlates of consciousness. Frontiers in psychology, 9, 1346. — This feels very fringe to me, but similar kinds of theories and thoughts have crossed my mind.
• Pepperell, R. (2018). Consciousness as a physical process caused by the organization of energy in the brain. Frontiers in Psychology, 2091.
• Fallon, F. (2016). Integrated information theory of consciousness. Internet Encyclopedia of Philosophy, 23.
• Tononi, G., Boly, M., Massimini, M., & Koch, C. (2016). Integrated information theory: from consciousness to its physical substrate. Nature Reviews Neuroscience, 17(7), 450-461.
• Tegmark, M. (2015). Consciousness as a state of matter. Chaos, Solitons & Fractals, 76, 238-270.
• Cerullo MA (2015) The Problem with Phi: A Critique of Integrated Information Theory. PLoS Comput Biol 11(9): e1004286.
• Maguire, P., Moser, P., Maguire, R., & Griffith, V. (2014). Is consciousness computable? Quantifying integrated information using algorithmic information theory. arXiv preprint arXiv:1405.0126.
• Oizumi M, Albantakis L, Tononi G (2014) From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0. PLoS Comput Biol 10(5): e1003588.
• (2012) Consciousness in Human and Non-human Animals. Francis Crick Memorial Conference. 7 July.
• Buckner, R. L. (2012). The serendipitous discovery of the brain's default network. Neuroimage, 62(2), 1137-1145.
• Craig, A. D. (2009). How do you feel—now? The anterior insula and human awareness. Nature reviews neuroscience, 10(1), 59-70.
• Baumeister, R. F., Vohs, K. D., Nathan DeWall, C., & Zhang, L. (2007). How emotion shapes behavior: Feedback, anticipation, and reflection, rather than direct causation. Personality and social psychology review, 11(2), 167-203.
• Northoff, G., Heinzel, A., De Greck, M., Bermpohl, F., Dobrowolny, H., & Panksepp, J. (2006). Self-referential processing in our brain—a meta-analysis of imaging studies on the self. Neuroimage, 31(1), 440-457.
• Tononi, G. (2004). An information integration theory of consciousness. BMC neuroscience, 5(1), 1-22.

Psychadelic Effects on Consciousness and Brain Networks

• Nani, A., & Hashmi, J. A. (2022). The interplay of consciousness and attention in atypical contexts of experience. Frontiers in Neuroscience, 658.
• Johnson, M. W., Hendricks, P. S., Barrett, F. S., & Griffiths, R. R. (2019). Classic psychedelics: An integrative review of epidemiology, therapeutics, mystical experience, and brain network function. Pharmacology & therapeutics, 197, 83-102.
• Kraehenmann, R. (2017). Dreams and psychedelics: neurophenomenological comparison and therapeutic implications. Current neuropharmacology, 15(7), 1032-1042.
• Preller, K. H., & Vollenweider, F. X. (2016). Phenomenology, structure, and dynamic of psychedelic states. Behavioral neurobiology of psychedelic drugs, 221-256.