7 References

*** Denotes references of particular interest.

7.1 Chapter 1

Anderson, B. (2014). Computational Neuroscience and Cognitive Modelling: A Student’s Introduction to Methods and Procedures: SAGE Publications.

Hodges, A. (2009). Alan Turing and the Turing Test. Epstein, R., Roberts G., & Beber, G. (Ed.) Parsing the Turing Test: Philosophical and Methodological issues in the Quest for the Thinking Computer. (pp. 13-22). Springer.

Lytton, W. W. (2002). From Computer to Brain: Foundations of Computational Neuroscience: Springer.

Markram, H. (2006). The Blue Brain Project. Nature Reviews Neuroscience, 7(2), 153-160. doi:10.1038/nrn1848

*** Marr, D. (1982). The Philosophy and the Approach. Vision. San Francisco: Freeman.

O’Reilly, R., & Munakata, Y. (2000). Computational Explorations in Cognitive Neuroscience Understanding the Mind by Simulating the Brain. MIT Press.

P. Trappenberg, T. (2002). Fundamentals of Computational Neuroscience: Oxford University Press UK.

*** Pessoa, L. (2017). Do Intelligent Robots Need Emotion? Trends in Cognitive Sciences, 21(11), 817-819. doi:https://doi.org/10.1016/j.tics.2017.06.010

Selfridge, O. G. (1955, March). Pattern recognition and modern computers. In Proceedings of the March 1-3, 1955, Western Joint Computer Conference (pp. 91-93). ACM.

Studios, BBC, director. The Chinese Room Experiment - The Hunt for AI. YouTube, YouTube, 17 Sept. 2015, www.youtube.com/watch?v=D0MD4sRHj1M.

7.2 Chapter 2

Anderson, B. (2014). Computational Neuroscience and Cognitive Modelling: A Student’s Introduction to Methods and Procedures: SAGE Publications.

Dayan, P. A., L. F. (2005). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (T. M. Press Ed.).

Discovery. “Dancing Zombie Squid Explained.” YouTube, YouTube, 10 Aug. 2011, www.youtube.com/watch?v=JGPfSSUlReM.

Lytton, W. W. (2002). From Computer to Brain: Foundations of Computational Neuroscience: Springer.

Mallot, H. A. (2013). Computational Neuroscience (S. International Ed.). Switzerland.

P. Trappenberg, T. (2002). Fundamentals of Computational Neuroscience: Oxford University Press UK.

Sterratt, D. G., Bruce; Gillies, Andrew; Willshaw, David. (2011). Principles of Computational Modelling in Neuroscience (Cambridge University Press ed.).

7.3 Chapter 3

Background: Spike Trains as Point Processes. (n.d.). Retrieved October 10, 2019, from http://www.stat.cmu.edu/~kass/contrib.html#background.

Jaeger, D., Jung, R., & Springer. (2015). “Spike Train.” Encyclopedia of Computational Neuroscience: Springer.

Mallot, H. A. (2015). “Chapter 2 Receptive Fields and the Specificity of Neuronal Firing.” Computational Neuroscience: A First Course. Berlin: Springer.

Dayan, P., & Abbott, L. F. (2001). “1.3 What Makes a Neuron Fire?” Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems: MIT Press.

Ringach, D., & Shapley, R. (2004). Reverse correlation in neurophysiology. Cognitive Science, 28(2), 147–166. doi: 10.1207/s15516709cog2802_2

Schwartz, O., Pillow, J. W., Rust, N. C., & Simoncelli, E. P. (2006). Spike-triggered neural characterization. Journal of Vision, 6(4), 13. doi: 10.1167/6.4.13

Rieke, F. (1999). Spikes: exploring the neural code. Cambridge, MA: MIT Press.

7.4 Chapter 4

3Blue1Brown. “Backpropagation Calculus | Deep Learning, Chapter 4.” YouTube,YouTube, Nov. 2017.

Anderson, B. (2014). Computational Neuroscience and Cognitive Modelling: A Student’s Introduction to Methods and Procedures: SAGE Publications.

Baker, Bowen. “Emergent Tool Use from Multi-Agent Interaction.” OpenAI, OpenAI, 29 Oct. 2019, openai.com/blog/emergent-tool-use/.

Glosser.ca. (2013). Colored neural network. Wikimedia.

Kang, N. (2017). Introducing Deep Learning and Neural Networks — Deep Learning for Rookies. Towards Data Science.

Kang, N. (2017). Multi-Layer Neural Networks with Sigmoid Function— Deep Learning for Rookies. Towards Data Science.

*** Lettvin, J. Y., Maturana, H. R., McCulloch, W. S., & Pitts, W. H. (1959). What the Frog’s Eye Tells the Frog’s Brain. Proceedings of the IRE, 47(11), 1940-1951. doi:10.1109/JRPROC.1959.287207

Lytton, W. W. (2002). From Computer to Brain: Foundations of Computational Neuroscience: Springer.

Mallot, H. A. (2013). Computational Neuroscience (S. International Ed.). Switzerland.

Murphy, K. P. (2012). Introduction Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) (1st ed.).

P. Trappenberg, T. (2002). Fundamentals of Computational Neuroscience: Oxford University Press UK.

Silver, David, et al. “AlphaZero: Shedding New Light on the Grand Games of Chess, Shogi and Go.” Deepmind, Dec. 2018, deepmind.com/blog/article/alphazero-shedding-new-light-grand-games-chess-shogi-and-go.

Tesla. “Full Self-Driving.” YouTube,YouTube,www.youtube.com/watch?v=tlThdr3O5Qo.

7.5 Chapter 5

Glover, G. H. (2011). Overview of Functional Magnetic Resonance Imaging. Neurosurgery Clinics of North America, 22(2), 133–139. doi: 10.1016/j.nec.2010.11.001

Grootswagers, T., Wardle, S. G., & Carlson, T. A. (2017). Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data. Journal of Cognitive Neuroscience, 29(4), 677–697. doi: 10.1162/jocn_a_01068

Haxby, J. V. (2012). Multivariate pattern analysis of fMRI: The early beginnings. NeuroImage, 62(2), 852–855. doi: 10.1016/j.neuroimage.2012.03.016

Lecture 2: k-nearest neighbors. (n.d.). Retrieved from http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote02_kNN.html.

Kriegeskorte, N., & Kreiman, G. (2012). Visual population codes: toward a common multivariate framework for cell recording and functional imaging. Cambridge, MA: MIT Press.

Singh, S. (2014). Magnetoencephalography: Basic principles. Annals of Indian Academy of Neurology, 17(5), 107. doi: 10.4103/0972-2327.128676