Chapter 11 References
*** Denotes references of particular interest.
11.3 Chapter 3: What is Computational Neuroscience?
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.
11.4 Chapter 4: Passive Membrane Models
Pivovarov, A.S., Calahorro, F. & Walker, R.J. Na+/K+-pump and neurotransmitter membrane receptors. Invert Neurosci 19, 1 (2019). https://doi.org/10.1007/s10158-018-0221-7
11.5 Chapter 5: Hodgkin and Huxley
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.).
11.6 Chapter 6: Firing Rates
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.
Mainen, Z. F., & Sejnowski, T. J. (1995). Reliability of spike timing in neocortical neurons. Science, 268(5216), 1503-1506.
11.7 Chapter 7: Reverse Correlation and Receptive Field Mapping
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.
Chichilnisky, E. J. “A Simple White Noise Analysis of Neuronal Light Responses.” Network: Computation in Neural Systems, vol. 12, no. 2, 2001, pp. 199–213., doi:10.1080/713663221.
Gerstner, Wulfram. “8.1 Noise Input.” 8.1 Noise Input | Neuronal Dynamics Online Book, neuronaldynamics.epfl.ch/online/Ch8.S1.html.
11.8 Chapter 8: Decoding
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
11.9 Chapter 9: Neural Networks
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.