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Papers read, reading, to read

Banzhaf Artifical Chemistries ??

Szerlip, Morse, Pugh, Stanley (2015). Unsupervised Feature Learning through Divergent Discriminative Feature Accumulation http://eplex.cs.ucf.edu/papers/szerlip_aaai15.pdf

Mouret and Clune (2015). Illuminating search spaces by mapping elites https://arxiv.org/pdf/1504.04909.pdf

MM Khan, AM Ahmad, GM Khan, JF Miller (2013). Fast learning neural networks using Cartesian genetic programming http://cartesiangp.co.uk/papers/neurocomp2013-khan.pdf

Pugh and Stanley (2013). Evolving Multimodal Controllers with HyperNEAT http://eplex.cs.ucf.edu/papers/pugh_gecco13_revised.pdf

Verbancsics and Stanley (2011). Constraining Connectivity to Encourage Modularity in HyperNEAT http://eplex.cs.ucf.edu/publications/2011/verbancsics-gecco11

Danesh Tarapore and Jean-Baptiste Mouret (2015). Evolvability signatures of generative encodings: beyond standard performance benchmarks https://arxiv.org/pdf/1410.4985.pdf

Junyoung Chung, Sungjin Ahn, Yoshua Bengio (2016). Hierarchical Multiscale Recurrent Neural Networks https://arxiv.org/abs/1609.01704v3

Joel Lehman, Sebastian Risi, David B. D'Ambrosio and Kenneth O. Stanley (2013). Encouraging Reactivity to Create Robust Machines http://eplex.cs.ucf.edu/publications/2013/lehman-ab13b

Stanley and Miikkulainen (2002). Evolving Neural Networks through Augmenting Topologies http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf

Joel Lehman and Kenneth O. Stanley (2011). Evolving a Diversity of Virtual Creatures through Novelty Search and Local Competition http://eplex.cs.ucf.edu/publications/2011/lehman-gecco11

Bryan Wilder and Kenneth O. Stanley (2015). Reconciling Explanations for the Evolution of Evolvability http://eplex.cs.ucf.edu/papers/wilder_ab15.pdf

Joel Lehman, and Kenneth O. Stanley (2010). Revising the Evolutionary Computation Abstraction: Minimal Criteria Novelty Search http://eplex.cs.ucf.edu/publications/2010/lehman-gecco10a

Stanley, K. (2007). Compositional Pattern Producing Networks: A Novel Abstraction of Development http://eplex.cs.ucf.edu/papers/stanley_gpem07.pdf

Stanley, K. and Miikkulainen, R. (2002) Evolving Neural Networks through Augmenting Topologies http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf

Rolls, E. (2016). Cerebral Cortex: Principles of Operation Appendices http://www.oxcns.org/papers/Cerebral%20Cortex%20Rolls%202016%20Contents%20and%20Appendices.pdf

Sylvain Cussat-Blanc, Kyle Harrington, and Jordan Pollack (2015). Gene Regulatory Network Evolution Through Augmenting Topologies https://www.researchgate.net/publication/273284677_Gene_Regulatory_Network_Evolution_Through_Augmenting_Topologies

Miconi, T. (2008). In Silicon No One Can Hear You Scream: Evolving Fighting Creatures http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.158.2020&rep=rep1&type=pdf

Rolls, E. (2016). Pattern completion and pattern separation mechanisms in the hippocampus

Richert et al (2016). Fundamental principles of cortical computation: unsupervised learning with prediction, compression and feedback. https://arxiv.org/pdf/1608.06277v1.pdf

Miconi, T. (2016).

Julian F. Miller (2014). Neuro-Centric and Holocentric Approaches to the Evolution of Developmental Neural Networks http://www.cartesiangp.co.uk/papers/devleann2012-miller.pdf

Aswolinskiy and Pipa (2015). RM-SORN: a reward-modulated self-organizing recurrent neural network https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4371712/

Chrol-Cannon and Jin (2015). Learning structure of sensory inputs with synaptic plasticity leads to interference https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4525052/

Yin, Meng, Jin (2012). A Developmental Approach to Structural Self-Organization in Reservoir Computing https://www.researchgate.net/profile/Yaochu_Jin/publication/260662653_A_Developmental_Approach_to_Structural_Self-Organization_in_Reservoir_Computing/links/53f122ff0cf23733e813a228.pdf

Schmidhuber (2015). On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models https://arxiv.org/abs/1511.09249

Chaumont, Adami (2016). Evolution of sustained foraging in three-dimensional environments with physics http://link.springer.com/article/10.1007%2Fs10710-016-9270-z

Schossau, Adami, Hintze (2016). Information-Theoretic Neuro-Correlates Boost Evolution of Cognitive Systems http://www.mdpi.com/1099-4300/18/1/6/html

Marstaller, Hintze, Adami (2013). The Evolution of Representation in Simple Cognitive Networks http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00475#.Vn9EJpO2k1g

Adami Lab. Markov Network Brains http://adamilab.msu.edu/markov-network-brains/

Adami. (2015). Evolving Intelligence ... With a Little Help http://adamilab.blogspot.hk/2015/12/evolving-intelligence-with-little-help.html

Joachimczak M., Wróbel B. (2010) Processing signals with evolving artificial gene regulatory networks. In: Artificial Life XII: Proceedings of the Twelfth International Conference on the Synthesis and Simulation of Living Systems pdf

Wróbel, B., Joachimczak, M., Montebelli, A., and Lowe, R. (2012). The search for beauty: Evolution of minimal cognition in an animat controlled by a gene regulatory network and powered by a metabolic system. In From Animals to Animats 12: The 12th International Conference on the Simulation of Adaptive Behavior (SAB 2012) pdf

Taras Kowaliw, Nicolas Bredeche, Sylvain Chevallier and René Doursat (2014) Chapter 1, Artificial Neurogenesis: An Introduction and Selective Review. In: Kowaliw, T., Bredeche, N. & Doursat, R., eds. "Growing Adaptive Machines: Combining Development and Learning in Artificial Neural Networks." pdf

Rene Doursat (2013) Bridging the Mind-Brain Gap by Morphogenetic “Neuron Flocking”: The Dynamic Self-Organization of Neural Activity into Mental Shapes pdf

Kemp, C., & Tenenbaum, J. B. (2009). Structured statistical models of inductive reasoning. Psychological Review. 116(1), 20-58. link

Lake, B. M. and Tenenbaum, J. B. (2010). Discovering Structure by Learning Sparse Graphs. In Proceedings of the 32nd Annual Conference of the Cognitive Science Society. link

Discovering Structure by Learning Sparse Graphs

Lake, B., Salakhutdinov, R., and Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction.Science 350(6266), 1332-1338. doi: 10.1126/science.aab3050 link

Rogers, T. T. & McClelland, J. L. (2014). Parallel Distributed Processing at 25: Further Explorations in the Microstructure of Cognition. Cognitive Science, 6, pp. 1024-1077. DOI: 10.1111/cogs.12148. link

Sadeghi, Z., Mcclelland, J. L., & Hoffman, P. (2015). You shall know an object by the company it keeps: An investigation of semantic representations derived from object co-occurrence in visual scenes. Neuropsychologia, 76, 52-61 link

Jern, A. & Kemp, C. (2013). A probabilistic account of exemplar and category generation. Cognitive Psychology. 66(1), 85-125.

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