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Created Feb 14, 2017
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Banzhaf Artifical Chemistries ??

Szerlip, Morse, Pugh, Stanley (2015). Unsupervised Feature Learning through Divergent Discriminative Feature Accumulation

Mouret and Clune (2015). Illuminating search spaces by mapping elites

MM Khan, AM Ahmad, GM Khan, JF Miller (2013). Fast learning neural networks using Cartesian genetic programming

Pugh and Stanley (2013). Evolving Multimodal Controllers with HyperNEAT

Verbancsics and Stanley (2011). Constraining Connectivity to Encourage Modularity in HyperNEAT

Danesh Tarapore and Jean-Baptiste Mouret (2015). Evolvability signatures of generative encodings: beyond standard performance benchmarks

Junyoung Chung, Sungjin Ahn, Yoshua Bengio (2016). Hierarchical Multiscale Recurrent Neural Networks

Joel Lehman, Sebastian Risi, David B. D'Ambrosio and Kenneth O. Stanley (2013). Encouraging Reactivity to Create Robust Machines

Stanley and Miikkulainen (2002). Evolving Neural Networks through Augmenting Topologies

Joel Lehman and Kenneth O. Stanley (2011). Evolving a Diversity of Virtual Creatures through Novelty Search and Local Competition

Bryan Wilder and Kenneth O. Stanley (2015). Reconciling Explanations for the Evolution of Evolvability

Joel Lehman, and Kenneth O. Stanley (2010). Revising the Evolutionary Computation Abstraction: Minimal Criteria Novelty Search

Stanley, K. (2007). Compositional Pattern Producing Networks: A Novel Abstraction of Development

Stanley, K. and Miikkulainen, R. (2002) Evolving Neural Networks through Augmenting Topologies

Rolls, E. (2016). Cerebral Cortex: Principles of Operation Appendices

Sylvain Cussat-Blanc, Kyle Harrington, and Jordan Pollack (2015). Gene Regulatory Network Evolution Through Augmenting Topologies

Miconi, T. (2008). In Silicon No One Can Hear You Scream: Evolving Fighting Creatures

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.

Miconi, T. (2016).

Julian F. Miller (2014). Neuro-Centric and Holocentric Approaches to the Evolution of Developmental Neural Networks

Aswolinskiy and Pipa (2015). RM-SORN: a reward-modulated self-organizing recurrent neural network

Chrol-Cannon and Jin (2015). Learning structure of sensory inputs with synaptic plasticity leads to interference

Yin, Meng, Jin (2012). A Developmental Approach to Structural Self-Organization in Reservoir Computing

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

Chaumont, Adami (2016). Evolution of sustained foraging in three-dimensional environments with physics

Schossau, Adami, Hintze (2016). Information-Theoretic Neuro-Correlates Boost Evolution of Cognitive Systems

Marstaller, Hintze, Adami (2013). The Evolution of Representation in Simple Cognitive Networks

Adami Lab. Markov Network Brains

Adami. (2015). Evolving Intelligence ... With a Little Help

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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