S. .. Modulated, , p.128

. .. , 132 6.2.3 Comparison of neuromodulation to standard optimization, 2 Neuromodulation of learning parameters in deep neural networks

.. .. Conclusion,

, Discussion and conclusion 143

, A framework for developmental neuroevolution

, Evolving to learn for data classification

, The evolution of learning

.. .. Conclusion,

M. Abadi, Tensorflow: a system for large-scale machine learning, In: OSDI, vol.16, pp.265-283, 2016.

L. F. Agnati, Volume transmission and wiring transmission from cellular to molecular networks: history and perspectives, Acta Physiologica, vol.187, issue.2, pp.329-344, 2006.

F. Luigi and . Agnati, Understanding wiring and volume transmission, Brain research reviews, vol.64, issue.1, pp.137-159, 2010.

A. R. Harvard-l-armus, J. L. Montgomery, and . Jellison, Discrimination learning in paramecia (P. caudatum), In: The Psychological Record, vol.56, issue.4, pp.489-498, 2006.

W. Lauren and . Ancel, Undermining the Baldwin expediting effect: does phenotypic plasticity accelerate evolution?, In: Theoretical population biology, vol.58, pp.307-319, 2000.

M. Andrychowicz, Learning to learn by gradient descent by gradient descent, Advances in Neural Information Processing Systems, pp.3981-3989, 2016.

L. Arias-darraz, A Transient Receptor Potential Ion Channel in Chlamydomonas Shares Key Features with Sensory Transduction-Associated TRP Channels in Mammals, In: The Plant Cell, vol.27, pp.177-188, 2015.

R. Baddeley, Responses of neurons in primary and inferior temporal visual cortices to natural scenes, Proceedings of the Royal Society of London B: Biological Sciences, vol.264, pp.1775-1783, 1997.

V. Alexander and . Badyaev, Evolutionary significance of phenotypic accommodation in novel environments: an empirical test of the Baldwin effect, In: Philosophical Transactions of the Royal Society B: Biological Sciences, vol.364, issue.1520, pp.1125-1141, 2009.

A. Balaam, Developmental Neural Networks for Agents, Advances in Artificial Life, Proceedings of the 7th European Conference on Artificial Life, pp.154-163, 2003.

J. Baldwin, In: The american naturalist, vol.30, pp.441-451, 1896.

W. Banzhaf, Genetic programming: an introduction, vol.1, 1998.

W. Banzhaf, Artificial regulatory networks and genetic programming, pp.43-61, 2003.

W. Banzhaf, On the dynamics of an artificial regulatory network, European Conference on Artificial Life, pp.217-227, 2003.

M. Parizad, A. Bilimoria, and . Bonni, Molecular Control of Axon Branching, The Neuroscientist, vol.19, pp.1073-8584, 2011.

J. W. Egbert, . Boers, V. Marko, I. G. Borst, and . Sprinkhuizen-kuyper, Evolving neural networks using the Baldwin effect, Artificial Neural Nets and Genetic Algorithms, pp.333-336, 1995.

M. Beyeler, D. Nikil, J. L. Dutt, and . Krichmar, Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule, Neural Networks, vol.48, pp.109-124, 2013.

M. G. Bellemare, The Arcade Learning Environment: An Evaluation Platform for General Agents, In: Journal of Artificial Intelligence Research, vol.47, pp.253-279, 2013.

H. Beyer, Towards a theory of evolution strategies: Results for (1,+ )-strategies on (nearly) arbitrary fitness functions, International Conference on Parallel Problem Solving from Nature, pp.57-67, 1994.

T. Bäck, B. David, Z. Fogel, and . Michalewicz, Evolutionary computation 1: Basic algorithms and operators, 2018.

E. Bonabeau, Swarm intelligence: from natural to artificial systems, vol.1, 1999.

M. Guo-qiang-bi and . Poo, Synaptic modification by correlated activity: Hebb's postulate revisited, In: Annual review of neuroscience, vol.24, pp.139-166, 2001.

R. Brette, Simulation of networks of spiking neurons: a review of tools and strategies, In: Journal of computational neuroscience, vol.23, pp.349-398, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00180662

G. Hans, H. Beyer, and . Schwefel, Evolution strategiesA comprehensive introduction, In: Natural computing 1, vol.1, pp.3-52, 2002.

A. John and . Bullinaria, Exploring the Baldwin effect in evolving adaptable control systems, pp.231-242, 2001.

A. John and . Bullinaria, Lifetime learning as a factor in life history evolution, Artificial Life, vol.15, pp.389-409, 2009.

. Sf-cooke and . Bliss, Plasticity in the human central nervous system, In: Brain, vol.129, issue.7, pp.1659-1673, 2006.

E. Coumans and Y. Bai, PyBullet, a Python module for physics simulation for games, robotics and machine learning, 2016.

N. Caporale and Y. Dan, Spike timingdependent plasticity: a Hebbian learning rule, Annu. Rev. Neurosci, vol.31, pp.25-46, 2008.

A. Chavoya and Y. Duthen, A cell pattern generation model based on an extended artificial regulatory network, In: BioSystems, vol.94, pp.95-101, 2008.

-. Sylvain-cussat, K. Blanc, and . Harrington, Genetically-regulated Neuromodulation Facilitates Multi-Task Reinforcement Learning, Proceedings of the 2015 on Genetic and Evolutionary Computation Conference -GECCO '15, pp.551-558, 2015.

J. K. Chilton, Molecular mechanisms of axon guidance, In: Developmental Biology, vol.292, issue.1, p.121606, 2006.

F. Chollet, , 2015.

K. Sylvain-cussat-blanc, J. Harrington, and . Pollack, Gene regulatory network evolution through augmenting topologies, IEEE Transactions on Evolutionary Computation, vol.19, pp.823-837, 2015.

S. Cussat-blanc, H. Luga, and Y. Duthen, From single cell to simple creature morphology and metabolism, http _ _ _ www . alifexi . org _ papers _ ALIFExi_pp134-141.pdf, pp.134-141, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00694989

A. Corl, The genetic basis of adaptation following plastic changes in coloration in a novel environment, Current Biology, vol.28, pp.2970-2977, 2018.

J. Clegg, J. A. Walker, and J. F. Miller, A new crossover technique for Cartesian genetic programming, p.1580, 2007.

P. Dayan, F. Laurence, and . Abbott, Theoretical neuroscience, vol.806, 2001.

E. George and . Dahl, Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition, IEEE Transactions on audio, speech, and language processing, vol.20, pp.30-42, 2012.

P. , D. Kuo, W. Banzhaf, and A. Leier, Network topology and the evolution of dynamics in an artificial genetic regulatory network model created by whole genome duplication and divergence, In: BioSystems, vol.85, issue.3, p.3032647, 2006.

U. Peter, M. Diehl, and . Cook, Unsupervised learning of digit recognition using spike-timing-dependent plasticity, In: Frontiers in computational neuroscience, vol.9, p.99, 2015.

J. Disset, S. Cussat-blanc, and Y. Duthen, MecaCell: an Opensource Efficient Cellular Physics Engine, 13th European Conference on Artificial Life, p.67, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01334681

J. Disset, S. Cussat-blanc, and Y. Duthen, Evolved Developmental Strategies of Artificial Multicellular Organisms, Artificial Life XV: Proceedings of Fifteenth International Symposium on the Synthesis and Simulation of Living Systems, p.1, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01511892

B. Doerr and C. Doerr, Optimal Static and Self-Adjusting Parameter Choices for the $1+(\lambda, \lambda)$ Genetic Algorithm, In: Algorithmica, vol.80, issue.5, pp.1658-1709, 2018.

K. Deb, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE transactions on evolutionary computation, vol.6, pp.182-197, 2002.

J. Duchi, E. Hazan, and Y. Singer, Adaptive subgradient methods for online learning and stochastic optimization, In: Journal of Machine Learning Research, vol.12, pp.2121-2159, 2011.

J. Disset, A comparison of genetic regulatory network dynamics and encoding, Proceedings of the Genetic and Evolutionary Computation Conference, pp.91-98, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01912802

S. Droste, T. Jansen, and I. Wegener, On the analysis of the (1+ 1) evolutionary algorithm, In: Theoretical Computer Science, vol.276, pp.51-81, 2002.

K. L. Downing, Supplementing evolutionary developmental systems with abstract models of neurogenesis, Proc. Conf. on Genetic and evolutionary Comp, pp.990-996, 2007.

L. Keith and . Downing, Intelligence emerging: adaptivity and search in evolving neural systems, 2015.

K. Doya, Metalearning and neuromodulation, In: Neural Networks, vol.15, pp.495-506, 2002.

K. Doya, Modulators of decision making, Nature neuroscience, vol.11, p.410, 2008.

R. Doursat and C. Sánchez, Growing fine-grained multicellular robots, In: Soft Robotics, vol.1, issue.2, pp.110-121, 2014.

S. Dasgupta, F. Charles, S. Stevens, and . Navlakha, A neural algorithm for a fundamental computing problem, In: Science, vol.358, pp.793-796, 2017.

L. Deng and D. Yu, Deep learning: methods and applications, In: Foundations and Trends? in Signal Processing, vol.7, pp.197-387, 2014.

E. Edsinger and G. Dölen, A conserved role for serotonergic neurotransmission in mediating social behavior in octopus, Current Biology, vol.28, pp.3136-3142, 2018.

L. Erskine and E. Herrera, The retinal ganglion cell axon's journey: insights into molecular mechanisms of axon guidance, Developmental biology, vol.308, pp.1-14, 2007.

E. Brent, . Eskridge, and . Dean-f-hougen, Nurturing promotes the evolution of learning in uncertain environments, 2012 IEEE International Conference on, pp.1-6, 2012.

K. Olav-ellefsen, Evolved Sensitive Periods in Learning, pp.409-416, 2013.

K. Olav-ellefsen, The Evolution of Learning Under Environmental Variability, The Evolution of Learning: Balancing Adaptivity and Stability in Artificial Agents, p.101, 2014.

T. Elsken, J. H. Metzen, and F. Hutter, Neural architecture search: A survey, 2018.

C. Finn, P. Abbeel, and S. Levine, Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, International Conference on Machine Learning, pp.1126-1135, 2017.

K. Fleischer and A. H. Barr, A Simulation Testbed for the Study of Multicellular Development: The Multiple Mechanisms of Morphogenesis, Artificial Life III: Proceedings of the Workshop on Artificial Life, p.389, 1992.

B. Farley and . Clark, Simulation of self-organizing systems by digital computer, Transactions of the IRE Professional Group on Information Theory, vol.4, pp.76-84, 1954.

A. Michael, A. L. Farries, and . Fairhall, Reinforcement Learning With Modulated Spike Timing-Dependent Synaptic Plasticity, In: Journal of neurophysiology, vol.98, pp.3648-3665, 2007.

N. Frémaux and W. Gerstner, Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules, Frontiers in Neural Circuits 9, 2016.

O. Firat, Multi-way, multilingual neural machine translation, Computer Speech & Language, vol.45, pp.236-252, 2017.

A. Ronald and . Fisher, The use of multiple measurements in taxonomic problems, In: Annals of eugenics, vol.7, issue.2, pp.179-188, 1936.

J. F. Fontanari and . Meir, Evolving a learning algorithm for the binary perceptron, Network: Computation in Neural Systems, vol.2, pp.353-359, 1991.

N. Feng, G. Ning, and X. Zheng, A framework for simulating axon guidance, In: Neurocomputing, vol.68, issue.1-4, p.9252312, 2005.

J. Lawrence, A. J. Fogel, M. Owens, and . Walsh, Artificial intelligence through simulated evolution, 1966.

. J-doyne, . Farmer, H. Norman, A. S. Packard, and . Perelson, The immune system, adaptation, and machine learning, In: Physica D: Nonlinear Phenomena, vol.22, pp.187-204, 1986.

X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp.249-256, 2010.

E. David, K. Goldberg, and . Deb, A comparative analysis of selection schemes used in genetic algorithms, Foundations of genetic algorithms, vol.1, pp.69-93, 1991.

S. Ginsburg and E. Jablonka, Epigenetic learning in non-neural organisms, In: Journal of biosciences, vol.34, p.633, 2009.

J. Guerguiev, P. Timothy, B. Lillicrap, and . Richards, Towards deep learning with segregated dendrites, In: ELife, vol.6, p.22901, 2017.

A. Daniel, L. Gibson, and . Ma, Developmental regulation of axon branching in the vertebrate nervous system, Development, vol.138, pp.950-1991, 2011.

W. B-w-goldman and . Punch, Length Bias and Search Limitations in Cartesian Genetic Programming, Gecco'13: Proceedings of the 2013 Genetic and Evolutionary Computation Conference, pp.932-940, 2013.

W. Brian, W. F. Goldman, and . Punch, Reducing wasted evaluations in cartesian genetic programming, European Conference on Genetic Programming, pp.61-72, 2013.

T. M. Gomez and N. C. Spitzer, Common mechanisms underlying growth cone guidance and axon branching, In: Journal of Neurobiology, vol.44, p.223034, 2000.

F. Gruau and D. Whitley, Adding learning to the cellular development of neural networks: Evolution and the Baldwin effect, In: Evolutionary computation, vol.1, issue.3, pp.213-233, 1993.

H. R. Richard and . Hahnloser, Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit, Nature 405, vol.6789, p.947, 2000.

N. Hansen, The CMA evolution strategy: a comparing review, Towards a new evolutionary computation, pp.75-102, 2006.

S. Harding, MT-CGP: Mixed Type Cartesian Genetic Programming, Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference -GECCO '12, p.751, 2012.

. Kyle-i-harrington, Autoconstructive Evolution for Structural Problems, 2012.

K. I. Harrington, Robot Coverage Control by Evolved Neuromodulation, The 2013 International Joint Conference on Neural Networks, pp.1-8, 2013.

S. Harding, Evolution of image filters on graphics processor units using cartesian genetic programming, pp.1921-1928, 2008.

M. Hausknecht, HyperNEAT-GGP: A HyperNEAT-based Atari general game player, Proceedings of the 14th annual conference on Genetic and evolutionary computation, pp.217-224, 2012.

M. Hausknecht, A neuroevolution approach to general atari game playing, IEEE Transactions on Computational Intelligence and AI in Games, vol.6, pp.355-366, 2014.

S. Harding, W. Banzhaf, and J. Miller, A Survey of Self Modifying Cartesian Genetic Programming, vol.8, pp.91-107, 2010.

O. Donald and . Hebb, The organization of behavior, 1949.

L. Alan, A. F. Hodgkin, and . Huxley, A quantitative description of membrane current and its application to conduction and excitation in nerve, In: The Journal of physiology, vol.117, pp.500-544, 1952.

J. Husa and R. Kalkreuth, A Comparative Study on Crossover in Cartesian Genetic Programming, European Conference on Genetic Programming, pp.203-219, 2018.

G. S. Hornby, H. Lipson, and J. B. Pollack, Generative Representations for the Automated Design of Modular Physical Robots, IEEE Trans. on Robotics and Automation, vol.19, pp.703-719, 2003.

S. Harding, J. Leitner, and J. Schmidhuber, Cartesian genetic programming for image processing, pp.31-44, 2013.

S. Harding, F. Julian, W. Miller, and . Banzhaf, SMCGP2: Self Modifying Cartesian Genetic Programming in Two Dimensions, Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp.1491-1498, 2011.

E. Geoffrey, . Hinton, and . Steven-j-nowlan, How learning can guide evolution, In: Complex systems, vol.1, issue.3, pp.495-502, 1987.

N. Hansen and A. Ostermeier, Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation, Proceedings of IEEE International Conference on, pp.312-317, 1996.

H. John and . Holland, Genetic algorithms, Scientific american, vol.267, pp.66-73, 1992.

J. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, 1992.

A. Jonas and . Hosp, Dopaminergic projections from midbrain to primary motor cortex mediate motor skill learning, In: Journal of Neuroscience, vol.31, pp.2481-2487, 2011.

L. James, R. M. Hindmarsh, and . Rose, A model of neuronal bursting using three coupled first order differential equations, In: Proc. R. Soc. Lond. B, vol.221, pp.87-102, 1984.

S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural computation 9, vol.8, pp.1735-1780, 1997.

D. Andrew and . Huberman, Ephrin-As mediate targeting of eye-specific projections to the lateral geniculate nucleus, vol.8, pp.1013-1021, 2009.

. Hge-hentschel and . Van-ooyen, Models of axon guidance and bundling during development, Proceedings of the Royal Society of London B: Biological Sciences, vol.266, pp.2231-2238, 1999.

. Ej-izquierdo and . Beer, From head to tail: a neuromechanical model of forward locomotion in Caenorhabditis elegans, Philosophical transactions of the Royal Society of London. Series B, vol.373, p.1758, 2018.

D. Izzo, F. Biscani, and A. Mereta, Differentiable Genetic Programming, European Conference on Genetic Programming, pp.35-51, 2017.

M. Eugene and . Izhikevich, Simple model of spiking neurons, IEEE Transactions on neural networks, vol.14, pp.1569-1572, 2003.

E. M. Izhikevich, Which model to use for cortical spiking neurons, IEEE Transactions on Neural Networks, vol.15, pp.1063-1070, 2004.

E. M. Izhikevich, Solving the distal reward problem through linkage of STDP and dopamine signaling, Cerebral Cortex, vol.17, pp.2443-2452, 2007.

R. Kristjan, R. Jessen, and . Mirsky, Glial cells in the enteric nervous system contain glial fibrillary acidic protein, Nature, vol.286, p.736, 1980.

M. Joachimczak, Spiral autowaves as minimal, distributed gait controllers for soft-bodied animats, Proceedings of the Artificial Life Conference, pp.140-141, 2016.

Y. Jia and . Shelhamer, Caffe model zoo, 2015.

M. Joachimczak and B. Wróbel, Evolution of the morphology and patterning of artificial embryos: scaling the tricolour problem to the third dimension, Proceedings of the European Conference on Artificial Life, pp.35-43, 2009.

M. Joachimczak and B. , Processing signals with evolving artificial gene regulatory networks, Artificial Life XII: Proceedings of the 12th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2010, pp.203-210, 2010.

M. Joachimczak and B. Wrobel, Evolving Gene Regulatory Networks for Real Time Control of Foraging Behaviours, Artificial Life XII. Proceedings of the 12th International Conference on the Synthesis and Simulation of Living Systems, pp.348-355, 2010.

P. Diederik, J. Kingma, and . Ba, Adam: A method for stochastic optimization, 2014.

A. Krizhevsky and G. Hinton, Learning multiple layers of features from tiny images, 2009.

R. Saeed and . Kheradpisheh, STDP-based spiking deep neural networks for object recognition, 2016.

H. Kitano, Designing neural networks using genetic algorithms with graph generation system, In: Complex Systems, vol.4, pp.461-476, 1990.

M. M. Khan, G. M. Khan, and J. F. Miller, Efficient representation of recurrent neural networks for markovian/non-markovian non-linear control problems, Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on, pp.615-620, 2010.

M. M. Khan, G. M. Khan, and J. F. Miller, Evolution of optimal anns for non-linear control problems using cartesian genetic programming, Proceedings of International Conference on Artificial Intelligence (ICAI 2010), 2010.

K. Kalil, L. Li, and B. I. Hutchins, Signaling Mechanisms in Cortical Axon Growth, Guidance, and Branching, In: Frontiers in Neuroanatomy, vol.5, pp.1662-5129, 2011.

G. M. Khan, J. F. Miller, and D. M. Halliday, A developmental model of neural computation using cartesian genetic programming, Proc. Conf. on Genetic And Evolutionary Computation, vol.7, pp.2535-2542, 2007.

G. M. Khan, J. F. Miller, and D. M. Halliday, Developing neural structure of two agents that play checkers using Cartesian Genetic Programming, Proc. Conf. on Genetic and evolutionary computation, pp.2169-2174, 2008.

J. F. Gul-muhammad-khan, D. M. Miller, and . Halliday, Evolution of cartesian genetic programs for development of learning neural architecture, In: Evolutionary computation, vol.19, pp.469-523, 2011.

J. Knabe, Evolving Biological Clocks using Genetic Regulatory Networks, Artificial Life X : Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems Alife, vol.10, pp.15-21, 2006.

. John-r-koza, Genetic programming as a means for programming computers by natural selection, In: Statistics and computing, vol.4, pp.87-112, 1994.

A. Krizhevsky, ImageNet Classification with Deep Convolutional Neural Networks, Advances in Neural Information and Processing Systems (NIPS) (2012), p.10495258

M. T. Killeen and S. S. Sybingco, Netrin, Slit and Wnt receptors allow axons to choose the axis of migration, Developmental Biology, vol.323, p.121606, 2008.

L. Lapicque, Recherches quantitatives sur l'excitation electrique des nerfs traitee comme une polarization, Journal de Physiologie et de Pathologie Generalej, vol.9, pp.620-635, 1907.

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.521, p.436, 2015.

G. Quoc-v-le, M. Brain, and . View, A Tutorial on Deep Learning Part 2: Autoencoders, Convolutional Neural Networks and Recurrent Neural Networks, pp.1-20, 2015.

L. Tommy, J. Lewis, F. Courchet, and . Polleux, Cell biology in neuroscience: Cellular and molecular mechanisms underlying axon formation, growth, and branching, In: The Journal of cell biology, vol.202, pp.1540-8140, 2013.

T. Jun-haeng-lee, M. Delbruck, and . Pfeiffer, Training Deep Spiking Neural Networks Using Backpropagation, In: Frontiers in Neuroscience, vol.10, pp.1662-453, 2016.

Y. Lecun, Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol.86, pp.2278-2324, 1998.

P. Timothy and . Lillicrap, Random synaptic feedback weights support error backpropagation for deep learning, Nature communications, vol.7, p.13276, 2016.

J. J. Letzkus, B. M. Kampa, and G. J. Stuart, Learning Rules for Spike Timing-Dependent Plasticity Depend on Dendritic Synapse Location, In: Journal of Neuroscience, vol.26, pp.10420-10429, 2006.

M. López-ibáñez, The irace package : Iterated racing for automatic algorithm configuration, pp.43-58, 2016.

R. Legenstein, D. Pecevski, and W. Maass, A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback, PLoS Computational Biology, vol.4, 2008.

J. Lehman, O. Kenneth, and . Stanley, Exploiting open-endedness to solve problems through the search for novelty, pp.329-336, 2008.

S. Luke and L. Spector, A Revised Comparison of Crossover and Mutation in Genetic Programming, 1997.

E. A. Maguire, Navigation-related structural change in the hippocampi of taxi drivers, PNAS 97, pp.4398-4403, 2000.

. Maryam-mahsal-khan, Fast learning neural networks using Cartesian genetic programming, In: Neurocomputing, vol.121, pp.274-289, 2013.

R. Menzel, The honeybee as a model for understanding the basis of cognition, Nature Reviews Neuroscience, vol.13, p.758, 2012.

. Risto-miikkulainen, Evolving deep neural networks, In: Artificial Intelligence in the Age of Neural Networks and Brain Computing, pp.293-312, 2018.

J. F. Miller, What bloat? Cartesian Genetic Programming on Boolean problems, Proc. Conf. Genetic and Evolutionary Computation, Late breaking papers, pp.295-302, 2001.

J. F. Miller, Evolving a Self-Repairing, Self-Regulating, French Flag Organism, Genetic and Evolutionary Computation Conference (GECCO'04)

F. Julian and . Miller, Cartesian Genetic Programming, 2011.

C. Morris and H. Lecar, Voltage oscillations in the barnacle giant muscle fiber, Biophysical journal 35, vol.1, pp.193-213, 1981.

E. David, R. Moriarty, and . Miikkulainen, Forming neural networks through efficient and adaptive coevolution, In: Evolutionary Computation, vol.5, pp.373-399, 1997.

V. Mnih, Human-level control through deep reinforcement learning, Nature, vol.518, pp.28-0836, 2015.

M. Mozafari, Combining STDP and Reward-Modulated STDP in Deep Convolutional Spiking Neural Networks for Digit Recognition, 2018.

M. Mozafari, First-Spike-Based Visual Categorization Using Reward-Modulated STDP, IEEE Transactions on Neural Networks and Learning Systems, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02341973

M. Minsky, A. Seymour, and . Papert, Perceptrons: An introduction to computational geometry, 1969.

J. F. Miller and S. L. Smith, Redundancy and computational efficiency in Cartesian Genetic Programming, IEEE Trans. on Evolutionary Computation, vol.10, issue.2, pp.167-174, 2006.

J. F. Miller and P. Thomson, Cartesian Genetic Programming, Proc. European Conf. on Genetic Programming, vol.10802, pp.121-132, 2000.

A. Héctor, J. L. Montes, and . Wyatt, Cartesian Genetic Programming for Image Processing Tasks, Neural Networks and Computational Intelligence, pp.185-190, 2003.

F. Julian, D. Miller, and . Wilson, A developmental artificial neural network model for solving multiple problems, Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp.69-70, 2017.

J. F. Miller, D. G. Wilson, and S. Cussat-blanc, Evolving developmental programs that build neural networks for solving multiple problems, 2018.

E. Yurii and . Nesterov, A method for solving the convex programming problem with convergence rate O(1/k 2 ), In: Dokl. Akad. Nauk SSSR, vol.269, pp.543-547, 1983.

S. Nolfi and D. Floreano, Learning and evolution, Autonomous robots, vol.7, pp.89-113, 1999.

M. Nicolau, M. Schoenauer, and W. Banzhaf, In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6021, LNCS, p.3029743, 2010.

C. Ofria and A. Lalejini, The evolutionary origins of phenotypic plasticity, Proceedings of the Artificial Life Conference 2016 13, pp.372-379, 2016.

D. Im-oliver, J. Smith, and . Holland, Study of permutation crossover operators on the traveling salesman problem, Genetic algorithms and their applications: proceedings of the second International Conference on Genetic Algorithms, 1987.

M. Pignatelli and A. Bonci, Role of Dopamine Neurons in Reward and Aversion: A Synaptic Plasticity Perspective, In: Neuron, vol.86, pp.1145-1157, 2015.

C. Pfeiffenberger, Ephrin-As and neural activity are required for eyespecific patterning during retinogeniculate mapping, vol.8, pp.1022-1027, 2007.

R. Poli and . William-b-langdon, On the search properties of different crossover operators in genetic programming, In: Genetic Programming, pp.293-301, 1998.

G. Perea, M. Navarrete, and A. Araque, Tripartite synapses: astrocytes process and control synaptic information, In: Trends in Neurosciences, vol.32, pp.421-431, 2009.

R. Poli, A field guide to genetic programming, Lulu. com, 2008.

R. Poli, Evolution of Graph-Like Programs with Parallel Distributed Genetic Programming, In: ICGA. Citeseer, pp.346-353, 1997.

F. Ponulak, ReSuMe-new supervised learning method for Spiking Neural Networks, In: Inst. Control Information Engineering, Poznan Univ, vol.22, pp.467-510, 2005.

A. B. Porto-pazos, Artificial astrocytes improve neural network performance, In: PLoS ONE, vol.6, pp.1-8, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01221307

P. Paris, E. C. Pedrino, and M. C. Nicoletti, Automatic learning of image filters using Cartesian genetic programming, In: Integrated Computer-Aided Engineering, vol.22, pp.135-151, 2015.

M. C-van-rossum, G. Bi, and G. Turrigiano, Stable Hebbian learning from spike timing-dependent plasticity, In: The Journal of neuroscience : the official journal of the Society for Neuroscience, vol.20, pp.8812-8821, 2000.

C. Ryan, J. J. Collins, and M. O. Neill, Grammatical evolution: Evolving programs for an arbitrary language, pp.83-96, 1998.

O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks for biomedical image segmentation, International Conference on Medical image computing and computer-assisted intervention, pp.234-241, 2015.

R. M. Rose and . Hindmarsh, The assembly of ionic currents in a thalamic neuron I. The three-dimensional model, In: Proc. R. Soc. Lond. B, vol.237, pp.267-288, 1989.

S. Risi, C. E. Hughes, and K. Stanley, Evolving plastic neural networks with novelty search, Adaptive Behavior, vol.18, pp.470-491, 2010.

S. Risi, J. Lehman, and K. O. Stanley, Evolving the Placement and Density of Neurons in the HyperNEAT Substrate, Proc. Conf. on Genetic and Evolutionary Computation, pp.563-570, 2010.

N. Richards, D. E. Moriarty, and R. Miikkulainen, Evolving neural networks to play Go, In: Applied Intelligence, vol.8, issue.1, pp.85-96, 1998.

F. Rosenblatt, The perceptron: a probabilistic model for information storage and organization in the brain, Psychological review, vol.65, p.386, 1958.

E. Ronald and M. Schoenauer, Genetic Lander: An experiment in accurate neuro-genetic control, International Conference on Parallel Problem Solving from Nature, pp.452-461, 1994.
URL : https://hal.archives-ouvertes.fr/hal-01079614

S. Ruder, An overview of gradient descent optimization algorithms, 2016.

A. Andrei and . Rusu, Progressive neural networks, 2016.

T. Salismans, Evolution Strategies as a scalable Alternative to Reinforcement Learning, ArXiv 170303864v2, pp.1-13, 2017.

H. Shayani, P. J. Bentley, and A. M. Tyrrell, A multi-cellular developmental representation for evolution of adaptive spiking neural microcircuits in an FPGA, Proceedings -2009 NASA/ESA Conference on Adaptive Hardware and Systems, pp.3-10, 2009.

S. Sanchez and S. Cussat-blanc, Gene regulated car driving: using a gene regulatory network to drive a virtual car, Genetic Programming and Evolvable Machines, vol.15, pp.477-511, 2014.

J. Schmidhuber, Curious model-building control systems, IEEE International Joint Conference on, pp.1458-1463, 1991.

J. Schmidhuber, Learning complex, extended sequences using the principle of history compression, Neural Computation, vol.4, pp.234-242, 1992.

N. Schweighofer and K. Doya, Meta-learning in reinforcement learning, Neural Networks, vol.16, issue.1, pp.5-9, 2003.

O. Kenneth, . Stanley, B. David, J. D'ambrosio, and . Gauci, A hypercubebased encoding for evolving large-scale neural networks, Artificial life, vol.15, pp.185-212, 2009.

H. Peter and . Seeburg, The NMDA receptor channel: molecular design of a coincidence detector, Proceedings of the 1993 Laurentian Hormone Conference, pp.19-34, 1995.

T. Serre, Robust object recognition with cortex-like mechanisms, IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.3, pp.411-426, 2007.

R. Susan, A. Sesack, and . Grace, Cortico-basal ganglia reward network: microcircuitry, In: Neuropsychopharmacology, vol.35, p.27, 2010.

L. Shapiro, Embodied cognition. Routledge, 2010.

S. Singh, Intrinsically motivated reinforcement learning: An evolutionary perspective, IEEE Transactions on Autonomous Mental Development, vol.2, issue.2, pp.70-82, 2010.

F. K. Skinner, Conductance-based models, In: Scholarpedia, vol.1, issue.11, p.1408, 2006.

P. Sterling and S. Laughlin, Principles of neural design, 2015.

O. Kenneth, R. Stanley, and . Miikkulainen, Evolving neural networks through augmenting topologies, Evolutionary computation, vol.10, pp.99-127, 2002.

S. Song, L. Miller, and . Abbott, Competitive Hebbian learning through spike-timing-dependent synaptic plasticity, Nature neuroscience, vol.3, pp.919-926, 2000.

D. Scherer, A. Müller, and S. Behnke, Evaluation of pooling operations in convolutional architectures for object recognition, Artificial Neural NetworksICANN, pp.92-101, 2010.

D. Gregory and . Smith, Fourier analysis of sinusoidally driven thalamocortical relay neurons and a minimal integrate-and-fire-or-burst model, In: Journal of Neurophysiology, vol.83, pp.588-610, 2000.

S. Shoham, H. Daniel, R. Oconnor, and . Segev, How silent is the brain: is there a dark matter problem in neuroscience?, In: Journal of Comparative Physiology A, vol.192, issue.8, pp.777-784, 2006.

L. Spector, Genetic Programming and Autoconstructive Evolution with the Push Programming Language, pp.7-40, 2002.

N. Spruston, Pyramidal neurons: dendritic structure and synaptic integration, Nature Reviews Neuroscience, vol.9, p.206, 2008.

M. Santos, E. Szathmáry, and J. Fontanari, Phenotypic plasticity, the baldwin effect, and the speeding up of evolution: The computational roots of an illusion, In: Journal of theoretical biology, vol.371, pp.127-136, 2015.

H. Song, F. Charles, F. H. Stevens, and . Gage, Astroglia induce neurogenesis from adult neural stem cells, Nature, vol.417, p.39, 2002.

A. Soltoggio, O. Kenneth, S. Stanley, and . Risi, Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks, Neural Networks, 2018.

K. O. Stanley, Compositional pattern producing networks: A novel abstraction of development, In: Genetic Programming and Evolvable Machines, vol.8, pp.131-162, 2007.

M. Stimberg, Equation-oriented specification of neural models for simulations, In: Frontiers in neuroinformatics, vol.8, p.6, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01686592

C. Stangor and J. L. Walinga-;-l, Introduction to Psychology, 2015.

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, p.ICLR, 2015.

J. Stork, M. Zaefferer, and T. Bartz-beielstein, Distance-based Kernels for Surrogate Model-based Neuroevolution, 2018.

S. Teki, Navigating the auditory scene: an expert role for the hippocampus, In: Journal of Neuroscience, vol.32, pp.12251-12257, 2012.

T. Tieleman and G. Hinton, Divide the gradient by a running average of its recent magnitude, 2018.

A. J. Turner and J. F. Miller, Recurrent cartesian genetic programming, International Conference on Parallel Problem Solving from Nature, pp.476-486, 2014.

A. Martin and . Trefzer, Evolution and analysis of a robot controller based on a gene regulatory network, International Conference on Evolvable Systems, pp.61-72, 2010.

. Joe-z-tsien, Linking Hebbs coincidence-detection to memory formation, In: Current opinion in neurobiology, vol.10, pp.266-273, 2000.

M. Alan and . Turing, Computing machinery and intelligence, pp.23-65, 2009.

F. Valverde, Rate and extent of recovery from dark rearing in the visual cortex of the mouse, Brain Res, vol.33, pp.1-11, 1971.

R. Velez and J. Clune, Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks, In: PloS one, vol.12, p.187736, 2017.

B. Wróbel and A. Abdelmotaleb, Evolving Spiking Neural Networks in the GReaNs ( Gene Regulatory evolving artificial Networks ) Plaftorm, EvoNet2012: Evolving Networks, from Systems/Synthetic Biology to Computational Neuroscience Workshop at Artificial Life XIII, pp.19-22, 2012.

B. Wrobel, A. Abdelmotaleb, and M. Joachimczak, Evolving networks processing signals with a mixed paradigm, inspired by gene regulatory networks and spiking neurons, Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, vol.134, p.18678211, 2014.

P. Werbos, New Tools for Prediction and Analysis in the Behavioral Sciences, 1974.

D. Wilson, On learning to generate wind farm layouts, In: Fifteenth annual conference on Genetic and evolutionary computation conference, pp.767-774, 2013.

D. Wilson, A continuous developmental model for wind farm layout optimization, Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp.745-752, 2014.

G. Dennis and . Wilson, Learning aquatic locomotion with animats, Artificial Life Conference Proceedings 14, pp.585-592, 2017.

G. Dennis and . Wilson, Evolving Differentiable Gene Regulatory Networks, 2018.

G. Dennis and . Wilson, Evolving simple programs for playing Atari games, Proceedings of the Genetic and Evolutionary Computation Conference, 2018.

. Dg-wilson, Positional Cartesian Genetic Programming, 2018.

B. Wróbel and M. Joachimczak, Using the Genetic Regulatory evolving Artificial Networks (GReaNs) platform for signal processing, animat control, and artificial multicellular development, pp.187-200, 2014.

J. , A. Walker, and J. F. Miller, Embedded cartesian genetic programming and the lawnmower and hierarchical-if-and-only-if problems, Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp.911-918, 2006.

C. David and . Wood, Habituation in Stentor: a response-dependent process, In: Journal of Neuroscience, vol.8, pp.2248-2253, 1988.

D. Matthew and . Zeiler, ADADELTA: an adaptive learning rate method, 2012.