. Bibliographie, . Adelson, E. H. Bergen-]-adelson, and J. R. Bergen, Spatiotemporal energy models for the perception of motion, J. Opt. Soc. Am. A, vol.2, issue.2, pp.284-299, 1985.

A. Alaghi, The Logic of Random Pulses : Stochastic Computing, 2015.

S. Amari, Dynamics of pattern formation in lateral-inhibition type neural fields, Biological Cybernetics, vol.13, issue.2, pp.77-87, 1977.
DOI : 10.1007/BF00337259

D. J. Amit, Modeling brain function : The world of attractor neural networks, 1992.
DOI : 10.1017/CBO9780511623257

S. L. Bade and B. L. Hutchings, FPGA-based stochastic neural networks-implementation, Proceedings of IEEE Workshop on FPGA's for Custom Computing Machines, pp.189-198, 1994.
DOI : 10.1109/FPGA.1994.315612

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.9400

. Bastian, Preshaping and continuous evolution of motor cortical representations during movement preparation, European Journal of Neuroscience, vol.41, issue.7, pp.2047-2058, 2003.
DOI : 10.1146/annurev.neuro.8.1.1

H. Benda, J. Benda, and A. V. Herz, A Universal Model for Spike-Frequency Adaptation, Neural Computation, vol.79, issue.11, pp.2523-2564, 2003.
DOI : 10.1038/26758

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.653.4577

. Benjamin, Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations, Proceedings of the IEEE, pp.699-716, 2014.
DOI : 10.1109/JPROC.2014.2313565

. Bicho, Integrating verbal and nonverbal communication in a dynamic neural field architecture for human-robot interaction, Frontiers in Neurorobotics, vol.4, 2010.
DOI : 10.3389/fnbot.2010.00005

P. C. Bressloff, Spatiotemporal dynamics of continuum neural fields, Journal of Physics A: Mathematical and Theoretical, vol.45, issue.3, p.33001, 2012.
DOI : 10.1088/1751-8113/45/3/033001

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.362.4885

. Canals, Noise-robust hardware implementation of neural networks, 2015 International Joint Conference on Neural Networks (IJCNN), pp.1-8, 2015.
DOI : 10.1109/IJCNN.2015.7280622

. Canals, A New Stochastic Computing Methodology for Efficient Neural Network Implementation, IEEE Transactions on Neural Networks and Learning Systems, vol.27, issue.3, pp.551-564, 2016.
DOI : 10.1109/TNNLS.2015.2413754

B. Vangel and J. Fix, In the quest of efficient hardware implementations of dynamic neural fields: An experimental study on the influence of the kernel shape, 2016 International Joint Conference on Neural Networks (IJCNN), 2016.
DOI : 10.1109/IJCNN.2016.7727446

URL : https://hal.archives-ouvertes.fr/hal-01482258

D. Chappet and . Vangel, Spiking dynamic neural fields architectures on fpga, ReConFigurable Computing and FPGAs (ReConFig), 2014 International Conference on, pp.1-6, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01071873

D. Chappet and . Vangel, Randomly spiking dynamic neural fields, ACM Journal of Emerging Technologies in Computing Systems, vol.11, issue.4, pp.1-26, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01071862

D. Chappet and . Vangel, Stochastic and asynchronous spiking dynamic neural fields, 2015 International Joint Conference on Neural Networks (IJCNN), pp.1-8, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01264903

D. Chappet and . Vangel, Event based visual attention with dynamic neural field on fpga, Proceedings of the 10th International Conference on Distributed Smart Cameras, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01482160

. Cheung, NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors, Frontiers in Neuroscience, vol.9, issue.180, 2016.
DOI : 10.3389/fnins.2015.00180

URL : http://doi.org/10.3389/fnins.2015.00516

. Chevallier, . Tarroux, S. Chevallier, and P. Tarroux, Visual focus with spiking neurons, European Symposium on Artificial Neural Networks, pp.385-389, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00472642

. Dalal, Low discrepancy sequences for Monte Carlo simulations on reconfigurable platforms, 2008 International Conference on Application-Specific Systems, Architectures and Processors, pp.108-113, 2008.
DOI : 10.1109/ASAP.2008.4580163

. Deneve, Efficient computation and cue integration with noisy population codes, Nature Neuroscience, vol.4, issue.8, pp.826-831, 2001.
DOI : 10.1038/90541

R. Desimone, Visual attention mediated by biased competition in extrastriate visual cortex, Philosophical Transactions of the Royal Society of London B : Biological Sciences, pp.1245-1255, 1373.
DOI : 10.1098/rstb.1998.0280

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1692333

G. I. Detorakis and N. P. Rougier, A Neural Field Model of the Somatosensory Cortex: Formation, Maintenance and Reorganization of Ordered Topographic Maps, PLoS ONE, vol.27, issue.7, p.40257, 2012.
DOI : 10.1371/journal.pone.0040257.t001

URL : https://hal.archives-ouvertes.fr/hal-00716355

M. Douglas, R. J. Douglas, and K. A. Martin, Behavioral architecture of the cortical sheet, Current Biology, vol.22, issue.24, pp.1033-1038, 2012.
DOI : 10.1016/j.cub.2012.11.017

S. Engels, C. Engels, and G. Schöner, Dynamic fields endow behavior-based robots with representations, Robotics and Autonomous Systems, vol.14, issue.1, pp.55-77, 1995.
DOI : 10.1016/0921-8890(94)00020-3

W. Erlhagen and E. Bicho, The dynamic neural field approach to cognitive robotics, Journal of Neural Engineering, vol.3, issue.3, pp.36-54, 2006.
DOI : 10.1088/1741-2560/3/3/R02

URL : http://repositorium.sdum.uminho.pt/bitstream/1822/5920/1/ErlhagenBicho-JNE06.pdf

W. Erlhagen and G. Schöner, Dynamic field theory of movement preparation., Psychological Review, vol.109, issue.3, pp.545-572, 2002.
DOI : 10.1037/0033-295X.109.3.545

. Ester, A density-based algorithm for discovering clusters in large spatial databases with noise, Kdd, pp.226-231, 1996.

. Faisal, Noise in the nervous system, Nature Reviews Neuroscience, vol.81, issue.4, pp.292-303, 2008.
DOI : 10.1126/science.1089662

N. Fates, A Guided Tour of Asynchronous Cellular Automata, International Workshop on Cellular Automata and Discrete Complex Systems, pp.15-30, 2013.
DOI : 10.1007/978-3-642-40867-0_2

URL : https://hal.archives-ouvertes.fr/hal-00845623

C. Faubel and G. Schöner, Learning to recognize objects on the fly: A neurally based dynamic field approach, Neural Networks, vol.21, issue.4, pp.562-576, 2008.
DOI : 10.1016/j.neunet.2008.03.007

J. Fix, Template based black-box optimization of dynamic neural fields, Neural Networks, vol.46, issue.0, pp.4640-4689, 2013.
DOI : 10.1016/j.neunet.2013.04.008

URL : https://hal.archives-ouvertes.fr/hal-00869726

. Fix, Dynamic neural field optimization using the unscented Kalman filter, 2011 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), pp.1-7, 2011.
DOI : 10.1109/CCMB.2011.5952113

URL : https://hal.archives-ouvertes.fr/hal-00618117

. Fix, A Dynamic Neural Field Approach to the Covert and Overt Deployment of Spatial Attention, Cognitive Computation, vol.25, issue.1A, pp.279-293, 2011.
DOI : 10.1007/s12559-010-9083-y

URL : https://hal.archives-ouvertes.fr/inria-00536374

. Fix, A Distributed Computational Model of Spatial Memory Anticipation During a Visual Search Task, Anticipatory Behavior in Adaptive Learning Systems : From Brains to Individual and Social Behavior, 2007.
DOI : 10.1007/978-3-540-74262-3_10

URL : https://hal.archives-ouvertes.fr/inria-00166535

A. Fung, C. Fung, and S. Amari, Spontaneous Motion on Two-Dimensional Continuous Attractors, Neural Computation, vol.16, issue.3, pp.507-547, 2015.
DOI : 10.1007/s10827-009-0172-4

URL : http://arxiv.org/abs/1502.00127

B. R. Gaines, Stochastic computing, Proceedings of the April 18-20, 1967, spring joint computer conference on, AFIPS '67 (Spring), pp.149-156, 1967.
DOI : 10.1145/1465482.1465505

M. A. Giese, Dynamic neural field theory for motion perception, 2012.
DOI : 10.1007/978-1-4615-5581-0

B. Girau, Du parallelisme des modeles connexionnistes a leur implantation parallele, Thèse de doctorat dirigée par Cosnard, Michel Sciences et techniques École normale supérieure, 1999.

. Girau, . Vlassopoulos, B. Girau, and N. Vlassopoulos, Tiled cellular automata for area-efficient distributed random number generators, 1st International conference on pervasive and embedded computing and communication systems -PECCS 2011, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00585495

. Golomb, . Amitai, D. Golomb, and Y. Amitai, Propagating neuronal discharges in neocortical slices : computational and experimental study, Journal of neurophysiology, vol.78, issue.3, pp.1199-1211, 1997.
DOI : 10.1007/978-1-4615-4831-7_64

. Holi, . Hwang, J. L. Holi, and J. Hwang, Finite precision error analysis of neural network hardware implementations, IEEE Transactions on Computers, vol.42, issue.3, pp.281-290, 1993.
DOI : 10.1109/12.210171

Z. Hu, X. Hu, and B. Zhang, A Gaussian Attractor Network for Memory and Recognition with Experience-Dependent Learning, Neural Computation, vol.16, issue.5, pp.1333-1357, 2010.
DOI : 10.1523/JNEUROSCI.1897-07.2007

. Hubel, D. Hubel, and T. Wiesel, Shape and arrangement of columns in cat's striate cortex, The Journal of Physiology, vol.165, issue.3, pp.559-568, 1963.
DOI : 10.1113/jphysiol.1963.sp007079

S. Hussein, J. Hussein, and G. Swift, Mitigating Single-Event Upsets, 2015.

. Igel, Optimization of dynamic neural fields, Neurocomputing, vol.36, issue.1-4, pp.225-233, 2001.
DOI : 10.1016/S0925-2312(00)00328-3

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.32.5415

G. Indiveri, Neuromorphic VLSI Models of Selective Attention: From Single Chip Vision Sensors to Multi-chip Systems, Sensors, vol.15, issue.1-2, pp.5352-5375, 2008.
DOI : 10.3390/s8095352

. Indiveri, . Horiuchi, G. Indiveri, and T. K. Horiuchi, Frontiers in Neuromorphic Engineering, Frontiers in Neuroscience, vol.5, 2011.
DOI : 10.3389/fnins.2011.00118

URL : http://doi.org/10.3389/fnins.2011.00118

. Indiveri, Neuromorphic Silicon Neuron Circuits, Frontiers in Neuroscience, vol.5, 2011.
DOI : 10.3389/fnins.2011.00073

URL : https://hal.archives-ouvertes.fr/hal-00597675

. Johnson, A Dynamic Neural Field Model of Visual Working Memory and Change Detection, Psychological Science, vol.453, issue.5, pp.568-577, 2009.
DOI : 10.1111/j.1467-9280.2009.02329.x

. Joubert, Hardware spiking neurons design: Analog or digital?, The 2012 International Joint Conference on Neural Networks (IJCNN), pp.1-5, 2012.
DOI : 10.1109/IJCNN.2012.6252600

. Kang, Mexican hats and pinwheels in visual cortex, Proceedings of the National Academy of Sciences, pp.2848-2853, 2003.
DOI : 10.1103/PhysRevLett.88.078102

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC151429

. Kopecz, . Schöner, K. Kopecz, and G. Schöner, Saccadic motor planning by integrating visual information and pre-information on neural dynamic fields, Biological Cybernetics, vol.15, issue.1, pp.49-60, 1995.
DOI : 10.1007/BF00199055

. Lefort, Coupling BCM and Neural Fields for the Emergence of Self-organization Consensus, From Brains to Systems, pp.41-56, 2011.
DOI : 10.1007/978-1-4614-0164-3_5

URL : https://hal.archives-ouvertes.fr/inria-00585493

. Lichtsteiner, A 128<formula formulatype="inline"><tex>$\times$</tex> </formula>128 120 dB 15 <formula><tex>$\mu$</tex></formula>s Latency Asynchronous Temporal Contrast Vision Sensor, IEEE Journal of Solid-State Circuits, vol.43, issue.2, pp.566-576, 2008.
DOI : 10.1109/JSSC.2007.914337

. London, Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex, Nature, vol.83, issue.7302, pp.466123-127, 2010.
DOI : 10.1038/nature09086

. Madison, D. Madison, and R. Nicoll, Control of the repetitive discharge of rat CA 1 pyramidal neurones in vitro., The Journal of Physiology, vol.354, issue.1, pp.319-331, 1984.
DOI : 10.1113/jphysiol.1984.sp015378

. Maggiani, Bio-inspired heterogeneous architecture for real-time pedestrian detection applications, Journal of Real-Time Image Processing, pp.1-14, 2016.
DOI : 10.1007/s11554-016-0581-3

URL : https://hal.archives-ouvertes.fr/hal-01298676

. Maguire, Challenges for large-scale implementations of spiking neural networks on FPGAs, Neurocomputing, vol.71, issue.1-3, pp.1-313, 2007.
DOI : 10.1016/j.neucom.2006.11.029

M. Mahowald, VLSI Analogs of Neuronal Visual Processing : A Synthesis of Form and Function, 1992.

R. Manohar, Comparing Stochastic and Deterministic Computing, IEEE Computer Architecture Letters, vol.14, issue.2, pp.119-122, 2015.
DOI : 10.1109/LCA.2015.2412553

H. Massoud, T. Massoud, and T. Horiuchi, A neuromorphic vlsi head direction cell system. Circuits and Systems I : Regular Papers, IEEE Transactions on, vol.58, issue.1, pp.150-163, 2011.
DOI : 10.1109/tcsi.2010.2055310

C. Mead, Neuromorphic electronic systems, Proceedings of the IEEE, vol.78, issue.10, pp.1629-1636, 1990.
DOI : 10.1109/5.58356

URL : http://authors.library.caltech.edu/53090/1/00058356.pdf

S. Misra, J. Misra, and I. Saha, Artificial neural networks in hardware: A survey of two decades of progress, Neurocomputing, vol.74, issue.1-3, pp.1-3239, 2010.
DOI : 10.1016/j.neucom.2010.03.021

V. B. Mountcastle, Modality and topographic properties of single neurons of cat's somatic sensory cortex, Journal of neurophysiology, vol.20, issue.4, pp.408-434, 1957.

L. Neil, D. Neil, and S. Liu, Minitaur, an event-driven fpga-based spiking network accelerator. Very Large Scale Integration (VLSI) Systems, IEEE Transactions on, issue.12, pp.222621-2628, 2014.
DOI : 10.1109/tvlsi.2013.2294916

. Palesi, . Daneshtalab, M. Palesi, and M. Daneshtalab, Routing algorithms in networks-on-chip, 2014.
DOI : 10.1007/978-1-4614-8274-1

. Patel, Neural Network Implementation Using Bit Streams, IEEE Transactions on Neural Networks, vol.18, issue.5, pp.1488-1504, 2007.
DOI : 10.1109/TNN.2007.895822

. Patel, Neural Network Implementation Using Bit Streams, IEEE Transactions on Neural Networks, vol.18, issue.5, pp.1488-1504, 2007.
DOI : 10.1109/TNN.2007.895822

J. Piaget, The construction of reality in the child, Routledge, vol.82, 2013.
DOI : 10.1037/11168-000

E. Pinto, D. J. Pinto, and G. B. Ermentrout, Spatially Structured Activity in Synaptically Coupled Neuronal Networks: I. Traveling Fronts and Pulses, SIAM Journal on Applied Mathematics, vol.62, issue.1, pp.206-225, 2001.
DOI : 10.1137/S0036139900346453

P. Posner, M. I. Posner, and S. E. Petersen, The Attention System of the Human Brain, Annual Review of Neuroscience, vol.13, issue.1, pp.25-42, 1990.
DOI : 10.1146/annurev.ne.13.030190.000325

G. Potthast, R. Potthast, and P. B. Graben, Inverse Problems in Neural Field Theory, SIAM Journal on Applied Dynamical Systems, vol.8, issue.4, pp.1405-1433, 2009.
DOI : 10.1137/080731220

URL : http://centaur.reading.ac.uk/29359/1/2009_Potthast_Graben_SIADS_IP_Neural_Field_Theory.pdf

J. Quinton, Exploring and optimizing dynamic neural fields parameters using Genetic Algorithms, The 2010 International Joint Conference on Neural Networks (IJCNN), 2010.
DOI : 10.1109/IJCNN.2010.5596293

URL : https://hal.archives-ouvertes.fr/inria-00488914

L. Rodriguez, Définition d'un substrat computationnel bio-inspiré : déclinaison de propriétés de plasticité cérébrale dans les architectures de traitement autoadaptatif, 2015.

. Rostro-gonzalez, Low-cost hardware implementations for discrete-time spiking neural networks, Cinquième conférence plénière française de Neurosciences Computationnelles, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00553431

. Rougier, N. P. Vitay-]-rougier, and J. Vitay, Emergence of attention within a neural population, Neural Networks, vol.19, issue.5, pp.573-581, 2006.
DOI : 10.1016/j.neunet.2005.04.004

URL : https://hal.archives-ouvertes.fr/inria-00000143

M. Samsonovich, A. Samsonovich, and B. L. Mcnaughton, Path integration and cognitive mapping in a continuous attractor neural network model, The Journal of neuroscience, issue.15, pp.175900-5920, 1997.

Y. Sandamirskaya, Dynamic neural fields as a step toward cognitive neuromorphic architectures, Frontiers in Neuroscience, vol.7, 2013.
DOI : 10.3389/fnins.2013.00276

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3898057

S. Sandamirskaya, Y. Sandamirskaya, and G. Schoner, Dynamic field theory of sequential action: A model and its implementation on an embodied agent, 2008 7th IEEE International Conference on Development and Learning, pp.133-138, 2008.
DOI : 10.1109/DEVLRN.2008.4640818

. Sarpeshkar, A low-power widedynamic-range analog vlsi cochlea, Neuromorphic systems engineering, pp.49-103, 1998.
DOI : 10.1007/978-0-585-28001-1_3

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.333.2578

. Schemmel, A wafer-scale neuromorphic hardware system for large-scale neural modeling, Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pp.1947-1950, 2010.
DOI : 10.1109/ISCAS.2010.5536970

. Schöner, Dynamics of behavior: Theory and applications for autonomous robot architectures, Moving the Frontiers between Robotics and Biology, pp.213-245, 1995.
DOI : 10.1016/0921-8890(95)00049-6

B. E. Shi, Spatio-temporal image filtering with cellular neural networks, Proceedings of International Conference on Neural Networks (ICNN'96), pp.1410-1415, 1996.
DOI : 10.1109/ICNN.1996.549106

B. E. Shi, Gabor-type filtering in space and time with cellular neural networks, IEEE Transactions on Circuits and Systems I : Fundamental Theory and Applications, pp.121-132, 1998.
DOI : 10.1109/81.661672

. Simmering, Generalizing the dynamic field theory of spatial cognition across real and developmental time scales, Brain Research, vol.1202, issue.0, pp.120268-86, 2008.
DOI : 10.1016/j.brainres.2007.06.081

M. Sipper, The emergence of cellular computing, Computer, vol.32, issue.7, pp.18-26, 1999.
DOI : 10.1109/2.774914

N. Swindale, The development of topography in the visual cortex: a review of models, Network: Computation in Neural Systems, vol.7, issue.2, pp.161-247, 1996.
DOI : 10.1088/0954-898X_7_2_002

. Taouali, On asynchronous dynamic neural field computation, Cinquième conférence plénière française de Neurosciences Computationnelles, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00553429

J. S. Taube and J. P. Bassett, Persistent Neural Activity in Head Direction Cells, Cerebral Cortex, vol.13, issue.11, pp.1162-1172, 2003.
DOI : 10.1093/cercor/bhg102

J. G. Taylor, Neural 'bubble' dynamics in two dimensions: foundations, Biological Cybernetics, vol.80, issue.6, pp.393-409, 1999.
DOI : 10.1007/s004220050534

. Thelen, The dynamics of embodiment: A field theory of infant perseverative reaching, Behavioral and Brain Sciences, vol.24, issue.1, pp.241-275, 2001.
DOI : 10.1017/S0140525X01003910

. Vainbrand, . Ginosar, D. Vainbrand, and R. Ginosar, Scalable network-on-chip architecture for configurable neural networks, Microprocessors and Microsystems, vol.35, issue.2, pp.152-166, 2011.
DOI : 10.1016/j.micpro.2010.08.005

. Vazquez, Visual attention using spiking neural maps, The 2011 International Joint Conference on Neural Networks, pp.2164-2171, 2011.
DOI : 10.1109/IJCNN.2011.6033496

URL : https://hal.archives-ouvertes.fr/inria-00603929

. Vitay, A Distributed Model of Spatial Visual Attention, Biomimetic Neural Learning for Intelligent Robotics, pp.54-72, 2005.
DOI : 10.1007/11521082_4

URL : https://hal.archives-ouvertes.fr/inria-00000145

G. Vlassopoulos, N. Vlassopoulos, and B. Girau, A Metric for Evolving 2- D Cellular Automata As Pseudo-Random Number Generators, Journal of Cellular Automata, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01071871

. Wang, Division of labor among distinct subtypes of inhibitory neurons in a cortical microcircuit of working memory, Proceedings of the National Academy of Sciences of the United States of America, pp.1368-1373, 2004.
DOI : 10.1159/000071957

. Wilimzig, The time course of saccadic decision making: Dynamic field theory, Neural Networks, vol.19, issue.8, pp.1059-1074, 2006.
DOI : 10.1016/j.neunet.2006.03.003

. Wilson, H. R. Cowan-]-wilson, and J. D. Cowan, Excitatory and Inhibitory Interactions in Localized Populations of Model Neurons, Biophysical Journal, vol.12, issue.1, pp.1-24, 1972.
DOI : 10.1016/S0006-3495(72)86068-5

. Wilson, H. R. Cowan-]-wilson, and J. D. Cowan, A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue, Kybernetik, vol.12, issue.2, pp.55-80, 1973.
DOI : 10.1007/BF00288786

G. Xie, X. Xie, and M. A. Giese, Nonlinear dynamics of direction-selective recurrent neural media, Physical Review E, vol.65, issue.5, p.65051904, 2002.
DOI : 10.1103/PhysRevE.65.051904

K. Zhang, Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble : a theory, The journal of neuroscience, vol.16, issue.6, pp.2112-2126, 1996.

. Zibner, Scene representation based on dynamic field theory : From human to machine, Frontiers in Computational Neuroscience, p.5, 2010.