R. Ananthanarayanan and D. S. Modha, Anatomy of a cortical simulator, Proceedings of the 2007 ACM/IEEE conference on Supercomputing , SC '07, 2007.
DOI : 10.1145/1362622.1362627

A. Bakhoda, G. Yuan, W. Fung, H. Wong, and T. Aamondt, Analyzing CUDA workloads using a detailed GPU simulator, 2009 IEEE International Symposium on Performance Analysis of Systems and Software, pp.163-174, 2009.
DOI : 10.1109/ISPASS.2009.4919648

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.507.8371

H. Berry and O. Temam, Modeling self-developping biological neural network, Neurocomputing, vol.70, pp.16-182723, 2007.

R. E. Brown and P. M. Milner, The legacy of Donald O. Hebb: more than the Hebb Synapse, Nature Reviews Neuroscience, vol.255, issue.12, pp.1013-1019, 2003.
DOI : 10.1038/nrn1257

N. Corporation, CUDA Programming Guide NVIDIA Corporation, 2701 San Toman Expressway, 2007.

A. Dehon, Nanowire-based programmable architectures, ACM Journal on Emerging Technologies in Computing Systems, vol.1, issue.2, pp.109-162, 2005.
DOI : 10.1145/1084748.1084750

M. Emmerson and R. Damper, Determining and improving the fault tolerance of multilayer perceptrons in a pattern-recognition application, IEEE Transactions on Neural Networks, vol.4, issue.5, 1993.
DOI : 10.1109/72.248456

J. Fieres, K. Meier, and J. Schemmel, A Convolutional Neural Network Tolerant of Synaptic Faults for Low-Power Analog Hardware, ANNPR, pp.122-132, 2006.
DOI : 10.1007/11829898_11

S. Haeusler and W. Maass, A Statistical Analysis of Information-Processing Properties of Lamina-Specific Cortical Microcircuit Models, Cerebral Cortex, vol.17, issue.1, pp.149-162, 2007.
DOI : 10.1093/cercor/bhj132

A. Hashmi, H. Berry, O. Temam, and M. H. Lipasti, Leveraging progress in neurobiology for computing systems, Proceedings of the Workshop on New Directions in Computer Architecture held in Conjunction with 42nd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO-42), 2009.

A. Hashmi and M. Lipasti, Cortical columns: Building blocks for intelligent systems, 2009 IEEE Symposium on Computational Intelligence for Multimedia Signal and Vision Processing, pp.21-28, 2009.
DOI : 10.1109/CIMSVP.2009.4925643

A. Hashmi and M. Lipasti, Discovering cortical algorithms, Proceedings of the International Conference on Neural Comp utation (ICNC), 2010.

S. Haykin, Neural Networks: A Comprehensive Foundation, 1998.

J. Hirsch and L. Martinez, Laminar processing in the visual cortical column, Current Opinion in Neurobiology, vol.16, issue.4, pp.377-384, 2006.
DOI : 10.1016/j.conb.2006.06.014

M. Holler, S. Tam, H. Castro, and R. Benson, An electrically trainable artificial neural network (ETANN) with 10240 'floating gate' synapses, International Joint Conference on Neural Networks, pp.191-196, 1989.
DOI : 10.1109/IJCNN.1989.118698

D. Hubel and T. Wiesel, Receptive fields, binocular interaction and functional architecture in the cat's visual cortex, The Journal of Physiology, vol.160, issue.1, pp.106-154, 1962.
DOI : 10.1113/jphysiol.1962.sp006837

K. Ibata, Q. Sun, and G. Turrigiano, Rapid Synaptic Scaling Induced by Changes in Postsynaptic Firing, Neuron, vol.57, issue.6, pp.819-826, 2008.
DOI : 10.1016/j.neuron.2008.02.031

M. H. Kalisman, N. Silberberg, and G. , The neocortical microcircuit as a tabula rasa, Proc. Natl. Acad. Sci. USA, pp.880-885, 2005.
DOI : 10.1073/pnas.0407088102

G. Kreiman, C. Koch, and I. Fried, Category-specific visual responses of single neurons in the human medial temporal lobe, LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, pp.946-9532278, 1998.

A. Losonczy and J. Magee, Integrative Properties of Radial Oblique Dendrites in Hippocampal CA1 Pyramidal Neurons, Neuron, vol.50, issue.2, pp.291-307, 2006.
DOI : 10.1016/j.neuron.2006.03.016

A. Nere and M. Lipasti, Cortical architectures on a GPGPU, Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units, GPGPU '10, pp.12-18, 2010.
DOI : 10.1145/1735688.1735693

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.212.4947

J. Peissig and M. Tarr, Visual Object Recognition: Do We Know More Now Than We Did 20 Years Ago?, Annual Review of Psychology, vol.58, issue.1, pp.75-96, 2007.
DOI : 10.1146/annurev.psych.58.102904.190114

M. Riesenhuber and T. Poggio, Hierarchical models of object recognition in cortex, Nature Neuroscience, vol.2, pp.1019-1025, 1999.

D. Ringach, Haphazard Wiring of Simple Receptive Fields and Orientation Columns in Visual Cortex, Journal of Neurophysiology, vol.92, issue.1, pp.468-476, 2004.
DOI : 10.1152/jn.01202.2003

S. Ryoo, C. Rodrigues, S. Babhsorkhi, S. Stone, D. Kirk et al., Optimization principles and application performance evaluation of a multithreaded GPU using CUDA, Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming , PPoPP '08, pp.73-82, 2008.
DOI : 10.1145/1345206.1345220

A. Sandberg and N. Bostrom, Whole brain emulation: A roadmap, 2008.
DOI : 10.1007/978-3-642-31674-6_19

J. Schemmel, J. Fieres, and K. Meier, Wafer-scale integration of analog neural networks, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp.431-438, 2008.
DOI : 10.1109/IJCNN.2008.4633828

T. Serre, A. Oliva, and T. Poggio, A feedforward architecture accounts for rapid categorization, Proc. Natl. Acad. Sci. USA, pp.6424-6429, 2007.
DOI : 10.1073/pnas.0700622104

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1847457

T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio, Robust Object Recognition with Cortex-Like Mechanisms, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.3, pp.411-426, 2007.
DOI : 10.1109/TPAMI.2007.56

URL : http://cbcl.mit.edu/projects/cbcl/publications/ps/serre-wolf-poggio-PAMI-07.pdf

. Sia, Semiconductor industry association 2007 roadmap, 2007.

G. Sperling, A model for visual memory tasks. Human Factor, pp.19-31, 1963.

M. Spratling, Pre-synaptic lateral inhibition provides a better architecture for self-organizing neural networks, Network: Computation in Neural Systems, vol.10, issue.4, pp.285-301, 1999.
DOI : 10.1088/0954-898X_10_4_301

R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, IEEE Transactions on Neural Networks, vol.9, issue.5, 1998.
DOI : 10.1109/TNN.1998.712192

L. Swanson, Mapping the human brain: past, present, and future, Trends in Neurosciences, vol.18, issue.11, pp.471-474, 1995.
DOI : 10.1016/0166-2236(95)92766-J