B. K. Aliabadi, C. Berrou, V. Gripon, and X. Jiang, Storing Sparse Messages in Networks of Neural Cliques, AGJ13] A. Aboudib, V. Gripon, and X. Jiang. A study of retrieval algorithms of sparse messages in networks of neural cliques. submitted to Neurocomputing, 2013.
DOI : 10.1109/TNNLS.2013.2285253

URL : https://hal.archives-ouvertes.fr/hal-00994216

G. [. Ackley and . Hinton, A Learning Algorithm for Boltzmann Machines*, Cognitive Science, vol.85, issue.1, pp.147-69, 1985.
DOI : 10.1207/s15516709cog0901_7

A. K. Aliabadi, Neural networks and sparse information acquisition, 2013.
URL : https://hal.archives-ouvertes.fr/tel-00962603

]. A. Bad94 and . Baddeley, The magical number seven: still magic after all these years?, Psychological Review, vol.101, issue.2, pp.353-56, 1994.

]. H. Bar72 and . Barlow, Single units and sensation: a neuron doctrine for perceptual psychology, Perception, vol.1, pp.371-94, 1972.

]. D. Bar03 and . Barber, Learning in spiking neural assemblies, Advances in Neural Information Processing Systems 15, pp.149-156, 2003.

E. [. Bassett and . Bullmore, Small-World Brain Networks, BIBLIOGRAPHY [Ben09] Y. Bengio. Learning deep architectures for AI. Foundations and Trends in Machine Learning, pp.512-231, 2006.
DOI : 10.1177/1073858406293182

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.114.794

V. [. Boguslawski, F. Gripon, F. Seguin, and . Heitzmann, Storing nonuniformly distributed messages in networks of neural cliques, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01058337

O. [. Bullmore and . Sporns, Complex brain networks: graph theoretical analysis of structural and functional systems, Nature Reviews Neuroscience, vol.8, issue.3, pp.186-98, 2009.
DOI : 10.1371/journal.pone.0002051

J. Brea, W. Senn, and J. Pfister, Sequence learning with hidden units in spiking neural networks, Proceeding of NIPS, pp.1422-1452, 2011.

L. Cruz, S. V. Buldyrev, S. Peng, D. L. Roe, B. Urbanc et al., A statistically based density map method for identification and quantification of regional differences in microcolumnarity in the monkey brain, Journal of Neuroscience Methods, vol.141, issue.2, pp.321-353, 2005.
DOI : 10.1016/j.jneumeth.2004.09.005

R. [. Coultrip, G. Granger, and . Lynch, A cortical model of winner-take-all competition via lateral inhibition, Neural Networks, vol.5, issue.1, pp.47-54, 1992.
DOI : 10.1016/S0893-6080(05)80006-1

]. N. Cow00 and . Cowan, The magical number 4 in short-term memory: a reconsideration of mental storage capacity, Behavioral and Brain Sciences, vol.24, pp.87-185, 2000.

]. D. Dra05 and . Drachman, Do we have brain to spare?, Neurology, vol.64, issue.12, pp.2004-2009, 2005.

N. [. Eichenbaum and . Cohen, From conditioning to conscious recollection: Memory systems of the the brain, 2001.
DOI : 10.1093/acprof:oso/9780195178043.001.0001

]. G. Eit99 and . Eitan, Backtracking algorithms, chapter 19 Data structures, 1999.

. [. Földiak, Sparse coding in the primate cortex The handbook of brain theory and neural networks, pp.1064-68, 2003.

D. [. Feldman and . Ballard, Connectionist Models and Their Properties, Cognitive Science, vol.284, issue.1, pp.205-54, 1982.
DOI : 10.1207/s15516709cog0603_1

C. [. Friston and . Price, Degeneracy and redundancy in cognitive anatomy, Trends in Cognitive Sciences, vol.7, issue.4, 2003.
DOI : 10.1016/S1364-6613(03)00054-8

]. R. Fre99 and . French, Catastrophic forgetting in connectionist networks: causes, consequences and solutions, Trends in Cognitive Science, vol.3, issue.4, pp.128-163, 1999.

]. J. Fus09 and . Fuster, Cortex and memory: emergence of a new paradigm, Journal of Cognitive Neurosciences, vol.21, pp.2047-72, 2009.

R. S. Fisher, W. V. Boas, W. Blume, C. Elger, P. Genton et al., Epileptic Seizures and Epilepsy: Definitions Proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE), Epilepsia, vol.32, issue.4, pp.470-72, 2005.
DOI : 10.1046/j.1528-1157.2001.22001.x

]. V. Gb11a, C. Gripon, and . Berrou, A simple and efficient way to store many messages using neural cliques, IEEE Symposium Series on Computational Intelligence, 2011.

]. V. Gb11b, C. Gripon, and . Berrou, Sparse neural networks with large learning diversity, IEEE Transactions on Neural Networks, vol.22, issue.7, 2011.

C. [. Gripon and . Berrou, Nearly-optimal associative memories based on distributed constant weight codes, 2012 Information Theory and Applications Workshop, pp.269-73, 2012.
DOI : 10.1109/ITA.2012.6181790

URL : https://hal.archives-ouvertes.fr/hal-01056531

W. [. Grossberg, H. Maass, and . Markram, Introduction, Gri11] Vincent Gripon. Networks of neural cliques, p.587, 2001.
DOI : 10.1016/S0893-6080(01)00102-2

]. C. Gro02 and . Gross, Genealogy of the " grandmother cell, The Neuroscientist, vol.8, issue.5, pp.512-530, 2002.

M. [. Gripon, V. Rabbat, W. J. Skachek, and . Gross, Compressing multisets using tries, 2012 IEEE Information Theory Workshop, pp.647-51, 2012.
DOI : 10.1109/ITW.2012.6404756

URL : https://hal.archives-ouvertes.fr/hal-01056521

D. [. Hanson and . Burr, What connectionist models learn: Learning and representation in connectionist networks, Behavioral and Brain Sciences, vol.3, issue.03, pp.471-489, 1990.
DOI : 10.1109/29.1640

A. [. Hinton and . Brown, Spiking Boltzmann machines, Advances in Neural Information Processing Systems, 2000.

]. J. Hop82 and . Hopfield, Neural networks and physical systems with emergent collective computational abilities, Proceedings of the National Academy of Sciences of the United States of America, vol.79, issue.8, pp.2554-58, 1982.

V. [. Jiang, C. Gripon, and . Berrou, Learning long sequences in binary neural networks, Proc. Cognitive Science, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01056538

A. [. Johansson and . Lansner, Towards cortex sized artificial neural systems, Neural Networks, vol.20, issue.1, pp.48-61, 2007.
DOI : 10.1016/j.neunet.2006.05.029

]. E. Jon00 and . Jones, Microcolumns in the cerebral cortex, Proc. National Academy of Sciences (PNAS), pp.5019-5040, 2000.

]. D. Kle86 and . Kleinfeld, Sequential state generation by model neural networks, Proceedings of the National Academy of Sciences of the United States of America, vol.83, issue.24, pp.9469-73, 1986.

G. [. Knoblauch, F. T. Palm, and . Sommer, Memory Capacities for Synaptic and Structural Plasticity, Neural Computation, vol.13, issue.1, pp.289-341, 2010.
DOI : 10.1002/(SICI)1097-4695(199610)31:2<219::AID-NEU7>3.0.CO;2-E

L. [. Lee, C. Itti, J. Koch, and . Braun, Attention activates winner-take-all competition among visual filters, Nature Neuroscience, vol.2, issue.4, pp.375-81, 1999.

]. J. Lis99 and . Lisman, Relating hippocampal circuitry to function: Recall of memory sequences by reciprocal dentate CA3 interactions, Neuron, vol.22, pp.233-275, 1999.

. Rauschecker, Brain activation during anticipation of sound sequences

. Lmz-+-12-]-c, L. Di-lanzo, F. Marzetti, F. D. Zappasodi, V. Fallani et al., Redundancy as a graph-based index of frequency specific meg functional connectivity, Computational and Mathematical Methods in Medicine, 2012.

L. [. Lisman, A. Talamini, and . Raffone, Recall of memory sequences by interaction of the dentate and CA3: A revised model of the phase precession, Neural Networks, vol.18, issue.9, pp.1191-1201, 2005.
DOI : 10.1016/j.neunet.2005.08.008

N. [. Mccloskey and . Cohen, Catastrophic interference in connectionist networks: The sequential learning problem. The Psychology of Learning and Motivation Extended hopfield network for sequence learning: Application to gesture recognition, Proceedings of the ICANN, pp.109-64, 1989.

]. G. Mil56 and . Miller, The magical number seven, plus or minus two: some limits on our capacity for processing information, Psychological Review, vol.63, pp.81-97, 1956.

W. [. Mcculloch and . Pitts, A logical calculus of the ideas immanent in nervous activity, The Bulletin of Mathematical Biophysics, vol.5, issue.4, pp.115-148, 1943.
DOI : 10.1007/BF02478259

D. [. Olshausen and . Field, Sparse coding of sensory outputs

D. [. Perfetti and . Bolger, The Brain Might Read That Way, Scientific Studies of Reading, vol.47, issue.3, pp.293-304, 2004.
DOI : 10.1037//0022-0663.91.3.415

G. [. Rumelhart, J. L. Hinton, and . Mcclelland, A general framework for parallel distributed processing, volume 1 of Parallel distributed processing: Explorations in the microstructure of cognition, 1986.

H. [. Starzyk and . He, Anticipation-Based Temporal Sequences Learning in Hierarchical Structure, IEEE Transactions on Neural Networks, vol.18, issue.2, 2007.
DOI : 10.1109/TNN.2006.884681

G. [. Sutskever, G. Hinton, and . Taylor, The recurrent temporal restricted boltzmann machine, Advances in Neural Information Processing Systems, pp.1601-1609, 2009.

I. [. Sompolinsky and . Kanter, Temporal Association in Asymmetric Neural Networks, Physical Review Letters, vol.57, issue.22, pp.2861-64, 1986.
DOI : 10.1103/PhysRevLett.57.2861

G. [. Sommer and . Palm, Improved bidirectional retrieval of sparse patterns stored by Hebbian learning, Neural Networks, vol.12, issue.2, pp.281-971067, 1999.
DOI : 10.1016/S0893-6080(98)00125-7

W. [. Shannon and . Weaver, The mathematical theory of communication, 1949.

M. [. Wang and . Arbib, Complex temporal sequence learning based on short-term memory, Proceedings of the IEEE, 1990.
DOI : 10.1109/5.58329

O. [. Willshaw, H. C. Bunetman, and . Longuet-higgins, Non-Holographic Associative Memory, Nature, vol.168, issue.5197, pp.960-62, 1969.
DOI : 10.1038/222960a0