Binding sparse spatiotemporal patterns in spiking computation

Imagine a two-dimensional spatial array of detectors temporally driven via an unknown number of mutually overlapping, unknown patterns. One at a time, these patterns are randomly, partially, sparsely and repeatedly presented, superimposed with omnipresent noise. The challenge is to design a scheme for detecting and recalling these patterns in an unsupervised, online and computationally efficient fashion.

[1]  Martin P Meyer,et al.  Evidence from In Vivo Imaging That Synaptogenesis Guides the Growth and Branching of Axonal Arbors by Two Distinct Mechanisms , 2006, The Journal of Neuroscience.

[2]  Daniel J. Amit,et al.  Spike-Driven Synaptic Dynamics Generating Working Memory States , 2003, Neural Computation.

[3]  H. Kalmus.  Biological Cybernetics , 1972, Nature.

[4]  Sandro Romani,et al.  Learning in realistic networks of spiking neurons and spike‐driven plastic synapses , 2005, The European journal of neuroscience.

[5]  D. J. Felleman,et al.  Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.

[6]  Ernest Van Someren.  Automat Und Mensch , 1962 .

[7]  D. Spalding.  The Principles of Psychology , 1873, Nature.

[8]  Gal Chechik,et al.  Spike-Timing-Dependent Plasticity and Relevant Mutual Information Maximization , 2003, Neural Computation.

[9]  Stephen Grossberg,et al.  Studies of mind and brain , 1982 .

[10]  Richard Granger,et al.  A cortical model of winner-take-all competition via lateral inhibition , 1992, Neural Networks.

[11]  J. Koenderink,et al.  Receptive field families , 1990, Biological Cybernetics.

[12]  David Zipser,et al.  Feature Discovery by Competive Learning , 1986, Cogn. Sci..

[13]  Rufin VanRullen,et al.  Temporal codes and sparse representations: A key to understanding rapid processing in the visual system , 2004, Journal of Physiology-Paris.

[14]  E. B. Baum,et al.  Internal representations for associative memory , 1988, Biological Cybernetics.

[15]  Christian Wozny,et al.  The Back and Forth of Dendritic Plasticity , 2007, Neuron.

[16]  S. Thorpe,et al.  The Time Course of Visual Processing: From Early Perception to Decision-Making , 2001, Journal of Cognitive Neuroscience.

[17]  Karl Steinbuch,et al.  Automat und Mensch , 1962 .

[18]  G. Palm,et al.  On associative memory , 2004, Biological Cybernetics.

[19]  R. Wurtz,et al.  Visual responses of inferior temporal neurons in awake rhesus monkey. , 1983, Journal of neurophysiology.

[20]  Richard T. Scalettar,et al.  Emergence of grandmother memory in feed forward networks: learning with noise and forgetfulness , 1988 .

[21]  Stephen Grossberg,et al.  The ART of adaptive pattern recognition by a self-organizing neural network , 1988, Computer.

[22]  Teuvo Kohonen,et al.  Content-addressable memories , 1980 .

[23]  H. C. LONGUET-HIGGINS,et al.  Non-Holographic Associative Memory , 1969, Nature.

[24]  E. Rolls.  Neural organization of higher visual functions , 1991, Current Opinion in Neurobiology.

[25]  J. Knott.  The organization of behavior: A neuropsychological theory , 1951 .

[26]  C. Malsburg.  Self-organization of orientation sensitive cells in the striate cortex , 2004, Kybernetik.

[27]  S. Grossberg,et al.  Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors , 1976, Biological Cybernetics.

[28]  Örjan Ekeberg,et al.  Reliability and Speed of Recall in an Associative Network , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  G. Kreiman,et al.  Timing, Timing, Timing: Fast Decoding of Object Information from Intracranial Field Potentials in Human Visual Cortex , 2009, Neuron.

[30]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.