Stationary log-normal distribution of weights stems from spontaneous ordering in adaptive node networks

[1]  L. Parra,et al.  Finding influential nodes for integration in brain networks using optimal percolation theory , 2018, Nature Communications.

[2]  I. Kanter,et al.  Adaptive nodes enrich nonlinear cooperative learning beyond traditional adaptation by links , 2018, Scientific Reports.

[3]  Federico Levi,et al.  The discovery of skewness , 2018, Nature Physics.

[4]  Bo Li,et al.  Exploring the Function Space of Deep-Learning Machines , 2017, Physical review letters.

[5]  Gerardo Iñiguez,et al.  Threshold driven contagion on weighted networks , 2017, Scientific Reports.

[6]  Ido Kanter,et al.  New Types of Experiments Reveal that a Neuron Functions as Multiple Independent Threshold Units , 2017, Scientific Reports.

[7]  Yee Lian Chew,et al.  Network control principles predict neuron function in the Caenorhabditis elegans connectome , 2017, Nature.

[8]  Youngjin Park,et al.  Symmetry of learning rate in synaptic plasticity modulates formation of flexible and stable memories , 2017, Scientific Reports.

[9]  Jacob G. Scott,et al.  Evolutionary dynamics of incubation periods , 2017, bioRxiv.

[10]  Y.-Y. Liu,et al.  The fundamental advantages of temporal networks , 2016, Science.

[11]  Rosario N. Mantegna,et al.  Plasticity of brain wave network interactions and evolution across physiologic states , 2015, Front. Neural Circuits.

[12]  M. Buchanan.  Depths of learning , 2015 .

[13]  Zoubin Ghahramani,et al.  Probabilistic machine learning and artificial intelligence , 2015, Nature.

[14]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[15]  Giacomo Luchetta,et al.  A New Tool in the Box? , 2015, European Journal of Risk Regulation.

[16]  D. R. Muir,et al.  Functional organization of excitatory synaptic strength in primary visual cortex , 2015, Nature.

[17]  Ido Kanter,et al.  Neuronal response impedance mechanism implementing cooperative networks with low firing rates and μs precision , 2014, Front. Neural Circuits.

[18]  Shivayogi M Hugar,et al.  An In Vivo Study , 2015 .

[19]  Ido Kanter,et al.  Synchronization among neuronal pools without common inputs: in vivo study , 2014, Brain Structure and Function.

[20]  G. Buzsáki,et al.  The log-dynamic brain: how skewed distributions affect network operations , 2014, Nature Reviews Neuroscience.

[21]  G. Laurent,et al.  Conditional modulation of spike-timing-dependent plasticity for olfactory learning , 2012, Nature.

[22]  Amir Bashan,et al.  Network physiology reveals relations between network topology and physiological function , 2012, Nature Communications.

[23]  Y. Loewenstein,et al.  Multiplicative Dynamics Underlie the Emergence of the Log-Normal Distribution of Spine Sizes in the Neocortex In Vivo , 2011, The Journal of Neuroscience.

[24]  N. Spruston.  Pyramidal neurons: dendritic structure and synaptic integration , 2008, Nature Reviews Neuroscience.

[25]  Y. Dan,et al.  Spike timing-dependent plasticity: from synapse to perception. , 2006, Physiological reviews.

[26]  Wulfram Gerstner,et al.  Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. , 2005, Journal of neurophysiology.

[27]  Sen Song,et al.  Highly Nonrandom Features of Synaptic Connectivity in Local Cortical Circuits , 2005, PLoS biology.

[28]  Christian Van den Broeck,et al.  Statistical Mechanics of Learning , 2001 .

[29]  T. Watkin,et al.  THE STATISTICAL-MECHANICS OF LEARNING A RULE , 1993 .

[30]  Moshe Abeles,et al.  Corticonics: Neural Circuits of Cerebral Cortex , 1991 .

[31]  M. Opper.  Learning in Neural Networks: Solvable Dynamics , 1989 .

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