Daniel D Ben Dayan Rubin

Position:
Postdoc -- ended Aug 2007
Email:
Work phone:
41 (0)44 63 53 027
Home page:

Memory models, dynamics of synaptic plasticity, neural network dynamics,
temporal processing in neural networks, the effect of noise in neural networks.

Noise, heterogeneity, and disorder can deteriorate the
performance of any communication substrate, either artificial
devices or complex systems like neural-networks. We study how to
exploit noise to better perform computational and memorization
tasks, how to model the noise sources, and which mechanism these
sources enhance/degrade. We isolate different components of noise
in networks: (1) structural—random connectivity and synaptic
efficacy distribution; (2) dynamical—input noise to each neuron,
output dynamics. Noise is a natural property of any system: noise
is necessary in neural networks in order to improve learning,
retaining, and information removal, making room for new memories.
We investigate the optimal range of parameters to exploit the
stochasticity of the involved processes.

Topology can also became a source of noise. By tuning the
randomness of the connections we can enhance computational
abilities such as associative memory and separating output
representations of temporal input patterns.

Publications

2010

2006

  • Ben Dayan Rubin, D and Fusi, S Storing sparse random patterns with cascade synapses, Proceedings CNS 2006 , 2006 pdf

2005

  • Astolfi, l and Babiloni, C and Carducci, F and Cincotti, F and Basilisco, A and Rossini, PM and Salinari, S and Cerutti, S and Ben Dayan Rubin, DD and Ding, L and Ni, Y and He, B and Babiloni, F Estimation of the Cortical Connectivity by High-Resolution EEG and Structural Equation Modeling: Simulations and Application to Finger Tapping Data, IEEE Transactions on Biomedical Engineering, 52:(5) 757-68, 2005 pdf

2004

  • Ben Dayan Rubin, D D Synaptic value bounds for optimizing retrieval in recurrent neural networks, Neurocomputing, 58: 745-741, 2004 pdf
  • Ben Dayan Rubin, D D and Baselli, G and Inbar, G F and Cerutti, S An adaptive neuro-Fuzzy method (ANFIS) for estimating single-trial movement-related potentials, Biological Cybernetics, 91: 63-75, 2004 pdf
  • Ben Dayan Rubin, D. D. and Chicca, E. and Indiveri, G. Characterizing the firing proprieties of an adaptive analog VLSI neuron, Lecture Notes in Computer Science, 3141: 189-200, 2004 pdf
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