Publications in Computational Neuroscience

  • 1- D. Hansel and H. Sompolinsky (1990). Learning from examples in a single layer neural network. Europhysics Letters, 11:677.

  • 2- E. Barkai, D. Hansel and I. Kanter (1990). Statistical mechanics of a multilayer perceptron. Physical Review Letters, 65:2312.

  • 3- E. Barkai, D. Hansel and H. Sompolinsky (1990). Broken symmetries inmultilayered perceptrons. Physical Review A, 45:4146.

  • 4- D. Hansel, G. Mato and C. Meunier (1992). Memorization without generalization in a multilayered neural network. Europhys. Lett., 20:471.

  • 5- D. Selingson, M. Griniasty, D. Hansel and N. Shoresh (1992). Computing with a difference neuron. Network, 3:187.

  • 6- D. Golomb, D. Hansel, B. Shraiman and H. Sompolinsky (1992). Clustering in globally coupled phase oscillators. Physical Review A, 45:3516

  • 7- D. Hansel and H. Sompolinsky (1993). A solvable model of spatiotemporal chaos. Phys. Rev. Letters, 71:2710.

  • 8- D. Hansel, G. Mato and C. Meunier (1993). Clustering and slow switching in globally coupled phase oscillators. Phys. Rev. E, 48:3470.

  • 9- D. Hansel and G. Mato (1993). Patterns of synchrony of a Hodgkin-Huxley neural network at weak coupling Physica A, 200:662.

  • 10- D. Hansel, G. Mato and C. Meunier (1993). Phase dynamics for weakly coupled Hodgkin-Huxley neurons. Europhysics Letters, 23:367.

  • 11- D. Hansel, G. Mato and C. Meunier (1993). Phase reduction and neural modeling. Concepts Neurosci. 4:192.

  • 12- D. Hansel, G. Mato and C. Meunier (1995). Synchronization in excitatory neural networks. Neural Computation 7: 307

  • 13- D. Hansel (1996). Synchronized chaos in local cortical circuits. Int. J. of Neur. Systems 7: 403.

  • 14- H. Bergman, A. Raz, A. Feingold, A. Nini, I. Nelken, D. Hansel, H. Ben-Pazi et A. Reches. Physiology of MPTP Tremor (1998) Movement Disorders 13:32.

  • 15- Hansel, D. and Sompolinsky, H. (1998). Modeling Feature Selectivity in Local Cortical Circuits. In Methods in Neuronal Modeling: From Synapse to Networks. Koch, C. and Segev, I. Eds. (MIT Press, Cambridge, MA, 1998), Chapter 13, second edition.
    introduction (ps file)

  • 16- Ben-Yishai, R., Hansel, D. and Sompolinsky, H. (1997). Traveling Waves and the Processing of Weakly Tuned Inputs in a Cortical Network Model.J. Comp. Neurosci. 4, 57-79.

  • 17- Hansel, D. and Sompolinsky, H. (1996). Chaos and Synchrony in a Model of a Hypercolumn in Visual Cortex. J. Comp. Neurosci. 3:7-34

  • 18- Hansel, D. and Sompolinsky, H. (1992). Synchronization and computation in a chaotic neural network. Physical Review Letters, 68:718.

  • 19- D. Hansel, G. Mato, C. Meunier and L. Neltner (1998). On numerical simulations of integrate-and-fire neural networks. Neural Computation, 10:467.

  • 20- D. Hansel and D. Golomb (2000). The number of synaptic input and the synchrony of large sparse neural networks. Neural Computation, in press.

  • 21- L. Neltner, D. Hansel, G. Mato and C. Meunier (2000). Synchrony in heterogeneous neural networks Neural Computation, in press. all figures for this paper

  • 22- O. Shriki, H. Sompolinsky and D. Hansel (1999). Modeling neuronal networks in cortex by rate models using the frequency-current response properties of cortical cells submitted.

  • 23- C. van Vreeswijk and D. Hansel (2000) Patterns of synchrony in neural networks with spike adaptation. submitted. all figures for this paper


    Abstracts

  • 1- M.L. Monnet, D. Hansel, G. Mato et C. Meunier (1995) Des proprétés cellulaires aux propriétés collectives des réseaux ; 2ieme Colloque de la Société des Neurosciences, Lyon.

  • 2- J. Goldberg, D. Hansel, and C. van Vreeswijk (1996) Israel J. of Med. Sci. 32:S22. The role of adaptation in synchrony and rhythmogenesis.

  • 3-D. Hansel, {\it Etats collectifs des grands syst\`emes de neurones: synchronisme, fluctuations, correlations}; 2ieme Colloque de la Soci\'et\'e des Neurosciences, Lyon, (1995).

  • 4- L. Neltner, D. Hansel, G. Mato and C. Meunier (1997). Recurrent excitatory and inhibitory interactions in neural synchronization Neural Coding 97, (Versailles).

  • 5- D. Golomb et D. Hansel (1998). Synchrony of large sparse neuronal networks, Workshop on Computational Neuroscience, (IMA, Minneapolis, USA).

  • 6- D.Golomb and D. Hansel (1998). Synchrony in sparse inhibitory cortical neuronal networks,Soc. Neurosci.

  • 7- D. Hansel (1995). Chaos and synchrony in a model of hypercolumn in visual cortex ; Cortical dynamics in Jerusalem.

  • 8- R. Ben-Yishai, D. Hansel and H. Sompolinsky (1995). Traveling waves and coding of movement in a cortical network module . Proceedings of the annual meeting of the Israel Society for Neurosciences Israel J. 31: 772.

  • 9- H. Sompolinsky, D. Hansel and R. Ben-Yshai (1996). Recurrent networks and sensory processing in visual cortea National Institute of Health, Bethesda, USA.

  • 10- D. Hansel (1997). The role of spike adaptation in shaping spatio-temporal patterns of neural activity SIAM conference on Aplication of Dynamical Systems.

  • 11- D. Hansel (1998). Spatio-temporal patterns of neural activity in large networks of neurons with spike adaptation neuronal networks Workshop on Computational Neuroscience, Institute of Applied Mathematics, (Minneapolis, USA).

  • 12- D. Hansel (1997). The role of adaptation in shaping spatiotemporal activity patterns in cortex Neural Coding 97, (Versailles).

  • 13- O. Shriki, D. Hansel and H. Sompolinsky (1998). Modeling neuronal networks in cortex by rate models using the current-frequency response properties of cortical cells, Soc. Neurosci.

  • 14- D. Hansel, H. Sompolinsky and O. Shriki (1998). A model of the orientation tuning of synaptic conductances in primary visual cortex Soc. Neurosci.

  • 15- J. Goldberg, H. Sompolinsky, D. Hansel and H. Bergman (1999). Network model of parkinsonian neuronal oscillations in the cortico-baso-cortical loop . Frontiers in Computational Neuroscience, Eilat.

  • 16- D. Hansel, H. Sompolinsky and O. Shriki (1999). A model of the orientation tuning of synaptic conductances in primary visual cortex , Frontiers in Computational Neuroscience, Eilat.

  • 17- O. Shriki, D. Hansel and H. Sompolinsky (1999). Modeling neuronal networks in cortex by rate models using the current-frequency response properties of cortical cells Frontiers in Computational Neuroscience, Eilat.


    Other publications