Selected publications:​​
  • Li N., Daie K., Svoboda K., Druckmann S. (2016) Robust neuronal dynamics in premotor cortex during motor planning. Nature
                Link to journal site ; News and views by Byron Yu
  • Druckmann, S. and Chklovskii, D. (2012) Neuronal circuits underlying persistent representations despite time varying activity, Current Biology
  • Druckmann S. and Chklovskii, D. (2010) Over-complete representations on recurrent neural networks can support persistent percepts, NIPS
Preprints
Wei Z., Inagaki H., Li N., Svoboda K., Druckmann S. (2018) An orderly single-trial organization of population dynamics in premotor cortex predicts behavioral variability, BioRxiv (link to pdf)
Full list of publications (chronological)
  1. Guo ZV, Inagaki HK, Daie K, Druckmann S, Gerfen CR, Svoboda K. (2017) Maintenance of persistent activity in a frontal thalamocortical loop. Nature
  2. Turner-Evans D, Wegener S, Rouault H, Franconville R, Wolff T, Seelig JD, Druckmann S, Jayaraman V. (2017) Angular velocity integration in a fly heading circuit. eLife
  3. Kim SS, Rouault H, Druckmann S* , Jayaraman V.* (2017) Ring attractor dynamics in the Drosophila central brain. Science; *:co-corresponding authors
  4. Li, N., Daie, K., Svoboda, K., Druckmann S. (2016) Robust neuronal dynamics in premotor cortex during motor planning. Nature
  5. Schulze A, Gomez-Marin A, Rajendran VG, Lott G, Musy M, Ahammad P, Deogade A, Sharpe J, Riedl J, Jarriault D, Trautman ET, Werner C, Venkadesan M, Druckmann S., Jayaraman V, Louis M (2015) Dynamical feature extraction at the sensory periphery guides chemotaxis, eLife
  6. Rah, J. C., L. Feng, Druckmann S., H. Lee and J. Kim (2015). From a meso- to micro-scale connectome: array tomography and mGRASP. Frontiers in Neuroanatomy
  7. Druckmann S., Feng L, Lee B, Yook C, Zhao T, Magee JC, Kim J. (2014), Structured synaptic connectivity between hippocampal regions. Neuron
  8. Yook C., Druckmann S., Kim, J. (2013) Mapping mammalian synaptic connectivity, Cellular and Molecular Life Sciences
  9. Druckmann, S. and Chklovskii, D. (2012) Neuronal circuits underlying persistent representations despite time varying activity, Current Biology
  10. Druckmann, S., Hu, T., and Chklovskii, D. (2012) A mechanistic model of early sensory processing based on subtracting sparse representations, NIPS
  11. Druckmann, S., Hill S., Schuermann F., Markarm H., and Segev, I. (2012) Heirarchical structure of cortical interneuron electrical diversity revealed by automated statistical analysis, Cerebral Cortex
  12. Druckmann, S., Berger, T.K., Hill S., Schuermann F., Markarm H., and Segev, I. (2011) Effective Stimuli for Faithful Neuron Models, PLoS Computational Biology
  13. Druckmann, S. and Chklovskii, D. (2010) Over-complete representations on recurrent neural networks can support persistent percepts, NIPS
  14. Druckmann, S., Berger, T.K., Hill S., Schuermann F., Markarm H., and Segev, I. (2008) Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data, Biological Cybernetics
  15. Druckmann, S., Banitt, Y., Gidon, A., Schuermann, F., Markram, H., and Segev, I. (2007) A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data. Frontiers in Neuroscience

Book chapters
1. Druckmann, S., (2014) Automated Parameter Constraining of Single-Neuron Models, in H. Cuntz, M. Remme, B. Torben-Nielsen (eds.), The Computing Dendrite: From Structure to Function. Springer New York
2. Druckmann S., (2014) Evolutionary Algorithms, in D. Jaeger, R. Jung (eds.) Encyclopedia of Computational Neuroscience. Springer New York
3. Druckmann S., Gidon, A., Segev, I. (2013) Computational Neuroscience: Capturing the Essence, in G. Galizia, Lledo, P. (eds.) Neurosciences-From Molecule to Behavior: a university textbook, Springer Berlin, 671-694