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
Kang B, Druckmann S (2021) Is recurrent connectivity necessary for object recognition under occlusion? biorxiv
Full list of publications (chronological)
  1. Daie K, Svoboda K, Druckmann S (2021) Targeted photostimulation uncovers circuit motifs supporting short-term memory, Nature Neuroscience
  2. Wilson GH, Stavisky SD, Willett FR,, Avansino DT, Kelemen JN, Hochberg LR, Ajiboye AB, Henderson JM, Druckmann S, Shenoy KV (2020) Decoding spoken English phonemes from intracortical electrode arrays in dorsal precentral gyrus. Journal of Neural Engineering
  3. Wei Z, Chen TW, Daie K, Svoboda K, Druckmann S (2020) A comparison of neuronal population dynamics measured with calcium imaging and electrophysiology, PLoS Computational Biology
  4. Kang B, Druckmann S (2020) Approaches to inferring multi-regional interactions from simultaneous population recordings, Current Opinion in Neurobiology (Review)
  5. Stavisky SD, Willett FR, Wilson GH, Murphy BA, Rezaii P, Avansino DT, Memberg WD, Miller JP, Kirsch RF, Hochberg LR, Ajiboye AB, Druckmann S, Shenoy KV, Henderson JM (2019) Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis. eLife
  6. Wan Y, Wei Z, Looger LL, Koyama M, Druckmann S*, Keller PJ* (2019) Single-Cell Reconstruction of Emerging Population Activity in an Entire Developing Circuit. Cell.  *Co-senior authors
  7. Kazemipour A, Novak O, Flickinger D, Marvin JS, Abdelfattah AS, King J, Borden PM, Kim JJ, Al-Abdullatif SH, Deal PE, Miller EW, Schreiter ER, Druckmann S, Svoboda K, Looger LL, Podgorski K. (2019) Kilohertz frame-rate two-photon tomography. Nature Methods
  8. Wei Z, Inagaki H, Li N, Svoboda K, Druckmann S. (2019) An orderly single-trial organization of population dynamics in premotor cortex predicts behavioral variability. Nature Communications 
  9. Ranganathan GN, Apostolides PF, Harnett MT, Xu NL, Druckmann S, Magee JC. (2018) Active dendritic integration and mixed neocortical network representations during an adaptive sensing behavior. Nature Neuroscience
  10. Kwon O, Feng L, Druckmann S, Kim J. (2018) Schaffer Collateral Inputs to CA1 Excitatory and Inhibitory Neurons Follow Different Connectivity Rules. J Neuroscience
  11. Guo ZV, Inagaki HK, Daie K, Druckmann S, Gerfen CR, Svoboda K. (2017) Maintenance of persistent activity in a frontal thalamocortical loop. Nature
  12. 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
  13. Kim SS, Rouault H, Druckmann S* , Jayaraman V.* (2017) Ring attractor dynamics in the Drosophila central brain. Science; *:co-corresponding authors
  14. Li, N., Daie, K., Svoboda, K., Druckmann S. (2016) Robust neuronal dynamics in premotor cortex during motor planning. Nature
  15. 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
  16. 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
  17. Druckmann S., Feng L, Lee B, Yook C, Zhao T, Magee JC, Kim J. (2014), Structured synaptic connectivity between hippocampal regions. Neuron
  18. Yook C., Druckmann S., Kim, J. (2013) Mapping mammalian synaptic connectivity, Cellular and Molecular Life Sciences
  19. Druckmann, S. and Chklovskii, D. (2012) Neuronal circuits underlying persistent representations despite time varying activity, Current Biology
  20. Druckmann, S., Hu, T., and Chklovskii, D. (2012) A mechanistic model of early sensory processing based on subtracting sparse representations, NIPS
  21. 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
  22. Druckmann, S., Berger, T.K., Hill S., Schuermann F., Markarm H., and Segev, I. (2011) Effective Stimuli for Faithful Neuron Models, PLoS Computational Biology
  23. Druckmann, S. and Chklovskii, D. (2010) Over-complete representations on recurrent neural networks can support persistent percepts, NIPS
  24. 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
  25. 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