Dr. Julijana Gjorgjieva

Post-Doctoral Researcher

Volen Center for Complex Systems
Brandeis University,
Mailstop 013
415 South Street
Waltham MA 02454
USA

gjorgjieva ‘at’ brandeis.edu

Tel: (781) 736-3134
Fax: (781) 736-3142

Education:

Harvard University, Cambridge, MA, Center for Brain Science
  • postdoc (Jun 2011 – Jun 2014)
University of Cambridge, Cambridge, UK, Department of Applied Mathematics and Theoretical Physics
  • PhD (Oct 2007 – Jun 2011)
  • Master of Advanced Studies in Mathematics (Oct 2006 – Jun 2007)
University of Washington, Seattle, WA, Department of Physiology and Biophysics
  • visiting research student (Feb 2011 – May 2011, Mar 2010 – May 2010, Mar 2009 – Apr 2009)
Harvey Mudd College, Claremont, CA
  • Bachelor of Science in Mathematics (Sep 2002 – May 2006)


J. Gjorgjieva, R. A. Mease, W. J. Moody and A. L. Fairhall (2014). Intrinsic neuronal properties govern information transmission in networks. PLoS Comp Biol 10(12): e1003962. Link

J. Gjorgjieva, H. Sompolinsky and M. Meister (2014). Benefits of Pathway Splitting in Sensory Coding. J Neurosci 34:12127-12144. Link

H. M. Barnett, J. Gjorgjieva, K. Weir, C. Comfort, A. L. Fairhall, W. J. Moody (2014). Relationship between individual neuron and network spontaneous activity in developing mouse cortex. J Neurophysiol, doi:10.1152/jn.00349.2014. Link

J. Gjorgjieva, D. Biron and G. Haspel (2014). Neurobiology of C. Elegans locomotion: Where do we stand? BioScience 64:476-486. Link

A. K. Barreiro, J. Gjorgjieva, F. Rieke and E. Shea-Brown (2014). When do microcircuits produce beyond-pairwise correlations? Frontiers Comp Neurosci 8(10). Link

R. A. Mease, M. Famulare, J. Gjorgjieva, W. J. Moody and A. L. Fairhall (2013). Emergence of adaptive computation by single neurons in the developing cortex. J Neurosci 33:12154-12170. Link

J. Gjorgjieva, J. Berni, J. F. Evers and S. J. Eglen (2013). Neural circuits for peristaltic wave propagation in crawling Drosophila larvae: analysis and modeling. Frontiers Comp Neurosci 7(24). Link

J. Gjorgjieva, C. Clopath, J. Audet and J.-P. Pfister (2011). A triplet spike-timing-dependent plasticity model generalizes the Bienenstock-Cooper-Munro rule to higher-order spatiotemporal correlations. Proc Natl Acad Sci USA 108:19383-19388. Link

J. Gjorgjieva and S. J. Eglen (2011). Modeling developmental patterns of spontaneous activity. Curr Opin Neurobiol 21:1-6. Link

J. Gjorgjieva, T. Toyoizumi and S. J. Eglen (2009). Burst-time-dependent plasticity robustly guides ON/OFF segregation in the lateral geniculate nucleus. PLoS Comp Biol 5(12): e1000618. Link

S. J. Eglen and J. Gjorgjieva (2009). Self-organisation in the Developing Nervous System: Theoretical Models. HFSP Journal 3:176-185. 

J. Gjorgjieva and J. Jacobsen (2007). Turing Patterns on Growing Spheres: The Exponential Case. Discrete Contin Dyn Syst Series A, suppl: 436-445.

J. Gjorgjieva, K. Smith, G. Chowell, F. Sanchez, J. Snyder, C. Castillo-Chavez (2005). The Role of Vaccination in the Control of SARS Math Biosci Eng 2: 753-769.

J. Gjorgjieva, J. F. Evers and S. J. Eglen (2015). Comparing two models for activity-dependent tuning of recurrent networks. Computational and Systems Neuroscience (COSYNE).

J. Gjorgjieva, M. Meister and H. Sompolinsky (2014). Optimal sensory coding by populations of ON and OFF neurons. Computational and Systems Neuroscience (COSYNE).

J. Gjorgjieva, R. Mease, W. J. Moody and A. L. Fairhall (2014). Intrinsic neuronal properties switch the mode of information transmission in networks. Computational and Systems Neuroscience (COSYNE), selected for talk.

J. Gjorgjieva, M. Meister and H. Sompolinsky (2013). Optimal sensory coding by populations of ON and OFF neurons. Society for Neuroscience 736.12.

J. Gjorgjieva, H. Sompolinsky and M. Meister (2013). Benefits for retinal ganglion cell diversification: the ON-OFF dichotomy. Symposium on the Computational Basis of Early Visionorganized by The Rank Prize Funds. 

J. Gjorgjieva, H. Sompolinsky and M. Meister (2012). Parallel channels for information transmission in the retina: ON-OFF and direction selectivity. Society for Neuroscience 880.06.

J. Gjorgjieva, H. Sompolinsky and M. Meister (2012). Parallel channels for information transmission in the retina: the ON-OFF dichotomy. Computational and Systems Neuroscience (COSYNE).

A. K. Barreiro, J. Gjorgjieva, F. Rieke and E.T. Shea-Brown (2012). Filtering and recurrent connectivity shape higher-order correlations in retinal circuits. Computational and Systems Neuroscience (COSYNE).

J. Gjorgjieva, M. Famulare, R. Mease, W. J. Moody and A. L. Fairhall (2011). Implications of single-neuron gain control for information transmission in networks. Computational and Systems Neuroscience (COSYNE). 

J. Gjorgjieva, C. Clopath, J. Audet and J.-P. Pfister (2010). When two is not enough: Functional consequences of a triplet model of spike time-dependent plasticity. Conference Abstract: Society for Neuroscience 142.1

J. Gjorgjieva, M. Famulare, R. Mease and A. L. Fairhall (2010). Implications of single-neuron gain control for information transmission in networks. Conference Abstract: Society for Neuroscience 370.16

J. Gjorgjieva, J. F. Evers, J. Berni, M. Bate and S. J. Eglen (2009). Computational modelling of crawling behaviour in Drosophila larvae: Minimal networks and developmental mechanisms. Conference Abstract: Society for Neuroscience 709.4

A. K. Barreiro, E.T. Shea-Brown, F. Rieke and J. Gjorgjieva (2009). When are microcircuits well-modeled by pairwise interactions? Conference Abstract: Society for Neuroscience 165.13

J. Gjorgjieva and S. J. Eglen (2009). Instructive cues for ON-OFF RGC segregation in the LGN of the developing mouse visual system. Front Syst Neurosci Conference Abstract: Computational and Systems Neuroscience (COSYNE).

My work is based on computational and mathematical approaches to understand how activity, generated spontaneously in the circuit, driven by external or descending inputs, guides network organization and resulting computation. In my work I aim to link descriptions at the level of single neuron and network computation to understand the specific functionality that intrinsic neuronal properties and synaptic connectivity confer on network dynamics.