Julijana Gjorgjieva

Julijana Gjorgjieva

gjorgjieva "at" fas.harvard.edu
gjorgjieva "at" brandeis.edu

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

In April 2016 I will be starting my research group at the Max Planck Institute for Brain Research in Frankfurt, Germany. Now recruiting highly motivated students and postdocs. Please apply here.

Publications          Conferences          CV


Efficient and stable transmission of information across neural circuits in the brain requires its components, the individual neurons and the connections between them, to be correctly tuned and maintained at stable levels. My work is based on computational and mathematical approaches to understand how activity, generated spontaneously in the circuit, or driven by external or descending inputs, guides network organization and resulting computation. 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. In my previous research I have addressed these two aspects separately, and the goal is now to study their interaction specifically to understand neural circuit development and coding.

There are many distinct types of neurons in the brain, which are distributed in a highly organized fashion and interconnected with remarkable specificity. Diversification of cell types is seen in different sensory modalities and species, suggesting an evolutionary fitness benefit of a very general nature. What is the computational role of cell type diversity in a neuronal population? My previous research has aimed at explaining the existence of multiple cell types in one of the best-characterized neural circuits, the retina. Using the principle of efficient coding, we have explored the benefits of ganglion cell diversification into ON and OFF types. My work proposes that ON and OFF pathways have emerged to maximize information transmission at lower metabolic costs. To achieve this, we derived predictions for the optimal response properties of a population of sensory neurons to natural visual stimuli (Gjorgjieva et al. J Neurosci, 2014). Revealing principles for cell type diversification in the retina will aid in understanding the benefits of pathway splitting in subsequent stages of the visual system and in other sensory systems.

Diverse response properties may also emerge due to different computational requirements during circuit development. An example of a single neuron property that changes over development is gain scaling: neurons adapt to the size of input fluctuations by scaling the gain of their responses (Mease et al. J Neurosci, 2014). We have recently shown that experimentally observed single neuron gain scaling profoundly influences the transmission of activity through a network (Gjorgjieva et al. PLoS Comp Biol, 2014). While young neurons generate spontaneous activity to guide network wiring and tuning, mature neurons stop transmitting waves, preventing epileptic activity and enabling the efficient processing of sensory information. This is a rare demonstration that experimentally observed single neuron properties influence network behavior, as previous work ascribes different network activity predominantly to changes in network connectivity.

During neural development, the complex activity generated spontaneously by the network contains cues that tune network connectivity. My PhD research focused on the role of spontaneous activity for network refinements in the mammalian visual (Gjorgjieva et al. PLoS Comp Biol, 2009; Gjorgjieva et al. PNAS, 2011) and invertebrate motor systems (Gjorgjieva et al. Front Comp Neurosci, 2013).

I am supported by a Burroughs-Wellcome Career Award at the Scientific Interface.