Dr. Andreea Lazar

Singer Lab · Post-Doc
  • andreea.lazar (at) esi-frankfurt.de
  • Deutschordenstr. 46 · 60528 Frankfurt am Main · Germany
  • +49 69 96769

Research Statement

The response of cortical networks to stimulation is a complicated interaction between external drive and the ongoing pattern of internally generated activity. I study how experience changes the spatio-temporal properties of cortical activity in simulated recurrent networks and data from in vivo electrophysiology. I am particularly interested in the role of spontaneous activity in neural circuits, its relevance to prediction and memory and its connection to statistical learning.


Shapcott KA, Schmiedt JT, Kouroupaki K, Kienitz R, Lazar A, Singer W, Schmid MC (2020). Reward-related suppression of neural activity in macaque visual area V4. Cereb Cortex, bhaa079. https://doi.org/10.1093/cercor/bhaa079

Bányai M, Lazar A, Klein L, Klon-Lipok J, Stippinger M, Singer W, Orbán G (2019). Stimulus complexity shapes response correlations in primary visual cortex. Proc Natl Acad Sci USA 116(7), 2723-2732. https://doi.org/10.1073/pnas.1816766116

Singer W, Lazar A (2016). Does the cerebral cortex exploit high-dimensional, non-linear dynamics for information processing? Front Comput Neurosci 10, 99. https://doi.org/10.3389/fncom.2016.00099

Hartmann C, Lazar A, Nessler B, Triesch J (2015). Where’s the noise? Key features of spontaneous activity and neural variability arise through learning in a deterministic network. PLoS Comput Biol 11(12), e1004640. https://doi.org/10.1371/journal.pcbi.1004640

Lewis CM, Lazar A (2013). Orienting towards ensembles: from single cells to neural populations. J Neurosci 33(1), 2–3. https://doi.org/10.1523/jneurosci.4658-12.2013

Schwiedrzik CM, Ruff CC, Lazar A, Leitner FC, Singer W, Melloni L (2012). Untangling perceptual memory: hysteresis and adaptation map into separate cortical networks. Cereb Cortex 24(5), 1152-1164. https://doi.org/10.1093/cercor/bhs396

Lazar A, Pipa G, Triesch J (2011). Emerging bayesian priors in a self-organizing recurrent network. Artificial neural networks and machine learning, ICANN 2011, Part II, Lecture Notes in Computer Science, Springer, 6792: 127-134.

Lazar A, Pipa G, Triesch J (2009). SORN: a self-organizing recurrent neural network. Front Comput Neurosci 3(23). https://doi.org/10.3389/neuro.10.023.2009

Lazar A, Pipa G, Triesch J (2008). Predictive coding in cortical microcircuits. Artificial neural networks and machine learning, ICANN 2008, Part II, Lecture Notes in Computer Science, Springer, 5164: 386-395.

Lazar A, Pipa G, Triesch J (2007). Fading memory and time series prediction in recurrent networks with different forms of plasticity. Neural Networks 20(3), 312-322.