Cognitive & computational neuroscience in insects
I study the neuronal basis of associative learning in the insect brain by combining behavioural analysis at the level of individual animals with computational models. Recent analysis showed (Pamir et al. 2011) that individual honeybees can rapidly acquire a stable conditioned response during classical conditioning of the proboscis extension response. In collaboration with Paul Szyszka from the University of Konstanz we further investigate the dynamics of learning and memory retention in individual honeybees. In collaboration with Joachim Hanicke we are simulating associative learning in the insect brain in order to explain a set of individual learning features by functional network components (Haenicke et al. 2012. BCCN conference abstract). In a side-project I was working on a model of innate and learned behaviour in drosophila larvae (Pamir et al. 2011. BCCN conference abstract).
I studied physics with a mayor in biophysics at the Ludwigs-Maximillians-Universität in Munich. I applied and further developed Atomic-force-Microscopy to study antibody-antigen bindings and mechano-electrical properties of single cells (Neuert et al. 2006, Pamir et al. 2008).
My PhD studies are funded by the DFG within the research training group Sensory Computation in Neural Systems (GRK 1589).
Pamir E, Chakroborty NK, Stollhoff N, Gehring KB, Antemann V, Morgenstern L, Felsenberg J, Eisenhardt D, Menzel R, Nawrot MP (2011) Average group behavior does not represent individual behavior in classical conditioning of the honeybee. Learning and Memory 18: 733-741 [PDF]
Pamir E, George M, Fertig N, Benoit M (2008) Planar patch-clamp force microscopy on living cells. Ultramicroscopy, 108(6), 552-557
Neuert G, Albrecht C, Pamir E, Gaub HE (2006) Dynamic force spectroscopy of the digoxigenin - antibody complex. FEBS Letters, 580(2), 505-509