Max Planck Institute for Dynamics and Self-Organization -- Department for Nonlinear Dynamics and Network Dynamics Group
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Tuesday, 22.10.2013 17 c.t.

Spatio-temporal encoding in pyramidal neuron models: the role of dendrites

by Prof. Dr. Panayiota Poirazi
from Institute of Molecular Biology and Biotechnology (IMBB) Foundation of Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece

Contact person: Detlev Schild


Ludwig Prandtl lecture hall


The goal of this presentation is to provide a set of predictions generated by biophysical models recently developed in our lab regarding the role of dendrites in information coding. Towards this goal, I will present modelling studies –along with supporting experimental evidence- that investigate how dendrites may be used to facilitate the learning and coding of both spatial and temporal information at the single cell and the microcircuit level. I will first discuss how the dendrites of individual CA1 pyramidal neurons may allow a single cell to discriminate between signals with varying spatio-temporal characteristics and propagate this information to down stream cells [1]. I will then discuss how these dendritic nonlinearities may enable stimulus specificity in individual PFC pyramidal neurons during working memory [2] and underlie the emergence of sustained activity at the single cell and the microcircuit level [2,3,4] 1. Pissadaki, E.K., Sidiropoulou K., Reczko M., and Poirazi, P. Encoding of spatio-temporal input characteristics by a single CA1 pyramidal neuron model PLoS Comp. Biology, 2010 Dec;6(12): e1001038. 2. Sidiropoulou, K. and Poirazi, P. Predictive features of persistent activity emergence in regular spiking and intrinsic bursting model neurons PLoS Comp. Biology, 2012 April; 8(4): e1002489 3. Papoutsi, A., Sidiropoulou, K., Cutsuridis, V., and Poirazi, P. Induction and modulation of persistent activity in a layer V PFC microcircuit model (accepted, Front. In Neural Circuits) 4. Papoutsi, A., Cutsuridis, V., Sidiropoulou, K., and Poirazi, P. Temporal Dynamics Predict State Transitions in a Prefrontal Cortex Microcircuit Model. (submitted)

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