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Panayiota Poirazi (Computational Biology Lab, IMBB, Foundation of Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece) | Dendrites and information coding: insights from biophysical model cells and circuits

When Mar 27, 2012
from 05:15 PM to 06:45 PM
Where Lecture Hall, Hansastr. 9a
Contact Name Ulrich Egert
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The goal of this presentation is to provide a set of predictions generated by biophysical models regarding the ways in which information may be encoded by single cells and/or neural assemblies and the role of dendrites in this process. Towards this goal, I will present modelling studies from our lab –along with supporting experimental evidence- that investigate how single pyramidal neurons and small neural networks in different brain regions process incoming signals that are associated with learning and memory. I will first briefly discuss the computational capabilities of individual pyramidal neurons in the hippocampus [1-3] and how these properties may allow a single cell to discriminate between familiar versus novel memories [4]. I will then present biophysical models of prefrontal layer V neurons and small networks that exhibit Up and Down states or sustained activity under realistic synaptic stimulation and discuss their potential role in working memory [5-7].


  1. Poirazi, P. Brannon, T. & Mel, B.W. “Arithmetic of Subthreshold Synaptic Summation in a Model CA1 Pyramidal Cell.” Neuron, vol 37, pg. 977- 987, March 2003.
  2. Poirazi, P. Brannon, T. & Mel, B.W. “Pyramidal Neuron as 2-Layer Neural Network.” Neuron, vol 37, pg. 989-999, March 2003.
  3. Poirazi, P. and Mel, B.W. “Impact of Active Dendritic Processing and Structural Plasticity on Learning and Memory.” Neuron, vol 29, pg. 779 -796, March 2001.
  4. 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.
  5. Sidiropoulou, K. and Poirazi, P. “Predictive features of persistent activity emergence in regular spiking and intrinsic bursting model neurons” (submitted)
  6. Papoutsi, A., Sidiropoulou, K., and Poirazi, P. “Temporal Dynamics Predict State Transitions in a Prefrontal Cortex Microcircuit Model.” (submitted)
  7. Krioneriti, D, Papoutsi, A, Poirazi, P “Mechanisms underlying the emergence of Up and Down states in a model PFC microcircuit” (CNS 2011).


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