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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Abstract
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].
Literature
- 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.
- Poirazi, P. Brannon, T. & Mel, B.W. “Pyramidal Neuron as 2-Layer Neural Network.” Neuron, vol 37, pg. 989-999, March 2003.
- 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.
- 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.
- Sidiropoulou, K. and Poirazi, P. “Predictive features of persistent activity emergence in regular spiking and intrinsic bursting model neurons” (submitted)
- Papoutsi, A., Sidiropoulou, K., and Poirazi, P. “Temporal Dynamics Predict State Transitions in a Prefrontal Cortex Microcircuit Model.” (submitted)
- Krioneriti, D, Papoutsi, A, Poirazi, P “Mechanisms underlying the emergence of Up and Down states in a model PFC microcircuit” (CNS 2011).