Literature DB >> 23727338

Coding and decoding with dendrites.

Athanasia Papoutsi1, George Kastellakis1, Maria Psarrou2, Stelios Anastasakis2, Panayiota Poirazi3.   

Abstract

Since the discovery of complex, voltage dependent mechanisms in the dendrites of multiple neuron types, great effort has been devoted in search of a direct link between dendritic properties and specific neuronal functions. Over the last few years, new experimental techniques have allowed the visualization and probing of dendritic anatomy, plasticity and integrative schemes with unprecedented detail. This vast amount of information has caused a paradigm shift in the study of memory, one of the most important pursuits in Neuroscience, and calls for the development of novel theories and models that will unify the available data according to some basic principles. Traditional models of memory considered neural cells as the fundamental processing units in the brain. Recent studies however are proposing new theories in which memory is not only formed by modifying the synaptic connections between neurons, but also by modifications of intrinsic and anatomical dendritic properties as well as fine tuning of the wiring diagram. In this review paper we present previous studies along with recent findings from our group that support a key role of dendrites in information processing, including the encoding and decoding of new memories, both at the single cell and the network level.
Copyright © 2013 Elsevier Ltd. All rights reserved.

Keywords:  Amygdala; CREB; Dendrites; Dendritic integration; Dendritic morphology; Dendritic plasticity; Memory; Prefrontal cortex

Mesh:

Year:  2013        PMID: 23727338     DOI: 10.1016/j.jphysparis.2013.05.003

Source DB:  PubMed          Journal:  J Physiol Paris        ISSN: 0928-4257


  6 in total

1.  Quantifying the Number of Discriminable Coincident Dendritic Input Patterns through Dendritic Tree Morphology.

Authors:  Antonio G Zippo; Gabriele E M Biella
Journal:  Sci Rep       Date:  2015-06-23       Impact factor: 4.379

2.  Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.

Authors:  Saeed Afshar; Libin George; Jonathan Tapson; André van Schaik; Tara J Hamilton
Journal:  Front Neurosci       Date:  2014-11-25       Impact factor: 4.677

3.  Traumatic Stress Produces Delayed Alterations of Synaptic Plasticity in Basolateral Amygdala.

Authors:  Huan-Huan Zhang; Shi-Qiu Meng; Xin-Yi Guo; Jing-Liang Zhang; Wen Zhang; Ya-Yun Chen; Lin Lu; Jian-Li Yang; Yan-Xue Xue
Journal:  Front Psychol       Date:  2019-10-25

4.  Input rate encoding and gain control in dendrites of neocortical pyramidal neurons.

Authors:  Nikolai C Dembrow; William J Spain
Journal:  Cell Rep       Date:  2022-02-15       Impact factor: 9.423

5.  Induction and modulation of persistent activity in a layer V PFC microcircuit model.

Authors:  Athanasia Papoutsi; Kyriaki Sidiropoulou; Vassilis Cutsuridis; Panayiota Poirazi
Journal:  Front Neural Circuits       Date:  2013-10-09       Impact factor: 3.492

6.  Spike-timing control by dendritic plateau potentials in the presence of synaptic barrages.

Authors:  Adam S Shai; Christof Koch; Costas A Anastassiou
Journal:  Front Comput Neurosci       Date:  2014-08-14       Impact factor: 2.380

  6 in total

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