Self-influencing synaptic plasticity: recurrent changes of synaptic weights can lead to specific functional properties

Tamosiunaite, M., Porr, B. and Worgotter, F. (2007) Self-influencing synaptic plasticity: recurrent changes of synaptic weights can lead to specific functional properties. Journal of Computational Neuroscience, 23(1), pp. 113-127. (doi:10.1007/s10827-007-0021-2)

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Abstract

Recent experimental results suggest that dendritic and back-propagating spikes can influence synaptic plasticity in different ways (Holthoff, 2004; Holthoff et al., 2005). In this study we investigate how these signals could interact at dendrites in space and time leading to changing plasticity properties at local synapse clusters. Similar to a previous study (Saudargiene et al., 2004) we employ a differential Hebbian learning rule to emulate spike-timing dependent plasticity and investigate how the interaction of dendritic and back-propagating spikes, as the post-synaptic signals, could influence plasticity. Specifically, we will show that local synaptic plasticity driven by spatially confined dendritic spikes can lead to the emergence of synaptic clusters with different properties. If one of these clusters can drive the neuron into spiking, plasticity may change and the now arising global influence of a back-propagating spike can lead to a further segregation of the clusters and possibly the dying-off of some of them leading to more functional specificity. These results suggest that through plasticity being a spatial and temporal local process, the computational properties of dendrites or complete neurons can be substantially augmented.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Porr, Dr Bernd
Authors: Tamosiunaite, M., Porr, B., and Worgotter, F.
College/School:College of Science and Engineering > School of Engineering > Biomedical Engineering
Journal Name:Journal of Computational Neuroscience
ISSN:0929-5313
ISSN (Online):1573-6873
Published Online:30 January 2007

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