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The effect of synaptic noise on dendritic morphology

Authors
Journal
BMC Neuroscience
1471-2202
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Volume
12
Identifiers
DOI: 10.1186/1471-2202-12-s1-p317
Keywords
  • Poster Presentation
Disciplines
  • Computer Science

Abstract

The effect of synaptic noise on dendritic morphology POSTER PRESENTATION Open Access The effect of synaptic noise on dendritic morphology Vandana Reddy Padala1*, Benjamin Torben-Nielsen2,3, Klaus M Stiefel1 From Twentieth Annual Computational Neuroscience Meeting: CNS*2011 Stockholm, Sweden. 23-28 July 2011 Neurons exhibit enormous diversity in their dendritic mor- phology and the highly structured dendrites help neurons to perform various computations. The complex relation- ship between the dendrite morphology and their emergent computational functionality is not well understood and is an active area of research. To study this relationship we previously developed an inverse approach [1] in which model neurons, including dendritic morphology, are opti- mized to perform a certain computational function. The synaptic inputs in the earlier study were deterministic. However, synaptic noise is known to have profound effects on computations performed by neurons [2]. In this work we investigate the influence of synaptic noise on the optimal morphology to perform a computa- tional task. We consider two types of synaptic noise, namely spatial and temporal noise. The former relates to failure in synaptic transmission while the latter relates to temporal jitter of events. We studied the effect of both types of noise in the input-order detection task. In this task, synaptic inputs are inserted into a model neuron when the dendrites of the model “grow” into two spatially separate regions; the number of synapses depends on the dendritic length in that region. Synapses in both groups are activated sequentially with some interval (Δt) between them. The model neuron is then optimized to respond strongly to a particular order of synaptic activation (for instance, region 1 before region 2) and not in the reverse order. We implement spatial synaptic noise as a probabil- ity of synaptic transmission failure, i.e., not all synapses will actually transmit a signal. Temporal synaptic noise is implemen

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