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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Statistical independence and neural computation in the leech ganglion
Organization Unit
Authors
  • Giulietta Pinato
  • Stefano Battiston
  • Vincent Torre
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Journal Title Biological Cybernetics
Publisher Springer
Geographical Reach international
ISSN 0340-1200
Volume 83
Number 2
Page Range 119 - 130
Date 2000
Abstract Text In this report, the input/output relations in an isolated ganglion of the leech Hirudo medicinalis were studied by simultaneously using six or eight suction pipettes and two intracellular electrodes. Sensory input was mimicked by eliciting action potentials in mechanosensory neurons with intracellular electrodes. The integrated neural output was measured by recording extracellular voltage signals with pipettes sucking the roots and the connectives. A single evoked action potential activated electrical activity in at least a dozen different neurons, some of which were identified. This electrical activity was characterized by a high degree of temporal and spatial variability. The action potentials of coactivated neurons, i.e. activated by the same mechanosensory neuron, did not show any significant pairwise correlation. Indeed, the analysis of evoked action potentials indicates clear statistical independence among coactivated neurons, presumably originating from the independence of synaptic transmission at distinct synapses. This statistical independence may be used to increase reliability when neuronal activity is averaged or pooled. It is suggested that statistical independence among coactivated neurons may be a usual property of distributed processing of neuronal networks and a basic feature of neural computation.
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Digital Object Identifier 10.1007/s004220000152
Other Identification Number merlin-id:10160
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Keywords Spatial variability, electrical activity, synaptic transmission, sensory input, neuronal network