How Does a Neuron Perform Subtraction? – Arithmetic Rules of Synaptic Integration of Excitation and Inhibition

  • Xu-Dong Wang
  • Jiang Hao
  • Mu-Ming Poo
  • Xiao-Hui Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


Numerous learning rules have been devised to carry out computational tasks in various neural network models. However, the rules for determining how a neuron integrates thousands of synaptic inputs on the dendritic arbors of a realistic neuronal model are still largely unknown. In this study, we investigated the properties of integration of excitatory and inhibitory postsynaptic potentials in a reconstructed pyramidal neuron in the CA1 region of the hippocampus. We found that the integration followed a nonlinear subtraction rule (the Cross-Shunting Rule, or CS rule). Furthermore, the shunting effect is dependent on the spatial location of inhibitory synapses, but not that of excitatory synapses. The shunting effect of inhibitory inputs was also found to promote the synchronization of neuronal firing when the CS rule was applied to a small scale neural network.


Pyramidal Neuron Synaptic Input Excitatory Input Inhibitory Input Excitatory Synapse 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xu-Dong Wang
    • 1
    • 2
  • Jiang Hao
    • 1
    • 2
  • Mu-Ming Poo
    • 1
    • 3
  • Xiao-Hui Zhang
    • 1
  1. 1.Institute of NeuroscienceChinese Academy of SciencesShanghaiChina
  2. 2.Graduate School of Chinese Academy of Sciences, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina
  3. 3.Division of Neurobiology, Department of Molecular and Cell BiologyUniversity of CaliforniaBerkeleyUSA

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