Synchrony is stubborn in feedforward cortical networks
Idan Segev
The author is in the Department of Neurobiology and Center for Neural Computation, The Hebrew University, Jerusalem 91904, Israel.
idan@lobster.ls.huji.ac.il
Action potential propagation has been studied extensively in model networks. Now a new paper describes an innovative method of combining neuronal recordings with real-time neuronal modeling to create multi-layer feedforward networks. Neurons in deep layers tend to fire in synchrony, suggesting such networks may code sensory information by groups of neurons that fire together.
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