Prolonged Synaptic Integration in Perirhinal Cortical Neurons

  1 Department of Psychology,   2 Interdepartmental Neuroscience Program, and   3 Department of Cellular and Molecular Physiology, Yale University, New Haven, Connecticut 06520 Beggs, John M., James R. Moyer Jr., John P. McGann, and Thomas H. Brown. Prolonged Synaptic Integration in Perirhinal Corti...

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Published inJournal of neurophysiology Vol. 83; no. 6; pp. 3294 - 3298
Main Authors Beggs, John M, Moyer, James R., Jr, McGann, John P, Brown, Thomas H
Format Journal Article
LanguageEnglish
Published United States Am Phys Soc 01.06.2000
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Summary:  1 Department of Psychology,   2 Interdepartmental Neuroscience Program, and   3 Department of Cellular and Molecular Physiology, Yale University, New Haven, Connecticut 06520 Beggs, John M., James R. Moyer Jr., John P. McGann, and Thomas H. Brown. Prolonged Synaptic Integration in Perirhinal Cortical Neurons. J. Neurophysiol. 83: 3294-3298, 2000. Layer II/III of rat perirhinal cortex (PR) contains numerous late-spiking (LS) pyramidal neurons. When injected with a depolarizing current step, these LS cells typically delay spiking for one or more seconds from the onset of the current step and then sustain firing for the duration of the step. This pattern of delayed and sustained firing suggested a specific computational role for LS cells in temporal learning. This hypothesis predicts and requires that some layer II/III neurons should also exhibit delayed and sustained spiking in response to a train of excitatory synaptic inputs. Here we tested this prediction using visually guided, whole cell recordings from rat PR brain slices. Most LS cells (19 of 26) exhibited delayed spiking to synaptic stimulation (>1 s latency from the train onset), and the majority of these cells (13 of 19) also showed sustained firing that persisted for the duration of the synaptic train (5-10 s duration). Delayed and sustained firing in response to long synaptic trains has not been previously reported in vertebrate neurons. The data are consistent with our model that a circuit containing late spiking neurons can be used for encoding long time intervals during associative learning.
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ISSN:0022-3077
1522-1598
DOI:10.1152/jn.2000.83.6.3294