Statistical computer model analysis of the reciprocal and recurrent inhibitions of the Ia-EPSP in α-Motoneurons

G. Gradwohl, Y. Grossman

Research output: Contribution to journalArticlepeer-review

Abstract

We simulate the inhibition of Ia-glutamatergic excitatory postsynaptic potential (EPSP) by preceding itwith glycinergic recurrent (REN) and reciprocal (REC) inhibitory postsynaptic potentials (IPSPs). The inhibition is evaluated in the presence of voltage-dependent conductances of sodium, delayed rectifier potassium, and slow potassium in five α-motoneurons (MNs).We distribute the channels along the neuronal dendrites using, alternatively, a density function of exponential rise (ER), exponential decay (ED), or a step function (ST).We examine the change in EPSP amplitude, the rate of rise (RR), and the time integral (TI) due to inhibition. The results yield six major conclusions. First, the EPSP peak and the kinetics depending on the time interval are either amplified or depressed by the REC and REN shunting inhibitions. Second, the mean EPSP peak, its TI, and RR inhibition of ST, ER, and ED distributions turn out to be similar for analogous ranges of G. Third, for identical G, the large variations in the parameters' values can be attributed to the sodium conductance step (gNa-step) and the active dendritic area. We find that small gNa-step on a few dendrites maintains the EPSP peak, its TI, and RR inhibition similar to the passive state, but high gNa-step on many dendrites decrease the inhibition and sometimes generates even an excitatory effect. Fourth, the MN's input resistance does not alter the efficacy of EPSP inhibition. Fifth, the REC and REN inhibitions slightly change the EPSP peak and its RR. However, EPSP TI is depressed by the REN inhibition more than the REC inhibition. Finally, only an inhibitory effect shows up during the EPSP TI inhibition, while there are both inhibitory and excitatory impacts on the EPSP peak and its RR.

Original languageEnglish
Pages (from-to)75-100
Number of pages26
JournalNeural Computation
Volume25
Issue number1
DOIs
StatePublished - 1 Dec 2012

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