Infrapyramidal mossy fibers and two-way avoidance learning: developmental modification of hippocampal circuitry and adult behavior of XXXX and @entity19 .

@entity19 @entity1955 @entity1378 @entity1251 @entity35 @entity7840
The extent of the intra- and infrapyramidal mossy fiber projection (IIP-MF) in the hippocampus of @entity19 and @entity35 is strain-specific, and correlates negatively with the strain-specific capacity of avoidance learning. If variations of the IIP-MF influence the capacity for 2-way avoidance learning, then developmental modification of the IIP-MF projection in an individual member of a strain should remain correlated with its adult behavior. Pups of strains with high avoidance capacity and small IIP-MF projections (RHA @entity35 , @entity7840 /2 and BALB/c @entity19 ) were injected with varying doses of @entity1955 during the postnatal period. This transient @entity1378 resulted in a strong, yet largely unpredictable, variability of the IIP-MF projection in the adult animals. Furthermore, postnatal saline injections also increased the variability of the IIP-MF projection; however, this was to a lesser degree than when using @entity1955 . The animals were tested for 2-way avoidance learning at the age of 90-120 d. Many showed strain-atypical avoidance scores. These deviations from the inherited level of 2-way avoidance learning were strongly correlated with the magnitude of the IIP-MF projection, regardless of whether the structural changes resulted from @entity1955 or saline injections. A multivariate analysis showed that the observed correlations could neither be explained by @entity1955 -induced changes in brain weight nor by individual differences of other terminal fields in the hippocampal region @entity1251 . These results suggest that the extent of the IIP-MF projection is influenced by several genetic and epigenetic factors. Irrespective of the underlying causes, the magnitude of the IIP-MF (or of an unknown but well-correlated variable) appears to bias the adult capacity for 2-way avoidance learning predictably.
