Social isolation impairs active avoidance performance and decreases neurogenesis in the dorsomedial telencephalon of rainbow trout.

Alterations in the social environment, such as isolating an individual that would normally live in a social group, can generate physiological responses that compromise an individual's capacity to learn. To investigate this, we tested whether social isolation impairs learning skills in the rainbow trout. We show that rainbow trout can achieve an active avoidance (AA) learning program with inter-individual variability. Moreover, c-Fos expression in dorsomedial telencephalon (Dm) correlates with the AA performance, indicating that this structure is involved in this cognitive task. Given that Dm participates in AA learning and this region is under plastic remodelling by addition of new-born neurons, we tested whether social isolation impinges on adult neurogenesis and, consequently, on the Dm cognitive outcome. Trout were reared for four weeks in control or isolated conditions. We found that social isolation diminished the percentage of adult-born neurons that are being incorporated into Dm network. Interestingly, the same isolation treatment also induced a severe deficit in the AA performance. Our results demonstrate a structure-to-function relationship between the Dm and the learning ability in an AA task, indicate that social isolation reduces the incorporation of adult-born neurons into Dm, and show that social isolation impairs the Dm-related cognitive function.

T2N as a new tool for robust electrophysiological modeling demonstrated for mature and adult-born dentate granule cells.

Compartmental models are the theoretical tool of choice for understanding single neuron computations. However, many models are incomplete, built ad hoc and require tuning for each novel condition rendering them of limited usability. Here, we present T2N, a powerful interface to control NEURON with Matlab and TREES toolbox, which supports generating models stable over a broad range of reconstructed and synthetic morphologies. We illustrate this for a novel, highly detailed active model of dentate granule cells (GCs) replicating a wide palette of experiments from various labs. By implementing known differences in ion channel composition and morphology, our model reproduces data from mouse or rat, mature or adult-born GCs as well as pharmacological interventions and epileptic conditions. This work sets a new benchmark for detailed compartmental modeling. T2N is suitable for creating robust models useful for large-scale networks that could lead to novel predictions. We discuss possible T2N application in degeneracy studies.