Mammalian cells repair DNA double-strand breaks (DSBs) through either homologous recombination or non-homologous end joining (NHEJ). V(D)J recombination, a cut-and-paste mechanism for generating diversity in antigen receptors, relies on NHEJ for repairing DSBs introduced by the Rag1-Rag2 protein complex. Animals lacking any of the seven known NHEJ factors are therefore immunodeficient. Nevertheless, DSB repair is not eliminated entirely in these animals: evidence of a third mechanism, 'alternative NHEJ', appears in the form of extremely rare V(D)J junctions and a higher rate of chromosomal translocations. The paucity of these V(D)J events has suggested that alternative NHEJ contributes little to a cell's overall repair capacity, being operative only (and inefficiently) when classical NHEJ fails. Here we find that removing certain portions of murine Rag proteins reveals robust alternative NHEJ activity in NHEJ-deficient cells and some alternative joining activity even in wild-type cells. We propose a two-tier model in which the Rag proteins collaborate with NHEJ factors to preserve genomic integrity during V(D)J recombination.

Learning arises through the activity of large ensembles of cells, yet most of the data neuroscientists accumulate is at the level of individual neurons; we need models that can bridge this gap. We have taken spatial learning as our starting point, computationally modeling the activity of place cells using methods derived from algebraic topology, especially persistent homology. We previously showed that ensembles of hundreds of place cells could accurately encode topological information about different environments (“learn” the space) within certain values of place cell firing rate, place field size, and cell population; we called this parameter space the learning region. Here we advance the model both technically and conceptually. To make the model more physiological, we explored the effects of theta precession on spatial learning in our virtual ensembles. Theta precession, which is believed to influence learning and memory, did in fact enhance learning in our model, increasing both speed and the size of the learning region. Interestingly, theta precession also increased the number of spurious loops during simplicial complex formation. We next explored how downstream readout neurons might define co-firing by grouping together cells within different windows of time and thereby capturing different degrees of temporal overlap between spike trains. Our model's optimum coactivity window correlates well with experimental data, ranging from ∼150–200 msec. We further studied the relationship between learning time, window width, and theta precession. Our results validate our topological model for spatial learning and open new avenues for connecting data at the level of individual neurons to behavioral outcomes at the neuronal ensemble level. Finally, we analyzed the dynamics of simplicial complex formation and loop transience to propose that the simplicial complex provides a useful working description of the spatial learning process.

The role of the hippocampus in spatial cognition is incontrovertible yet controversial. Place cells, initially thought to be location-specifiers, turn out to respond promiscuously to a wide range of stimuli. Here we test the idea, which we have recently demonstrated in a computational model, that the hippocampal place cells may ultimately be interested in a space's topological qualities (its connectivity) more than its geometry (distances and angles); such higher-order functioning would be more consistent with other known hippocampal functions. We recorded place cell activity in rats exploring morphing linear tracks that allowed us to dissociate the geometry of the track from its topology. The resulting place fields preserved the relative sequence of places visited along the track but did not vary with the metrical features of the track or the direction of the rat's movement. These results suggest a reinterpretation of previous studies and new directions for future experiments.DOI: http://dx.doi.org/10.7554/eLife.03476.001