Grafted neuronal precursor cells differentiate and integrate in injured hippocampus in experimental pneumococcal meningitis

Bacterial meningitis (BM) frequently causes persisting neurofunctional sequelae. Autopsy studies in patients dying from BM show characteristic apoptotic brain injury to the stem cell niche in the subgranular zone of the hippocampal dentate gyrus (DG), and this form of brain damage is associated with learning and memory deficits in experimental BM. With an eye to potential regenerative therapies, the survival, migration, and differentiation of neuronal precursor cells (NPCs) were evaluated after engraftment into the injured hippocampus in vitro and in vivo in an infant rat model of pneumococcal meningitis. Green fluorescent protein (GFP)-expressing NPCs were grafted into the DG of organotypic hippocampal slice cultures injured by challenge with live Streptococcus pneumoniae. Seven days after engraftment, NPCs had migrated from the site of injection into the injured granular layer of the DG and electro-functionally integrated into the hippocampal network. In vivo, GFP-expressing NPCs migrated within 1 week from the injection site in the hilus region to the injured granular layer of the hippocampal DG and showed neuronal differentiation at 2 and 4 weeks after transplantation. Hippocampal injury induced by BM guides grafted NPCs to the area of brain damage and provides a microenvironment for neuronal differentiation and functional integration.

Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States

Oscillations between high and low values of the membrane potential (UP and DOWN states respectively) are an ubiquitous feature of cortical neurons during slow wave sleep and anesthesia. Nevertheless, a surprisingly small number of quantitative studies have been conducted only that deal with this phenomenon’s implications for computation. Here we present a novel theory that explains on a detailed mathematical level the computational benefits of UP states. The theory is based on random sampling by means of interspike intervals (ISIs) of the exponential integrate and fire (EIF) model neuron, such that each spike is considered a sample, whose analog value corresponds to the spike’s preceding ISI. As we show, the EIF’s exponential sodium current, that kicks in when balancing a noisy membrane potential around values close to the firing threshold, leads to a particularly simple, approximative relationship between the neuron’s ISI distribution and input current. Approximation quality depends on the frequency spectrum of the current and is improved upon increasing the voltage baseline towards threshold. Thus, the conceptually simpler leaky integrate and fire neuron that is missing such an additional current boost performs consistently worse than the EIF and does not improve when voltage baseline is increased. For the EIF in contrast, the presented mechanism is particularly effective in the high-conductance regime, which is a hallmark feature of UP-states. Our theoretical results are confirmed by accompanying simulations, which were conducted for input currents of varying spectral composition. Moreover, we provide analytical estimations of the range of ISI distributions the EIF neuron can sample from at a given approximation level. Such samples may be considered by any algorithmic procedure that is based on random sampling, such as Markov Chain Monte Carlo or message-passing methods. Finally, we explain how spike-based random sampling relates to existing computational theories about UP states during slow wave sleep and present possible extensions of the model in the context of spike-frequency adaptation.