An emerging generation of high-density microelectrode arrays (MEAs) is now capable of recording spiking activity simultaneously from thousands of neurons with closely spaced electrodes. Reliable spike detection and analysis in such recordings is challenging due to the large amount of raw data and the dense sampling of spikes with closely spaced electrodes. Here, we present a highly efficient, online capable spike detection algorithm, and an offline method with improved detection rates, which enables estimation of spatial event locations at a resolution higher than that provided by the array by combining information from multiple electrodes. Data acquired with a 4096 channel MEA from neuronal cultures and the neonatal retina, as well as synthetic data, was used to test and validate these methods. We demonstrate that these algorithms outperform conventional methods due to a better noise estimate and an improved signal-to-noise ratio (SNR) through combining information from multiple electrodes. Finally, we present a new approach for analyzing population activity based on the characterization of the spatio-temporal event profile, which does not require the isolation of single units. Overall, we show how the improved spatial resolution provided by high density, large scale MEAs can be reliably exploited to characterize activity from large neural populations and brain circuits.

Various plasticity mechanisms, including experience-dependent, spontaneous, as well as homeostatic ones, continuously remodel neural circuits. Yet, despite fluctuations in the properties of single neurons and synapses, the behavior and function of neuronal assemblies are generally found to be very stable over time. This raises the important question of how plasticity is coordinated across the network. To address this, we investigated the stability of network activity in cultured rat hippocampal neurons recorded with high-density multielectrode arrays over several days. We used parametric models to characterize multineuron activity patterns and analyzed their sensitivity to changes. We found that the models exhibited sloppiness, a property where the model behavior is insensitive to changes in many parameter combinations, but very sensitive to a few. The activity of neurons with sloppy parameters showed faster and larger fluctuations than the activity of a small subset of neurons associated with sensitive parameters. Furthermore, parameter sensitivity was highly correlated with firing rates. Finally, we tested our observations from cell cultures on an in vivo recording from monkey visual cortex and we confirm that spontaneous cortical activity also shows hallmarks of sloppy behavior and firing rate dependence. Our findings suggest that a small subnetwork of highly active and stable neurons supports group stability, and that this endows neuronal networks with the flexibility to continuously remodel without compromising stability and function.

Memory formation can be influenced by sleep and sex hormones in both men and women, and by the menstrual cycle in women. Though many studies have shown that sleep benefits the consolidation of memories, it is not clear whether this effect differs between men and women in general or according to menstrual phase in women. The present study investigated the effect of sex and menstrual cycle on memory consolidation of face-name associations (FNA) following a daytime nap. Recognition memory was tested using a face-name paired associates task with a polysomnographic nap between morning and evening testing. Seventeen healthy women (age: 20.75 (1.98) years) were studied at two time points of their menstrual cycles, defined from self-report and separated by 2 weeks (perimenses: -5 days to + 6 days from the start of menses, and non-perimenses: outside of the perimenses phase) and compared with eighteen healthy men (age: 22.01 (2.91) years). Regardless of menstrual phase, women had better pre-nap performance than men. Further, menstrual phase affected post-nap memory consolidation, with women showing greater forgetting in their perimenses phase compared with their nonperimenses phase, and men. Interestingly, post-nap performance correlated with electrophysiological events during sleep (slow oscillations, spindles, and temporal coupling between the two), however, these correlations differed according to menstrual phase and sex.Men's performance improvement was associated with the temporal coupling of spindles and slow oscillations (i.e., spindle/SO coincidence) as well as spindles. Women, however, showed an association with slow oscillations during non-perimenses, whereas when they were in their perimenses phase of their cycle, women appeared to show an association only with sleep spindle events for consolidation. These findings add to the growing literature demonstrating sex and menstrual phase effects on memory formation during sleep. THE EFFECT OF SEX AND MENSTRUAL CYCLE ON MEMORY 3

Spindles - a hallmark of stage II sleep - are a transient oscillatory phenomenon in the EEG believed to reflect thalamocortical activity contributing to unresponsiveness during sleep. Currently spindles are often classified into two classes: fast spindles, with a frequency of around 14 Hz, occurring in the centro-parietal region; and slow spindles, with a frequency of around 12 Hz, prevalent in the frontal region. Here we aim to establish whether the spindle generation process also exhibits spatial heterogeneity. Electroencephalographic recordings from 20 subjects were automatically scanned to detect spindles and the time occurrences of spindles were used for statistical analysis. Gamma distribution parameters were fit to each inter-spindle interval distribution, and a modified Wald-Wolfowitz lag-1 correlation test was applied. Results indicate that not all spindles are generated by the same statistical process, but this dissociation is not spindle-type specific. Although this dissociation is not topographically specific, a single generator for all spindle types appears unlikely.

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