Synchronous excitation in the superficial and deep layers of the medial entorhinal cortex precedes early sharp waves in the neonatal rat hippocampus

Early Sharp Waves (eSPWs) are the earliest pattern of network activity in the developing hippocampus of neonatal rodents. eSPWs were originally considered to be an immature prototype of adult SPWs, which are spontaneous top-down hippocampal events that are self-generated in the hippocampal circuitry. However, recent studies have shifted this paradigm to a bottom-up model of eSPW genesis, in which eSPWs are primarily driven by the inputs from the layers 2/3 of the medial entorhinal cortex (MEC). A hallmark of the adult SPWs is the relay of information from the CA1 hippocampus to target structures, including deep layers of the EC. Whether and how deep layers of the MEC are activated during eSPWs in the neonates remains elusive. In this study, we investigated activity in layer 5 of the MEC of neonatal rat pups during eSPWs using silicone probe recordings from the MEC and CA1 hippocampus. We found that neurons in deep and superficial layers of the MEC fire synchronously during MEC sharp potentials, and that neuronal firing in both superficial and deep layers of the MEC precedes the activation of CA1 neurons during eSPWs. Thus, the sequence of activation of CA1 hippocampal neurons and deep EC neurons during sharp waves reverses during development, from a lead of deep EC neurons during eSPWs in neonates to a lead of CA1 neurons during adult SPWs. These findings suggest another important difference in the generative mechanisms and possible functional roles of eSPWs compared to adult SPWs.

Somatosensory-Evoked Early Sharp Waves in the Neonatal Rat Hippocampus

The developing entorhinal-hippocampal system is embedded within a large-scale bottom-up network, where spontaneous myoclonic movements, presumably via somatosensory feedback, trigger hippocampal early sharp waves (eSPWs). The hypothesis, that somatosensory feedback links myoclonic movements with eSPWs, implies that direct somatosensory stimulation should also be capable of evoking eSPWs. In this study, we examined hippocampal responses to electrical stimulation of the somatosensory periphery in urethane-anesthetized, immobilized neonatal rat pups using silicone probe recordings. We found that somatosensory stimulation in ~33% of the trials evoked local field potential (LFP) and multiple unit activity (MUA) responses identical to spontaneous eSPWs. The somatosensory-evoked eSPWs were delayed from the stimulus, on average, by 188 ms. Both spontaneous and somatosensory-evoked eSPWs (i) had similar amplitude of ~0.5 mV and half-duration of ~40 ms, (ii) had similar current-source density (CSD) profiles, with current sinks in CA1 strata radiatum, lacunosum-moleculare and DG molecular layer and (iii) were associated with MUA increase in CA1 and DG. Our results indicate that eSPWs can be triggered by direct somatosensory stimulations and support the hypothesis that sensory feedback from movements is involved in the association of eSPWs with myoclonic movements in neonatal rats.

Neural activity in the suprachiasmatic circadian clock of nocturnal mice anticipating a daytime meal

Circadian rhythms in mammals are regulated by a system of circadian oscillators that includes a light-entrainable pacemaker in the suprachiasmatic nucleus (SCN) and food-entrainable oscillators (FEOs) elsewhere in the brain and body. In nocturnal rodents, the SCN promotes sleep in the day and wake at night, while FEOs promote an active state in anticipation of a predictable daily meal. For nocturnal animals to anticipate a daytime meal, wake-promoting signals from FEOs must compete with sleep-promoting signals from the SCN pacemaker. One hypothesis is that FEOs impose a daily rhythm of inhibition on SCN output that is timed to permit the expression of activity prior to a daytime meal. This hypothesis predicts that SCN activity should decrease prior to the onset of anticipatory activity and remain suppressed through the scheduled mealtime. To assess the hypothesis, neural activity in the SCN of mice anticipating a 4-5-h daily meal in the light period was measured using FOS immunohistochemistry and in vivo multiple unit electrophysiology. SCN FOS, quantified by optical density, was significantly reduced at the expected mealtime in food-anticipating mice with access to a running disk, compared to ad libitum-fed and acutely fasted controls. Group differences were not significant when FOS was quantified by other methods, or in mice without running disks. SCN electrical activity was markedly decreased during locomotion in some mice but increased in others. Changes in either direction were concurrent with locomotion, were not specific to food anticipation, and were not sustained during longer pauses. Reduced FOS indicates a net suppression of SCN activity that may depend on the intensity or duration of locomotion. The timing of changes in SCN activity relative to locomotion suggests that any effect of FEOs on SCN output is mediated indirectly, by feedback from neural or systemic correlates of locomotion.

Higher-order correlations in non-stationary parallel spike trains: statistical modeling and inference

The extent to which groups of neurons exhibit higher-order correlations in their spiking activity is a controversial issue in current brain research. A major difficulty is that currently available tools for the analysis of massively parallel spike trains (N >10) for higher-order correlations typically require vast sample sizes. While multiple single-cell recordings become increasingly available, experimental approaches to investigate the role of higher-order correlations suffer from the limitations of available analysis techniques. We have recently presented a novel method for cumulant-based inference of higher-order correlations (CuBIC) that detects correlations of higher order even from relatively short data stretches of length T = 10-100 s. CuBIC employs the compound Poisson process (CPP) as a statistical model for the population spike counts, and assumes spike trains to be stationary in the analyzed data stretch. In the present study, we describe a non-stationary version of the CPP by decoupling the correlation structure from the spiking intensity of the population. This allows us to adapt CuBIC to time-varying firing rates. Numerical simulations reveal that the adaptation corrects for false positive inference of correlations in data with pure rate co-variation, while allowing for temporal variations of the firing rates has a surprisingly small effect on CuBICs sensitivity for correlations.