Theta oscillations play a major role in temporarily defining the hippocampal rate code by translating behavioral sequences into neuronal representations. However, mechanisms constraining phase timing and cell-type-specific phase preference are unknown. Here, we employ computational models tuned with evolutionary algorithms to evaluate phase preference of individual CA1 pyramidal cells recorded in mice and rats not engaged in any particular memory task. We applied unbiased and hypothesis-free approaches to identify effects of intrinsic and synaptic factors, as well as cell morphology, in determining phase preference. We found that perisomatic inhibition delivered by complementary populations of basket cells interacts with input pathways to shape phase-locked specificity of deep and superficial pyramidal cells. Somatodendritic integration of fluctuating glutamatergic inputs defined cycle-by-cycle by unsupervised methods demonstrated that firing selection is tuneable across sublayers. Our data identify different mechanisms of phase-locking selectivity that are instrumental for flexible dynamical representations of theta sequences.

Decades of rodent research have established the role of hippocampal sharp wave ripples (SPW-Rs) in consolidating and guiding experience. More recently, intracranial recordings in humans have suggested their role in episodic and semantic memory. Yet, common standards for recording, detection, and reporting do not exist. Here, we outline the methodological challenges involved in detecting ripple events and offer practical recommendations to improve separation from other high-frequency oscillations. We argue that shared experimental, detection, and reporting standards will provide a solid foundation for future translational discovery.

Theta oscillations play a major role in temporarily defining the hippocampal rate code by translating behavioural sequences into neuronal representations. However, mechanisms constraining phase timing and cell-type specific phase preference are unknown. Here, we employ computational models tuned with evolutionary algorithms to evaluate phase preference of individual CA1 pyramidal cells recorded in mice and rats not engaged in any particular memory task. We applied unbiased and hypothesis-free approaches to identify effects of intrinsic and synaptic factors, as well as cell morphology, in determining phase preference. We found that perisomatic inhibition delivered by complementary populations of basket cells interacts with input pathways to shape phase-locked specificity of deep and superficial pyramidal cells. Somatodendritic integration of fluctuating glutamatergic inputs defined cycle-by-cycle by unsupervised methods demonstrated that firing selection is tuneable across sublayers. Our data identify different mechanisms of phase-locking selectivity that are instrumental for high-level flexible dynamical representations.

Local field potential (LFP) deflections and oscillations define hippocampal sharp-wave ripples (SWR), one of the most synchronous events of the brain. SWR reflect firing and synaptic current sequences emerging from cognitively relevant neuronal ensembles. While spectral analysis have permitted advances, the surge of ultra-dense recordings now call for new automatic detection strategies. Here, we show how one-dimensional convolutional networks operating over high-density LFP hippocampal recordings allowed for automatic identification of SWR from the rodent hippocampus. When applied without retraining to new datasets and ultra-dense hippocampus-wide recordings, we discovered physiologically relevant processes associated to the emergence of SWR, prompting for novel classification criteria. To gain interpretability, we developed a method to interrogate the operation of the artificial network. We found it relied in feature-based specialization, which permit identification of spatially segregated oscillations and deflections, as well as synchronous population firing typical of replay. Thus, using deep learning based approaches may change the current heuristic for a better mechanistic interpretation of these relevant neurophysiological events.

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Cajal-Retzius cells (CRs) are transient neurons, disappearing almost completely in the postnatal neocortex by programmed cell death (PCD), with a percentage surviving up to adulthood in the hippocampus. Here, we evaluate CR’s role in the establishment of adult neuronal and cognitive function using a mouse model preventing Bax-dependent PCD. CRs abnormal survival resulted in impairment of hippocampus-dependent memory, associated in vivo with attenuated theta oscillations and enhanced gamma activity in the dorsal CA1. At the cellular level, we observed transient changes in the number of NPY+ cells and altered CA1 pyramidal cell spine density. At the synaptic level, these changes translated into enhanced inhibitory currents in hippocampal pyramidal cells. Finally, adult mutants displayed an increased susceptibility to lethal tonic-clonic seizures in a kainate model of epilepsy. Our data reveal that aberrant survival of a small proportion of postnatal hippocampal CRs results in cognitive deficits and epilepsy-prone phenotypes in adulthood.

Review of Donoso et al. Hippocampal sharp waves are transient deflections of local field potentials (LFPs) recorded in the stratum radiatum of CA1 during immobility and slow-wave sleep. Ripple oscillations are superimposed on sharp waves, but they emerge from the stratum pyramidale. These sharp-wave ripple complexes are considered a biomarker for episodic memory (Buzsáki, 2015). Data from normal (Sullivan et al., 2011) and epileptic hippocampus (Valero et al., 2017) suggest that the complex spectral structure of ripples cannot be explained by simple mechanisms. Nonetheless, a recent computational study published in The Journal of Neuroscience shed light on a microcircuit mechanism that can explain part of the spectral phenomenology of ripples (Donoso et al., 2018). Ripples associated with sharp waves were initially identified at the high-frequency band (150-200 Hz), but subsequent data revealed a contribution at the fast gamma band (90-150 Hz) (Sullivan et al., 2011; Buzsaki, 2015). High-fre

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