Influence of slow oscillation on hippocampal activity and ripples through cortico-hippocampal synaptic interactions, analyzed by a cortical-CA3-CA1 network model

Hippocampal sharp wave-ripple complexes (SWRs) involve the synchronous discharge of thousands of cells throughout the CA3-CA1-subiculum-entorhinal cortex axis. Their strong transient output affects cortical targets, rendering SWRs a possible means for memory transfer from the hippocampus to the neocortex for long-term storage. Neurophysiological observations of hippocampal activity modulation by the cortical slow oscillation (SO) during deep sleep and anesthesia, and correlations between ripples and UP states, support the role of SWRs in memory consolidation through a cortico-hippocampal feedback loop. We couple a cortical network exhibiting SO with a hippocampal CA3-CA1 computational network model exhibiting SWRs, in order to model such cortico-hippocampal correlations and uncover important parameters and coupling mechanisms controlling them. The cortical oscillatory output entrains the CA3 network via connections representing the mossy fiber input, and the CA1 network via the temporoammonic pathway (TA). The spiking activity in CA3 and CA1 is shown to depend on the excitation-to-inhibition ratio, induced by combining the two hippocampal inputs, with mossy fiber input controlling the UP-state correlation of CA3 population bursts and corresponding SWRs, whereas the temporoammonic input affects the overall CA1 spiking activity. Ripple characteristics and pyramidal spiking participation to SWRs are shaped by the strength of the Schaffer collateral drive. A set of in vivo recordings from the rat hippocampus confirms a model-predicted segregation of pyramidal cells into subgroups according to the SO state where they preferentially fire and their response to SWRs. These groups can potentially play distinct functional roles in the replay of spike sequences

Cross-frequency phase-phase coupling between theta and gamma oscillations in the hippocampus

Neuronal oscillations allow for temporal segmentation of neuronal spikes. Interdependent oscillators can integrate multiple layers of information. We examined phase–phase coupling of theta and gamma oscillators in the CA1 region of rat hippocampus during maze exploration and rapid eye movement sleep. Hippocampal theta waves were asymmetric, and estimation of the spatial position of the animal was improved by identifying the waveform-based phase of spiking, compared to traditional methods used for phase estimation. Using the waveform-based theta phase, three distinct gamma bands were identified: slow gammaS (gammaS; 30–50 Hz), midfrequency gammaM (gammaM; 50–90 Hz), and fast gammaF (gammaF; 90–150 Hz or epsilon band). The amplitude of each sub-band was modulated by the theta phase. In addition, we found reliable phase–phase coupling between theta and both gammaS and gammaM but not gammaF oscillators. We suggest that cross-frequency phase coupling can support multiple time-scale control of neuronal spikes within and across structures.Fil: Belluscio, Mariano Andres. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Mizuseki, Kenji. Rutgers University; Estados UnidosFil: Schmidt, Robert. Rutgers University; Estados UnidosFil: Kempter, Richard. Rutgers University; Estados UnidosFil: Buzsáki, György. Rutgers University; Estados Unido

Spatially distributed local fields in the hippocampus encode rat position

Although neuronal spikes can be readily detected from extracellular recordings, synaptic and subthreshold activity remains undifferentiated within the local field potential (LFP). In the hippocampus, neurons discharge selectively when the rat is at certain locations, while LFPs at single anatomical sites exhibit no such place-tuning. Nonetheless, because the representation of position is sparse and distributed, we hypothesized that spatial information can be recovered from multiple-site LFP recordings. Using high-density sampling of LFP and computational methods, we show that the spatiotemporal structure of the theta rhythm can encode position as robustly as neuronal spiking populations. Because our approach exploits the rhythmicity and sparse structure of neural activity, features found in many brain regions, it is useful as a general tool for discovering distributed LFP codes

Theta Phase Segregation of Input-Specific Gamma Patterns in Entorhinal-Hippocampal Networks

Precisely how rhythms support neuronal communication remains obscure. We investigated interregional coordination of gamma oscillations using high-density electrophysiological recordings in the rat hippocampus and entorhinal cortex. We found that 30–80 Hz gamma dominated CA1 local field potentials (LFPs) on the descending phase of CA1 theta waves during navigation, with 60–120 Hz gamma at the theta peak. These signals corresponded to CA3 and entorhinal input, respectively. Above 50 Hz, interregional phase-synchronization of principal cell spikes occurred mostly for LFPs in the axonal target domain. CA1 pyramidal cells were phase-locked mainly to fast gamma (>100 Hz) LFP patterns restricted to CA1, which were strongest at the theta trough. While theta phase coordination of spiking across entorhinal-hippocampal regions depended on memory demands, LFP gamma patterns below 100 Hz in the hippocampus were consistently layer specific and largely reflected afferent activity. Gamma synchronization as a mechanism for interregional communication thus rapidly loses efficacy at higher frequencies

Preconfigured, Skewed Distribution of Firing Rates in the Hippocampus and Entorhinal Cortex

Despite the importance of the discharge frequency in neuronal communication, little is known about the firing-rate patterns of cortical populations. Using large-scale recordings from multiple layers of the entorhinal-hippocampal loop, we found that the firing rates of principal neurons showed a lognormal-like distribution in all brain states. Mean and peak rates within place fields of hippocampal neurons were also strongly skewed. Importantly, firing rates of the same neurons showed reliable correlations in different brain states and testing situations, as well as across familiar and novel environments. The fraction of neurons that participated in population oscillations displayed a lognormal pattern. Such skewed firing rates of individual neurons may be due to a skewed distribution of synaptic weights, which is supported by our observation of a lognormal distribution of the efficacy of spike transfer from principal neurons to interneurons. The persistent skewed distribution of firing rates implies that a preconfigured, highly active minority dominates information transmission in cortical networks