Hippocampal Ripple Oscillations and Inhibition-First Network Models: Frequency Dynamics and Response to GABA Modulators

Differences in the consolidation by spontaneous and evoked ripples in the presence of active dendrites

Ripples are a typical form of neural activity in hippocampal neural networks associated with the replay of episodic memories during sleep as well as sleep-related plasticity and memory consolidation. The emergence of ripples has been observed both dependent as well as independent of input from other brain areas and often coincides with dendritic spikes. Yet, it is unclear how input-evoked and spontaneous ripples as well as dendritic excitability affect plasticity and consolidation. Here, we use mathematical modeling to compare these cases. We find that consolidation as well as the emergence of spontaneous ripples depends on a reliable propagation of activity in feed-forward structures which constitute memory representations. This propagation is facilitated by excitable dendrites, which entail that a few strong synapses are sufficient to trigger neuronal firing. In this situation, stimulation-evoked ripples lead to the potentiation of weak synapses within the feed-forward structure and, thus, to a consolidation of a more general sequence memory. However, spontaneous ripples that occur without stimulation, only consolidate a sparse backbone of the existing strong feed-forward structure. Based on this, we test a recently hypothesized scenario in which the excitability of dendrites is transiently enhanced after learning, and show that such a transient increase can strengthen, restructure and consolidate even weak hippocampal memories, which would be forgotten otherwise. Hence, a transient increase in dendritic excitability would indeed provide a mechanism for stabilizing memories.

Oxytocin attenuates neuroinflammation-induced anxiety through restoration of excitation and inhibition balance in the anterior cingulate cortex in mice

BACKGROUND

Accumulative evidence suggested that the oxytocin system plays a role in socio-emotional disorders, although its role in neuroinflammation-induced anxiety remains unclear.

METHOD

In the present study, anxiety-like behavior was induced in cohorts of animals through repeated lipopolysaccharide (LPS, 0.5 mg/kg, daily, Escherichia coli O55:B5) i.p. injections for seven consecutive days. These different cohorts were subsequently used for anxiety-like behavior assessment with open field test, elevated plus maze, and novelty-suppressed feeding test or for electrophysiology (EEG) recordings of miniature excitatory postsynaptic currents (mEPSCs), miniature inhibitory postsynaptic currents (mIPSCs), or local field potential (LFP) in vivo or ex vivo settings. Samples of the anterior cingulate cortex (ACC) from some cohorts were harvested to conduct immunostaining or western blotting analysis of oxytocin, oxytocin receptor, CamkII, GABA, vGAT, vGLUT2, and c-fos. The dendritic spine density was assessed by Golgi-Cox staining.

RESULTS

Repeated LPS injections induced anxiety-like behavior with concurrent decreases of oxytocin, vGLUT2, mEPSC, dendritic spine, c-fos, membrane excitability, and EEG beta and gamma oscillations, but increased oxytocin receptor and vGAT expressions in the ACC; all these changes were ameliorated by oxytocin intranasal or local brain (via cannula) administration.

CONCLUSION

Taken together, our data suggested that oxytocin system may be a therapeutic target for developing treatment to tackle neuroinflammation-induced anxiety.

Intra-ripple frequency accommodation in an inhibitory network model for hippocampal ripple oscillations

Hippocampal ripple oscillations have been implicated in important cognitive functions such as memory consolidation and planning. Multiple computational models have been proposed to explain the emergence of ripple oscillations, relying either on excitation or inhibition as the main pacemaker. Nevertheless, the generating mechanism of ripples remains unclear. An interesting dynamical feature of experimentally measured ripples, which may advance model selection, is intra-ripple frequency accommodation (IFA): a decay of the instantaneous ripple frequency over the course of a ripple event. So far, only a feedback-based inhibition-first model, which relies on delayed inhibitory synaptic coupling, has been shown to reproduce IFA. Here we use an analytical mean-field approach and numerical simulations of a leaky integrate-and-fire spiking network to explain the mechanism of IFA. We develop a drift-based approximation for the oscillation dynamics of the population rate and the mean membrane potential of interneurons under strong excitatory drive and strong inhibitory coupling. For IFA, the speed at which the excitatory drive changes is critical. We demonstrate that IFA arises due to a speed-dependent hysteresis effect in the dynamics of the mean membrane potential, when the interneurons receive transient, sharp wave-associated excitation. We thus predict that the IFA asymmetry vanishes in the limit of slowly changing drive, but is otherwise a robust feature of the feedback-based inhibition-first ripple model.

A developmental increase of inhibition promotes the emergence of hippocampal ripples

Sharp wave-ripples (SPW-Rs) are a hippocampal network phenomenon critical for memory consolidation and planning. SPW-Rs have been extensively studied in the adult brain, yet their developmental trajectory is poorly understood. While SPWs have been recorded in rodents shortly after birth, the time point and mechanisms of ripple emergence are still unclear. Here, we combine in vivo electrophysiology with optogenetics and chemogenetics in 4 to 12-day-old mice to address this knowledge gap. We show that ripples are robustly detected and induced by light stimulation of channelrhodopsin-2-transfected CA1 pyramidal neurons only from postnatal day 10 onwards. Leveraging a spiking neural network model, we mechanistically link the maturation of inhibition and ripple emergence. We corroborate these findings by reducing ripple rate upon chemogenetic silencing of CA1 interneurons. Finally, we show that early SPW-Rs elicit a more robust prefrontal cortex response than SPWs lacking ripples. Thus, development of inhibition promotes ripples emergence.

Computational models of Idling brain activity for memory processing

Studying the underlying neural mechanisms of cognitive functions of the brain is one of the central questions in modern biology. Moreover, it has significantly impacted the development of novel technologies in artificial intelligence. Spontaneous activity is a unique feature of the brain and is currently lacking in many artificially constructed intelligent machines. Spontaneous activity may represent the brain's idling states, which are internally driven by neuronal networks and possibly participate in offline processing during awake, sleep, and resting states. Evidence is accumulating that the brain's spontaneous activity is not mere noise but part of the mechanisms to process information about previous experiences. A bunch of literature has shown how previous sensory and behavioral experiences influence the subsequent patterns of brain activity with various methods in various animals. It seems, however, that the patterns of neural activity and their computational roles differ significantly from area to area and from function to function. In this article, I review the various forms of the brain's spontaneous activity, especially those observed during memory processing, and some attempts to model the generation mechanisms and computational roles of such activities.

High-frequency oscillations and sequence generation in two-population models of hippocampal region CA1

Hippocampal sharp wave/ripple oscillations are a prominent pattern of collective activity, which consists of a strong overall increase of activity with superimposed (140 − 200 Hz) ripple oscillations. Despite its prominence and its experimentally demonstrated importance for memory consolidation, the mechanisms underlying its generation are to date not understood. Several models assume that recurrent networks of inhibitory cells alone can explain the generation and main characteristics of the ripple oscillations. Recent experiments, however, indicate that in addition to inhibitory basket cells, the pattern requires in vivo the activity of the local population of excitatory pyramidal cells. Here, we study a model for networks in the hippocampal region CA1 incorporating such a local excitatory population of pyramidal neurons. We start by investigating its ability to generate ripple oscillations using extensive simulations. Using biologically plausible parameters, we find that short pulses of external excitation triggering excitatory cell spiking are required for sharp/wave ripple generation with oscillation patterns similar to in vivo observations. Our model has plausible values for single neuron, synapse and connectivity parameters, random connectivity and no strong feedforward drive to the inhibitory population. Specifically, whereas temporally broad excitation can lead to high-frequency oscillations in the ripple range, sparse pyramidal cell activity is only obtained with pulse-like external CA3 excitation. Further simulations indicate that such short pulses could originate from dendritic spikes in the apical or basal dendrites of CA1 pyramidal cells, which are triggered by coincident spike arrivals from hippocampal region CA3. Finally we show that replay of sequences by pyramidal neurons and ripple oscillations can arise intrinsically in CA1 due to structured connectivity that gives rise to alternating excitatory pulse and inhibitory gap coding; the latter denotes phases of silence in specific basket cell groups, which induce selective disinhibition of groups of pyramidal neurons. This general mechanism for sequence generation leads to sparse pyramidal cell and dense basket cell spiking, does not rely on synfire chain-like feedforward excitation and may be relevant for other brain regions as well.

Hippocampal sharp wave-ripples and the associated sequence replay emerge from structured synaptic interactions in a network model of area CA3

Hippocampal place cells are activated sequentially as an animal explores its environment. These activity sequences are internally recreated (‘replayed’), either in the same or reversed order, during bursts of activity (sharp wave-ripples [SWRs]) that occur in sleep and awake rest. SWR-associated replay is thought to be critical for the creation and maintenance of long-term memory. In order to identify the cellular and network mechanisms of SWRs and replay, we constructed and simulated a data-driven model of area CA3 of the hippocampus. Our results show that the chain-like structure of recurrent excitatory interactions established during learning not only determines the content of replay, but is essential for the generation of the SWRs as well. We find that bidirectional replay requires the interplay of the experimentally confirmed, temporally symmetric plasticity rule, and cellular adaptation. Our model provides a unifying framework for diverse phenomena involving hippocampal plasticity, representations, and dynamics, and suggests that the structured neural codes induced by learning may have greater influence over cortical network states than previously appreciated.

Mapping input noise to escape noise in integrate-and-fire neurons: a level-crossing approach

Noise in spiking neurons is commonly modeled by a noisy input current or by generating output spikes stochastically with a voltage-dependent hazard rate ("escape noise"). While input noise lends itself to modeling biophysical noise processes, the phenomenological escape noise is mathematically more tractable. Using the level-crossing theory for differentiable Gaussian processes, we derive an approximate mapping between colored input noise and escape noise in leaky integrate-and-fire neurons. This mapping requires the first-passage-time (FPT) density of an overdamped Brownian particle driven by colored noise with respect to an arbitrarily moving boundary. Starting from the Wiener-Rice series for the FPT density, we apply the second-order decoupling approximation of Stratonovich to the case of moving boundaries and derive a simplified hazard-rate representation that is local in time and numerically efficient. This simplification requires the calculation of the non-stationary auto-correlation function of the level-crossing process: For exponentially correlated input noise (Ornstein-Uhlenbeck process), we obtain an exact formula for the zero-lag auto-correlation as a function of noise parameters, mean membrane potential and its speed, as well as an exponential approximation of the full auto-correlation function. The theory well predicts the FPT and interspike interval densities as well as the population activities obtained from simulations with colored input noise and time-dependent stimulus or boundary. The agreement with simulations is strongly enhanced across the sub- and suprathreshold firing regime compared to a first-order decoupling approximation that neglects correlations between level crossings. The second-order approximation also improves upon a previously proposed theory in the subthreshold regime. Depending on a simplicity-accuracy trade-off, all considered approximations represent useful mappings from colored input noise to escape noise, enabling progress in the theory of neuronal population dynamics.

The essential role of hippocampo-cortical connections in temporal coordination of spindles and ripples

The predominant activity of slow wave sleep is cortical slow oscillations (SOs), thalamic spindles and hippocampal sharp wave ripples. While the precise temporal nesting of these rhythms was shown to be essential for memory consolidation, the coordination mechanism is poorly understood. Here we develop a minimal hippocampo-cortico-thalamic network that can explain the mechanism underlying the SO-spindle-ripple coupling indicating of the succession of regional neuronal interactions. Further we verify the model predictions experimentally in naturally sleeping rodents showing our simple model provides a quantitative match to several experimental observations including the nesting of ripples in the spindle troughs and larger duration but lower amplitude of the ripples co-occurring with spindles or SOs compared to the isolated ripples. The model also predicts that the coupling of ripples to SOs and spindles monotonically enhances by increasing the strength of hippocampo-cortical connections while it is stronger at intermediate values of the cortico-hippocampal projections.

A computational study of suppression of sharp wave ripple complexes by controlling calcium and gap junctions in pyramidal cells

The hippocampus plays a key role in memory formation and learning. According to the concept of active systems memory consolidation, transiently stored memory traces are transferred from the hippocampus into the neocortex for permanent storage. This phenomenon relies on hippocampal network oscillations, particularly sharp wave ripples [SPW-Rs). In this process prior saved data in the hippocampus may be reactivated. Recent investigations reveal that several neurotransmitters and neuromodulators including norepinephrine, acetylcholine, serotonin, etc., suppress SPW-Rs activity in rodents’ hippocampal slices. This suppression of SPW-Rs may depend on various presynaptic and postsynaptic parameters including decrease in calcium influx, hyperpolarization/depolarization and alteration in gap junctions’ function in pyramidal cells. In this study, we demonstrate the impact of calcium influx and gap junctions on pyramidal cells for the modulation of SPW-Rs in a computational model of CA1.

We used,SPW-Rs model with some modifications. SPW-Rs are simulated with gradual reduction of calcium and with decreasing conductance through gap junctions in PCs. Both, with calcium reduction as well as with conductance reduction through gap junctions, SPW-Rs are suppressed. Both effects add up synergistically in combination.

Regulating synchronous oscillations of cerebellar granule cells by different types of inhibition

Synchronous oscillations in neural populations are considered being controlled by inhibitory neurons. In the granular layer of the cerebellum, two major types of cells are excitatory granular cells (GCs) and inhibitory Golgi cells (GoCs). GC spatiotemporal dynamics, as the output of the granular layer, is highly regulated by GoCs. However, there are various types of inhibition implemented by GoCs. With inputs from mossy fibers, GCs and GoCs are reciprocally connected to exhibit different network motifs of synaptic connections. From the view of GCs, feedforward inhibition is expressed as the direct input from GoCs excited by mossy fibers, whereas feedback inhibition is from GoCs via GCs themselves. In addition, there are abundant gap junctions between GoCs showing another form of inhibition. It remains unclear how these diverse copies of inhibition regulate neural population oscillation changes. Leveraging a computational model of the granular layer network, we addressed this question to examine the emergence and modulation of network oscillation using different types of inhibition. We show that at the network level, feedback inhibition is crucial to generate neural oscillation. When short-term plasticity was equipped on GoC-GC synapses, oscillations were largely diminished. Robust oscillations can only appear with additional gap junctions. Moreover, there was a substantial level of cross-frequency coupling in oscillation dynamics. Such a coupling was adjusted and strengthened by GoCs through feedback inhibition. Taken together, our results suggest that the cooperation of distinct types of GoC inhibition plays an essential role in regulating synchronous oscillations of the GC population. With GCs as the sole output of the granular network, their oscillation dynamics could potentially enhance the computational capability of downstream neurons.

Subiculum as a generator of sharp wave-ripples in the rodent hippocampus

Sharp wave-ripples (SWRs) represent synchronous discharges of hippocampal neurons and are believed to play a major role in memory consolidation. A large body of evidence suggests that SWRs are exclusively generated in the CA3-CA2 network. In contrast, here, we provide several lines of evidence showing that the subiculum can function as a secondary SWRs generator. SWRs with subicular origin propagate forward into the entorhinal cortex as well as backward into the hippocampus proper. Our findings suggest that the output structures of the hippocampus are not only passively facilitating the transfer of SWRs to the cortex, but they also can actively contribute to the genesis of SWRs. We hypothesize that SWRs with a subicular origin may be important for the consolidation of information conveyed to the hippocampus via the temporoammonic pathway.

Imbrosci et al. show that the subiculum can work as a secondary generator of sharp wave-ripples (SWRs). SWRs with their origin in subiculum can propagate to the entorhinal cortex and backward to CA1 and CA3.

Gating of hippocampal rhythms and memory by synaptic plasticity in inhibitory interneurons

Mental experiences can become long-term memories if the hippocampal activity patterns that encode them are broadcast during network oscillations. The activity of inhibitory neurons is essential for generating these neural oscillations, but molecular control of this dynamic process during learning remains unknown. Here, we show that hippocampal oscillatory strength positively correlates with excitatory monosynaptic drive onto inhibitory neurons (E→I) in freely behaving mice. To establish a causal relationship between them, we identified γCaMKII as the long-sought mediator of long-term potentiation for E→I synapses (LTP_E→I), which enabled the genetic manipulation of experience-dependent E→I synaptic input/plasticity. Deleting γCaMKII in parvalbumin interneurons selectively eliminated LTP_E→I and disrupted experience-driven strengthening in theta and gamma rhythmicity. Behaviorally, this manipulation impaired long-term memory, for which the kinase activity of γCaMKII was required. Taken together, our data suggest that E→I synaptic plasticity, exemplified by LTP_E→I, plays a gatekeeping role in tuning experience-dependent brain rhythms and mnemonic function.

The effects of eszopiclone on sleep spindles and memory consolidation in schizophrenia: a randomized clinical trial

Sleep spindles, defining oscillations of stage 2 non-rapid eye movement sleep (N2), mediate memory consolidation. Schizophrenia is characterized by reduced spindle activity that correlates with impaired sleep-dependent memory consolidation. In a small, randomized, placebo-controlled pilot study of schizophrenia, eszopiclone (Lunesta®), a nonbenzodiazepine sedative hypnotic, increased N2 spindle density (number/minute) but did not significantly improve memory. This larger double-blind crossover study that included healthy controls investigated whether eszopiclone could both increase N2 spindle density and improve memory. Twenty-six medicated schizophrenia outpatients and 29 healthy controls were randomly assigned to have a placebo or eszopiclone (3 mg) sleep visit first. Each visit involved two consecutive nights of high density polysomnography with training on the Motor Sequence Task (MST) on the second night and testing the following morning. Patients showed a widespread reduction of spindle density and, in both groups, eszopiclone increased spindle density but failed to enhance sleep-dependent procedural memory consolidation. Follow-up analyses revealed that eszopiclone also affected cortical slow oscillations: it decreased their amplitude, increased their duration, and rendered their phase locking with spindles more variable. Regardless of group or visit, the density of coupled spindle-slow oscillation events predicted memory consolidation significantly better than spindle density alone, suggesting that they are a better biomarker of memory consolidation. In conclusion, sleep oscillations are promising targets for improving memory consolidation in schizophrenia, but enhancing spindles is not enough. Effective therapies also need to preserve or enhance cortical slow oscillations and their coordination with thalamic spindles, an interregional dialog that is necessary for sleep-dependent memory consolidation.

Generation of Sharp Wave-Ripple Events by Disinhibition

Sharp wave-ripple complexes (SWRs) are hippocampal network phenomena involved in memory consolidation. To date, the mechanisms underlying their occurrence remain obscure. Here, we show how the interactions between pyramidal cells, parvalbumin-positive (PV⁺) basket cells, and an unidentified class of anti-SWR interneurons can contribute to the initiation and termination of SWRs. Using a biophysically constrained model of a network of spiking neurons and a rate-model approximation, we demonstrate that SWRs emerge as a result of the competition between two interneuron populations and the resulting disinhibition of pyramidal cells. Our models explain how the activation of pyramidal cells or PV⁺ cells can trigger SWRs, as shown in vitro, and suggests that PV⁺ cell-mediated short-term synaptic depression influences the experimentally reported dynamics of SWR events. Furthermore, we predict that the silencing of anti-SWR interneurons can trigger SWRs. These results broaden our understanding of the microcircuits supporting the generation of memory-related network dynamics.

SIGNIFICANCE STATEMENT The hippocampus is a part of the mammalian brain that is crucial for episodic memories. During periods of sleep and inactive waking, the extracellular activity of the hippocampus is dominated by sharp wave-ripple events (SWRs), which have been shown to be important for memory consolidation. The mechanisms regulating the emergence of these events are still unclear. We developed a computational model to study the emergence of SWRs and to explain the roles of different cell types in regulating them. The model accounts for several previously unexplained features of SWRs and thus advances the understanding of memory-related dynamics.

Modelling unitary fields and the single-neuron contribution to local field potentials in the hippocampus

Key points

We simulate the unitary local field potential (uLFP) generated in the hippocampus CA3, using morphologically detailed models.

The model suggests that cancelling effects between apical and basal dendritic synapses explain the low amplitude of excitatory uLFPs.

Inhibitory synapses around the soma do not cancel and could explain the high‐amplitude inhibitory uLFPs.

These results suggest that somatic inhibition constitutes a strong component of LFPs, which may explain a number of experimental observations.

Abstract

Synaptic currents represent a major contribution to the local field potential (LFP) in brain tissue, but the respective contribution of excitatory and inhibitory synapses is not known. Here, we provide estimates of this contribution by using computational models of hippocampal pyramidal neurons, constrained by in vitro recordings. We focus on the unitary LFP (uLFP) generated by single neurons in the CA3 region of the hippocampus. We first reproduce experimental results for hippocampal basket cells, and in particular how inhibitory uLFP are distributed within hippocampal layers. Next, we calculate the uLFP generated by pyramidal neurons, using morphologically reconstructed CA3 pyramidal cells. The model shows that the excitatory uLFP is of small amplitude, smaller than inhibitory uLFPs. Indeed, when the two are simulated together, inhibitory uLFPs mask excitatory uLFPs, which might create the illusion that the inhibitory field is generated by pyramidal cells. These results provide an explanation for the observation that excitatory and inhibitory uLFPs are of the same polarity, in vivo and in vitro. These results suggest that somatic inhibitory currents are large contributors to the LFP, which is important information for interpreting this signal. Finally, the results of our model might form the basis of a simple method to compute the LFP, which could be applied to point neurons for each cell type, thus providing a simple biologically grounded method for calculating LFPs from neural networks.

In conclusion, computational models constrained by in vitro recordings suggest that: (1) Excitatory uLFPs are of smaller amplitude than inhibitory uLFPs. (2) Inhibitory uLFPs form the major contribution to LFPs. (3) uLFPs can be used as a simple model to generate LFPs from spiking networks.

We simulate the unitary local field potential (uLFP) generated in the hippocampus CA3, using morphologically detailed models.

The model suggests that cancelling effects between apical and basal dendritic synapses explain the low amplitude of excitatory uLFPs.

Inhibitory synapses around the soma do not cancel and could explain the high‐amplitude inhibitory uLFPs.

These results suggest that somatic inhibition constitutes a strong component of LFPs, which may explain a number of experimental observations.

Targeting sleep oscillations to improve memory in schizophrenia

Although schizophrenia is defined by waking phenomena, a growing literature documents a deficit in sleep spindles, a defining oscillation of stage 2 non-rapid eye movement sleep. Compelling evidence supports an important role for spindles in cognition, and particularly memory. In schizophrenia, although the spindle deficit correlates with impaired sleep-dependent memory consolidation, recent clinical trials find that increasing spindles does not improve memory. This may reflect that sleep-dependent memory consolidation relies not on spindles alone, but also on their precise temporal coordination with cortical slow oscillations and hippocampal sharp-wave ripples. Consequently, interventions to improve memory in schizophrenia must not only increase spindles, but also preserve or enhance slow oscillations, hippocampal ripples and their temporal relations. Because hippocampal ripples and the activity of the thalamic spindle generator are difficult to measure noninvasively, screening potential interventions requires complementary animal and human studies. In this review we (i) propose that sleep oscillations are novel pathophysiological targets for therapy to improve cognition in schizophrenia; (ii) summarize our understanding of how these oscillations interact to consolidate memory; (iii) suggest that a systems neuroscience strategy is essential to selecting and evaluating effective treatments, and illustrate this with findings from clinical trials; and (iv) selectively review the interventional literature relevant to sleep and cognition, covering both pharmacological and noninvasive brain stimulation approaches. We conclude that coordinated sleep oscillations are promising targets for improving cognition in schizophrenia and that effective therapies will need to preserve or enhance sleep oscillatory dynamics and restore function at the network level.

Lactate Attenuates Synaptic Transmission and Affects Brain Rhythms Featuring High Energy Expenditure

Lactate shuttled from blood, astrocytes, and/or oligodendrocytes may serve as the major glucose alternative in brain energy metabolism. However, its effectiveness in fueling neuronal information processing underlying complex cortex functions like perception and memory is unclear. We show that sole lactate disturbs electrical gamma and theta-gamma oscillations in hippocampal networks by either attenuation or neural bursts. Bursting is suppressed by elevating the glucose fraction in substrate supply. By contrast, lactate does not affect electrical sharp wave-ripple activity featuring lower energy use. Lactate increases the oxygen consumption during the network states, reflecting enhanced oxidative ATP synthesis in mitochondria. Finally, lactate attenuates synaptic transmission in excitatory pyramidal cells and fast-spiking, inhibitory interneurons by reduced neurotransmitter release from presynaptic terminals, whereas action potential generation in the axon is regular. In conclusion, sole lactate is less effective and potentially harmful during gamma-band rhythms by omitting obligatory ATP delivery through fast glycolysis at the synapse.

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Lactate fuels network oscillations featuring low energy expenditure

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Lactate can disturb the neuronal excitation-inhibition balance

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Lactate attenuates neurotransmission at glutamatergic and GABAergic synapses

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Lactate increases oxygen consumption, whereas neural activity can even decrease

Propofol Modulates Early Memory Consolidation in Humans

Maintenance of memory across time is crucial for adaptive behavior. Current theories posit that the underlying consolidation process depends on stabilization of synapses and reorganization of interactions between hippocampus and neocortex. However, the temporal properties of hippocampal-neocortical network reconfiguration during consolidation are still a matter of debate. Translational research on this issue is challenged by the paucity of techniques to transiently interfere with memory in the healthy human brain. Here, we report a neuro-pharmacological approach with the GABA_Aergic anesthetic propofol and a memory task sensitive to hippocampal dysfunction. Patients undergoing minor surgery learned word lists before injection of an anesthetic dose of propofol. Results show that administration of the drug shortly after learning (∼13 min) impairs recall after awakening but spares recognition. By contrast, later administration (∼105 min) has no effect. These findings suggest significant changes in memory networks very early after learning that are decisive for later recall. Propofol general anesthesia provides an experimental tool to modulate the first steps of hippocampus-mediated memory consolidation in humans.

Mechanisms of neural organization and rhythmogenesis during hippocampal and cortical ripples

Neural activity during ripples has attracted great theoretical and experimental attention over the last three decades. Perhaps one reason for such interest is that ripples occur during quiet waking moments and during sleep, times when we reflect and dream about what has just occurred and what we expect to happen next. The hope is that understanding such 'offline' activity may yield insights into reflection, planning, and the purposes of sleep. This review focuses on the mechanisms by which neurons organize during these high-frequency events. In studying ripples, broader principles have emerged that relate intrinsic neural properties, network topology and synaptic plasticity in controlling neural activity. Ripples, therefore, serve as an excellent model for studying how properties of a neural network relate to neural dynamics. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.

Impairment of Sharp-Wave Ripples in a Murine Model of Dravet Syndrome

Dravet syndrome (DS) is a severe early-onset epilepsy associated with heterozygous loss-of-function mutations in SCN1A Animal models of DS with global Scn1a haploinsufficiency recapitulate the DS phenotype, including seizures, premature death, and impaired spatial memory performance. Spatial memory requires hippocampal sharp-wave ripples (SPW-Rs), which consist of high-frequency field potential oscillations (ripples, 100-260 Hz) superimposed on a slower SPW. Published in vitro electrophysiologic recordings in DS mice demonstrate reduced firing of GABAergic inhibitory neurons, which are essential for the formation of SPW-R complexes. Here, in vivo electrophysiologic recordings of hippocampal local field potential in both male and female mice demonstrate that Scn1a haploinsufficiency slows intrinsic ripple frequency and reduces the rate of SPW-R occurrence. In DS mice, peak ripple-band power is shifted to lower frequencies, average intertrough intervals of individually detected ripples are slower, and the rate of SPW-R generation is reduced, while SPW amplitude remains unaffected. These alterations in SPW-R properties, in combination with published reductions in interneuron function in DS, suggest a direct link between reduced inhibitory neuron excitability and impaired SPW-R function. A simple interconnected, conductance-based in silico interneuron network model was used to determine whether reduced sodium conductance is sufficient to slow ripple frequency, and stimulation with a modeled SPW demonstrates that reduced sodium conductance alone is sufficient to slow oscillatory frequencies. These findings forge a potential mechanistic link between impaired SPW-R generation and Scn1a mutation in DS mice, expanding the set of disorders in which SPW-R dysfunction contributes to impaired memory.SIGNIFICANCE STATEMENT Disruption of sharp-wave ripples, a characteristic hippocampal rhythm coordinated by the precise timing of GABAergic interneurons, impairs spatial learning and memory. Prior in vitro patch-clamp recordings in brain slices from genetic mouse models of Dravet syndrome (DS) reveal reduced sodium current and excitability in GABAergic interneurons but not excitatory cells, suggesting a causal role for impaired interneuron activity in seizures and cognitive impairment. Here, heterozygous Scn1a mutation in DS mice reduces hippocampal sharp-wave ripple occurrence and slows internal ripple frequency in vivo and a simple in silico model demonstrates reduction in interneuron function alone is sufficient to slow model oscillations. Together, these findings provide a plausible pathophysiologic mechanism for Scn1a gene mutation to impair spatial memory.

Interspike interval correlations in networks of inhibitory integrate-and-fire neurons

We study temporal correlations of interspike intervals, quantified by the network-averaged serial correlation coefficient (SCC), in networks of both current- and conductance-based purely inhibitory integrate-and-fire neurons. Numerical simulations reveal transitions to negative SCCs at intermediate values of bias current drive and network size. As bias drive and network size are increased past these values, the SCC returns to zero. The SCC is maximally negative at an intermediate value of the network oscillation strength. The dependence of the SCC on two canonical schemes for synaptic connectivity is studied, and it is shown that the results occur robustly in both schemes. For conductance-based synapses, the SCC becomes negative at the onset of both a fast and slow coherent network oscillation. We then show by means of offline simulations using prerecorded network activity that a neuron's SCC is highly sensitive to its number of presynaptic inputs. Finally, we devise a noise-reduced diffusion approximation for current-based networks that accounts for the observed temporal correlation transitions.

On the origin of lognormal network synchrony in CA1

The sharp wave ripple complex in rodent hippocampus is associated with a network burst in CA3 (NB) that triggers a synchronous event in the CA1 population (SE). The number of CA1 pyramidal cells participating in a SE has been observed to follow a lognormal distribution. However, the origin of this skewed and heavy-tailed distribution of population synchrony in CA1 remains unknown. Because the size of SEs is likely to originate from the size of the NBs and the underlying neural circuitry, we model the CA3-CA1 circuit to study the underlying mechanisms and their functional implications. We show analytically that if the size of a NB in CA3 is distributed according to a normal distribution, then the size of the resulting SE in CA1 follows a lognormal distribution. Our model predicts the distribution of the NB size in CA3, which remains to be tested experimentally. Moreover, we show that a putative lognormal NB size distribution leads to an extremely heavy-tailed SE size distribution in CA1, contradicting experimental evidence. In conclusion, our model provides general insight on the origin of lognormally distributed network synchrony as a consequence of synchronous synaptic transmission of normally distributed input events.