Synaptic gating at axonal branches, and sharp-wave ripples with replay: a simulation study

Gamma oscillation plasticity is mediated via parvalbumin interneurons

Understanding the plasticity of neuronal networks is an emerging field of (patho-) physiological research, yet the underlying cellular mechanisms remain poorly understood. Gamma oscillations (30 to 80 hertz), a biomarker of cognitive performance, require and potentiate glutamatergic transmission onto parvalbumin-positive interneurons (PVIs), suggesting an interface for cell-to-network plasticity. In ex vivo local field potential recordings, we demonstrate long-term potentiation of hippocampal gamma power. Gamma potentiation obeys established rules of PVI plasticity, requiring calcium-permeable AMPA receptors (CP-AMPARs) and metabotropic glutamate receptors (mGluRs). A microcircuit computational model of CA3 gamma oscillations predicts CP-AMPAR plasticity onto PVIs critically outperforms pyramidal cell plasticity in increasing gamma power and completely accounts for gamma potentiation. We reaffirm this ex vivo in three PVI-targeting animal models, demonstrating that gamma potentiation requires PVI-specific signaling via a Gq/PKC pathway comprising mGluR5 and a Gi-sensitive, PKA-dependent pathway. Gamma activity-dependent, metabotropically mediated CP-AMPAR plasticity on PVIs may serve as a guiding principle in understanding network plasticity in health and disease.

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.

Could electrical coupling contribute to the formation of cell assemblies?

Cell assemblies and central pattern generators (CPGs) are related types of neuronal networks: both consist of interacting groups of neurons whose collective activities lead to defined functional outputs. In the case of a cell assembly, the functional output may be interpreted as a representation of something in the world, external or internal; for a CPG, the output ‘drives’ an observable (i.e. motor) behavior. Electrical coupling, via gap junctions, is critical for the development of CPGs, as well as for their actual operation in the adult animal. Electrical coupling is also known to be important in the development of hippocampal and neocortical principal cell networks. We here argue that electrical coupling – in addition to chemical synapses – may therefore contribute to the formation of at least some cell assemblies in adult animals.

Synaptic entrainment of ectopic action potential generation in hippocampal pyramidal neurons

KEY POINTS

Ectopic action potentials (EAPs) arise at distal locations in axonal fibres and are often associated with neuronal pathologies such as epilepsy or nerve injury, but they also occur during physiological network conditions. This study investigates whether initiation of such EAPs is modulated by subthreshold synaptic activity. Somatic subthreshold potentials invade the axonal compartment to considerable distances (>350 μm), whereas spread of axonal subthreshold potentials to the soma is inefficient. Ectopic spike generation is entrained by conventional synaptic signalling mechanisms. Excitatory synaptic potentials promote EAPs, whereas inhibitory synaptic potentials block EAPs. The modulation of ectopic excitability depends on propagation of somatic voltage deflections to the axonal EAP initiation site. Synaptic modulation of EAP initiation challenges the view of the distal axon being independent of synaptic activity and may contribute to mechanisms underlying fast network oscillations and pathological network activity.

ABSTRACT

While most action potentials are generated at the axon initial segment, they can also be triggered at more distal sites along the axon. Such ectopic action potentials (EAPs) occur during several neuronal pathologies such as epilepsy, nerve injuries and inflammation but have also been observed during physiological network activity. EAPs propagate antidromically towards the somato-dendritic compartment where they modulate synaptic plasticity. Here we investigate the converse signal direction: do somato-dendritic synaptic potentials affect the generation of ectopic spikes? We measured anti- and orthodromic spikes in the soma and axon of mouse hippocampal CA1 pyramidal cells. We found that synaptic potentials propagate reliably through the axon, causing significant voltage transients at distances >350 μm. At these sites, excitatory input efficiently facilitated EAP initiation in distal axons and, conversely, inhibitory input suppressed EAP initiation. Our data reveal a new mechanism by which ectopically generated spikes can be entrained by conventional synaptic signalling during normal and pathological network activity.

Electrical coupling between hippocampal neurons: contrasting roles of principal cell gap junctions and interneuron gap junctions

There is considerable experimental evidence, anatomical and physiological, that gap junctions exist in the hippocampus. Electrical coupling through these gap junctions may be divided into three types: between principal neurons, between interneurons and at mixed chemical (glutamatergic)/electrical synapses. An approach, combining in vitro experimental with modeling techniques, sheds some light on the functional consequences of electrical coupling, for network oscillations and for seizures. Additionally, in vivo experiments, using mouse connexin knockouts, suggest that the presence of electrical coupling is important for optimal performance on selected behavioral tasks; however, the interpretation of such data, in cellular terms, has so far proven difficult. Given that invertebrate central pattern generators so often depend on both chemical and electrical synapses, our hypothesis is that hippocampus-mediated and -influenced behaviors will act likewise. Experiments, likely hard ones, will be required to test this intuition.

Electrical synapses in mammalian CNS: Past eras, present focus and future directions

Gap junctions provide the basis for electrical synapses between neurons. Early studies in well-defined circuits in lower vertebrates laid the foundation for understanding various properties conferred by electrical synaptic transmission. Knowledge surrounding electrical synapses in mammalian systems unfolded first with evidence indicating the presence of gap junctions between neurons in various brain regions, but with little appreciation of their functional roles. Beginning at about the turn of this century, new approaches were applied to scrutinize electrical synapses, revealing the prevalence of neuronal gap junctions, the connexin protein composition of many of those junctions, and the myriad diverse neural systems in which they occur in the mammalian CNS. Subsequent progress indicated that electrical synapses constitute key elements in synaptic circuitry, govern the collective activity of ensembles of electrically coupled neurons, and in part orchestrate the synchronized neuronal network activity and rhythmic oscillations that underlie fundamental integrative processes. This article is part of a Special Issue entitled: Gap Junction Proteins edited by Jean Claude Herve.

An in Silico, Biomarker-Based Method for the Evaluation of Virtual Neuropsychiatric Drug Effects

The recent explosion in neuroscience research has markedly increased our understanding of the neurobiological correlates of many psychiatric illnesses, but this has unfortunately not translated into more effective pharmacologic treatments for these conditions. At the same time, researchers have increasingly sought out biological markers, or biomarkers, as a way to categorize psychiatric illness, as these are felt to be closer to underlying genetic and neurobiological vulnerabilities. While biomarker-based drug discovery approaches have tended to employ in vivo (e.g., rodent) or in vitro test systems, relatively little attention has been paid to the potential of computational, or in silico, methodologies. Here we describe such a methodology, using as an example a biophysically detailed computational model of hippocampus that is made to generate putative schizophrenia biomarkers by the inclusion of a number of neuropathological changes that have been associated with the illness (NMDA system deficit, decreased neural connectivity, hyperdopaminergia). We use the specific inability to attune to gamma band (40 Hz) auditory stimulus as our illness biomarker. We expose this system to a large number of virtual medications, defined by systematic variation of model parameters corresponding to five cellular-level effects. The potential efficacy of virtual medications is determined by a wellness metric (WM) that we have developed. We identify a number of virtual agents that consist of combinations of mechanisms, which are not simply reversals of the causative lesions. The manner in which this methodology could be extended to other neuropsychiatric conditions, such as Alzheimer's disease, autism, and fragile X syndrome, is discussed.

Memory replay in balanced recurrent networks

Complex patterns of neural activity appear during up-states in the neocortex and sharp waves in the hippocampus, including sequences that resemble those during prior behavioral experience. The mechanisms underlying this replay are not well understood. How can small synaptic footprints engraved by experience control large-scale network activity during memory retrieval and consolidation? We hypothesize that sparse and weak synaptic connectivity between Hebbian assemblies are boosted by pre-existing recurrent connectivity within them. To investigate this idea, we connect sequences of assemblies in randomly connected spiking neuronal networks with a balance of excitation and inhibition. Simulations and analytical calculations show that recurrent connections within assemblies allow for a fast amplification of signals that indeed reduces the required number of inter-assembly connections. Replay can be evoked by small sensory-like cues or emerge spontaneously by activity fluctuations. Global-potentially neuromodulatory-alterations of neuronal excitability can switch between network states that favor retrieval and consolidation.

From Neuron Biophysics to Orientation Selectivity in Electrically Coupled Networks of Neocortical L2/3 Large Basket Cells

In the neocortex, inhibitory interneurons of the same subtype are electrically coupled with each other via dendritic gap junctions (GJs). The impact of multiple GJs on the biophysical properties of interneurons and thus on their input processing is unclear. The present experimentally based theoretical study examined GJs in L2/3 large basket cells (L2/3 LBCs) with 3 goals in mind: (1) To evaluate the errors due to GJs in estimating the cable properties of individual L2/3 LBCs and suggest ways to correct these errors when modeling these cells and the networks they form; (2) to bracket the GJ conductance value (0.05-0.25 nS) and membrane resistivity (10 000-40 000 Ω cm(2)) of L2/3 LBCs; these estimates are tightly constrained by in vitro input resistance (131 ± 18.5 MΩ) and the coupling coefficient (1-3.5%) of these cells; and (3) to explore the functional implications of GJs, and show that GJs: (i) dynamically modulate the effective time window for synaptic integration; (ii) improve the axon's capability to encode rapid changes in synaptic inputs; and (iii) reduce the orientation selectivity, linearity index, and phase difference of L2/3 LBCs. Our study provides new insights into the role of GJs and calls for caution when using in vitro measurements for modeling electrically coupled neuronal networks.

High-frequency brain networks undergo modular breakdown during epileptic seizures

OBJECTIVE

Cortical high-frequency oscillations (HFOs; 100-500 Hz) play a critical role in the pathogenesis of epilepsy; however, whether they represent a true epileptogenic process remains largely unknown. HFOs have been recorded in the human cortex but their network dynamics during the transitional period from interictal to ictal phase remain largely unknown. We sought to determine the high-frequency network dynamics of these oscillations in patients with epilepsy who were undergoing intracranial electroencephalographic recording for seizure localization.

METHODS

We applied a graph theoretical analysis framework to high-resolution intracranial electroencephalographic recordings of 24 interictal and 24 seizure periods to identify the spatiotemporal evolution of community structure of high-frequency cortical networks at rest and during multiple seizure episodes in patients with intractable epilepsy.

RESULTS

Cortical networks at all examined frequencies showed temporally stable community architecture in all 24 interictal periods. During seizure periods, high-frequency networks showed a significant breakdown of their community structure, which was characterized by the emergence of numerous small nodal communities, not limited to seizure foci and encompassing the entire recorded network. Such network disorganization was observed on average 225 s before the electrographic seizure onset and extended on average 190 s after termination of the seizure. Gamma networks were characterized by stable community dynamics during resting and seizure periods.

SIGNIFICANCE

Our findings suggest that the modular breakdown of high-frequency cortical networks represents a distinct functional pathology that underlies epileptogenesis and corresponds to a cortical state of highest propensity to generate seizures.

A Unified Dynamic Model for Learning, Replay, and Sharp-Wave/Ripples

Hippocampal activity is fundamental for episodic memory formation and consolidation. During phases of rest and sleep, it exhibits sharp-wave/ripple (SPW/R) complexes, which are short episodes of increased activity with superimposed high-frequency oscillations. Simultaneously, spike sequences reflecting previous behavior, such as traversed trajectories in space, are replayed. Whereas these phenomena are thought to be crucial for the formation and consolidation of episodic memory, their neurophysiological mechanisms are not well understood. Here we present a unified model showing how experience may be stored and thereafter replayed in association with SPW/Rs. We propose that replay and SPW/Rs are tightly interconnected as they mutually generate and support each other. The underlying mechanism is based on the nonlinear dendritic computation attributable to dendritic sodium spikes that have been prominently found in the hippocampal regions CA1 and CA3, where SPW/Rs and replay are also generated. Besides assigning SPW/Rs a crucial role for replay and thus memory processing, the proposed mechanism also explains their characteristic features, such as the oscillation frequency and the overall wave form. The results shed a new light on the dynamical aspects of hippocampal circuit learning.

SIGNIFICANCE STATEMENT

During phases of rest and sleep, the hippocampus, the "memory center" of the brain, generates intermittent patterns of strongly increased overall activity with high-frequency oscillations, the so-called sharp-wave/ripples. We investigate their role in learning and memory processing. They occur together with replay of activity sequences reflecting previous behavior. Developing a unifying computational model, we propose that both phenomena are tightly linked, by mutually generating and supporting each other. The underlying mechanism depends on nonlinear amplification of synchronous inputs that has been prominently found in the hippocampus. Besides assigning sharp-wave/ripples a crucial role for replay generation and thus memory processing, the proposed mechanism also explains their characteristic features, such as the oscillation frequency and the overall wave form.

Memory trace replay: the shaping of memory consolidation by neuromodulation

The consolidation of memories for places and events is thought to rely, at the network level, on the replay of spatially tuned neuronal firing patterns representing discrete places and spatial trajectories. This occurs in the hippocampal-entorhinal circuit during sharp wave ripple events (SWRs) that occur during sleep or rest. Here, we review theoretical models of lingering place cell excitability and behaviorally induced synaptic plasticity within cell assemblies to explain which sequences or places are replayed. We further provide new insights into how fluctuations in cholinergic tone during different behavioral states might shape the direction of replay and how dopaminergic release in response to novelty or reward can modulate which cell assemblies are replayed.

Action potential generation in an anatomically constrained model of medial superior olive axons

Neurons in the medial superior olive (MSO) encode interaural time differences (ITDs) with sustained firing rates of >100 Hz. They are able to generate such high firing rates for several hundred milliseconds despite their extremely low-input resistances of only few megaohms and high synaptic conductances in vivo. The biophysical mechanisms by which these leaky neurons maintain their excitability are not understood. Since action potentials (APs) are usually assumed to be generated in the axon initial segment (AIS), we analyzed anatomical data of proximal MSO axons in Mongolian gerbils and found that the axon diameter is <1 μm and the internode length is ∼100 μm. Using a morphologically constrained computational model of the MSO axon, we show that these thin axons facilitate the excitability of the AIS. However, for ongoing high rates of synaptic inputs the model generates a substantial fraction of APs in its nodes of Ranvier. These distally initiated APs are mediated by a spatial gradient of sodium channel inactivation and a strong somatic current sink. The model also predicts that distal AP initiation increases the dynamic range of the rate code for ITDs.

Re-encoding of associations by recurrent plasticity increases memory capacity

Recurrent networks have been proposed as a model of associative memory. In such models, memory items are stored in the strength of connections between neurons. These modifiable connections or synapses constitute a shared resource among all stored memories, limiting the capacity of the network. Synaptic plasticity at different time scales can play an important role in optimizing the representation of associative memories, by keeping them sparse, uncorrelated and non-redundant. Here, we use a model of sequence memory to illustrate how plasticity allows a recurrent network to self-optimize by gradually re-encoding the representation of its memory items. A learning rule is used to sparsify large patterns, i.e., patterns with many active units. As a result, pattern sizes become more homogeneous, which increases the network's dynamical stability during sequence recall and allows more patterns to be stored. Last, we show that the learning rule allows for online learning in that it keeps the network in a robust dynamical steady state while storing new memories and overwriting old ones.

A computational model for preplay in the hippocampus

The hippocampal network produces sequences of neural activity even when there is no time-varying external drive. In offline states, the temporal sequence in which place cells fire spikes correlates with the sequence of their place fields. Recent experiments found this correlation even between offline sequential activity (OSA) recorded before the animal ran in a novel environment and the place fields in that environment. This preplay phenomenon suggests that OSA is generated intrinsically in the hippocampal network, and not established by external sensory inputs. Previous studies showed that continuous attractor networks with asymmetric patterns of connectivity, or with slow, local negative feedback, can generate sequential activity. This mechanism could account for preplay if the network only represented a single spatial map, or chart. However, global remapping in the hippocampus implies that multiple charts are represented simultaneously in the hippocampal network and it remains unknown whether the network with multiple charts can account for preplay. Here we show that it can. Driven with random inputs, the model generates sequences in every chart. Place fields in a given chart and OSA generated by the network are highly correlated. We also find significant correlations, albeit less frequently, even when the OSA is correlated with a new chart in which place fields are randomly scattered. These correlations arise from random correlations between the orderings of place fields in the new chart and those in a pre-existing chart. Our results suggest two different accounts for preplay. Either an existing chart is re-used to represent a novel environment or a new chart is formed.