Synaptic and dendritic architecture of different types of hippocampal somatostatin interneurons.

GABAergic inhibitory neurons fundamentally shape the activity and plasticity of cortical circuits. A major subset of these neurons contains somatostatin (SOM); these cells play crucial roles in neuroplasticity, learning, and memory in many brain areas including the hippocampus, and are implicated in several neuropsychiatric diseases and neurodegenerative disorders. Two main types of SOM-containing cells in area CA1 of the hippocampus are oriens-lacunosum-moleculare (OLM) cells and hippocampo-septal (HS) cells. These cell types show many similarities in their soma-dendritic architecture, but they have different axonal targets, display different activity patterns in vivo, and are thought to have distinct network functions. However, a complete understanding of the functional roles of these interneurons requires a precise description of their intrinsic computational properties and their synaptic interactions. In the current study we generated, analyzed, and make available several key data sets that enable a quantitative comparison of various anatomical and physiological properties of OLM and HS cells in mouse. The data set includes detailed scanning electron microscopy (SEM)-based 3D reconstructions of OLM and HS cells along with their excitatory and inhibitory synaptic inputs. Combining this core data set with other anatomical data, patch-clamp electrophysiology, and compartmental modeling, we examined the precise morphological structure, inputs, outputs, and basic physiological properties of these cells. Our results highlight key differences between OLM and HS cells, particularly regarding the density and distribution of their synaptic inputs and mitochondria. For example, we estimated that an OLM cell receives about 8,400, whereas an HS cell about 15,600 synaptic inputs, about 16% of which are GABAergic. Our data and models provide insight into the possible basis of the different functionality of OLM and HS cell types and supply essential information for more detailed functional models of these neurons and the hippocampal network.

Huygens synchronization of medial septal pacemaker neurons generates hippocampal theta oscillation.

Episodic learning and memory retrieval are dependent on hippocampal theta oscillation, thought to rely on the GABAergic network of the medial septum (MS). To test how this network achieves theta synchrony, we recorded MS neurons and hippocampal local field potential simultaneously in anesthetized and awake mice and rats. We show that MS pacemakers synchronize their individual rhythmicity frequencies, akin to coupled pendulum clocks as observed by Huygens. We optogenetically identified them as parvalbumin-expressing GABAergic neurons, while MS glutamatergic neurons provide tonic excitation sufficient to induce theta. In accordance, waxing and waning tonic excitation is sufficient to toggle between theta and non-theta states in a network model of single-compartment inhibitory pacemaker neurons. These results provide experimental and theoretical support to a frequency-synchronization mechanism for pacing hippocampal theta, which may serve as an inspirational prototype for synchronization processes in the central nervous system from Nematoda to Arthropoda to Chordate and Vertebrate phyla.

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.

HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data.

Anatomically and biophysically detailed data-driven neuronal models have become widely used tools for understanding and predicting the behavior and function of neurons. Due to the increasing availability of experimental data from anatomical and electrophysiological measurements as well as the growing number of computational and software tools that enable accurate neuronal modeling, there are now a large number of different models of many cell types available in the literature. These models were usually built to capture a few important or interesting properties of the given neuron type, and it is often unknown how they would behave outside their original context. In addition, there is currently no simple way of quantitatively comparing different models regarding how closely they match specific experimental observations. This limits the evaluation, re-use and further development of the existing models. Further, the development of new models could also be significantly facilitated by the ability to rapidly test the behavior of model candidates against the relevant collection of experimental data. We address these problems for the representative case of the CA1 pyramidal cell of the rat hippocampus by developing an open-source Python test suite, which makes it possible to automatically and systematically test multiple properties of models by making quantitative comparisons between the models and electrophysiological data. The tests cover various aspects of somatic behavior, and signal propagation and integration in apical dendrites. To demonstrate the utility of our approach, we applied our tests to compare the behavior of several different rat hippocampal CA1 pyramidal cell models from the ModelDB database against electrophysiological data available in the literature, and evaluated how well these models match experimental observations in different domains. We also show how we employed the test suite to aid the development of models within the European Human Brain Project (HBP), and describe the integration of the tests into the validation framework developed in the HBP, with the aim of facilitating more reproducible and transparent model building in the neuroscience community.

Data-driven integration of hippocampal CA1 synaptic physiology in silico.

The anatomy and physiology of monosynaptic connections in rodent hippocampal CA1 have been extensively studied in recent decades. Yet, the resulting knowledge remains disparate and difficult to reconcile. Here, we present a data-driven approach to integrate the current state-of-the-art knowledge on the synaptic anatomy and physiology of rodent hippocampal CA1, including axo-dendritic innervation patterns, number of synapses per connection, quantal conductances, neurotransmitter release probability, and short-term plasticity into a single coherent resource. First, we undertook an extensive literature review of paired recordings of hippocampal neurons and compiled experimental data on their synaptic anatomy and physiology. The data collected in this manner is sparse and inhomogeneous due to the diversity of experimental techniques used by different groups, which necessitates the need for an integrative framework to unify these data. To this end, we extended a previously developed workflow for the neocortex to constrain a unifying in silico reconstruction of the synaptic physiology of CA1 connections. Our work identifies gaps in the existing knowledge and provides a complementary resource toward a more complete quantification of synaptic anatomy and physiology in the rodent hippocampal CA1 region.

Dynamics of sleep oscillations is coupled to brain temperature on multiple scales.

KEY POINTS: Sleep spindle frequency positively, duration negatively correlates with brain temperature. Local heating of the thalamus produces similar effects in the heated area. Thalamic network model corroborates temperature dependence of sleep spindle frequency. Brain temperature shows spontaneous microfluctuations during both anesthesia and natural sleep. Larger fluctuations are associated with epochs of REM sleep. Smaller fluctuations correspond to the alteration of spindling and delta epochs of infra-slow oscillation. ABSTRACT: Every form of neural activity depends on temperature, yet its relationship to brain rhythms is poorly understood. In this work we examined how sleep spindles are influenced by changing brain temperatures and how brain temperature is influenced by sleep oscillations. We employed a novel thermoelectrode designed for measuring temperature while recording neural activity. We found that spindle frequency is positively correlated and duration negatively correlated with brain temperature. Local heating of the thalamus replicated the temperature dependence of spindle parameters in the heated area only, suggesting biophysical rather than global modulatory mechanisms, a finding also supported by a thalamic network model. Finally, we show that switches between oscillatory states also influence brain temperature on a shorter and smaller scale. Epochs of paradoxical sleep as well as the infra-slow oscillation were associated with brain temperature fluctuations below 0.2°C. Our results highlight that brain temperature is massively intertwined with sleep oscillations on various time scales.

The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow.

Every neuron is part of a network, exerting its function by transforming multiple spatiotemporal synaptic input patterns into a single spiking output. This function is specified by the particular shape and passive electrical properties of the neuronal membrane, and the composition and spatial distribution of ion channels across its processes. For a variety of physiological or pathological reasons, the intrinsic input/output function may change during a neuron's lifetime. This process results in high variability in the peak specific conductance of ion channels in individual neurons. The mechanisms responsible for this variability are not well understood, although there are clear indications from experiments and modeling that degeneracy and correlation among multiple channels may be involved. Here, we studied this issue in biophysical models of hippocampal CA1 pyramidal neurons and interneurons. Using a unified data-driven simulation workflow and starting from a set of experimental recordings and morphological reconstructions obtained from rats, we built and analyzed several ensembles of morphologically and biophysically accurate single cell models with intrinsic electrophysiological properties consistent with experimental findings. The results suggest that the set of conductances expressed in any given hippocampal neuron may be considered as belonging to two groups: one subset is responsible for the major characteristics of the firing behavior in each population and the other is responsible for a robust degeneracy. Analysis of the model neurons suggests several experimentally testable predictions related to the combination and relative proportion of the different conductances that should be expressed on the membrane of different types of neurons for them to fulfill their role in the hippocampus circuitry.

The Effects of Realistic Synaptic Distribution and 3D Geometry on Signal Integration and Extracellular Field Generation of Hippocampal Pyramidal Cells and Inhibitory Neurons.

In vivo and in vitro multichannel field and somatic intracellular recordings are frequently used to study mechanisms of network pattern generation. When interpreting these data, neurons are often implicitly considered as electrotonically compact cylinders with a homogeneous distribution of excitatory and inhibitory inputs. However, the actual distributions of dendritic length, diameter, and the densities of excitatory and inhibitory input are non-uniform and cell type-specific. We first review quantitative data on the dendritic structure and synaptic input and output distribution of pyramidal cells (PCs) and interneurons in the hippocampal CA1 area. Second, using multicompartmental passive models of four different types of neurons, we quantitatively explore the effect of differences in dendritic structure and synaptic distribution on the errors and biases of voltage clamp measurements of inhibitory and excitatory postsynaptic currents. Finally, using the 3-dimensional distribution of dendrites and synaptic inputs we calculate how different inhibitory and excitatory inputs contribute to the generation of local field potential in the hippocampus. We analyze these effects at different realistic background activity levels as synaptic bombardment influences neuronal conductance and thus the propagation of signals in the dendritic tree. We conclude that, since dendrites are electrotonically long and entangled in 3D, somatic intracellular and field potential recordings miss the majority of dendritic events in some cell types, and thus overemphasize the importance of perisomatic inhibitory inputs and belittle the importance of complex dendritic processing. Modeling results also suggest that PCs and inhibitory neurons probably use different input integration strategies. In PCs, second- and higher-order thin dendrites are relatively well-isolated from each other, which may support branch-specific local processing as suggested by studies of active dendritic integration. In the electrotonically compact parvalbumin- and cholecystokinincontaining interneurons, synaptic events are visible in the whole dendritic arbor, and thus the entire dendritic tree may form a single integrative element. Calretinin-containing interneurons were found to be electrotonically extended, which suggests the possibility of complex dendritic processing in this cell type. Our results also highlight the need for the integration of methods that allow the measurement of dendritic processes into studies of synaptic interactions and dynamics in neural networks.

Mechanisms of sharp wave initiation and ripple generation.

Replay of neuronal activity during hippocampal sharp wave-ripples (SWRs) is essential in memory formation. To understand the mechanisms underlying the initiation of irregularly occurring SWRs and the generation of periodic ripples, we selectively manipulated different components of the CA3 network in mouse hippocampal slices. We recorded EPSCs and IPSCs to examine the buildup of neuronal activity preceding SWRs and analyzed the distribution of time intervals between subsequent SWR events. Our results suggest that SWRs are initiated through a combined refractory and stochastic mechanism. SWRs initiate when firing in a set of spontaneously active pyramidal cells triggers a gradual, exponential buildup of activity in the recurrent CA3 network. We showed that this tonic excitatory envelope drives reciprocally connected parvalbumin-positive basket cells, which start ripple-frequency spiking that is phase-locked through reciprocal inhibition. The synchronized GABA(A) receptor-mediated currents give rise to a major component of the ripple-frequency oscillation in the local field potential and organize the phase-locked spiking of pyramidal cells. Optogenetic stimulation of parvalbumin-positive cells evoked full SWRs and EPSC sequences in pyramidal cells. Even with excitation blocked, tonic driving of parvalbumin-positive cells evoked ripple oscillations. Conversely, optogenetic silencing of parvalbumin-positive cells interrupted the SWRs or inhibited their occurrence. Local drug applications and modeling experiments confirmed that the activity of parvalbumin-positive perisomatic inhibitory neurons is both necessary and sufficient for ripple-frequency current and rhythm generation. These interneurons are thus essential in organizing pyramidal cell activity not only during gamma oscillation, but, in a different configuration, during SWRs.

A flexible, interactive software tool for fitting the parameters of neuronal models.

The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool.

Dendritic spikes induce ripples in parvalbumin interneurons during hippocampal sharp waves.

Sharp-wave ripples are transient oscillatory events in the hippocampus that are associated with the reactivation of neuronal ensembles within specific circuits during memory formation. Fast-spiking, parvalbumin-expressing interneurons (FS-PV INs) are thought to provide fast integration in these oscillatory circuits by suppressing regenerative activity in their dendrites. Here, using fast 3D two-photon imaging and a caged glutamate, we challenge this classical view by demonstrating that FS-PV IN dendrites can generate propagating Ca(2+) spikes during sharp-wave ripples. The spikes originate from dendritic hot spots and are mediated dominantly by L-type Ca(2+) channels. Notably, Ca(2+) spikes were associated with intrinsically generated membrane potential oscillations. These oscillations required the activation of voltage-gated Na(+) channels, had the same frequency as the field potential oscillations associated with sharp-wave ripples, and controlled the phase of action potentials. Furthermore, our results demonstrate that the smallest functional unit that can generate ripple-frequency oscillations is a segment of a dendrite.

Physiological sharp wave-ripples and interictal events in vitro: what's the difference?

Sharp wave-ripples and interictal events are physiological and pathological forms of transient high activity in the hippocampus with similar features. Sharp wave-ripples have been shown to be essential in memory consolidation, whereas epileptiform (interictal) events are thought to be damaging. It is essential to grasp the difference between physiological sharp wave-ripples and pathological interictal events to understand the failure of control mechanisms in the latter case. We investigated the dynamics of activity generated intrinsically in the Cornu Ammonis region 3 of the mouse hippocampus in vitro, using four different types of intervention to induce epileptiform activity. As a result, sharp wave-ripples spontaneously occurring in Cornu Ammonis region 3 disappeared, and following an asynchronous transitory phase, activity reorganized into a new form of pathological synchrony. During epileptiform events, all neurons increased their firing rate compared to sharp wave-ripples. Different cell types showed complementary firing: parvalbumin-positive basket cells and some axo-axonic cells stopped firing as a result of a depolarization block at the climax of the events in high potassium, 4-aminopyridine and zero magnesium models, but not in the gabazine model. In contrast, pyramidal cells began firing maximally at this stage. To understand the underlying mechanism we measured changes of intrinsic neuronal and transmission parameters in the high potassium model. We found that the cellular excitability increased and excitatory transmission was enhanced, whereas inhibitory transmission was compromised. We observed a strong short-term depression in parvalbumin-positive basket cell to pyramidal cell transmission. Thus, the collapse of pyramidal cell perisomatic inhibition appears to be a crucial factor in the emergence of epileptiform events.

The effect of dendritic voltage-gated conductances on the neuronal impedance: a quantitative model.

Neuronal impedance characterizes the magnitude and timing of the subthreshold response of a neuron to oscillatory input at a given frequency. It is known to be influenced by both the morphology of the neuron and the presence of voltage-gated conductances in the cell membrane. Most existing theoretical accounts of neuronal impedance considered the effects of voltage-gated conductances but neglected the spatial extent of the cell, while others examined spatially extended dendrites with a passive or spatially uniform quasi-active membrane. We derived an explicit mathematical expression for the somatic input impedance of a model neuron consisting of a somatic compartment coupled to an infinite dendritic cable which contained voltage-gated conductances, in the more general case of non-uniform dendritic membrane potential. The validity and generality of this model was verified through computer simulations of various model neurons. The analytical model was then applied to the analysis of experimental data from real CA1 pyramidal neurons. The model confirmed that the biophysical properties and predominantly dendritic localization of the hyperpolarization-activated cation current I (h) were important determinants of the impedance profile, but also predicted a significant contribution from a depolarization-activated fast inward current. Our calculations also implicated the interaction of I (h) with amplifying currents as the main factor governing the shape of the impedance-frequency profile in two types of hippocampal interneuron. Our results provide not only a theoretical advance in our understanding of the frequency-dependent behavior of nerve cells, but also a practical tool for the identification of candidate mechanisms that determine neuronal response properties.

Differences in subthreshold resonance of hippocampal pyramidal cells and interneurons: the role of h-current and passive membrane characteristics.

The intrinsic properties of distinct types of neuron play important roles in cortical network dynamics. One crucial determinant of neuronal behaviour is the cell's response to rhythmic subthreshold input, characterised by the input impedance, which can be determined by measuring the amplitude and phase of the membrane potential response to sinusoidal currents as a function of input frequency. In this study, we determined the impedance profiles of anatomically identified neurons in the CA1 region of the rat hippocampus (pyramidal cells as well as interneurons located in the stratum oriens, including OLM cells, fast-spiking perisomatic region-targeting interneurons and cells with axonal arbour in strata oriens and radiatum). The basic features of the impedance profiles, as well as the passive membrane characteristics and the properties of the sag in the voltage response to negative current steps, were cell-type specific. With the exception of fast-spiking interneurons, all cell types showed subthreshold resonance, albeit with distinct features. The HCN channel blocker ZD7288 (10 microM) eliminated the resonance and changed the shape of the impedance curves, indicating the involvement of the hyperpolarization-activated cation current I(h). Whole-cell voltage-clamp recordings uncovered differences in the voltage-dependent activation and kinetics of I(h) between different cell types. Biophysical modelling demonstrated that the cell-type specificity of the impedance profiles can be largely explained by the properties of I(h) in combination with the passive membrane characteristics. We conclude that differences in I(h) and passive membrane properties result in a cell-type-specific response to inputs at given frequencies, and may explain, at least in part, the differential involvement of distinct types of neuron in various network oscillations.

Models, structure, function: the transformation of cortical signals in the dentate gyrus.

Our central question is why the hippocampal CA3 region is the only cortical area capable of forming interference-free representations of complex environmental events (episodes), given that apparently all cortical regions have recurrent excitatory circuits with modifiable synapses, the basic substrate for autoassociative memory networks. We review evidence for the radical (but classic) view that a unique transformation of incoming cortical signals by the dentate gyrus and the subsequent faithful transfer of the resulting code by the mossy fibers are absolutely critical for the appropriate association of memory items by CA3 and, in general, for hippocampal function. In particular, at the gate of the hippocampal formation, the dentate gyrus possesses a set of unusual properties, which selectively evolved for the task of code transformation between cortical afferents and the hippocampus. These evolutionarily conserved anatomical features enable the dentate gyrus to translate the noisy signal of the upstream cortical areas into the sparse and specific code of hippocampal formation, which is indispensable for the efficient storage and recall of multiple, multidimensional memory items. To achieve this goal the mossy fiber pathway maximally utilizes the opportunity to differentially regulate its postsynaptic partners. Selective innervation of CA3 pyramidal cells and interneurons by distinct terminal types creates a favorable condition to differentially regulate the short-term and long-term plasticity and the motility of various mossy terminal types. The utility of this highly dynamic system appears to be the frequency-dependent fine-tuning the excitation and inhibition evoked by the large and the small mossy terminals respectively. This will determine exactly which CA3 cell population is active and induces permanent modification in the autoassociational network of the CA3 region.

Distinct properties of two major excitatory inputs to hippocampal pyramidal cells: a computational study.

The two main sources of excitatory input to CA1 pyramidal cells, the Schaffer collaterals (SC) and the perforant path (PP), target different regions of the dendritic tree. This spatial segregation may have important consequences for the way in which different inputs affect the activity of principal neurons. We constructed detailed biophysical models of CA1 pyramidal cells, incorporating a variety of active conductances, and investigated the ability of synapses located in different dendritic segments to elicit a somatic voltage response. Synaptic efficacy as seen by the soma was strongly dependent on the site of the synapse, with PP inputs being more severely attenuated than SC inputs. Variability within SC inputs, but not between SC inputs and PP inputs, could be eliminated by appropriate scaling of synaptic efficacy. The spatial and temporal summation of multiple synaptic inputs was also investigated. While summation of SC inputs was linear up to the somatic spike threshold, PP inputs summed in a strongly sublinear fashion, with the somatic response remaining subthreshold even following the simultaneous activation of a large number of synapses and during stimulation with high-frequency trains. Finally, the relative impact of different pathways on somatic activity could be effectively altered by modulating the kinetic properties of dendritic transient K+ channels, corresponding to the activation of ascending modulatory neurotransmitter systems. In this case, the efficacy of the PP was enhanced by the dendritic generation and limited spread of action potentials. Strong PP activation could also evoke dendritic Ca++ spikes, which often triggered a somatic burst.

Off-line replay maintains declarative memories in a model of hippocampal-neocortical interactions.

During sleep, neural activity in the hippocampus and neocortex seems to recapitulate aspects of its earlier, awake form. This replay may be a substrate for the consolidation of long-term declarative memories, whereby they become independent of the hippocampus and are stored in neocortex. In contrast to storage, other crucial facets of competent long-term memory, such as maintenance of access to stored traces and preservation of their correct interpretation, have received little attention. We investigate long-term episodic and semantic memory in a theoretical model of neocortical-hippocampal interaction. We find that, in the absence of regular hippocampal reactivation, even supposedly consolidated episodic memories are fragile in the face of cortical semantic plasticity. Replay allows access to episodes stored in the hippocampus to be maintained, by keeping them in appropriate register with changing neocortical representations. Hippocampal storage and replay also has a constructive role in the recall of structured, semantic information.