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![The 3D reconstructions of CA1 cells in rat hippocampus used in this study. (Top) Pyramidal cells; dendrites are shown in black, axons in red; cell identifier, from left: 990803, oh140807_A0_idJ, oh140807_A0_idH, oh140807_A0_idG, oh140807_A0_idF, 050921AM2, oh140807_A0_idC, oh140807_A0_idB, oh140807_A0_idA; (Bottom) Interneurons, from left to right: basket cell (dendrites in black, axon in pink [Cell number 990111HP2]); bistratified cell (dendrites in black, axon in blue [Cell number 980513B]); axo-axonic cell (dendrites in black, axon in purple [Cell number 970911C]); OLM cell (dendrites in black, axon in dark blue [Cell number 011017HP2]); Ivy cell (dendrites in black; axon in light pink [Cell number 010710HP2]); perforant path associated cell (dendrites in black, axon in red [Cell number 011127HP1]); Schaffer collateral-associated cell (dendrites in black, axon in green [Cell number 990827IN5HP3]). Reconstructions by Joanne Falck and Sigrun Lange. SO Stratum Oriens, SP Stratum Pyramidale, SR Stratum Radiatum, SLM Stratum Lacunosum-Moleculare. 3D reconstructions of the PPA, OLM, axo-axonic cells and of other examples of different types of cells are available in S1 Fig of Mercer and Thomson [17]. https://doi.org/10.1371/journal.pcbi.1006423.g001](profile/Sigrun-Lange/publication/327710144/figure/fig1/AS:706730841350145@1545509171338/The-3D-reconstructions-of-CA1-cells-in-rat-hippocampus-used-in-this-study-Top.png)
The 3D reconstructions of CA1 cells in rat hippocampus used in this study. (Top) Pyramidal cells; dendrites are shown in black, axons in red; cell identifier, from left: 990803, oh140807_A0_idJ, oh140807_A0_idH, oh140807_A0_idG, oh140807_A0_idF, 050921AM2, oh140807_A0_idC, oh140807_A0_idB, oh140807_A0_idA; (Bottom) Interneurons, from left to right: basket cell (dendrites in black, axon in pink [Cell number 990111HP2]); bistratified cell (dendrites in black, axon in blue [Cell number 980513B]); axo-axonic cell (dendrites in black, axon in purple [Cell number 970911C]); OLM cell (dendrites in black, axon in dark blue [Cell number 011017HP2]); Ivy cell (dendrites in black; axon in light pink [Cell number 010710HP2]); perforant path associated cell (dendrites in black, axon in red [Cell number 011127HP1]); Schaffer collateral-associated cell (dendrites in black, axon in green [Cell number 990827IN5HP3]). Reconstructions by Joanne Falck and Sigrun Lange. SO Stratum Oriens, SP Stratum Pyramidale, SR Stratum Radiatum, SLM Stratum Lacunosum-Moleculare. 3D reconstructions of the PPA, OLM, axo-axonic cells and of other examples of different types of cells are available in S1 Fig of Mercer and Thomson [17]. https://doi.org/10.1371/journal.pcbi.1006423.g001
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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...
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... From the functional side, optical imaging via genetically encoded voltage indicators (Knöpfel and Song, 2019) will provide in vivo voltage traces for defined neuron types that can greatly enhance the repertoire of firing pattern phenotypes to utilize in simulations (Adam et al., 2019). Data-driven computational models can provide a useful conceptual bridge between molecular sequencing and activity imaging by investigating the effects of specific subcellular distributions of voltage-and ligand-gated conductances on neuronal excitability (Migliore et al., 2018). With the converging maturation of these young techniques and the advent of others yet on the horizon, Hippocampome. ...
Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Previously, Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression (Wheeler et al., 2015). Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.
... From the functional side, optical imaging via genetically encoded voltage indicators (Knöpfel and Song, 2019) will provide in vivo voltage traces for defined neuron types that can greatly enhance the repertoire of firing pattern phenotypes to utilize in simulations (Adam et al., 2019). Data-driven computational models can provide a useful conceptual bridge between molecular sequencing and activity imaging by investigating the effects of specific subcellular distributions of voltage-and ligand-gated conductances on neuronal excitability (Migliore et al., 2018). With the converging maturation of these young techniques and the advent of others yet on the horizon, Hippocampome. ...
Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression. Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.
... Original The model of rat CA1 pyramidal neuron from (R. Migliore et al., 2018) showed a DBLO of only 1 mV (Fig 2A). Since the model did not correct for the experimental recordings for liquid junction potential (LJP), we compensated for the LJP in the models by decreasing the leak reversal potential (Em) of the model such that the resulting E rest of the model was 15 mV lower than the E_rest of the original model. ...
... This is the kinetics used in all the simulations up until now. For Migliore models we used Na_T kinetics used in (R. Migliore et al., 2018). This kinetics was further based on (M. ...
... Na_T kinetics from three different sources - Gouwens et al., 2018, Royeck et al., 2008, and Migliore et, al., 2018. The upper panel shows the steady state curves of the activation (solid lines) and inactivation (dashed lines) gates. ...
Somatic current-step injection is a routinely used protocol to characterize neurons and measure their electrophysiological properties. A signature feature of the responses of many neuronal types is an elevated baseline of action potential firing from the resting membrane potential, the depolarization baseline level offset (DBLO). We find that four key factors together account for high DBLO: Liquid Junction Potential correction, subthreshold impedance amplitude profile, fast potassium delayed rectifier kinetics, and appropriate transient sodium current kinetics. We show that simple mechanisms for DBLO, such as Ohmic depolarization due to current input, fail to explain the effect, and many sophisticated conductance-based models also do not correctly manifest DBLO. Neither low pass filtering effect of membrane nor high reversal potential of potassium channels are able to explain high DBLO. Using stochastic parameter search in conductance-based models of CA1 pyramidal neurons, we explore cellular morphology configurations and channel kinetics. Multi-compartment models which matched experimental subthreshold impedance amplitude profiles had higher DBLO than single compartment models. DBLO was further increased in models that exhibited rapid deactivation time-constants in the dominant potassium conductance. We also saw that certain transient sodium current kinetics resulted in higher DBLO than others. We emphasize that correct expression of DBLO in conductance-based models is important to make quantitative predictions about the levels of ion channels in a neuron and also to correctly predict mechanisms underlying cellular function, such as the role of persistent sodium in imparting firing bistability to a neuron.
Abstract Figure
... In particular, we used 6 positive injections, belonging to the protocol APwaveform, and 4 negative injections from the protocol HyperDePol. Together with the recordings previously obtained and analyzed from rats (Migliore et al., 2018), they were organized in 64 sets of recordings, with 20 neurons from mice (10 of which used in the modeling), 53 neurons from rats. For those features requiring an Action Potential threshold, a value of -20 mV was used. ...
... Except for the non-specific I h current, separately optimized in preliminary optimization runs, channel kinetics were based on those used in many previously published papers on hippocampal neurons (Magee, 1998;Bianchi et al., 2012;Migliore et al., 2018), and validated against a number of experimental findings on CA1 pyramidal neurons. The complete set of active membrane properties included a sodium current (Nax or Na3, for axon or other compartments, respectively), four types of potassium (K DR , K A -proximal or distal for soma and axon or dendrites respectively-, K M , and K D ). ...
... A simple Calcium extrusion mechanism, with a single exponential decay of 100 ms, was also included in all compartments containing Calcium channels. In general, channels were uniformly distributed in all dendritic compartments, except K Ap , K D and I h channels, that were arranged following an appropriate distribution as a function of distance from the soma (Migliore et al., 2018). The values for the peak conductance of each channel were independently optimized in each type of compartment (soma, axon, basal and apical dendrites). ...
The fundamental role of any neuron within a network is to transform complex spatiotemporal synaptic input patterns into individual output spikes. These spikes, in turn, act as inputs for other neurons in the network. Neurons must execute this function across a diverse range of physiological conditions, often based on species-specific traits. Therefore, it is crucial to determine the extent to which findings can be extrapolated between species and, ultimately, to humans. In this study, we employed a multidisciplinary approach to pinpoint the factors accounting for the observed electrophysiological differences between mice and rats, the two species most used in experimental and computational research. After analyzing the morphological properties of their hippocampal CA1 pyramidal cells, we conducted a statistical comparison of rat and mouse electrophysiological features in response to somatic current injections. This analysis aimed to uncover the parameters underlying these distinctions. Using a well-established computational workflow, we created ten distinct single-cell computational models of mouse CA1 pyramidal neurons, ready to be used in a full-scale hippocampal circuit. By comparing their responses to a variety of somatic and synaptic inputs with those of rat models, we generated experimentally testable hypotheses regarding species-specific differences in ion channel distribution, kinetics, and the electrophysiological mechanisms underlying their distinct responses to synaptic inputs during the behaviorally relevant Gamma and Sharp-Wave rhythms.
... We used a modified NMDAR subunit-dependent voltagebased model of synaptic weight change at hippocampal CA3-CA1 synapses (Dainauskas et al., 2023) to investigate the effect of elevated levels of Aβ and AICD. We embedded this model into a multicompartmental CA1 pyramidal neuron model (Peng et al., 2016;Migliore et al., 2018) to study the influence of AICD and of Aβ on synaptic weights within a cluster of randomly distributed CA3-CA1 synapses onto apical dendrites of a CA1 neuron in the stratum radiatum (SR) region. We modeled LTP and LTD, induced by high and low frequency stimulation, respectively, and the alterations due to the increased levels of AD peptides. ...
... The channel kinetics were based on those used in previously published articles on CA1 hippocampal neurons and validated against a number of experimental findings. The model was implemented with channel kinetics used by Migliore et al. (2018) (ModelDB accession number 244688). In particular, we used a delayed-rectifier type current (K DR ), two A-type potassium (K A ) currents (for proximal and distal dendrites), a delayed type current (K D ), three types of voltage-dependent Ca 2+ currents (CaL, CaT, and CaN), the slow afterhyperpolarization (AHP) Ca 2+ -dependent K + current (kCa), a Na + current, and a calcium pump. ...
Alzheimer's disease (AD) is a progressive memory loss and cognitive dysfunction brain disorder brought on by the dysfunctional amyloid precursor protein (APP) processing and clearance of APP peptides. Increased APP levels lead to the production of AD-related peptides including the amyloid APP intracellular domain (AICD) and amyloid beta (A β ), and consequently modify the intrinsic excitability of the hippocampal CA1 pyramidal neurons, synaptic protein activity, and impair synaptic plasticity at hippocampal CA1–CA3 synapses. The goal of the present study is to build computational models that incorporate the effect of AD-related peptides on CA1 pyramidal neuron and hippocampal synaptic plasticity under the AD conditions and investigate the potential pharmacological treatments that could normalize hippocampal synaptic plasticity and learning in AD. We employ a phenomenological N-methyl-D-aspartate (NMDA) receptor-based voltage-dependent synaptic plasticity model that includes the separate receptor contributions on long-term potentiation (LTP) and long-term depression (LTD) and embed it into the a detailed compartmental model of CA1 pyramidal neuron. Modeling results show that partial blockade of Glu2NB-NMDAR-gated channel restores intrinsic excitability of a CA1 pyramidal neuron and rescues LTP in AICD and A β conditions. The model provides insight into the complex interactions in AD pathophysiology and suggests the conditions under which the synchronous activation of a cluster of synaptic inputs targeting the dendritic tree of CA1 pyramidal neuron leads to restored synaptic plasticity.
... Despite the linearity of the equations, the A-GLIF model can reproduce a wide range of physiological firing patterns like bursting, non adapting, and continuous adapting (Migliore et al 2018), provided that the initial data sequences, I 0 dep and I 0 adap , are suitably chosen. For this purpose, a fundamental step is to carry out an analytical study of the spiking properties as a function of the model parameters. ...
... To test and validate our model we considered a set of somatic voltage traces recorded from 84 cells: 58 pyramidal and 26 interneurons, obtained from in vitro rat hippocampal CA1 slices (Migliore et al 2018), in response to somatic constant current injections, from I min stim =200pA to I max stim =1000pA with a step of 200pA. The 314 traces from pyramidal neurons were all classified as continuous accommodating cells (cAC); for interneurons, 54 traces were classified as cAC, 72 traces as bursting cells (bAC), and 62 traces as continuous non-accommodating cells (cNAC). ...
... For a set of specific current injection protocols, which were not available experimentally and that we wanted to use as a reference for model validation, we considered a NEURON (Hines and Carnevale (1997),v8.0.0) model. For this purpose, a realistic morphological and biophysical reconstruction of a hippocampal pyramidal CA1 neuron (cell oh140807_A0_idB from Migliore et al (2018), see Fig. 6a), was adapted to generate the specific cAC firing patterns illustrated in Fig. 6B. We tested both con- Fig. 10 Reference experimental traces. ...
Full-scale morphologically and biophysically realistic model networks, aiming at modeling multiple brain areas, provide an invaluable tool to make significant scientific advances from in-silico experiments on cognitive functions to digital twin implementations. Due to the current technical limitations of supercomputer systems in terms of computational power and memory requirements, these networks must be implemented using (at least) simplified neurons. A class of models which achieve a reasonable compromise between accuracy and computational efficiency is given by generalized leaky integrate-and fire models complemented by suitable initial and update conditions. However, we found that these models cannot reproduce the complex and highly variable firing dynamics exhibited by neurons in several brain regions, such as the hippocampus. In this work, we propose an adaptive generalized leaky integrate-and-fire model for hippocampal CA1 neurons and interneurons, in which the nonlinear nature of the firing dynamics is successfully reproduced by linear ordinary differential equations equipped with nonlinear and more realistic initial and update conditions after each spike event, which strictly depends on the external stimulation current. A mathematical analysis of the equilibria stability as well as the monotonicity properties of the analytical solution for the membrane potential allowed (i) to determine general constraints on model parameters, reducing the computational cost of an optimization procedure based on spike times in response to a set of constant currents injections; (ii) to identify additional constraints to quantitatively reproduce and predict the experimental traces from 85 neurons and interneurons in response to any stimulation protocol using constant and piecewise constant current injections. Finally, this approach allows to easily implement a procedure to create infinite copies of neurons with mathematically controlled firing properties, statistically indistinguishable from experiments, to better reproduce the full range and variability of the firing scenarios observed in a real network.
Supplementary Information
The online version contains supplementary material available at 10.1007/s11538-023-01206-8.
... From the functional side, optical imaging via genetically encoded voltage indicators (Knöpfel and Song, 2019 ) will provide in vivo voltage traces for defined neuron types that can greatly enhance the repertoire of firing pattern phenotypes to utilize in simulations (Adam et al., 2019 ). Data-driven computational models can provide a useful conceptual bridge between molecular sequencing and activity imaging by investigating the effects of specific subcellular distributions of voltage-and ligand-gated conductances on neuronal excitability (Migliore et al., 2018 ). With the converging maturation of these young techniques and the advent of others yet on the horizon, Hippocampome .org ...
Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression. Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, incorporates over 50 new neuron types and extends the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.
... Powerlaws with α between 0-4 have been reported depending on the measured scale for frequencies below 1 kHz [41]- [43]. Dipole moment calculation were performed on morphologically accurate neuron models [44], [45] to gain more insight in the energy content of realistic current dipoles for f > 1 kHz [46]. Our calculations showed fitted power law coefficients of 4 to 5 for f ϵ [1,25] kHz (see supplementary section IV). ...
... The used neuron models are multi-compartmental. Two hippocampal pyramidal models (morphologies: mpg141208 B idA and mpg141209 A idA) from Migliore et al. (2018) [45] were tested, and one L2/3 cortical pyramidal cell from Aberra et al. (2018) [44]. The models were obtained from ModelDB [62]. ...
... The used neuron models are multi-compartmental. Two hippocampal pyramidal models (morphologies: mpg141208 B idA and mpg141209 A idA) from Migliore et al. (2018) [45] were tested, and one L2/3 cortical pyramidal cell from Aberra et al. (2018) [44]. The models were obtained from ModelDB [62]. ...
Acousto-electrophysiological neuroimaging is a technique hypothesized to record electrophysiological activity of the brain with millimeter spatial and sub-millisecond temporal resolution. This improvement is obtained by tagging areas with focused ultrasound (fUS). Due to mechanical vibration with respect to the measuring electrodes, the electrical activity of the marked region will be modulated onto the ultrasonic frequency. The region's electrical activity can subsequently be retrieved via demodulation of the measured signal. In this study, the feasibility of this hypothesized technique is tested. This is done by calculating the forward electroencephalography (EEG) response under quasi-static assumptions. The head is simplified as a set of concentric spheres. Two sizes are evaluated representing human and mouse brains. Moreover, feasibility is assessed for wet and dry transcranial, and for cortically placed electrodes. The activity sources are modeled by dipoles, with their current intensity profile drawn from a power-law power spectral density. It is shown that mechanical vibration modulates the endogenous activity onto the ultrasonic frequency. The signal strength depends non-linearly on the alignment between dipole orientation, vibration direction and recording point. The strongest signal is measured when these three dependencies are perfectly aligned. The signal strengths are in the pV-range for a dipole moment of 5~nAm and ultrasonic pressures within FDA-limits. The endogenous activity can then be accurately reconstructed via demodulation. Two interference types are investigated: vibrational and static. Depending on the vibrational interference, it is shown that millimeter resolution signal detection is possible also for deep brain regions. Subsequently, successful demodulation depends on the static interference, that at MHz-range has to be sub-picovolt.
... A Graphical representation of the 3D reconstructed CA1 cell models used in this study: two pyramidal cells (pyr 1 and pyr 2 ), 1 bistratified (int 1 ) and 1 basket cell (int 2 ). The models are adopted from Migliore et al. (2018) [35]. This reference frame is applicable throughout the whole study, i.e. the soma is at z = 0 mm, and the somato-dendritic axis is parallel to the z-axis. ...
... A Graphical representation of the 3D reconstructed CA1 cell models used in this study: two pyramidal cells (pyr 1 and pyr 2 ), 1 bistratified (int 1 ) and 1 basket cell (int 2 ). The models are adopted from Migliore et al. (2018) [35]. This reference frame is applicable throughout the whole study, i.e. the soma is at z = 0 mm, and the somato-dendritic axis is parallel to the z-axis. ...
... In this study, the optogenetic excitability of cornu ammonis (CA1) cells is investigated with the aim of gaining insights that could help guide optogenetic experiments concerning the suppression and initiation of seizures. The novelty is the use of morphologically reconstructed and data-driven biophysical models of CA1 pyramidal neurons and interneurons [35] that are extended to include ChR2(H134R) dynamics, and are subsequently subjected to a Monte Carlo simulated optic field. The effect of opsin expression level and spatial confinement on stimulation thresholds for multiple pulse durations is determined. ...
Optogenetics has emerged as a promising technique for modulating neuronal activity and holds potential for the treatment of neurological disorders such as temporal lobe epilepsy (TLE). However, clinical translation still faces many challenges. This in-silico study aims to enhance the understanding of optogenetic excitability in CA1 cells and to identify strategies for improving stimulation protocols. Employing state-of-the-art computational models, the optogenetic excitability of four CA1 cells, two pyramidal and two interneurons, expressing ChR2(H134R) is investigated. The results demonstrate that confining the opsin to specific neuronal membrane compartments significantly improves excitability. An improvement is also achieved by focusing the light beam on the most excitable cell region. Moreover, the perpendicular orientation of the optical fiber relative to the somato-dendritic axis yields superior results. Inter-cell variability is observed, highlighting the importance of considering neuron degeneracy when designing optogenetic tools. Opsin confinement to the basal dendrites of the pyramidal cells renders the neuron the most excitability. A global sensitivity analysis identified opsin location and expression level as having the greatest impact on simulation outcomes. The error reduction of simulation outcome due to coupling of neuron modeling with light propagation is shown. The results promote spatial confinement and increased opsin expression levels as important improvement strategies. On the other hand, uncertainties in these parameters limit precise determination of the irradiance thresholds. This study provides valuable insights on optogenetic excitability of CA1 cells useful for the development of improved optogenetic stimulation protocols for, for instance, TLE treatment.
... The question is therefore whether these multiple spatial constraints on function, imposed on the same neuronal morphology, would limit the expression of degeneracy in these neurons. However, several lines of evidence, spanning different neuronal subtypes, demonstrate the emergence of similar somatodendritic functional properties despite widespread heterogeneity in the underlying ion-channel expression profiles and gating properties 20,21,48,[62][63][64][65][66][67] . Thus, there are independent lines of evidence for different ion channels mediating epilepsy-associated changes to somato-dendritic excitability, and for how different combinations of ion channels might yield similar somato-dendritic functional maps. ...
Due to its complex and multifaceted nature, developing effective treatments for epilepsy is still a major challenge. To deal with this complexity we introduce the concept of degeneracy to the field of epilepsy research: the ability of disparate elements to cause an analogous function or malfunction. Here, we review examples of epilepsy-related degeneracy at multiple levels of brain organisation, ranging from the cellular to the network and systems level. Based on these insights, we outline new multiscale and population modelling approaches to disentangle the complex web of interactions underlying epilepsy and to design personalised multitarget therapies.
