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IntroductionRepetitive transcranial magnetic stimulation (rTMS) is a widely used therapeutic tool in neurology and psychiatry, but its cellular and molecular mechanisms are not fully understood. Standardizing stimulus parameters, specifically electric field strength, is crucial in experimental and clinical settings. It enables meaningful comparisons across studies and facilitates the translation of findings into clinical practice. However, the impact of biophysical properties inherent to the stimulated neurons and networks on the outcome of rTMS protocols remains not well understood. Consequently, achieving standardization of biological effects across different brain regions and subjects poses a significant challenge.MethodsThis study compared the effects of 10 Hz repetitive magnetic stimulation (rMS) in entorhino-hippocampal tissue cultures from mice and rats, providing insights into the impact of the same stimulation protocol on similar neuronal networks under standardized conditions.ResultsWe observed the previously described plastic changes in excitatory and inhibitory synaptic strength of CA1 pyramidal neurons in both mouse and rat tissue cultures, but a higher stimulation intensity was required for the induction of rMS-induced synaptic plasticity in rat tissue cultures. Through systematic comparison of neuronal structural and functional properties and computational modeling, we found that morphological parameters of CA1 pyramidal neurons alone are insufficient to explain the observed differences between the groups. Although morphologies of mouse and rat CA1 neurons showed no significant differences, simulations confirmed that axon morphologies significantly influence individual cell activation thresholds. Notably, differences in intrinsic cellular properties were sufficient to account for the 10% higher intensity required for the induction of synaptic plasticity in the rat tissue cultures.ConclusionThese findings demonstrate the critical importance of axon morphology and intrinsic cellular properties in predicting the plasticity effects of rTMS, carrying valuable implications for the development of computer models aimed at predicting and standardizing the biological effects of rTMS.

Pattern separation is a valuable computational function performed by neuronal circuits, such as the dentate gyrus, where dissimilarity between inputs is increased, reducing noise and increasing the storage capacity of downstream networks. Pattern separation is studied from both in vivo experimental and computational perspectives and, a number of different measures (such as orthogonalisation, decorrelation, or spike train distance) have been applied to quantify the process of pattern separation. However, these are known to give conclusions that can differ qualitatively depending on the choice of measure and the parameters used to calculate it. We here demonstrate that arbitrarily increasing sparsity, a noticeable feature of dentate granule cell firing and one that is believed to be key to pattern separation, typically leads to improved classical measures for pattern separation even, inappropriately, up to the point where almost all information about the inputs is lost. Standard measures therefore both cannot differentiate between pattern separation and pattern destruction, and give results that may depend on arbitrary parameter choices. We propose that techniques from information theory, in particular mutual information, transfer entropy, and redundancy, should be applied to penalise the potential for lost information (often due to increased sparsity) that is neglected by existing measures. We compare five commonly-used measures of pattern separation with three novel techniques based on information theory, showing that the latter can be applied in a principled way and provide a robust and reliable measure for comparing the pattern separation performance of different neurons and networks. We demonstrate our new measures on detailed compartmental models of individual dentate granule cells and a dentate microcircuit, and show how structural changes associated with epilepsy affect pattern separation performance. We also demonstrate how our measures of pattern separation can predict pattern completion accuracy. Overall, our measures solve a widely acknowledged problem in assessing the pattern separation of neural circuits such as the dentate gyrus, as well as the cerebellum and mushroom body. Finally we provide a publicly available toolbox allowing for easy analysis of pattern separation in spike train ensembles.

Investigating and modelling the functionality of human neurons remains challenging due to the technical limitations, resulting in scarce and incomplete 3D anatomical reconstructions. Here we used a morphological modelling approach based on optimal wiring to repair the parts of a dendritic morphology that were lost due to incomplete tissue samples. In Drosophila, where dendritic regrowth has been studied experimentally using laser ablation, we found that modelling the regrowth reproduced a bimodal distribution between regeneration of cut branches and invasion by neighbouring branches. Interestingly, our repair model followed growth rules similar to those for the generation of a new dendritic tree. To generalise the repair algorithm from Drosophila to mammalian neurons, we artificially sectioned reconstructed dendrites from mouse and human hippocampal pyramidal cell morphologies, and showed that the regrown dendrites were morphologically similar to the original ones. Furthermore, we were able to restore their electrophysiological functionality, as evidenced by the recovery of their firing behaviour. Importantly, we show that such repairs also apply to other neuron types including hippocampal granule cells and cerebellar Purkinje cells. We then extrapolated the repair to incomplete human CA1 pyramidal neurons, where the anatomical boundaries of the particular brain areas innervated by the neurons in question were known. Interestingly, the repair of incomplete human dendrites helped to simulate the recently observed increased synaptic thresholds for dendritic NMDA spikes in human versus mouse dendrites. To make the repair tool available to the neuroscience community, we have developed an intuitive and simple graphical user interface (GUI), which is available in the TREES toolbox (www.treestoolbox.org).

Abstract 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.

Dendritic spines are crucial for excitatory synaptic transmission as the size of a spine head correlates with the strength of its synapse. The distribution of spine head sizes follows a lognormal-like distribution with more small spines than large ones. We analysed the impact of synaptic activity and plasticity on the spine size distribution in adult-born hippocampal granule cells from rats with induced homo- and heterosynaptic long-term plasticity in vivo and CA1 pyramidal cells from Munc13–1/Munc13–2 knockout mice with completely blocked synaptic transmission. Neither the induction of extrinsic synaptic plasticity nor the blockage of presynaptic activity degrades the lognormal-like distribution but changes its mean, variance and skewness. The skewed distribution develops early in the life of the neuron. Our findings and their computational modelling support the idea that intrinsic synaptic plasticity is sufficient for the generation, while a combination of intrinsic and extrinsic synaptic plasticity maintains lognormal-like distribution of spines.

The cation-chloride cotransporters KCC2 and NKCC1 regulate the intracellular Cl− concentration and cell volume of neurons and/or glia. The Cl− extruder KCC2 is expressed at higher levels than the Cl− transporter NKCC1 in mature compared to immature neurons, accounting for the developmental shift from high to low Cl− concentration and from depolarizing to hyperpolarizing currents through GABA-A receptors. Previous studies have shown that KCC2 expression is downregulated following central nervous system injury, returning neurons to a more excitable state, which can be pathological or adaptive. Here, we show that deafferentation of the dendritic segments of granule cells in the outer (oml) and middle (mml) molecular layer of the dentate gyrus via entorhinal denervation in vivo leads to cell-type- and layer-specific changes in the expression of KCC2 and NKCC1. Microarray analysis validated by reverse transcription-quantitative polymerase chain reaction revealed a significant decrease in Kcc2 mRNA in the granule cell layer 7 days post-lesion. In contrast, Nkcc1 mRNA was upregulated in the oml/mml at this time point. Immunostaining revealed a selective reduction in KCC2 protein expression in the denervated dendrites of granule cells and an increase in NKCC1 expression in reactive astrocytes in the oml/mml. The NKCC1 upregulation is likely related to the increased activity of astrocytes and/or microglia in the deafferented region, while the transient KCC2 downregulation in granule cells may be associated with denervation-induced spine loss, potentially also serving a homeostatic role via boosting GABAergic depolarization. Furthermore, the delayed KCC2 recovery might be involved in the subsequent compensatory spinogenesis.

The axon initial segment (AIS) is the site of action potential initiation and important for the integration of synaptic input. Length and localization of the AIS are dynamic, modulated by afferent activity and contribute to the homeostatic control of neuronal excitability. Synaptopodin is a plasticity-related protein expressed by the majority of telencephalic neurons. It is required for the formation of cisternal organelles within the AIS and an excellent marker to identify these enigmatic organelles at the light microscopic level. Here we applied 2 h of high frequency stimulation of the medial perforant path in rats in vivo to induce a strong long-term potentiation of dentate gyrus granule cells. Immunolabeling for βIV-spectrin and synaptopodin were performed to study structural changes of the AIS and its cisternal organelles. Three-dimensional analysis of the AIS revealed a shortening of the AIS and a corresponding reduction of the number of synaptopodin clusters. These data demonstrate a rapid structural plasticity of the AIS and its cisternal organelles to strong stimulation, indicating a homeostatic response of the entire AIS compartment.

Background: Transcranial Magnetic Stimulation (TMS) is a widely used non-invasive brain stimulation method. However, its mechanism of action and the neural response to TMS are still poorly understood. Multi-scale modeling can complement experimental research to study the subcellular neural effects of TMS. At the macroscopic level, sophisticated numerical models exist to estimate the induced electric fields. However, multi-scale computational modeling approaches to predict TMS cellular and subcellular responses, crucial to understanding TMS plasticity inducing protocols, are not available so far. Objective: We develop an open-source multi-scale toolbox Neuron Modeling for TMS (NeMo-TMS) to address this problem. Methods: NeMo-TMS generates accurate neuron models from morphological reconstructions, couples them to the external electric fields induced by TMS, and simulates the cellular and subcellular responses of single-pulse and repetitive TMS. Results: We provide examples showing some of the capabilities of the toolbox. Conclusion: NeMo-TMS toolbox allows researchers a previously not available level of detail and precision in realistically modeling the physical and physiological effects of TMS.

Abstract Background Amyloid precursor protein (APP) processing is central to Alzheimer’s disease (AD) etiology. As early cognitive alterations in AD are strongly correlated to abnormal information processing due to increasing synaptic impairment, it is crucial to characterize how peptides generated through APP cleavage modulate synapse function. We previously described a novel APP processing pathway producing η-secretase-derived peptides (Aη) and revealed that Aη–α, the longest form of Aη produced by η-secretase and α-secretase cleavage, impaired hippocampal long-term potentiation (LTP) ex vivo and neuronal activity in vivo. Methods With the intention of going beyond this initial observation, we performed a comprehensive analysis to further characterize the effects of both Aη-α and the shorter Aη-β peptide on hippocampus function using ex vivo field electrophysiology, in vivo multiphoton calcium imaging, and in vivo electrophysiology. Results We demonstrate that both synthetic peptides acutely impair LTP at low nanomolar concentrations ex vivo and reveal the N-terminus to be a primary site of activity. We further show that Aη-β, like Aη–α, inhibits neuronal activity in vivo and provide confirmation of LTP impairment by Aη–α in vivo. Conclusions These results provide novel insights into the functional role of the recently discovered η-secretase-derived products and suggest that Aη peptides represent important, pathophysiologically relevant, modulators of hippocampal network activity, with profound implications for APP-targeting therapeutic strategies in AD.

Transcranial magnetic stimulation (TMS) can depolarize cortical neurons through the intact skin and skull. The characteristics of the induced electric field (E-field) have a major impact on specific outcomes of TMS. Using multi-scale computational modeling, we explored whether the stimulation parameters derived from the primary motor cortex (M1) induce comparable macroscopic E-field strengths and subcellular/cellular responses in the dorsolateral prefrontal cortex (DLPFC). To this aim, we calculated the TMS-induced E-field in 16 anatomically realistic head models and simulated the changes in membrane voltage and intracellular calcium levels of morphologically and biophysically realistic human pyramidal cells in the M1 and DLPFC. We found that the conventional intensity selection methods (i.e., motor threshold and fixed intensities) produce variable macroscopic E-fields. Consequently, it was challenging to produce comparable subcellular/cellular responses across cortical regions with distinct folding characteristics. Prospectively, personalized stimulation intensity selection could standardize the E-fields and the subcellular/cellular responses to repetitive TMS across cortical regions and individuals. The suggested computational approach points to the shortcomings of the conventional intensity selection methods used in clinical settings. We propose that multi-scale modeling has the potential to overcome some of these limitations and broaden our understanding of the neuronal mechanisms for TMS.

Neurons encounter unavoidable evolutionary trade-offs between multiple tasks. They must consume as little energy as possible while effectively fulfilling their functions. Cells displaying the best performance for such multi-task trade-offs are said to be Pareto optimal, with their ion channel configurations underpinning their functionality. Ion channel degeneracy, however, implies that multiple ion channel configurations can lead to functionally similar behaviour. Therefore, instead of a single model, neuroscientists often use populations of models with distinct combinations of ionic conductances. This approach is called population (database or ensemble) modelling. It remains unclear, which ion channel parameters in the vast population of functional models are more likely to be found in the brain. Here we argue that Pareto optimality can serve as a guiding principle for addressing this issue by helping to identify the subpopulations of conductance-based models that perform best for the trade-off between economy and functionality. In this way, the high-dimensional parameter space of neuronal models might be reduced to geometrically simple low-dimensional manifolds, potentially explaining experimentally observed ion channel correlations. Conversely, Pareto inference might also help deduce neuronal functions from high-dimensional Patch-seq data. In summary, Pareto optimality is a promising framework for improving population modelling of neurons and their circuits.

Summary: The development of epilepsy (epileptogenesis) involves a complex interplay of neuronal and immune processes. Here, we present a first-of-its-kind mathematical model to better understand the relationships among these processes. Our model describes the interaction between neuroinflammation, blood-brain barrier disruption, neuronal loss, circuit remodeling, and seizures. Formulated as a system of nonlinear differential equations, the model reproduces the available data from three animal models. The model successfully describes characteristic features of epileptogenesis such as its paradoxically long timescales (up to decades) despite short and transient injuries or the existence of qualitatively different outcomes for varying injury intensity. In line with the concept of degeneracy, our simulations reveal multiple routes toward epilepsy with neuronal loss as a sufficient but non-necessary component. Finally, we show that our model allows for in silico predictions of therapeutic strategies, revealing injury-specific therapeutic targets and optimal time windows for intervention.

Abstract Modeling long-term neuronal dynamics may require running long-lasting simulations. Such simulations are computationally expensive, and therefore it is advantageous to use simplified models that sufficiently reproduce the real neuronal properties. Reducing the complexity of the neuronal dendritic tree is one option. Therefore, we have developed a new reduced-morphology model of the rat CA1 pyramidal cell which retains major dendritic branch classes. To validate our model with experimental data, we used HippoUnit, a recently established standardized test suite for CA1 pyramidal cell models. The HippoUnit allowed us to systematically evaluate the somatic and dendritic properties of the model and compare them to models publicly available in the ModelDB database. Our model reproduced (1) somatic spiking properties, (2) somatic depolarization block, (3) EPSP attenuation, (4) action potential backpropagation, and (5) synaptic integration at oblique dendrites of CA1 neurons. The overall performance of the model in these tests achieved higher biological accuracy compared to other tested models. We conclude that, due to its realistic biophysics and low morphological complexity, our model captures key physiological features of CA1 pyramidal neurons and shortens computational time, respectively. Thus, the validated reduced-morphology model can be used for computationally demanding simulations as a substitute for more complex models.

Previously we showed that the vitamin A metabolite all-trans retinoic acid (atRA) induces synaptic plasticity in acute brain slices prepared from the mouse and human neocortex (Lenz et al., 2021). Depending on the brain region studied, distinct effects of atRA on excitatory and inhibitory neurotransmission have been reported. Here, we used intraperitoneal injections of atRA (10 mg/kg) in adult C57BL/6J mice to study the effects of atRA on excitatory and inhibitory neurotransmission in the mouse fascia dentata—a brain region implicated in memory acquisition. No major changes in synaptic transmission were observed in the ventral hippocampus while a significant increase in both spontaneous excitatory postsynaptic current frequencies and synapse numbers were evident in the dorsal hippocampus 6 hr after atRA administration. The intrinsic properties of hippocampal dentate granule cells were not significantly different and hippocampal transcriptome analysis revealed no essential neuronal changes upon atRA treatment. In light of these findings, we tested for the metaplastic effects of atRA, that is, for its ability to modulate synaptic plasticity expression in the absence of major changes in baseline synaptic strength. Indeed, in vivo long-term potentiation (LTP) experiments demonstrated that systemic atRA treatment improves the ability of dentate granule cells to express LTP. The plasticity-promoting effects of atRA were not observed in synaptopodin-deficient mice, therefore, extending our previous results regarding the relevance of synaptopodin in atRA-mediated synaptic strengthening in the mouse prefrontal cortex. Taken together, our data show that atRA mediates synaptopodin-dependent metaplasticity in mouse dentate granule cells.

Artificial neural networks, taking inspiration from biological neurons, have become an invaluable tool for machine learning applications. Recent studies have developed techniques to effectively tune the connectivity of sparsely-connected artificial neural networks, which have the potential to be more computationally efficient than their fully-connected counterparts and more closely resemble the architectures of biological systems. We here present a normalisation, based on the biophysical behaviour of neuronal dendrites receiving distributed synaptic inputs, that divides the weight of an artificial neuron's afferent contacts by their number. We apply this dendritic normalisation to various sparsely-connected feedforward network architectures, as well as simple recurrent and self-organised networks with spatially extended units. The learning performance is significantly increased, providing an improvement over other widely-used normalisations in sparse networks. The results are two-fold, being both a practical advance in machine learning and an insight into how the structure of neuronal dendritic arbours may contribute to computation.

The impact of GABAergic transmission on neuronal excitability depends on the Cl--gradient across membranes. However, the Cl--fluxes through GABAA receptors alter the intracellular Cl- concentration ([Cl-]i) and in turn attenuate GABAergic responses, a process termed ionic plasticity. Recently it has been shown that coincident glutamatergic inputs significantly affect ionic plasticity. Yet how the [Cl-]i changes depend on the properties of glutamatergic inputs and their spatiotemporal relation to GABAergic stimuli is unknown. To investigate this issue, we used compartmental biophysical models of Cl- dynamics simulating either a simple ball-and-stick topology or a reconstructed CA3 neuron. These computational experiments demonstrated that glutamatergic co-stimulation enhances GABA receptor-mediated Cl- influx at low and attenuates or reverses the Cl- efflux at high initial [Cl-]i. The size of glutamatergic influence on GABAergic Cl--fluxes depends on the conductance, decay kinetics, and localization of glutamatergic inputs. Surprisingly, the glutamatergic shift in GABAergic Cl--fluxes is invariant to latencies between GABAergic and glutamatergic inputs over a substantial interval. In agreement with experimental data, simulations in a reconstructed CA3 pyramidal neuron with physiological patterns of correlated activity revealed that coincident glutamatergic synaptic inputs contribute significantly to the activity-dependent [Cl-]i changes. Whereas the influence of spatial correlation between distributed glutamatergic and GABAergic inputs was negligible, their temporal correlation played a significant role. In summary, our results demonstrate that glutamatergic co-stimulation had a substantial impact on ionic plasticity of GABAergic responses, enhancing the attenuation of GABAergic inhibition in the mature nervous systems, but suppressing GABAergic [Cl-]i changes in the immature brain. Therefore, glutamatergic shift in GABAergic Cl--fluxes should be considered as a relevant factor of short-term plasticity.

Class I ventral posterior dendritic arborisation (c1vpda) proprioceptive sensory neurons respond to contractions in the Drosophila larval body wall during crawling. Their dendritic branches run along the direction of contraction, possibly a functional requirement to maximise membrane curvature during crawling contractions. Although the molecular machinery of dendritic patterning in c1vpda has been extensively studied, the process leading to the precise elaboration of their comb-like shapes remains elusive. Here, to link dendrite shape with its proprioceptive role, we performed long-term, non-invasive, in vivo time-lapse imaging of c1vpda embryonic and larval morphogenesis to reveal a sequence of differentiation stages. We combined computer models and dendritic branch dynamics tracking to propose that distinct sequential phases of stochastic growth and retraction achieve efficient dendritic trees both in terms of wire and function. Our study shows how dendrite growth balances structure–function requirements, shedding new light on general principles of self-organisation in functionally specialised dendrites.

Summary: A large number of experiments have indicated that precise spike times, firing rates, and synapse locations crucially determine the dynamics of long-term plasticity induction in excitatory synapses. However, it remains unknown how plasticity mechanisms of synapses distributed along dendritic trees cooperate to produce the wide spectrum of outcomes for various plasticity protocols. Here, we propose a four-pathway plasticity framework that is well grounded in experimental evidence and apply it to a biophysically realistic cortical pyramidal neuron model. We show in computer simulations that several seemingly contradictory experimental landmark studies are consistent with one unifying set of mechanisms when considering the effects of signal propagation in dendritic trees with respect to synapse location. Our model identifies specific spatiotemporal contributions of dendritic and axo-somatic spikes as well as of subthreshold activation of synaptic clusters, providing a unified parsimonious explanation not only for rate and timing dependence but also for location dependence of synaptic changes. : Synaptic plasticity is shaped by local dynamic processes within dendritic trees. Ebner et al. present a biologically inspired plasticity rule and study its implications in a detailed model of a pyramidal cell. They provide a unified description of rate, timing, and location dependence and predict cooperative plasticity in dendrites. Keywords: compartmental modeling, synaptic plasticity, spike timing dependent plasticity (STDP), dendritic spikes, NMDA spikes, long term potentiation (LTP), long term depression (LTD), synaptic cooperativity

Giant depolarizing potentials (GDPs) represent a typical spontaneous activity pattern in the immature hippocampus. GDPs are mediated by GABAergic and glutamatergic synaptic inputs and their initiation requires an excitatory GABAergic action, which is typical for immature neurons due to their elevated intracellular Cl- concentration ([Cl-]i). Because GABAA receptors are ligand-gated Cl- channels, activation of these receptors can potentially influence [Cl-]i. However, whether the GABAergic activity during GDPs influences [Cl-]i is unclear. To address this question we performed whole-cell and gramicidin-perforated patch-clamp recordings from visually identified CA3 pyramidal neurons in immature hippocampal slices of mice at postnatal days 4–7. These experiments revealed that the [Cl-]i of CA3 neurons displays a considerable heterogeneity, ranging from 13 to 70 mM (average 38.1 ± 3.2 mM, n = 36). In accordance with this diverse [Cl-]i, GDPs induced either Cl--effluxes or Cl--influxes. In high [Cl-]i neurons with a negative Cl--driving force (DFCl) the [Cl-]i decreased after a GDP by 12.4 ± 3.4 mM (n = 10), while in low [Cl-]i neurons with a positive DFCl [Cl-]i increased by 4.4 ± 0.9 mM (n = 6). Inhibition of GDP activity by application of the AMPA receptor antagonist CNQX led to a [Cl-]i decrease to 24.7 ± 2.9 mM (n = 8). We conclude from these results, that Cl--fluxes via GABAA receptors during GDPs induced substantial [Cl-]i changes and that this activity-dependent ionic plasticity in neuronal [Cl-]i contributes to the functional consequences of GABAergic responses, emphasizing the concept that [Cl-]i is a state- and compartment-dependent parameter of individual cells.

The hippocampal dentate gyrus plays a role in spatial learning and memory and is thought to encode differences between similar environments. The integrity of excitatory and inhibitory transmission and a fine balance between them is essential for efficient processing of information. Therefore, identification and functional characterization of crucial molecular players at excitatory and inhibitory inputs is critical for understanding the dentate gyrus function. In this minireview, we discuss recent studies unraveling molecular mechanisms of excitatory/inhibitory synaptic transmission, long-term synaptic plasticity, and dentate granule cell excitability in the hippocampus of live animals. We focus on the role of three major postsynaptic proteins localized at excitatory (neuroligin-1) and inhibitory synapses (neuroligin-2 and collybistin). In vivo recordings of field potentials have the advantage of characterizing the effects of the loss of these proteins on the input-output function of granule cells embedded in a network with intact connectivity. The lack of neuroligin-1 leads to deficient synaptic plasticity and reduced excitation but normal granule cell output, suggesting unaltered excitation-inhibition ratio. In contrast, the lack of neuroligin-2 and collybistin reduces inhibition resulting in a shift towards excitation of the dentate circuitry.

The nervous system is a non-linear dynamical complex system with many feedback loops. A conventional wisdom is that in the brain the quantum fluctuations are self-averaging and thus functionally negligible. However, this intuition might be misleading in the case of non-linear complex systems. Because of an extreme sensitivity to initial conditions, in complex systems the microscopic fluctuations may be amplified and thereby affect the system’s behavior. In this way quantum dynamics might influence neuronal computations. Accumulating evidence in non-neuronal systems indicates that biological evolution is able to exploit quantum stochasticity. The recent rise of quantum biology as an emerging field at the border between quantum physics and the life sciences suggests that quantum events could play a non-trivial role also in neuronal cells. Direct experimental evidence for this is still missing but future research should address the possibility that quantum events contribute to an extremely high complexity, variability and computational power of neuronal dynamics.

Compartmental models are the theoretical tool of choice for understanding single neuron computations. However, many models are incomplete, built ad hoc and require tuning for each novel condition rendering them of limited usability. Here, we present T2N, a powerful interface to control NEURON with Matlab and TREES toolbox, which supports generating models stable over a broad range of reconstructed and synthetic morphologies. We illustrate this for a novel, highly detailed active model of dentate granule cells (GCs) replicating a wide palette of experiments from various labs. By implementing known differences in ion channel composition and morphology, our model reproduces data from mouse or rat, mature or adult-born GCs as well as pharmacological interventions and epileptic conditions. This work sets a new benchmark for detailed compartmental modeling. T2N is suitable for creating robust models useful for large-scale networks that could lead to novel predictions. We discuss possible T2N application in degeneracy studies.

The effects of ionotropic γ-aminobutyric acid receptor (GABA-A, GABAA) activation depends critically on the Cl−-gradient across neuronal membranes. Previous studies demonstrated that the intracellular Cl−-concentration ([Cl−]i) is not stable but shows a considerable amount of activity-dependent plasticity. To characterize how membrane properties and different molecules that are directly or indirectly involved in GABAergic synaptic transmission affect GABA-induced [Cl−]i changes, we performed compartmental modeling in the NEURON environment. These simulations demonstrate that GABA-induced [Cl−]i changes decrease at higher membrane resistance, revealing a sigmoidal dependency between both parameters. Increase in GABAergic conductivity enhances [Cl−]i with a logarithmic dependency, while increasing the decay time of GABAA receptors leads to a nearly linear enhancement of the [Cl−]i changes. Implementing physiological levels of HCO3−-conductivity to GABAA receptors enhances the [Cl−]i changes over a wide range of [Cl−]i, but this effect depends on the stability of the HCO3− gradient and the intracellular pH. Finally, these simulations show that pure diffusional Cl−-elimination from dendrites is slow and that a high activity of Cl−-transport is required to improve the spatiotemporal restriction of GABA-induced [Cl−]i changes. In summary, these simulations revealed a complex interplay between several key factors that influence GABA-induced [Cl]i changes. The results suggest that some of these factors, including high resting [Cl−]i, high input resistance, slow decay time of GABAA receptors and dynamic HCO3− gradient, are specifically adapted in early postnatal neurons to facilitate limited activity-dependent [Cl−]i decreases.

Long-term potentiation (LTP) and long-term depression (LTD) are widely accepted to be synaptic mechanisms involved in learning and memory. It remains uncertain, however, which particular activity rules are utilized by hippocampal neurons to induce LTP and LTD in behaving animals. Recent experiments in the dentate gyrus of freely moving rats revealed an unexpected pattern of LTP and LTD from high-frequency perforant path stimulation. While 400 Hz theta-burst stimulation (400-TBS) and 400 Hz delta-burst stimulation (400-DBS) elicited substantial LTP of the tetanized medial path input and, concurrently, LTD of the non-tetanized lateral path input, 100 Hz theta-burst stimulation (100-TBS, a normally efficient LTP protocol for in vitro preparations) produced only weak LTP and concurrent LTD. Here we show in a biophysically realistic compartmental granule cell model that this pattern of results can be accounted for by a voltage-based spike-timing-dependent plasticity (STDP) rule combined with a relatively fast Bienenstock-Cooper-Munro (BCM)-like homeostatic metaplasticity rule, all on a background of ongoing spontaneous activity in the input fibers. Our results suggest that, at least for dentate granule cells, the interplay of STDP-BCM plasticity rules and ongoing pre- and postsynaptic background activity determines not only the degree of input-specific LTP elicited by various plasticity-inducing protocols, but also the degree of associated LTD in neighboring non-tetanized inputs, as generated by the ongoing constitutive activity at these synapses.

Neuronal death and subsequent denervation of target areas are major consequences of several neurological conditions such asischemia or neurodegeneration (Alzheimer's disease). The denervation-induced axonal loss results in reorganization of the dendritic tree of denervated neurons. The dendritic reorganization has been previously studied using entorhinal cortex lesion (ECL). ECL leads to shortening and loss of dendritic segments in the denervated outer molecular layer of the dentate gyrus. However, the functional importance of these long-term dendritic alterations is not yet understood and their impact on neuronal electrical properties remains unclear. Here we analyzed what happens to the electrotonic structure and excitability of dentate granule cells after lesion-induced alterations of their dendritic morphology, assuming all other parameters remain equal. We performed comparative electrotonic analysis in anatomically and biophysically realistic compartmental models of 3D-reconstructed healthy and denervated granule cells. Using the method of morphological modeling based on optimization principles minimizing the amount of wiring and maximizing synaptic democracy, we built artificial granule cells which replicate morphological features of their real counterparts. Our results show that somatofugal and somatopetal voltage attenuation in the passive cable model are strongly reduced in denervated granule cells. In line with these predictions, the attenuation both of simulated backpropagating action potentials and forward propagating EPSPs was significantly reduced in dendrites of denervated neurons. Intriguingly, the enhancement of action potential backpropagation occurred specifically in the denervated dendritic layers. Furthermore, simulations of synaptic f-I curves revealed a homeostatic increase of excitability in denervated granule cells. In summary, our morphological and compartmental modeling indicates that unless modified by changes of passive and/or active membrane properties, the plastic remodeling of dendrites following lesion of entorhinal inputs to granule cells will boost the efficacy of action potential backpropagation significantly and maintain their firing rate. Our results suggest that in addition to synaptic and intrinsic plasticity, a novel form of homeostatic plasticity,structural plasticity due todendritic remodeling.is operating in denervated neurons.

Denervation-induced changes in excitatory synaptic strength were studied following entorhinal deafferentation of hippocampal granule cells in mature (≥ 3 weeks old) mouse organotypic entorhino-hippocampal slice cultures. Whole-cell patch-clamp recordings revealed an increase in excitatory synaptic strength in response to denervation during the first week after denervation. By the end of the second week synaptic strength had returned to baseline. Because these adaptations occurred in response to the loss of excitatory afferents, they appeared to be in line with a homeostatic adjustment of excitatory synaptic strength. To test whether denervation-induced changes in synaptic strength exploit similar mechanisms as homeostatic synaptic scaling following pharmacological activity blockade, we treated denervated cultures at 2 days post lesion for 2 days with tetrodotoxin. In these cultures, the effects of denervation and activity blockade were not additive, suggesting that similar mechanisms are involved. Finally, we investigated whether entorhinal denervation, which removes afferents from the distal dendrites of granule cells while leaving the associational afferents to the proximal dendrites of granule cells intact, results in a global or a local up-scaling of granule cell synapses. By using computational modeling and local electrical stimulations in Strontium (Sr(2+))-containing bath solution, we found evidence for a lamina-specific increase in excitatory synaptic strength in the denervated outer molecular layer at 3-4 days post lesion. Taken together, our data show that entorhinal denervation results in homeostatic functional changes of excitatory postsynapses of denervated dentate granule cells in vitro.

During postnatal development hippocampal dentate granule cells (GCs) often extend dendrites from the basal pole of their cell bodies into the hilar region. These so-called hilar basal dendrites (hBD) usually regress with maturation. However, hBDs may persist in a subset of mature GCs under certain conditions (both physiological and pathological). The functional role of these hBD-GCs remains not well understood. Here, we have studied hBD-GCs in mature (≥18 days in vitro) mouse entorhino-hippocampal slice cultures under control conditions and have compared their basic functional properties (basic intrinsic and synaptic properties) and structural properties (dendritic arborisation and spine densities) to those of neighboring GCs without hBDs in the same set of cultures. Except for the presence of hBDs, we did not detect major differences between the two GC populations. Furthermore, paired recordings of neighboring GCs with and without hBDs did not reveal evidence for a heavy aberrant GC-to-GC connectivity. Taken together, our data suggest that in control cultures the presence of hBDs on GCs is neither sufficient to predict alterations in the basic functional and structural properties of these GCs nor indicative of a heavy GC-to-GC connectivity between neighboring GCs.