Hippocampome.org: a knowledge base of neuron types in the rodent hippocampus

Wheeler, Diek W.; White, Charise M.; Rees, Colin; Komendantov, Alexander O.; Hamilton, D. R.; Ascoli, Giorgio A.

doi:10.7554/elife.09960

Cited by 148 publications

(155 citation statements)

References 96 publications

(107 reference statements)

Supporting

Mentioning

146

Contrasting

Order By: Relevance

“…Multicompartmental models were created by reading the reconstructed morphologies into the Neuron simulation tool (Carnevale and Hines, 2006), and adding appropriate, spatially uniform membrane and intracellular properties (specific capacitance, membrane conductance, and axial resistance) to match the basic subthreshold physiological properties (somatic input resistance and membrane time constant) of each cell type based on the online database Hippocampome.org (Wheeler et al, 2015). The resulting values of the passive parameters were as follows: C m = 0.01 F/m 2 , g l = 0.8 S/m 2 , R a = 1 Ωm for the PC; C m = 0.01 F/m 2 , g l = 0.5 S/m 2 , R a = 1 Ωm for the PV-positive cell; C m = 0.005 F/m 2 , g l = 0.1 S/m 2 , R a = 2 Ωm for the CCK-positive cell; C m = 0.01 F/m2, g l = 0.5 S/m 2 , R a = 1 Ωm for the CR-positive cell.…”

Section: Methodsmentioning

confidence: 99%

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

Gulyás

Freund

Káli

2016

Front. Neural Circuits

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

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

Gulyás

Freund

Káli

2016

Front. Neural Circuits

View full text Add to dashboard Cite

show abstract

“…Additional specific efforts for experimental data sharing include Collaborative Research in Computational Neuroscience [141] (crcns.org) hosting Neurodata Without Borders [143], Hippocampome [160] (hippocampome.org), and NeuroElectro [161] (neuroelectro.org). …”

Section: Broader Issues – Experimental Data Sharing and Model Matmentioning

confidence: 99%

Reproducibility in Computational Neuroscience Models and Simulations

McDougal

Bulanova

Lytton

2016

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

Objective Like all scientific research, computational neuroscience research must be reproducible. Big data science, including simulation research, cannot depend exclusively on journal articles as the method to provide the sharing and transparency required for reproducibility. Methods Ensuring model reproducibility requires the use of multiple standard software practices and tools, including version control, strong commenting and documentation, and code modularity. Results Building on these standard practices, model sharing sites and tools have been developed that fit into several categories: 1. standardized neural simulators, 2. shared computational resources, 3. declarative model descriptors, ontologies and standardized annotations; 4. model sharing repositories and sharing standards. Conclusion A number of complementary innovations have been proposed to enhance sharing, transparency and reproducibility. The individual user can be encouraged to make use of version control, commenting, documentation and modularity in development of models. The community can help by requiring model sharing as a condition of publication and funding. Significance Model management will become increasingly important as multiscale models become larger, more detailed and correspondingly more difficult to manage by any single investigator or single laboratory. Additional big data management complexity will come as the models become more useful in interpreting experiments, thus increasing the need to ensure clear alignment between modeling data, both parameters and results, and experiment.

show abstract

“…However, because neurons are often named on an ad hoc basis without full mappings to previous names and descriptors (Hamilton et al, 2016), author-provided names of types were treated warily. Instead, neuron types were identified chiefly based on their primary neurotransmitter (i.e., glutamate or GABA) and for having a unique binary pattern of axonal and dendritic presence or absence across the 26 parcels (Wheeler et al, 2015). In rare cases (e.g., fast-spiking/parvalbumin-positive and regular-spiking/cholecystokinin-positive basket cells, ivy and bistratified cells), aligned molecular marker and electrophysiological evidence was sufficiently different to support the creation of two distinct types out of neurons with the same morphological pattern and primary neurotransmitter.…”

Section: Methodsmentioning

confidence: 99%

“…Although neurons are indeed unique cellular units, they may be readily grouped according to sets of properties that cluster along a continuum. Over the past 6 years, we mounted a massive literature search to catalog all known neuron types in the rodent hippocampal formation based on their main neurotransmitter, axonal-dendritic morphologies, somatic location, molecular expression, and electrophysiological parameters (Wheeler et al, 2015). All the properties (and underlying experimental evidence) of the resulting 122 neuron types are collated in a publicly available, highly curated knowledge base (Hippocampome.org) that is ripe for analysis along multiple dimensions.…”

Section: Introductionmentioning

confidence: 99%

Graph Theoretic and Motif Analyses of the Hippocampal Neuron Type Potential Connectome

Rees

Wheeler

Hamilton

et al. 2016

eNeuro

Self Cite

View full text Add to dashboard Cite

We computed the potential connectivity map of all known neuron types in the rodent hippocampal formation by supplementing scantly available synaptic data with spatial distributions of axons and dendrites from the open-access knowledge base Hippocampome.org. The network that results from this endeavor, the broadest and most complete for a mammalian cortical region at the neuron-type level to date, contains more than 3200 connections among 122 neuron types across six subregions. Analyses of these data using graph theory metrics unveil the fundamental architectural principles of the hippocampal circuit. Globally, we identify a highly specialized topology minimizing communication cost; a modular structure underscoring the prominence of the trisynaptic loop; a core set of neuron types serving as information-processing hubs as well as a distinct group of particular antihub neurons; a nested, two-tier rich club managing much of the network traffic; and an innate resilience to random perturbations. At the local level, we uncover the basic building blocks, or connectivity patterns, that combine to produce complex global functionality, and we benchmark their utilization in the circuit relative to random networks. Taken together, these results provide a comprehensive connectivity profile of the hippocampus, yielding novel insights on its functional operations at the computationally crucial level of neuron types.

show abstract

Hippocampome.org: a knowledge base of neuron types in the rodent hippocampus

Cited by 148 publications

References 96 publications

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

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

Reproducibility in Computational Neuroscience Models and Simulations

Graph Theoretic and Motif Analyses of the Hippocampal Neuron Type Potential Connectome

Contact Info

Product

Resources

About