Hippocampome.org 2.0 is a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits

Abstract

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.

Full text

Wheeler etal. eLife 2023;13:RP90597. DOI: https://doi.org/10.7554/eLife.90597 1 of 27
Hippocampome. org 2.0 is a knowledge
base enabling data- driven spiking
neural network simulations of rodent
hippocampalcircuits
Diek W Wheeler1,2*, Jeffrey D Kopsick1,3, Nate Sutton1,2, Carolina Tecuatl1,2,
Alexander O Komendantov1,2, Kasturi Nadella1,2, Giorgio A Ascoli1,2,3*
1Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for
Advanced Study, George Mason University, Fairfax, United States; 2Bioengineering
Department and Center for Neural Informatics, Structures, & Plasticity, College
of Engineering and Computing, George Mason University, Fairfax, United States;
3Interdisciplinary Program in Neuroscience, College of Science, George Mason
University, Fairfax, United States
Abstract Hippocampome. org is a mature open- access knowledge base of the rodent hippo-
campal 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 aggrega-
tion of literature- mined data, including among others neuron counts, spiking patterns, synaptic phys-
iology, 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 incor-
porating 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 down-
loadable 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.
eLife assessment
The authors have greatly expanded their important hippocampome. org resource about rodent
hippocampal cell types, their physiological properties, and their interactions. With version 2.0, they
make a significant advance in providing a user- friendly means to make computer models of hippo-
campal circuits. The work is convincing, and there are only minor reservations that the figures may
be too complex.
Introduction
Neuroscience knowledge continues to increase every year (Eke etal., 2022; Yeung et al., 2017),
making it challenging for researchers to keep abreast of mounting data and evolving information
RESEARCH ADVANCE
*For correspondence:
dwheele5@gmu.edu (DWW);
ascoli@gmu.edu (GAA)
Competing interest: The authors
declare that no competing
interests exist.
Funding: See page 20
Preprint posted
29 June 2023
Sent for Review
09 July 2023
Reviewed preprint posted
27 September 2023
Reviewed preprint revised
26 January 2024
Version of Record published
12 February 2024
Reviewing Editor: Helen
E Scharfman, Nathan Kline
Institute and New York University
Langone Medical Center, United
States
Copyright Wheeler etal. This
article is distributed under the
terms of the Creative Commons
Attribution License, which
permits unrestricted use and
redistribution provided that the
original author and source are
credited.

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even in their own domain of expertise. Large- scale endeavors, such as the Brain Research through
Advancing Innovative Neurotechnologies (BRAIN) Initiative (Insel et al., 2013) and the European
Union’s Human Brain Project (Amunts etal., 2016), are contributing to this tremendous growth along
with the ‘long tail’ of independent labs and individual scientists (Ferguson etal., 2014). A key orga-
nizing principle for neuroscience knowledge is the seminal notion of neuron types (Petilla Interneuron
Nomenclature Group etal., 2008; Zeng and Sanes, 2017), which constitute the conceptual ‘parts
list’ of functional circuits. The National Institutes of Health launched the BRAIN Initiative Cell Census
Network (BICCN) to help establish a comprehensive reference of the cell type diversity in the human,
mouse, and non- human primate brains (Hawrylycz etal., 2022). This multi- institution collaboration
is already producing innovative results (Muñoz- Castañeda etal., 2021) and actionable community
resources (Hawrylycz etal., 2022).
Hippocampome. org (https://www.hippocampome.org) is an open- access knowledge base of the
rodent hippocampal circuit (dentate gyrus, CA3, CA2, CA1, subiculum, and entorhinal cortex) at the
mesoscopic level of neuron types (Wheeler etal., 2015). This resource has proven popular and effec-
tive thanks to the adoption of a simple yet powerful classification system for defining neuron types.
Specifically, a key property for the identification of neuron types in Hippocampome. org is the loca-
tion of axons and dendrites across the subregions and layers of the hippocampal formation. This
approach can be broadly extended to classify neurons in other brain regions and neural systems
(Ascoli and Wheeler, 2016). Focusing on axonal and dendritic distributions provides several consid-
erable advantages. First, these features mediate neuronal connectivity, thus immediately revealing
the underlying blueprint of network circuitry (Rees etal., 2017). Second, they are widely used in the
neuroscience community as a reliable and concrete anchoring signature to correlate electrophysio-
logical and transcriptomic profiles (DeFelipe etal., 2013). Third, to coherently classify neuron types,
we are not reliant on the inconsistent nomenclature that authors provide (Hamilton etal., 2017a).
Therefore, starting from the foundational morphology- based identification of 122 neuron types in the
first release (version 1.0 or v1.0), Hippocampome. org progressively amassed an increasing amount
of complementary data, such as firing patterns (Komendantov etal., 2019), molecular expression
(White etal., 2020), cell counts (Attili etal., 2022), synaptic communication (Moradi etal., 2022;
Moradi and Ascoli, 2020), in vivo oscillations (Sanchez- Aguilera etal., 2021), and connection prob-
abilities (Tecuatl etal., 2021b). In all cases, the public repository provided direct links to the specific
peer- reviewed empirical evidence supporting the added knowledge.
Since the inception of Hippocampome. org, we have attempted to maintain the naming styling
for already established neuron types by adopting either canonical names, only- cited names, most
frequently cited names or hybridizations of cited names, and only as last resort crafting our own
names (see Figure 6 in Hamilton etal., 2017a). In the entorhinal cortex (EC), where many of the
hybridizations occur, we have followed the authors’ own definitions for the six layers and the distinc-
tion between medial and lateral, when incorprating such terms into Hippocampome. org type names.
If a neuron type exists in both medial and lateral entorhinal cortex, then the name is simply prefixed
by EC, rather than MEC or LEC. As another example, in the dentate gyrus (DG), we established
HIPROM (Hilar Interneuron with PRojections to the Outer Molecular layer) and MOCAP (MOlecular
Commissural- Associational Pathway- related axons and dendrites) in the same vein as HIPP (HIlar
Perforant Path- associated), MOPP (MOlecular layer Perforant Path- associated), and HICAP (HIlar
Commissural- Associational Pathway- related), where the outer two- thirds of the stratum moleculare
(SMo) is distinguished by the region intercepted by the Perforant Path from the entorhinal cortex,
and the inner one- third (SMi) is characterized by the commissural- associational pathway that often
recurrently connects stratum moleculare with the hilus.
Having established a web- based integrated storehouse of hippocampal information, Hippo-
campome. org also expanded its scope by including data- driven computational models of neuronal
excitability and synaptic signaling, as well as ties to community resources such as NeuroMorpho.Org
(https://www.neuromorpho.org; Akram et al., 2018), SenseLab ModelDB (McDougal etal., 2017),
CARLsim (Niedermeier et al., 2022), and the Allen Brain Atlas (Jones et al., 2009). Altogether,
these extensions resulted in the emergence of a complete framework in Hippocampome. org v2.0 that
makes the original vision of this project, to enable data- driven spiking neural network simulations of
rodent hippocampal circuits (Ascoli, 2010), finally achievable. The present report thus marks a new
phase in the life cycle of this community resource.

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One line of research pertaining to the state of simulation readiness of Hippocampome. org
involves a real- scale mouse model of CA3 (Kopsick etal., 2023) investigating the cellular mecha-
nisms of pattern completion, which includes Pyramidal cells and seven main inhibitory interneuron
types. Another avenue of research investigates spatial representation involving in vivo firing grid cells
(Sargolini etal., 2006), which utilizes Medial Entorhinal Cortex Layer II Stellate cells, two types of
pyramidal cells, and three interneuron types. Both lines of research make use of Hippocampome. org
parameters for properties such as cell census, Izhikevich models (Izhikevich, 2003), synaptic signals,
and connection probabilities.
The following ‘Description of resource’ section begins with a concise, referenced overview of
the neural properties collated from Hippocampome. org v1.0 through release v1.12. We then briefly
describe the new neuron types and data currently being added in Hippocampome. org v2.0. Next is
an abridged summary of the usage and recognition of this online portal in biomedical research. This is
followed by an explanation of the latest capabilities of Hippocampome. org v2.0 to search, filter, and
download the complete set of computational parameters enabling quantitative connectomic analyses
and spiking neural network simulations. The section concludes with an outlook of possible research
applications allowed by the expansion of this scientific resource.
Description of resource
Characterizing properties of hippocampal neuron types
Hippocampome. org v1.0 (Wheeler etal., 2015) established the morphological encoding of axonal
and dendritic locations and the main neurotransmitter (glutamate or GABA) as the primary determi-
nants of neuron types in the rodent hippocampal formation. For example, a Dentate Gyrus Basket
cell (with name capitalized to indicate a formally defined neuron type) is a GABAergic cell with axon
contained in the granular layer and dendrites spanning all dentate gyrus layers (Figure1A1- 4). In
this framework, two neurons releasing the same neurotransmitter belong to different types if the
axon or dendrites of only one of them invades any of the 26 layers across 6 subregions of the hippo-
campal formation ( hippocampome. org/ morphology). In other words, neurons of the same type share
the same potential inputs, outputs, and excitatory vs. inhibitory function. These properties were
initially supplemented with additional empirical evidence for molecular expression of major protein
biomarkers (Figure 1A5; hippocampome. org/ markers) and membrane biophysics (Figure 1A6- 7;
hippocampome. org/ electrophysiology).
Many neuronal properties and functionalities were progressively added in 12 subsequent releases
(Table1). The numerical sequencing of these Hippocampome. org versions depended on the order
of peer- review and publication of the corresponding scientific reports. Here we will describe them
instead in logical groupings. The first two updates enhanced the user functionality of the knowl-
edge base. Specifically, v1.1 integrated a web- based interactive thesaurus mapping of synonyms and
definitions (Hamilton etal., 2017a; hippocampome. org/ find- term) to help disambiguate the many
terminological inconsistencies in the neuroscience literature (Shepherd etal., 2019; Yuste etal.,
2020). Release v1.2 introduced the capability to browse, search, and analyze the potential connec-
tivity between neuron types (Rees etal., 2016; Hippocampome. org/ connectivity) as derived from the
compiled overlapping locations of all the presynaptic axons and postsynaptic dendrites. Transcrip-
tomic information was greatly expanded in both v1.3 (Hamilton etal., 2017b), which incorporated
in situ hybridization data from the Allen Brain Atlas (Lein etal., 2007), and v1.5 (White etal., 2020),
which leveraged relational inferences interlinking the region- specific expression of two or more genes.
The quantifications of firing pattern phenotypes, such as rapid adapting spiking, transient stut-
tering, and persistent slow- wave bursting, in v1.6 (Komendantov etal., 2019; hippocampome. org/
firing_ patterns) were fitted by dynamical systems modeling (Izhikevich, 2003) in v1.7 (Venkadesh
etal., 2019; hippocampome. org/ Izhikevich). Although the above properties were largely measured
from slice preparations, v1.9 made available measurements from in vivo recordings (Sanchez- Aguilera
et al., 2021; hippocampome. org/ in- vivo). Release v1.10 provided a compendium of cognitive
functions linked to specific hippocampal neurons (Sutton and Ascoli, 2021; hippocampome. org/
cognome), while the v1.11 neuron type census estimated the population counts for each neuron
type (Attili etal., 2022; hippocampome. org/ census). Finally, there are a set of properties pertaining
not to individual neuron types but to synaptic connections between a pair of pre- and post- synaptic

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Figure 1. Dening neuron types in Hippocampome.org. (A)Properties of a Hippocampome.org v1.0 neuron type.
(A1)Morphology of a Dentate Gyrus (i) 2232 Basket cell (NeuroMorpho.Org cell NMO_34300: Figure S3A in Hosp
etal., 2014) with axons (red) in stratum granulosum (SG) and dendrites (blue) in all four layers. (A2)Schematic
interpretation of the morphological tracing, where the circle represents the location of the soma in SG, the red
triangle the location of the axons in SG, and the blue rectangles the locations of the dendrites in all four layers.
(A3)Hippocampome.org representation of the morphology, where a blue square with a vertical line (|) indicates
dendritic presence in the outer two- thirds of the stratum moleculare (SMo) and the inner one- third of the stratum
moleculare (SMi) and the hilus (H),a purple square with a cross (+) indicates both axonal and dendritic presence
in SG, and a black dot (•) indicates the soma location in SG. (A4)Hippocampome.org numerical coding of the
reconstructed neuron, where 2 indicates the presence of dendrites (in SMo, SMi, and H); and 3 indicates the
presence of both axons and dendrites (in SG). (A5)Biomarker expressions, where a green triangle indicates
positive expression for parvalbumin (PV), and blue triangles indicate negative expression for cholecystokinin
(CCK) and vasoactive intestinal polypeptide (VIP). (A6)Firing pattern phenotype (non- adapting spiking (NASP);
adapted from Figure 1B1 in Savanthrapadian etal., 2014). (A7)Membrane biophysics values (from Figure 3C
and Table 1 in Lübke etal., 1998) recorded at 35–37°C. (B)Properties for a Hippocampome.org v2.0 neuron
Figure 1 continued on next page

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neuron types. In particular, v1.8 calculated the synaptic probabilities and the numbers of contacts per
connected pair (Tecuatl et al., 2021b; hippocampome. org/ syn_ probabilities), and v1.4 data mined
synaptic physiology (Moradi and Ascoli, 2020; hippocampome. org/ synaptome), with conductance,
time constant, and short- term plasticity values normalized by age, temperature, species, sex, and
type. (B1)Morphology of a DG (i) 2210 Basket GRALDEN (NeuroMorpho.Org cell NMO_146159: Figure 4S3 in
Vaden etal., 2020) with red axons in SG and blue dendrites in SMo and Smi. (B2)Schematic interpretation of
the reconstruction (same symbols as in A2). (B3- 4) Hippocampome.org representation and numerical coding of
the morphology (same symbols as in A3- 4). (B5)Biomarker expression. (B6)Firing pattern phenotype (silence
preceded by transient stuttering (TSTUT.SLN); adapted from Figure S4 in Markwardt etal., 2011). (B7)Membrane
biophysics values recorded at room temperature (from Figure 4D in Vaden etal., 2020), and at 22°C (from Figure
S4 in Markwardt etal., 2011); emboldened values were extracted from the ring pattern trace in B6. Membrane
biophysics abbreviations: Vrest: resting membrane potential; Vthresh: ring threshold potential; APampl: action potential
amplitude; APwidth: action potential width; Rin: input resistance; τm: membrane time constant; Max FR: maximum
ring rate; fAHP: fast after- hyperpolarizing potential; sAHP: slow after- hyperpolarizing potential; Sag ratio: ratio of
the steady- state membrane potential to the minimum membrane potential.
Figure 1 continued
Table 1. Added knowledge and functioning in Hippocampome.org releases v1.1–12.
Version Contribution Article
v1.1 • definitions for terms and phrases relevant to Hippocampome.org
Hamilton etal.,
2017a
v1.2
• clickable connectivity matrix
• interactive connectivity navigator Java applet
• searching by connectivity Rees etal., 2016
v1.3
• downloadable list of ABA predictions of marker expressions
• utility for viewing the effects of thresholds on ABA marker expres-
sion predictions
Hamilton etal.,
2017b
v1.4 • access to the synapse knowledge base
Moradi and Ascoli,
2020
v1.5 • relational biomarker expression inferences White etal., 2020
v1.6
• firing pattern phenotypes
• clickable firing pattern matrix
• clickable firing pattern parameters matrix
• search by firing pattern
• search by firing pattern parameter
Komendantov etal.,
2019
v1.7
• Izhikevich models
• clickable Izhikevich model parameters matrix
• downloadable single- neuron parameter files
• downloadable single- neuron CARLSim4 simulation files
• ability to perform single- neuron simulations of the firing patterns
Venkadesh etal.,
2019
v1.8
• clickable/downloadable neurite lengths matrix
• clickable/downloadable somatic path distances matrix
• clickable/downloadable numbers of potential synapses matrix
• clickable/downloadable numbers of contacts matrix
• clickable/downloadable connection probabilities matrix Tecuatl etal., 2021b
v1.9 • clickable matrix for in vivo recordings
Sanchez- Aguilera
etal., 2021
v1.10
• Cognome knowledge base of spiking neural circuit functions and
network simulations of the hippocampal formation
Sutton and Ascoli,
2021
v1.11 • clickable matrix of neuron type census values for rat and mouse Attili etal., 2022
v1.12
• clickable/downloadable matrices of synaptic physiology param-
eter values (g, τd, τr, τf, U) for combinations of species, sex, age,
temperature, and recording mode Moradi etal., 2022

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recording method in v1.12 (Moradi etal., 2022; hippocampome. org/ synapse), leveraging machine
learning and a phenomenological model (Tsodyks etal., 1998).
Expanding the catalog of neuron types and properties from
Hippocampome. org v1.x to v2.0
The Hippocampome. org framework to classify neuron types and collate their properties allows
agile content updates as new data are continuously reported in the peer- reviewed literature. For
example, the description of a parvalbumin- positive dentate gyrus GABAergic interneuron with
axon contained in the granular layer and dendrites invading the molecular layer but not the hilus
(Vaden etal., 2020) supported the definition of a new neuron type (Figure 1B1- 5), referred to
in Hippocampome. org v2.0 as DG Basket GRALDEN (GRAnular Layer DENdrites) cell. Moreover,
such an identification made it possible to unequivocally ascribe to this neuron type previously
Figure 2. New neuron types added to Hippocampome.org v2.0. (A)Morphology encodings of the 56 new neuron types that extend the 124 types in
Hippocampome.org v1.12. (Left) Increase in number of neuron types for each subregion. For the neuron type names, excitatory types (e)are in black
font and inhibitory types (i)are in gray font. The 3–6 digit numbers encode the patterns of axons and dendrites in the layers of the home subregion
of the neuron type: 0=no axons or dendrites, 1=only axons, 2=only dendrites, 3=both axons and dendrites. A “p” indicates that the neuron types
projects across into other subregions. (B)Biomarker expressions of the neuron types. (C)Membrane biophysics values for the neuron types. Morphology
abbreviations: DG: dentate gyrus; SMo: outer two- thirds of stratum moleculare; SMi: inner one- third of stratum moleculare; SG: stratum granulosum;
H: hilus; SLM: stratum lacunosum- moleculare; SR: stratum radiatum; SL: stratum lucidum; SP: stratum pyramidale; SO: stratum oriens; Sub: subiculum;
PL: polymorphic layer; EC: entorhinal cortex. Marker abbreviations: CB: calbindin; CR: calretinin; PV: parvalbumin; 5HT- 3: serotonin receptor 3; CB1:
cannabinoid receptor type 1; GABAa α1: GABA- a alpha 1 subunit; mGluR1a: metabotropic glutamate receptor 1 alpha; Mus2R: muscarinic type 2
receptor; vGluT3: vesicular glutamate transporter 3; CCK: cholecystokinin; ENK: enkephalin; NPY: neuropeptide Y; SOM: somatostatin; VIP: vasoactive
intestinal polypeptide; nNOS: neuronal nitric oxide synthase; RLN: reelin. Membrane biophysics abbreviations: see Figure1.

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reported electrophysiological characteristics (Figure1B6- 7; Markwardt etal., 2011). Comprehen-
sive literature mining following the same process expanded the Hippocampome. org v2.0 catalog
with 56 new neuron types across 5 of the 6 subregions of the hippocampal formation (Figure2),
including axonal- dendritic morphological patterns (Figure2A), molecular expression (Figure2B),
and membrane biophysics (Figure2C).
Besides identifying new neuron types, the Hippocampome. org classification system also allows
the ongoing accumulation of new properties onto existing neuron types as well as the reconciliation
of fragmented descriptions from scientific publications (Figure3). For instance, converging evidence
indicates that Entorhinal Cortex Layer III Pyramidal cells have axonal projections in all layers of CA1
(Deller etal., 1996; Takács etal., 2012), not just in stratum lacunosum- moleculare (SLM) as originally
reported (Steward, 1976). Hippocampome. org v2.0 captures both the new extracted knowledge
and the corresponding experimental evidence (Figure3A). The annotation of neuron type- specific
firing phases relative to in vivo oscillations in v1.9 highlighted a clear distinction between Superficial
and Deep CA1 Pyramidal cells (Sanchez- Aguilera etal., 2021). The present release enriches that
description with accompanying novel molecular markers (Figure3B1), membrane biophysics values
(Figure3B2), and differential connectivity with other subregions and neuron types (Figure3B3). Simi-
larly, numerous additional firing patterns (Figure3C) have been datamined for existing neuron types,
such as adapting spiking in CA1 Oriens- Bistratified cells (Craig and McBain, 2015), non- adapting
spiking in CA3 Basket Cholecystokinin- positive (CCK+) cells (Szabadics and Soltesz, 2009) or tran-
sient stuttering in CA1 Radiatum Giant cells (Kirson and Yaari, 2000). Notably, this includes a novel
phenotype, transient stuttering followed by persistent stuttering (TSTUT.PSTUT) in CA1 Interneuron
Specific O- targeting QuadD cells (Chamberland etal., 2010). With this report, we also release new
differential connection probabilities to various CA1 neuron type targets from traditional CA3 Pyra-
midal cells vs. CA3c Pyramidal cells (Figure3D) and from Dentate Gyrus Granule cells to mossy fiber
CA3 targets (Table2).
Quantifying the content and impact of Hippocampome. org
Over the course of subsequent releases, we have measured Hippocampome. org content using
two metrics. The number of pieces of knowledge (PoK) tallies the distinct units of structured infor-
mation, such as the statements that Dentate Gyrus Granule cell axons invade the hilus or that
CA1 Basket cells express parvalbumin. The pieces of evidence (PoE) are specific excerpts of peer
reviewed publications (portion of text, figure, or table) or database entries (e.g. from the Allen
Brain Atlas) always linked to each PoK. Both PoK and PoE continued to grow with successive
releases of Hippocampome. org (Figure4A). Notably, the largest increases in PoK and PoE were
related to synaptic properties (Moradi and Ascoli, 2020; Tecuatl etal., 2021b; Moradi etal.,
2022). Specifically, the data underlying synaptic physiology and connection probabilities were
supported by over 23,000 PoE and yielded a remarkable 500,000 PoK thanks to the normalized
collection of signaling and short- term plasticity modeling parameters for multiple combinations of
experimental conditions.
To assess community usage of Hippocampome. org, we tracked the number of citations of the
original publication (Wheeler etal., 2015) and of the subsequent versions (Figure4B), separating
simple references from actual employment of information extracted from Hippocampome. org for
secondary analyses (Table3). At the time of this writing, year 2021 proved to be the most prolific
citation- wise; however, more than a third of the releases (v1.8–12) appeared after 2021 and most PoK
were added in 2022, so usage could potentially accelerate further in coming years. An early appli-
cation of Hippocampome. org- sourced data used subthreshold biophysical measures, such as input
resistance and membrane time constant, for multicompartmental models of signal integration and
extracellular field generation (Gulyás etal., 2016). That study concluded that somatic and proximal
dendritic intracellular recordings in pyramidal cells and calretinin- positive interneurons, in particular,
do not capture a sizable portion of the synaptic inputs. As a recent usage example, another lab
employed Hippocampome. org as the primary information resource for neuron types in DG, CA3, and
CA1 (Schumm etal., 2022). They discovered that mild traumatic brain injury, in the form of alterations
in spike- timing- dependent plasticity, may affect the broadband power in CA3 and CA1 and the phase
coherence between CA3 and CA1.

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Figure 3. Extensions to the neuronal properties of Hippocampome.org v1.x. (A)Additions to the axonal projections (circled in green) for two v1.0
neuron types, based on information derived from Figure 2b in Deller etal., 1996. (B1)Biomarker expressions for the two CA1 Pyramidal sub- types
added in Hippocampome.org v1.9 (Sanchez- Aguilera etal., 2021). (B2)Membrane biophysics values for the two sub- types. (B3)CA2 projects
preferentially to the deep sublayer of CA1 (Kohara etal., 2014). More perisomatic parvalbumin- positive (PV+) GABAergic boutons are found at CA1
Deep Pyramidal cells (Valero etal., 2015). CA1 Supercial Pyramidal cells form more frequent connections to PV + CA1 Basket cells, and PV + CA1
Basket cells form signicantly more perisomatic axon terminals on CA1 Deep Pyramidal cells (Lee etal., 2014). (C1)Additions to the ring pattern
phenotypes of v1.0 neuron types. (C2a) Example of adapting spiking (ASP.) in a CA1 Oriens- Bistratied cell (adapted from Figure 4B in Craig and
Figure 3 continued on next page

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From experimental data to biologically realistic computational models
Several key neural properties collated into Hippocampome. org have gradually transformed the site
from an organized repository of hippocampal knowledge to a computational framework for launching
real- scale neural network simulations. Specifically, building a data- driven circuit model of a neural
system (such as the hippocampal formation or portion thereof) requires four essential quantities
besides the full list of neuron types (Bahmer etal., 2023; DePasquale etal., 2023): (i) the number of
neurons in each type; (ii) the input- output response function for each neuron type; (iii) the connection
probability for each pair of interacting neuron types; and (iv) the unitary synaptic signals for each pair
of connected neuron types (Figure5). Of those quantities, (i) and (ii) are neuron type properties, while
(iii) and (iv) are properties of directional connections, defined as a distinct pair of a presynaptic and a
postsynaptic neuron type. Moreover, (i) and (iii) are structural features, while (ii) and (iv) are electro-
physiological ones.
Hippocampome. org v1.11 provides estimates of the number of neurons in each neuron type (i)
for both rats and mice (Figure5A). These values were derived in a two- step process (Attili et al.,
2020): first, literature mining extracted suitable quantitative relations such as the cellular density in a
given layer (Attili etal., 2019), the total count of neurons expressing a certain gene, or the fraction
of sampled cells with a particular morphology; second, numerical optimization of the corresponding
equations yielded a complete census for all neuron types. As of v1.7, Hippocampome. org represents
McBain, 2015). (C2b) Example of non- adapting spiking (NASP) in a CA3 Basket CCK + cell (adapted from Figure 3A in Szabadics and Soltesz, 2009).
(C2c) Example of transient stuttering (TSTUT.) in a CA1 Radiatum Giant cell (reproduced from Figure 2Bb in Kirson and Yaari, 2000). (C2d) Example
of transient stuttering followed by persistent stuttering (TSTUT.PSTUT) in a CA1 Interneuron Specic O- targeting QuadD (adapted from Figure 2D in
Chamberland etal., 2010). (C2e) Example of silence preceded by transient stuttering (TSTUT.SLN) in a DG MOLAX cell (adapted from Figure S2c in
Lee etal., 2016). (D)Synaptic probabilities for projecting connections and the corresponding number of contacts in brackets between the two CA3
Pyramidal neuron types and a selection of CA1 neuron types. Morphology abbreviations: see Figure2. Marker abbreviations: CB: calbindin; Astn2:
astrotactin 2; Dcn: decorin; Gpc3: glypican 3; Grp: gastrin releasing peptide; Htr2c: 5- hydroxytryptamine receptor 2c; Ndst4: N- deacetylase and N-
sulfotransferase 4; Nov: nephroblastoma overexpressed; Nr3c2: nuclear receptor subfamily 3 group C member 2; Nr4a1: nuclear receptor subfamily
4 group A member 1; Prss12: serine protease 12; Prss23: serine protease 23; Wfs1: wolframin ER transmembrane glycoprotein. Membrane biophysics
abbreviations: see Figure1.
© 2000, Society for Neuroscience. Figure 3C2c is reproduced from Figure 2Bb from Kirson and Yaari, 2000, with permission from Society for
Neuroscience. It is not covered by the CC- BY 4.0 licence and further reproduction of this panel would need permission from the copyright holder.
Figure 3 continued
Table 2. Probabilities of connection and number of contacts per connected pair from DG Granule
cell to mossy fiber targets in CA3.
Postsynaptic neuron type Probability # contacts
CA3 Pyramidal 1.11E- 04 1.08
CA3c Pyramidal 3.91E- 04 1.31
CA3 Spiny Lucidum Dentate- Projecting 5.89E- 04 1.69
CA3 Mossy Fiber- Associated ORDEN 4.44E- 04 1.27
CA3 Basket 6.55E- 04 1.50
CA3 Basket CCK+ 2.14E- 04 1.16
CA3 Ivy 3.35E- 04 1.29
CA3 Mossy Fiber- Associated 3.78E- 05 1.04
CA3 LMR- Targeting 1.31E- 04 1.21
CA3 Lucidum ORAX 2.62E- 04 1.19
CA3 Lucidum- Radiatum 3.25E- 04 1.13
CA3 Axo- Axonic 7.56E- 04 1.50
CA3 Bistratied 8.25E- 04 1.45
CA3 QuadD- LM 2.91E- 04 1.25

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the neuronal input- output response function (ii)
in the form of single- and multi- compartment
Izhikevich models (Figure 5B) fitted by evolu-
tionary algorithms to accurately reproduce the
observed firing behavior of each neuron type
(Venkadesh etal., 2018). For v1.8, the connection
probability (iii) from one neuron type to another
(Figure 5C) was computed from measurements
of the appropriate axonal and dendritic lengths
in each invaded subregion and layer ( hippocam-
pome. org/ A- D_ lengths). Additionally, users can
also access the presynaptic and postsynaptic
path distances from the respective somata (
hippocampome. org/ soma_ distances) and the
number of contacts per connected neuron pairs
( hippocampome. org/ num_ contacts). As for the
synaptic communication between neurons (iv),
Hippocampome. org v1.12 adopted the Tsodyks-
Pawelzik- Markram formulation, representing
unitary signals and short- term plasticity with five
constants for each directional pair of interacting
neuron types: the synaptic conductance, decay
time, recovery time, facilitation time, and the
utilization ratio (Tsodyks et al., 1998; Moradi
etal., 2022). Once again, these parameters were
fitted from the experimental data (Cutsuridis
etal., 2018) employing deep learning to account
for (and predict the effects of) numerous exper-
imental variables (Figure 5D), including species
(rat vs. mouse), sex (male vs. female), age (young
vs. adult), recording temperature (room vs. body),
and clamping configuration (voltage vs. current).
The above description underscores the crucial
interconnectedness of individually measured
neuronal properties forming a cohesive whole
in Hippocampome. org (Figure 6). In particular,
normalized simulation parameters (e.g. the sensitivity of recovery variable in Izhikevich models)
are derived from quantitative experimental measurements, such as the spiking adaptation rate
(Figure6a). Those in turn are linked to an identified neuron type based on qualitative features, like
calbindin expression or laminar distribution of axons and dendrites. In addition to enabling computa-
tional applications as described below, such integration also allows the meta- analysis of correlations
between morphological features, molecular profiles, electrophysiological properties, and dynamic
circuit functions. At the same time, several components of Hippocampome. org are also synergistically
linked to external community resources (Figure6B). For example, each neuron page links out to all
three- dimensional morphological reconstructions of the same cell type available in NeuroMorpho.Org
(Ascoli etal., 2007), and selected data from NeuroMorpho.Org were used to compute axonal and
dendritic length and connection probabilities. Each neuron page also links out to all computational
models (including Hodgkin- Huxley, stochastic diffusion, mean firing rate, etc.) involving the same cell
type on ModelDB (McDougal etal., 2017), while conversely ModelDB includes the Izhikevich models
for all Hippocampome. org neuron types. Moreover, simulation parameters from Hippocampome. org
are exportable to the CARLsim simulation environment (Nageswaran etal., 2009), enabling fast
execution of spiking neural network models optimized for GPUs. Furthermore, Hippocampome. org
harnessed data from the Allen Brain Atlas (Lein etal., 2007) to infer gene expression for principal
neurons and cell densities for use in the neuron type census.
Figure 4. Trends in Hippocampome.org data,
knowledge, citations, and usage since v1.0. (A)Increase
in pieces of knowledge (blue) and evidence (red) with
Hippocampome.org version number. (B)Number of
citations, in which the publication is simply referenced
(blue and gray portions), and usage cases, in which the
citing work makes use of the information contained
within the Hippocampome.org- related work (orange
and yellow portions), by year.

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To facilitate construction of spiking neural network simulations, Hippocampome. org v2.0 also
includes a new graphical user interface (GUI). With this GUI, users can download sets of simulation
parameter values for arbitrarily selected neuron types, a subregion of interest, or the whole hippo-
campal formation (Figure 7). The sets consist of files for the instantiation of CARLsim simulations
and a comma- separated values (CSV) spreadsheet of parameters for use in a different simulation
environment of the user’s choice. For the convenience of users interested in simplified circuit models,
Hippocampome. org informally ranks the importance of each neuron type in terms of the functional
role it plays in the hippocampal circuitry from 1 (essential) to 5 (dispensable). For instance, a user may
choose to simulate only the canonical, or rank 1, neuron types of the tri- synaptic circuit and entorhinal
cortex, consisting of Dentate Gyrus Granule, CA3 Pyramidal, CA1 Pyramidal, and Medial Entorhinal
Cortex Layer II Stellate cells. When Hippocampome. org is missing a parameter value due to insuffi-
cient experimental evidence, the GUI exports a default value clearly indicating so in the downloadable
files. For missing Izhikevich and synaptic signaling parameters, the default values are those provided
by the CARLsim simulation environment. For missing synaptic probabilities, Hippocampome. org
Table 3. Examples of independent studies utilizing unique neuronal properties from
Hippocampome.org v1.0.
Article Usage
Gulyás etal., 2016
Lists of subthreshold physiological properties for
multicompartmental modeling
Skene and Grant, 2016 Catalog of CA1 Interneuron types
Faghihi and Moustafa, 2017
Diversity of hippocampal neuron types and
morphological neuronal features
Puighermanal etal., 2017 Biomarker expression in CA1 interneurons
Depannemaecker etal., 2020
Parameter values for a model of synaptic
neurotransmission
Ecker etal., 2020
Evidence that CA1 interneurons express multiple
overlapping chemical markers
Hunsberger and Mynlieff, 2020 Cell identication based on ring properties
Schumm etal., 2020 Directionality of connections in the hippocampus
Aery Jones etal., 2021 Local connectivity of CA1 PV + interneurons
Ciarpella etal., 2021 Lists of hippocampal genes
Luo etal., 2021 Conrmation of multiple hippocampal neuron types
Mehta etal., 2021 Connectome model inspired by entorhinal- CA1 circuit
Obafemi etal., 2021
Principal channels of information processing are DG
Granule cells and CA1- 3 Pyramidal cells
Sáray etal., 2021 Membrane biophysics values for CA1 Pyramidal cells
Smith etal., 2021 Omni- directionality of axons of CA1 Pyramidal cells
Venkadesh and Van Horn, 2021
Example of a brain region’s mesoscopic structural
connectivity
Walker etal., 2021
Reference to morphological and molecular characteristics
of hippocampal principal cells and interneurons
Wynne etal., 2021 Example brain region with a variety of cell types
Kopsick etal., 2023
Utilize accumulated knowledge as the basis for
simulations
Schumm etal., 2022
Hippocampal morphology, biomarker expression,
connectivity, and typing of neurons
Zagrean etal., 2022
Diversity of hippocampal neuronal types and their
properties

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precomputes values averaged by connection type, namely excitatory- excitatory (0.0117), excitatory-
inhibitory (0.0237), inhibitory- excitatory (0.00684), and inhibitory- inhibitory (0.00423).
Potential applications to connectomic analyses and spiking neural
networks simulations
Hippocampome. org v2.0 enables the multiscale analysis of circuit connectivity (Figure 8). At the
highest echelon are the connections between hippocampal subregions, which are comprised of the
mesoscopic level potential connectivity between individual neuron types (Figure 8A). Expanding,
for example, upon the 147 connections between the dentate gyrus and CA3 neuron types reveals
all the connections every individual neuron type forms with the other neuron types within and across
the subregions (Figure8B). Zooming in onto a single neuron type each from the dentate gyrus and
CA3, it is possible to quantify the efferent and afferent connections with other neuron types from
throughout the hippocampal formation in terms of synaptic probabilities and number of neuronal
Figure 5. Transitional knowledge enabling Hippocampome.org to support spiking neural network simulations. Center: General diagram of the
hippocampal formation and the number of cell types in Hippocampome.org v1.0. (A)Neuron type census. Top: General pipeline for obtaining
cell counts for specic collections of neurons from the peer reviewed literature. Left: Neuron count proportions for the different subregions of the
hippocampal formation. Insert: Normalized neuron counts for the inhibitory vs. excitatory balance by subregion. Right: Neuron counts for ve identied
CA2 neuron types (green schematic: excitatory, red schematic: inhibitory). (B)Neuron dynamics. Top: General pipeline for obtaining Izhikevich models to
reproduce the ring pattern phenotypes from peer reviewed data. Right: Simulated ring pattern from a Sub CA1- Projecting Pyramidal cell in response
to a 250pA current injection pulse lasting 1s. Izhikevich model parameters are shown in bold and the membrane biophysics properties are shown in
regular font. (C)Synaptic probabilities. Left: General pipeline for obtaining the connection probabilities, number of contacts, and dendritic and axonal
path lengths from 2D reconstructions. Middle: Example of a connectivity diagram of a DG Granule cell and two interneurons across the different parcels
of DG. Probabilities of connection (mean ± SD) are shown in black, numbers of contacts in gray, dendritic path lengths in blue, and axonal lengths in
red. Right top: Total number of connections within DG by connection type. Right bottom: Breakdown of the total number of connections by parcel
and connection type. (D)Synaptic physiology. Left: General pipeline for obtaining normalized synaptic parameters from paired recordings with a TPM
model. Right top: Digitized synaptic data between two EC LII- III Pyramidal- Tripolar cells. Experimental data are shown in blue, initiation synaptic points
in pink, model data in orange, and corrected data in green. Right bottom: Simulated modeling conditions, electrophysiological parameters, and TPM
parameters. Abbreviations by panel: (A) PC: Pyramidal cell; BC: Basket cell; WA BC: Wide- Arbor Basket cell; BiC: Bistratied cell; Exc: excitatory; Inh:
inhibitory. (B)Vr: resting membrane potential; Vt: ring threshold potential; Vpeak: spike cutoff potential; Vmin: post- spike reset potential. (C)E- E: excitatory-
excitatory; E- I: excitatory- inhibitory; I- I: inhibitory- inhibitory; I- E: inhibitory- excitatory. (D)Vh: holding potential; τr: recovery time constant; τf: facilitation
time constant; g: conductance; τd: deactivation time constant; U: utilization ratio.

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partners (Figure8C). Diving even deeper into the isolated connection between two neuron types,
such as the mossy fiber contacts from Dentate Gyrus Granule cells to CA3 Basket cells, expands the
connectivity analysis to several physiological factors affecting neuronal communication: the subcel-
lular location of the synaptic contact (e.g. soma in stratum pyramidale and proximal dendrites in
stratum lucidum), the transfer function (product of synaptic conductance and decay time constant),
the in vivo firing rate of the presynaptic neuron type, and the relationship between input current and
resulting output spiking frequency (F- I curve) of the post- synaptic neuron (Figure8D).
Release of v2.0 makes the original objective of Hippocampome. org, to enable data- driven spiking
neural network simulations of rodent hippocampal circuits (Ascoli, 2010), finally achievable. An
ongoing line of research in this regard focuses on a real- scale mouse model of CA3, with the eventual
Figure 6. Hippocampome.org data provenance. (A)The internal web of constituent neuron- type properties (black
thin arrows) that ultimately contribute to the instantiation of spiking neural simulations (orange thick arrows).
Properties described qualitatively, such as morphological presence of axons in a layer or molecular biomarker
expressions, are in black font. Properties described by quantitative values, such as membrane biophysics and
neurite lengths, are in red font. Properties with v2.0 updated information, such as connectivity and ring pattern
phenotypes, are depicted by blue hexagons, and v1.x information, such as Izhikevich modeling parameter values
and neuron- type census values, is visualized by black circles. (B)External resources that contribute data to and
receive data from Hippocampome.org (the ModelDB logo has been modied from the original).

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goal of investigating the cellular mechanisms of pattern completion. Initial work included excitatory
Pyramidal cells and seven main inhibitory interneuron types: Axo- axonic cells (AAC), Basket cells
(BC), Basket Cholecystokinin- positive cells (BC CCK+), Bistratified cells (BiC), Ivy cells, Mossy Fiber-
Associated ORDEN (MFA ORDEN) cells, and QuadD- LM (QuadD) cells (Kopsick etal., 2023). Use of
Hippocampome. org parameters for cell census, Izhikevich models, synaptic signals, and connection
probabilities resulted in robust, realistic, rhythmic resting state activity for all neuron types (Figure9A).
Building off the constructed network of CA3, we seek to understand how the neuron type circuit may
allow for the formation of cell assemblies that correspond to distinct memories. Additionally, we will
evaluate the network’s pattern completion capabilities when presented with degraded input patterns
(Guzman et al., 2021). Furthermore, associations between memories in CA3 may be encoded by
cells shared between cell assemblies (Gastaldi etal., 2021). Therefore, we will investigate how cell
assembly size and overlap may impact memory storage and recall and the role of different neuron
types in associating cell assemblies with one another.
Another pursuit using a spiking neural network seeks to replicate the spatial representation in grid
cells (Sargolini etal., 2006), modeled utilizing Hippocampome. org Medial Entorhinal Cortex Layer II
Stellate cells (SC), and supported by various GABAergic interneuron types (Dhillon and Jones, 2000):
Axo- axonic (AA), Basket cells (BC), and Entorhinal Cortex Layer II Basket- Multipolar cells (BC MP).
This study aims to reproduce the in vivo firing of these neuron types as a virtual rodent explores an
open field (Figure9B). Different theories offer potential neural mechanisms underlying the grid cell
phenomenon, but it remains challenging to test them comprehensively for anatomical and electrophys-
iological consistency with experimental data (Sutton and Ascoli, 2021; Zilli, 2012). Our work, which
is in preparation for publication, demonstrates that a spiking neural network implementation of one
such theory, the continuous attractor model, generates grid field activity highly compatible with that
measured in behaving animals when utilizing Hippocampome. org model parameters. While prelim-
inary, these illustrative examples highlight the potential of Hippocampome. org enabled data- driven
Figure 7. CARLsim simulation parameters selection and le generation interface. (A)The user chooses which subset of the available neuron types
to include in the generated downloadable parameter le. Neuron types can be selected (check boxes and gray highlights) either individually or
by groupings, such as by subregion and/or by importance rank. (B)Representative user selection. (C)Downloadable neuron- level parameters.
(D)Downloadable connection- level parameters.

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Figure 8. Hierarchy of neuronal connectivity in Hippocampome.org. (A)Subregional connectivity, where the number of connections between
subregions is shown, and the node size is proportional to the number of neuron types in each subregion. (B)The reciprocal connectivity between DG
and CA3 neuron types consists of 147 connections. The node size is proportional to the census size for each neuron type. (C)The full connectivity
involving DG Granule and CA3 Basket neuron types consists of 98 connections. The node size is proportional to the census size for each neuron type,
and the thicknesses of the connecting arrows are proportional to the synaptic probability. The dashed lines are connections for which the synaptic
probability has been approximated based on the means of known values. (D)The electrophysiological connection between a DG Granule cell and a
Figure 8 continued on next page

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spiking neural network simulations to investigate computational theories of cognitive functions in
hippocampal circuits at the level of biologically detailed mechanisms.
Discussion
Hippocampome. org, through its continuous updates and conspicuous usage, has established itself
prominently amongst other readily accessible, evidence- based, expert- curated bioscience public
resources of note, such as FlyBase for Drosophila molecular biology (The FlyBase Consortium, 1994;
dos Santos et al., 2015), WormBase for nematode genomics (Stein etal., 2001), the Blue Brain
Project for somatosensory cortex (Markram, 2006), SynGO for synaptic functions (Koopmans etal.,
2019), and RegenBase for spinal cord injury biology (Callahan etal., 2016). Hippocampome. org has
evolved from being a storehouse of information in v1.0–1.12, along the lines of FlyBase, WormBase,
SynGO, and RegenBase, to a platform in v2.0 for launching detailed simulations of the hippocampal
formation, in the vein of the Blue Brain Project. However, Hippocampome. org distinguishes itself in its
reliance wholly on already published data and the more tailored focus on a single portion of the brain.
The foundation for Hippocampome. org has always been the data that are published in the litera-
ture. Although a certain level of interpretation is always necessary to make the data machine readable
and suitable for database incorporation, data inclusion does not depend on how the data are modeled.
Nevertheless, some of the simulation- ready parameters now also included in Hippocampome. org
are indeed the result of modeling, such as the neuronal input/output functions (Izhikevich model;
Izhikevich, 2003) and the unitary synaptic values (Tsodyks- Pawelzik- Markram model; Tsodyks etal.,
1998). Other simulation- ready parameters are the result of specific analysis approaches, including the
connection probabilities (axonal- dendritic spatial overlaps) and the neuron type census (numerical
optimization of all constraints).
The growth of Hippocampome. org since the initial release of v1.0 (Wheeler et al., 2015) has
been prodigious. To date, the site has been visited over 136,000 times with over 33,000 unique
visits, and the original publication has been cited more than hundred times. Each successive release
of Hippocampome. org v1.X has added new dimensions of knowledge and/or functionality and has
been building toward assembling all the components necessary to produce real- scale computational
models of the rodent hippocampal formation. The culmination of all this work is the release of Hippo-
campome. org v2.0, which introduces a framework for launching computer simulations directly from
the accumulated knowledge. However, achieving simulations does not mark the end point for this
project, because Hippocampome. org will continue to aggregate new knowledge as it is published
in the peer- reviewed literature. Gradually, the focus of this resource will shift from development to
exploitation through the in silico emulation of complex dynamics observed in vivo and in vitro, with
the goal of shedding light on the underlying synaptic- level computational mechanisms.
The creation of real- scale spiking neural network models of the hippocampal formation and its
subregions can foster biologically realistic, data- driven, mesoscopic simulations of cognitive function
and dysfunction (Sutton and Ascoli, 2021). For instance, simulations with Hippocampome. org’s real-
scale model of the dentate gyrus can build on previous network models of epileptogenesis (Dyhrfjeld-
Johnsen etal., 2007) by providing further clarity to the roles of all documented neuron types and
their corresponding potential connections in seizure initiation and propagation. A real- scale model of
CA1 can aim to further the insights into the spatiotemporal dynamics of the circuit during theta oscil-
lations (Bezaire etal., 2016; Navas- Olive etal., 2020; Romani etal., 2023). Furthermore, network
models involving multiple subregions can open new vistas on unexplored territories, such as the use
of real- scale models of the entorhinal cortex and CA2 to simulate the neuron- and connection- type
specific mechanisms of social memory (Lopez- Rojas etal., 2022). Moreover, open source sharing of
the real- scale models replicating those functions (Gleeson etal., 2017) will facilitate cross- talk within
CA3 Basket cell. The in vivo ring rate is shown for the presynaptic neuron. The transfer function between the two neuron types is proportional to the
synaptic conductance times the single- exponential decay time constant (g · τd; rat, male, P56, 32°C, current clamp). The frequency- current (F–I)curve
of the single- compartment Izhikevich model of a CA3 Basket cell was obtained with 10pA current steps. Inset: Izhikevich model ring pattern of a CA3
Basket cell simulated with 430pA of current applied for 500 ms (vertical and horizontal scale bars, respectively).
Figure 8 continued

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Figure 9. Spiking neural network simulations. (A)Full- scale CA3 model. (A1) Neuron type connectivity schematic.
(A2) Theta (4–12Hz; top), Gamma (25–100Hz; middle), and Sharp- Wave Ripple (150–200Hz; bottom) ltered
local eld potentials from 175ms of the simulation. (A3) Raster plot of 500 Pyramidal cells and 50 interneurons of
each type (top), and representative voltage traces for each neuron type (bottom) during the same 175ms of the
Figure 9 continued on next page

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the systems neuroscience community to better understand the role of distinct neuron types in hippo-
campal function.
A notable aspect of Hippocampome. org is that all freely downloadable model parameters are
directly linked to the specific peer- reviewed empirical evidence from which they were derived. Thus, if
users disagree with a specific interpretation, or are not fully convinced by an individual experimental
measurement, they maintain control in selecting the information sources. Conversely, researchers can
choose to reuse the collated experimental data to constrain different computational models they may
prefer, such as adopting the Hodgkin- Huxley formalism instead of Izhikevich dynamics. At the same
time, Hippocampome. org is not only a collection of model parameters and corresponding empirical
evidence, but it also provides an opportunity to unearth knowledge gaps, as facilitated by an intuitive
search functionality ( hippocampome. org/ find- neuron). Missing data can serve to guide the design of
targeted ‘low hanging fruit’ experiments or to generate new hypotheses.
Another important element of Hippocampome. org is the careful annotation of the experimental
metadata for each piece of evidence, including the species (rat or mouse), sex (male or female), age
(young or adult) as well as any and all reported details that could affect the recorded neuronal prop-
erty. Examples of these confounding factors abound especially for in vitro electrophysiological data,
such as the exact chemical composition of the solution in the electrode and in the bath, slice thick-
ness and orientation, clamping configuration, recording temperature, and animal weight. Because
these covariates, when reported by the original investigators, are also stored in the database, it is
possible to account for them in subsequent analyses and simulations. Hippocampome. org therefore
constitutes a considerably rich one- stop resource to compare and ‘translate’ key parameters, such as
the amplitude and duration of a synaptic signal between two specifically identified neuron types, for
instance, from 14- day- old male rat at 22°C in voltage clamp to a 56- day- old female mouse at 32°C in
current clamp. When fed into spiking neural network simulations, these differential parameter values
can foster intuition while attempting to reconcile neuroscience theories and observations.
Hippocampome. org is yet poised for the onset of an information deluge from current and future
big science projects, which will need to be integrated into a complete cohesive picture (de la Prida
and Ascoli, 2021). Although morphological identification will continue to play a fundamental role in
defining neuron types and circuit connectivity, the manner in which knowledge is cross- referenced in
this resource will allow its effective linkage to rapidly accumulating molecular and imaging data. The
ongoing spatial transcriptomics revolution is already transforming the frontiers of cellular neuro-
science, often using the hippocampus as its favorite sandbox (Lein etal., 2017; Yao etal., 2021;
Zeisel etal., 2015). Single- cell transcriptomics via scRNAseq can bolster the current morphological
information by offering distinct transcription factor codes for existing neuron types and assist in
defining new ones (Cembrowski and Spruston, 2019; Winnubst etal., 2020; Yuste etal., 2020).
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, Hippocam-
pome. org will be able to integrate multidimensional knowledge on the solid foundation of neuronal
classification.
simulation in (A2). (B)A mock up of a spatial representation through grid cell ring. (B1) Neuron type connectivity
schematic. (B2) Simulated animal trajectory (black) with red dots indicating the ring of a neuron in those locations.
(B3) Raster plot of 300 neurons from each type (top), and representative voltage traces for each neuron type.
Abbreviations by panel: (A) PC: Pyramidal cell; BiC: Bistratied cell; QuadD: QuadD- LM cell; AAC: Axo- axonic cell;
MFA ORDEN: Mossy Fiber- Associated ORDEN cell; BC CCK+: cholecystokinin- positive Basket cell. (B)SC: Medial
Entorhinal Cortex Layer II Stellate cell; PC: Entorhinal Cortex Layer III Pyramidal cell; MP PC: Entorhinal Cortex
Layer I- II Multipolar Pyramidal cell; AA: Entorhinal Cortex Layer II Axo- axonic; BC: Medial Entorhinal Cortex Layer II
Basket; BC MP: Entorhinal Cortex Layer II Basket- Multipolar Interneuron.
Figure 9 continued

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Materials and methods
Hippocampome. org v2.0 vs. the legacy status of v1.12
With the release of v2.0 of Hippocampome. org upon publication of this article, v1.12 of the website
will no longer be updated and will transition to legacy status ( hippocampome. org/ legacy_ v1). In this
way, users may avail themselves of the full benefits of the new content and functionality of v2.0, while
maintaining access to reference content as published through v1.12. In the near term, neuron types
new to v2.0 are tagged with an asterisk on the web site to differentiate them from v1.X types.
Linking neuron types to NeuroMorpho.Org morphological
reconstructions
Hippocampome. org neuron types are regularly linked to appropriately identified digital reconstruc-
tions of neuronal morphology from NeuroMorpho.Org (Ascoli etal., 2007). Identification of suitable
reconstructions with individual neuron types depends on the correspondence of dendritic and axonal
locations across hippocampal subregions and layers, as they appear in the reference publication.
Alternatively, direct cell typing by the authors in the reference publication text is accepted as evidence
for canonical (principal cell) types, such as CA1 Pyramidal cells or Dentate Gyrus Granule cells. Recon-
structions are not linked to a neuron type if the experimental conditions are inconsistent with the
inclusion criteria of Hippocampome. org, as in the case of cell cultures or embryonic development.
Lack of either axonal or dendritic tracing also disqualifies reconstructions of non- canonical neurons
from being linked.
Connections from DG Granule cells to CA3
To compute estimates of connection probabilities and numbers of contacts per connected pair for the
rat mossy fiber- CA3 circuit, we used previously calculated average convex hull volume (Tecuatl etal.,
2021b) and several measurements from a seminal anatomical study (Acsády etal., 1998): DG Granule
cell axonal length within CA3 (3,236 µm), inter- bouton distances for mossy boutons on Pyramidal
cell targets in CA3c (162µm) and in the rest of CA3 (284µm), and inter- bouton distances for en- pas-
sant and filipodia boutons onto CA3 interneurons (67.4µm, considering that 48 interneurons can be
contacted by a single GC). Given that the mossy fibers innervate mainly CA3 SL, and due to the lack
of information regarding the exact proportion of axons innervating CA3 SP, these calculations assume
that GCs only innervate SL. The probabilities of connection and numbers of contacts per connected
pair (Table2) are estimated as previously described (Tecuatl etal., 2021a) utilizing the CA3 dendritic
lengths reported in Hippocampome. org.
Connections from CA3 and CA3c Pyramidal cells to CA1
To compute estimates of connection probabilities and numbers of contacts per connected pair
for the rat Schaffer collaterals- CA1 circuit, we utilized previously reported values for the distinct
axonal innervation patterns (Ropireddy et al., 2011; Sik et al., 1993; Wittner etal., 2007) in
CA1 stratum radiatum (SR) and stratum oriens (SO) from CA3 Pyramidal cells (27.5% of total axonal
length: 64% to SR, 15% to stratum pyramidale (SP), 21% to SO) and CA3c Pyramidal cells (64.1% of
total axonal length: 94% to SR, 3% to SP, 3% to SO). In addition, we used the average inter- bouton
distance reported for the Schaffer collaterals (Li et al., 1994) in SR (4.47 µm) and SO (5.8 µm).
Total axonal length was measured with L- Measure (Scorcioni etal., 2008) from three NeuroMorpho.
Org reconstructions for CA3c (NMO_00187, NMO_00191) and CA3b (NMO_00931). We extracted
parcel- specific convex hull volumes from Janelia MouseLight (Winnubst et al., 2019) Pyramidal
cell reconstructions (AA0304, AA0307, AA0420, AA0960, AA0997, AA0999, AA1548) mapped to
the 2022 version of the Allen Institute Common Coordinate Framework (CCF). The probabilities
of connection and number of contacts per connected pair were estimated as previously described
(Tecuatl et al., 2021a) using CA1 dendritic lengths from Hippocampome. org. We used separate
values for inter- bouton distances in CA1 SR for CA3c Pyramidal cells (5.5µm: Wittner etal., 2007)
and CA3 Pyramidal cells (3.7µm: Shepherd etal., 2019; 4.4 µm: Li etal., 1994; 4.29 µm: Sik etal.,
1993; averaged as 4.1µm).

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Wheeler etal. eLife 2023;13:RP90597. DOI: https://doi.org/10.7554/eLife.90597 20 of 27
Constructing Hippocampome. org spiking neural simulations
Hippocampome. org utilizes CARLsim (Nageswaran etal., 2009) as its default simulation environ-
ment (https://sites.socsci.uci.edu/~jkrichma/CARLsim/). CARLsim is a graphics processing unit (GPU)-
accelerated library of functions for simulating spiking neural networks based on Izhikevich neuron
models (Izhikevich, 2003). We selected CARLsim due to this ability to run on collections of GPUs, as
the power of a GPU supercomputer is needed to simulate the millions of neurons that comprise a full-
scale spiking neural network simulation of the complete hippocampal formation. The current version
is CARLsim 6 (Niedermeier etal., 2022), and the most up- to- date Hippocampome. org- optimized
code base, including features not yet released in the main CARLsim version, can be found at hippo-
campome. org/ CARLsim (Kopsick etal., 2023).
Web portal, database, and source code
Hippocampome. org runs on current versions of Chrome, Safari, and Edge web browsers, and it is
deployed on a CentOS server running Apache. The website runs off of PHP from a MySQL database.
The code for Hippocampome. org v2.0 is available open source at GitHub (copy archived at Wheeler
etal., 2023). This includes all code for displaying the pages of the website, all scripts for importing
spreadsheets into the database, code for using evolutionary algorithms to optimize Izhikevich model
parameters, code for the graph theory analysis of the potential connectome, code for the implemen-
tation of the firing pattern classification algorithm, and code for analyzing network simulations in
CARLsim.
Acknowledgements
We thank David J Hamilton, Charise M White, Christopher L Rees, Maurizio Bergamino, Keivan
Moradi, Siva Venkadesh, Alberto Sanchez- Aguilera, Teresa Jurado- Parras, Manuel Valero, Miriam S
Nokia, Elena Cid, Ivan Fernandez- Lamo, Daniel García- Rincón, Liset M de la Prida, Sarojini M Attili,
Iqbal Addou, and the many student interns ( hippocampome. org/ thx) for invaluable help advancing
from v1.0 to v2.0. This work was supported in part by grants R01NS39600, RF1MH128693, and
U01MH114829 from the National Institutes of Health (NIH). The funding sources were not involved in
study design, data collection and interpretation, or the decision to submit the work for publication.
Additional information
Funding
Funder Grant reference number Author
National Institutes of
Health
R01NS39600 Giorgio A Ascoli
National Institutes of
Health
RF1MH128693 Giorgio A Ascoli
National Institutes of
Health
U01MH114829 Giorgio A Ascoli
The funders had no role in study design, data collection and interpretation, or the
decision to submit the work for publication.
Author contributions
Diek W Wheeler, Conceptualization, Data curation, Software, Supervision, Investigation, Visualiza-
tion, Methodology, Writing – original draft, Project administration; Jeffrey D Kopsick, Data curation,
Software, Formal analysis, Investigation, Visualization, Methodology, Writing – review and editing;
Nate Sutton, Data curation, Software, Investigation, Visualization, Methodology, Writing – review
and editing; Carolina Tecuatl, Data curation, Formal analysis, Supervision, Validation, Investiga-
tion, Visualization, Methodology, Writing – review and editing; Alexander O Komendantov, Formal
analysis, Visualization, Writing – review and editing; Kasturi Nadella, Software, Writing – review

Research advance Neuroscience
Wheeler etal. eLife 2023;13:RP90597. DOI: https://doi.org/10.7554/eLife.90597 21 of 27
and editing; Giorgio A Ascoli, Conceptualization, Resources, Data curation, Supervision, Funding
acquisition, Investigation, Visualization, Methodology, Project administration, Writing – review and
editing
Author ORCIDs
Diek W Wheeler
https://orcid.org/0000-0001-8635-0033
Jeffrey D Kopsick
https://orcid.org/0000-0002-8175-0246
Nate Sutton
http://orcid.org/0000-0002-4424-3886
Carolina Tecuatl
http://orcid.org/0000-0001-6398-3444
Alexander O Komendantov
http://orcid.org/0009-0005-2360-4805
Kasturi Nadella
http://orcid.org/0009-0008-5905-5328
Giorgio A Ascoli
https://orcid.org/0000-0002-0964-676X
Peer review material
Reviewer #2 (Public Review): https://doi.org/10.7554/eLife.90597.3.sa1
Reviewer #3 (Public Review): https://doi.org/10.7554/eLife.90597.3.sa2
Author Response https://doi.org/10.7554/eLife.90597.3.sa3
Additional files
Supplementary files
• MDAR checklist
Data availability
The current manuscript is a description of an updated resource, so no data have been generated for
this manuscript. The source code for our resource is available from GitHub (copy archived at Wheeler
etal., 2023).
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