Hippocampome.org: a knowledge base of neuron types in the rodent hippocampus
A century after the advent of the neuron doctrine ( Jones , 2006 ) , neuroscience is challenged with data on neuronal characteristics spread throughout thousands of articles in hundreds of journals , growing at a rate of dozens of new papers each day . The sweeping array of research designs , data formats , and presentation styles , reflecting the tremendous diversity in neuronal structure and functions , makes it extremely difficult to assess the available information . Overcoming the hindrances of this variety is problematic because of limited consensus on which properties to use for neuron classification and how to define them effectively ( Petilla Interneuron Nomenclature Group et al . , 2008; Hamilton et al . , 2012 ) . The lack of a complete accounting of neuron types is recognized as a critical omission for experimental and computational progress alike ( Lichtman and Denk , 2011 ) . As a case in point , the rodent hippocampus is among the most intensively studied neural systems . Numerous seminal efforts have summarized the enormous amount of information on the morphology , connectivity , biochemistry , and electrophysiology of hippocampal neurons ( Bernard and Wheal , 1994; Patton and McNaughton , 1995; Freund and Buzsáki , 1996; Parra et al . , 1998; McBain and Fisahn , 2001; Maccaferri and Lacaille , 2003; Amaral and Lavenex , 2007; Canto et al . , 2008; Klausberger and Somogyi , 2008; Klausberger , 2009; Somogyi , 2010; Bezaire and Soltesz , 2013 ) . However , these notable advances have yet to translate into an integrated understanding of corresponding functions . The hippocampus plays a critical role in the consolidation and retrieval of episodic memory ( Nadel and Moscovitch , 1997; Wang and Morris , 2010 ) as well as in spatial representation and navigation ( Foster and Knierim , 2012; Moser et al . , 2015 ) . Many theories have been formulated to connect these important cognitive functions to hippocampal architecture ( Clark and Squire , 2010; Eichenbaum and Cohen , 2014 ) , rhythmic activity ( Burgess and O'Keefe , 2011; Hasselmo and Stern , 2014 ) , and synaptic plasticity ( Rolls and Treves , 1994; Bliss and Collingridge , 2013 ) . However , current models are far from comprehensively accounting for the available experimental data on all neuron types and relevant properties . As a stepping stone towards filling this gap , we mounted a large-scale literature mining effort to assemble a knowledge base of neuron types in the rodent hippocampal formation: dentate gyrus ( DG ) , CA3 , CA2 , CA1 , subiculum ( Sub ) , and entorhinal cortex ( EC ) . Our data-driven analysis of thousands of peer-reviewed publications identified a specific set of neuron properties suitable to define a basic classification scheme for collating , organizing , and integrating available knowledge . A cornerstone of this approach is the distributions of axons and dendrites across the distinct areas and layers of the hippocampal formation , such as CA1 stratum radiatum ( SR ) or EC layer II . Axonal and dendritic arbors perennially have been central to the experimental identification of neurons ( Jones , 2006; DeFelipe et al . , 2013 ) , as they underlie network connectivity and profoundly influence information processing . Systematic mining of published data on axonal and dendritic profiles , augmented with information on neurotransmitter and synaptic specificity , led to the tentative definition of over 100 distinct neuron types across the hippocampal formation . Importantly , this initial identification of ‘morphological’ types enabled the unambiguous incorporation of existing molecular and electrophysiological data into the knowledge base . Other key features , such as developmental origin , firing dynamics , synaptic properties , neuron counts , and connectivity ratios , are being progressively integrated into the same classification framework . Essential to this effort is the digestion of information from original publications into both human- and machine-readable forms . We present Hippocampome . org , a publicly accessible web portal to browse and query data with direct links to specific evidence in the scientific literature . Information summaries are available for each neuron type , anatomical parcel , molecular marker , and cited author . Searching for combined morphological , molecular , and electrophysiological properties returns lists of all known neuron types with those features . These functionalities , coupled with the knowledge base's comprehensiveness and inter-relatedness , reveal novel insights on neuron types , their properties , and circuit connectivity , which are impractical or impossible to derive from traditional literature searches . Moreover , the collation of data enables quantification of available information , unearthing residual knowledge gaps . The ‘Description of resource’ section of this article aims to offer a clear description of both the conceptual foundation and practical utility of Hippocampome . org according to the following organization . First , we explain the neuron type classification criteria , including the definition of morphological patterns and the distinctions by main neurotransmitter , synaptic specificity , biomarkers , and electrophysiology . We then provide a summary of the knowledge base content and describe its online accessibility . Next , we illustrate the usefulness and possible applications of Hippocampome . org through analyses of pairwise correlations among neuron properties , potential circuit connectivity , and use case scenarios in experimental and computational investigations . A discussion follows the ‘Description of resource’ section . In the final ‘Materials and methods’ section , we provide practical details of how the Hippocampome . org knowledge base was assembled . We define parcels and neuron types , explain how biomarker and electrophysiological data are linked to morphological data , describe how names are assigned to neuron types , and expand upon how the knowledge base will be maintained going forward . For optimal comprehension of this resource , we recommend online consultation of Hippocampome . org while reading this article .
As a first step in the knowledge-base design , we sought the optimal level of description to capture the largest possible extent of available information . For example , just distinguishing neurons into projection cells and local interneurons is too coarse to reflect the known variety of hippocampal neuron types . In contrast , demanding a detailed quantification of axonal and dendritic morphology and connectivity excludes the majority of available scientific reports that only depict neuronal arbors qualitatively . An intermediate approach is to describe axons and dendrites based on the specific areas and layers they invade . The orderly anatomical organization of the hippocampal formation is commonly delineated into 26 cytoarchitectural parcels ( Figure 1A , B ) : DG outer stratum ( s . ) moleculare ( SMo ) , inner s . moleculare ( SMi ) , s . granulosum ( SG ) , hilus ( H ) ; CA3 s . lacunosum-moleculare ( SLM ) , s . radiatum ( SR ) , s . lucidum ( SL ) , s . pyramidale ( SP ) , s . oriens ( SO ) ; 4 each in CA2 and CA1 ( same as CA3 except SL ) ; Sub s . moleculare ( SM ) , SP , polymorphic layer ( PL ) ; and EC layers I–VI . Most publications that report morphological information on hippocampal neurons include evidence of axonal and dendritic presence in at least a subset of these parcels in the form of reconstructions , tracings , microscopic images , schematics , or text descriptions ( Figure 1C–E ) . 10 . 7554/eLife . 09960 . 003Figure 1 . Defining neuron types with anatomical parcels and morphological patterns . ( A , B ) Nissl staining of a P56 mouse ( coronal sections 74 and 85 from the Allen Brain Atlas ) overlaid by color-coded parcels ( in accord with Temporal-Lobe . com ) of ( A ) the hippocampus proper and ( B ) the rest of the hippocampal formation . See main text for abbreviations . ( C–E ) Example morphological reconstructions from NeuroMorpho . Org ( left ) with red axons and blue dendrites; their estimated axonal and dendritic breakdown by layers ( middle ) ; and Hippocampome . org representation ( right ) with blue square and vertical line ( | ) indicating dendritic presence , red square and horizontal line ( − ) indicating axonal presence , purple square and cross ( + ) indicating both axonal and dendritic presence , and a black dot ( • ) indicating soma location . ( C ) Dentate gyrus ( DG ) Outer Molecular Layer cell with axons in SMo and dendrites in all layers ( NMO_00179; [Mott et al . , 1997] ) . ( D ) CA1 O-LM cell with axons in SLM and dendrites in SO ( NMO_02347; [Cossart et al . , 2003] ) . ( E ) MEC LII Pyramidal-Multiform cell with axons in layers II–VI and dendrites in layers I-III ( NMO_07252; ( Quilichini et al . , 2010] ) . ( F ) Axon and dendrite locations for 100 representative neuron types ( glutamatergic: black; GABAergic: gray; full matrix: hippocampome . org/morphology or hippocampome . org/php/images/morphology/Morphology_Matrix . jpg ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09960 . 003 This relatively simple description is surprisingly effective to map neuronal diversity in the hippocampus . Specifically , the binary representation ( present or not ) of both axons and dendrites across the 26 hippocampal parcels ( see ‘Materials and methods’ ) is sufficient to identify >100 unique morphological profiles based on existing literature ( selection in Figure 1F; complete data: hippocampome . org/morphology ) . A key assumption of this framework is that neurons differing in axonal or dendritic patterns are different types . The same morphological pattern is further separated into distinct types if the neurons can be discriminated by neurotransmitter , synaptic selectivity , or consistent molecular and electrophysiological differences ( see below ) , giving rise to 122 neuron types distinguished so far ( 18 in DG , 25 in CA3 , 5 in CA2 , 40 in CA1 , 3 in Sub , and 31 in EC ) . The soma layer location is annotated for all neuron types , but neurons are not considered of separate types solely due to this feature if their other main characteristics are the same . Several neuron types have somata distributed across both sides of , or just along , a layer boundary , such as CA1 quadrilaminar cells ( Pawelzik et al . , 2002 ) or DG hilar commissural-associational pathway ( HICAP ) cells ( Mott et al . , 1997 ) . If only dendrites or axons are observed ( but not both ) , or a single instance of a neuron morphology is described ( n = 1 ) , the characterization is deemed insufficient to include a neuron type in the knowledge base . We often attempt to contact authors to ascertain whether additional data may exist . If no further evidence is available , the existing information is extracted but not integrated with the rest of the knowledge base , and the possible neuron type is placed ‘on hold’ until more data are published . Currently , 151 potential neuron types are in this state ( hippocampome . org/on-hold ) . Hippocampome . org links information from various publications to specific neuron types primarily based on parcel-delineated neurite morphology and does not rely on author given names for neuron classification . The reason is that the same name is frequently employed in the literature to describe neurons with different morphological patterns . For example , the term ‘bistratified’ has been used to describe different morphologies corresponding to Hippocampome cell types CA1 Bistratified ( Freund and Buzsáki , 1996 ) , CA1 Radial Trilaminar ( Daw et al . , 2009 ) , and Schaffer Collateral-receiving Radiatum-targeting ( Leão et al . , 2012 ) . Furthermore , neurons of the same type are often given different names due to varying emphases of researchers . For instance , CA1 Radiatum cells ( Cope et al . , 2002 ) have also been called CaBP-positive non-pyramidal cells ( Toth and Freund , 1992 ) and Schaffer collateral associated cells ( Vida , 2010 ) . The authors' original names are always included as synonyms of the Hippocampome . org-assigned identifiers ( see ‘Materials and methods’ ) to facilitate tracing information provenance and for ease of communication . An advantage of defining neuron types by their axonal and dendritic patterns is the practical cross-species invariance of these features , at least within rodents . Hippocampome . org collates data from all healthy young or adult rats , mice , and guinea pigs , but all evidence is meta-annotated with species and age information to allow separate tracking . The few cross-species morphological differences identified so far , such as the extent of CA3c infrapyramidal mossy fibers ( Blaabjerg and Zimmer , 2007 ) , are immaterial to the current granularity of the knowledge base . Hippocampal neurons mainly release glutamate or gamma-aminobutyric acid ( GABA ) ( Kullmann , 2007 ) . When the literature does not present explicit evidence for determining the neurotransmitter , a neuron type is deemed putatively glutamatergic or GABAergic depending on ancillary characteristics including ( a ) symmetry of synapses , excitatory or inhibitory effect , relative somatic abundance within an area , presence of dendritic spines , and local or projecting nature of the axons . Although exceptions exist for all these criteria , taken together , they are sufficiently indicative to enable the tentative inference of the main neurotransmitter for all identified neuron types based on published information ( Figure 1F: black , presumed glutamatergic; gray , presumed GABAergic ) . Neurons with the same axonal and dendritic patterns , but different neurotransmitters , belong to different types . Interestingly , only one such case is known: in CA1 , Cajal–Retzius cells , which were recently characterized as glutamatergic and more abundant than previously assumed in adult rats ( Quattrocolo and Maccaferri , 2014 ) , and ( GABAergic ) Neurogliaform cells ( Price et al . , 2005 ) have both axons and dendrites confined to SLM , though with extremely different arbor densities and shapes . Thus , putatively excitatory and inhibitory neurons in the hippocampus tend to have completely distinct morphologies . Although most excitatory neurons in all hippocampal areas have long-range axons , Hippocampome . org also includes several local glutamatergic types in DG , CA3 , and CA1 . Similarly , a substantial minority of GABAergic types in these areas ( 16/71 ) have projecting axons . Knowledge is sparser in CA2 , Sub , and EC . The axons of several presumed glutamatergic types in EC have not been reconstructed beyond layer VI or the angular bundle , so their participation in long-range pathways remains largely unknown ( Canto and Witter , 2012a , 2012b ) . Certain interneurons selectively discriminate between GABAergic and glutamatergic post-synaptic partners . Like the neurotransmitter , preferential connectivity profoundly affects circuit function . This characteristic is thus essential to distinguish neuron types with the same axonal–dendritic pattern . For example , Axo-axonic and Basket cells in CA3 and CA1 have dendrites spanning all layers and axons limited to SP ( e . g . , Freund and Buzsáki , 1996; Klausberger and Somogyi , 2008 ) . Axo-axonic cells , however , contact exclusively Pyramidal cells on the axon initial segments ( Li et al . , 1992; Ganter et al . , 2004 ) , whereas Basket cells synapse perisomatically on both principal neurons and interneurons ( Buhl et al . , 1994; Vida , 2010 ) . The separation of Axo-axonic and Basket cells yields two additional types with the same axonal–dendritic patterns . Several ‘interneuron-specific’ neuron types preferentially target other GABAergic neurons over glutamatergic types . Since all known cases correspond to unique morphological patterns , such selectivity does not separate further neuron types . However , this core property is indicated in their adopted names , as in CA1 Interneuron-specific O-R cells ( cf . Gulyás et al . , 1996 ) . The hippocampal literature offers a wealth of biochemical and electrophysiological data ( see below ) that are linked in the knowledge base to specific morphologically defined neuron types ( ‘Materials and methods’ ) . Differences in individual molecular or biophysical features , such as the presence/absence of a single biomarker or the high/low value in one membrane property , are frequently reported for a given axonal–dendritic pattern . These cases are consistently annotated in Hippocampome . org as indicating potential sub-types , but do not establish full neuron types . In contrast , when opposite expression of multiple markers and large discrepancies in several membrane properties strongly support the existence of distinct neuron types by converging molecular and electrophysiological evidence , the same morphological pattern is divided into two types . For example , one type of CA1 Basket cells expresses parvalbumin ( PV ) and µ-opioid receptor , but not cholecystokinin ( CCK ) , cannabinoid receptor 1 ( CB1 ) , and substance P receptor ( sub P rec ) , while another type displays the opposite expression profile ( Pawelzik et al . , 2002 ) ; moreover , PV+ cells have significantly lower input resistance ( Rin ) , faster membrane time constant ( τm ) , narrower action potential width ( APwidth ) , and smaller slow after-hyperpolarization ( sAHP ) than CCK+ cells ( Bartos and Elgueta , 2012 ) . Similarly , both CA1 Bistratified and Ivy cells have axons and dendrites extending from SO to SR , but the former are positive for PV and somatostatin ( SOM ) and negative for neuronal nitric oxide synthase ( nNOS ) , while the opposite holds for the latter; furthermore , relative to Ivy cells , Bistratified cells have lower firing threshold ( Vthresh ) and maximum firing rates ( maxFR ) , larger action potential amplitude ( APampl ) , and narrower APwidth ( Fuentealba et al . , 2008; Tricoire et al . , 2011 ) . Equally convincing data support the distinction of CA3 Ivy and Bistratified cells as well as the separation of basket cells in DG and CA3 . In all , five additional types with non-unique axonal–dendritic patterns are established on the basis of clear and substantial molecular and electrophysiological information . Expression data are obtained from studies detecting proteins ( immunohistochemistry ) or mRNA ( in situ hybridization , promoter constructs , or single-cell RT-PCR ) . The knowledge base has information on 96 biomarkers , and positive or negative expression of at least one biomarker is known for two-thirds ( 81/122 ) of the neuron types ( Figure 2; extended listing: hippocampome . org/markers ) . The most cited biomarker is parvalbumin ( PV ) , appearing in ∼25% of the biomarker sources in Hippocampome . org , and whose expression is known for 56 neuron types . 10 . 7554/eLife . 09960 . 004Figure 2 . Expression of 20 common biomarkers for 100 representative neuron types ( full matrix: hippocampome . org/markers or hippocampome . org/php/images/marker/Marker_Matrix . jpg; complete list of abbreviations: hippocampome . org/help ) . Positive expression: left green flags; negative expression: right blue; mixed expression ( possible subtypes ) : left/right green blue; mixed expression ( different experimental protocols , species , or sub-cellular localization ) : top orange; unresolved mixed expression: bottom red; empty gray boxes indicate that morphologically linkable information was not found . The right summary column reports the number of additional biomarkers with known expression for each neuron type . Bottom values are counts of neuron types with available information for each biomarker . DOI: http://dx . doi . org/10 . 7554/eLife . 09960 . 004 In 79 of the 566 cases with available expression data for the 20 most cited biomarkers ( non-gray squares in Figure 2 and hippocampome . org/markers ) , evidence exists for a neuron type to both express and not express a given biomarker . In 13 of these cases , the incongruity can be attributed to species , technique , or sub-cellular localization differences ( Figure 2 , orange , down-facing flags ) . For example , mossy cells express calretinin ( CR ) in mice ( Blasco-Ibáñez and Freund , 1997 ) , but not in rats ( Freund et al . , 1997 ) . In another case , in situ hybridization showed GABAA-α1 expression in rat CA1 Pyramidal cells ( Miralles et al . , 1994 ) , yet somatic immunohistochemistry of these neurons is negative; however , their dendrites show positive expression ( Lopez-Tellez et al . , 2004 ) . Another 58 mixed cases are designated as possible subtypes ( Figure 2 , blue and green flags in the same square ) , when in a population of cells with equivalent morphology , from the same experiment , a sizeable proportion expresses a biomarker and a sizeable proportion does not . The interpretation of potential subtypes is sometimes supported by distinct somatic locations . For example , CA3 Pyramidal cells are positive for chicken ovalbumin upstream promoter transcription factor II ( COUP-TFII ) in the temporal , but not septal hippocampus ( Fuentealba et al . , 2010 ) . Similarly , superficial CA1 Pyramidal cells express calbindin ( CB ) while those deep in CA1 SP do not ( Jinno et al . , 1999; Sadowski et al . , 2002 ) . In the remaining eight cases of mixed expression ( red , upward-facing flags ) , data come from multiple sources that use the same species and technique ( e . g . , rat immunohistochemistry ) ; however , non-identical experimental details ( e . g . , the antibodies used ) prevent conclusive interpretation . Electrophysiological characteristics vary extensively across hippocampal neurons . Passive , spike , and other response parameters are commonly recorded in the slice preparation . Slice recording is highly dependent on experimental method ( patch clamp or sharp microelectrode ) , animal species and age , bathing and intra-electrode solution , and temperature . Thus , summary values ( Table 1 for 50 representative neuron types; complete listing: hippocampome . org/electrophysiology ) are not averaged across all studies , but rather reflect data from a single report under ‘preferred’ conditions ( operationally defined as rat patch clamp at body temperature ) with the largest number of measurements ( n ) . These settings represent 61% of the data in Table 1 . If these conditions are unavailable , the precedence order is patch clamp over microelectrodes , body temperature over room temperature , and rats over mice over guinea pigs . However , all collected parameter values ( preferred or otherwise ) for every neuron type are accessible at Hippocampome . org , each complete with metadata , available statistics , and direct links to underlying experimental evidence . Three-quarters of the neuron types ( 93/122 ) have measurements reported for four or more parameters , the most common being APampl , fast afterhyperpolarization amplitude ( fAHP ) , and Vthresh . 10 . 7554/eLife . 09960 . 005Table 1 . Electrophysiological properties for 50 representative neuron types ( full table: hippocampome . org/electrophysiology or hippocampome . org/php/images/electrophysiology/Electrophysiology_Table . jpg ) DOI: http://dx . doi . org/10 . 7554/eLife . 09960 . 005Vrest ( mV ) Rin ( MΩ ) τm ( ms ) Vthresh ( mV ) fAHP ( mV ) APampl ( mV ) APwidth ( ms ) maxFR ( Hz ) sAHP ( mV ) Sag ratioDG ( 8 ) Granule−75 ± 2 ( 16 ) ∈ 5228 ± 79 . 1 ( 31 ) ∈ 526 . 9 ± 6 . 7 ( 31 ) ∈ 535 ∈ 511 . 7 ± 1 . 1 ( 16 ) ∈ 391 ∈ 50 . 87 ± 0 . 06 ( 16 ) ∈ 272 ± 8 ( 16 ) 0 . 6 ± 0 . 4 ( 16 ) ∈ 30 . 97 ± 0 . 01 ( 16 ) ∈ 3Mossy−62 ± 1 ( 9 ) 199 ± 19 ( 9 ) 41 ± 3 ( 8 ) 23 . 76 . 2 ± 0 . 9 ( 9 ) 62 . 50 . 78 ± 0 . 04 ( 9 ) 50 ± 6 ( 9 ) 2 . 8 ± 0 . 7 ( 9 ) 0 . 81 ± 0 . 03 ( 9 ) AIPRIM−64 ± 2 ( 4 ) 363 ± 62 ( 4 ) 30 ± 6 ( 4 ) 1612 . 3 ± 2 . 3 ( 4 ) [76 , 77 . 9]0 . 5 ± 0 . 02 ( 4 ) 81 ± 9 ( 4 ) 9 ± 2 . 7 ( 4 ) 0 . 8 ± 0 . 04 ( 4 ) DG Axo-axonic−65 . 1 ± 3 . 9 ( 14 ) e73 . 9 ± 23 . 8 ( 14 ) e7 . 7 ± 3 . 8 ( 14 ) e13r ∈ 27r ∈ 278r ∈ 20 . 42r ∈ 285r4 . 5r ∈ 2–DG Basket−62 ± 3 ( 3 ) 43 ± 5 ( 3 ) ∈ 310 ± 1 ( 3 ) ∈ 2[17 . 6 , 19 . 0] ∈ 320 ± 2 . 3 ( 3 ) ∈ 2[71 . 6 , 73 . 6] ∈ 20 . 25 ± 0 . 04 ( 3 ) ∈ 2230 ± 15 ( 3 ) ∈ 22 . 3 ± 0 . 2 ( 3 ) 0 . 97 ± 0 . 02 ( 3 ) ∈ 2HIPROM−65 ± 6 ( 3 ) 371 ± 47 ( 3 ) 35[25 . 1 , 27 . 3]13 . 1 ± 3 . 0 ( 3 ) 80 . 80 . 72 ± 0 . 08 ( 3 ) 69 ± 4 ( 3 ) 3 . 1 ± 1 . 0 ( 3 ) 0 . 82 ± 0 . 02 ( 3 ) MOLAX−54 . 5 ± 1 . 9 ( 13 ) ∈ 2198 . 2 ± 23 . 8 ( 13 ) ∈ 218 . 4 ± 1 . 1 ( 13 ) 15 . 2 ∈ 211 ∈ 2[43 . 2 , 44 . 4] ∈ 2[1 . 26 , 1 . 64]503 . 5mr ∈ 2–Total Molecular Layer−54 . 5 ± 1 . 9 ( 13 ) 198 . 2 ± 23 . 8 ( 13 ) 18 . 4 ± 1 . 1 ( 13 ) 15 . 211[43 . 2 , 44 . 4][1 . 26 , 1 . 64]504 . 5–CA3 ( 8 ) CA3 Pyramidal−60 . 5 ± 5 . 4 ( 43 ) ∈ 8126 ± 8 ( 35 ) ∈ 861 ± 24 ( 36 ) ∈ 713 ∈ 510 . 2 ± 0 . 5 ( 43 ) ∈ 397 . 6 ± 1 . 9 ( 43 ) ∈ 41 ± 0 . 1 ( 43 ) ∈ 440 ± 20 . 8 ( 3 ) 7 . 5m ∈ 31 . 01 ± 0 . 01 ( 7 ) ∈ 4CA3 Giant−57 ± 1 . 2 ( 28 ) r595 ± 224 ( 28 ) r67 ± 23 ( 28 ) r22r14r76 ± 7 . 5 ( 28 ) r1 . 1 ± 0 . 1 ( 28 ) r>50 ± 3 ( 13 ) 7 . 1r0 . 68rCA3 Granule−78 ± 0 . 5 ( 15 ) 139 ± 11 ( 15 ) 17 . 1 ± 1 . 8 ( 15 ) 27 . 47 . 95104 . 3[0 . 821 , 1 . 072]>1001 . 750 . 99CA3 Basket−58 . 5 ± 2 . 8 ( 6 ) mr122 . 9 ± 26 . 7 ( 6 ) mr11 . 2 ± 2 . 9 ( 6 ) mr25 . 6mr35mr77 . 1mr0 . 54 ± 0 . 1 ( 8 ) mr33 . 6 ± 6 . 4 ( 6 ) mr3mr0 . 93mrLucidum ORAX−61 ± 6 ( 9 ) ∈ 2284 ± 180 ( 9 ) ∈ 242 ± 17 ( 8 ) ∈ 216 . 3 ∈ 215 ± 6 . 6 ( 9 ) ∈ 277 . 7 ∈ 20 . 53 ± 0 . 2 ( 9 ) ∈ 275 ± 28 . 3 ( 8 ) ∈ 26 . 6 ± 3 . 9 ( 9 ) ∈ 20 . 9 ± 0 . 1 ( 8 ) ∈ 2MFA ORDEN−57 ± 5 ( 13 ) 225 ± 93 ( 13 ) 29 . 1 ± 14 . 6 ( 13 ) 2013 . 1 ± 1 . 9 ( 13 ) 740 . 72 ± 0 . 15 ( 13 ) 73 ± 16 ( 13 ) 6 . 5–CA3 O-LM−60 ± 12 ( 15 ) mr315 . 1 ± 161 . 1 ( 15 ) mr33 . 3 ± 5 . 4 ( 15 ) mr[16 , 37]mr34 . 8mr109mr0 . 84 ± 0 . 2 ( 15 ) mr182 ± 161 . 1 ( 15 ) mr1 . 3mr0 . 79 ± 0 . 1 ( 10 ) mrCA3 Trilaminar−61 . 2 ± 13 ( 8 ) mr167 . 3 ± 59 . 1 ( 8 ) mr16 . 9 ± 8 . 8 ( 8 ) mr12mr30mr[69 , 99]mr0 . 57 ± 0 ( 8 ) mr101 . 5 ± 62 . 2 ( 8 ) mr0 . 1mr0 . 88mrCA2 ( 4 ) CA2 Basket−71 . 2 ± 4 ( 6 ) e77 ± 19 . 3 ( 6 ) e8 . 2 ± 3 . 2 ( 6 ) e[17 , 27]e22 ± 5 . 1 ( 6 ) e62 . 7 ± 9 ( 6 ) e0 . 5 ± 0 . 1 ( 6 ) e>180e3e0 . 99eCA2 Wide-arbor BC−74 . 9 ± 5 . 6 ( 10 ) e111 . 8 ± 36 . 7 ( 10 ) e12 . 6 ± 4 . 2 ( 10 ) e[26 , 34]e19 . 5 ± 9 ( 10 ) e65 . 5 ± 7 . 1 ( 10 ) e0 . 6 ± 0 . 1 ( 10 ) e>125e7e0 . 55eCA2 Bistratified−72 . 7 ± 1 . 1 ( 3 ) e83 . 3 ± 16 . 7 ( 3 ) e13 . 7 ± 10 ( 3 ) e[42 , 56]e14 . 4 ± 11 . 8 ( 3 ) e65 . 2 ± 7 . 8 ( 3 ) e0 . 4 ± 0 . 1 ( 3 ) e–3 . 2e0 . 99eCA2 SP-SR−71 ± 4 . 5 ( 8 ) e82 . 6 ± 24 . 4 ( 8 ) e12 . 7 ± 3 . 8 ( 8 ) e19e11 . 5e67e0 . 5 ± 0 . 1 ( 8 ) e>160e3 . 3e0 . 86eCA1 ( 16 ) CA1 Pyramidal−62 . 4 ± 2 . 4 ( 21 ) ∈ 765 . 6 ± 4 . 4 ( 20 ) ∈ 722 . 4 ± 1 . 5 ( 20 ) ∈ 616 . 6 ∈ 76 . 8 ∈ 290 ∈ 41 ∈ 3>321 . 7 ∈ 30 . 74 ∈ 3CA1 Radiatum Giant−66 ± 3 . 6 ( 16 ) ∈ 256 ± 14 ( 25 ) ∈ 250 ± 7 . 9 ( 7 ) r ∈ 231 ∈ 28 ∈ 270 ∈ 21 . 9 ± 0 . 2 ( 7 ) r>268 . 3r ∈ 20 . 86 ∈ 2CA1 Horizontal AA−57 ± 5 ( 15 ) m[186 , 252]r[16 , 32]r25m[6 . 0 , 13 . 7]r[71 . 4 , 90 . 2]r[0 . 7 , 1 . 1]r87r ∈ 26 . 1m0 . 74mCA1 Basket−57 ± 5 ( 15 ) m ∈ 2116 ± 63 ( 15 ) m ∈ 213 ± 8 ( 15 ) m ∈ 219 ∈ 310 ∈ 362 ∈ 30 . 54 ± 0 . 11 ( 15 ) m ∈ 2>602e ∈ 30 . 84 ± 0 . 06 ( 15 ) mCA1 BC CCK+−61 . 4 ± 3 . 2 ( 5 ) 281 . 68 ± 79 . 7 ( 5 ) 25 . 07 ± 5 . 6 ( 5 ) 21 . 5 ∈ 315 . 17 ± 3 . 4 ( 5 ) ∈ 376 . 92 ± 11 . 7 ( 5 ) ∈ 30 . 84 ± 0 . 1 ( 5 ) >307 ∈ 20 . 825CA1 Horizontal BC−55 . 4 ± 9 . 5 ( 17 ) ∈ 2[116 , 199] ∈ 2[15 . 4 , 25 . 5] ∈ 224 ∈ 211 ∈ 2130 ∈ 20 . 77 ± 0 . 10 ( 18 ) m ∈ 2>50m4 . 8m ∈ 20 . 6 ∈ 2CA1 Ivy−7172 . 8 ± 53 . 6 ( 5 ) 7 . 6 ± 4 . 1 ( 5 ) 30 . 113 . 6 ± 3 . 8 ( 5 ) 440 . 8 ± 0 . 2 ( 5 ) –30 . 98CA1 LMR−53 . 1 ± 4 . 0 ( 48 ) r ∈ 3352 ± 107 ( 49 ) r ∈ 332 . 9 ± 12 . 7 ( 11 ) r ∈ 313 . 2r ∈ 321 . 5r ∈ 386 . 9 ± 11 . 0 ( 49 ) r ∈ 31 . 3r ∈ 3–0 . 2r ∈ 40 . 92 ± 0 . 11 ( 15 ) r ∈ 3CA1 Neurogliaform−63 . 1 ± 5 . 6 ( 33 ) ∈ 2215 . 3 ± 92 . 8 ( 32 ) ∈ 212 . 43 ± 4 . 59 ( 32 ) ∈ 232 . 4 ∈ 220 . 4 ± 4 . 1 ( 34 ) ∈ 273 ∈ 20 . 9 ± 0 . 18 ( 26 ) ∈ 252 . 8 ± 31 . 0 ( 26 ) 9 ∈ 20 . 99 ∈ 2CA1 NGF Projecting−63 . 1 ± 5 . 6 ( 33 ) 215 . 3 ± 92 . 8 ( 32 ) 12 . 43 ± 4 . 59 ( 32 ) 32 . 420 . 4 ± 4 . 1 ( 34 ) 730 . 9 ± 0 . 18 ( 26 ) 52 . 8 ± 31 . 0 ( 26 ) 90 . 99CA1 Recurrent O-LM[−85 , −65]e70 ± 13 . 72 ( 8 ) e12 . 8 ± 1 . 5 ( 8 ) e25e16 . 1 ± 10 . 7 ( 8 ) e58 . 75 ± 7 . 2 ( 8 ) e0 . 6 ± 0 . 3 ( 8 ) e>150e5e0 . 71eCA1 PPA QuadD−64 ± 7 ( 23 ) m216 ± 124 ( 23 ) m46 ± 18 ( 23 ) m27m22 ± 3 ( 23 ) m61 ± 7 ( 23 ) m0 . 77 ± 0 . 12 ( 23 ) m>40m1 . 3m0 . 79 ± 0 . 09 ( 23 ) mSchaffer Collateral-Assoc−55 . 8 ± 2 . 8 ( 10 ) e ∈ 296 . 3 ± 36 . 0 ( 10 ) e ∈ 216 . 2 ± 8 . 9 ( 10 ) e ∈ 211e ∈ 211 . 4 ± 2 . 8 ( 10 ) e ∈ 270 . 8 ± 8 . 0 ( 10 ) e ∈ 20 . 74 ± 0 . 1 ( 10 ) e ∈ 2>1005 . 5e ∈ 20 . 86eCA1 SO–SO−59 ± 10 ( 19 ) m ∈ 2401 ± 212 ( 19 ) m38 ± 13 ( 19 ) m24m ∈ 215 ± 4 ( 19 ) m ∈ 248 ± 10 ( 19 ) m ∈ 21 . 12 ± 0 . 14 ( 19 ) m>160m4 . 8m ∈ 20 . 69m ∈ 2CA1 Trilaminar−64 ± 7 ( 23 ) m216 ± 124 ( 23 ) m46 ± 18 ( 23 ) m27m22 ± 3 ( 23 ) m61 ± 7 ( 23 ) m0 . 77 ± 0 . 12 ( 23 ) m>130m4 . 7m0 . 79mCA1 Radial Trilaminar−57 ± 5 ( 15 ) m116 ± 63 ( 15 ) m13 ± 8 ( 15 ) m29m ∈ 325 ± 4 ( 15 ) m ∈ 348 ± 8 ( 15 ) m ∈ 30 . 54 ± 0 . 11 ( 15 ) m>120m0 . 82m ∈ 30 . 89mEC ( 14 ) LI-II Multipolar-Pyramidal[−70 , −56]430 ± 121 . 7 ( 37 ) 25 ± 15 . 2 ( 37 ) [8 . 1 , 23 . 1][3 . 7 , 7 . 3]77 . 5 ± 15 . 2 ( 37 ) 1 . 22 ± 0 . 4 ( 37 ) >504 . 70 . 78 ± 0 . 06 ( 37 ) LI-II Pyramidal-Fan[−62 , −59] ∈ 2400 ± 98 ( 96 ) ∈ 215 . 8 ± 14 . 2 ( 96 ) ∈ 2[7 . 5 , 8 . 4] ∈ 2[7 . 1 , 7 . 3] ∈ 269 ± 19 . 6 ( 96 ) ∈ 21 . 25 ± 0 . 49 ( 96 ) ∈ 2>40[0 , 6] ∈ 20 . 66 ± 0 . 05 ( 96 ) ∈ 2MEC LII PC-Multiform[−70 , −56]430 ± 121 . 7 ( 37 ) 25 ± 15 . 2 ( 37 ) [8 . 1 , 23 . 1][3 . 7 , 7 . 3]77 . 5 ± 15 . 2 ( 37 ) 1 . 22 ± 0 . 4 ( 37 ) >254 . 70 . 78 ± 0 . 06 ( 37 ) MEC LII Oblique Pyramidal[−62 , −59]400 ± 98 ( 96 ) 15 . 8 ± 14 . 2 ( 96 ) [7 . 5 , 8 . 4][7 . 1 , 7 . 3]69 ± 19 . 6 ( 96 ) 1 . 25 ± 0 . 49 ( 96 ) >40[0 , 6]0 . 66 ± 0 . 05 ( 96 ) MEC LII Stellate[−62 , −59] ∈ 230 . 2 ± 12 . 5 ( 112 ) e ∈ 28 . 9 ± 1 . 9 ( 112 ) e ∈ 2[7 . 5 , 8 . 4] ∈ 2[7 . 1 , 7 . 3] ∈ 269 ± 19 . 6 ( 96 ) ∈ 21 . 25 ± 0 . 49 ( 96 ) ∈ 2>40[0 , 6]0 . 66 ± 0 . 05 ( 96 ) ∈ 2LII-III Pyramidal-Tripolar[−70 , −56] ∈ 2400 ± 98 ( 96 ) ∈ 215 . 8 ± 14 . 2 ( 96 ) ∈ 2[8 . 1 , 23 . 1] ∈ 2[3 . 7 , 7 . 3] ∈ 269 ± 19 . 6 ( 96 ) ∈ 21 . 25 ± 0 . 49 ( 96 ) ∈ 2>404 . 7 ∈ 20 . 66 ± 0 . 05 ( 96 ) ∈ 2LEC LIII Multipolar Principal[−68 , −65]450 ± 78 ( 27 ) 29 ± 12 . 5 ( 27 ) [21 . 17 , 26 . 03][10 . 23 , 11 . 04]70 ± 15 . 6 ( 27 ) 1 . 38 ± 0 . 26 ( 27 ) >254 . 060 . 9 ± 0 . 1 ( 27 ) LEC LIII Complex Pyramidal[−68 , −65]450 ± 78 ( 27 ) 29 ± 12 . 5 ( 27 ) [21 . 17 , 26 . 03][10 . 23 , 11 . 04]70 ± 15 . 6 ( 27 ) 1 . 38 ± 0 . 26 ( 27 ) >254 . 060 . 9 ± 0 . 1 ( 27 ) LIII Pyramidal-Stellate[−68 , −65] ∈ 2450 ± 78 ( 27 ) ∈ 229 ± 12 . 5 ( 27 ) ∈ 2[21 . 17 , 26 . 03] ∈ 2[10 . 23 , 11 . 04] ∈ 270 ± 15 . 6 ( 27 ) ∈ 21 . 38 ± 0 . 26 ( 27 ) ∈ 2[85 , 105]e ∈ 24 . 06 ∈ 20 . 9 ± 0 . 1 ( 27 ) LIII-V Bipolar Pyramidal[−69 , −67]490 ± 79 ( 28 ) 36 ± 16 ( 28 ) [18 . 2 , 21 . 8][5 . 6 , 8 . 7]70 ± 15 . 9 ( 28 ) 1 . 48 ± 0 . 2 ( 28 ) >29[2 , 7]0 . 86 ± 0 . 11 ( 28 ) LIV-VI Deep Multipolar−59 . 8 ± 6 . 8 ( 8 ) ∈ 4272 . 3 ± 105 . 8 ( 8 ) ∈ 426 . 6 ± 6 . 5 ( 8 ) ∈ 436 ∈ 415 ∈ 475 ∈ 41 . 39 ± 0 . 6 ( 8 ) ∈ 4>704 . 4 ∈ 40 . 9 ∈ 3LV Deep Pyramidal−65 . 02 ± 3 . 8 ( 38 ) e ∈ 275 . 67 ± 28 . 72 ( 38 ) e ∈ 212 . 83 ± 3 . 65 ( 38 ) e ∈ 222 . 14e ∈ 217 . 44 ± 2 . 64 ( 38 ) e ∈ 264 . 96 ± 6 . 53 ( 38 ) e ∈ 21 . 53 ± 0 . 28 ( 38 ) e ∈ 2>700 . 1e ∈ 20 . 88e ∈ 2MEC LV-VI PC-Polymorph−63480 ± 94 ( 14 ) 41 ± 19 ( 14 ) [26 . 7 , 28 . 7][7 . 1 , 9 . 1]73 ± 8 ( 14 ) 2 . 1 ± 0 . 8 ( 14 ) >331 . 60 . 91 ± 0 . 09 ( 14 ) LEC LVI Multipolar-PC−64450 ± 90 ( 13 ) 26 ± 14 ( 13 ) [26 . 8 , 29 . 0][6 . 0 , 8 . 8]74 ± 14 ( 13 ) 1 . 7 ± 0 . 4 ( 13 ) >101 . 20 . 9 ± 0 . 1 ( 13 ) Values are mean values ± standard deviations or [range]; parentheses indicate the number of data points ( default = 1; 2 for ranges ) . Values of maxFR reported as relative lower limits ( e . g . , >30 Hz ) are measured from limited spike trains . The data are selected from a set ( ∈ ) of available values based on experimental conditions and number of measurements . Preferred conditions ( rat/body temperature/patch clamp ) are not indicated; otherwise , m = mice , r = room temperature , e = microelectrodes . Although Hippocampome . org primarily categorizes neuron types according to their morphologies ( locations of axons and dendrites across the 26 parcels of the hippocampal formation ) , several other properties are also considered ( Figure 3 ) . When evaluating publications describing a neuron type , we first apply a set of interpretive rules to the morphological information ( see ‘Materials and methods’ and hippocampome . org/full-interp ) . We then follow a series of systematic criteria that can result in one of three end points: the mined information is consistent with ( and supplementary of ) a currently active , fully defined neuron type ( hippocampome . org/supplemental ) ; the information is distinct enough to establish a new active neuron type; or the information is insufficient , thus generating an ‘on hold’ neuron type ( hippocampome . org/on-hold ) . 10 . 7554/eLife . 09960 . 006Figure 3 . Flow chart of inclusion criteria for neuron types . Beginning with a reconstruction , schematic , or text description of a neuron morphology , the flow chart ends with either a new ‘on hold’ neuron type , supplemental information for an existing active neuron type , or a new active neuron type . Intermediate decision points evaluate the presence of both axons and dendrites , the determination of the main neurotransmitter , the uniqueness of the new type , and whether information is sufficient to create a new active type . DOI: http://dx . doi . org/10 . 7554/eLife . 09960 . 006 Hippocampome . org provides free , user-friendly online access to the entire information content . The annotated evidence supports 3697 distinct ‘pieces of knowledge’ ( PoK ) regarding the presence or absence of axons or dendrites within any of the 26 parcels ( e . g . , Granule cells axons are found in CA3 SL represents one PoK ) , the expression or non-expression of a biomarker ( e . g . , Granule cells are positive for CB ) , and individual electrophysiological properties ( e . g . , the APwidth of Granule cells is 1 . 71 ± 0 . 58 ms , mean ± standard deviation ) . This knowledge exceeds by 2–3 orders of magnitude ( Figure 4A ) ; the average numbers of PoK collated from relevant articles ( ∼5 ) and reviews ( ∼27 ) . Integration of published information from multiple sources , by linking together molecular , electrophysiological , and morphological data , produces an explicit knowledge web of references , properties , and neuron types interlaced in a many-to-many fashion ( Figure 4B ) . Of equal importance , this resource explicitly highlights the non-uniform distribution of knowledge among neuron types and across morphological , molecular , and electrophysiological properties ( Figure 4C ) . The approximate ratio of morphological , molecular , and electrophysiological PoK is 5:3:2 . While CA1 is the most studied hippocampal area ( 1321 PoK ) , outstanding knowledge gaps remain on interneuron diversity in subiculum ( only Axo-axonic cells have enough information for inclusion as an active neuron type in Hippocampome . org ) and EC ( no GABAergic cells characterized with either soma or axons in deep layers ) . 10 . 7554/eLife . 09960 . 007Figure 4 . Quantifying knowledge in Hippocampome . org about morphology , biomarkers , and electrophysiology of hippocampal neuron types . ( A ) Histograms comparing the sum of pieces of knowledge ( PoK ) in relevant journal articles or book chapters , in reviews , and in Hippocampome . org . ( B ) Interconnected knowledge graph of neuron type properties mined from two typical journal articles . ( C ) Balloon plot of collated knowledge for a majority of GABAergic neuron types . The balloon size indicates the sum of PoK for that type across all three dimensions; balloon color denotes the subregion ( as in Figure 1 ) . Note the dearth of biomarker information in entorhinal cortex ( EC ) and the uneven distribution of data between CA3 and CA1 . DOI: http://dx . doi . org/10 . 7554/eLife . 09960 . 007 The massive amount of knowledge and online availability of Hippocampome . org enable both broad-scope analytics and quick-use information checks . All information can be browsed or searched . Browsing starts with summaries of morphological ( hippocampome . org/morphology ) , molecular ( hippocampome . org/markers ) , or electrophysiological ( hippocampome . org/electrophysiology ) data , similar to hyperlinked versions of Figures 1F , 2 , and Table 1 , respectively . Searching for neuron types is accomplished through selections from dynamically updated pull-down menus of any combination of properties , such as ‘axons located in DG’ , ‘PV-negative’ , and ‘Vthresh > 20 mV’ ( Figure 5A ) . Compound queries with AND & OR Boolean connectors can uncover unexpected results even for the most experienced hippocampal researchers . Searches can also be conducted by PubMed identifiers or author names ( Figure 5B ) . Returned results contain full bibliographic information , a link to the article , and a list of neuron types with information from the article . 10 . 7554/eLife . 09960 . 008Figure 5 . Hippocampome . org enables searching neuron types by neurotransmitter; axon , dendrite , and soma locations; molecular expression; electrophysiological parameters; and input/output connectivity . ( A ) Sample query for parvalbumin-negative neuron types with axons in DG and firing threshold >20 mV . ( B ) The knowledge base may also be queried for a specific PubMed ID or author name ( e . g . , ‘Fuentealba P’ ) . ( C , D ) Returned results link to ( C ) neuron type summary pages ( Figure 1A from Price et al . , 2005 , J . Neurosci . 25:6775–6786 [permission to reuse granted by SfN] ) and ( D ) evidence from published figures , tables , and text quotes supporting all reported properties ( Figure 6B from Fuentealba et al . , 2010 , J . Neurosci . 30:1595–1609; Table 2 and Figure 3A from Price et al . , 2005 , J . Neurosci . 25:6775–6786 [permission to reuse granted by SfN] ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09960 . 008 Both browsing and searching lead to summaries of all information associated with a given neuron type ( Figure 5C ) : synonyms , morphology , electrophysiology , biomarkers , a representative figure , and known pre- and post-synaptic connectivity ( see below ) . Every property on each neuron page or browse summary links to an evidence page that lists all supporting bibliographic citations complete with extracted quotes , figures , tables , and exact pointers to the relevant pages and paragraphs ( Figure 5D ) . Hippocampome . org contains 13 , 888 pieces of evidence , including all known sources for many neuron types and properties , but only an adequate number of sources to firmly support established knowledge ( e . g . , we have only annotated a fraction of all published evidence that granule cells extend axons in CA3 SL ) . Knowledge integration facilitates the discovery of relations between neuronal properties that would ordinarily remain hidden in the scattered literature . Most PoK in Hippocampome . org , such as axonal presence in a layer or expression of a biomarker , are categorical , and their statistical co-occurrence can be analyzed with contingency tables ( ‘Materials and methods’ ) . To allow comparison across experiments , electrophysiological parameters are converted ( for this analysis only ) from continuous to categorical variables by labeling values in the top and bottom one-third of the range , respectively , as high and low . This approach reveals several interesting relationships ( Box 1 ) : for example , positive or negative expression of neuropeptide Y ( NPY ) tends to co-occur , respectively , with high or low fAHP ( p < 0 . 001 with Barnard's exact test; post hoc t-test p < 0 . 02 with real values from all n = 42 pieces of evidence ) ; vasoactive intestinal polypeptide ( VIP ) is mutually exclusive with CB ( p < 0 . 04 ) ; and of 31 entorhinal neuron types , only Oblique Pyramidal cells in LII of medial EC have no layer overlap between axons and dendrites . 10 . 7554/eLife . 09960 . 009Box 1 . Trends assessed from data collated in the Hippocampome . The trend across clear expression patterns of calbindin ( CB ) , calretinin ( CR ) , and parvalbumin ( PV ) supports the general idea that only one of these calcium binding proteins is expressed at a time within a cell . While CB is never co-expressed with either CR ( p < 0 . 05 , n = 19 ) or PV ( NS , n = 31 ) , PV and CR do co-occur in 2 out of 12 neuron types known to express at least one of them ( p > 0 . 49 , n = 31 ) . A less known but more stringent relationship exists between CB and vasoactive intestinal peptide ( VIP ) : among the 24 neuron types for which the expression information on both of these markers is known , 14 are positive for either CB or VIP , but none express both ( p < 0 . 04 , n = 24 ) . Clearly positive expression of neuropeptide Y ( NPY ) tends to coincide with very high values of the fast afterhyperpolarization ( fAHP ) ( p < 0 . 001 , n = 23 ) and membrane firing threshold ( Vthresh ) ( p < 0 . 005 , n = 21 ) , and clearly negative expression tends to be associated with very low values of both parameters . fAHP is similarly related also to expression of chicken ovalbumin upstream promoter transcription factor II ( CoupTF II ) ( p < 0 . 005 , n = 14 ) , which tends to clearly co-express either both positively or both negatively with NPY ( p < 0 . 005 , n = 18 ) . Confirming a well-known relationship , of 45 cell types with extreme values of input resistance ( Rin ) and membrane time constant ( τm ) ( p < 0 . 001 , n = 45 ) , 43 are positively correlated ( R2 = 0 . 70 ) . Likewise , high values of the spike width ( APwidth ) are associated with high values of Rin ( p < 0 . 001 , n = 45 ) , and vice versa for the low values of both ( R2 = 0 . 36 ) . Of the 69 cell types with axons in more than one layer , only DG Granule and CA1 O-LMR do not have overlapping axons and dendrites ( p < 0 . 001 , n = 122 ) . All of the 43 cell types that have axons in three or more layers have axons that overlap with their dendrites ( p < 0 . 001 , n = 122 ) . Furthermore , cell types with axons in three or more layers tend to have very high Rin ( p < 0 . 001 , n = 58 ) and τm ( p < 0 . 001 , n = 59 ) . Clear expression of cannabinoid receptor 1 ( CB1 ) implies ( in 5/5 cases ) clear expression of cholecystokinin ( CCK ) ( if known ) , and clear absence of CCK implies ( in 8/8 cases ) clear absence of CB1 ( if known ) ( p < 0 . 005 , n = 15 ) . Of the two neuron types expressing CCK and not CB1 , one expresses VIP; however , independent of CCK expression , CB1 and VIP are not co-expressed in any of the eight types that are positive for either biomarker ( NS , n = 17 ) . Overall , these patterns suggest that CCK+/VIP+ and CCK+/CB1+ neurons constitute completely separate groups . The p values and sample sizes ( n ) pertain to Bernard's exact test on 2 × 2 contingency tables ( see ‘Materials and methods’ ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09960 . 009 Many observed trends also reinforce expected associations and serve as validation for the dataset . For example , 96% ( 43/45 ) of neuron types with high or low input resistance have correspondingly high or low τm , consistent with RC-circuit theory . The two outliers correspond to data from a paper on CA1 Radiatum Giant cells ( Kirson and Yaari , 2000 ) , for which the particularly high τm/Rin ratio is explained by the oversized soma and consequently large capacitance . Hippocampome . org contains known connectivity information ( synapses or lack thereof , established e . g . , by electron microscopy or paired electrophysiological recordings ) among its neuron types . Such data , however , are only available for 202 out of 14 , 884 possible pairs ( <2% ) . In the remaining cases , co-existence of axons and dendrites across parcels allows inference of ‘potential connectivity’ between cell types ( Braitenberg , 1991 ) . Specifically , potential connectivity is computed as the dot-product of the 26-dimensional binary vectors encoding the presence/absence of axons for one neuron and dendrites for another across hippocampal parcels . The possibility of connections can be tentatively excluded for 11 , 668 pairs of neuron types ( 78 . 4% ) based on non-overlapping distributions of their respective axons and dendrites . However , incomplete axons and the binary thresholding of the morphological encoding ( see ‘Materials and methods’ ) might yield false negatives . In 3014 pairs ( 20% ) the axons and dendrites of the two neuron types share at least one parcel . Although the connection probability may not be estimated in the absence of experimental evidence , even the opportunity to make synapses is computationally relevant due to the superior structural plasticity of the hippocampal formation throughout the lifespan ( Leuner and Gould , 2010 ) . All ( known and potential ) connectivity data are summarized in a matrix with rows as pre-synaptic and columns as post-synaptic neuron types ( 100 neuron types in Figure 6A; complete data: hippocampome . org/connectivity ) . Filled boxes along the main diagonal , corresponding to neurons with axons and dendrites co-located in any parcel ( purple boxes in Figure 1F ) , indicate within-type connectivity ( not necessarily autapses ) , and are more frequent among excitatory ( 87% ) than inhibitory ( 67% ) types . 10 . 7554/eLife . 09960 . 010Figure 6 . Neuron type connectivity ( area color coding and numbering as in Figure 1 ) . ( A ) Known and potential connections for 100 neuron types ( full matrix: hippocampome . org/connectivity or hippocampome . org/php/images/connectivity/Connectivity_Matrix . jpg ) , with pre-synaptic types in rows and post-synaptic types in columns . Black squares indicate potential glutamatergic connections and gray squares GABAergic . Pairs of neuron types with experimentally established and refuted synapses are shown using green and red squares , respectively . Colored boundaries demarcate intra-area connections . ( B ) Regional pathways with 33 glutamatergic connections ( full lines ending in arrows ) and 20 GABAergic connections ( dashed lines ending in open circles ) ; line thickness represents the numbers of connected neuron types between areas . DOI: http://dx . doi . org/10 . 7554/eLife . 09960 . 010 Neuron type connectivity as depicted in Figure 6A constitutes an intermediate level of description between the neuron-by-neuron connectome ( ‘synaptome’ ) and the region-by-region connectome ( ‘projectome’ ) . In order for two regions to be connected , there must be at least one neuron type in one region connecting to one neuron type in the other ( Martone and Ascoli , 2013 ) . Within the hippocampal formation , this relationship is illustrated explicitly in Figure 6B: all six areas have potential excitatory , and with the exception of Sub and EC , inhibitory connections to at least one other area , most of which are not considered in the canonical trisynaptic loop ( cf . Burns and Young , 2000; van Strien et al . , 2009 ) . The only potential monosynaptic connection between DG and CA1 is inhibitory . While this regional wiring diagram appears simpler than the corresponding seminal illustration for the visual cortex ( Felleman and Van Essen , 1991 ) , the underlying neuron type circuitry is highly complex . In fact , the explicit wiring diagram of all neuron type potential connections is impractical to render on a single page . Selecting only 15 representative neuron types in the DG entails 253 connections among 43 somatic and dendritic compartments ( Figure 7A ) . This compartmental representation of network connectivity captures the computationally distinct subunits that emerge from layer-specific axonal targeting , a key component of mesoscopic neuron type circuitry . 10 . 7554/eLife . 09960 . 011Figure 7 . Neuron type circuitry . ( A ) Circuit diagram of selected neuron types in DG ( full diagram: hippocampome . org/php/images/connectivity/DG_Circuit_Diagram . jpg or hippocampome . org/php/images/connectivity/DG_Circuit_Diagram . graffle . zip ) . Axonal connections from pre-synaptic somata ( orange glutamatergic , green GABAergic ) to post-synaptic somata or dendrites ( blue ) have unique line colors for each pre-synaptic type for clarity . Lines ending in arrows and open circles indicate , respectively , glutamatergic and GABAergic connections; 22 known ( thick lines ) and 231 potential ( thin ) connections are depicted . 1: Granule . 2: Semilunar Granule . 3: Mossy . 4: AIPRIM . 5: Axo-axonic . 6: Basket . 7: Basket CCK+ . 8: HICAP . 9: HIPP . 10: HIPROM . 11: MOCAP . 12: MOLAX . 13: MOPP . 14: Neurogliaform . 15: Outer Molecular Layer . ( B ) Pre- and post-synaptic connections for DG Granule and CA3 Basket cells . Red lines ending in arrows indicate glutamatergic connections , and blue lines ending in open circles indicate GABAergic connections . Thick and thin lines indicate , respectively , known and potential connections . Neuron types are color coded by area of origin ( as in Figure 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09960 . 011 The richness of connectivity is also revealed when focusing on a specific interaction between two neuron types . For instance , in a dual recording of a Granule cell and a CA3 Basket cell , circuit dynamics are potentially affected by many contributors ( Figure 7B ) . Granule cells can receive input from 26 neuron types ( 9 excitatory ) across DG , CA3 , CA1 , and EC . CA3 Basket cells can receive input from 31 types ( 8 excitatory ) , including Granule cells and other Basket cells . The above numbers include five neuron types that provide possible input to both Granule and CA3 Basket cells . Granule cells potentially send output to 29 neuron types , counting all 13 that could receive information from CA3 Basket cells , including CA3 Basket cells themselves . Hippocampome . org can deliver valuable information in many day-to-day research scenarios ( Box 2 ) and at multiple stages of a neuroscience study ( hippocampome . org/usage ) . They include determining whether a hippocampal neuron with a particular morphological pattern is known , obtaining a list of candidate neuron types based on partial reconstructions , and finding biomarker and electrophysiological properties for most neuron types . In all highlighted use cases , Hippocampome . org delivers the needed information with very few mouse clicks , in a matter of seconds . Even queries that are nearly impossible with literature search engines ( e . g . , finding an unnamed neuron by its axonal pattern ) allow , at Hippocampome . org , straightforward retrieval of the original evidence . 10 . 7554/eLife . 09960 . 012Box 2 . Representative Hippocampome . org use case scenarios . Property-based recognition of known neuron typesWhile patching in DG stratum granulosum ( SG ) , a researcher encounters several interneurons with high input resistance ( Rin ) , membrane time constant ( τm ) , and spike amplitude ( APampl ) . Biocytin filling reveals both axonal and dendritic presences in the inner stratum moleculare ( SMi ) but only axons in the hilus ( H ) . Is this a newly discovered neuron type ? Hippocampome . orgThese characteristics are consistent with those reported for MOCAP neurons , first described by Markwardt et al . ( 2011 ) as non-Ivy/NG cells . Comprehensive listing of potential pre-synaptic sourcesIn the presence of outward channel blockers , CA1 Pyramidal cells display rebound spikes upon repeated GABA puffing on their distal apical dendrites . What neurons besides O-LM and Neurogliaform cells might trigger such post-inhibitory firing ? Hippocampome . org14 different interneurons have axons in stratum lacunosum-moleculare ( SLM ) , including CA1 Quadrilaminar , Back-projection , Radiatum-receiving Apical-targeting , Perforant Path-associated , Oriens-Bistratified Projecting , and LMR cells . Comprehensive listing of potential post-synaptic targetsGlutamatergic Cajal–Retzius neurons have recently been observed in the adult rodent hippocampus in larger numbers than previously assumed . What interneurons could they excite in CA1 ? Hippocampome . orgAlthough the axons of CA1 Cajal–Retzius are confined to SLM , they could nonetheless contact no fewer than 17 types of distinct GABAergic cells , such as Basket , Axo-axonic , Radial Trilaminar , Oriens/Alveus , Schaffer Collateral-associated , as well as several interneuron-specific interneurons . Discrimination of perisomatic neuron types by electrophysiological measuresCA3 Basket and Axo-axonic neurons have similar somatic locations , dendritic tree shapes ( invading all layers from stratum oriens ( SO ) to SLM ) , and overall axonal patterns . Short of the labor-intensive determination of their post-synaptic targets , could they be tentatively distinguished by patch-clamp recording ? Hippocampome . orgWhile CA3 Axo-axonic and Basket cells have practically indistinguishable resting potential ( Vrest ) , τm , and APampl , the former tend to display lower firing threshold ( Vthresh ) as well as greater slow afterhyperpolarizations ( sAHP ) and Rin . Positive identification of neuronal phenotypesTwo varieties of CA2 basket cells have been described: a classic type with dendrites confined within CA2 and a ‘wide-arbor’ type , whose dendrites enter CA1 and CA3 . How can the borders between CA2 and adjacent subregions be reliably demarcated ? Hippocampome . orgCA2 pyramidal neurons , unlike their CA1 and CA3 counterparts , are positive for Purkinje cell protein 4 ( PCP4 ) . Conversely , pyramidal neurons in CA1 and CA3 express α-mannosidase-1 ( Man1a ) , while those in CA2 do not . Immunostaining with either of these markers can thus delineate CA2 boundaries . Constraining simulation parametersTo build a circuit model of grid cell activity , a computational neuroscientist is searching for plausible values for Vrest , Vthresh , fast afterhyperpolarizations ( fAHP ) , and APampl for the principal cells and main interneurons above the lamina dissecans ( LIV ) of the medial entorhinal cortex ( MEC ) . Hippocampome . orgThese data are available for MEC LII Stellate cells and EC LIII Pyramidal cells ( the two major glutamatergic neurons ) , for EC LII Basket-Multipolar and MEC LIII Superficial Trilayered interneurons ( the major perisomatic and dendritic-targeting GABAergic cells , respectively ) , as well as for eight additional , if less prominent , neuron types ( 5 excitatory and 3 inhibitory ) of layers I-III . Distilling information relevant to specific hypothesesA novel theory of hippocampal function requires direct feedback inhibition from CA3 and CA1 to DG Granule cells , contrary to the canonical trisynaptic loop and the common assumption of non-projecting GABAergic cells . Does the literature provide any experimental evidence to support the new assumption ? Hippocampome . orgBased on axonal–dendritic overlap , both the Granule cell page and the connectivity matrix indicate as ( potential ) sources of monosynaptic input one CA3 ( Spiny Lucidum ) and three CA1 ( LMR Projecting , Perforant Path-associated , and Neurogliaform Projecting ) neurons . Of these , Perforant Path-associated neurons have already been shown to form synapses onto DG Granule cells ( Vida et al . , 1998 ) . See hippocampome . org/usage for an extended example . DOI: http://dx . doi . org/10 . 7554/eLife . 09960 . 012
Systematic organization of present knowledge on neuron types might revolutionize neuroscience akin to the impact the Periodic Table of the Elements had on chemistry 150 years ago . Key to this endeavor is the judicious selection of pivotal variables with the most discriminant and explanatory power . Notably , the initial choice may prove to be a useful proxy even if later recognized as unprincipled: Mendeleev's first attempt to order chemical elements by atomic mass subsequently led to predicting their correct atomic numbers four decades before the discovery of the proton . The distribution of axons and dendrites across anatomical areas and layers is known for the majority of hippocampal neurons identified to date and effectively discriminates among them . Among the features commonly used to describe cortical neurons , morphology is also considerably robust to experimental conditions . Furthermore , just like the positions of the elements in the Periodic Table indicate their potential to combine into molecules , axonal and dendritic patterns provide the blueprint for the potential network connectivity . Our classification scheme links biochemical , physiological , and synaptic data to structural knowledge of neuron types by methodical literature mining . The cost of this integration is the need to interpret published information to abstract specific evidence from neuron instances into general properties of neuron types . The resulting knowledge base constitutes the first comprehensive machine-readable neuron inventory for a mammalian cortical region . Parallel efforts are underway for the retina ( Siegert et al . , 2009 ) , somatosensory ( Markram , 2006 ) and visual cortex ( celltypes . brain-map . org ) , and drosophila ( dos Santos et al . , 2015 ) . Complementary synergies in the rat hippocampal formation include Temporal-lobe . com ( van Strien et al . , 2009 ) , Rat Hippocampus Atlas ( Kjonigsen et al . , 2011 ) , and Hippocampus 3D ( Ropireddy et al . , 2012 ) . The vision driving Hippocampome . org is a real-scale computer model of the entire hippocampal formation . Accurate simulations require knowledge of the component parts , their locations , numbers , properties , and connectivity . We have started with an accounting of the neuron types defined by their most essential morphological , molecular , and electrophysiological features . Further works-in-progress include neuron counts ( Bezaire and Soltesz , 2013 ) , firing patterns , synaptic profiles , developmental origins ( Tricoire et al . , 2011 ) , and rhythmic phase-locking ( Somogyi and Klausberger , 2005 ) . Moreover , the binary assignment of axonal and dendritic distribution across parcels will gradually be substituted by quantitative morphological estimates ( Ropireddy and Ascoli , 2011 ) . Neuronal classification will likely benefit by advances in optogenetics , sequencing , and machine learning ( Fenno et al . , 2014; Kohara et al . , 2014; Roux et al . , 2014; Armañanzas and Ascoli , 2015; Zeisel et al . , 2015 ) . Rapid growth is expected particularly in the knowledge of molecular markers . Thousands of the ∼20 , 000 genes mapped in the Allen Mouse Brain Atlas ( Lein et al . , 2007 ) are expressed in the hippocampus . Connecting such large-scale information to the morphology and physiology of particular neuron types will likely answer many open questions while raising new ones . By integrating available information , Hippocampome . org aims to accelerate discovery . NeuroMorpho . Org ( Ascoli et al . , 2007 ) , a digital archive of neuronal reconstructions , has enabled numerous secondary findings from amounts of data far outreaching the collection means of individual labs ( Parekh and Ascoli , 2014 ) . While this article offered selected examples , further in-depth analyses of neuron type connectivity and pairwise property correlations are ongoing . Consolidation of knowledge also allows assessing the available information to prioritize missing data in the hippocampal formation ( cf . Olshausen and Field , 2005 for V1 ) . The limited interneuron diversity in CA3 , CA2 , Sub , and EC compared to CA1 suggests several to-be-discovered neurons . Many cell types found in CA1 ( e . g . , Basket , O-LM , Ivy , Neurogliaform , Axo-axonic , Trilaminar , and Bistratified ) have counterparts in some other area ( s ) , but they are probably present in more . As experimental techniques advance , several new neuron types will likely be reported . The Hippocampome . org framework also sets a standard for the minimal information that must be included in publications describing hippocampal neuron types in order to link the resulting data to the existing body of knowledge . Initially enforcing strict information granularity has proven critical for the launch of this resource by preventing unending mining of published neuronal properties with clear-cut delimiters . The germinal selection of information to include in Hippocampome . org was pragmatically guided by the density of available published data . For example , in vivo neuronal reconstructions and recordings , although extremely valuable , are too sparse to be adopted as defining dimensions in a comprehensive census of neuron types . Similarly , axonal and dendritic patterns are more commonly identified across layers than in the longitudinal ( septo-temporal ) and transverse ( e . g . , CA3a , b , c ) axes . In limited cases , information is sufficient to distinguish specific neuron types based on finer anatomical sub-divisions . For instance , CA3c Pyramidal cells are differentiated from other CA3 Pyramidal cells , because of their axonal back-projection to DG and lack of dendrites in SLM . Some neuron types are similarly separated between medial and lateral EC . At the same time , Hippocampome . org is well equipped to manage the expected ‘data deluge’ from ongoing big science projects ( Kandel et al . , 2013 ) . With accelerating knowledge expansion , our linking protocol to interrelate each additional piece of data with pre-existing information will ensure continuous integration , facilitating user-friendly analysis and modeling . Moreover , Hippocampome . org infrastructure is designed to accommodate additions along many dimensions: new areas ( e . g . , septum ) , finer parcels ( medial vs lateral EC ) , additional neuron types ( hippocampome . org/future ) , and graded reporting of neurite densities . The open-source code of this resource also encourages community-led extension to other brain regions , inter-operability with related initiatives , and progressive adoption of unique identifiers for neuron types and properties .
Several attempts are underway to establish definitive layer and area boundaries through molecular expression ( Thompson et al . , 2008; Boccara et al . , 2014 ) . Until formal definitions are in place , generally agreed upon anatomical and histochemical characteristics provide for the delineation of the hippocampal formation into six areas ( DG , CA3 , CA2 , CA1 , Sub , and EC ) and their respective layers ( e . g . , CA1 SO , SR , etc . ; Figure 1A , B ) . Most of these parcels are standard and can be found in all reviews of the hippocampal formation . Part of the DG literature only defines a single s . moleculare ( SM ) between the fissure and SG . Hippocampome . org divides SM into the outer two-thirds ( SMo ) and the inner one-third ( SMi ) , because SMo receives perforant path input from EC and SMi receives hilar collaterals from mossy cells ( Amaral et al . , 2007 ) . In Sub , Hippocampome . org lists a single SM superficial to SP and a PL below SP ( van Strien et al . , 2009 ) , differing from the Allen Mouse Brain Atlas ( Lein et al . , 2007 ) delineation of two parcels above SP and none below . A neuron type must satisfy four criteria to be included in Hippocampome . org . ( 1 ) The soma must be located in the hippocampal formation . ( 2 ) The major neurotransmitter must be at least tentatively specifiable; at present , only glutamatergic and GABAergic types have qualified for inclusion in Hippocampome . org ( see below for cholinergic ) . ( 3 ) The locations of both axons and dendrites within any of the 26 hippocampal parcels ( Figure 1A , B ) must be clearly presented , either textually or graphically . ( 4 ) Experimental evidence of more than a single neuron of the type must be reported . Hippocampome . org neuron types are primarily distinguished by criterion 3 ( axonal–dendritic patterning ) independent of author provided names or grouping , but are further differentiated by their synaptic specificity ( see Figure 3 ) . Neurite densities within parcels , beyond a minimal threshold ( see below ) , are not currently a factor , as most reconstructions are incomplete; this first approximation is likely to change when more quantitative data become available for most neuron types . All evidence collated in Hippocampome . org comes from peer-reviewed articles or book chapters ( hippocampome . org/bibliography ) and pertains to healthy rodents , predominantly rats and mice ≥13 days old . When incomplete information prevents inclusion in Hippocampome . org , neuron types are placed ‘on hold’ ( hippocampome . org/on-hold ) . For example , none of the references providing evidence for cholinergic neurons in CA1 clearly describe the axonal and dendritic arbors of individual neurons . On-hold types also include neurons from animals younger than P13 ( e . g . , DG Cajal–Retzius cells ) . Hippocampome . org binarizes the locations of axons and dendrites in the 26 parcels: neurites either have ‘sufficient’ presence in a given parcel or they do not . Lexical and visual thresholds , briefly described here , specify sufficiency criteria ( full explanation and examples: hippocampome . org/full-interp ) . For neuronal reconstructions and schematics , a layer must contain either ≥15% of the overall arbor or at least half the amount included in the most abundant layer . Moreover , the neurite must penetrate ≥15% of the layer depth . In particular , the axonal tree of the CA1 basket cell can spill over from SP into SO and SR , but the penetration threshold is not crossed ( cf . Figure 3E from Pawelzik et al . , 2002 ) . Thus , if the layers are not delineated , the figure may be unusable for neuron typing . For text descriptions , categorical statements such as ‘The dendrites ( of CA1 Horizontal Basket cells ) are restricted to stratum oriens’ ( Maccaferri et al . , 2000 ) , are straightforward . However , quotes that are ambiguous in terms of neurite quantity within a parcel require an interpretive threshold . The term ‘most’ referring to branches is considered as evidence for presence within that parcel , while the term ‘some’ is not , although the quote is still included in Hippocampome . org as evidential information . Interpretation of other equivocal statements is carefully annotated . Although the authors of an article may group a set of neurons together , differing axonal and dendritic patterns can lead to multiple Hippocampome neuron types . This occurs if the number of neurons in each subset is greater than the square root of the total number of neurons described ( e . g . , if 4 neurons out of 15 have distinct neurite distributions , they would be split into a different cell type because 4 > √15 ) . For instance , DG neurogliaform cells ( Armstrong et al . , 2011 ) are divided into two separate neuron types: those that extend their axons into Sub ( keeping the name ‘DG Neurogliaform’ ) and those that remain local within DG SMo . This latter type is merged with MOPP cells , which have the same axo-dendritic pattern in DG and no projection . Future accumulation of neurite density , molecular expression , and electrophysiology data may result in separating local DG neurogliaform and MOPP cells . Conversely , neurons assigned to different groups in a paper might belong to the same Hippocampome type , such as R-LM and P-LM cells ( Oliva et al . , 2000 ) , that differing only in somatic location , are merged into a single CA1 OR-LM type in Hippocampome . org . Hippocampome . org neuron names were assigned progressing from the most to the least prominent types . When a single name is used in the literature , that name is adopted into the Hippocampome . org neuron name . For example , DG granule cells in the knowledge base are referred to as DG ( e ) 2201p-CA3_00110 Granule , a formal name encoding the pattern of axons and dendrites ( hippocampome . org/brief-interp ) , and Granule , a common name . The formal name is composed of multiple parts ( hippocampome . org/formal-name ) , the first of which ( e . g . , DG ( e ) 2201p ) encodes information for the area where the neuron resides: DG designates the home area of the soma; ( e ) informs about the putative major neurotransmitter being glutamategric; 2201 is the axo–dendritic pattern in the home area ordered from the most superficial to the deepest layer ( e . g . , dendrites in SMo and SMi , no neurites in SG , and axons in H ) ; and the p indicates that the axons and/or dendrites project out of the home area . The second part of the formal name encoding ( e . g . , CA3_00110 ) describes the pattern of the projecting neurites ( e . g . , axons in SL and SP of CA3 ) . The final portion of the formal name is the same as the common name . Common neuron names in Hippocampome . org are assigned a prefix denoting their hippocampal area if the same name is used for neurons in different areas ( e . g . , CA3 Bistratified and CA1 Bistratified ) , but not if the name is unique ( as in the case of HIPP cells , which only exist in DG ) . Furthermore , the prefix MEC is assigned to neurons that have been characterized solely ( or are known to exist predominantly ) in the medial EC , such as MEC Layer II Stellate cells . Similarly , the prefix LEC is assigned to lateral entorhinal neurons . In contrast , neurons that are believed to exist in both the medial and lateral ECs are generically referred to with the prefix EC . When multiple names appear in the literature for the same neuron type , the most frequent or best-known is selected , such as CA1 O-LM over CA1 oriens interneurone of the second type ( McBain et al . , 1994 ) or CA1 horizontal oriens-alveus interneurone ( Ali and Thomson , 1998 ) . If no literature name clearly emerges , or if all suitable names are already adopted for more prominent neuron types , we adopt or combine multiple author-originated names , as in EC LI-II Pyramidal-Fan ( Germroth et al . , 1991; Lingenhöhl and Finch , 1991; Empson et al . , 1995; Tahvildari and Alonso , 2005 ) . All terms used in publications are always reported as synonyms in Hippocampome . org . To search for molecular and electrophysiological data , the articles defining the axonal–dendritic pattern of each Hippocampome . org neuron type are mined first . All references citing ( webofknowledge . com ) or cited by these sources are mined next . Lastly , full-text searches using all known synonyms of the target property and neuron type are performed ( scholar . google . com and previously scirus . com ) . If these searches fail to return usable molecular or electrophysiological information for a given neuron type , the corresponding property is labeled as ‘currently unknown’ ( gray boxes in Figure 2 and empty entries in Table 1 ) . For inclusion in the knowledge base , molecular and electrophysiological data must be linked to a morphologically defined neuron type . As with the establishment of morphological types , the linking process is blind to neuron names used by authors . Rather , links require either the co-presentation of axonal–dendritic information or the citation , for that specific evidence , of a source that has a morphological description of the neuron . Only in two cases can linking be achieved without complete knowledge of the morphology . The first applies to principal cells within their layers ( i . e . , granule cells in DG and pyramidal cells in CA1 SP ) . These neuron types are readily identified as positive or negative for a biomarker because the layers are >90% homogeneous for the principal cells ( Czéh et al . , 2013 ) and the somata are densely packed . In the second case , certain groups of neurons can be identified by their axonal tracts , for example , granule cells by their mossy fibers and medial EC LII stellate cells by their perforant path projection . In all cases , the linking information is explicitly included with the evidence reported at Hippocampome . org . The 20 most studied biomarkers in hippocampal research ( Figure 2; extended listing: hippocampome . org/markers ) were targeted for literature searches across all 122 neuron types . These include calcium-binding proteins ( PV , CB , CR ) , receptors and transporters ( CB1 , sub P rec , muscarinic receptor 2 , serotonin receptor 3 , vesicular glutamate transporter 3 , metabotropic glutamate receptor 1α , GABAA-α1 subunit ) , neuropeptides ( CCK , SOM , enkephalin , NPY , VIP , neurogranin ) , and a miscellaneous group of cytoskeletal and extracellular matrix proteins ( α-actinin 2 , reelin ) , transcription factors ( COUP-TFII ) , and enzymes ( nNOS ) . When information is available , a particular neuron type is characterized as positive , negative , or mixed positive-negative for a biomarker . In the case of mixed expression , the data are evaluated to determine whether the mixed information might be attributed to differences in species ( e . g . , rats vs mice ) , techniques ( e . g . , protein detection vs mRNA detection ) , or subcellular expression localization ( soma vs neurite ) . When a single population of neurons is shown to be divisible into clearly negative and clearly positive subpopulations , this is taken as an indication of biomarker subtypes . When we are unable to determine whether mixed biomarker data is attributable to species/technique/subcellular localization differences or subtypes , the data are annotated as unresolved . Electrophysiological property values are extracted from the literature and compiled , when available , for all Hippocampome . org neuron types . The knowledge base includes passive ( Rin , τm , resting membrane potential or Vrest ) , spike ( APampl , APwidth , Vthresh , fAHP ) , and other response parameters ( maxFR , sAHP , hyperpolarization sag ratio ) . Values are extracted from published reports either from text ( or tables ) or by digitizing figures ( plotdigitizer . sourceforge . net ) and reported as mean , range , standard deviation , and number of measurements . Although these properties can be defined and measured in multiple ways , Hippocampome . org standardizes the data according to a single definition ( hippocampome . org/ephys-defs ) . This standardization , along with full hand-curation , distinguishes our approach from the semi-automated mining by NeuroElectro ( Tripathy et al . , 2014 ) . The growth , evolution , and accuracy of Hippocampone . org content rely on two main mechanisms: continuous literature mining ( hippocampome . org/ongoing-mining ) and community feedback ( hippocampome . org/feedback ) . We update the bibliographic listing at quarterly intervals ( based on citation alerts for many of the core review articles as well as perusals of new issues of most mainline neuroscience publications ) , adding new relevant references and linking to the knowledge base the articles that have been annotated . Moreover , we welcome suggestions for improvements , corrections , and additions . Addressing of this feedback will also be incorporated into the Frequently Asked Questions ( FAQs ) listing ( hippocampome . org/FAQ ) for future reference by all users . In order to ensure reliable resource citation , we adopt a numbered versioning system to release additions of new neuron types and specification of additional properties for existing neuron types . Publication of this article marks the v . 1 . 0 release of Hippocampome . org . The web portal and associated database infrastructures facilitate access to and utilization of morphological , molecular , electrophysiological , and connectivity information . The implementation leverages the model-view-control software design . The model component , which defines the database interface , is provided solely by server-side code . The view component , which renders the web pages , and the control code , which implements decision logic , are both served up by the server , but are run in the user's browser . The underlying relational database ensures flexibility in establishing relations between data records . Hippocampome . org is deployed on a CentOS 5 . 11 server running Apache 2 . 2 . 22 and runs on current versions of Firefox , Chrome , Safari , and Explorer . Knowledge base content is served up to the PHP 5 . 3 . 27 website from a MySQL 5 . 1 . 73 database . Django 1 . 7 . 1 and Python 3 . 4 . 2 provide database ingest capability of comma separated value annotation files derived from human-interpreted peer-reviewed literature . Hippocampome . org code is available open source at github . com/Hippocampome . org . We explored pairwise correlations between 205 properties of Hippocampome neuron types , including neurotransmitter; axonal , dendritic , and somatic locations in the 26 partitions and 6 areas; the projecting ( inter-areas ) or local ( intra-area ) nature of axons and dendrites; axon and dendrite co-presence within any partition; axonal and dendritic presence in a single layer only or in ≥3 layers; clear positive or negative expression of any biomarkers; and high ( top third ) or low ( bottom third ) values for seven electrophysiological properties ( excluding highly stimulus-dependent sag ratio , sAHP , and maxFR ) . To evaluate the correlations between these categorical properties , we use 2 × 2 contingency matrices with Barnard's exact test , which provides the greatest statistical power when row and column totals are free to vary ( Lydersen et al . , 2009 ) .
Hippocampome . org is a comprehensive knowledge base of neuron types in the rodent hippocampal formation ( dentate gyrus , CA3 , CA2 , CA1 , subiculum , and entorhinal cortex ) . Although the hippocampal literature is remarkably information-rich , neuron properties are often reported with incompletely defined and notoriously inconsistent terminology , creating a formidable challenge for data integration . Our extensive literature mining and data reconciliation identified 122 neuron types based on neurotransmitter , axonal and dendritic patterns , synaptic specificity , electrophysiology , and molecular biomarkers . All ∼3700 annotated properties are individually supported by specific evidence ( ∼14 , 000 pieces ) in peer-reviewed publications . Systematic analysis of this unprecedented amount of machine-readable information reveals novel correlations among neuron types and properties , the potential connectivity of the full hippocampal circuitry , and outstanding knowledge gaps . User-friendly browsing and online querying of Hippocampome . org may aid design and interpretation of both experiments and simulations . This powerful , simple , and extensible neuron classification endeavor is unique in its detail , utility , and completeness .
The hippocampus is a seahorse-shaped region of the brain that is responsible for learning , emotions , and memory . Like other regions of the brain , it contains many types of neurons that send information to each other by releasing chemicals called neurotransmitters across junctions known as synapses . Identifying all the different neuron types in the hippocampus is an important step towards understanding in detail how this brain region works . Thousands of articles have been published that attempt to characterize the neurons in the hippocampus , but many of these studies report only some of the properties of a new neuron type . It is also often difficult to compare the results of different studies , as many different approaches have been used to investigate neuron types , and different studies often use different terms to describe similar features . Wheeler et al . have now created a resource called Hippocampome . org that combines approximately 14 , 000 pieces of experimental evidence about neuron types in the rat hippocampus into a unified database . Analyzing these data has revealed about 3700 different neuron properties . By primarily considering the pattern formed by the branched axons and dendrites , the outputs and inputs that extend out of a neuron , Wheeler et al . have identified over a hundred different neuron types . This classification system also considers how selectively the neuron forms synapses with other cells and the identity of the neurotransmitter released by the neuron . In the future , other features of the neurons will also be incorporated into the system to help refine the classifications . All of this information is online and freely available at Hippocampome . org . This resource is expected to provide a solid basis for analyzing how the hippocampus works , by helping researchers to design and interpret experiments and simulations .