started working on Jan's discussion comments

eneuro.bst

@@ -43,6 +43,7 @@ ENTRY
     URL
     volume
     year
+    doi
   }
   { field.used etal.allowed etal.required }
   { extra.label sort.label list.year }
@@ -170,6 +171,7 @@ FUNCTION {write.url}
 
 INTEGERS { nameptr namesleft numnames }
 
+
 FUNCTION {format.names}
 { 's :=
   'f :=
@@ -196,6 +198,7 @@ FUNCTION {format.names}
   while$
 }
 
+
 FUNCTION {format.authors}
 { author empty$
     { "" }

manuscript.tex

@@ -242,7 +242,7 @@ with slope \(k\), voltage for half-maximal activation or inactivation (\(V_{1/2}
     
     To obtain the rheobase, the current step interval preceding the occurrence of action potentials was explored at higher resolution with 100 current steps spanning the interval. Membrane responses to these current steps were then analyzed for action potentials and the rheobase was considered the lowest current step for which an action potential was elicited.
     
-    All models exhibit tonic firing and any instances of bursting were excluded to simplify the characterization of firing.
+    All models exhibit tonic firing and any instances of bursting were excluded to simplify the characterization of firing.  Firing characterization was performed on steady-state firing and as such adaptation processes are neglected in our analysis. \notenk{moved here from discussion}
     
 \subsection*{Sensitivity Analysis and Comparison of Models}
     
@@ -359,34 +359,61 @@ Mutations in \Kv are associated with episodic ataxia type~1 (EA1) and have been
 
 \section*{Discussion (3000 Words Maximum - Currently 2145)}
 % \textit{The discussion section should include a brief statement of the principal findings, a discussion of the validity of the observations, a discussion of the findings in light of other published work dealing with the same or closely related subjects, and a statement of the possible significance of the work. Extensive discussion of the literature is discouraged.}\\
+
 Using a set of diverse conductance-based neuronal models, the effects of changes to properties of ionic currents on neuronal firing were determined to be heterogenous for the AUC of the steady state fI curve but more homogenous for rheobase. For a known channelopathy, episodic ataxia type~1 associated \Kv mutations, the effects on rheobase are consistent across model cell types, whereas the effects on AUC depend on cell type. Our results demonstrate that LOF and GOF on the biophysical level cannot be uniquely transfered to the level of neuronal firing. The effects depend on the properties of the other currents expressed in a cell and are therefore depending on cell type.
 
+\subsection*{Neuronal Diversity} 
+\notejb{Before we start questioning our models we should have a paragraph pointing out that neurons are diverse and differ in their ion channel composition. Cite for example those recent Nature/Science papers where Phillip Berens is part of on neuron types in cerebellum. Thomas Euler Retina ganglien cell types. Then the paper defining Regular/fast spiking interneurons. And many more... like Eve Marder as you have it in a paragraph further down.}
+\notenk{Added this section - it needs more work, but what do you think of the direction I'm going?} \notenk{Also I'm not sure which regular/fast spiking interneuron paper you mean}\\
+Advances in high-throughput techniques have enable large-scale investigation into single-cell properties across the CNS \citep{Poulin2016} that have revealed large diversity in neuronal gene expression, morphology and neuronal types in the motor cortex \citep{Scala2021}, neocortex \cite{Cadwell2016, Cadwell2020}, GABAergic neurons \citep{Huang2019} and interneruons \citep{Laturnus2020}, cerebellum \citep{Kozareva2021}, spinal cord \citep{Alkaslasi2021}, visual cortex \citep{Gouwens2019} as well as the retina \citep{Baden2016, Voigt2019, Berens2017,  Yan2020, Yan2020a}. \notenk{Yan2020 and Yan2020a are not ``et al.''  - need to fix}
+
+% Functional differences: reg/fat spiking, Ephys, models
+Diversity across neurons is not limited to gene expression and can also be seen electrophysiologically \citep{Tripathy2017, Gouwens2018, Tripathy2015, Scala2021, Cadwell2020, Gouwens2019, Baden2016, Berens2017} with correlations existing between gene expression and electrophysiological properties \citep{Tripathy2017}. 
+
+At the ion channel level, diversity exists not only between the specific ion channels cell types express but heterogeneity also exists in ion channel expression levels within cell types \citep{marder_multiple_2011,  goaillard_ion_2021}. 
+
+Taken together, the nervous system consists of a vastly diverse and heterogenous collection of neurons with variable properties and characteristics including diverse combinations and expression levels of ion channels.
+
+
+
+
+
+
+
 \subsection*{Validity of Neuronal Models}
 \notels{should we move this to a less prominent position? How much of this part could be counted as common knowledge and be left out?, for example model complexity in terms of currents and compartments, I just think that this part might be too harsh on the models, even if the criticism doesn't apply for the main points of the paper}
 \notenk{I think a large part of this section, although hgihlighting the problems with the models, helps make the case that there is a vast amount of complexity and heterogeneity not just in what we show with the models, but also unaccounted for by the models. That is to say that we are in a sense underestimating the amount of variability in responses of different cells to the same mutation. Perhaps it would be good to change this section to emphasize that perspective?}
 
-\notejb{Before we start questioning our models we should have a paragraph pointing out that neurons are diverse and differ in their ion channel composition. Cite for example those recent Nature/Science papers where Phillip Berens is part of on neuron types in cerebellum. Thomas Euler Retina ganglien cell types. Then the paper defining Regular/fast spiking interneurons. And many more... like Eve Marder as you have it in a paragrph further down.}
 
-\notejb{The following three paragraphs are rather technical and if possible should be shorter.}
-Our findings are based on simulations of a range of single-compartment conductance-based models. Many aspects of these models can be questioned.
 
-The \Kv model from \cite{ranjan_kinetic_2019} is based on expression of only \Kv in CHO cells and represents the biophysical properties of \Kv homotetramers and not heteromers. Thus the \Kv model used here neglects the complex reality of these channels \textit{in vivo} including their expression as heteromers and the altered biophyiscal properties of these heteromers \citep{wang__1999, roeper_nip_1998, coleman_subunit_1999, ruppersberg_heteromultimeric_1990, isacoff_evidence_1990, rettig_inactivation_1994}.  Furthermore, dynamic modulation of \Kv channels, although physiologically relevant, is neglected here. For example, \(\textrm{K}_{\textrm{V}}\upbeta\)2 plays a role in \(\textrm{K}_{\textrm{V}}\textrm{1}\) channel trafficking and cell membrane expression \citep{shi_efficacy_2016, campomanes_kv_2002, manganas_identification_2001} and  \Kv phosphorylation increases cell membrane \Kv \citep{jonas_regulation_1996}. It should be noted that the discrete classification of potassium currents into delayed rectifier and A-type is likely not biological, but rather highlights the characteristics of a spectrum of potassium channel inactivation that arises in part due to additional factors such as heteromer composition \citep{stuhmer_molecular_1989, glasscock_kv11_2019}, non-pore forming subunits (e.g. \(\textrm{K}_{\textrm{V}}\upbeta\) subunits) \citep{rettig_inactivation_1994, xu_kv2_1997}, and temperature \citep{ranjan_kinetic_2019} modulating channel properties.
+\notejb{The following three paragraphs are rather technical and if possible should be shorter.} \notenk{shortened single vs multicompartment model paragraphs. We could remove the \Kv paragraph I've shortened - see below}
+Our findings are based on simulations of a range of single-compartment conductance-based models. Single-compartment models do not take into consideration differential effects on neuronal compartments (i.e. axon, soma, dendrites), possible different spatial cellular distribution of channel expression across and within these neuronal compartments. More realistic models are more computationally expensive, and require knowledge of the distribution of conductances across the cell. However, each of the single-compartment models used here can reproduce physiological firing behaviour of the neurons they represent \citep{pospischil_minimal_2008, alexander_cerebellar_2019, otsuka_conductance-based_2004} and capture key aspects of the dynamics of these cell types. 
+%Many aspects of these models can be questioned.
 
-Additionally, the single-compartment models do not take into consideration differential effects on neuronal compartments (i.e. axon, soma, dendrites), possible different spatial cellular distribution of channel expression across and within these neuronal compartments or across CNS regions nor does it consider different channel types (e.g \(\textrm{Na}_{\textrm{V}}\text{1.1}\) vs \(\textrm{Na}_{\textrm{V}}\text{1.8}\)). More realistic models would consist of multiple compartments, take more ionic currents into account and take the spatial distribution of channels into account, however these models are more computationally expensive, require current specific models and knowledge of the distribution of conductances across the cell. Despite these limitations, each of the models can reproduce physiological firing behaviour of the neurons they represent \citep{pospischil_minimal_2008, alexander_cerebellar_2019, otsuka_conductance-based_2004} and capture key aspects of the dynamics of these cell types. 
+\notenk{We could remove this paragraph about \Kv}
+The \Kv model from \cite{ranjan_kinetic_2019} is based on expression of only \Kv in CHO cells and simplifies the complex reality of these channels \textit{in vivo} including their function as heteromers, and dynamic modulation and regulation  \citep{wang__1999, roeper_nip_1998, coleman_subunit_1999, ruppersberg_heteromultimeric_1990, isacoff_evidence_1990, rettig_inactivation_1994, shi_efficacy_2016, campomanes_kv_2002, manganas_identification_2001, jonas_regulation_1996, stuhmer_molecular_1989, glasscock_kv11_2019, xu_kv2_1997, ranjan_kinetic_2019}. 
 
-\notejb{If this could be enriched with some citations than fine. Otherwise move this as a half sentence into methods/results}
-The firing characterization was performed on steady-state firing and as such adaptation processes are neglected in our analysis. These could be seen as further dimensions to analyze the influence of mutations on neuronal firing and can only increase the uncertainty of these estimations.
     
-Despite all these shortcomings of the models we used in our simulations, they do not touch our main conclusion that the quantitative as well as qualitative effects of a given ionic current variant in general depend on the specific properties of all the other ionic currents expressed in a given cell.
 
-\subsection*{Ionic Current Environments Determine the Effect of Ion Channel Mutations}
-\notenk{We could add a brief discussion somewhere in this section about time constants and why we neglect them despite likely being important in determining the outcome of a mutation.} \notejb{If we have citations for the time constant issue then yes, do it.}
 
-\notejb{Too technical, shorter! These aspects do not questions our result.}
+\notejb{If this could be enriched with some citations than fine. Otherwise move this as a half sentence into methods/results} \notenk{moved steady-state firing characterization to methods}
-One-factor-at-a-time (OFAT) sensitivity analyses such as the one performed here are predicated on assumptions of model linearity, and cannot account for interactions between factors \citep{czitrom_one-factor-at--time_1999, saltelli_how_2010}. OFAT approaches are local and not global (i.e. always in reference to a baseline point in the parameter space) and therefore cannot be generalized to the global parameter space unless linearity is met \citep{saltelli_how_2010}. The local space around the wild type neuron is explored with an OFAT sensitivity analysis without taking interactions between parameters into account. Comparisons between the effects of changes in similar parameters across different models can be made at the wild type locale indicative of experimentally observed neuronal behaviour. In this case, the role of deviations in the ionic current properties from their wild type in multiple neuronal models presented here provides a starting point for understanding the general role of these current properties in neurons. However, a more global approach would provide a more holistic understanding of the parameter space and provide insight into interactions between properties.
+%The firing characterization was performed on steady-state firing and as such adaptation processes are neglected in our analysis. These could be seen as further dimensions to analyze the influence of mutations on neuronal firing and can only increase the uncertainty of these estimations.
+
+Despite all these shortcomings of the models we used in our simulations, they do not touch our main conclusion that the quantitative as well as qualitative effects of a given ionic current variant in general depend on the specific properties of all the other ionic currents expressed in a given cell.
     
-\notejb{Too technical, shorter! These aspects do not questions our result.}
+\subsection*{Ionic Current Environments Determine the Effect of Ion Channel Mutations}
-Characterization of the effects of a parameter on firing with non-parametric Kendall \(\tau\) correlations takes into account the sign and monotonicity of the correlation. In other words Kendall \(\tau\) coefficients provide information as to whether changing a parameter is positively or negatively correlated with AUC or rheobase as well as the extent to which this correlation is positive or negative across the parameter range examined. Therefore, Kendall \(\tau\) coefficients provide general information as to the sensitivity of different models to a change in a given current property, however more nuanced difference between the sensitivities of models to current property changes, such as the slope of the relationship between parameter change and firing are not included in our analysis. 
+\notenk{We could add a brief discussion somewhere in this section about time constants and why we neglect them despite likely being important in determining the outcome of a mutation.} \notejb{\textcolor{red}{If we have citations for the time constant issue then yes, do it.}}
+\notenk{I think that these 2 papers might be useful but it's late and I need to look at them with fresh eyes tomorrow}
+https://doi.org/10.1016/j.bpj.2019.08.001   \\
+https://doi.org/10.1002/wsbm.1482
+
+\notejb{Too technical, shorter! These aspects do not questions our result.} \notenk{Made a little shorter}
+%One-factor-at-a-time (OFAT) sensitivity analyses such as the one performed here are predicated on assumptions of model linearity, and cannot account for interactions between factors \citep{czitrom_one-factor-at--time_1999, saltelli_how_2010}. OFAT approaches are local and not global (i.e. always in reference to a baseline point in the parameter space) and therefore cannot be generalized to the global parameter space unless linearity is met \citep{saltelli_how_2010}. 
+The local current parameter space around the wild type neuron is explored here with a one-factor-at-a-time (OFAT) sensitivity analysis without taking interactions between parameters into account. Comparisons between the effects of changes in similar parameters across different models can be made at the wild type locale indicative of experimentally observed neuronal behaviour. In this case, the role of deviations in the ionic current properties from their wild type in multiple neuronal models presented here provides a starting point for understanding the general role of these current properties in neurons. However, a more global approach would provide a more holistic understanding of the parameter space and provide insight into interactions between properties.
+
+\notejb{Too technical, shorter! These aspects do not questions our result.} \notenk{Tried to shorten, not sure about it...}
+%Characterization of the effects of a parameter on firing with non-parametric Kendall \(\tau\) correlations takes into account the sign and monotonicity of the correlation. In other words Kendall \(\tau\) coefficients provide information as to whether changing a parameter is positively or negatively correlated with AUC or rheobase as well as the extent to which this correlation is positive or negative across the parameter range examined. 
+Kendall \(\tau\) coefficients provide general information as to whether different models exhibit positive or negative correlation of AUC or rheobase to a change in a given current property, however more nuanced difference between the sensitivities of models to current property changes, such which models show faster/slower increases/decreases in firing properties in response to a given current property change are not included in our analysis. 
 % The inter-model differences seen with the OFAT sensitivity analysis highlight the need for cell specific models. The observed dependence of neuronal firing on voltage-gated sodium channels and delayed-rectifier potassium channels is known \citep{verma_computational_2020, arhem_channel_2006} and substantiated by OFAT analysis across models. It is suggested that variability in these currents may underlie within cell population variability in neuronal firing behaviour \citep{verma_computational_2020}. Although increases in low-voltage activated inward currents are generally accepted to increase firing rates and outward currents to decrease firing rates \citep{nowacki_sensitivity_2011}, this was not always observed in AUC. The heterogeneity in outcomes of model OFAT analysis, especialy with AUC, suggest that the effects of changes in current properties are neuronal dependent and the current environment encompassing the relative conductances, gating \(V_{1/2}\) positions, and gating slopes of other currents plays an important role in modulating firing behaviour and in determining the outcome of a current property change such as a mutation.
 
 \notejb{Super important paragraph!}
@@ -399,7 +426,14 @@ Variability in ion channel expression often correlates with the expression of ot
 The variability of ion currents and degeneracy of neurons may account, at least in part, for the observation that the effect of toxins within a neuronal type is frequently not constant \citep{khaliq_relative_2006, puopolo_roles_2007, ransdell_neurons_2013}.
 
 \subsection*{Effects of KCNA1 Mutations}
-Moderate changes in delayed rectifier potassium currents change the bifurcation structure of Hodgkin Huxley model, with changes analogous to those seen with \Kv mutations resulting in increased excitability due to reduced thresholds for repetitive firing \citep{hafez_altered_2020} \notejb{I do not get this first sentence. Where are the bifurcations (citation?) and why is the increased excitatbility a bifurcation?}. Although the Hodgkin Huxley delayed rectifier lacks inactivation, the increases in excitability seen \notejb{seen where? Here in this manuscript or in which citation?} are in line with simulation-based predictions of the outcomes of \textit{KCNA1} mutations \notejb{our simulations?}.  
+Moderate changes in delayed rectifier potassium currents change the bifurcation structure
+of Hodgkin Huxley model, with changes analogous to those seen with KV1.1 mutations resulting
+in increased excitability due to reduced thresholds for repetitive firing (Hafez and
+Gottschalk, 2020). Although the Hodgkin Huxley delayed rectifier lacks inactivation, the increases
+in excitability seen are in line with both score-based and simulation-based predictions
+of the outcomes of KCNA1 mutations.
+
+Moderate changes in delayed rectifier potassium currents change the bifurcation structure of Hodgkin Huxley model, analogous to those seen in \Kv mutations, result in reduced thresholds for repetitive firing and thus contribute to increased excitability \citep{hafez_altered_2020} \notejb{I do not get this first sentence. Where are the bifurcations (citation?) and why is the increased excitatbility a bifurcation?} \notenk{I have tried to fix this section to make it more understandable. The bifurcations change by changing the delayed rectifier in the HH model and as a result of that there is a lower threshold for tonic firing. This lower threshold is what they (Hafez and Gottschalk) use to say that excitability has changed.}. Although the Hodgkin Huxley delayed rectifier lacks inactivation, the increases in excitability seen by \citet{hafez_altered_2020}\notejb{seen where? Here in this manuscript or in which citation?} are in line with our simulation-based predictions of the outcomes of \Kv mutations \notejb{our simulations?}\notenk{Yes}.  
 LOF KCNA1 mutations generally increase neuronal excitability, however the varying susceptibility on rheobase and different effects on AUC of KCNA1 mutations across models are indicative that a certain cell type specific complexity exists.
 %LOF KCNA1 mutations generally increase neuronal excitability, however the different effects of KCNA1 mutations across models on AUC are indicative that a certain cell type specific complexity exists. 
 Increased excitability is seen experimentally with \Kv null mice \citep{smart_deletion_1998, zhou_temperature-sensitive_1998}, with pharmacological \Kv block \citep{chi_manipulation_2007, morales-villagran_protection_1996} and by \citet{hafez_altered_2020} with simulation-based predictions of KCNA1 mutations. Contrary to these results,  \citet{zhao_common_2020} predicted \textit{in silico} that the depolarizing shifts seen as a result of KCNA1 mutations broaden action potentials and interfere negatively with high frequency action potential firing, however they varied stimulus duration between different models and therefore comparability of firing rates is lacking in this study.     

ref.bib

@@ -1,4 +1,24 @@
  
+@Article{Tripathy2017,
+  author    = {Tripathy, Shreejoy J. and Toker, Lilah and Li, Brenna and Crichlow, Cindy-Lee and Tebaykin, Dmitry and Mancarci, B. Ogan and Pavlidis, Paul},
+  journal   = {PLOS Computational Biology},
+  title     = {Transcriptomic correlates of neuron electrophysiological diversity},
+  year      = {2017},
+  issn      = {1553-7358},
+  month     = oct,
+  number    = {10},
+  pages     = {e1005814},
+  volume    = {13},
+  abstract  = {How neuronal diversity emerges from complex patterns of gene expression remains poorly understood. Here we present an approach to understand electrophysiological diversity through gene expression by integrating pooled- and single-cell transcriptomics with intracellular electrophysiology. Using neuroinformatics methods, we compiled a brain-wide dataset of 34 neuron types with paired gene expression and intrinsic electrophysiological features from publically accessible sources, the largest such collection to date. We identified 420 genes whose expression levels significantly correlated with variability in one or more of 11 physiological parameters. We next trained statistical models to infer cellular features from multivariate gene expression patterns. Such models were predictive of gene-electrophysiological relationships in an independent collection of 12 visual cortex cell types from the Allen Institute, suggesting that these correlations might reflect general principles relating expression patterns to phenotypic diversity across very different cell types. Many associations reported here have the potential to provide new insights into how neurons generate functional diversity, and correlations of ion channel genes like Gabrd and Scn1a (Nav1.1) with resting potential and spiking frequency are consistent with known causal mechanisms. Our work highlights the promise and inherent challenges in using cell type-specific transcriptomics to understand the mechanistic origins of neuronal diversity.},
+  doi       = {10.1371/journal.pcbi.1005814},
+  file      = {:Tripathy2017 - Transcriptomic Correlates of Neuron Electrophysiological Diversity.pdf:PDF},
+  keywords  = {Gene expression, Electrophysiology, Transcriptome analysis, Brain electrophysiology, Neurons, Action potentials, Ion channels, Electrophysiological properties},
+  language  = {en},
+  publisher = {Public Library of Science},
+
+}
+
+ 
 @Book{Izhikevich2006,
   author     = {Izhikevich, Eugene M.},
   editor     = {Sejnowski, Terrence J. and Poggio, Tomaso A.},
@@ -28,8 +48,6 @@
   abstract = {Mutations in SCN2A, a gene encoding the voltage-gated sodium channel Nav1.2, have been associated with a spectrum of epilepsies and neurodevelopmental disorders. Here, we report the phenotypes of 71 patients and review 130 previously reported patients. We found that (i) encephalopathies with infantile/childhood onset epilepsies (≥3 months of age) occur almost as often as those with an early infantile onset (\<3 months), and are thus more frequent than previously reported; (ii) distinct phenotypes can be seen within the late onset group, including myoclonic-atonic epilepsy (two patients), Lennox-Gastaut not emerging from West syndrome (two patients), and focal epilepsies with an electrical status epilepticus during slow sleep-like EEG pattern (six patients); and (iii) West syndrome constitutes a common phenotype with a major recurring mutation (p.Arg853Gln: two new and four previously reported children). Other known phenotypes include Ohtahara syndrome, epilepsy of infancy with migrating focal seizures, and intellectual disability or autism without epilepsy. To assess the response to antiepileptic therapy, we retrospectively reviewed the treatment regimen and the course of the epilepsy in 66 patients for which well-documented medical information was available. We find that the use of sodium channel blockers was often associated with clinically relevant seizure reduction or seizure freedom in children with early infantile epilepsies (\<3 months), whereas other antiepileptic drugs were less effective. In contrast, sodium channel blockers were rarely effective in epilepsies with later onset (≥3 months) and sometimes induced seizure worsening. Regarding the genetic findings, truncating mutations were exclusively seen in patients with late onset epilepsies and lack of response to sodium channel blockers. Functional characterization of four selected missense mutations using whole cell patch-clamping in tsA201 cells—together with data from the literature—suggest that mutations associated with early infantile epilepsy result in increased sodium channel activity with gain-of-function, characterized by slowing of fast inactivation, acceleration of its recovery or increased persistent sodium current. Further, a good response to sodium channel blockers clinically was found to be associated with a relatively small gain-of-function. In contrast, mutations in patients with late-onset forms and an insufficient response to sodium channel blockers were associated with loss-of-function effects, including a depolarizing shift of voltage-dependent activation or a hyperpolarizing shift of channel availability (steady-state inactivation). Our clinical and experimental data suggest a correlation between age at disease onset, response to sodium channel blockers and the functional properties of mutations in children with SCN2A-related epilepsy.},
   doi      = {10.1093/brain/awx054},
   file     = {:wolff_genetic_2017 - Genetic and Phenotypic Heterogeneity Suggest Therapeutic Implications in SCN2A Related Disorders.pdf:PDF},
-  url      = {https://doi.org/10.1093/brain/awx054},
-  urldate  = {2022-05-17},
 }
 
  
@@ -1425,7 +1443,6 @@ SIGNIFICANCE: Bromide is most effective and is a well-tolerated drug among DS pa
   keywords  = {Neuronal physiology, RNA sequencing},
   language  = {en},
   publisher = {Nature Publishing Group},
-  urldate   = {2022-05-06},
 }
 
  
@@ -1631,7 +1648,276 @@ SIGNIFICANCE: Bromide is most effective and is a well-tolerated drug among DS pa
   pmcid      = {PMC6045440},
   pmid       = {29542386},
   shorttitle = {Potassium channel gain of function in epilepsy},
-  urldate    = {2022-05-16},
+}
+
+ 
+@Article{Huang2019,
+  author    = {Huang, Z. Josh and Paul, Anirban},
+  journal   = {Nature Reviews Neuroscience},
+  title     = {The diversity of {GABAergic} neurons and neural communication elements},
+  year      = {2019},
+  issn      = {1471-0048},
+  month     = sep,
+  number    = {9},
+  pages     = {563--572},
+  volume    = {20},
+  abstract  = {The phenotypic diversity of cortical GABAergic neurons is probably necessary for their functional versatility in shaping the spatiotemporal dynamics of neural circuit operations underlying cognition. Deciphering the logic of this diversity requires comprehensive analysis of multi-modal cell features and a framework of neuronal identity that reflects biological mechanisms and principles. Recent high-throughput single-cell analyses have generated unprecedented data sets characterizing the transcriptomes, morphology and electrophysiology of interneurons. We posit that cardinal interneuron types can be defined by their synaptic communication properties, which are encoded in key transcriptional signatures. This conceptual framework integrates multi-modal cell features, captures neuronal input–output properties fundamental to circuit operation and may advance understanding of the appropriate granularity of neuron types, towards a biologically grounded and operationally useful interneuron taxonomy.},
+  copyright = {2019 The Publisher},
+  doi       = {10.1038/s41583-019-0195-4},
+  file      = {Full Text PDF:https\://www.nature.com/articles/s41583-019-0195-4.pdf:application/pdf},
+  keywords  = {Cellular neuroscience, Molecular neuroscience, Neural circuits},
+  language  = {en},
+  publisher = {Nature Publishing Group},
+
+}
+
+ 
+@Article{Gouwens2019,
+  author    = {Gouwens, Nathan W. and Sorensen, Staci A. and Berg, Jim and Lee, Changkyu and Jarsky, Tim and Ting, Jonathan and Sunkin, Susan M. and Feng, David and Anastassiou, Costas A. and Barkan, Eliza and Bickley, Kris and Blesie, Nicole and Braun, Thomas and Brouner, Krissy and Budzillo, Agata and Caldejon, Shiella and Casper, Tamara and Castelli, Dan and Chong, Peter and Crichton, Kirsten and Cuhaciyan, Christine and Daigle, Tanya L. and Dalley, Rachel and Dee, Nick and Desta, Tsega and Ding, Song-Lin and Dingman, Samuel and Doperalski, Alyse and Dotson, Nadezhda and Egdorf, Tom and Fisher, Michael and de Frates, Rebecca A. and Garren, Emma and Garwood, Marissa and Gary, Amanda and Gaudreault, Nathalie and Godfrey, Keith and Gorham, Melissa and Gu, Hong and Habel, Caroline and Hadley, Kristen and Harrington, James and Harris, Julie A. and Henry, Alex and Hill, DiJon and Josephsen, Sam and Kebede, Sara and Kim, Lisa and Kroll, Matthew and Lee, Brian and Lemon, Tracy and Link, Katherine E. and Liu, Xiaoxiao and Long, Brian and Mann, Rusty and McGraw, Medea and Mihalas, Stefan and Mukora, Alice and Murphy, Gabe J. and Ng, Lindsay and Ngo, Kiet and Nguyen, Thuc Nghi and Nicovich, Philip R. and Oldre, Aaron and Park, Daniel and Parry, Sheana and Perkins, Jed and Potekhina, Lydia and Reid, David and Robertson, Miranda and Sandman, David and Schroedter, Martin and Slaughterbeck, Cliff and Soler-Llavina, Gilberto and Sulc, Josef and Szafer, Aaron and Tasic, Bosiljka and Taskin, Naz and Teeter, Corinne and Thatra, Nivretta and Tung, Herman and Wakeman, Wayne and Williams, Grace and Young, Rob and Zhou, Zhi and Farrell, Colin and Peng, Hanchuan and Hawrylycz, Michael J. and Lein, Ed and Ng, Lydia and Arkhipov, Anton and Bernard, Amy and Phillips, John W. and Zeng, Hongkui and Koch, Christof},
+  journal   = {Nature Neuroscience},
+  title     = {Classification of electrophysiological and morphological neuron types in the mouse visual cortex},
+  year      = {2019},
+  issn      = {1546-1726},
+  month     = jul,
+  number    = {7},
+  pages     = {1182--1195},
+  volume    = {22},
+  abstract  = {Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To systematically profile morpho-electric properties of mammalian neurons, we established a single-cell characterization pipeline using standardized patch-clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly accessible online database, the Allen Cell Types Database, to display these datasets. Intrinsic physiological properties were measured from 1,938 neurons from the adult laboratory mouse visual cortex, morphological properties were measured from 461 reconstructed neurons, and 452 neurons had both measurements available. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We established a taxonomy of morphologically and electrophysiologically defined cell types for this region of the cortex, with 17 electrophysiological types, 38 morphological types and 46 morpho-electric types. There was good correspondence with previously defined transcriptomic cell types and subclasses using the same transgenic mouse lines.},
+  copyright = {2019 The Author(s), under exclusive licence to Springer Nature America, Inc.},
+  doi       = {10.1038/s41593-019-0417-0},
+  keywords  = {Cellular neuroscience, Striate cortex},
+  language  = {en},
+  publisher = {Nature Publishing Group},
+}
+
+ 
+@Article{Kozareva2021,
+  author    = {Kozareva, Velina and Martin, Caroline and Osorno, Tomas and Rudolph, Stephanie and Guo, Chong and Vanderburg, Charles and Nadaf, Naeem and Regev, Aviv and Regehr, Wade G. and Macosko, Evan},
+  journal   = {Nature},
+  title     = {A transcriptomic atlas of mouse cerebellar cortex comprehensively defines cell types},
+  year      = {2021},
+  issn      = {1476-4687},
+  month     = oct,
+  number    = {7879},
+  pages     = {214--219},
+  volume    = {598},
+  abstract  = {The cerebellar cortex is a well-studied brain structure with diverse roles in motor learning, coordination, cognition and autonomic regulation. However,  a complete inventory of cerebellar cell types is currently lacking. Here, using recent advances in high-throughput transcriptional profiling1–3, we molecularly define cell types across individual lobules of the adult mouse cerebellum. Purkinje neurons showed considerable regional specialization, with the greatest diversity occurring in the posterior lobules. For several types of cerebellar interneuron, the molecular variation within each type was more continuous, rather than discrete. In particular, for the unipolar brush cells—an interneuron population previously subdivided into discrete populations—the continuous variation in gene expression was associated with a graded continuum of electrophysiological properties. Notably, we found that molecular layer interneurons were composed of two molecularly and functionally distinct types. Both types show a continuum of morphological variation through the thickness of the molecular layer, but electrophysiological recordings revealed marked differences between the two types in spontaneous firing, excitability and electrical coupling. Together, these findings provide a comprehensive cellular atlas of the cerebellar cortex, and outline a methodological and conceptual framework for the integration of molecular, morphological and physiological ontologies for defining brain cell types.},
+  copyright = {2021 The Author(s)},
+  doi       = {10.1038/s41586-021-03220-z},
+  file      = {Full Text PDF:https\://www.nature.com/articles/s41586-021-03220-z.pdf:application/pdf},
+  keywords  = {Cellular neuroscience, Genomics},
+  language  = {en},
+  publisher = {Nature Publishing Group},
+}
+
+ 
+@Article{Baden2016,
+  author   = {Baden, Tom and Berens, Philipp and Franke, Katrin and Rosón, Miroslav Román and Bethge, Matthias and Euler, Thomas},
+  journal  = {Nature},
+  title    = {The functional diversity of retinal ganglion cells in the mouse},
+  year     = {2016},
+  issn     = {0028-0836},
+  month    = jan,
+  number   = {7586},
+  pages    = {345--350},
+  volume   = {529},
+  abstract = {In the vertebrate visual system, all output of the retina is carried by retinal ganglion cells. Each type encodes distinct visual features in parallel for transmission to the brain. How many such “output channels” exist and what each encodes is an area of intense debate. In mouse, anatomical estimates range between 15–20 channels, and only a handful are functionally understood. Combining two-photon calcium imaging to obtain dense retinal recordings and unsupervised clustering of the resulting sample of {\textgreater}11,000 cells, we here show that the mouse retina harbours substantially more than 30 functional output channels. These include all known and several new ganglion cell types, as verified by genetic and anatomical criteria. Therefore, information channels from the mouse’s eye to the mouse’s brain are considerably more diverse than shown thus far by anatomical studies, suggesting an encoding strategy resembling that used in state-of-the-art artificial vision systems.},
+  doi      = {10.1038/nature16468},
+  file     = {PubMed Central Link:https\://www.ncbi.nlm.nih.gov/pmc/articles/PMC4724341/:text/html},
+  pmcid    = {PMC4724341},
+  pmid     = {26735013},
+}
+
+ 
+@Article{Poulin2016,
+  author    = {Poulin, Jean-Francois and Tasic, Bosiljka and Hjerling-Leffler, Jens and Trimarchi, Jeffrey M. and Awatramani, Rajeshwar},
+  journal   = {Nature Neuroscience},
+  title     = {Disentangling neural cell diversity using single-cell transcriptomics},
+  year      = {2016},
+  issn      = {1546-1726},
+  month     = sep,
+  number    = {9},
+  pages     = {1131--1141},
+  volume    = {19},
+  abstract  = {Although single-cell gene expression profiling has been possible for the past two decades, a number of recent technological advances in microfluidic and sequencing technology have recently made the procedure much easier and less expensive. Awatramani and colleagues discuss the use of single-cell gene expression profiling for classifying neuronal cell types.},
+  copyright = {2016 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
+  doi       = {10.1038/nn.4366},
+  file      = {Full Text PDF:https\://www.nature.com/articles/nn.4366.pdf:application/pdf},
+  keywords  = {Cellular neuroscience, Molecular neuroscience, Transcriptomics},
+  language  = {en},
+  publisher = {Nature Publishing Group},
+}
+
+ 
+@Article{Alkaslasi2021,
+  author    = {Alkaslasi, Mor R. and Piccus, Zoe E. and Hareendran, Sangeetha and Silberberg, Hanna and Chen, Li and Zhang, Yajun and Petros, Timothy J. and Le Pichon, Claire E.},
+  journal   = {Nature Communications},
+  title     = {Single nucleus {RNA}-sequencing defines unexpected diversity of cholinergic neuron types in the adult mouse spinal cord},
+  year      = {2021},
+  issn      = {2041-1723},
+  month     = apr,
+  number    = {1},
+  pages     = {2471},
+  volume    = {12},
+  abstract  = {In vertebrates, motor control relies on cholinergic neurons in the spinal cord that have been extensively studied over the past hundred years, yet the full heterogeneity of these neurons and their different functional roles in the adult remain to be defined. Here, we develop a targeted single nuclear RNA sequencing approach and use it to identify an array of cholinergic interneurons, visceral and skeletal motor neurons. Our data expose markers for distinguishing these classes of cholinergic neurons and their rich diversity. Specifically, visceral motor neurons, which provide autonomic control, can be divided into more than a dozen transcriptomic classes with anatomically restricted localization along the spinal cord. The complexity of the skeletal motor neurons is also reflected in our analysis with alpha, gamma, and a third subtype, possibly corresponding to the elusive beta motor neurons, clearly distinguished. In combination, our data provide a comprehensive transcriptomic description of this important population of neurons that control many aspects of physiology and movement and encompass the cellular substrates for debilitating degenerative disorders.},
+  copyright = {2021 This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply},
+  doi       = {10.1038/s41467-021-22691-2},
+  file      = {Full Text PDF:https\://www.nature.com/articles/s41467-021-22691-2.pdf:application/pdf},
+  keywords  = {Cellular neuroscience, Molecular neuroscience, Motor neuron, Spinal cord, Transcriptomics},
+  language  = {en},
+  publisher = {Nature Publishing Group},
+}
+
+ 
+@Article{Voigt2019,
+  author   = {Voigt, A. P. and Whitmore, S. S. and Flamme-Wiese, M. J. and Riker, M. J. and Wiley, L. A. and Tucker, B. A. and Stone, E. M. and Mullins, R. F. and Scheetz, T. E.},
+  journal  = {Experimental Eye Research},
+  title    = {Molecular characterization of foveal versus peripheral human retina by single-cell {RNA} sequencing},
+  year     = {2019},
+  issn     = {0014-4835},
+  month    = jul,
+  pages    = {234--242},
+  volume   = {184},
+  abstract = {The human retina is a complex tissue responsible for detecting photons of light and converting information from these photons into the neurochemical signals interpreted as vision. Such visual signaling not only requires sophisticated interactions between multiple classes of neurons, but also spatially-dependent molecular specialization of individual cell types. In this study, we performed single-cell RNA sequencing on neural retina isolated from both the fovea and peripheral retina in three human donors. We recovered a total of 8,217 cells, with 3,578 cells originating from the fovea and 4,639 cells originating from the periphery. Expression profiles for all major retinal cell types were compiled, and differential expression analysis was performed between cells of foveal versus peripheral origin. Globally, mRNA for the serum iron binding protein transferrin (TF), which has been associated with age-related macular degeneration pathogenesis, was enriched in peripheral samples. Cone photoreceptor cells were of particular interest and formed two predominant clusters based on gene expression. One cone cluster had 96\% of cells originating from foveal samples, while the second cone cluster consisted exclusively of peripherally isolated cells. A total of 148 genes were differentially expressed between cones from the fovea versus periphery. Interestingly, peripheral cones were enriched for the gene encoding Beta-Carotene Oxygenase 2 (BCO2). A relative deficiency of this enzyme may account for the accumulation of carotenoids responsible for yellow pigment deposition within the macula. Overall, this data set provides rich expression profiles of the major human retinal cell types and highlights transcriptomic features that distinguish foveal and peripheral cells.},
+  doi      = {10.1016/j.exer.2019.05.001},
+  file     = {ScienceDirect Full Text PDF:https\://www.sciencedirect.com/science/article/pii/S0014483519302726/pdfft?md5=099a053a74940e3ca26986bb68c2e4ec&pid=1-s2.0-S0014483519302726-main.pdf&isDTMRedir=Y:application/pdf},
+  keywords = {Retina, Cones, Fovea, Single-cell, Transferrin},
+  language = {en},
+}
+
+ 
+@Article{Berens2017,
+  author    = {Berens, Philipp and Euler, Thomas},
+  journal   = {e-Neuroforum},
+  title     = {Neuronal {Diversity} {In} {The} {Retina}},
+  year      = {2017},
+  issn      = {1868-856X},
+  month     = may,
+  number    = {2},
+  pages     = {93--101},
+  volume    = {23},
+  abstract  = {The retina in the eye performs complex computations, to transmit only behaviourally relevant information about our visual environment to the brain. These computations are implemented by numerous different cell types that form complex circuits. New experimental and computational methods make it possible to study the cellular diversity of the retina in detail – the goal of obtaining a complete list of all the cell types in the retina and, thus, its “building blocks”, is within reach. We review the current state of this endeavour and highlight possible directions for future research.},
+  chapter   = {Neuroforum},
+  doi       = {10.1515/nf-2016-A055},
+  keywords  = {Retina, Eye, Cell Types, Networks, Neural circuits},
+  language  = {en},
+  publisher = {De Gruyter},
+}
+
+ 
+@Article{Cadwell2020,
+  author    = {Cadwell, Cathryn R and Scala, Federico and Fahey, Paul G and Kobak, Dmitry and Mulherkar, Shalaka and Sinz, Fabian H and Papadopoulos, Stelios and Tan, Zheng H and Johnsson, Per and Hartmanis, Leonard and Li, Shuang and Cotton, Ronald J and Tolias, Kimberley F and Sandberg, Rickard and Berens, Philipp and Jiang, Xiaolong and Tolias, Andreas Savas},
+  journal   = {eLife},
+  title     = {Cell type composition and circuit organization of clonally related excitatory neurons in the juvenile mouse neocortex},
+  year      = {2020},
+  issn      = {2050-084X},
+  month     = mar,
+  pages     = {e52951},
+  volume    = {9},
+  abstract  = {Clones of excitatory neurons derived from a common progenitor have been proposed to serve as elementary information processing modules in the neocortex. To characterize the cell types and circuit diagram of clonally related excitatory neurons, we performed multi-cell patch clamp recordings and Patch-seq on neurons derived from Nestin-positive progenitors labeled by tamoxifen induction at embryonic day 10.5. The resulting clones are derived from two radial glia on average, span cortical layers 2–6, and are composed of a random sampling of transcriptomic cell types. We find an interaction between shared lineage and connection type: related neurons are more likely to be connected vertically across cortical layers, but not laterally within the same layer. These findings challenge the view that related neurons show uniformly increased connectivity and suggest that integration of vertical intra-clonal input with lateral inter-clonal input may represent a developmentally programmed connectivity motif supporting the emergence of functional circuits.},
+  doi       = {10.7554/eLife.52951},
+  editor    = {West, Anne E and Behrens, Timothy E and Hevner, Robert and Fishell, Gordon},
+  file      = {:Cadwell2020 - Cell Type Composition and Circuit Organization of Clonally Related Excitatory Neurons in the Juvenile Mouse Neocortex.pdf:PDF},
+  keywords  = {cell lineage, connectivity, clonally related, excitatory neurons, cortical development, transcriptomics},
+  publisher = {eLife Sciences Publications, Ltd},
+}
+
+ 
+@Article{Laturnus2020,
+  author   = {Laturnus, Sophie and Kobak, Dmitry and Berens, Philipp},
+  journal  = {Neuroinformatics},
+  title    = {A {Systematic} {Evaluation} of {Interneuron} {Morphology} {Representations} for {Cell} {Type} {Discrimination}},
+  year     = {2020},
+  issn     = {1559-0089},
+  month    = oct,
+  number   = {4},
+  pages    = {591--609},
+  volume   = {18},
+  abstract = {Quantitative analysis of neuronal morphologies usually begins with choosing a particular feature representation in order to make individual morphologies amenable to standard statistics tools and machine learning algorithms. Many different feature representations have been suggested in the literature, ranging from density maps to intersection profiles, but they have never been compared side by side. Here we performed a systematic comparison of various representations, measuring how well they were able to capture the difference between known morphological cell types. For our benchmarking effort, we used several curated data sets consisting of mouse retinal bipolar cells and cortical inhibitory neurons. We found that the best performing feature representations were two-dimensional density maps, two-dimensional persistence images and morphometric statistics, which continued to perform well even when neurons were only partially traced. Combining these feature representations together led to further performance increases suggesting that they captured non-redundant information. The same representations performed well in an unsupervised setting, implying that they can be suitable for dimensionality reduction or clustering.},
+  doi      = {10.1007/s12021-020-09461-z},
+  file     = {:Laturnus2020 - A Systematic Evaluation of Interneuron Morphology Representations for Cell Type Discrimination.pdf:PDF},
+  keywords = {Neuroanatomy, Benchmarking, Cell types, Mouse, Visual cortex},
+  language = {en},
+}
+
+ 
+@Article{Yan2020,
+  author    = {Yan, Wenjun and Peng, Yi-Rong and van Zyl, Tavé and Regev, Aviv and Shekhar, Karthik and Juric, Dejan and Sanes, Joshua R.},
+  journal   = {Scientific Reports},
+  title     = {Cell {Atlas} of {The} {Human} {Fovea} and {Peripheral} {Retina}},
+  year      = {2020},
+  issn      = {2045-2322},
+  month     = jun,
+  number    = {1},
+  pages     = {9802},
+  volume    = {10},
+  abstract  = {Most irreversible blindness results from retinal disease. To advance our understanding of the etiology of blinding diseases, we used single-cell RNA-sequencing (scRNA-seq) to analyze the transcriptomes of {\textasciitilde}85,000 cells from the fovea and peripheral retina of seven adult human donors. Utilizing computational methods, we identified 58 cell types within 6 classes: photoreceptor, horizontal, bipolar, amacrine, retinal ganglion and non-neuronal cells. Nearly all types are shared between the two retinal regions, but there are notable differences in gene expression and proportions between foveal and peripheral cohorts of shared types. We then used the human retinal atlas to map expression of 636 genes implicated as causes of or risk factors for blinding diseases. Many are expressed in striking cell class-, type-, or region-specific patterns. Finally, we compared gene expression signatures of cell types between human and the cynomolgus macaque monkey, Macaca fascicularis. We show that over 90\% of human types correspond transcriptomically to those previously identified in macaque, and that expression of disease-related genes is largely conserved between the two species. These results validate the use of the macaque for modeling blinding disease, and provide a foundation for investigating molecular mechanisms underlying visual processing.},
+  copyright = {2020 The Author(s)},
+  doi       = {10.1038/s41598-020-66092-9},
+  keywords  = {Diseases of the nervous system, Eye diseases, Neuroscience, Sensory systems},
+  language  = {en},
+  publisher = {Nature Publishing Group},
+}
+
+ 
+@Article{Yan2020a,
+  author     = {Yan, Wenjun and Laboulaye, Mallory A. and Tran, Nicholas M. and Whitney, Irene E. and Benhar, Inbal and Sanes, Joshua R.},
+  journal    = {Journal of Neuroscience},
+  title      = {Mouse {Retinal} {Cell} {Atlas}: {Molecular} {Identification} of over {Sixty} {Amacrine} {Cell} {Types}},
+  year       = {2020},
+  issn       = {0270-6474, 1529-2401},
+  month      = jul,
+  number     = {27},
+  pages      = {5177--5195},
+  volume     = {40},
+  abstract   = {Amacrine cells (ACs) are a diverse class of interneurons that modulate input from photoreceptors to retinal ganglion cells (RGCs), rendering each RGC type selectively sensitive to particular visual features, which are then relayed to the brain. While many AC types have been identified morphologically and physiologically, they have not been comprehensively classified or molecularly characterized. We used high-throughput single-cell RNA sequencing to profile {\textgreater}32,000 ACs from mice of both sexes and applied computational methods to identify 63 AC types. We identified molecular markers for each type and used them to characterize the morphology of multiple types. We show that they include nearly all previously known AC types as well as many that had not been described. Consistent with previous studies, most of the AC types expressed markers for the canonical inhibitory neurotransmitters GABA or glycine, but several expressed neither or both. In addition, many expressed one or more neuropeptides, and two expressed glutamatergic markers. We also explored transcriptomic relationships among AC types and identified transcription factors expressed by individual or multiple closely related types. Noteworthy among these were Meis2 and Tcf4, expressed by most GABAergic and most glycinergic types, respectively. Together, these results provide a foundation for developmental and functional studies of ACs, as well as means for genetically accessing them. Along with previous molecular, physiological, and morphologic analyses, they establish the existence of at least 130 neuronal types and nearly 140 cell types in the mouse retina. SIGNIFICANCE STATEMENT The mouse retina is a leading model for analyzing the development, structure, function, and pathology of neural circuits. A complete molecular atlas of retinal cell types provides an important foundation for these studies. We used high-throughput single-cell RNA sequencing to characterize the most heterogeneous class of retinal interneurons, amacrine cells, identifying 63 distinct types. The atlas includes types identified previously as well as many novel types. We provide evidence for the use of multiple neurotransmitters and neuropeptides, and identify transcription factors expressed by groups of closely related types. Combining these results with those obtained previously, we proposed that the mouse retina contains ∼130 neuronal types and is therefore comparable in complexity to other regions of the brain.},
+  chapter    = {Research Articles},
+  copyright  = {Copyright © 2020 the authors},
+  doi        = {10.1523/JNEUROSCI.0471-20.2020},
+  keywords   = {GABA, glycine, Meis2, neuropeptide, RNA-seq, TCF4},
+  language   = {en},
+  pmid       = {32457074},
+  publisher  = {Society for Neuroscience},
+  shorttitle = {Mouse {Retinal} {Cell} {Atlas}},
+}
+
+ 
+@Article{Gouwens2018,
+  author    = {Gouwens, Nathan W. and Berg, Jim and Feng, David and Sorensen, Staci A. and Zeng, Hongkui and Hawrylycz, Michael J. and Koch, Christof and Arkhipov, Anton},
+  journal   = {Nature Communications},
+  title     = {Systematic generation of biophysically detailed models for diverse cortical neuron types},
+  year      = {2018},
+  issn      = {2041-1723},
+  month     = feb,
+  number    = {1},
+  pages     = {710},
+  volume    = {9},
+  abstract  = {The cellular components of mammalian neocortical circuits are diverse, and capturing this diversity in computational models is challenging. Here we report an approach for generating biophysically detailed models of 170 individual neurons in the Allen Cell Types Database to link the systematic experimental characterization of cell types to the construction of cortical models. We build models from 3D morphologies and somatic electrophysiological responses measured in the same cells. Densities of active somatic conductances and additional parameters are optimized with a genetic algorithm to match electrophysiological features. We evaluate the models by applying additional stimuli and comparing model responses to experimental data. Applying this technique across a diverse set of neurons from adult mouse primary visual cortex, we verify that models preserve the distinctiveness of intrinsic properties between subsets of cells observed in experiments. The optimized models are accessible online alongside the experimental data. Code for optimization and simulation is also openly distributed.},
+  copyright = {2018 The Author(s)},
+  doi       = {10.1038/s41467-017-02718-3},
+  file      = {Full Text PDF:https\://www.nature.com/articles/s41467-017-02718-3.pdf:application/pdf},
+  keywords  = {Biophysical models, Cellular neuroscience, Striate cortex},
+  language  = {en},
+  publisher = {Nature Publishing Group},
+}
+
+ 
+@Article{Tripathy2015,
+  author    = {Tripathy, Shreejoy J. and Burton, Shawn D. and Geramita, Matthew and Gerkin, Richard C. and Urban, Nathaniel N.},
+  journal   = {Journal of Neurophysiology},
+  title     = {Brain-wide analysis of electrophysiological diversity yields novel categorization of mammalian neuron types},
+  year      = {2015},
+  issn      = {0022-3077},
+  month     = jun,
+  number    = {10},
+  pages     = {3474--3489},
+  volume    = {113},
+  abstract  = {For decades, neurophysiologists have characterized the biophysical properties of a rich diversity of neuron types. However, identifying common features and computational roles shared across neuron types is made more difficult by inconsistent conventions for collecting and reporting biophysical data. Here, we leverage NeuroElectro, a literature-based database of electrophysiological properties (www.neuroelectro.org), to better understand neuronal diversity, both within and across neuron types, and the confounding influences of methodological variability. We show that experimental conditions (e.g., electrode types, recording temperatures, or animal age) can explain a substantial degree of the literature-reported biophysical variability observed within a neuron type. Critically, accounting for experimental metadata enables massive cross-study data normalization and reveals that electrophysiological data are far more reproducible across laboratories than previously appreciated. Using this normalized dataset, we find that neuron types throughout the brain cluster by biophysical properties into six to nine superclasses. These classes include intuitive clusters, such as fast-spiking basket cells, as well as previously unrecognized clusters, including a novel class of cortical and olfactory bulb interneurons that exhibit persistent activity at theta-band frequencies.},
+  doi       = {10.1152/jn.00237.2015},
+  file      = {:Tripathy2015 - Brain Wide Analysis of Electrophysiological Diversity Yields Novel Categorization of Mammalian Neuron Types.pdf:PDF},
+  keywords  = {neuron biophysics, intrinsic membrane properties, electrophysiology, neuron diversity, neuroinformatics, text mining, databases},
+  publisher = {American Physiological Society},
+
 }
 
 @Comment{jabref-meta: databaseType:bibtex;}