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{"title"=>"Successful Reconstruction of a Physiological Circuit with Known Connectivity from Spiking Activity Alone", "type"=>"journal", "authors"=>[{"first_name"=>"Felipe", "last_name"=>"Gerhard", "scopus_author_id"=>"39761401300"}, {"first_name"=>"Tilman", "last_name"=>"Kispersky", "scopus_author_id"=>"35797579500"}, {"first_name"=>"Gabrielle J.", "last_name"=>"Gutierrez", "scopus_author_id"=>"55656157900"}, {"first_name"=>"Eve", "last_name"=>"Marder", "scopus_author_id"=>"7005218059"}, {"first_name"=>"Mark", "last_name"=>"Kramer", "scopus_author_id"=>"36016738900"}, {"first_name"=>"Uri", "last_name"=>"Eden", "scopus_author_id"=>"6602248001"}], "year"=>2013, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"84880767917", "doi"=>"10.1371/journal.pcbi.1003138", "issn"=>"1553734X", "pui"=>"369438558", "isbn"=>"1553-7358", "pmid"=>"23874181", "scopus"=>"2-s2.0-84880767917"}, "id"=>"f7005f01-c2e8-3a43-86d3-b86bcf27a84a", "abstract"=>"Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology. More recently, causal network inference algorithms have been proposed to deduce connectivity directly from electrophysiological signals, such as extracellularly recorded spiking activity. Usually, these algorithms have not been validated on a neurophysiological data set for which the actual circuitry is known. Recent work has shown that traditional network inference algorithms based on linear models typically fail to identify the correct coupling of a small central pattern generating circuit in the stomatogastric ganglion of the crab Cancer borealis. In this work, we show that point process models of observed spike trains can guide inference of relative connectivity estimates that match the known physiological connectivity of the central pattern generator up to a choice of threshold. We elucidate the necessary steps to derive faithful connectivity estimates from a model that incorporates the spike train nature of the data. We then apply the model to measure changes in the effective connectivity pattern in response to two pharmacological interventions, which affect both intrinsic neural dynamics and synaptic transmission. Our results provide the first successful application of a network inference algorithm to a circuit for which the actual physiological synapses between neurons are known. The point process methodology presented here generalizes well to larger networks and can describe the statistics of neural populations. In general we show that advanced statistical models allow for the characterization of effective network structure, deciphering underlying network dynamics and estimating information-processing capabilities.", "link"=>"http://www.mendeley.com/research/successful-reconstruction-physiological-circuit-known-connectivity-spiking-activity-alone", "reader_count"=>157, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>9, "Researcher"=>39, "Student > Doctoral Student"=>11, "Student > Ph. D. Student"=>47, "Student > Postgraduate"=>5, "Student > Master"=>18, "Other"=>2, "Student > Bachelor"=>11, "Lecturer"=>1, "Professor"=>12}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>9, "Researcher"=>39, "Student > Doctoral Student"=>11, "Student > Ph. D. Student"=>47, "Student > Postgraduate"=>5, "Student > Master"=>18, "Other"=>2, "Student > Bachelor"=>11, "Lecturer"=>1, "Professor"=>12}, "reader_count_by_subject_area"=>{"Unspecified"=>8, "Engineering"=>26, "Biochemistry, Genetics and Molecular Biology"=>2, "Mathematics"=>9, "Agricultural and Biological Sciences"=>49, "Medicine and Dentistry"=>7, "Neuroscience"=>26, "Philosophy"=>1, "Sports and Recreations"=>1, "Physics and Astronomy"=>12, "Psychology"=>3, "Computer Science"=>13}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>26}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>7}, "Neuroscience"=>{"Neuroscience"=>26}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>12}, "Psychology"=>{"Psychology"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>49}, "Computer Science"=>{"Computer Science"=>13}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Mathematics"=>{"Mathematics"=>9}, "Unspecified"=>{"Unspecified"=>8}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"United States"=>12, "Japan"=>2, "United Kingdom"=>3, "Switzerland"=>1, "Portugal"=>2, "Spain"=>1, "Greece"=>1, "Canada"=>1, "Turkey"=>1, "Iran"=>1, "Brazil"=>1, "Israel"=>1, "Chile"=>1, "Germany"=>3}, "group_count"=>8}

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• http://doi.org/10.1016/j.neuron.2018.09.030
• http://doi.org/10.1152/jn.00086.2017
• http://doi.org/10.1073/pnas.1704663114
• http://doi.org/10.1007/s10827-015-0548-6
• http://doi.org/10.1162/neco_a_01138
• http://doi.org/10.1371/journal.pcbi.1005738
• http://doi.org/10.1007/s10827-020-00770-5
• http://doi.org/10.1039/C5LC00133A
• http://doi.org/10.1162/NETN_a_00032
• http://doi.org/10.1371/journal.pcbi.1005390
• http://doi.org/10.3390/math8020300
• http://doi.org/10.1146/annurev-statistics-041715-033733
• http://doi.org/10.1152/jn.00429.2015
• http://doi.org/10.1371/journal.pcbi.1004762
• http://doi.org/10.1007/s10827-018-0678-8
• http://doi.org/10.1371/journal.pone.0215473
• http://doi.org/10.1016/j.jphysparis.2017.02.004
• http://doi.org/10.1371/journal.pcbi.1004167
• http://doi.org/10.1007/s10827-015-0565-5
• http://doi.org/10.1016/j.tins.2015.09.001
• http://doi.org/10.1007/s10827-020-00739-4
• http://doi.org/10.1088/1361-6528/ab3534
• http://doi.org/10.7554/eLife.52460
• http://doi.org/10.1109/TIT.2017.2700464
• http://doi.org/10.1371/journal.pone.0087636
• http://doi.org/10.1016/j.neuron.2016.05.039
• http://doi.org/10.1146/annurev-neuro-070815-013851
• http://doi.org/10.1016/j.conb.2017.06.004
• http://doi.org/10.1016/j.heliyon.2020.e03760
• http://doi.org/10.1103/PhysRevLett.121.054101
• http://doi.org/10.1371/journal.pone.0115764
• http://doi.org/10.1371/journal.pcbi.1004464
• http://doi.org/10.1016/j.jneumeth.2013.10.018

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• {"id"=>"355764373165309953", "text"=>"PLOS Comp Biol: Successful Reconstruction of a Physiological Circuit with Known Connectivity from Spiking Activity.. http://t.co/ahMxl24RQ2", "created_at"=>"2013-07-12T19:03:31Z", "user"=>"neuroethology", "user_name"=>"Neuroethology (ISN)", "user_profile_image"=>"http://a0.twimg.com/profile_images/456050586/ISN_avatar_normal.png"}
• {"id"=>"355846666307383296", "text"=>"Not buying it: http://t.co/V0lwuRKvXe Reconstruction of a Physiological Circuit with Known Connectivity from Spiking Activity Alone.", "created_at"=>"2013-07-13T00:30:31Z", "user"=>"BWJones", "user_name"=>"Bryan William Jones", "user_profile_image"=>"http://a0.twimg.com/profile_images/422335502/BWJONES__normal.gif"}
• {"id"=>"356400886668460032", "text"=>"Paper of Poster P118 just out (and makes up for the landscape-vs-portrait-confusion)! #PLOSCompBio #CNSParis http://t.co/88cVSvORv3", "created_at"=>"2013-07-14T13:12:47Z", "user"=>"neuroflips", "user_name"=>"Felipe", "user_profile_image"=>"http://a0.twimg.com/profile_images/378800000127473976/027e7f2a47b4fdbc518f2b44707ca711_normal.jpeg"}
• {"id"=>"359252453104947202", "text"=>"Reconstruction of a Physiological Circuit with Known Connectivity from Spiking Activity: http://t.co/FLao6Y5RqM", "created_at"=>"2013-07-22T10:03:54Z", "user"=>"antoniozippo", "user_name"=>"Antonio G. Zippo", "user_profile_image"=>"http://a0.twimg.com/profile_images/3239295885/c8f09bcaa9a58f8b7f4c085e37956f84_normal.jpeg"}
• {"id"=>"362945445590351873", "text"=>"#Reverse #engineering of circuits? Another step for #neuronal #circuit #reconstruction from #spiking activity http://t.co/sKjuR2GwLp", "created_at"=>"2013-08-01T14:38:32Z", "user"=>"ai_research", "user_name"=>"Roberto Santana", "user_profile_image"=>"http://a0.twimg.com/profile_images/1720491627/Santana1_normal.jpg"}
• {"id"=>"399706240390946816", "text"=>"How we reconstructed the \"connectome\" of the crab's pyloric rhythm circuit from functional signals: http://t.co/88cVSvORv3 #SfN13", "created_at"=>"2013-11-11T01:12:48Z", "user"=>"neuroflips", "user_name"=>"Felipe Gerhard", "user_profile_image"=>"http://pbs.twimg.com/profile_images/378800000127473976/027e7f2a47b4fdbc518f2b44707ca711_normal.jpeg"}

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/1117266"], "description"=>"<p><b>A</b>, Statistical models fitted on spike train activity (left) can be used to infer the effective coupling. The effective coupling should match the physiologically known diagram of the pyloric circuit (right). All synaptic couplings in the pyloric circuit are inhibitory. <b>B</b>, Comparison of algorithms for network inference. Neural activity can be either described in a firing rate model, e.g., in classical time series analysis, or using a point process model or generalized linear model (GLM). For both models, couplings are introduced as interaction kernels between the stochastic processes. The strength of the interaction can be either quantified through its statistical significance, i.e., a Granger causality-type measure, or through the magnitude of the interaction, as measured by the net area under the interaction kernel. Only the combination of a point-process-based generalized linear model with the definition of coupling strength as the magnitude of the interaction is able to recover a connectivity that is consistent with physiology (lower right). All other combinations of models and measures infer inaccurate connectivity patterns.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "neuroscience", "neural networks", "signal processing", "Statistical signal processing", "connectivity", "pyloric", "circuit", "crab", "stomatogastric", "ganglion", "extracellular", "spike"], "article_id"=>744299, "categories"=>["Engineering", "Biological Sciences"], "users"=>["Felipe Gerhard", "Tilman Kispersky", "Gabrielle J. Gutierrez", "Eve Marder", "Mark Kramer", "Uri Eden"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003138.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Inferring_network_connectivity_of_the_pyloric_circuit_of_the_crab_stomatogastric_ganglion_STG_based_on_extracellular_spike_train_recordings_/744299", "title"=>"Inferring network connectivity of the pyloric circuit of the crab stomatogastric ganglion (STG) based on extracellular spike train recordings.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-11 02:49:41"}
• {"files"=>["https://ndownloader.figshare.com/files/1117268"], "description"=>"<p><b>A</b>, Schematic overview of the point process generalized linear model (GLM). One model is fit per neuron, conditioning its activity on its own previous activity (Y) and the activity of all other simultaneously recorded neurons (X, Z). Spike trains are convolved with filters conceptually similar to spike-triggered currents. All contributions are linearly summed and passed through a static sigmoidal nonlinearity. Spikes are assumed to be a sample from the instantaneous intensity function. Coupling strength between two neurons is defined as the net area under the coupling filter. <b>B</b>, Maximum-likelihood filters. Filters indicate how much the firing activity of the postsynaptic neuron is modulated by a spike in the presynaptic neuron at a specified lag. Self-couplings (on the diagonal) have a maximal time lag of 0.4 s, cross-couplings have a maximal time lag of 0.1 s. These values were determined by a model selection procedure. <b>C</b>, Simulated spike trains from the estimated model reproduce the pyloric rhythm. Spike trains were taken from the experimental recording for one second. Then, spike trains were simulated as a random sample from the point process model. The simulated three-neuron network reproduces the stereotypical pyloric rhythm. <b>D</b>, Simulation with PY-to-PD connection forced to zero. If the PY-to-PD connection is removed from the model, the remaining model network still exhibits a pyloric rhythm. Spike trains obtained from experiments were used for one second, afterward spikes were simulated using the maximum-likelihood fit of the model. <b>E</b>, The strengths of all six directed couplings are plotted as a function of the length of the data set used for fitting the model. <b>F</b>, Coupling strengths as a function of the minimal parameter value. Relative strengths remain invariant for all reasonable choices of . The value used throughout the analysis is indicated by a vertical, dashed line. <b>G</b>, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003138#s2\" target=\"_blank\">Results</a> obtained from the point process model are reproducible across independent data sets. Data set 1 corresponds to the data set used in all analysis and other subpanels, except where otherwise noted. Inferred network strengths are shown for three additional preparations. For all data sets, the physiologically nonexistent connection is weakest. Horizontal scatter is for visualization only.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "neuroscience", "neural networks", "signal processing", "Statistical signal processing", "provides", "recovers", "physiological"], "article_id"=>744301, "categories"=>["Engineering", "Biological Sciences"], "users"=>["Felipe Gerhard", "Tilman Kispersky", "Gabrielle J. Gutierrez", "Eve Marder", "Mark Kramer", "Uri Eden"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003138.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_point_process_model_provides_a_good_fit_to_the_experimental_data_and_recovers_the_known_physiological_connectivity_/744301", "title"=>"A point process model provides a good fit to the experimental data and recovers the known physiological connectivity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-11 02:49:41"}
• {"files"=>["https://ndownloader.figshare.com/files/1117270"], "description"=>"<p><b>A</b>, Granger causality (GC) scores as a function of time used for fitting. <b>B</b>, GC scores as a function of the maximal time lag used for fitting (same color scheme as in <b>A</b>). <b>C</b>, Network inference for all four data sets, using the Granger causality score. The physiologically nonexistent connection does not correspond to the weakest one in any case. Horizontal scatter is for visualization only. <b>D</b>, Coupling strengths (CS) and Granger causality scores (GC) are uncorrelated for the point process model. For the point process model, the strength of the coupling can be either defined by the net integral of the interaction filter (horizontal axis) or by the statistical Granger causality score (vertical axis). The scatter plot shows the six cross-couplings for each of the four data sets. The two measures of coupling strength are not significantly correlated (, ).</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "neuroscience", "neural networks", "signal processing", "Statistical signal processing", "granger", "causality", "physiological"], "article_id"=>744303, "categories"=>["Engineering", "Biological Sciences"], "users"=>["Felipe Gerhard", "Tilman Kispersky", "Gabrielle J. Gutierrez", "Eve Marder", "Mark Kramer", "Uri Eden"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003138.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Using_a_Granger_causality_score_with_the_point_process_model_does_not_recover_the_physiological_connectivity_/744303", "title"=>"Using a Granger causality score with the point process model does not recover the physiological connectivity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-11 02:49:41"}
• {"files"=>["https://ndownloader.figshare.com/files/1117272"], "description"=>"<p><b>A</b>, Schematic overview of the linear rate model. First, spike trains are convolved with a smoothing filter to obtain smoothed time series of firing rates for all neurons (here denoted by X, Y and Z). Linear models are estimated for each neuron (Y) by including auto- and cross-regressive terms from the filtered input of putatively presynaptic neurons (here, X and Z). The firing rate is assumed to be Gaussian with the linearly predicted mean and fixed standard deviation. <b>B</b>, Estimated interactions. Coupling coefficients for the auto- (diagonal) and cross-regressive terms (off-diagonal) of the linear model are shown. The maximal time lag was chosen to be 400 ms to match the model of Kispersky et al. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003138#pcbi.1003138-Kispersky1\" target=\"_blank\">[29]</a>. Coupling strength is defined as the net area under the interaction kernel. <b>C</b>, The linear rate model fails to generate pyloric-like activity. Neural activity was simulated from the fitted model. First, model output was clamped to the observed activity traces for one second (vertical line). Subsequent activity was simulated using the predictions of the model and a stochastic realization of the noise term. The triphasic burst rhythm is not maintained and modeled neural firing rates diverge after a few seconds of simulated time. <b>D–E</b>, The linear model does not accurately reproduce the known physiological connectivity for a wide range of parameter choices, such as the length of the data set (<b>D</b>) or a maximal time lag different from 400 ms (<b>E</b>). <b>F</b>, Network inference using the linear model for all four data sets. The physiologically nonexistent connection corresponds to the weakest one in only two out of the four cases. Horizontal scatter is for visualization only. <b>G</b>, Coupling strengths (CS) for the point process model (horizontal axis) and linear rate model (vertical axis) are uncorrelated. The scatter plot shows the six cross-couplings for each of the four data sets. The coupling strength for the two models are not significantly correlated (, ).</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "neuroscience", "neural networks", "signal processing", "Statistical signal processing", "linear", "firing", "insufficient", "reconstruct"], "article_id"=>744305, "categories"=>["Engineering", "Biological Sciences"], "users"=>["Felipe Gerhard", "Tilman Kispersky", "Gabrielle J. Gutierrez", "Eve Marder", "Mark Kramer", "Uri Eden"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003138.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_linear_firing_rate_model_is_insufficient_to_reconstruct_the_correct_connectivity_/744305", "title"=>"A linear firing rate model is insufficient to reconstruct the correct connectivity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-11 02:49:41"}
• {"files"=>["https://ndownloader.figshare.com/files/1117275"], "description"=>"<p><b>A</b>, Exemplary spike trains from the control condition (left) and after application of CsCl, which blocks the h-current in all neurons (right). The pyloric rhythm is maintained in both conditions. <b>B</b>, Inferred coupling strengths for the control (left) and CsCl condition (right). All coupling strengths (self- and cross-couplings) become stronger, that is, more inhibitory. The mean of all eight coupling strengths (thick line, all except PY-to-PD) increases significantly between the control and CsCl condition. The nonexistent PY-to-PD coupling remains the weakest coupling in both conditions (blue line). <b>C</b>, Relative change of (signed) coupling strengths between the two conditions. Same data as in <b>B</b>, but expressed as the change of coupling strength relative to the control condition. All couplings become more inhibitory with a mean relative change of 450% (left). The relative change of the PY-to-PD coupling has the opposite sign (right). <b>D</b>, Exemplary spike trains from the control condition (left) and after application of PTX, blocking glutamatergic synaptic transmission (right). The pyloric rhythm is qualitatively maintained in both conditions. <b>E</b>, Inferred coupling strengths for the control (left) and PTX condition (right). All coupling strengths become weaker, that is, less inhibitory. The mean of all five existing cross-couplings (thick line) decreases significantly between the control and PTX condition. The nonexistent PY-to-PD coupling remains the weakest coupling in both conditions (blue line). <b>F</b>, Relative change of coupling strengths between the two conditions. Same data as in <b>E</b>, but expressed as the change of coupling strength relative to the control condition. All couplings become weaker with a mean relative change of −85% (left). The relative change of the PY-to-PD coupling has the opposite sign (right). <b>G</b>, Spike trains generated from the model of the PTX data set with various network constraints. Spike trains obtained from the PTX condition were used for five seconds, afterward spikes were simulated using either the full model (left top), a model with the PY-to-PD link forced to zero (right top), a network structure allowing only for non-glutamatergic synapses (left bottom) or a model with no cross-interactions (right bottom). All models except the last one produce spike trains comparable to the real data. The model without any cross-interactions show burst-like and tonic activity but neurons do not fire with a fixed relative phase.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "neuroscience", "neural networks", "signal processing", "Statistical signal processing", "synaptic", "coupling", "strengths", "pharmacological"], "article_id"=>744308, "categories"=>["Engineering", "Biological Sciences"], "users"=>["Felipe Gerhard", "Tilman Kispersky", "Gabrielle J. Gutierrez", "Eve Marder", "Mark Kramer", "Uri Eden"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003138.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_point_process_model_predicts_changes_of_synaptic_coupling_strengths_due_to_pharmacological_conditions_/744308", "title"=>"The point process model predicts changes of synaptic coupling strengths due to pharmacological conditions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-11 02:49:41"}
• {"files"=>["https://ndownloader.figshare.com/files/1117279", "https://ndownloader.figshare.com/files/1117280", "https://ndownloader.figshare.com/files/1117282", "https://ndownloader.figshare.com/files/1117284", "https://ndownloader.figshare.com/files/1117285"], "description"=>"<div><p>Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology. More recently, causal network inference algorithms have been proposed to deduce connectivity directly from electrophysiological signals, such as extracellularly recorded spiking activity. Usually, these algorithms have not been validated on a neurophysiological data set for which the actual circuitry is known. Recent work has shown that traditional network inference algorithms based on linear models typically fail to identify the correct coupling of a small central pattern generating circuit in the stomatogastric ganglion of the crab <i>Cancer borealis</i>. In this work, we show that point process models of observed spike trains can guide inference of relative connectivity estimates that match the known physiological connectivity of the central pattern generator up to a choice of threshold. We elucidate the necessary steps to derive faithful connectivity estimates from a model that incorporates the spike train nature of the data. We then apply the model to measure changes in the effective connectivity pattern in response to two pharmacological interventions, which affect both intrinsic neural dynamics and synaptic transmission. Our results provide the first successful application of a network inference algorithm to a circuit for which the actual physiological synapses between neurons are known. The point process methodology presented here generalizes well to larger networks and can describe the statistics of neural populations. In general we show that advanced statistical models allow for the characterization of effective network structure, deciphering underlying network dynamics and estimating information-processing capabilities.</p></div>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "neuroscience", "neural networks", "signal processing", "Statistical signal processing", "reconstruction", "physiological", "circuit", "connectivity", "spiking"], "article_id"=>744310, "categories"=>["Engineering", "Biological Sciences"], "users"=>["Felipe Gerhard", "Tilman Kispersky", "Gabrielle J. Gutierrez", "Eve Marder", "Mark Kramer", "Uri Eden"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003138.s001", "https://dx.doi.org/10.1371/journal.pcbi.1003138.s002", "https://dx.doi.org/10.1371/journal.pcbi.1003138.s003", "https://dx.doi.org/10.1371/journal.pcbi.1003138.s004", "https://dx.doi.org/10.1371/journal.pcbi.1003138.s005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Successful_Reconstruction_of_a_Physiological_Circuit_with_Known_Connectivity_from_Spiking_Activity_Alone_/744310", "title"=>"Successful Reconstruction of a Physiological Circuit with Known Connectivity from Spiking Activity Alone", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-07-11 02:49:41"}

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Relative Metric

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F1000Prime