Spike-Threshold Adaptation Predicted by Membrane Potential Dynamics In Vivo
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{"title"=>"Spike-Threshold Adaptation Predicted by Membrane Potential Dynamics In Vivo", "type"=>"journal", "authors"=>[{"first_name"=>"Bertrand", "last_name"=>"Fontaine", "scopus_author_id"=>"22234095500"}, {"first_name"=>"José Luis", "last_name"=>"Peña", "scopus_author_id"=>"55434111500"}, {"first_name"=>"Romain", "last_name"=>"Brette", "scopus_author_id"=>"55513197100"}], "year"=>2014, "source"=>"PLoS Computational Biology", "identifiers"=>{"doi"=>"10.1371/journal.pcbi.1003560", "sgr"=>"84901293963", "scopus"=>"2-s2.0-84901293963", "issn"=>"15537358", "pmid"=>"24722397", "isbn"=>"1553-734X", "pui"=>"373162996"}, "id"=>"72394117-2cb0-3ae2-a082-98d224c7ef8f", "abstract"=>"Neurons encode information in sequences of spikes, which are triggered when their membrane potential crosses a threshold. In vivo, the spiking threshold displays large variability suggesting that threshold dynamics have a profound influence on how the combined input of a neuron is encoded in the spiking. Threshold variability could be explained by adaptation to the membrane potential. However, it could also be the case that most threshold variability reflects noise and processes other than threshold adaptation. Here, we investigated threshold variation in auditory neurons responses recorded in vivo in barn owls. We found that spike threshold is quantitatively predicted by a model in which the threshold adapts, tracking the membrane potential at a short timescale. As a result, in these neurons, slow voltage fluctuations do not contribute to spiking because they are filtered by threshold adaptation. More importantly, these neurons can only respond to input spikes arriving together on a millisecond timescale. These results demonstrate that fast adaptation to the membrane potential captures spike threshold variability in vivo.", "link"=>"http://www.mendeley.com/research/spikethreshold-adaptation-predicted-membrane-potential-dynamics-vivo", "reader_count"=>63, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>5, "Student > Doctoral Student"=>5, "Researcher"=>13, "Student > Ph. D. Student"=>24, "Student > Postgraduate"=>3, "Student > Master"=>4, "Student > Bachelor"=>4, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>5, "Student > Doctoral Student"=>5, "Researcher"=>13, "Student > Ph. D. Student"=>24, "Student > Postgraduate"=>3, "Student > Master"=>4, "Student > Bachelor"=>4, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>5, "Environmental Science"=>1, "Mathematics"=>1, "Agricultural and Biological Sciences"=>26, "Neuroscience"=>14, "Arts and Humanities"=>1, "Physics and Astronomy"=>8, "Psychology"=>1, "Computer Science"=>6}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>5}, "Neuroscience"=>{"Neuroscience"=>14}, "Physics and Astronomy"=>{"Physics and Astronomy"=>8}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>26}, "Computer Science"=>{"Computer Science"=>6}, "Mathematics"=>{"Mathematics"=>1}, "Environmental Science"=>{"Environmental Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Netherlands"=>1, "Finland"=>1, "Israel"=>1, "France"=>4, "Germany"=>4}, "group_count"=>0}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1458824"], "description"=>"<p><b>a</b>, Top: voltage trace (top, black) and predicted threshold (red). Bottom: steady-state threshold in the fitted model. <b>b</b>, vs. predicted threshold for the trace in (a). The identity line (red) sharply separates subthreshold fluctuations from spikes.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "applied", "intracellular", "voltage"], "article_id"=>994528, "categories"=>["Biological Sciences"], "users"=>["Bertrand Fontaine", "José Luis Peña", "Romain Brette"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003560.g004", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Fitting_procedure_applied_on_an_intracellular_voltage_trace_/994528", "title"=>"Fitting procedure applied on an intracellular voltage trace.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-10 03:01:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1458825"], "description"=>"<p>Threshold curves resulting from optimizing the threshold model to recordings in 16 cells. The dashed line is the diagonal and the shaded area represents the average ± standard deviation over all recording conditions in each cell.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience"], "article_id"=>994529, "categories"=>["Biological Sciences"], "users"=>["Bertrand Fontaine", "José Luis Peña", "Romain Brette"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003560.g005", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Steady_state_threshold_curves_/994529", "title"=>"Steady-state threshold curves.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-10 03:01:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1458826"], "description"=>"<p>The optimization results for all cells are shown for three parameters: high voltage slope (<b>a</b>), low voltage slope (<b>b</b>) and time constant (<b>c</b>). Blue bars correspond to mean ± standard deviation over all recordings categorized by average membrane potential, and red bars (when available) correspond to mean ± standard deviation over all recordings categorized by stimulus condition (e.g. varying ITD with fixed IID). <b>d</b>, Distribution of average distance within cells between steady-state threshold functions (grey) and between steady-state threshold functions and the diagonal (green). <b>e</b>, Distribution of false alarm rates when the models are tested against recordings with a different mean (blue) and with different sound stimulation (red) than used for fitting. <b>f</b>, Same as (d) for the explained variance of measured spike threshold.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience"], "article_id"=>994530, "categories"=>["Biological Sciences"], "users"=>["Bertrand Fontaine", "José Luis Peña", "Romain Brette"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003560.g006", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Fitting_results_/994530", "title"=>"Fitting results.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-10 03:01:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1458827"], "description"=>"<p>To show that the optimized threshold time constant (about 260 µs on average) is accurate, we fitted the threshold model to the recordings while setting the time constant to a fixed value, i.e., the time constant is no longer a parameter to be optimized. The plots show the resulting gamma factor (in black, right ordinate) and explained variance (in red, left ordinate) as a function of threshold time constant for 9 cells. Moving the time constant away from its optimal value results in large increases in the fitting error.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience"], "article_id"=>994531, "categories"=>["Biological Sciences"], "users"=>["Bertrand Fontaine", "José Luis Peña", "Romain Brette"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003560.g007", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Fit_quality_vs_threshold_time_constant_/994531", "title"=>"Fit quality vs. threshold time constant.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-10 03:01:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1458828"], "description"=>"<p><b>a</b>, Top: voltage trace (black) and the corresponding fitted threshold (red). Bottom: the effective signal (black) is the difference. A spike occurs when it crosses 0 mV (red). <b>b</b>, Distribution of (top) and of the effective signal (bottom). <b>c</b>, Autocorrelogram of (top) and of the effective signal (bottom), showing the half-height width (HHW). <b>d</b>, Top: postsynaptic potential (PSP, black) and its effect on the threshold (red). Bottom: effective PSP. <b>e</b>, Standard deviation of the effective signal vs. standard deviation of (line: identity). <b>f</b>, HHW of the effective signal's autocorrelogram vs. HHW of 's autocorrelogram.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience"], "article_id"=>994532, "categories"=>["Biological Sciences"], "users"=>["Bertrand Fontaine", "José Luis Peña", "Romain Brette"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003560.g008", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effective_signal_/994532", "title"=>"Effective signal.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-10 03:01:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1458818"], "description"=>"<p><b>a</b>, Intracellular recordings () in the owl's ICx, with binaural stimuli (L: left, R: right). Either ITD is varied at best IID (top) or IID is varied at best ITD (bottom). Owl picture source: <a href=\"http://openclipart.org/detail/17566/cartoon-owl-by-lemmling\" target=\"_blank\">http://openclipart.org/detail/17566/cartoon-owl-by-lemmling</a>. <b>b</b>, Two spikes from the traces in (a); red dots indicate the estimated spiking threshold. <b>c</b>, Trace from (a) shown in phase space: vs. . Spike threshold is detected when exceeds a fixed value (red dashed line). <b>d</b>, Distribution of subthreshold membrane potential (blue) and spike threshold (green). <b>e</b>, Spike threshold vs. average before spike. <b>f</b>, Spike threshold vs. depolarization slope before spike. <b>g</b>, Spike threshold vs. preceding interspike interval. Red lines are linear regressions.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "intracellular"], "article_id"=>994522, "categories"=>["Biological Sciences"], "users"=>["Bertrand Fontaine", "José Luis Peña", "Romain Brette"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003560.g001", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_In_vivo_intracellular_recordings_/994522", "title"=>"<i>In vivo</i> intracellular recordings.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-10 03:01:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1458820"], "description"=>"<p><b>a</b>, Steady-state threshold function, defined by 5 parameters. <b>b</b>, Illustration of the model fitness computation, Voltage trace (blue) and the corresponding dynamic threshold in the model (red). A spike is predicted when the curves cross, and a refractory period follows (grey). Prediction is considered correct when the actual and predicted spikes are within a fixed coincidence window (green). Left: incorrect predictions, right: correct prediction. Note that for the sake of illustration the coincidence window is drawn larger than what it is in reality. <b>c–f</b>, Top: output of the fitting procedure on neuron models with explicit dynamic threshold (green: actual dynamic threshold, red: model prediction), with four different steady-state threshold functions and threshold time constants (bottom). <b>g</b>, The fitting procedure was run for the same model shown in <b>f</b>, but with input currents varying in mean (20–200 pA) and standard deviation (50–400 pA). The shaded area shows the mean and standard deviation of the fitted steady-state threshold function: optimization results were not strongly dependent on the input current used for training. <b>h</b>, Same as <b>g</b>, but with ms and input current with short autocorrelation time constant (0.5 ms).</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "fitting"], "article_id"=>994524, "categories"=>["Biological Sciences"], "users"=>["Bertrand Fontaine", "José Luis Peña", "Romain Brette"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003560.g002", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_fitting_approach_/994524", "title"=>"Model fitting approach.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-10 03:01:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1458822"], "description"=>"<p><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003560#pcbi.1003560-Hu1\" target=\"_blank\">[<b>7</b>]</a><b>. </b><b>a</b>, Spike threshold measured at the soma vs. logarithm of the sodium inactivation variable h at the axonal initiation site. The dashed line shows the linear regression (slope 3.2 mV). <b>b</b>, The fitting procedure is run on the somatic voltage trace (blue), and the predicted threshold (red) is compared to the threshold calculated from the value of ionic channel variables (green; as in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003560#pcbi.1003560-Platkiewicz2\" target=\"_blank\">[26]</a>). <b>c</b>, Predicted threshold resulting from the fitting procedure vs. measured threshold for all spikes. The dashed line is the identity. <b>d</b>, Steady-state threshold function of the optimized model (red) compared to the corresponding function calculated from the properties of sodium channel inactivation. <b>e</b>, Estimated time constant of threshold adaptation (red) vs. time constant of sodium inactivation. The estimation is correct in the spike initiation zone (−50 to −40 mV). <b>f</b>, Logarithm of the sodium inactivation variable h at the axonal initiation site plotted against predicted threshold for the entire simulation, excluding spikes.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "applied", "multicompartmental", "cortical"], "article_id"=>994526, "categories"=>["Biological Sciences"], "users"=>["Bertrand Fontaine", "José Luis Peña", "Romain Brette"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003560.g003", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Fitting_procedure_applied_on_a_multicompartmental_model_of_a_cortical_neuron_/994526", "title"=>"Fitting procedure applied on a multicompartmental model of a cortical neuron", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-10 03:01:09"}

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