Burst-Time-Dependent Plasticity Robustly Guides ON/OFF Segregation in the Lateral Geniculate Nucleus
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Mendeley | Further Information

{"title"=>"Burst-time-dependent plasticity robustly guides ON/OFF segregation in the lateral geniculate nucleus", "type"=>"journal", "authors"=>[{"first_name"=>"Julijana", "last_name"=>"Gjorgjieva", "scopus_author_id"=>"35317490800"}, {"first_name"=>"Taro", "last_name"=>"Toyoizumi", "scopus_author_id"=>"8380165100"}, {"first_name"=>"Stephen J.", "last_name"=>"Eglen", "scopus_author_id"=>"6601993657"}], "year"=>2009, "source"=>"PLoS Computational Biology", "identifiers"=>{"doi"=>"10.1371/journal.pcbi.1000618", "sgr"=>"74549206712", "issn"=>"1553734X", "pui"=>"358137742", "isbn"=>"1553-7358 (Electronic)\r1553-734X (Linking)", "pmid"=>"20041207", "scopus"=>"2-s2.0-74549206712"}, "id"=>"683f923f-98b7-3bed-b84a-1f7c5579b809", "abstract"=>"Spontaneous retinal activity (known as \"waves\") remodels synaptic connectivity to the lateral geniculate nucleus (LGN) during development. Analysis of retinal waves recorded with multielectrode arrays in mouse suggested that a cue for the segregation of functionally distinct (ON and OFF) retinal ganglion cells (RGCs) in the LGN may be a desynchronization in their firing, where ON cells precede OFF cells by one second. Using the recorded retinal waves as input, with two different modeling approaches we explore timing-based plasticity rules for the evolution of synaptic weights to identify key features underlying ON/OFF segregation. First, we analytically derive a linear model for the evolution of ON and OFF weights, to understand how synaptic plasticity rules extract input firing properties to guide segregation. Second, we simulate postsynaptic activity with a nonlinear integrate-and-fire model to compare findings with the linear model. We find that spike-time-dependent plasticity, which modifies synaptic weights based on millisecond-long timing and order of pre- and postsynaptic spikes, fails to segregate ON and OFF retinal inputs in the absence of normalization. Implementing homeostatic mechanisms results in segregation, but only with carefully-tuned parameters. Furthermore, extending spike integration timescales to match the second-long input correlation timescales always leads to ON segregation because ON cells fire before OFF cells. We show that burst-time-dependent plasticity can robustly guide ON/OFF segregation in the LGN without normalization, by integrating pre- and postsynaptic bursts irrespective of their firing order and over second-long timescales. We predict that an LGN neuron will become ON- or OFF-responsive based on a local competition of the firing patterns of neighboring RGCs connecting to it. Finally, we demonstrate consistency with ON/OFF segregation in ferret, despite differences in the firing properties of retinal waves. Our model suggests that diverse input statistics of retinal waves can be robustly interpreted by a burst-based rule, which underlies retinogeniculate plasticity across different species.", "link"=>"http://www.mendeley.com/research/bursttimedependent-plasticity-robustly-guides-onoff-segregation-lateral-geniculate-nucleus", "reader_count"=>51, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>4, "Researcher"=>13, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>1, "Student > Master"=>8, "Student > Bachelor"=>2, "Lecturer"=>1, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>4, "Researcher"=>13, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>1, "Student > Master"=>8, "Student > Bachelor"=>2, "Lecturer"=>1, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Engineering"=>3, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>2, "Agricultural and Biological Sciences"=>22, "Medicine and Dentistry"=>2, "Neuroscience"=>4, "Physics and Astronomy"=>1, "Psychology"=>3, "Computer Science"=>11}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Neuroscience"=>{"Neuroscience"=>4}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>22}, "Computer Science"=>{"Computer Science"=>11}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"Turkey"=>1, "United States"=>2, "United Kingdom"=>4, "Malaysia"=>1, "Switzerland"=>2, "Germany"=>6, "India"=>1}, "group_count"=>1}

Scopus | Further Information

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  • {"files"=>["https://ndownloader.figshare.com/files/432048", "https://ndownloader.figshare.com/files/432117", "https://ndownloader.figshare.com/files/432164", "https://ndownloader.figshare.com/files/432218", "https://ndownloader.figshare.com/files/432246"], "description"=>"<div><p>Spontaneous retinal activity (known as “waves”) remodels synaptic connectivity to the lateral geniculate nucleus (LGN) during development. Analysis of retinal waves recorded with multielectrode arrays in mouse suggested that a cue for the segregation of functionally distinct (ON and OFF) retinal ganglion cells (RGCs) in the LGN may be a desynchronization in their firing, where ON cells precede OFF cells by one second. Using the recorded retinal waves as input, with two different modeling approaches we explore timing-based plasticity rules for the evolution of synaptic weights to identify key features underlying ON/OFF segregation. First, we analytically derive a linear model for the evolution of ON and OFF weights, to understand how synaptic plasticity rules extract input firing properties to guide segregation. Second, we simulate postsynaptic activity with a nonlinear integrate-and-fire model to compare findings with the linear model. We find that spike-time-dependent plasticity, which modifies synaptic weights based on millisecond-long timing and order of pre- and postsynaptic spikes, fails to segregate ON and OFF retinal inputs in the absence of normalization. Implementing homeostatic mechanisms results in segregation, but only with carefully-tuned parameters. Furthermore, extending spike integration timescales to match the second-long input correlation timescales always leads to ON segregation because ON cells fire before OFF cells. We show that burst-time-dependent plasticity can robustly guide ON/OFF segregation in the LGN without normalization, by integrating pre- and postsynaptic bursts irrespective of their firing order and over second-long timescales. We predict that an LGN neuron will become ON- or OFF-responsive based on a local competition of the firing patterns of neighboring RGCs connecting to it. Finally, we demonstrate consistency with ON/OFF segregation in ferret, despite differences in the firing properties of retinal waves. Our model suggests that diverse input statistics of retinal waves can be robustly interpreted by a burst-based rule, which underlies retinogeniculate plasticity across different species.</p></div>", "links"=>[], "tags"=>["burst-time-dependent", "plasticity", "robustly", "guides", "segregation", "lateral", "geniculate", "nucleus"], "article_id"=>145266, "categories"=>["Neuroscience", "Medicine"], "users"=>["Julijana Gjorgjieva", "Taro Toyoizumi", "Stephen J. Eglen"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000618.s001", "https://dx.doi.org/10.1371/journal.pcbi.1000618.s002", "https://dx.doi.org/10.1371/journal.pcbi.1000618.s003", "https://dx.doi.org/10.1371/journal.pcbi.1000618.s004", "https://dx.doi.org/10.1371/journal.pcbi.1000618.s005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Burst_Time_Dependent_Plasticity_Robustly_Guides_ON_OFF_Segregation_in_the_Lateral_Geniculate_Nucleus/145266", "title"=>"Burst-Time-Dependent Plasticity Robustly Guides ON/OFF Segregation in the Lateral Geniculate Nucleus", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2009-12-24 01:27:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/869566"], "description"=>"<p>(A, left) An LGN neuron receives feedforward weak synaptic input from neighboring ON (red) and OFF (blue) RGC inputs early in development. (A, right) Spontaneous retinal waves selectively refine RGC inputs, such that synaptic weights of one RGC type (ON) strengthen, while weights of the other RGC type (OFF) decay to 0, resulting in an ON-responsive LGN neuron. Sample P12 spike rasters in the middle demonstrate that ON cells fire shorter bursts of higher spike frequency, while OFF cells fire longer bursts of lower spike frequency, 1 second after ON <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000618#pcbi.1000618-Kerschensteiner1\" target=\"_blank\">[20]</a>. Also shown are spike rasters for a developing LGN neuron receiving weak mixed ON and OFF inputs, and for a refined LGN neuron, receiving selective input solely from ON RGCs. LGN spiking activity was generated using the integrate-and-fire model in Equations 12–13. (B) Correlation functions for the input spike trains shown in (A) (mouse data set 1). The input correlation function for RGCs of the same type (ON/ON and OFF/OFF) peaks at 0 seconds, while for RGCs of different type peaks at second for ON/OFF and second for OFF/ON pairs. Symmetric decaying exponentials were fit to the pairwise input correlations using Equation 11. Peak amplitudes and decay time constants for pairs with maximal peaks are reported in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000618#pcbi-1000618-t001\" target=\"_blank\">Table 1</a>.</p>", "links"=>[], "tags"=>["computational biology/computational neuroscience", "computational biology/systems biology", "neuroscience/neurodevelopment"], "article_id"=>540025, "categories"=>["Neuroscience", "Medicine"], "users"=>["Julijana Gjorgjieva", "Taro Toyoizumi", "Stephen J. Eglen"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000618.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_description_and_input_correlation_functions_/540025", "title"=>"Model description and input correlation functions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-12-24 00:00:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/869647"], "description"=>"<p>(A) STDP modifies synaptic strength based on the timing, (indicated by horizontal arrows), and firing order of pairs of pre- and postsynaptic spikes <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000618#pcbi.1000618-Zhang1\" target=\"_blank\">[10]</a>. Synaptic change occurs in a time window on the order of tens of milliseconds determined by and , with maximum change at given by and in EPSC (evoked postsynaptic current) size. (B) BTDP governs synaptic change based on the timing (but not order) of pre- and postsynaptic bursts over a second-long time window, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000618#pcbi.1000618-Butts2\" target=\"_blank\">[16]</a>. We fit the blue symmetric exponential curve to the linear experimental fit, where and denote the amounts of maximum synaptic potentiation and depression in of EPSC size, respectively. (C) We detected ON (top) and OFF (bottom) bursts by accumulating burst detection variables and . At the arrival of a spike, increases by 1, and otherwise decays exponentially with a time constant ms. A burst was detected once reached a fixed threshold (here 1.5) denoted with an asterisk; was not allowed to exceed the threshold value. The location of the asterisk (instead of the start time of the burst) was used to evaluate the burst latency for synaptic change. Parameters for STDP and BTDP are listed in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000618#pcbi-1000618-t003\" target=\"_blank\">Table 3</a>. Figure modified from <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000618#pcbi.1000618-Eglen2\" target=\"_blank\">[68]</a> with permission from the HFSP journal.</p>", "links"=>[], "tags"=>["btdp"], "article_id"=>540110, "categories"=>["Neuroscience", "Medicine"], "users"=>["Julijana Gjorgjieva", "Taro Toyoizumi", "Stephen J. Eglen"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000618.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_STDP_versus_BTDP_and_burst_detection_/540110", "title"=>"STDP versus BTDP and burst detection.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-12-24 00:01:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/869730"], "description"=>"<p>(A) STDP with spike integration time windows of 20 ms results in ON/OFF segregation only for two data sets out of six (data set 1 shown here; data set 2 not shown), assuming a depression-to-potentiation ratio, , much larger than experimentally-observed. Theory and simulation show similar trends, although actual values of differ. (Theory) Vector fields and example trajectories of the ON-OFF weight dynamics illustrate results from the linear model. The direction of two eigenvectors of the plasticity matrix (pointing out of the origin for a positive eigenvalue, and into the origin for a negative eigenvalue) determine how affects segregation: as increases, regions of initial conditions where both weights potentiate (trajectories in black) become areas where segregation occurs (trajectories in red for ON and blue for OFF). (Simulation) In the simulated integrate-and-fire model (here, data set 1 with 3 ON and 3 OFF inputs), a separate simulation was run for each initial condition uniformly sampled between 0 and the maximum weight value, , for the ON and the OFF weights. Here and the discrete steps for the initial weights were . The colored symbol indicates the segregation outcome according to the legend, matching the color of the trajectories in the linear model. The size of the colored circles denotes the percentage of synaptic weights of a particular RGC type which potentiated maximally out of all RGCs of the same type initially wired to the LGN neuron (the three dots denote , and , respectively), while all weights of the other RGC type depressed to 0. Initial weights too small to generate postsynaptic activity result in no synaptic change (dots in the bottom left region of each plot). (B) Extending the spike integration window to match the timescale of the input correlations to ms, results in pure dominance of the ON cells, both under the theoretical and the simulated model and for any value of the ratio (data set 1 shown here, but all others show the same behavior).</p>", "links"=>[], "tags"=>["normalization"], "article_id"=>540192, "categories"=>["Neuroscience", "Medicine"], "users"=>["Julijana Gjorgjieva", "Taro Toyoizumi", "Stephen J. Eglen"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000618.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_STDP_without_normalization_does_not_result_in_segregation_/540192", "title"=>"STDP without normalization does not result in segregation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-12-24 00:03:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/869875"], "description"=>"<p>(A) Segregation indices using Equation 16 for all data sets following the linear modeling approach with STDP using unbiased initial conditions of 4.0 for both ON and OFF weights. By design the theoretical model always results in segregation for large enough ; the minimum for segregation is written in each bar. (B) Segregation indices from numerically implementing STDP for all data sets illustrate the absence of segregation. A high was used here; increasing beyond 3.0 results in segregation only for data sets 1 (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000618#pcbi-1000618-g002\" target=\"_blank\">Figure 2A</a>) and 2 (data not shown), inconsistent with linear model predictions in (A). (C) Segregation indices from the theoretical and numerical implementation of BTDP show consistent segregation across all data sets using the experimentally-observed ratio <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000618#pcbi.1000618-Butts2\" target=\"_blank\">[16]</a>. (D) STDP with subtractive normalization can induce weight competition and segregation in the linear model. For realistic depression-to-potentiation values of , the outcome is always OFF segregation. Increasing to the values which generated segregation in (A) (denoted by the black vertical lines) results in ON segregation for sets 4 and 6, matching results from (A). Note that in the linear model simply scales the weights and thus, it does not affect segregation outcome. (E) STDP with subtractive normalization also results in segregation in the simulated integrate-and-fire model for realistic values of . However, results depend on parameters: for instance, changes in (horizontal axis) sometimes result in a switch from ON to OFF segregation (data set 2), and sometimes from ON to no segregation (data set 3). Also, the amount of total synaptic weight maintained by the postsynaptic neuron (top bar for each set and bottom bar ) affects segregation outcome (for example, data sets 4 and 5). While subtractive normalization rescues segregation for the STDP rule, results are inconsistent across the two different modeling approaches (compare to D). Uncolored sections in each bar denote no segregation.</p>", "links"=>[], "tags"=>["segregation", "stdp"], "article_id"=>540334, "categories"=>["Neuroscience", "Medicine"], "users"=>["Julijana Gjorgjieva", "Taro Toyoizumi", "Stephen J. Eglen"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000618.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_summary_of_segregation_under_STDP_and_BTDP_/540334", "title"=>"A summary of segregation under STDP and BTDP.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-12-24 00:05:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/869977"], "description"=>"<p>(A) Segregation results using BTDP for data set 1, a representative spike train recording of data sets 1–3 which have similar peaks of the pairwise input correlation for ON/ON and OFF/OFF pairs. Drawing conventions and legend as in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000618#pcbi-1000618-g002\" target=\"_blank\">Figure 2</a>, and as before with discrete steps for the initial weights of . As the depression-to-potentiation ratio in BTDP, , increases towards the experimentally-observed ratio, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000618#pcbi.1000618-Butts2\" target=\"_blank\">[16]</a>, theory and simulation show the segregation of ON and OFF RGC inputs (red and blue trajectories), emerging from a state where the weights of both cell types potentiate maximally for small (black trajectories). The region of initial conditions resulting in ON segregation is larger than the region resulting in OFF segregation, indicative of ON dominance (see main text). Similarly, unbiased initial conditions located along the main diagonal in each plot show ON segregation. (B) Segregation results using BTDP for data set 4, a representative spike train recording of data sets 4–6, which have higher OFF/OFF input correlation peaks than ON/ON peaks. As increases, the dominant segregation outcome for a larger set of initial conditions is OFF segregation, in contrast to (A). While the match between theory and simulation is consistent for data sets 4–6 and experimentally-observed <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000618#pcbi.1000618-Butts2\" target=\"_blank\">[16]</a>, for data sets 4 and 5 a narrow range of large ratios (denoted by ) resulted in OFF segregation only in simulations (bottom panels). To match predictions of the two models for this larger than experimentally-observed , was made larger than in the theoretical model (from 0.327 to 0.500, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000618#pcbi-1000618-t001\" target=\"_blank\">Table 1</a>).</p>", "links"=>[], "tags"=>["robust"], "article_id"=>540440, "categories"=>["Neuroscience", "Medicine"], "users"=>["Julijana Gjorgjieva", "Taro Toyoizumi", "Stephen J. Eglen"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000618.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_BTDP_results_in_robust_segregation_/540440", "title"=>"BTDP results in robust segregation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-12-24 00:07:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/870152"], "description"=>"<p>(A) Temporal evolution of synaptic weights with for data set 1 (3 ON and 3 OFF cells), as in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000618#pcbi-1000618-g004\" target=\"_blank\">Figure 4A</a>, for initial conditions when the ON (first column) or the OFF cells (second column) segregate. Similarly, sample trajectories for data set 4 (5 ON and 2 OFF cells), as in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000618#pcbi-1000618-g004\" target=\"_blank\">Figure 4B</a>, for initial conditions where the ON (third column) or the OFF cells (fourth column) segregate. Upper bound on the weights . Even though there are more ON than OFF inputs in data set 4, the outcome does not depend on the number of ON and OFF inputs, but on the initial conditions of the synaptic weights. Furthermore, a simple bias in the initial conditions does not always result in segregation towards the biased cell (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000618#pcbi-1000618-g004\" target=\"_blank\">Figure 4</a> for the segregation outcome for all initial condition combinations). (B) Increasing the upper bound of the weights to does not affect the segregation outcome; weights simply take longer to segregate. (C) Delaying the time of the spikes of ON cells by 1 second, such that they are synchronous with OFF cells, eliminates segregation.</p>", "links"=>[], "tags"=>["segregation"], "article_id"=>540621, "categories"=>["Neuroscience", "Medicine"], "users"=>["Julijana Gjorgjieva", "Taro Toyoizumi", "Stephen J. Eglen"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000618.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Features_which_guide_segregation_under_BTDP_/540621", "title"=>"Features which guide segregation under BTDP.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-12-24 00:10:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/870299"], "description"=>"<p>(A) (left) ON dominance. Factors which determine segregation for data sets 1–3 (data set 1 in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000618#pcbi-1000618-g004\" target=\"_blank\">Figure 4A</a>) with similar correlation peaks of ON/ON and OFF/OFF pairs. Segregation is the result of a trade-off between the relative areas of the input correlation functions for ON/ON (red) and OFF/OFF (dark blue) pairs integrated by the positive and negative parts of BTDP (green). BTDP favors ON segregation because the smaller area of the ON/ON correlation under the negative part of BTDP (denoted by ) dominates the larger area of the OFF/OFF correlation under the positive part of BTDP (denoted by ). Therefore, in the plasticity matrix (Equation 1). (middle) OFF dominance. Factors which determine segregation for data sets 4–6 (data set 4 in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000618#pcbi-1000618-g004\" target=\"_blank\">Figure 4B</a>) with larger correlation peaks of OFF/OFF than ON/ON pairs. BTDP favors OFF segregation because the larger area of the OFF/OFF correlation under the positive part of BTDP dominates the smaller area of the ON-ON correlation under the negative part of BTDP, such that in the plasticity matrix (Equation 1). (right) The reflected (about 0 seconds) input correlations between cells of different type (light blue) have a larger part of their areas under the negative part of BTDP, ensuring negative off-diagonal terms in (Equation 1) and introducing competition. (B) (left) Input correlation functions for two of the 15 ferret data sets illustrate dominance of ON segregation. (middle) Input correlation functions for the remaining 13 ferret data sets illustrate dominance of OFF segregation. (right) The input correlation function between cells of different type peaks at 0 seconds for ferret, however, the wide correlation timescale (wider than in mouse) still produces negative off-diagonal terms in (Equation 1), and correspondingly, competition between the ON and OFF weights. (C) STDP drawn to scale to compare its spike integration time window with the timescale of input correlations shown in (A) and (B). The near-0 off-diagonal terms in (Equation 3) inhibit segregation.</p>", "links"=>[], "tags"=>["segregation", "linear"], "article_id"=>540760, "categories"=>["Neuroscience", "Medicine"], "users"=>["Julijana Gjorgjieva", "Taro Toyoizumi", "Stephen J. Eglen"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000618.g007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Insights_into_ON_OFF_segregation_from_the_linear_model_/540760", "title"=>"Insights into ON/OFF segregation from the linear model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-12-24 00:12:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/870483"], "description"=>"<p>The number of ON and OFF cells used in the simulated integrate-and-fire model are also listed for each data set. The OFF/ON correlation function is a reflection about 0 of the ON/OFF correlation function, such that , and .</p>", "links"=>[], "tags"=>["amplitudes", "decay", "constants", "symmetric", "fall-off", "exponential", "fits", "functions", "most-correlated", "spontaneous", "retinal", "recordings", "kerschensteiner", "wong", "denote", "firing", "rates", "correlated", "cells", "parameters", "illustrated"], "article_id"=>540905, "categories"=>["Neuroscience", "Medicine"], "users"=>["Julijana Gjorgjieva", "Taro Toyoizumi", "Stephen J. Eglen"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000618.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Peak_amplitudes_and_decay_time_constants_of_the_symmetric_fall_off_exponential_fits_to_the_correlation_functions_of_the_most_correlated_input_pair_from_spontaneous_retinal_wave_recordings_by_Kerschensteiner_and_Wong_20_values_given_as_estimate_standard_e/540905", "title"=>"Peak amplitudes and decay time constants of the symmetric fall-off exponential fits to the correlation functions of the most-correlated input pair from spontaneous retinal wave recordings by Kerschensteiner and Wong [20] (values given as estimatestandard error). and denote the average firing rates of the two most correlated ON and OFF cells in each data set; other parameters are illustrated in Figure 1B.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-12-24 00:15:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/870508"], "description"=>"<p>Segregation results are shown for the reduced two-dimensional linear model, the full linear model with all RGC inputs, and the nonlinear integrate-and-fire model with all RGC inputs for a biologically-plausible range of the depression-to-potentiation ratio . ON denotes ON segregation, OFF denotes OFF segregation, and a dash denotes no segregation.</p>", "links"=>[], "tags"=>["stdp", "subtractive"], "article_id"=>540972, "categories"=>["Neuroscience", "Medicine"], "users"=>["Julijana Gjorgjieva", "Taro Toyoizumi", "Stephen J. Eglen"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000618.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parameter_sensitivity_of_STDP_with_subtractive_normalization_/540972", "title"=>"Parameter sensitivity of STDP with subtractive normalization.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-12-24 00:16:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/870549"], "description"=>"<p>Multiple values were tested for some parameters, marked by asterisks.</p>", "links"=>[], "tags"=>["parameters", "lgn", "linear", "integrate-and-fire", "synaptic", "modification", "induced", "stdp"], "article_id"=>541002, "categories"=>["Neuroscience", "Medicine"], "users"=>["Julijana Gjorgjieva", "Taro Toyoizumi", "Stephen J. Eglen"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000618.t003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_parameters_for_generation_of_LGN_activity_using_the_linear_model_theory_and_the_integrate_and_fire_model_simulation_and_for_synaptic_modification_induced_by_STDP_or_BTDP_/541002", "title"=>"Model parameters for generation of LGN activity using the linear model (theory) and the integrate-and-fire model (simulation), and for synaptic modification induced by STDP or BTDP.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-12-24 00:16:42"}

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Relative Metric

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