Measuring Predictability of Autonomous Network Transitions into Bursting Dynamics
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{"title"=>"Measuring predictability of autonomous network transitions into bursting dynamics", "type"=>"journal", "authors"=>[{"first_name"=>"Sima", "last_name"=>"Mofakham", "scopus_author_id"=>"36976759100"}, {"first_name"=>"Michal", "last_name"=>"Zochowski", "scopus_author_id"=>"6701611987"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"604108248", "doi"=>"10.1371/journal.pone.0122225", "scopus"=>"2-s2.0-84929990575", "sgr"=>"84929990575", "issn"=>"19326203", "pmid"=>"25855975"}, "id"=>"55c22ce9-19c8-3fa3-85da-154213047dd1", "abstract"=>"Understanding spontaneous transitions between dynamical modes in a network is of significant importance. These transitions may separate pathological and normal functions of the brain. In this paper, we develop a set of measures that, based on spatio-temporal features of network activity, predict autonomous network transitions from asynchronous to synchronous dynamics under various conditions. These metrics quantify spike-timing distributions within a narrow time window as a function of the relative location of the active neurons. We applied these metrics to investigate the properties of these transitions in excitatory-only and excitatory-and-inhibitory networks and elucidate how network topology, noise level, and cellular heterogeneity affect both the reliability and the timeliness of the predictions. The developed measures can be calculated in real time and therefore potentially applied in clinical situations.", "link"=>"http://www.mendeley.com/research/measuring-predictability-autonomous-network-transitions-bursting-dynamics", "reader_count"=>5, "reader_count_by_academic_status"=>{"Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>1, "Student > Postgraduate"=>1, "Student > Master"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>1, "Student > Postgraduate"=>1, "Student > Master"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Agricultural and Biological Sciences"=>2, "Neuroscience"=>2}, "reader_count_by_subdiscipline"=>{"Neuroscience"=>{"Neuroscience"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>2}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"United States"=>1}, "group_count"=>0}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2012604"], "description"=>"<p>(A-D) Raster plots and ISI histograms associated with deterministic dynamics of networks having P<sub>e</sub> = 0, 0.15, 0.4 and 1 respectively (blue dots denote timing of neuronal action potential). (E-H) Same as panels (A-D) for noise driven networks (noise frequency = 0.00005). (I) Changes of mean ISIs as a function of rewiring parameter for noise driven identical (I<sup>e</sup><sub>ext</sub> = 1.05, f<sub>N</sub> = 0.00005 blue line), non-identical (I<sup>e</sup><sub>ext</sub> = 0.95–1.15, f<sub>N</sub> = 0.00005, green line) and deterministic dynamics for non-identical neurons (I<sup>e</sup><sub>ext</sub> = 0.95–1.15, red line). (J) Changes in mean ISI duration as a function of noise level for an excitatory network with P<sub>e</sub> = 0.15 for both identical (I<sup>e</sup><sub>ext</sub> = 1.05, blue line), and non-identical neurons (I<sup>e</sup><sub>ext</sub> = 0.95–1.15, green line). Spike timings used for analysis in this figure is provided as the supplemental data in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122225#pone.0122225.s001\" target=\"_blank\">S1</a>–<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122225#pone.0122225.s008\" target=\"_blank\">S8</a> Dataset.</p>", "links"=>[], "tags"=>["network transitions", "noise level", "metric", "network topology", "function", "network activity", "time window", "Autonomous Network Transitions"], "article_id"=>1373453, "categories"=>["Uncategorised"], "users"=>["Sima Mofakham", "Michal Zochowski"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122225.g001", "stats"=>{"downloads"=>3, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Dynamics_of_a_network_of_200_excitatory_integrate_and_fire_neurons_with_deterministic_left_column_and_noise_driven_right_column_dynamics_/1373453", "title"=>"Dynamics of a network of 200 excitatory integrate-and-fire neurons with deterministic (left column) and noise-driven (right column) dynamics.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-09 04:01:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2012618"], "description"=>"<p>(A) P<sub>e</sub> = 0.0, (B) P<sub>e</sub> = 0.15 and (C) <b>P</b><sub>e</sub><b>= 1</b>.0 where blue, red and green lines are standing for obtained T<sub>L</sub> based on the T<sub>M</sub>, variance of T<sub>M</sub> and variance of dT<sub>M</sub> measures respectively.</p>", "links"=>[], "tags"=>["network transitions", "noise level", "metric", "network topology", "function", "network activity", "time window", "Autonomous Network Transitions"], "article_id"=>1373467, "categories"=>["Uncategorised"], "users"=>["Sima Mofakham", "Michal Zochowski"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122225.g009", "stats"=>{"downloads"=>7, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_T_L_varies_as_a_function_of_inhibitory_connectivity_P_i_for_excitatory_networks_with_different_values_of_P_e_/1373467", "title"=>"T<sub>L</sub> varies as a function of inhibitory connectivity (P<sub>i</sub>) for excitatory networks with different values of P<sub>e.</sub>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-09 04:01:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2012617"], "description"=>"<p>(A-B) the ratio of measures (T<sub>M</sub>: blue line, Variance Of T<sub>M</sub>: red line, Variance Of dT<sub>M</sub>: green line) before and after the onset of the transition into the bursting is shown for P<sub>i</sub> = 0.2 and 1, respectively; P<sub>e</sub> = 0.15, f<sub>N</sub> = 0.00005. Based on these ratios, T<sub>L</sub> is calculated as a function of the inhibitory connectivity pattern (C). T<sub>L</sub> peaks for P<sub>i</sub> = 0.2 and then decreases for more random inhibitory topologies.</p>", "links"=>[], "tags"=>["network transitions", "noise level", "metric", "network topology", "function", "network activity", "time window", "Autonomous Network Transitions"], "article_id"=>1373466, "categories"=>["Uncategorised"], "users"=>["Sima Mofakham", "Michal Zochowski"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122225.g008", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_effect_of_inhibitory_connectivity_on_the_lead_time_T_L_/1373466", "title"=>"The effect of inhibitory connectivity on the lead-time, T<sub>L</sub>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-09 04:01:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2012612"], "description"=>"<p>Where the left, middle and right column are associated with P<sub>e</sub> = 0, P<sub>e</sub> = 0.15, P<sub>e</sub> = 1 respectively. (A) Raster plots; (B) Spatio-temporal changes of T<sub>D</sub>; (C) Examples of T<sub>D</sub> evolution with distance for selected time windows (marked of B); (D) examples of derivative of T<sub>D</sub> at the same timepoints.</p>", "links"=>[], "tags"=>["network transitions", "noise level", "metric", "network topology", "function", "network activity", "time window", "Autonomous Network Transitions"], "article_id"=>1373461, "categories"=>["Uncategorised"], "users"=>["Sima Mofakham", "Michal Zochowski"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122225.g005", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Raster_plots_and_corresponding_T_D_and_dT_D_for_selected_time_windows_in_an_excitatory_network_/1373461", "title"=>"Raster plots and corresponding T<sub>D</sub> and dT<sub>D</sub> for selected time windows in an excitatory network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-09 04:01:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2012608"], "description"=>"<p>(A) Topology of interacting network of excitatory and inhibitory neurons. Here P<sub>e</sub> = 0.15 and inhibitory connectivity changes from local (P<sub>I</sub> = 0) to random (P<sub>I</sub> = 1). (B) Excitatory only neurons with P<sub>e</sub> = 0.15 when there is no inhibitory feedback. (C) P<sub>i</sub> = 0, (D) P<sub>i</sub> = 0.2, the local propagating waves in the asynchronous regime are destroyed. (E) Random inhibitory connections (P<sub>i</sub> = 1), the firing frequency reduces significantly, while the propagating waves are longer and the synchronous bursting is suppressed. Spike timings used for analysis in this figure is provided as the supplemental data in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122225#pone.0122225.s009\" target=\"_blank\">S9</a>–<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122225#pone.0122225.s012\" target=\"_blank\">S12</a> Dataset.</p>", "links"=>[], "tags"=>["network transitions", "noise level", "metric", "network topology", "function", "network activity", "time window", "Autonomous Network Transitions"], "article_id"=>1373457, "categories"=>["Uncategorised"], "users"=>["Sima Mofakham", "Michal Zochowski"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122225.g003", "stats"=>{"downloads"=>3, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Interaction_of_excitatory_and_inhibitory_networks_for_varying_inhibitory_connectivity_in_networks_with_deterministic_dynamics_/1373457", "title"=>"Interaction of excitatory and inhibitory networks for varying inhibitory connectivity in networks with deterministic dynamics.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-09 04:01:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2012606"], "description"=>"<p>(A, B) An example of raster plot and cumulative network activity pattern for a system composed of 200 excitatory neurons, transitioning from asynchronous to synchronous dynamics. (C) Example of voltage traces of two pairs of neurons ([10, 11],[90,91]) where neurons in each pair are neighbors but the pairs are distant from each other.</p>", "links"=>[], "tags"=>["network transitions", "noise level", "metric", "network topology", "function", "network activity", "time window", "Autonomous Network Transitions"], "article_id"=>1373455, "categories"=>["Uncategorised"], "users"=>["Sima Mofakham", "Michal Zochowski"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122225.g002", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Network_activity_and_individual_neurons_8217_voltage_profiles_before_and_after_transition_into_synchronous_dynamics_/1373455", "title"=>"Network activity and individual neurons’ voltage profiles before and after transition into synchronous dynamics.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-09 04:01:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2012619"], "description"=>"<p>(A) Excitatory networks (P<sub>e</sub> = 0.15); (B) excitatory and inhibitory networks (P<sub>e</sub> = 0.15, P<sub>i</sub> = 0.0). Solid lines denote simulations in which all neurons receive identical external current (I<sub>e</sub> = 1.05, I<sub>i</sub> = 0.95), while dashed-lines are representing simulations with distribution of external currents (I<sub>e</sub> = 0.95–1.15, I<sub>i</sub> = 0.9–1.0).</p>", "links"=>[], "tags"=>["network transitions", "noise level", "metric", "network topology", "function", "network activity", "time window", "Autonomous Network Transitions"], "article_id"=>1373468, "categories"=>["Uncategorised"], "users"=>["Sima Mofakham", "Michal Zochowski"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122225.g010", "stats"=>{"downloads"=>3, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_effect_of_noise_on_the_lead_time_of_the_transitions_for_both_excitatory_and_interacting_excitatory_inhibitory_networks_/1373468", "title"=>"The effect of noise on the lead-time of the transitions for both excitatory and interacting excitatory-inhibitory networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-09 04:01:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2012614"], "description"=>"<p>The synchronous fraction of dynamics as a function of inhibitory connectivity when excitatory connectivity is in small-world regime (P<sub>e</sub> = 0.15), for: 1) noise driven identical neurons (f<sub>N</sub> = 0.00005, I<sub>e</sub> = 1.05,I<sub>i</sub> = 0.95; blue line), 2) non-identical neurons (f<sub>N</sub> = 0.00005, I<sub>e</sub> = 0.95–1.15, I<sub>i</sub> = 0.9–1.0; green line), and 3) deterministic dynamics of non-identical neurons (no noise, I<sub>e</sub> = 0.95–1.15, I<sub>i</sub> = 0.9–1.0; red line).</p>", "links"=>[], "tags"=>["network transitions", "noise level", "metric", "network topology", "function", "network activity", "time window", "Autonomous Network Transitions"], "article_id"=>1373463, "categories"=>["Uncategorised"], "users"=>["Sima Mofakham", "Michal Zochowski"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122225.g007", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Fraction_of_time_that_the_network_spent_in_the_synchronous_regime_/1373463", "title"=>"Fraction of time that the network spent in the synchronous regime.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-09 04:01:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2012613"], "description"=>"<p>(A) Fraction of time network adopts synchronous dynamics as a function of rewiring parameter for noise driven identical (I<sup>e</sup><sub>ext</sub> = 1.05 for all neurons f<sub>N</sub> = 0.00005, blue line), non-identical (I<sup>e</sup><sub>ext</sub> = 0.95–1.15, f<sub>N</sub> = 0.00005, green line) and deterministic dynamics for network of non-identical cells (I<sup>e</sup><sub>ext</sub> = 0.95–1.15, red line). (B) The effect of the increasing noise level on the dynamics for the excitatory-only network with P<sub>e</sub> = 0.15 for noise driven identical (I<sup>e</sup><sub>ext</sub> = 1.05 for all neurons, blue line) and non-identical (I<sup>e</sup><sub>ext</sub> = 0.95–1.15, green line).</p>", "links"=>[], "tags"=>["network transitions", "noise level", "metric", "network topology", "function", "network activity", "time window", "Autonomous Network Transitions"], "article_id"=>1373462, "categories"=>["Uncategorised"], "users"=>["Sima Mofakham", "Michal Zochowski"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122225.g006", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Characterizing_the_effect_of_noise_level_and_connectivity_structure_on_the_dynamics_of_excitatory_network_/1373462", "title"=>"Characterizing the effect of noise level and connectivity structure on the dynamics of excitatory network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-09 04:01:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2012651", "https://ndownloader.figshare.com/files/2012652", "https://ndownloader.figshare.com/files/2012653", "https://ndownloader.figshare.com/files/2012654", "https://ndownloader.figshare.com/files/2012655", "https://ndownloader.figshare.com/files/2012656", "https://ndownloader.figshare.com/files/2012657", "https://ndownloader.figshare.com/files/2012658", "https://ndownloader.figshare.com/files/2012659", "https://ndownloader.figshare.com/files/2012660", "https://ndownloader.figshare.com/files/2012661", "https://ndownloader.figshare.com/files/2012662"], "description"=>"<div><p>Understanding spontaneous transitions between dynamical modes in a network is of significant importance. These transitions may separate pathological and normal functions of the brain. In this paper, we develop a set of measures that, based on spatio-temporal features of network activity, predict autonomous network transitions from asynchronous to synchronous dynamics under various conditions. These metrics quantify spike-timing distributions within a narrow time window as a function of the relative location of the active neurons. We applied these metrics to investigate the properties of these transitions in excitatory-only and excitatory-and-inhibitory networks and elucidate how network topology, noise level, and cellular heterogeneity affect both the reliability and the timeliness of the predictions. The developed measures can be calculated in real time and therefore potentially applied in clinical situations.</p></div>", "links"=>[], "tags"=>["network transitions", "noise level", "metric", "network topology", "function", "network activity", "time window", "Autonomous Network Transitions"], "article_id"=>1373491, "categories"=>["Uncategorised"], "users"=>["Sima Mofakham", "Michal Zochowski"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0122225.s001", "https://dx.doi.org/10.1371/journal.pone.0122225.s002", "https://dx.doi.org/10.1371/journal.pone.0122225.s003", "https://dx.doi.org/10.1371/journal.pone.0122225.s004", "https://dx.doi.org/10.1371/journal.pone.0122225.s005", "https://dx.doi.org/10.1371/journal.pone.0122225.s006", "https://dx.doi.org/10.1371/journal.pone.0122225.s007", "https://dx.doi.org/10.1371/journal.pone.0122225.s008", "https://dx.doi.org/10.1371/journal.pone.0122225.s009", "https://dx.doi.org/10.1371/journal.pone.0122225.s010", "https://dx.doi.org/10.1371/journal.pone.0122225.s011", "https://dx.doi.org/10.1371/journal.pone.0122225.s012"], "stats"=>{"downloads"=>102, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Measuring_Predictability_of_Autonomous_Network_Transitions_into_Bursting_Dynamics_/1373491", "title"=>"Measuring Predictability of Autonomous Network Transitions into Bursting Dynamics", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-04-09 04:01:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2012609"], "description"=>"<p>(A) To characterize instantaneous spatial patterning in the network, we calculate the minimum time interval between each neuron’s spike in the time-window (blue filled circles) with all other neurons’ spikes and sort these timings based on their spatial distance. The left side of panel A shows the calculation for the a time window that has a asynchronous activity with relatively large and highly variable time intervals; the right side panel depicts calculation for the time window with a synchronous activity and minimal time differences between the spikes. (B) An example of raster plot obtained from noise driven excitatory only network P<sub>E</sub> = 0.15, noise frequency f = 0.00005 (C) Color plot of consecutive T<sub>D</sub> calculations; colors indicate the closest timing between spikes of neurons in the given window with all other neurons in the network. (D) Spatial derivative of T<sub>D</sub> (dT<sub>D</sub>) in each time window. (E) Mean of T<sub>D</sub> for consecutive time windows (to which we refer as T<sub>M</sub>). The dotted line is a cutoff, which we will use to identify the initiation of the bursting dynamics.</p>", "links"=>[], "tags"=>["network transitions", "noise level", "metric", "network topology", "function", "network activity", "time window", "Autonomous Network Transitions"], "article_id"=>1373458, "categories"=>["Uncategorised"], "users"=>["Sima Mofakham", "Michal Zochowski"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122225.g004", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Characterization_of_the_spatio_temporal_dynamics_of_the_network_/1373458", "title"=>"Characterization of the spatio-temporal dynamics of the network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-09 04:01:59"}

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{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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