Oscillation, Conduction Delays, and Learning Cooperate to Establish Neural Competition in Recurrent Networks
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{"title"=>"Oscillation, conduction delays, and learning cooperate to establish neural competition in recurrent networks", "type"=>"journal", "authors"=>[{"first_name"=>"Hideyuki", "last_name"=>"Kato", "scopus_author_id"=>"57131816600"}, {"first_name"=>"Tohru", "last_name"=>"Ikeguchi", "scopus_author_id"=>"7003664912"}], "year"=>2016, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84959036299", "sgr"=>"84959036299", "doi"=>"10.1371/journal.pone.0146044", "pui"=>"608396010", "issn"=>"19326203"}, "id"=>"2a0a319f-cd87-34e4-b5b9-063891065c5e", "abstract"=>"Specific memory might be stored in a subnetwork consisting of a small population of neu- rons. To select neurons involved in memory formation, neural competition might be essen- tial. In this paper, we show that excitable neurons are competitive and organize into two assemblies in a recurrent network with spike timing-dependent synaptic plasticity (STDP) and axonal conduction delays. Neural competition is established by the cooperation of spontaneously induced neural oscillation, axonal conduction delays, and STDP. We also suggest that the competition mechanism in this paper is one of the basic functions required to organize memory-storing subnetworks into fine-scale cortical networks.", "link"=>"http://www.mendeley.com/research/oscillation-conduction-delays-learning-cooperate-establish-neural-competition-recurrent-networks", "reader_count"=>11, "reader_count_by_academic_status"=>{"Researcher"=>3, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>6, "Student > Bachelor"=>1}, "reader_count_by_user_role"=>{"Researcher"=>3, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>6, "Student > Bachelor"=>1}, "reader_count_by_subject_area"=>{"Agricultural and Biological Sciences"=>3, "Neuroscience"=>5, "Psychology"=>1, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Neuroscience"=>{"Neuroscience"=>5}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>3}, "Computer Science"=>{"Computer Science"=>2}}, "reader_count_by_country"=>{"United States"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/4260226"], "description"=>"<p>(A) Schematic diagram of assortativity and disassortativity that are evaluated using the instrength- and the outstrength-correlation coefficient. Positive (negative) values of the coefficients represent assortativity (disassortativity) of networks, and zero corresponds to random networks. (B), (C) The time traces of the instrength- and the outstrength-correlation coefficient during STDP with external input firing rates of 1 spk/s (gray line), 10 spk/s (red line), and 40 spk/s (blue line). The coefficients are computed at every second.</p>", "links"=>[], "tags"=>["Neural Competition", "excitable neurons", "competition mechanism", "memory formation", "axonal conduction delays", "Recurrent Networks", "Learning Cooperate", "Conduction Delays", "STDP", "Neural competition", "subnetwork"], "article_id"=>2610403, "categories"=>["Cell Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Developmental Biology", "Infectious Diseases"], "users"=>["Hideyuki Kato", "Tohru Ikeguchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0146044.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Connectivity_of_winner_and_loser_neurons_/2610403", "title"=>"Connectivity of winner and loser neurons.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-03 23:08:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/4260262"], "description"=>"<p>Same as <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146044#pone.0146044.g006\" target=\"_blank\">Fig 6</a> but when the external input rate is 1 spk/s.</p>", "links"=>[], "tags"=>["Neural Competition", "excitable neurons", "competition mechanism", "memory formation", "axonal conduction delays", "Recurrent Networks", "Learning Cooperate", "Conduction Delays", "STDP", "Neural competition", "subnetwork"], "article_id"=>2610439, "categories"=>["Cell Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Developmental Biology", "Infectious Diseases"], "users"=>["Hideyuki Kato", "Tohru Ikeguchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0146044.g007", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Same_as_Fig_6_but_when_the_external_input_rate_is_1_spk_s_/2610439", "title"=>"Same as Fig 6 but when the external input rate is 1 spk/s.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-03 23:08:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/4260232"], "description"=>"<p>Scattergram (A) shows the number of excitatory presynaptic neurons versus the instrength after learning at <i>t</i> = 3,600 s (<i>τ</i><sub><i>K</i></sub> = 0.18), and (B) shows the number of inhibitory presynaptic neurons versus the instrength after learning at <i>t</i> = 3,600 s (<i>τ</i><sub><i>K</i></sub> = − 0.3).</p>", "links"=>[], "tags"=>["Neural Competition", "excitable neurons", "competition mechanism", "memory formation", "axonal conduction delays", "Recurrent Networks", "Learning Cooperate", "Conduction Delays", "STDP", "Neural competition", "subnetwork"], "article_id"=>2610409, "categories"=>["Cell Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Developmental Biology", "Infectious Diseases"], "users"=>["Hideyuki Kato", "Tohru Ikeguchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0146044.g004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Influence_of_the_initial_conditions_on_the_neuronal_competition_/2610409", "title"=>"Influence of the initial conditions on the neuronal competition.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-03 23:08:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/4260265"], "description"=>"<p>(A) The lower panel is the time courses of the membrane potential of (red) a winner and (blue) a loser neuron. The upper panel is the average firing rates of (black) all excitatory neurons, (red) presynaptic terminals of the winner neuron, and (blue) presynaptic terminals of the loser neuron. These average firing rages are estimated by <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146044#pone.0146044.e019\" target=\"_blank\">Eq (11)</a>. For the black line, timings of somatic firings are used. The red and the blue line are estimated with firing timings of presynaptic terminals, at which delay lengths are added to somatic firing timings of presynaptic neurons of the winner and the loser neuron. The winner and the loser neuron are randomly picked up from the identified groups at <i>t</i> = 3,600 s. (B) Schematic of a phase difference of the presynaptic oscillations from the global oscillation. The black, red and blue lines represent the mean firing rate of all excitatory neurons, the presynaptic terminals of the winner neuron, and the presynaptic terminals of the loser neuron, respectively. (C) Phase distributions of the local maxima in presynaptic terminal firing rate for the winner (red) and the loser (blue) neurons. The local maxima for the presynaptic firing rates are characterized by estimated phases relative to phases of a global firing rate of the excitatory neurons with <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146044#pone.0146044.e018\" target=\"_blank\">Eq (10)</a>. The data for 45 s (from 5 s to 50 s) is used for the estimation. Results are however not different when using data after <i>t</i> = 50 s. Watson’s <i>U</i><sup>2</sup>-test is used to test for significant differences in the distribution pattern between the winner and the loser neurons. This is the non-parametric test for phase data used to indicate any significant differences in the mean value or the variance (<i>P</i> < 0.001). (D) Schematic of the phase difference between the mean firing rate of presynaptic terminals and a postsynaptic firing in a winner neuron (left) and a loser neuron (right). The dashed and the solid lines represent the mean firing rate of presynaptic terminals and the postsynaptic potential, respectively. (E) Same as (C), but the local maxima of the presynaptic firing rates are replaced by the spikes of the winner (red) and the loser (blue) neuron firing rates (<i>P</i> < 0.001).</p>", "links"=>[], "tags"=>["Neural Competition", "excitable neurons", "competition mechanism", "memory formation", "axonal conduction delays", "Recurrent Networks", "Learning Cooperate", "Conduction Delays", "STDP", "Neural competition", "subnetwork"], "article_id"=>2610442, "categories"=>["Cell Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Developmental Biology", "Infectious Diseases"], "users"=>["Hideyuki Kato", "Tohru Ikeguchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0146044.g008", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Mechanisms_of_emergence_of_winner_and_loser_neurons_in_the_neural_network_/2610442", "title"=>"Mechanisms of emergence of winner and loser neurons in the neural network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-03 23:08:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/4260301"], "description"=>"<p>The upper bound of synaptic weights is (A) <i>w</i><sub><i>max</i></sub> = 7, (B) <i>w</i><sub><i>max</i></sub> = 15, and (C) <i>w</i><sub><i>max</i></sub> = 20. The other parameters are the same as those in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146044#pone.0146044.g001\" target=\"_blank\">Fig 1B</a>. The instrength- and outstrength-correlation coefficients are (A) 0.002 and −0.006, (B) 0.13 and 0.05, and (C) 0.3 and 0.2, respectively. All the results are obtained from the networks at <i>t</i> = 3,600 s.</p>", "links"=>[], "tags"=>["Neural Competition", "excitable neurons", "competition mechanism", "memory formation", "axonal conduction delays", "Recurrent Networks", "Learning Cooperate", "Conduction Delays", "STDP", "Neural competition", "subnetwork"], "article_id"=>2610478, "categories"=>["Cell Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Developmental Biology", "Infectious Diseases"], "users"=>["Hideyuki Kato", "Tohru Ikeguchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0146044.g010", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Influence_of_the_upper_bound_of_synaptic_weights_on_the_organized_networks_/2610478", "title"=>"Influence of the upper bound of synaptic weights on the organized networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-03 23:08:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/4260211"], "description"=>"<p>(A)–(C) Histograms of plastic synaptic weights. (D)–(F) Joint strength distribution matrices (JSDMs) of excitatory neurons. The mean firing rates of the external inputs are (A), (D) 1 spk/s, (B), (E) 10 spk/s, and (C), (F) 40 spk/s. In the JSDMs, the colors represent the frequency of the excitatory neurons. In (E), the schematics of the winner and the loser neurons are illustrated. The winner neurons obtain many strong incoming connections but their many outgoing connections are weak. The loser neurons have properties opposite to those in winner neurons. All the results in this figure are generated from the networks at <i>t</i> = 3,600 s.</p>", "links"=>[], "tags"=>["Neural Competition", "excitable neurons", "competition mechanism", "memory formation", "axonal conduction delays", "Recurrent Networks", "Learning Cooperate", "Conduction Delays", "STDP", "Neural competition", "subnetwork"], "article_id"=>2610388, "categories"=>["Cell Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Developmental Biology", "Infectious Diseases"], "users"=>["Hideyuki Kato", "Tohru Ikeguchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0146044.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Neuronal_competition_in_the_neural_network_induced_by_STDP_/2610388", "title"=>"Neuronal competition in the neural network induced by STDP.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-03 23:08:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/4260244"], "description"=>"<p>(A) The conduction delay distribution in excitatory presynaptic connections of a neuron. The value in each bin is averaged over all the winner neurons or all the loser neurons. (B) The average synaptic weight of each conduction delay. Red and blue bars represent winner and loser neurons, respectively. The data are generated from the network at <i>t</i> = 3,600 s.</p>", "links"=>[], "tags"=>["Neural Competition", "excitable neurons", "competition mechanism", "memory formation", "axonal conduction delays", "Recurrent Networks", "Learning Cooperate", "Conduction Delays", "STDP", "Neural competition", "subnetwork"], "article_id"=>2610421, "categories"=>["Cell Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Developmental Biology", "Infectious Diseases"], "users"=>["Hideyuki Kato", "Tohru Ikeguchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0146044.g005", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Statistics_of_conduction_delays_of_synaptic_connections_to_the_winner_and_the_loser_neurons_and_their_relation_with_the_plastic_synaptic_weights_/2610421", "title"=>"Statistics of conduction delays of synaptic connections to the winner and the loser neurons and their relation with the plastic synaptic weights.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-03 23:08:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/4260214"], "description"=>"<p>The red line indicates the number of neurons that move from the winner assembly to the loser assembly. The winner and the loser group are identified at each second. The blue line is the same as the red one, but progresses from loser to winner status.</p>", "links"=>[], "tags"=>["Neural Competition", "excitable neurons", "competition mechanism", "memory formation", "axonal conduction delays", "Recurrent Networks", "Learning Cooperate", "Conduction Delays", "STDP", "Neural competition", "subnetwork"], "article_id"=>2610391, "categories"=>["Cell Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Developmental Biology", "Infectious Diseases"], "users"=>["Hideyuki Kato", "Tohru Ikeguchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0146044.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Stability_of_the_neural_competition_in_the_STDP_network_/2610391", "title"=>"Stability of the neural competition in the STDP network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-03 23:08:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/4260253"], "description"=>"<p>Rastergrams and the average firing rates of all excitatory (light blue) and all inhibitory (dark red) neurons at (A) <i>t</i> = 0 to 0.5 s, (B) 3 to 3.5 s, and (C) 5 to 5.5 s. The lower panel of (C) is the enlargement of the selected area 5–5.2 s of the upper panel. Two black arrows represent the peaks of the firing rates of excitatory and inhibitory neurons. The average firing rates are estimated by <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146044#pone.0146044.e019\" target=\"_blank\">Eq (11)</a>. (D) Time course of the mean synaptic weight of the plastic synapses. (E) Power spectra of the firing rates of excitatory (upper) and inhibitory (lower) neurons. The colors indicate the normalized power intensity.</p>", "links"=>[], "tags"=>["Neural Competition", "excitable neurons", "competition mechanism", "memory formation", "axonal conduction delays", "Recurrent Networks", "Learning Cooperate", "Conduction Delays", "STDP", "Neural competition", "subnetwork"], "article_id"=>2610427, "categories"=>["Cell Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Developmental Biology", "Infectious Diseases"], "users"=>["Hideyuki Kato", "Tohru Ikeguchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0146044.g006", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Neuronal_activity_and_the_mean_synaptic_weight_in_the_network_for_10_spk_s_external_input_/2610427", "title"=>"Neuronal activity and the mean synaptic weight in the network for 10 spk/s-external input.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-03 23:08:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/4260283"], "description"=>"<p>The firing rates of the external inputs are (A) 1 spk/s, (B) 10 spk/s, and (C) 40 spk/s. Colors correspond to the values of the instrength- (upper) and the outstrength- (lower) correlation coefficient. We plotted the instrength- and the outstrength-correlation coefficients of the neural network at <i>t</i> = 3,600 s for all the parameters.</p>", "links"=>[], "tags"=>["Neural Competition", "excitable neurons", "competition mechanism", "memory formation", "axonal conduction delays", "Recurrent Networks", "Learning Cooperate", "Conduction Delays", "STDP", "Neural competition", "subnetwork"], "article_id"=>2610460, "categories"=>["Cell Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Developmental Biology", "Infectious Diseases"], "users"=>["Hideyuki Kato", "Tohru Ikeguchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0146044.g009", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Influence_of_the_balance_between_LTD_and_LTP_and_inhibition_level_on_the_organized_networks_/2610460", "title"=>"Influence of the balance between LTD and LTP and inhibition level on the organized networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-03 23:08:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/4260181", "https://ndownloader.figshare.com/files/4260187", "https://ndownloader.figshare.com/files/4260190", "https://ndownloader.figshare.com/files/4260193"], "description"=>"<div><p>Specific memory might be stored in a subnetwork consisting of a small population of neurons. To select neurons involved in memory formation, neural competition might be essential. In this paper, we show that excitable neurons are competitive and organize into two assemblies in a recurrent network with spike timing-dependent synaptic plasticity (STDP) and axonal conduction delays. Neural competition is established by the cooperation of spontaneously induced neural oscillation, axonal conduction delays, and STDP. We also suggest that the competition mechanism in this paper is one of the basic functions required to organize memory-storing subnetworks into fine-scale cortical networks.</p></div>", "links"=>[], "tags"=>["Neural Competition", "excitable neurons", "competition mechanism", "memory formation", "axonal conduction delays", "Recurrent Networks", "Learning Cooperate", "Conduction Delays", "STDP", "Neural competition", "subnetwork"], "article_id"=>2610367, "categories"=>["Cell Biology", "Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Developmental Biology", "Infectious Diseases"], "users"=>["Hideyuki Kato", "Tohru Ikeguchi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0146044.s001", "https://dx.doi.org/10.1371/journal.pone.0146044.s002", "https://dx.doi.org/10.1371/journal.pone.0146044.s003", "https://dx.doi.org/10.1371/journal.pone.0146044.s004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Oscillation_Conduction_Delays_and_Learning_Cooperate_to_Establish_Neural_Competition_in_Recurrent_Networks/2610367", "title"=>"Oscillation, Conduction Delays, and Learning Cooperate to Establish Neural Competition in Recurrent Networks", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2016-02-03 23:08:47"}

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{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
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