Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity
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{"title"=>"Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity", "type"=>"journal", "authors"=>[{"first_name"=>"Sadra", "last_name"=>"Sadeh", "scopus_author_id"=>"55928013100"}, {"first_name"=>"Claudia", "last_name"=>"Clopath", "scopus_author_id"=>"16241056400"}, {"first_name"=>"Stefan", "last_name"=>"Rotter", "scopus_author_id"=>"7004581948"}], "year"=>2015, "source"=>"PLoS Computational Biology", "identifiers"=>{"doi"=>"10.1371/journal.pcbi.1004307", "sgr"=>"84953337491", "pui"=>"607497643", "scopus"=>"2-s2.0-84953337491", "issn"=>"15537358", "pmid"=>"26090844"}, "id"=>"9e377f93-8386-3123-adb1-cc802d0dfb73", "abstract"=>"In rodent visual cortex, synaptic connections between orientation-selective neurons are unspecific at the time of eye opening, and become to some degree functionally specific only later during development. An explanation for this two-stage process was proposed in terms of Hebbian plasticity based on visual experience that would eventually enhance connections between neurons with similar response features. For this to work, however, two conditions must be satisfied: First, orientation selective neuronal responses must exist before specific recurrent synaptic connections can be established. Second, Hebbian learning must be compatible with the recurrent network dynamics contributing to orientation selectivity, and the resulting specific connectivity must remain stable for unspecific background activity. Previous studies have mainly focused on very simple models, where the receptive fields of neurons were essentially determined by feedforward mechanisms, and where the recurrent network was small, lacking the complex recurrent dynamics of large-scale networks of excitatory and inhibitory neurons. Here we studied the emergence of functionally specific connectivity in large-scale recurrent networks with synaptic plasticity. Our results show that balanced random networks, which already exhibit highly selective responses at eye opening, can develop feature-specific connectivity if appropriate rules of synaptic plasticity are invoked within and between excitatory and inhibitory populations. If these conditions are met, the initial orientation selectivity guides the process of Hebbian learning and, as a result, functionally specific and a surplus of bidirectional connections emerge. Our results thus demonstrate the cooperation of synaptic plasticity and recurrent dynamics in large-scale functional networks with realistic receptive fields, highlight the role of inhibition as a critical element in this process, and paves the road for further computational studies of sensory processing in neocortical network models equipped with synaptic plasticity.", "link"=>"http://www.mendeley.com/research/emergence-functional-specificity-balanced-networks-synaptic-plasticity", "reader_count"=>106, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>5, "Researcher"=>24, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>39, "Student > Postgraduate"=>5, "Student > Master"=>17, "Other"=>2, "Student > Bachelor"=>3, "Lecturer > Senior Lecturer"=>3, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>5, "Researcher"=>24, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>39, "Student > Postgraduate"=>5, "Student > Master"=>17, "Other"=>2, "Student > Bachelor"=>3, "Lecturer > Senior Lecturer"=>3, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Engineering"=>3, "Biochemistry, Genetics and Molecular Biology"=>2, "Mathematics"=>3, "Agricultural and Biological Sciences"=>28, "Medicine and Dentistry"=>3, "Neuroscience"=>28, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>12, "Psychology"=>4, "Computer Science"=>17}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Neuroscience"=>{"Neuroscience"=>28}, "Physics and Astronomy"=>{"Physics and Astronomy"=>12}, "Psychology"=>{"Psychology"=>4}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>28}, "Computer Science"=>{"Computer Science"=>17}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>5}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"United States"=>2, "China"=>1, "United Kingdom"=>4, "France"=>2, "Belarus"=>1, "Portugal"=>1, "Switzerland"=>1, "Germany"=>6}, "group_count"=>6}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2127676"], "description"=>"<p>Default parameters are given, and only the exceptions are noted for figures with parameters different from the default values.</p>", "links"=>[], "tags"=>["neocortical network models", "neuron", "dynamic", "eye opening", "synaptic connections", "connectivity", "unspecific background activity", "response", "orientation selectivity guides", "hebbian", "Synaptic plasticity"], "article_id"=>1455565, "categories"=>["Uncategorised"], "users"=>["Sadra Sadeh", "Claudia Clopath", "Stefan Rotter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004307.t001", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Table_of_parameters_/1455565", "title"=>"Table of parameters.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-06-19 04:11:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/2127675"], "description"=>"<p><b>(A, B)</b> The robustness of the results in the default configuration of the network is studied by decreasing (A) or increasing (B) 5 parameters of plasticity by 10%. The parameters are <i>A</i><sub>LTD</sub>, <i>A</i><sub>LTP</sub>, <i>θ</i><sub>−</sub>, <i>θ</i><sub>+</sub> and <math><mrow><msubsup><mi>u</mi><mrow><mi>r</mi><mi>e</mi><mi>f</mi></mrow><mn>2</mn></msubsup></mrow></math>, respectively. The learning phase for each network is again organized in 20 batches. <b>(C)</b> Selectivity of the final weights is quantified (blue and red; similar to <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004307#pcbi.1004307.g009\" target=\"_blank\">Fig 9</a>) and compared with the selectivity in the default case (black).</p>", "links"=>[], "tags"=>["neocortical network models", "neuron", "dynamic", "eye opening", "synaptic connections", "connectivity", "unspecific background activity", "response", "orientation selectivity guides", "hebbian", "Synaptic plasticity"], "article_id"=>1455564, "categories"=>["Uncategorised"], "users"=>["Sadra Sadeh", "Claudia Clopath", "Stefan Rotter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004307.g010", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Robustness_of_the_results_to_changes_in_the_plasticity_parameters_/1455564", "title"=>"Robustness of the results to changes in the plasticity parameters.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-19 04:11:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/2127637"], "description"=>"<p>(<b>A</b>) In random networks of excitatory (E; triangles) and inhibitory (I; circles) neurons, synaptic connections are established disregarding the stimulus selectivities (preferred orientation) of pre- and post-synaptic neurons. (<b>B</b>) In specific networks, synapses between neurons of similar preferred orientations are stronger, while dissimilar feature selectivity of pre- and post-synaptic neurons imply weaker synapses between them. (<b>C–F</b>) Stimulus-induced response of a network before, during and after learning. The middle of each panel shows the raster plot of two seconds of stimulation. Spikes of excitatory and inhibitory neurons are displayed in red and blue, respectively. Within each population, neurons are sorted according to the preferred orientations of their weakly tuned inputs. Average firing rates of individual neurons during the period of stimulation are shown in the histogram on the right. The average firing rate of each subpopulation is indicated to the right of it, in the corresponding color. The lower panel depicts the time-resolved histograms of population activity for excitatory (red) and inhibitory (blue) neurons, respectively. Population spike counts are extracted from bins of size 10 ms. The colored bar at the bottom of the main panel shows the sequence of stimulus orientations applied during the simulation (color code is indicated between panels (D) and (E)). For the simulations before (C) and after (F) learning, the initial or final weights are frozen, respectively, and network activity is simulated with static weights in response to a stimulus of orientation 90°. During learning, a network with plastic synapses is stimulated with 40 batches of oriented bar stimuli. Each batch consists of a random sequence of 20 different stimulus orientations, each stimulus lasting for 100 ms. Therefore, the “visual experience” lasts 20 × 20 × 0.1 s = 40 s in total. The responses to the first and the last batch are shown in (D) and (E), respectively.</p>", "links"=>[], "tags"=>["neocortical network models", "neuron", "dynamic", "eye opening", "synaptic connections", "connectivity", "unspecific background activity", "response", "orientation selectivity guides", "hebbian", "Synaptic plasticity"], "article_id"=>1455530, "categories"=>["Uncategorised"], "users"=>["Sadra Sadeh", "Claudia Clopath", "Stefan Rotter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004307.g001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulating_the_effect_of_synaptic_plasticity_in_balanced_random_networks_/1455530", "title"=>"Simulating the effect of synaptic plasticity in balanced random networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-19 04:11:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/2127648"], "description"=>"<p><b>(A)</b> The membrane potential trace of the same excitatory neuron shown in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004307#pcbi.1004307.g002\" target=\"_blank\">Fig 2A</a> in response to its preferred orientation is depicted here for 500 ms. The black trace is its actual membrane potential, with the thin vertical lines denoting spikes when the membrane potential crosses the spiking threshold (the dashed line). The red and the blue traces reflect the excitatory and inhibitory components of the free membrane potential (by not allowing the neuron to spike), respectively. <b>(B)</b> Temporal average of the membrane potential, ⟨<i>u</i>⟩, over two seconds of stimulation is extracted for each stimulus orientation and plotted as a tuning curve. The tuning curve of the free membrane potential, <i>u</i><sub>free</sub>, is also computed, by correcting for the total reset voltage induced as a result of spiking, i.e. <i>u</i><sub>free</sub> = <i>u</i> + <i>τ</i><sub><i>m</i></sub><i>u</i><sub>th</sub><i>r</i>, where <i>r</i> is the mean firing rate of the neuron. <b>(C)</b> The tuning curves introduced in (B) for a single cell are now computed for the whole network, by aligning the individual tuning curves and computing the mean and standard deviation of values in each 180 discrete bins (similar to <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004307#pcbi.1004307.g002\" target=\"_blank\">Fig 2C</a>). <b>(D–F)</b> Same as (A–C), respectively, for the network after learning.</p>", "links"=>[], "tags"=>["neocortical network models", "neuron", "dynamic", "eye opening", "synaptic connections", "connectivity", "unspecific background activity", "response", "orientation selectivity guides", "hebbian", "Synaptic plasticity"], "article_id"=>1455541, "categories"=>["Uncategorised"], "users"=>["Sadra Sadeh", "Claudia Clopath", "Stefan Rotter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004307.g004", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Balanced_activity_regime_before_and_after_learning_/1455541", "title"=>"Balanced activity regime before and after learning.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-19 04:11:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/2127638"], "description"=>"<p><b>(A)</b> Tuning curves of a sample excitatory neuron from the network before and after learning. The mean firing rate of a neuron for different stimulus orientations was extracted from simulations of network activity for two seconds, as explained for Fig <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004307#pcbi.1004307.g001\" target=\"_blank\">1C</a> and <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004307#pcbi.1004307.g001\" target=\"_blank\">1F</a>. This was repeated 10 times for each orientation, and the mean and standard deviation over trials of the firing rates are depicted by solid lines and corresponding shadings, respectively. <b>(B)</b> Same as (A) for a sample inhibitory neuron. <b>(C)</b> All tuning curves of excitatory neurons, similar to the tuning curve in (A), were aligned to the preferred orientation of the input, and the average tuning curves (over 180 bins) before and after learning were computed from them. The solid line indicates the mean value in each bin, and the shading is ± one standard deviation. The orientation selectivity index indicated in the plot (OSI, see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004307#sec011\" target=\"_blank\">Materials and Methods</a>) was computed from the average tuning curves, respectively. <b>(D)</b> Same as (C) for the inhibitory population. <b>(E)</b> The OSI was computed for each individual tuning curve, and its distribution for the entire excitatory population is shown for a network before (solid) and after (outlined) learning. The average OSI in the network is indicated for each case. <b>(F)</b> Same as (E), for the inhibitory population.</p>", "links"=>[], "tags"=>["neocortical network models", "neuron", "dynamic", "eye opening", "synaptic connections", "connectivity", "unspecific background activity", "response", "orientation selectivity guides", "hebbian", "Synaptic plasticity"], "article_id"=>1455531, "categories"=>["Uncategorised"], "users"=>["Sadra Sadeh", "Claudia Clopath", "Stefan Rotter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004307.g002", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Orientation_selectivity_before_and_after_learning_/1455531", "title"=>"Orientation selectivity before and after learning.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-19 04:11:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/2127664"], "description"=>"<p><b>(A)</b> Sensitivity of the final weights to an over-representation of certain stimulus orientations. When the stimulation is repeated with cardinal orientations being more frequent than others (0° and 90° stimuli represent 50% of all stimuli), the final weights also reflect this over-representation of the corresponding subnetworks. <b>(B)</b> Total weight change of each presynaptic neuron is plotted vs. its initial preferred orientation (PO). Specifically for the excitatory population, neurons with POs close to 0° and 90° show the highest increase in their synaptic weights.</p>", "links"=>[], "tags"=>["neocortical network models", "neuron", "dynamic", "eye opening", "synaptic connections", "connectivity", "unspecific background activity", "response", "orientation selectivity guides", "hebbian", "Synaptic plasticity"], "article_id"=>1455555, "categories"=>["Uncategorised"], "users"=>["Sadra Sadeh", "Claudia Clopath", "Stefan Rotter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004307.g007", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Stimulus_statistics_is_reflected_in_the_learned_weights_/1455555", "title"=>"Stimulus statistics is reflected in the learned weights.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-19 04:11:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/2127674"], "description"=>"<p><b>(A)</b> Initial (before learning) response selectivity for networks with different amounts of inhibition dominance, <i>g</i>. <b>(B)</b> Each network is simulated for 20 batches of learning, and the final selectivity of E → E weights is illustrated by plotting the weights vs. the difference in the PO of pre- and post-synaptic neurons (pre-post dPO). <b>(C)</b> The selectivity of the initial network tuning curve (A) and the final weight tuning (B) is quantified by computing their OSI (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004307#sec011\" target=\"_blank\">Materials and Methods</a>) for each value of <i>g</i>. The more selective the initial orientation selectivity is, the more feature-specificity is observed in the final connectivity.</p>", "links"=>[], "tags"=>["neocortical network models", "neuron", "dynamic", "eye opening", "synaptic connections", "connectivity", "unspecific background activity", "response", "orientation selectivity guides", "hebbian", "Synaptic plasticity"], "article_id"=>1455563, "categories"=>["Uncategorised"], "users"=>["Sadra Sadeh", "Claudia Clopath", "Stefan Rotter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004307.g009", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relation_between_orientation_selectivity_and_feature_specific_connectivity_induced_by_learning_/1455563", "title"=>"Relation between orientation selectivity and feature-specific connectivity induced by learning.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-19 04:11:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/2127693", "https://ndownloader.figshare.com/files/2127694", "https://ndownloader.figshare.com/files/2127695", "https://ndownloader.figshare.com/files/2127696", "https://ndownloader.figshare.com/files/2127697"], "description"=>"<div><p>In rodent visual cortex, synaptic connections between orientation-selective neurons are unspecific at the time of eye opening, and become to some degree functionally specific only later during development. An explanation for this two-stage process was proposed in terms of Hebbian plasticity based on visual experience that would eventually enhance connections between neurons with similar response features. For this to work, however, two conditions must be satisfied: First, orientation selective neuronal responses must exist before specific recurrent synaptic connections can be established. Second, Hebbian learning must be compatible with the recurrent network dynamics contributing to orientation selectivity, and the resulting specific connectivity must remain stable for unspecific background activity. Previous studies have mainly focused on very simple models, where the receptive fields of neurons were essentially determined by feedforward mechanisms, and where the recurrent network was small, lacking the complex recurrent dynamics of large-scale networks of excitatory and inhibitory neurons. Here we studied the emergence of functionally specific connectivity in large-scale recurrent networks with synaptic plasticity. Our results show that balanced random networks, which already exhibit highly selective responses at eye opening, can develop feature-specific connectivity if appropriate rules of synaptic plasticity are invoked within and between excitatory and inhibitory populations. If these conditions are met, the initial orientation selectivity guides the process of Hebbian learning and, as a result, functionally specific and a surplus of bidirectional connections emerge. Our results thus demonstrate the cooperation of synaptic plasticity and recurrent dynamics in large-scale functional networks with realistic receptive fields, highlight the role of inhibition as a critical element in this process, and paves the road for further computational studies of sensory processing in neocortical network models equipped with synaptic plasticity.</p></div>", "links"=>[], "tags"=>["neocortical network models", "neuron", "dynamic", "eye opening", "synaptic connections", "connectivity", "unspecific background activity", "response", "orientation selectivity guides", "hebbian", "Synaptic plasticity"], "article_id"=>1455580, "categories"=>["Uncategorised"], "users"=>["Sadra Sadeh", "Claudia Clopath", "Stefan Rotter"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1004307.s001", "https://dx.doi.org/10.1371/journal.pcbi.1004307.s002", "https://dx.doi.org/10.1371/journal.pcbi.1004307.s003", "https://dx.doi.org/10.1371/journal.pcbi.1004307.s004", "https://dx.doi.org/10.1371/journal.pcbi.1004307.s005"], "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Emergence_of_Functional_Specificity_in_Balanced_Networks_with_Synaptic_Plasticity_/1455580", "title"=>"Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-06-19 04:11:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/2127652"], "description"=>"<p><b>(A)</b> Evolution of synaptic weights in the network during plasticity. After each batch of learning, weights are frozen and compared to the weights in the previous batch. The difference in the weights is averaged over populations, for all synapses, respectively. On average, weight changes decrease over batches, indicating the convergence of the learning process. The weights established under evoked activity are also stable for spontaneous activity, in absence of any specific stimulation (batch 41 and later, after the vertical dashed line). Here, the plasticity period is continued for extra 10 batches, where neurons are only receiving an untuned background input (with an input rate of <i>s</i><sub><i>b</i></sub>/2 and <i>μ</i> = 0, see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004307#sec011\" target=\"_blank\">Materials and Methods</a>). <b>(B)</b> Distribution of weights are shown separately for E → E, E → I and I → E weights, for evoked and spontaneous states, respectively. In each case, two distributions are shown, one sampling the beginning and one sampling the end of the corresponding learning phase. <b>(C)</b> Distribution of weights extracted at the end of each trial batch (every 2 s) is plotted for all plastic connections in a pseudo-color map. Note the logarithmic scale of the color code.</p>", "links"=>[], "tags"=>["neocortical network models", "neuron", "dynamic", "eye opening", "synaptic connections", "connectivity", "unspecific background activity", "response", "orientation selectivity guides", "hebbian", "Synaptic plasticity"], "article_id"=>1455545, "categories"=>["Uncategorised"], "users"=>["Sadra Sadeh", "Claudia Clopath", "Stefan Rotter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004307.g006", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Dynamics_and_stability_of_learned_weights_/1455545", "title"=>"Dynamics and stability of learned weights.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-19 04:11:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/2127672"], "description"=>"<p>The format of (A–D) is identical to <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004307#pcbi.1004307.g002\" target=\"_blank\">Fig 2</a>, (C–F), and (E–K) identical to <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004307#pcbi.1004307.g003\" target=\"_blank\">Fig 3</a>, respectively, for a network with initial random connectivity and strongly reduced inhibition dominance, <i>g</i>. Instead of <i>g</i> = 8, the inhibition-dominance ratio is now decreased to <i>g</i> = 4 for the network “before plasticity”. As before, the learning was run for 20 batches.</p>", "links"=>[], "tags"=>["neocortical network models", "neuron", "dynamic", "eye opening", "synaptic connections", "connectivity", "unspecific background activity", "response", "orientation selectivity guides", "hebbian", "Synaptic plasticity"], "article_id"=>1455562, "categories"=>["Uncategorised"], "users"=>["Sadra Sadeh", "Claudia Clopath", "Stefan Rotter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004307.g008", "stats"=>{"downloads"=>2, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Emergence_of_feature_selectivity_and_feature_specific_connectivity_/1455562", "title"=>"Emergence of feature selectivity and feature-specific connectivity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-19 04:11:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/2127649"], "description"=>"<p><b>(A)</b> Simulations of the network in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004307#pcbi.1004307.g002\" target=\"_blank\">Fig 2</a> (before learning) are repeated with the following changes in the parameters: <i>s</i><sub><i>b</i></sub> (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004307#pcbi.1004307.e007\" target=\"_blank\">Eq 2</a> in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004307#sec011\" target=\"_blank\">Materials and Methods</a>) is scaled by a connectivity factor (<i>C</i> = 1, 2, 4), which emulates an effective increase in connectivity. The relative modulation of the feedforward input to excitatory neurons, <i>μ</i><sub>exc</sub> (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004307#pcbi.1004307.e007\" target=\"_blank\">Eq 2</a>), is reduced by a factor <math><mrow><mn>1</mn><mo>/</mo><msqrt><mi>C</mi></msqrt></mrow></math>. Finally, to keep the absolute size of modulation the same, feedforward synaptic strength is scaled by a factor <math><mrow><mn>1</mn><mo>/</mo><msqrt><mi>C</mi></msqrt></mrow></math>. The output tuning curve of the sample neuron in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004307#pcbi.1004307.g002\" target=\"_blank\">Fig 2A</a> for the three different values of <i>C</i> is then plotted. <b>(B)</b> Same as (A) for the network after learning. <b>(C)</b> Average tuning curves of all the cells in the network (extracted similarly to <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004307#pcbi.1004307.g002\" target=\"_blank\">Fig 2C</a>) are plotted for different values of <i>C</i>, along with the OSI computed for each average tuning curve. <b>(D)</b> Same as (C) for the network after learning. <b>(E, F)</b> Distribution of OSI of individual tuning curves (similar to Fig <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004307#pcbi.1004307.g002\" target=\"_blank\">2E</a> and <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004307#pcbi.1004307.g002\" target=\"_blank\">2F</a>) for the networks before and after learning, respectively.</p>", "links"=>[], "tags"=>["neocortical network models", "neuron", "dynamic", "eye opening", "synaptic connections", "connectivity", "unspecific background activity", "response", "orientation selectivity guides", "hebbian", "Synaptic plasticity"], "article_id"=>1455542, "categories"=>["Uncategorised"], "users"=>["Sadra Sadeh", "Claudia Clopath", "Stefan Rotter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004307.g005", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Orientation_selectivity_of_network_responses_to_more_weakly_tuned_inputs_/1455542", "title"=>"Orientation selectivity of network responses to more weakly tuned inputs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-19 04:11:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/2127645"], "description"=>"<p><b>(A)</b> Initial connectivity matrix of the random network. Each excitatory neuron is connected to a random sample of 30% of all post-synaptic neurons, with a weight EPSP = 0.5 mV. Inhibitory neurons form synapses of weight IPSP = −4 mV. Neurons are sorted according to their preferred orientations (POs) within each population (1–400: excitatory, 401–500: inhibitory). <b>(B)</b> Matrix of synaptic weights in the network, after learning. <b>(C)</b> Change in the weights as a result of plasticity and “sensory experience”. The overall weight increase on the diagonal shows the specific potentiation of synapses between pairs of excitatory neurons with similar POs. <b>(D–F)</b> The final weights (black) are plotted against the difference in the preferred orientations (dPO) of pre-synaptic and post-synaptic neurons, for E → E(D), E → I (E) and I → E (F) connections, respectively. The initial weights are shown in red, for comparison. <b>(G)</b> Mean of the weight distributions are shown for all synapses between neurons with similar (dPO < 30°), indifferent (30° < dPO < 60°) and dissimilar POs (dPO > 60°). The unspecific distribution of initial weights (red) has now changed to an over-emphasis of connections between neurons with similar POs, indicating the emergence of specific connectivity in the network through synaptic plasticity and visual experience.</p>", "links"=>[], "tags"=>["neocortical network models", "neuron", "dynamic", "eye opening", "synaptic connections", "connectivity", "unspecific background activity", "response", "orientation selectivity guides", "hebbian", "Synaptic plasticity"], "article_id"=>1455538, "categories"=>["Uncategorised"], "users"=>["Sadra Sadeh", "Claudia Clopath", "Stefan Rotter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004307.g003", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Emergence_of_specific_connectivity_as_a_result_of_synaptic_plasticity_/1455538", "title"=>"Emergence of specific connectivity as a result of synaptic plasticity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-19 04:11:54"}

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  • {"unique-ip"=>"7", "full-text"=>"6", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"3"}
  • {"unique-ip"=>"2", "full-text"=>"3", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"1"}
  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"5"}
  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"4"}
  • {"unique-ip"=>"4", "full-text"=>"3", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
  • {"unique-ip"=>"4", "full-text"=>"1", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"3", "cited-by"=>"0", "year"=>"2018", "month"=>"7"}
  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"1", "full-text"=>"0", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"2", "full-text"=>"1", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"4", "full-text"=>"5", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"3", "full-text"=>"2", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}

Relative Metric

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