The Emergence of Up and Down States in Cortical Networks
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{"title"=>"The emergence of up and down states in cortical networks", "type"=>"journal", "authors"=>[{"first_name"=>"David", "last_name"=>"Holcman", "scopus_author_id"=>"6701643334"}, {"first_name"=>"Misha", "last_name"=>"Tsodyks", "scopus_author_id"=>"7003469335"}], "year"=>2006, "source"=>"PLoS Computational Biology", "identifiers"=>{"issn"=>"1553734X", "scopus"=>"2-s2.0-33645789850", "pui"=>"43555792", "doi"=>"10.1371/journal.pcbi.0020023", "isbn"=>"1553-734X", "sgr"=>"33645789850", "pmid"=>"16557293"}, "id"=>"4858dc50-b221-334b-8926-00703adaadb2", "abstract"=>"The cerebral cortex is continuously active in the absence of external stimuli. An example of this spontaneous activity is the voltage transition between an Up and a Down state, observed simultaneously at individual neurons. Since this phenomenon could be of critical importance for working memory and attention, its explanation could reveal some fundamental properties of cortical organization. To identify a possible scenario for the dynamics of Up-Down states, we analyze a reduced stochastic dynamical system that models an interconnected network of excitatory neurons with activity-dependent synaptic depression. The model reveals that when the total synaptic connection strength exceeds a certain threshold, the phase space of the dynamical system contains two attractors, interpreted as Up and Down states. In that case, synaptic noise causes transitions between the states. Moreover, an external stimulation producing a depolarization increases the time spent in the Up state, as observed experimentally. We therefore propose that the existence of Up-Down states is a fundamental and inherent property of a noisy neural ensemble with sufficiently strong synaptic connections.", "link"=>"http://www.mendeley.com/research/emergence-up-down-states-cortical-networks", "reader_count"=>245, "reader_count_by_academic_status"=>{"Unspecified"=>5, "Professor > Associate Professor"=>13, "Librarian"=>1, "Researcher"=>78, "Student > Doctoral Student"=>11, "Student > Ph. D. Student"=>66, "Student > Postgraduate"=>7, "Student > Master"=>12, "Other"=>8, "Student > Bachelor"=>15, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>7, "Professor"=>18}, "reader_count_by_user_role"=>{"Unspecified"=>5, "Professor > Associate Professor"=>13, "Librarian"=>1, "Researcher"=>78, "Student > Doctoral Student"=>11, "Student > Ph. D. Student"=>66, "Student > Postgraduate"=>7, "Student > Master"=>12, "Other"=>8, "Student > Bachelor"=>15, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>7, "Professor"=>18}, "reader_count_by_subject_area"=>{"Unspecified"=>12, "Agricultural and Biological Sciences"=>107, "Arts and Humanities"=>3, "Business, Management and Accounting"=>1, "Chemistry"=>1, "Computer Science"=>15, "Engineering"=>14, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>2, "Mathematics"=>10, "Medicine and Dentistry"=>16, "Neuroscience"=>25, "Physics and Astronomy"=>22, "Psychology"=>11, "Social Sciences"=>5}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>16}, "Social Sciences"=>{"Social Sciences"=>5}, "Physics and Astronomy"=>{"Physics and Astronomy"=>22}, "Psychology"=>{"Psychology"=>11}, "Mathematics"=>{"Mathematics"=>10}, "Unspecified"=>{"Unspecified"=>12}, "Environmental Science"=>{"Environmental Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>3}, "Engineering"=>{"Engineering"=>14}, "Chemistry"=>{"Chemistry"=>1}, "Neuroscience"=>{"Neuroscience"=>25}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>107}, "Computer Science"=>{"Computer Science"=>15}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}}, "reader_count_by_country"=>{"Hungary"=>1, "United States"=>8, "United Kingdom"=>7, "Malaysia"=>1, "Belarus"=>1, "Switzerland"=>1, "Portugal"=>1, "India"=>1, "Spain"=>2, "Russia"=>1, "Greece"=>1, "Canada"=>2, "Netherlands"=>1, "Belgium"=>2, "Norway"=>1, "China"=>1, "Finland"=>1, "Brazil"=>1, "Poland"=>1, "Italy"=>1, "Israel"=>1, "Australia"=>1, "France"=>2, "Germany"=>9}, "group_count"=>13}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/959586"], "description"=>"<div><p>(A) The depression <i>μ</i> is plotted on the <i>x</i>-axis, while the variable <i>V</i> is on the <i>y</i>-axis (for the schematic representation, no scale is given). The recurrent sets are the attractor points <i>P</i><sub>1</sub> (the Down state), the saddle point <i>P</i><sub>3</sub><b>,</b> and the attractor <i>P</i><sub>2</sub> (the Up state), separated by an unstable limit cycle C (dashed line). The region inside the unstable limit cycle is the basin of attraction of <i>P</i><sub>2</sub>. By definition, it is the Up state. For a specific value of the parameter, an homoclinic curve can appear. The unstable branch of the separatrix starting from <i>P</i><sub>3</sub> terminates inside the basin of attraction of the Down state. The noise drives the dynamics beyond the separatrices (lines with arrow converging to <i>P</i><sub>3</sub>) but a second transition is necessary for a stochastic trajectory to enter inside the Up state delimited by C. However a single noise transition is enough to destabilize the dynamics from the Up to the Down state.</p><p>(B) Example of a trajectory in the phase space.</p></div>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "computational biology/systems biology"], "article_id"=>629618, "categories"=>["Biological Sciences", "Neuroscience", "Medicine"], "users"=>["David Holcman", "Misha Tsodyks"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.0020023.g001", "stats"=>{"downloads"=>1, "page_views"=>30, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_Representation_of_the_Phase_Space_956_V_/629618", "title"=>"Schematic Representation of the Phase Space (<i>μ,V</i>)", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-22 08:44:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/471576"], "description"=>"<div><p>The cerebral cortex is continuously active in the absence of external stimuli. An example of this spontaneous activity is the voltage transition between an Up and a Down state, observed simultaneously at individual neurons. Since this phenomenon could be of critical importance for working memory and attention, its explanation could reveal some fundamental properties of cortical organization. To identify a possible scenario for the dynamics of Up–Down states, we analyze a reduced stochastic dynamical system that models an interconnected network of excitatory neurons with activity-dependent synaptic depression. The model reveals that when the total synaptic connection strength exceeds a certain threshold, the phase space of the dynamical system contains two attractors, interpreted as Up and Down states. In that case, synaptic noise causes transitions between the states. Moreover, an external stimulation producing a depolarization increases the time spent in the Up state, as observed experimentally. We therefore propose that the existence of Up–Down states is a fundamental and inherent property of a noisy neural ensemble with sufficiently strong synaptic connections.</p></div>", "links"=>[], "tags"=>["emergence", "states", "cortical", "networks"], "article_id"=>152982, "categories"=>["Biological Sciences", "Neuroscience", "Medicine"], "users"=>["David Holcman", "Misha Tsodyks"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.0020023", "stats"=>{"downloads"=>2, "page_views"=>37, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/The_Emergence_of_Up_and_Down_States_in_Cortical_Networks_/152982", "title"=>"The Emergence of Up and Down States in Cortical Networks", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2006-03-24 00:49:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/960350"], "description"=>"<div><p>(A) Effect of increasing <i>w<sub>T</sub></i>, which is equivalent to the LTP effect. The effect of LTP is obtained by increasing the total synaptic connections <i>w<sub>T</sub></i> to a value of 15. The effect is similar to a depolarization injection current.</p><p>(B) Effect of LTD on Up–Down state dynamics<i>.</i> The effect of LTD is obtained by decreasing the total synaptic connections <i>w<sub>T</sub></i> to a value of 11. The effect is similar to hyperpolarizing the ensemble of neurons.</p></div>", "links"=>[], "tags"=>["synaptic", "plasticity"], "article_id"=>630361, "categories"=>["Biological Sciences", "Neuroscience", "Medicine"], "users"=>["David Holcman", "Misha Tsodyks"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.0020023.g004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effect_of_Synaptic_Plasticity_on_Up_8211_Down_State_Dynamics_/630361", "title"=>"Effect of Synaptic Plasticity on Up–Down State Dynamics", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-22 08:49:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/960040"], "description"=>"<p>The top graph represents the frequency of Up states as a function of Input I. Hyperpolarizing the neurons is generated for <i>I</i> < 0, while depolarization is generated for <i>I</i> > 0. The result suggests that there is a unique value of I for which the frequency of the Up state is maximal. In the bottom graph, the mean duration of the Up states is plotted as a function of I.</p>", "links"=>[], "tags"=>["duration", "spent"], "article_id"=>630060, "categories"=>["Biological Sciences", "Neuroscience", "Medicine"], "users"=>["David Holcman", "Misha Tsodyks"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.0020023.g003", "stats"=>{"downloads"=>4, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Frequency_and_Duration_of_the_Mean_Time_Spent_in_the_Up_State_as_a_Function_of_I_/630060", "title"=>"Frequency and Duration of the Mean Time Spent in the Up State as a Function of I", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-22 08:47:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/960503"], "description"=>"<p>The average voltage response is computed. The blue curve shows the response in the Down state. The red curve shows the response in the Up state. The horizontal black bar indicates the duration of the stimulus (starting at <i>t</i> = 0 ms). The time response is different in each state, and in agreement with the experimental results of [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020023#pcbi-0020023-b027\" target=\"_blank\">27</a>].</p>", "links"=>[], "tags"=>["excitatory", "produces", "depolarization"], "article_id"=>630509, "categories"=>["Biological Sciences", "Neuroscience", "Medicine"], "users"=>["David Holcman", "Misha Tsodyks"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.0020023.g005", "stats"=>{"downloads"=>2, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Average_Response_to_a_Brief_Excitatory_Pulse_Which_Produces_a_Small_Depolarization_in_Each_State_/630509", "title"=>"Average Response to a Brief Excitatory Pulse Which Produces a Small Depolarization in Each State", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-22 08:50:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/959892"], "description"=>"<div><p>(A) A typical realization of Up–Down transition dynamics in the stochastic model with <i>w<sub>T</sub></i> = 12.6.</p><p>Top graph: (Up) average population voltage (mV), baseline at 0 mV; (Down) plot of the depression variable <i>μ</i> as a function of time: when the system arrives in the Down state, it is depressed. Then the depression recovers slowly toward 1. Usually the noise is able to produce a transition, before full recovery is achieved.</p><p>Bottom graph: Histogram of the population voltage: fraction of the time spent in a given state. Units are in ms on the <i>y</i>-axis and in mV on the <i>x</i>-axis: the fraction of total time spent in the Up and Down state is about the same during the 20 s of simulations. The maximum time in the Up state is about 500 ms but the mean maximum time is about 3 s.</p><p>Right column graph of (A): histogram of the duration of the Up states.</p><p>(B) Effect of an external stimulation: depolarization. When a constant external input (I = 0.8) is added into the model, the neural network spends on average more time in the Up state than in Down state. In that case, the attractor point <i>P</i><sub>1</sub> is shifted and is now closer to the separatrix.</p><p>(C) Hyperpolarization, simulated by a current injection<i>.</i> A hyperpolarization (I = −0.3) drives the ensemble of neurons at a lower mean potential, and as a result the neural network spends on average more time in the Down state.</p></div>", "links"=>[], "tags"=>["clamp"], "article_id"=>629917, "categories"=>["Biological Sciences", "Neuroscience", "Medicine"], "users"=>["David Holcman", "Misha Tsodyks"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.0020023.g002", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Up_8211_Down_State_Dynamics_under_Normal_and_Clamp_Conditions_/629917", "title"=>"Up–Down State Dynamics under Normal and Clamp Conditions", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-22 08:46:18"}

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Relative Metric

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