A New Framework for Cortico-Striatal Plasticity: Behavioural Theory Meets In Vitro Data at the Reinforcement-Action Interface
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{"title"=>"A New Framework for Cortico-Striatal Plasticity: Behavioural Theory Meets In Vitro Data at the Reinforcement-Action Interface", "type"=>"journal", "authors"=>[{"first_name"=>"Kevin N.", "last_name"=>"Gurney", "scopus_author_id"=>"23766890200"}, {"first_name"=>"Mark D.", "last_name"=>"Humphries", "scopus_author_id"=>"7101607621"}, {"first_name"=>"Peter", "last_name"=>"Redgrave", "scopus_author_id"=>"7005371657"}], "year"=>2015, "source"=>"PLoS Biology", "identifiers"=>{"sgr"=>"84922228056", "doi"=>"10.1371/journal.pbio.1002034", "issn"=>"15457885", "pui"=>"601975864", "isbn"=>"1545-7885 (Electronic)\\r1544-9173 (Linking)", "pmid"=>"25562526", "scopus"=>"2-s2.0-84922228056"}, "id"=>"f4f6de86-bcbc-3a2a-8c4f-20663a9aa83c", "abstract"=>"<sec> <title></title> <p>A computational model yields new insights into the bewildering complexity of cortico-striatal plasticity and its rationale for supporting operant learning.</p> </sec>", "link"=>"http://www.mendeley.com/research/new-framework-corticostriatal-plasticity-behavioural-theory-meets-vitro-data-reinforcementaction-int", "reader_count"=>173, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>8, "Librarian"=>1, "Researcher"=>50, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>54, "Student > Postgraduate"=>5, "Student > Master"=>19, "Other"=>6, "Student > Bachelor"=>7, "Lecturer > Senior Lecturer"=>1, "Professor"=>12}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>8, "Librarian"=>1, "Researcher"=>50, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>54, "Student > Postgraduate"=>5, "Student > Master"=>19, "Other"=>6, "Student > Bachelor"=>7, "Lecturer > Senior Lecturer"=>1, "Professor"=>12}, "reader_count_by_subject_area"=>{"Engineering"=>7, "Unspecified"=>8, "Mathematics"=>5, "Agricultural and Biological Sciences"=>54, "Medicine and Dentistry"=>8, "Neuroscience"=>39, "Arts and Humanities"=>1, "Physics and Astronomy"=>2, "Chemical Engineering"=>1, "Psychology"=>29, "Computer Science"=>18, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>7}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>8}, "Neuroscience"=>{"Neuroscience"=>39}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Psychology"=>{"Psychology"=>29}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>54}, "Computer Science"=>{"Computer Science"=>18}, "Mathematics"=>{"Mathematics"=>5}, "Unspecified"=>{"Unspecified"=>8}, "Chemical Engineering"=>{"Chemical Engineering"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"United States"=>10, "Japan"=>1, "United Kingdom"=>8, "Portugal"=>2, "Switzerland"=>1, "Spain"=>1, "New Zealand"=>1, "Canada"=>2, "Sweden"=>2, "Netherlands"=>1, "Austria"=>1, "Belgium"=>1, "France"=>2, "Germany"=>7}, "group_count"=>5}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1861362"], "description"=>"<p>Intrinsic and synaptic parameters for the medium spiny neuron model.</p>", "links"=>[], "tags"=>["activity changes", "dopamine reinforcement signal", "reinforcement prediction error", "dopamine receptor type", "striatal output neuron", "plasticity", "interface", "extinction", "action selection", "Behavioural Theory Meets", "operant task", "data", "model"], "article_id"=>1285182, "categories"=>["Uncategorised"], "users"=>["Kevin N. Gurney", "Mark D. Humphries", "Peter Redgrave"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002034.t002", "stats"=>{"downloads"=>0, "page_views"=>28, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Intrinsic_and_synaptic_parameters_for_the_medium_spiny_neuron_model_/1285182", "title"=>"Intrinsic and synaptic parameters for the medium spiny neuron model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-01-06 02:42:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1861359"], "description"=>"<p>Each bar is the log of the ratio of final-to-initial EPSP amplitude after a period of plasticity induction. Solid/open bars are for the model/data respectively and each condition refers to a level of dopamine concentration (“hi” or “lo”) and spike pair timing (+/− for positive/negative pre-post timing). For each of D1- and D2-MSNs, plasticity coefficients were drawn from the sets deemed successful in the exhaustive search. In each case (D1 or D2) the four outcomes were uniformly scaled (under linearity of learning) to the best least-squares fit.</p>", "links"=>[], "tags"=>["activity changes", "dopamine reinforcement signal", "reinforcement prediction error", "dopamine receptor type", "striatal output neuron", "plasticity", "interface", "extinction", "action selection", "Behavioural Theory Meets", "operant task", "data", "model"], "article_id"=>1285179, "categories"=>["Uncategorised"], "users"=>["Kevin N. Gurney", "Mark D. Humphries", "Peter Redgrave"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002034.g012", "stats"=>{"downloads"=>0, "page_views"=>26, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_STDE_model_s_replication_of_Shen_and_colleagues_plasticity_results_plotted_in_the_insets_of_Figure_4_/1285179", "title"=>"STDE model’s replication of Shen and colleagues’ plasticity results (plotted in the insets of <b>Figure 4</b>).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-01-06 02:42:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1861358"], "description"=>"<p>In (A), each plot shows the range of the resulting plasticity functions for positive and negative spike-pair timing in pale red and blue, respectively, with the mean shown by solid lines. The plot layout is the same as that used in <a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002034#pbio-1002034-g004\" target=\"_blank\">Figures 4</a> and <a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002034#pbio-1002034-g010\" target=\"_blank\">10</a>. The box-and-whisker inset plots show the quartiles, medians, extrema, and means (black dots) of the sum , for each function pair in <a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002034#pbio-1002034-g010\" target=\"_blank\">Figure 10</a>; this gives a measure of the overall direction of plasticity, given a random sampling of pre-post spike pair timings. (B) The extent and mean of the plasticity functions at high dopamine levels across D1 and D2 type MSNs combined (from left column of plots in (A)).</p>", "links"=>[], "tags"=>["activity changes", "dopamine reinforcement signal", "reinforcement prediction error", "dopamine receptor type", "striatal output neuron", "plasticity", "interface", "extinction", "action selection", "Behavioural Theory Meets", "operant task", "data", "model"], "article_id"=>1285178, "categories"=>["Uncategorised"], "users"=>["Kevin N. Gurney", "Mark D. Humphries", "Peter Redgrave"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002034.g011", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_plasticity_rules_obtained_under_the_exhaustive_search_with_the_plasticity_coefficients_of_Figure_10_/1285178", "title"=>"The plasticity rules obtained under the exhaustive search with the plasticity coefficients of <b>Figure 10</b>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-01-06 02:42:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1861353"], "description"=>"<p>The 3D plots in (A and B) are for D1 and D2 MSNs, respectively. In these plots, for constant levels of dopamine , the thick, light-blue lines show the STDP functions at high and low dopamine levels corresponding to those in <a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002034#pbio-1002034-g004\" target=\"_blank\">Figure 4</a>. For other (constant) values of , the STDP function is obtained by smoothly “blending” together for positive timing, and for negative timing. Thinner black lines show some examples of this, and the tonic dopamine level in our model gives functions shown in dark blue. With time-dependent levels of dopamine and eligibility, the generalised plasticity function can change dynamically with time for a given . The green line in (A) shows a typical such trajectory as a phasic dopamine burst is received, starting at tonic level, moving to the peak of phasic amplitude and back again. (D) The mixing function that determines how much of each of the functions, are blended together across the range of dopamine . (C and E) The resultant plasticity factors for D1 (C) and D2 (E) MSNs, respectively, giving the amplitude of the STDP functions at in (A) and (B).</p>", "links"=>[], "tags"=>["activity changes", "dopamine reinforcement signal", "reinforcement prediction error", "dopamine receptor type", "striatal output neuron", "plasticity", "interface", "extinction", "action selection", "Behavioural Theory Meets", "operant task", "data", "model"], "article_id"=>1285173, "categories"=>["Uncategorised"], "users"=>["Kevin N. Gurney", "Mark D. Humphries", "Peter Redgrave"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002034.g005", "stats"=>{"downloads"=>0, "page_views"=>25, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_8220_function_mixing_8221_model_of_dopamine_dependent_cortico_striatal_plasticity_/1285173", "title"=>"The “function mixing” model of dopamine-dependent cortico-striatal plasticity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-01-06 02:42:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1861349"], "description"=>"<p>The timeline at the top shows the experiment’s epochs. Below that we plot target response profiles of D1 and D2 type MSNs over each epoch of trials. These are based on the analysis in <a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002034#pbio-1002034-g003\" target=\"_blank\">Figure 3</a> with the key points from that analysis shown by open symbols; grey lines between them show direction of change over the epoch. Stability is indicated by horizontal lines, and continuous (but not necessarily linear) plastic change is shown by lines with arrows between two open symbols. Bottom plot: trial-by-trial envelope-of-amplitudes of individual phasic dopamine events within each trial. This amplitude is governed by a variable , whose value decays exponentially when describing positive dopamine signals (bursts) from some maximal value . For negative going dopamine signals (dips) rises exponentially over a trajectory that can be negative (dotted grey line). However, the phasic excursions of the level of dopamine itself, , are always positive or zero, for when . The use of in this way expediently fixes the interval over which . In both cases the time constant of the dynamics of is .</p>", "links"=>[], "tags"=>["activity changes", "dopamine reinforcement signal", "reinforcement prediction error", "dopamine receptor type", "striatal output neuron", "plasticity", "interface", "extinction", "action selection", "Behavioural Theory Meets", "operant task", "data", "model"], "article_id"=>1285169, "categories"=>["Uncategorised"], "users"=>["Kevin N. Gurney", "Mark D. Humphries", "Peter Redgrave"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002034.g001", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Stylised_behavioural_experiment_for_action_discovery_with_associated_dynamics_of_MSN_responses_and_phasic_dopamine_/1285169", "title"=>"Stylised behavioural experiment for action discovery, with associated dynamics of MSN responses and phasic dopamine.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-01-06 02:42:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1861361"], "description"=>"<p>Parameters for mixing STDP functions (kernels) and plasticity rules.</p>", "links"=>[], "tags"=>["activity changes", "dopamine reinforcement signal", "reinforcement prediction error", "dopamine receptor type", "striatal output neuron", "plasticity", "interface", "extinction", "action selection", "Behavioural Theory Meets", "operant task", "data", "model"], "article_id"=>1285181, "categories"=>["Uncategorised"], "users"=>["Kevin N. Gurney", "Mark D. Humphries", "Peter Redgrave"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002034.t001", "stats"=>{"downloads"=>0, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parameters_for_mixing_STDP_functions_kernels_and_plasticity_rules_/1285181", "title"=>"Parameters for mixing STDP functions (kernels) and plasticity rules.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-01-06 02:42:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1861360"], "description"=>"<p>Here we plot the mean (line) and range (shading) of the overall weight change (sum of the plasticity factors ) at a given dopamine level , across every set of plasticity coefficients found by the search (<a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002034#pbio-1002034-g010\" target=\"_blank\">Figure 10</a>). (A) Separate plots for the found sets of D1 and D2 MSN coefficients, showing the dopamine dependence of each neuron type. (B) The sum of the individual MSN-type contributions in (A).</p>", "links"=>[], "tags"=>["activity changes", "dopamine reinforcement signal", "reinforcement prediction error", "dopamine receptor type", "striatal output neuron", "plasticity", "interface", "extinction", "action selection", "Behavioural Theory Meets", "operant task", "data", "model"], "article_id"=>1285180, "categories"=>["Uncategorised"], "users"=>["Kevin N. Gurney", "Mark D. Humphries", "Peter Redgrave"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002034.g013", "stats"=>{"downloads"=>0, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Expected_overall_weight_change_as_a_function_of_dopamine_concentration_/1285180", "title"=>"Expected overall weight change as a function of dopamine concentration.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-01-06 02:42:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1861357"], "description"=>"<p>The plot layout corresponds to that in <a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002034#pbio-1002034-g004\" target=\"_blank\">Figure 4</a>. In each plot, the red crosses show the coefficient value, the area of the bubble is proportional to the number of times that value was found, and the blue squares are the hand-chosen values used to create the activity profiles in <a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002034#pbio-1002034-g007\" target=\"_blank\">Figure 7</a>. The discovered set for D1-MSNs comprised the 26 best profiles from <a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002034#pbio-1002034-g009\" target=\"_blank\">Figure 9D</a> and are reported in the top row. For D2 MSNs, there were 32 candidates with satisfactory profiles at the end of both coarse and focused searches; they yield the plots in the bottom row.</p>", "links"=>[], "tags"=>["activity changes", "dopamine reinforcement signal", "reinforcement prediction error", "dopamine receptor type", "striatal output neuron", "plasticity", "interface", "extinction", "action selection", "Behavioural Theory Meets", "operant task", "data", "model"], "article_id"=>1285177, "categories"=>["Uncategorised"], "users"=>["Kevin N. Gurney", "Mark D. Humphries", "Peter Redgrave"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002034.g010", "stats"=>{"downloads"=>0, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Results_of_exhaustive_search_of_plasticity_coefficients_for_the_STDE_model_/1285177", "title"=>"Results of exhaustive search of plasticity coefficients for the STDE model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-01-06 02:42:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1861356"], "description"=>"<p>(A) and (D) are the response profiles, for D1-MSNs and D2-MSNs, repectively, plotted as spike count per trial against trial number; key trials delimit epochs defined in <a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002034#pbio-1002034-g001\" target=\"_blank\">Figure 1</a>. Coefficients for D1-MSNs were: ; and for D2-MSNs were: . (B) and (E) the mean AMPA conductances for D1-, D2-MSNs of each synapse at trials 1 and 55. Synapses in the set, , (50 synapses consistently subject to stronger input during the learning and extinction epochs), are collected at the left hand side of each plot, and delimited by the vertical dotted line. The horizontal dotted line shows the initial mean AMPA conductance of 0.458 nS. (C) and (F) The mean conductance of the set against trial number (D1-, D2-MSNs, respectively).</p>", "links"=>[], "tags"=>["activity changes", "dopamine reinforcement signal", "reinforcement prediction error", "dopamine receptor type", "striatal output neuron", "plasticity", "interface", "extinction", "action selection", "Behavioural Theory Meets", "operant task", "data", "model"], "article_id"=>1285176, "categories"=>["Uncategorised"], "users"=>["Kevin N. Gurney", "Mark D. Humphries", "Peter Redgrave"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002034.g007", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Behaviour_of_MSNs_obtained_with_the_STDE_plasticity_rules_we_plot_here_means_obtained_over_ten_experiments_with_different_random_initialisation_of_AMPA_conductances_/1285176", "title"=>"Behaviour of MSNs obtained with the STDE plasticity rules (we plot here means obtained over ten experiments with different random initialisation of AMPA conductances).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-01-06 02:42:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1861351"], "description"=>"<p>In all plots, neural “responsiveness” is the ratio of the population′s input value to output response; we abbreviate to “response” in axis labelling for brevity. (A and B) relate to action learning and extinction, respectively. The pairs of “bubble plots” in the top left of each panel show (i) an idealised selection template for a two-channel competition (left plot in each pair), with the key action on channel 1 and the control action on channel 2; and (ii) the best match to that template (at the D1 and D2 responsiveness noted above the plot). In each bubble plot, open symbols show an outcome of channel 2 selected, closed symbols show channel 1 selected, dots are no selection, and the crossed-circle shows both channels selected. The 2D colour plots (“heat maps”) show the template match for each D1/D2 responsiveness pair. The pairs of line plots show details of the corresponding colour map. The left hand line plots (open symbols) show the maximum template match for a given D1-MSN responsiveness; results at 1 and 1.25 are highlighted by the dashed lines. The right hand line plots (closed symbols) show cross sections through the 2D heat map (indicated by dashed grey lines therein) at D1 responsiveness of 1 (circles) and 1.25 (squares).</p>", "links"=>[], "tags"=>["activity changes", "dopamine reinforcement signal", "reinforcement prediction error", "dopamine receptor type", "striatal output neuron", "plasticity", "interface", "extinction", "action selection", "Behavioural Theory Meets", "operant task", "data", "model"], "article_id"=>1285171, "categories"=>["Uncategorised"], "users"=>["Kevin N. Gurney", "Mark D. Humphries", "Peter Redgrave"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002034.g003", "stats"=>{"downloads"=>0, "page_views"=>62, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Linking_action_selection_in_basal_ganglia_to_MSN_responses_/1285171", "title"=>"Linking action selection in basal ganglia to MSN responses.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-01-06 02:42:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1861350"], "description"=>"<p>(A) Schematic of model architecture. It contains all major nuclei: STN, subthalamic nucleus; GPe, globus pallidus external segment; output nuclei (collectively)— SNr, substantia nigra pars compacta, and GPi, globus pallidus internal segment; striatum, with MSNs preferentially expressing D1 and D2 type dopamine receptors. Red and blue lines indicate excitatory and inhibitory connections, respectively. Circles indicate action-representing populations within each nucleus, each population modelled by its normalised mean firing rate, with relative rates represented by degree of shading (dark is highly active, pale grey is less so). In the interests of clarity, only two of the six channels are shown, and the diffuse projection from the channel on the right hand side in STN is shown as a single, wide red arrow (but mirrors its left-hand counterpart in terms of its individual connections to SNr/GPi and GPe). (B) Selection (left) and suppression (right) in the dynamical model. A phasic signal from cortex is input to a single channel in the model. Left: If the cortico-striatal weight is stronger to the channel’s D1 MSN population, then selection results: the corresponding SNr/GPi population’s activity is inhibited. Right: if the cortico-striatal weight is stronger to the channel’s D2 MSN population, then suppression results: via the effect of the enhanced D2 MSN input to the STN-GPe loop, the corresponding SNr/GPi population’s activity is excited. The model thus shows that a single cortical input drives coincident activity in D1 and D2 MSN populations, and that even within a single action-representing channel the two pathways are antagonistic.</p>", "links"=>[], "tags"=>["activity changes", "dopamine reinforcement signal", "reinforcement prediction error", "dopamine receptor type", "striatal output neuron", "plasticity", "interface", "extinction", "action selection", "Behavioural Theory Meets", "operant task", "data", "model"], "article_id"=>1285170, "categories"=>["Uncategorised"], "users"=>["Kevin N. Gurney", "Mark D. Humphries", "Peter Redgrave"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002034.g002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_of_basal_ganglia_dynamics_/1285170", "title"=>"Model of basal ganglia dynamics.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-01-06 02:42:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1861363", "https://ndownloader.figshare.com/files/1861364", "https://ndownloader.figshare.com/files/1861365", "https://ndownloader.figshare.com/files/1861366"], "description"=>"<div><p>Operant learning requires that reinforcement signals interact with action representations at a suitable neural interface. Much evidence suggests that this occurs when phasic dopamine, acting as a reinforcement prediction error, gates plasticity at cortico-striatal synapses, and thereby changes the future likelihood of selecting the action(s) coded by striatal neurons. But this hypothesis faces serious challenges. First, cortico-striatal plasticity is inexplicably complex, depending on spike timing, dopamine level, and dopamine receptor type. Second, there is a credit assignment problem—action selection signals occur long before the consequent dopamine reinforcement signal. Third, the two types of striatal output neuron have apparently opposite effects on action selection. Whether these factors rule out the interface hypothesis and how they interact to produce reinforcement learning is unknown. We present a computational framework that addresses these challenges. We first predict the expected activity changes over an operant task for both types of action-coding striatal neuron, and show they co-operate to promote action selection in learning and compete to promote action suppression in extinction. Separately, we derive a complete model of dopamine and spike-timing dependent cortico-striatal plasticity from in vitro data. We then show this model produces the predicted activity changes necessary for learning and extinction in an operant task, a remarkable convergence of a bottom-up data-driven plasticity model with the top-down behavioural requirements of learning theory. Moreover, we show the complex dependencies of cortico-striatal plasticity are not only sufficient but necessary for learning and extinction. Validating the model, we show it can account for behavioural data describing extinction, renewal, and reacquisition, and replicate in vitro experimental data on cortico-striatal plasticity. By bridging the levels between the single synapse and behaviour, our model shows how striatum acts as the action-reinforcement interface.</p></div>", "links"=>[], "tags"=>["activity changes", "dopamine reinforcement signal", "reinforcement prediction error", "dopamine receptor type", "striatal output neuron", "plasticity", "interface", "extinction", "action selection", "Behavioural Theory Meets", "operant task", "data", "model"], "article_id"=>1285183, "categories"=>["Uncategorised"], "users"=>["Kevin N. Gurney", "Mark D. Humphries", "Peter Redgrave"], "doi"=>["https://dx.doi.org/10.1371/journal.pbio.1002034.s001", "https://dx.doi.org/10.1371/journal.pbio.1002034.s002", "https://dx.doi.org/10.1371/journal.pbio.1002034.s003", "https://dx.doi.org/10.1371/journal.pbio.1002034.s004"], "stats"=>{"downloads"=>8, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_New_Framework_for_Cortico_Striatal_Plasticity_Behavioural_Theory_Meets_In_Vitro_Data_at_the_Reinforcement_Action_Interface_/1285183", "title"=>"A New Framework for Cortico-Striatal Plasticity: Behavioural Theory Meets In Vitro Data at the Reinforcement-Action Interface", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-01-06 02:42:18"}

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  • {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2018", "month"=>"7"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"6", "full-text"=>"6", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"4", "full-text"=>"3", "pdf"=>"5", "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"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"6", "full-text"=>"7", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"7", "full-text"=>"85", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"8", "full-text"=>"8", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"8", "full-text"=>"8", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"11", "full-text"=>"10", "pdf"=>"7", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"4", "full-text"=>"4", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
  • {"unique-ip"=>"6", "full-text"=>"1", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}

Relative Metric

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