ModelDB: Model of memory linking through memory allocation (Kastellakis et al. 2016) Advanced search SenseLab SimToolDB ModelDB Help User account Login Register Find models by Model name First author Each author Region(circuits) Find models for Cell type Current Receptor Gene Transmitters Concept Simulators Methods Find models of Realistic Networks Neurons Electrical synapses (gap junctions) Chemical synapses Ion channels Neuromuscular junctions Axons Pathophysiology Other resources SenseLab mailing list ModelDB related resources Computational neuroscience ecosystem Models in a git repository Model of memory linking through memory allocation (Kastellakis et al. 2016) Download zip file Help downloading and running models Model Information Model File Citations Versions on GitHub Accession:206249 Here, we present a simplified, biophysically inspired network model that incorporates multiple plasticity processes and explains linking of information at three different levels: (a) learning of a single associative memory (b) rescuing of a weak memory when paired with a strong one and (c) linking of multiple memories across time. By dissecting synaptic from intrinsic plasticity and neuron-wide from dendritically restricted protein capture, the model reveals a simple, unifying principle: Linked memories share synaptic clusters within the dendrites of overlapping populations of neurons Reference: 1 . Kastellakis G, Silva AJ, Poirazi P (2016) Linking Memories across Time via Neuronal and Dendritic Overlaps in Model Neurons with Active Dendrites. Cell Rep 17:1491-1504 [PubMed] Model Information (Click on a link to find other models with that property) Model Type: Realistic Network; Brain Region(s)/Organism: Cell Type(s): Abstract integrate-and-fire leaky neuron with dendritic subunits; Channel(s): Gap Junctions: Receptor(s): Gene(s): Transmitter(s): Simulation Environment: C or C++ program; C or C++ program (web link to model); Model Concept(s): Active Dendrites; Implementer(s): Kastellakis, George [gkastel at gmail.com]; Download the displayed file / stdmodel distributionPlot exportfig figs mtrand README allgraphs.m allrun.m an_brtest.m an_stats.m anmulti.py ansims.py barwitherr.m * Other models using barwitherr.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) btagstats.m * Other models using btagstats.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) CImg.h * Other models using CImg.h: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) constructs.cpp constructs.h defaults.m dir2.m * Other models using dir2.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) getspikedata.m * Other models using getspikedata.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) getsynstate.m * Other models using getsynstate.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) getsynstate2.m * Other models using getsynstate2.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) graphs.m * Other models using graphs.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) hist_percents.m * Other models using hist_percents.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) hist_with_errs.m * Other models using hist_with_errs.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) interact.m * Other models using interact.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) intexp_constructs.cpp job_sims.sh kurtos.m * Other models using kurtos.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) lamodel.cpp LICENSE * Other models using LICENSE: A Method for Prediction of Receptor Activation in the Simulation of Synapses (Montes et al. 2013) An attractor network model of grid cells and theta-nested gamma oscillations (Pastoll et al 2013) Coincidence detection in MSO principal cells (Goldwyn et al. 2019) Cortical oscillations and the basal ganglia (Fountas & Shanahan 2017) Deep belief network learns context dependent behavior (Raudies, Zilli, Hasselmo 2014) Duration-tuned neurons from the inferior colliculus of the big brown bat (Aubie et al. 2009) Fast Spiking Basket cells (Tzilivaki et al 2019) Grid cell model with compression effects (Raudies & Hasselmo, 2015) Hierarchical Gaussian Filter (HGF) model of conditioned hallucinations task (Powers et al 2017) Hierarchical network model of perceptual decision making (Wimmer et al 2015) Hippocampal spiking model for context dependent behavior (Raudies & Hasselmo 2014) Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) Inhibitory network bistability explains increased activity prior to seizure onset (Rich et al 2020) PyMUS: A Python based Motor Unit Simulator (Kim & Kim 2018) Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation (Luque et al 2019) Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation (Luque et al 2019) make_graphs.m * Other models using make_graphs.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) Makefile * Other models using Makefile: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) matlab.mat * Other models using matlab.mat: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) mtest.py mtrand.cpp * Other models using mtrand.cpp: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) mtrand.h * Other models using mtrand.h: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) multi.py multistats.m * Other models using multistats.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) nextplot.m * Other models using nextplot.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) pairstrong.m * Other models using pairstrong.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) repeated.m * Other models using repeated.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) rotateXLabels.m * Other models using rotateXLabels.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) run_1.sh run_2strong.sh run_2weak.sh run_3.sh run_all.sh run_brov.sh run_brtest.sh run_btag.sh run_dir.sh run_ep.sh run_gp.sh run_gp2.sh run_mult.sh run_Nsparse.sh run_pairstrong.sh run_rep.sh run_sims.sh run_sparse.sh run_sparseS2.sh runloc.sh runmany.sh S2sparse.m * Other models using S2sparse.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) savefig.m * Other models using savefig.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) scratch.m * Other models using scratch.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) sensitivity.m * Other models using sensitivity.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) stats.m * Other models using stats.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) stats.py * Other models using stats.py: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) stderr.m * Other models using stderr.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) strong2.m * Other models using strong2.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) strongstrong.m * Other models using strongstrong.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) submit_lamodel.sh * Other models using submit_lamodel.sh: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) three.m * Other models using three.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) trevrolls.m * Other models using trevrolls.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) vis.py * Other models using vis.py: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) weastrong.m * Other models using weastrong.m: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) wxglmodel * Other models using wxglmodel: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) wxglmodel.cpp * Other models using wxglmodel.cpp: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) wxglmodel.h * Other models using wxglmodel.h: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) wxmodel.cpp * Other models using wxmodel.cpp: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) wxmodel.h * Other models using wxmodel.h: Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) %************************************************************************** % %   This is a simple extension of the bar plot to include error bars.  It %   is called in exactly the same way as bar but with an extra input %   parameter "errors" passed first. % %   Parameters: %   errors - the errors to be plotted (extra dimension used if assymetric) %   varargin - parameters as passed to conventional bar plot %   See bar and errorbar documentation for more details. % %   Output: %   [hBar hErrorbar] = barwitherr(..) returns a vector of handles to the  %                      barseries (hBar) and error bar (hErrorbar) objects % %   Symmetric Example: %   y = randn(3,4);         % random y values (3 groups of 4 parameters)  %   errY = 0.1.*y;          % 10% error %   h = barwitherr(errY, y);% Plot with errorbars % %   set(gca,'XTickLabel',{'Group A','Group B','Group C'}) %   legend('Parameter 1','Parameter 2','Parameter 3','Parameter 4') %   ylabel('Y Value') %   set(h(1),'FaceColor','k'); % % %   Asymmetric Example: %   y = randn(3,4);         % random y values (3 groups of 4 parameters) %   errY = zeros(3,4,2); %   errY(:,:,1) = 0.1.*y;   % 10% lower error %   errY(:,:,2) = 0.2.*y;   % 20% upper error %   barwitherr(errY, y);    % Plot with errorbars % %   set(gca,'XTickLabel',{'Group A','Group B','Group C'}) %   legend('Parameter 1','Parameter 2','Parameter 3','Parameter 4') %   ylabel('Y Value') % % %   Notes: %   Ideally used for group plots with non-overlapping bars because it %   will always plot in bar centre (so can look odd for over-lapping bars)  %   and for stacked plots the errorbars will be at the original y value is  %   not the stacked value so again odd appearance as is. % %   The data may not be in ascending order.  Only an issue if x-values are  %   passed to the fn in which case their order must be determined to  %   correctly position the errorbars. % % %   24/02/2011  Martina F. Callaghan    Created %   12/08/2011  Martina F. Callaghan    Updated for random x-values    %   24/10/2011  Martina F. Callaghan    Updated for asymmetric errors %   15/11/2011  Martina F. Callaghan    Fixed bug for assymetric errors & %                                       vector plots %   14/06/2013  Martina F. Callaghan    Returning handle as recommended by %                                       Eric (see submission comments) %   08/07/2013  Martina F. Callaghan    Only return handle if requested. %   18/07/2013  Martina F. Callaghan    Bug fix for single group data that  %                                       allows assymetric errors. %                                       Also removed dot from display as %                                       per Charles Colin comment. The  %                                       handle can be returned to control %                                       appearance. %   27/08/2013  Martina F. Callaghan    Ensuring errors are always stored %                                       as lowerErrors and upperErrors even %                                       if symmetric. % %**************************************************************************