Tag Trigger Consolidation (Clopath and Ziegler et al. 2008)

 Download zip file 
Help downloading and running models
Accession:118199
This model simulates different phases of LTP/D, i.e. the induction or early phase, the setting of synaptic tags, a trigger process for protein synthesis, and a slow transition leading to synaptic consolidation namely the late phase of synaptic plasticity. The model explains a large body of experimental data on synaptic tagging and capture, cross-tagging, and the late phases of LTP and LTD. Moreover, the model accounts for the dependence of LTP and LTD induction on voltage and presynaptic stimulation frequency.
Reference:
1 . Clopath C, Ziegler L, Vasilaki E, Busing L, Gerstner W (2008) Tag-trigger-consolidation: a model of early and late long-term-potentiation and depression. PLoS Comput Biol 4:e1000248 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Synapse;
Brain Region(s)/Organism:
Cell Type(s): Hippocampus CA1 pyramidal cell; Abstract integrate-and-fire adaptive exponential (AdEx) neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Dopamine;
Simulation Environment: Python;
Model Concept(s): Simplified Models; Synaptic Plasticity; Long-term Synaptic Plasticity; Maintenance;
Implementer(s): Ziegler, Lorric ;
Search NeuronDB for information about:  Hippocampus CA1 pyramidal cell; Dopamine;
  
/
tagtric
readme.txt
tagtric.py
                            
This is the readme for the TagTriC model from:

C.Clopath, L.Ziegler, E.Vasilaki, L.Buesing and W.Gerstner
Tag-Trigger-Consolidation: A Model of Early and Late
Long-Term-Potentiation and Depression
PLoS Comput Biol. 4(12): e1000248, 2008

Execute or import tagtric.py in an interactive python shell (ipython).

The function 'sim' simulates a neuron with 2 pathways of 100 input
synapses each (see U.Frey and R.Morris, Nature 385:533-536, 1997). It
takes three string arguments: 1st stimulation protocol, 2nd
stimulation, and what to plot.

- The 2 first arguments should be one of 'wtet','stet','wlfs','slfs'
  or 'nothing' which stand for weak/strong tetanus, weak/stonrg low
  frequency prpotocol and nothing, respectively.

- The 3rd one is for plotting. The string should contain: 'w1' and/or
  'w2' for the weight (decomposed in h, l and z, see the article for
  details) of the 1st and/or 2nd pathway(s); 'hl' for a two
  dimensional graph of the decomposed weights; 'p' for the protein
  production.

The function 'simoconn' simulates the experience by O'Connor
(D.O'Connor, G.Wittenberg and S.Wang, J Neurophysiol 94:1565-1573,
2005) on the frequency dependence of LTP/D. It takes a frequency (in
[Hz]) as argument.

For any question on the code contact
lorric.ziegler@epfl.ch
April 20th, 2009 - updated with corrected amplitudes for
depression/potentiation.

Clopath C, Ziegler L, Vasilaki E, Busing L, Gerstner W (2008) Tag-trigger-consolidation: a model of early and late long-term-potentiation and depression. PLoS Comput Biol 4:e1000248[PubMed]

References and models cited by this paper

References and models that cite this paper

Abraham WC (2003) How long will long-term potentiation last? Philos Trans R Soc Lond B Biol Sci 358:735-44 [PubMed]

Amit DJ, Fusi S (1994) Learning in neural networks with material synapses Neural Comput 6:957-982

Arleo A, Gerstner W (2000) Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity. Biol Cybern 83:287-99 [PubMed]

Artola A, Brocher S, Singer W (1990) Different voltage-dependent thresholds for inducing long-term depression and long-term potentiation in slices of rat visual cortex. Nature 347:69-72 [PubMed]

Artola A, Singer W (1993) Long-term depression of excitatory synaptic transmission and its relationship to long-term potentiation. Trends Neurosci 16:480-7 [PubMed]

Badel L, Lefort S, Brette R, Petersen CC, Gerstner W, Richardson MJ (2007) Dynamic I-V curves are reliable predictors of naturalistic pyramidal-neuron voltage traces. J Neurophysiol 99(2):656-66 [PubMed]

Bi G, Poo M (2001) Synaptic modification by correlated activity: Hebb's postulate revisited. Annu Rev Neurosci 24:139-66 [PubMed]

Bienenstock EL, Cooper LN, Munro PW (1982) Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. J Neurosci 2:32-48 [PubMed]

Bliss TV, Collingridge GL (1993) A synaptic model of memory: long-term potentiation in the hippocampus. Nature 361:31-9 [PubMed]

Bliss TV, Gardner-Medwin AR (1973) Long-lasting potentiation of synaptic transmission in the dentate area of the unanaestetized rabbit following stimulation of the perforant path. J Physiol 232:357-74 [PubMed]

Brette R, Gerstner W (2005) Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J Neurophysiol 94:3637-42 [Journal] [PubMed]

  • Adaptive exponential integrate-and-fire model (Brette & Gerstner 2005) [Model]

Buonomano DV, Merzenich MM (1998) Cortical plasticity: from synapses to maps. Annu Rev Neurosci 21:149-86 [PubMed]

Carpenter G, Grossberg S (1987) Art 2: Self-organization of stable category recognition codes for analog input patterns. Appl Opt 26:4919-4930

Crick F (2004) Memory and molecular turnover. Nature 312:101-4 [PubMed]

Dudek SM, Bear MF (1992) Homosynaptic long-term depression in area CA1 of hippocampus and effects of N-methyl-D-aspartate receptor blockade. Proc Natl Acad Sci U S A 89:4363-7 [PubMed]

Fonseca R, Nagerl UV, Bonhoeffer T (2006) Neuronal activity determines the protein synthesis dependence of long-term potentiation. Nat Neurosci 9:478-80

Frey U, Morris RG (1997) Synaptic tagging and long-term potentiation. Nature 385:533-6 [PubMed]

Frey U, Schroeder H, Matthies H (1990) Dopaminergic antagonists prevent long-term maintenance of posttetanic LTP in the CA1 region of rat hippocampal slices. Brain Res 522:69-75 [PubMed]

Fusi S (2002) Hebbian spike-driven synaptic plasticity for learning patterns of mean firing rates. Biol Cybern 87:459-70 [PubMed]

Fusi S, Drew PJ, Abbott LF (2005) Cascade models of synaptically stored memories. Neuron 45:599-611 [PubMed]

Gerstner W, Kempter R, van Hemmen JL, Wagner H (1996) A neuronal learning rule for sub-millisecond temporal coding. Nature 383:76-81 [PubMed]

Gerstner W, Kistler WM (2002) Spiking neuron models

Govindarajan A, Kelleher RJ, Tonegawa S (2006) A clustered plasticity model of long-term memory engrams. Nat Rev Neurosci 7:575-83 [PubMed]

Graupner M, Brunel N (2007) STDP in a bistable synapse model based on CaMKII and associated signaling pathways. PLoS Comput Biol 3:e221 [Journal] [PubMed]

  • CaMKII system exhibiting bistability with respect to calcium (Graupner and Brunel 2007) [Model]

Hayer A, Bhalla US (2005) Molecular switches at the synapse emerge from receptor and kinase traffic. PLoS Comput Biol 1:137-54 [PubMed]

Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci U S A 79:2554-8 [PubMed]

Izhikevich EM (2007) Solving the Distal Reward Problem through Linkage of STDP and Dopamine Signaling. Cereb Cortex 17(10):2443-2452 [Journal] [PubMed]

  • Linking STDP and Dopamine action to solve the distal reward problem (Izhikevich 2007) [Model]

Kempter R, Gerstner W, van_Hemmen JL (1999) Hebbian learning and spiking neurons Physical Review E 59:4498-4514 [Journal]

Krug M, Lossner B, Ott T (1984) Anisomycin blocks the late phase of long-term potentiation in the dentate gyrus of freely moving rats. Brain Res Bull 13:39-42 [PubMed]

Legenstein R, Pecevski D, Maass W (2008) A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback. PLoS Comput Biol 4:e1000180 [Journal] [PubMed]

  • Reward modulated STDP (Legenstein et al. 2008) [Model]

Lisman J (1989) A mechanism for the Hebb and the anti-Hebb processes underlying learning and memory. Proc Natl Acad Sci U S A 86:9574-8 [PubMed]

Lisman J (2003) Long-term potentiation: outstanding questions and attempted synthesis. Philos Trans R Soc Lond B Biol Sci 358:829-42 [PubMed]

Lisman J, Schulman H, Cline H (2002) The molecular basis of CaMKII function in synaptic and behavioural memory. Nat Rev Neurosci 3:175-90 [PubMed]

Lisman JE (1985) A mechanism for memory storage insensitive to molecular turnover: a bistable autophosphorylating kinase. Proc Natl Acad Sci U S A 82:3055-7 [PubMed]

Malenka RC, Bear MF (2004) LTP and LTD: an embarrassment of riches. Neuron 44:5-21 [PubMed]

Markram H, Lubke J, Frotscher M, Sakmann B (1997) Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275:213-5 [PubMed]

Markram H, Wang Y, Tsodyks M (1998) Differential signaling via the same axon of neocortical pyramidal neurons. Proc Natl Acad Sci U S A 95:5323-8 [PubMed]

Miller P, Zhabotinsky AM, Lisman JE, Wang XJ (2005) The stability of a stochastic CaMKII switch: dependence on the number of enzyme molecules and protein turnover. PLoS Biol 3:e107-8 [PubMed]

Nadal JP, Toulouse G, Changeux JP, Dehaene S (1986) Networks of formal neurons and memory palimpsests Europhys Lett 1:535

Navakkode S, Sajikumar S, Frey JU (2007) Synergistic requirements for the induction of dopaminergic D1/D5-receptor-mediated LTP in hippocampal slices of rat CA1 in vitro. Neuropharmacology 52:1547-54

Newpher TM, Ehlers MD (2008) Glutamate receptor dynamics in dendritic microdomains. Neuron 58:472-97 [PubMed]

Ngezahayo A, Schachner M, Artola A (2000) Synaptic activity modulates the induction of bidirectional synaptic changes in adult mouse hippocampus. J Neurosci 20:2451-8 [PubMed]

O'Connor DH, Wittenberg GM, Wang SS (2005) Graded bidirectional synaptic plasticity is composed of switch-like unitary events. Proc Natl Acad Sci U S A 102:9679-84 [PubMed]

O'Connor DH, Wittenberg GM, Wang SS (2005) Dissection of bidirectional synaptic plasticity into saturable unidirectional processes. J Neurophysiol 94:1565-73 [PubMed]

Otmakhov N, Griffith LC, Lisman JE (1997) Postsynaptic inhibitors of calcium-calmodulin-dependent protein kinase type II block induction but not maintenance of pairing-induced long-term potentiation. J Neurosci 17:5357-65 [PubMed]

Pastalkova E, Serrano P, Pinkhasova D, Wallace E, Fenton AA, Sacktor TC (2006) Storage of spatial information by the maintenance mechanism of LTP. Science 313:1141-4 [PubMed]

Petersen CC, Malenka RC, Nicoll RA, Hopfield JJ (1998) All-or-none potentiation at CA3-CA1 synapses. Proc Natl Acad Sci U S A 95:4732-7 [PubMed]

Pfister JP, Gerstner W (2006) Triplets of spikes in a model of spike timing-dependent plasticity. J Neurosci 26:9673-82 [PubMed]

Pfister JP, Toyoizumi T, Barber D, Gerstner W (2006) Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning. Neural Comput 18:1318-48 [PubMed]

Reymann KG, Frey JU (2007) The late maintenance of hippocampal LTP: requirements, phases, 'synaptic tagging', 'late-associativity' and implications. Neuropharmacology 52:24-40 [PubMed]

Sajikumar S, Frey JU (2004) Late-associativity, synaptic tagging, and the role of dopamine during LTP and LTD. Neurobiol Learn Mem 82:12-25 [PubMed]

Sajikumar S, Frey JU (2004) Resetting of 'synaptic tags' is time- and activity-dependent in rat hippocampal CA1 in vitro. Neuroscience 129:503-7 [PubMed]

Sajikumar S, Navakkode S, Frey JU (2007) Identification of compartment- and process-specific molecules required for "synaptic tagging" during long-term potentiation and long-term depression in hippocampal CA1. J Neurosci 27:5068-80 [PubMed]

Sajikumar S, Navakkode S, Sacktor TC, Frey JU (2005) Synaptic tagging and cross-tagging: the role of protein kinase Mzeta in maintaining long-term potentiation but not long-term depression. J Neurosci 25:5750-6 [PubMed]

Schultz W, Dayan P, Montague PR (1997) A neural substrate of prediction and reward. Science 275:1593-9 [PubMed]

Smolen P, Baxter DA, Byrne JH (2006) A model of the roles of essential kinases in the induction and expression of late long-term potentiation. Biophys J 90:2760-75 [Journal] [PubMed]

  • Roles of essential kinases in induction of late hippocampal LTP (Smolen et al., 2006) [Model]

Song S, Miller KD, Abbott LF (2000) Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat Neurosci 3:919-26 [PubMed]

Sutton RS, Barto AG (1998) Reinforcement learning: an introduction [Journal]

  • A reinforcement learning example (Sutton and Barto 1998) [Model]

Toyoizumi T, Pfister JP, Aihara K, Gerstner W (2007) Optimality model of unsupervised spike-timing-dependent plasticity: synaptic memory and weight distribution. Neural Comput 19:639-71 [PubMed]

Clopath C, Busing L, Vasilaki E, Gerstner W (2010) Connectivity reflects coding: a model of voltage-based STDP with homeostasis. Nat Neurosci 13:344-52 [Journal] [PubMed]

  • Voltage-based STDP synapse (Clopath et al. 2010) [Model]

Esposito U, Giugliano M, van Rossum M, Vasilaki E (2014) Measuring symmetry, asymmetry and randomness in neural network connectivity. PLoS One 9:e100805 [Journal] [PubMed]

  • Statistics of symmetry measure for networks of neurons (Esposito et al. 2014) [Model]

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 [Journal] [PubMed]

  • Model of memory linking through memory allocation (Kastellakis et al. 2016) [Model]

Manninen T, Hituri K, Kotaleski JH, Blackwell KT, Linne ML (2010) Postsynaptic signal transduction models for long-term potentiation and depression. Front Comput Neurosci 4:152 [Journal] [PubMed]

Vasilaki E, Giugliano M (2014) Emergence of connectivity motifs in networks of model neurons with short- and long-term plastic synapses. PLoS One 9:e84626 [Journal] [PubMed]

  • Emergence of Connectivity Motifs in Networks of Model Neurons (Vasilaki, Giugliano 2014) [Model]

(64 refs)