Abstract
Computational neuroscientists have been playing around with plastic synapses for several decades. Interestingly, mechanistically detailed models of synaptic plasticity started around the same time as the CNS meetings. This was when the associative properties of the N-methyl-d-aspartate (NMDA) receptor were demonstrated, first setting out the molecular and mechanistic underpinnings of synaptic plasticity. Some 20 years ago there was little reason to expect that the underlying biology would turn out to be as outrageously complicated as we now find it. Associativity seemed to be established by the NMDA receptor especially through the work of Collingridge, and there were already a couple of candidate mechanisms for how to maintain synaptic weights: the CaMKII autocatalytic process found by several people and first modeled by Lisman, and the PKA story from Kandel. These leads led into a maze. Even 10 years ago, there were over a 100 known molecules implicated in synaptic plasticity. The first major molecular models of synaptic plasticity had some dozen signaling pathways—a far cry from what was known. The field as a whole is still playing catch-up. Nevertheless, most of the key properties of plasticity have had a good share of models, at various levels of detail. I suggest that there has been a recent shift in perspective, from enumerating molecules to looking at functional roles that may involve different, often overlapping sets of molecules. It is the identification and integration of these diverse functions of the synapse that is the key conceptual direction of the field. This has combined with technical and data-driven advances in managing and modeling multiscale phenomena spanning single-molecule reaction–diffusion, through chemistry, electrical and structural effects, and the network. As many of us felt, 20 years ago, we are again at a fascinating time where the experiments, the databases, and the computational tools are just coming together to address these questions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ajay SM, Bhalla US (2004) A role for ERKII in synaptic pattern selectivity on the time-scale of minutes. Eur J Neurosci 20(10):2671–2680. doi:10.1111/j.1460-9568.2004.03725.x
Ajay SM, Bhalla US (2007) A propagating ERKII switch forms zones of elevated dendritic activation correlated with plasticity. HFSP J 1(1):49–66. doi:10.2976/1.2721383
Alon U (2007) Network motifs: theory and experimental approaches. Nat Rev Genet 8(6):450–461. doi:10.1038/nrg2102
Andrews SS, Addy NJ, Brent R, Arkin AP (2010) Detailed simulations of cell biology with Smoldyn 2.1. PLoS Comput Biol 6(3):e1000705. doi:10.1371/journal.pcbi.1000705
Anglister L, Stiles JR, Salpeter MM (1994) Acetylcholinesterase density and turnover number at frog neuromuscular junctions, with modeling of their role in synaptic function. Neuron 12(4):783–794
Appleby PA, Elliott T (2005) Synaptic and temporal ensemble interpretation of spike-timing-dependent plasticity. Neural Comput 17(11):2316–2336. doi:10.1162/0899766054796879
Aslam N, Kubota Y, Wells D, Shouval HZ (2009) Translational switch for long-term maintenance of synaptic plasticity. Mol Syst Biol 5:284. doi:10.1038/msb.2009.38
Badoual M, Zou Q, Davison AP, Rudolph M, Bal T, Frégnac Y, Destexhe A (2006) Biophysical and phenomenological models of multiple spike interactions in spike-timing dependent plasticity. Int J Neural Syst 16(2):79–97
Barkai E, Hasselmo ME (1994) Modulation of the input/output function of rat piriform cortex pyramidal cells. J Neurophysiol 72(2):644–658
Bennett MR, Gibson WG, Robinson J (1995) Probabilistic secretion of quanta: spontaneous release at active zones of varicosities, boutons, and endplates. Biophys J 69(1):42–56. doi:10.1016/S0006-3495(95)79873-3
Bhalla US (2002a) Biochemical signaling networks decode temporal patterns of synaptic input. J Comput Neurosci 13(1):49–62
Bhalla US (2002b) Mechanisms for temporal tuning and filtering by postsynaptic signaling pathways. Biophys J 83(2):740–752. doi:10.1016/S0006-3495(02)75205-3
Bhalla US (2003) Understanding complex signaling networks through models and metaphors. Prog Biophys Mol Biol 81(1):45–65
Bhalla US (2004) Signaling in small subcellular volumes. II. Stochastic and diffusion effects on synaptic network properties. Biophys J 87(2):745–753. doi:10.1529/biophysj.104.040501
Bhalla US, Iyengar R (1999) Emergent properties of networks of biological signaling pathways. Science 283(5400):381–387
Bhalla US, Ram PT, Iyengar R (2002) MAP kinase phosphatase as a locus of flexibility in a mitogen-activated protein kinase signaling network. Science 297(5583):1018–1023
Bi GQ, Poo MM (1998) Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci 18(24):10464–10472
Bialek W (2001) Stability and noise in biochemical switches. Adv Neural Inf Process Syst 13:103–109
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(1):32–48
Bliss TV, Collingridge GL (1993) A synaptic model of memory: long-term potentiation in the hippocampus. Nature 361(6407):31–39. doi:10.1038/361031a0
Byrne MJ, Putkey JA, Waxham MN, Kubota Y (2009) Dissecting cooperative calmodulin binding to CaM kinase II: a detailed stochastic model. J Comput Neurosci 27(3):621–638. doi:10.1007/s10827-009-0173-3
Clopath C, Ziegler L, Vasilaki E, BĂĽsing L, Gerstner W (2008) Tag-trigger-consolidation: a model of early and late long-term-potentiation and depression. PLoS Comput Biol 4(12):e1000248. doi:10.1371/journal.pcbi.1000248
Coggan JS, Bartol TM, Esquenazi E, Stiles JR, Lamont S, Martone ME, Berg DK et al (2005) Evidence for ectopic neurotransmission at a neuronal synapse. Science 309(5733):446–451. doi:10.1126/science.1108239
Crook SM, Dur-E-Ahmad M, Baer SM (2007a) A model of activity-dependent changes in dendritic spine density and spine structure. Math Biosci Eng 4(4):617–631
Crook S, Gleeson P, Howell F, Svitak J, Silver RA (2007b) MorphML: level 1 of the NeuroML standards for neuronal morphology data and model specification. Neuroinformatics 5(2):96–104
D’Alcantara P, Schiffmann SN, Swillens S (2003) Bidirectional synaptic plasticity as a consequence of interdependent Ca2+-controlled phosphorylation and dephosphorylation pathways. Eur J Neurosci 17(12):2521–2528
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 USA 89(10):4363–4367
Ehlers MD (2003) Activity level controls postsynaptic composition and signaling via the ubiquitin-proteasome system. Nat Neurosci 6(3):231–242. doi:10.1038/nn1013
Ferrell JE, Xiong W (2001) Bistability in cell signaling: how to make continuous processes discontinuous, and reversible processes irreversible. Chaos 11(1):227–236
Froemke RC, Poo M, Dan Y (2005) Spike-timing-dependent synaptic plasticity depends on dendritic location. Nature 434(7030):221–225
Gillespie DT (1977) Exact stochastic simulation of coupled chemical reactions. J Phys Chem 81(25):2340–2361. doi:10.1021/j100540a008
Gold JI, Bear MF (1994) A model of dendritic spine Ca2+ concentration exploring possible bases for a sliding synaptic modification threshold. Proc Natl Acad Sci USA 91(9):3941–3945
Graupner M, Brunel N (2007) STDP in a bistable synapse model based on CaMKII and associated signaling pathways. PLoS Comput Biol 3(11):e221. doi:10.1371/journal.pcbi.0030221
Hasselmo ME, Anderson BP, Bower JM (1992) Cholinergic modulation of cortical associative memory function. J Neurophysiol 67(5):1230–1246
Hayer A, Bhalla US (2005) Molecular switches at the synapse emerge from receptor and kinase traffic. PLoS Comput Biol 1(2):137–154
Hebb D (1949) The organization of behavior. Wiley, New York
Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79(8):2554–2558
Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, Arkin AP et al (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19(4):524–531
Ito M (1989) Long-term depression. Annu Rev Neurosci 12:85–102. doi:10.1146/annurev.ne.12.030189.000505
Jaeger D, De Schutter E, Bower JM (1997) The role of synaptic and voltage-gated currents in the control of Purkinje cell spiking: a modeling study. J Neurosci 17(1):91–106
Jain P, Bhalla US (2009) Signaling logic of activity-triggered dendritic protein synthesis: an mTOR gate but not a feedback switch. PLoS Comput Biol 5(2):e1000287. doi:10.1371/journal.pcbi.1000287
Kim M, Huang T, Abel T, Blackwell KT (2010) Temporal sensitivity of protein kinase a activation in late-phase long term potentiation. PLoS Comput Biol 6(2):e1000691. doi:10.1371/journal.pcbi.1000691
Kuroda S, Schweighofer N, Kawato M (2001) Exploration of signal transduction pathways in cerebellar long-term depression by kinetic simulation. J Neurosci 21(15):5693–5702
Lee CJ, Anton M, Poon C, McRae GJ (2009) A kinetic model unifying presynaptic short-term facilitation and depression. J Comput Neurosci 26(3):459–473. doi:10.1007/s10827-008-0122-6
Lindskog M, Kim M, Wikström MA, Blackwell KT, Kotaleski JH (2006) Transient calcium and dopamine increase PKA activity and DARPP-32 phosphorylation. PLoS Comput Biol 2(9):e119. doi:10.1371/journal.pcbi.0020119
Lisman J (1989) A mechanism for the Hebb and the anti-Hebb processes underlying learning and memory. Proc Natl Acad Sci USA 86(23):9574–9578
Lisman JE, Zhabotinsky AM (2001) A model of synaptic memory: a CaMKII/PP1 switch that potentiates transmission by organizing an AMPA receptor anchoring assembly. Neuron 31(2):191–201
Liu Z, Golowasch J, Marder E, Abbott LF (1998) A model neuron with activity-dependent conductances regulated by multiple calcium sensors. J Neurosci 18(7):2309–2320
Magee JC, Johnston D (1997) A synaptically controlled, associative signal for Hebbian plasticity in hippocampal neurons. Science 275(5297):209–213
Malinow R, Schulman H, Tsien RW (1989) Inhibition of postsynaptic PKC or CaMKII blocks induction but not expression of LTP. Science 245(4920):862–866
Marder E, Goaillard J (2006) Variability, compensation and homeostasis in neuron and network function. Nat Rev Neurosci 7(7):563–574. doi:10.1038/nrn1949
Markevich NI, Hoek JB, Kholodenko BN (2004) Signaling switches and bistability arising from multisite phosphorylation in protein kinase cascades. J Cell Biol 164(3):353–359
Markram H, Lübke J, Frotscher M, Sakmann B (1997) Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275(5297):213–215
Miller P, Zhabotinsky AM, Lisman JE, Wang X (2005) The stability of a stochastic CaMKII switch: dependence on the number of enzyme molecules and protein turnover. PLoS Biol 3(4):e107
Minsky M, Papert S (1969) Perceptrons: an introduction to computational geometry. MIT Press, Cambridge, MA
Mulkey RM, Malenka RC (1992) Mechanisms underlying induction of homosynaptic long-term depression in area CA1 of the hippocampus. Neuron 9(5):967–975
Nakano T, Doi T, Yoshimoto J, Doya K (2010) A kinetic model of dopamine- and calcium-dependent striatal synaptic plasticity. PLoS Comput Biol 6(2):e1000670. doi:10.1371/journal.pcbi.1000670
Nowak L, Bregestovski P, Ascher P, Herbet A, Prochiantz A (1984) Magnesium gates glutamate-activated channels in mouse central neurones. Nature 307(5950):462–465
Oliveira RF, Terrin A, Di Benedetto G, Cannon RC, Koh W, Kim M, Zaccolo M et al (2010) The role of type 4 phosphodiesterases in generating microdomains of cAMP: large scale stochastic simulations. PLoS One 5(7):e11725. doi:10.1371/journal.pone.0011725
Olypher AV, Prinz AA (2010) Geometry and dynamics of activity-dependent homeostatic regulation in neurons. J Comput Neurosci 28(3):361–374. doi:10.1007/s10827-010-0213-z
Pepke S, Kinzer-Ursem T, Mihalas S, Kennedy MB (2010) A dynamic model of interactions of Ca2+, calmodulin, and catalytic subunits of Ca2+/calmodulin-dependent protein kinase II. PLoS Comput Biol 6(2):e1000675. doi:10.1371/journal.pcbi.1000675
Pfister J, Gerstner W (2006) Triplets of spikes in a model of spike timing-dependent plasticity. J Neurosci 26(38):9673–9682
Ramakrishnan N, Bhalla US (2008) Memory switches in chemical reaction space. PLoS Comput Biol 4(7):e1000122. doi:10.1371/journal.pcbi.1000122
Rosenblatt F (1962) Principles of perceptrons. Spartan, Washington, DC
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533–536
Sanes JR, Lichtman JW (1999) Can molecules explain long-term potentiation? Nat Neurosci 2(7):597–604. doi:10.1038/10154
Santamaria F, Wils S, De Schutter E, Augustine GJ (2006) Anomalous diffusion in Purkinje cell dendrites caused by spines. Neuron 52(4):635–648. doi:10.1016/j.neuron.2006.10.025
Sanyal S, Sandstrom DJ, Hoeffer CA, Ramaswami M (2002) AP-1 functions upstream of CREB to control synaptic plasticity in Drosophila. Nature 416(6883):870–874. doi:10.1038/416870a
Shema R, Sacktor TC, Dudai Y (2007) Rapid erasure of long-term memory associations in the cortex by an inhibitor of PKM zeta. Science 317(5840):951–953. doi:10.1126/science.1144334
Shouval HZ (2005) Clusters of interacting receptors can stabilize synaptic efficacies. Proc Natl Acad Sci USA 102(40):14440–14445. doi:10.1073/pnas.0506934102
Smolen P, Baxter DA, Byrne JH (2008) Bistable MAP kinase activity: a plausible mechanism contributing to maintenance of late long-term potentiation. Am J Physiol Cell Physiol 294(2):C503–C515. doi:10.1152/ajpcell.00447.2007
Smolen P, Baxter DA, Byrne JH (2009) Interlinked dual-time feedback loops can enhance robustness to stochasticity and persistence of memory. Phys Rev E Stat Nonlin Soft Matter Phys 79(3 Pt 1):031902
Song H, Smolen P, Av-Ron E, Baxter DA, Byrne JH (2006) Bifurcation and singularity analysis of a molecular network for the induction of long-term memory. Biophys J 90(7):2309–2325. doi:10.1529/biophysj.105.074500
Stanton PK, Sejnowski TJ (1989) Associative long-term depression in the hippocampus induced by hebbian covariance. Nature 339(6221):215–218. doi:10.1038/339215a0
Steuber V, Willshaw D (2004) A biophysical model of synaptic delay learning and temporal pattern recognition in a cerebellar Purkinje cell. J Comput Neurosci 17(2):149–164. doi:10.1023/B:JCNS.0000037678.26155.b5
Tanaka K, Augustine GJ (2008) A positive feedback signal transduction loop determines timing of cerebellar long-term depression. Neuron 59(4):608–620. doi:10.1016/j.neuron.2008.06.026
Turrigiano GG (1999) Homeostatic plasticity in neuronal networks: the more things change, the more they stay the same. Trends Neurosci 22(5):221–227
Turrigiano GG (2008) The self-tuning neuron: synaptic scaling of excitatory synapses. Cell 135(3):422–435. doi:10.1016/j.cell.2008.10.008
Tyson JJ, Chen KC, Novak B (2003) Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr Opin Cell Biol 15(2):221–231
Wils S, De Schutter E (2009) STEPS: modeling and simulating complex reaction-diffusion systems with python. Front Neuroinform 3:15. doi:10.3389/neuro.11.015.2009
Zador A, Koch C, Brown TH (1990) Biophysical model of a Hebbian synapse. Proc Natl Acad Sci USA 87(17):6718–6722
Zou Q, Destexhe A (2007) Kinetic models of spike-timing dependent plasticity and their functional consequences in detecting correlations. Biol Cybern 97(1):81–97
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this chapter
Cite this chapter
Bhalla, U.S. (2013). Still Looking for the Memories: Molecules and Synaptic Plasticity. In: Bower, J. (eds) 20 Years of Computational Neuroscience. Springer Series in Computational Neuroscience, vol 9. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1424-7_9
Download citation
DOI: https://doi.org/10.1007/978-1-4614-1424-7_9
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-1423-0
Online ISBN: 978-1-4614-1424-7
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)