Skip to main content

Spike-Timing-Dependent Plasticity, Learning Rules

  • Living reference work entry
  • First Online:
  • 535 Accesses

Synonyms

Spike-dependent synaptic learning rules; Spike-timing-dependent synaptic plasticity; STDP

Definition

Biological phenomenon. Spike-timing-dependent plasticity (STDP) in its narrow sense refers to the change in the synaptic strength as a result of repeatedly triggering pairs of action potentials (“spikes”) with a fixed time difference between the pre- and postsynaptic action potentials (Markram et al. 1997; Bi and Poo 1998; Sjostrom et al. 2001). STDP is typically observed for synapses between hippocampal or cortical pyramidal neurons in slices of juvenile rodents, and the spike pairings are repeated 50–100 times with various frequencies, e.g. 1 or 10 Hz. This protocol induces a change in the amplitude of a single excitatory postsynaptic potential (EPSP) which is plotted against the spike time difference Δt = t postt pre between the postsynaptic spike and the presynaptic spike (Fig. 1). The change takes in many cases a few minutes to be expressed and lasts at least for the...

This is a preview of subscription content, log in via an institution.

References

  • Artola A, Bröcher 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

    Article  PubMed  CAS  Google Scholar 

  • Bi G, Poo M (1998) Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci 18:10464–10472

    PubMed  CAS  Google Scholar 

  • Bi G, Poo M (2001) Synaptic modification by correlated activity: Hebb’s postulate revisited. Annu Rev Neurosci 24:139–166

    Article  PubMed  CAS  Google Scholar 

  • 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  CAS  Google Scholar 

  • Brea J, Senn W, Pfister JP (2013) Matching recall and storage in sequence learning with spiking neural networks. J Neurosci 33:9565–9575

    Article  PubMed  CAS  Google Scholar 

  • Buchs NJ, Senn W (2002) Spike-based synaptic plasticity and the emergence of direction selective simple cells: simulation results. J Comput Neurosci 13:167–168

    Article  PubMed  CAS  Google Scholar 

  • Clopath C, Gerstner W (2010) Voltage and Spike Timing Interact in STDP – a unified model. Front Synaptic Neurosci 2:25

    PubMed  PubMed Central  Google Scholar 

  • Clopath C, Büsing L, Vasilaki E, Gerstner W (2010) Connectivity reflects coding: a model of voltage-based STDP with homeostasis. Nat Neurosci 13:344–352

    Article  PubMed  CAS  Google Scholar 

  • Florian RV (2007) Reinforcement learning through modulation of spike-timing-dependent synaptic plasticity. Neural Comput 19:1468–1502

    Article  PubMed  Google Scholar 

  • Frémaux N, Sprekeler H, Gerstner W (2010) Functional requirements for reward-modulated spike-timing-dependent plasticity. J Neurosci 30:13326–13337

    Article  PubMed  Google Scholar 

  • Friedrich J, Urbanczik R, Senn W (2011) Spatio-temporal credit assignment in neuronal population learning. PLoS Comput Biol 7:e1002092

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  • Friedrich J, Urbanczik R, Senn W (2014) Code-specific learning rules improve action selection by populations of spiking neurons. Int J Neural Syst 24:1–17

    Article  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  • Gjorgjieva J, Clopath C, Audet J, Pfister JP (2011) A triplet spike-timing-dependent plasticity model generalizes the Bienenstock-Cooper-Munro rule to higher-order spatiotemporal correlations. Proc Natl Acad Sci U S A 108:19383–19388

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  • Gordon U, Polsky A, Schiller J (2006) Plasticity compartments in basal dendrites of neocortical pyramidal neurons. J Neurosci 26:12717–12726

    Article  PubMed  CAS  Google Scholar 

  • Graupner M, Brunel N (2012) Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location. Proc Natl Acad Sci U S A 109:3991–3996

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  • Izhikevich EM (2007) Solving the distal reward problem through linkage of STDP and dopamine signaling. Cereb Cortex 17:2443–2452

    Article  PubMed  Google Scholar 

  • Karmarkar UR, Buonomano DV (2002) A model of spike-timing dependent plasticity: one or two coincidence detectors? J Neurophysiol 88:507–513

    PubMed  Google Scholar 

  • Kempter R, Gerstner W, van Hemmen JL (2001) Intrinsic stabilization of output rates by spike-based Hebbian learning. Neural Comput 13:2709–2741

    Article  PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  PubMed Central  Google Scholar 

  • Markram H, Lübke J, Frotscher M, Sakmann B (1997) Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275:213–215

    Article  PubMed  CAS  Google Scholar 

  • Pfister JP, Gerstner W (2006) Triplets of spikes in a model of spike timing-dependent plasticity. J Neurosci 26:9673–9682

    Article  PubMed  CAS  Google Scholar 

  • Pfister J, Toyoizumi T, Barber D, Gerstner W (2006) Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning. Neural Comput 18:1318–1348

    Article  PubMed  Google Scholar 

  • Rubin JE, Gerkin RC, Bi GQ, Chow CC (2005) Calcium time course as a signal for spike-timing-dependent plasticity. J Neurophysiol 93:2600–2613

    Article  PubMed  Google Scholar 

  • Schiess M, Urbanczik R, Senn W (2012) Gradient estimation in dendritic reinforcement learning. J Math Neurosci 2:2

    PubMed  PubMed Central  Google Scholar 

  • Senn W (2002) Beyond spike timing: the role of nonlinear plasticity and unreliable synapses. Biol Cybern 87:344–355

    Article  PubMed  Google Scholar 

  • Senn W, Markram H, Tsodyks M (2001) An algorithm for modifying neurotransmitter release probability based on pre- and postsynaptic spike timing. Neural Comput 13:35–67

    Article  PubMed  CAS  Google Scholar 

  • Seung HS (2003) Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron 40:1063–1073

    Article  PubMed  CAS  Google Scholar 

  • Shouval HZ, Bear MF, Cooper LN (2002) A unified model of NMDA receptor-dependent bidirectional synaptic plasticity. Proc Natl Acad Sci U S A 99:10831–10836

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  • Sjöström J, Gerstner W (2010) Spike-timing dependent plasticity. Scholarpedia 5:1362

    Article  Google Scholar 

  • Sjostrom PJ, Hausser M (2006) A cooperative switch determines the sign of synaptic plasticity in distal dendrites of neocortical pyramidal neurons. Neuron 51:227–238

    Article  PubMed  CAS  Google Scholar 

  • Sjostrom PJ, Turrigiano GG, Nelson SB (2001) Rate, timing, and cooperativity jointly determine cortical synaptic plasticity. Neuron 32:1149–1164

    Article  PubMed  CAS  Google Scholar 

  • Sjostrom PJ, Turrigiano GG, Nelson SB (2004) Endocannabinoid-dependent neocortical layer-5 LTD in the absence of postsynaptic spiking. J Neurophysiol 92:3338–3343

    Article  PubMed  CAS  Google Scholar 

  • Sjostrom PJ, Rancz EA, Roth A, Hausser M (2008) Dendritic excitability and synaptic plasticity. Physiol Rev 88:769–840

    Article  PubMed  CAS  Google Scholar 

  • Song S, Abbott LF (2001) Cortical development and remapping through spike timing-dependent plasticity. Neuron 32:339–350

    Article  PubMed  CAS  Google Scholar 

  • Toyoizumi T, Pfister JP, Aihara K, Gerstner W (2007) Optimality model of unsupervised spike-timing-dependent plasticity: synaptic memory and weight distribution. Neural Comput 19:639–671

    Article  PubMed  Google Scholar 

  • Urbanczik R, Senn W (2009) Reinforcement learning in populations of spiking neurons. Nat Neurosci 12:250–252

    Article  PubMed  CAS  Google Scholar 

  • Urbanczik R, Senn W (2014) Learning by the dendritic prediction of somatic spiking. Neuron (in press)

    Google Scholar 

  • Xie X, Seung HS (2004) Learning in neural networks by reinforcement of irregular spiking. Phys Rev E Stat Nonlin Soft Matter Phys 69:041909

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Walter Senn .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this entry

Cite this entry

Senn, W., Pfister, JP. (2014). Spike-Timing-Dependent Plasticity, Learning Rules. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_683-1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-7320-6_683-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4614-7320-6

  • eBook Packages: Springer Reference Biomedicine and Life SciencesReference Module Biomedical and Life Sciences

Publish with us

Policies and ethics