Biological Cybernetics

, Volume 98, Issue 6, pp 459-478

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

Phenomenological models of synaptic plasticity based on spike timing

  • Abigail MorrisonAffiliated withComputational Neuroscience Group, RIKEN Brain Science Institute
  • , Markus DiesmannAffiliated withComputational Neuroscience Group, RIKEN Brain Science InstituteBernstein Center for Computational Neuroscience, Albert-Ludwigs-University
  • , Wulfram GerstnerAffiliated withLaboratory of Computational Neuroscience, LCN, Brain Mind Institute and School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL) Email author 


Synaptic plasticity is considered to be the biological substrate of learning and memory. In this document we review phenomenological models of short-term and long-term synaptic plasticity, in particular spike-timing dependent plasticity (STDP). The aim of the document is to provide a framework for classifying and evaluating different models of plasticity. We focus on phenomenological synaptic models that are compatible with integrate-and-fire type neuron models where each neuron is described by a small number of variables. This implies that synaptic update rules for short-term or long-term plasticity can only depend on spike timing and, potentially, on membrane potential, as well as on the value of the synaptic weight, or on low-pass filtered (temporally averaged) versions of the above variables. We examine the ability of the models to account for experimental data and to fulfill expectations derived from theoretical considerations. We further discuss their relations to teacher-based rules (supervised learning) and reward-based rules (reinforcement learning). All models discussed in this paper are suitable for large-scale network simulations.


Spike-timing dependent plasticity Short term plasticity Modeling Simulation Learning