Learning in spiking neural networks by reinforcement of stochastic synaptic transmission

Neuron. 2003 Dec 18;40(6):1063-73. doi: 10.1016/s0896-6273(03)00761-x.


It is well-known that chemical synaptic transmission is an unreliable process, but the function of such unreliability remains unclear. Here I consider the hypothesis that the randomness of synaptic transmission is harnessed by the brain for learning, in analogy to the way that genetic mutation is utilized by Darwinian evolution. This is possible if synapses are "hedonistic," responding to a global reward signal by increasing their probabilities of vesicle release or failure, depending on which action immediately preceded reward. Hedonistic synapses learn by computing a stochastic approximation to the gradient of the average reward. They are compatible with synaptic dynamics such as short-term facilitation and depression and with the intricacies of dendritic integration and action potential generation. A network of hedonistic synapses can be trained to perform a desired computation by administering reward appropriately, as illustrated here through numerical simulations of integrate-and-fire model neurons.

MeSH terms

  • Action Potentials / physiology*
  • Learning / physiology*
  • Neural Networks, Computer*
  • Reinforcement, Psychology*
  • Stochastic Processes
  • Synaptic Transmission / physiology*