A Quaternionic Rate-Based Synaptic Learning Rule Derived from Spike-Timing Dependent Plasticity
- 1.8k Downloads
Most of the differential Hebbian rules derived from Spike-Timing Dependent Plasticity (STDP) focus on the rates of change of post-synaptic activity that carries the information about the future and enables the neural network to predict. And the current model mainly consider three factors for the adjustment of synaptic weight, namely, the rate of pre- and post-synaptic activity and the rate of change of post-synaptic activity. We argue that the rate of change of pre-synaptic activity also plays an important role on the adjustment of synaptic weight. Hence, this paper proposes a quaternionic rate-based synaptic learning rule that depends on four elements, namely, the instantaneous firing rates of both pre- and post-synaptic neurons and their time derivatives.
KeywordsSpike-Timing Dependent Plasticity Quaternionic rate-based synaptic learning rule Instantaneous firing rate
This study was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB02060007), and Beijing Municipal Commission of Science and Technology (Z151100000915070, Z161100000216124). This research is conducted at Institute of Automation, Chinese Academy of Science.
- 1.Bengio, Y., Lee, D.H., Bornschein, J., Lin, Z.: Towards biologically plausible deep learning. arXiv preprint arXiv:1502.04156 (2015)
- 2.Bengio, Y., Mesnard, T., Fischer, A., Zhang, S., Wu, Y.: STDP as presynaptic activity times rate of change of postsynaptic activity. arXiv preprint arXiv:1509.05936 (2015)
- 3.Bi, G.Q., Poo, M.M.: Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 18(24), 10464–10472 (1998)Google Scholar
- 4.Chrol-Cannon, J., Grüning, A., Jin, Y.: The emergence of polychronous groups under varying input patterns, plasticity rules and network connectivities. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–6 (2012)Google Scholar
- 5.Froemke, R.C., Letzkus, J.J., Kampa, B.M., Hang, G.B., Stuart, G.J.: Dendritic synapse location and neocortical spike-timing-dependent plasticity. Front. Synaptic Neurosci. 2, 29 (2010)Google Scholar
- 10.Henneman, E., Mendell, L.M.: Functional organization of motoneuron pool and its inputs. In: Comprehensive Physiology, pp. 423–507 (2011). http://onlinelibrary.wiley.com/doi/10.1002/cphy.cp010211/abstract;jsessionid=DC8038A8B928C849064AFF986290D55A.f04t01
- 15.Markram, H., Sakmann, B.: Action potentials propagating back into dendrites trigger changes in efficacy of single-axon synapses between layer V pyramidal neurons. Soc. Neurosci. Abstr. 21, 2007 (1995)Google Scholar
- 16.Nessler, B., Pfeiffer, M., Maass, W.: STDP enables spiking neurons to detect hidden causes of their inputs. In: Advances in Neural Information Processing Systems, pp. 1357–1365 (2009)Google Scholar
- 19.Scellier, B., Bengio, Y.: Equilibrium propagation: bridging the gap between energy-based models and backpropagation. arXiv preprint arXiv:1602.05179 (2016)
- 20.Senn, W., Pfister, J.P.: Spike-timing-dependent plasticity, learning rules. In: Jaeger, D., Jung, R. (eds.) Encyclopedia of Computational Neuroscience, pp. 1–10. Springer, Heidelberg (2014)Google Scholar
- 24.Xie, X., Seung, H.S.: Spike-based learning rules and stabilization of persistent neural activity. Adv. Neural Inf. Process. Syst. 12, 199–208 (2000)Google Scholar