Formation of feedforward networks and frequency synchrony by spike-timing-dependent plasticity
Spike-timing-dependent plasticity (STDP) with asymmetric learning windows is commonly found in the brain and useful for a variety of spike-based computations such as input filtering and associative memory. A natural consequence of STDP is establishment of causality in the sense that a neuron learns to fire with a lag after specific presynaptic neurons have fired. The effect of STDP on synchrony is elusive because spike synchrony implies unitary spike events of different neurons rather than a causal delayed relationship between neurons. We explore how synchrony can be facilitated by STDP in oscillator networks with a pacemaker. We show that STDP with asymmetric learning windows leads to self-organization of feedforward networks starting from the pacemaker. As a result, STDP drastically facilitates frequency synchrony. Even though differences in spike times are lessened as a result of synaptic plasticity, the finite time lag remains so that perfect spike synchrony is not realized. In contrast to traditional mechanisms of large-scale synchrony based on mutual interaction of coupled neurons, the route to synchrony discovered here is enslavement of downstream neurons by upstream ones. Facilitation of such feedforward synchrony does not occur for STDP with symmetric learning windows.
KeywordsSpike-timing-dependent plasticity Synchronization Feedforward networks Complex networks
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- Bienenstock, E. (1991). Notes on the growth of a composition machine. In D. Andler, E. Bienenstock, & B. Laks (Eds.), Proceedings of the First Interdisciplinary Workshop on Compositionality in Cognition and Neural Networks (pp. 25–43). Abbaye de Royaumont, France.Google Scholar
- Gerstner, W., & Kistler, W. M. (2002). Spiking neuron models. Cambridge: Cambridge University Press.Google Scholar
- Glass, L., & Mackey, M. C. (1988). From clocks to chaos—The rhythms of life. Princeton: Princeton University Press.Google Scholar
- Hansel, D., Mato, G., & Meunier, C. (1993). Phase dynamics for weakly coupled Hodgkin–Huxley neurons. Europhysics Letters, 23(5), 367–372.Google Scholar
- Horn, D., Levy, N., Meilijson, I., & Ruppin, E. (2000). Distributed synchrony of spiking neurons in a Hebbian cell assembly. In S. A. Solla, T. K. Leen, & K.-R. Müller (Eds.), Advances in Neural Information Processing Systems, (Vols. 25–43, pp. 129–135). Cambridge, MA: MIT.Google Scholar
- Kori, H., & Mikhailov A. S. (2006). Strong effects of network architecture in the entrainment of coupled oscillator systems. Physical Review E, 74, 066115Google Scholar
- Kuramoto, Y. (1984). Chemical oscillations, waves, and turbulence. Berlin: Springer.Google Scholar
- Pikovsky, A., Rosenblum, M., & Kurths, J. (2001). Synchronization—A universal concept in nonlinear sciences. Cambridge, UK: Cambridge University Press.Google Scholar
- Reyes, A. D. (2003). Synchrony-dependent propagation of firing rate in iteratively constructed networks in vitro. Nature Neuroscience, 6(6), 593–599.Google Scholar
- Winfree, A. T. (1980). The geometry of biological time. New York: Springer.Google Scholar
- Zhigulin, V. P., & Rabinovich, M. I. (2004). An important role of spike timing dependent synaptic plasticity in the formation of synchronized neural ensembles. Neurocomputing, 58–60, 373–378.Google Scholar