TY - JOUR
T1 - Formation of feedforward networks and frequency synchrony by spike-timing-dependent plasticity
AU - Masuda, Naoki
AU - Kori, Hiroshi
N1 - Funding Information:
Acknowledgements We thank Brent Doiron and Taro Toyoizumi for critical reading of the manuscript and Tohru Ikeguchi and Tomoya Suzuki for discussion. Naoki Masuda thanks the Special Postdoctoral Researchers Program of RIKEN. Hiroshi Kori thanks financial support from the Humboldt foundation (Germany) and from 21st Century COE program “Nonlinearity via Singularity” in Department of Mathematics, Hokkaido University.
PY - 2007/6
Y1 - 2007/6
N2 - 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.
AB - 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.
KW - Complex networks
KW - Feedforward networks
KW - Spike-timing-dependent plasticity
KW - Synchronization
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U2 - 10.1007/s10827-007-0022-1
DO - 10.1007/s10827-007-0022-1
M3 - Article
C2 - 17393292
AN - SCOPUS:34249891338
SN - 0929-5313
VL - 22
SP - 327
EP - 345
JO - Journal of Computational Neuroscience
JF - Journal of Computational Neuroscience
IS - 3
ER -