Abstract
Brain functions such as learning and memory rely on synaptic plasticity. Many studies have shown that synaptic plasticity can be driven by the timings between pre- and postsynaptic spikes, also known as spike-timing-dependent plasticity (STDP). In most of the modeling studies exploring STDP functions, presynaptic spikes have been postulated to be Poisson (random) spikes and postsynaptic neurons have been described using an integrate-and-fire model, for simplicity. However, experimental data suggest this is not necessarily true. In this study, we investigated how STDP worked in synaptic competition if more neurophysiologically realistic properties for pre- and postsynaptic dynamics were incorporated; presynaptic (input) spikes obeyed a gamma process and the postsynaptic neuron was a multi-timescale adaptive threshold model. Our results showed that STDP strengthened specific combinations of pre- and postsynaptic properties; regular spiking neurons favored regular input spikes whereas random spiking neurons did random ones, suggesting neural information coding utilizes both the properties.
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Ito, H., Kitano, K. (2014). Pre- and Postsynaptic Properties Regulate Synaptic Competition through Spike-Timing-Dependent Plasticity. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_92
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DOI: https://doi.org/10.1007/978-3-319-11179-7_92
Publisher Name: Springer, Cham
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