“School of Cognitive%20Sciences”
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| Paper IPM / Cognitive%20Sciences / 15483 |
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In plastic neuronal networks, the synaptic strengths are adapted to the neuronal activity. Specifically, spike-timing-dependent plasticity (STDP) is a fundamental mechanism that modifies the synaptic strengths based on the relative timing of pre- and postsynaptic spikes, taking into account the spikes temporal order. In many studies, propagation delays were neglected to avoid additional dynamic complexity or computational costs. So far, networks equipped with a classic STDP rule typically rule out bidirectional couplings (i.e., either loops or uncoupled states) and are, hence, not able to reproduce fundamental experimental findings. In this review paper, we consider additional features, e.g., extensions of the classic STDP rule or additional aspects like noise, in order to overcome the contradictions between theory and experiment. In addition, we review in detail recent studies showing that a classic STDP rule combined with realistic propagation patterns is able to capture relevant experimental findings. In two coupled oscillatory neurons with propagation delays, bidirectional synapses can be preserved and potentiated. This result also holds for large networks of type-II phase oscillators. In addition, not only the mean of the initial distribution of synaptic weights, but also its standard deviation crucially determines the emergent structural connectivity, i.e., the mean final synaptic weight, the number of two-neuron loops, and the symmetry of the final connectivity pattern. The latter is affected by the firing rates, where more symmetric synaptic configurations emerge at higher firing rates. Finally, we discuss these findings in the context of the computational neuroscience-based development of desynchronizing brain stimulation techniques.
Activity patterns of plastic neuronal networks determine their connectivity patterns and vice versa. Spike-timing-dependent plasticity (STDP) is a fundamental mechanism by which neurons adapt the strength of their mutual synaptic connections to the timing of their discharges. STDP adds a relevant amount of complexity to the dynamics of a neuronal network. Accordingly, in many studies, propagation delays between neurons were neglected. To overcome discrepancies between theoretical and experimental findings, more complex STDP mechanisms or additional noise sources were taken into account. We here review recent studies that have shown that a simple classic STDP rule combined with realistic dendritic and axonal propagation delays is able to explain relevant experimental findings and overcome the aforementioned discrepancies. Taking into account propagation delays is important for understanding the dynamics of neuronal networks and may help to further improve therapeutic brain stimulation techniques which aim at modulating plastic neuronal networks in diseased brains.
I. INTRODUCTION
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