The model is used to illustrate the role of neuromodulators in cortical plasticity. The model consists of a feedforward network with 1 postsynaptic neuron with plastic synaptic weights. These weights are updated through a spike-timing-dependent plasticity rule. "First, we explore the ability of neuromodulators to gate plasticity by reshaping the learning window for spike-timing-dependent plasticity. Using a simple computational model, we implement four different learning rules and demonstrate their effects on receptive field plasticity. We then compare the neuromodulatory effects of upregulating learning rate versus the effects of upregulating neuronal activity. "
Cell Type(s): Abstract integrate-and-fire fractional leaky neuron
Model Concept(s): STDP; Synaptic Plasticity; Learning; Neuromodulation
Simulation Environment: Python
Implementer(s): Pedrosa, Victor [v.pedrosa15 at imperial.ac.uk]
References:
Clopath C, Pedrosa V. (2017). The role of neuromodulators in cortical plasticity. A computational perspective. Front. Synaptic Neurosci.. 8