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Rewarded spike timing dependent plasticity (STDP) has been implicated as a possible learning mechanism in a variety of brain systems. This mechanism combines unsupervised STDP that modifies synaptic strength depending on the relative timing of presynaptic input and postsynaptic spikes together with a reinforcement signal that modulates synaptic changes. In this study, rewarded STDP was implemented as part of a spiking network model of excitatory cells and inhibitory interneurons. The network was used to model basic foraging behavior ...

Spike timing dependent plasticity (STDP) modifies synaptic strength depending on the relative timing of pre-synaptic input and post-synaptic spikes. The effects of STDP in computational models often lead to physiologically inaccurate or unstable distributions of synaptic strength. Examples of stable but physiologically unrealistic synaptic strength distributions include bimodal distributions and highly skewed distributions. Connection strengths also tend to be excessively unstable for learning. Preventing this “run-away” dynamics using basic STDP mechanisms requires fine tuning of learning rules and ...