STDP for Correlated Inputs under a Calcium-Dependent Learning Rule

We used a model of spike-timing-dependent plasticity (STDP) based on calcium signaling to test the effects of correlated inputs on synaptic weight distributions. Gilson and Fukai (2011) used amplitudes for the STDP curve that depended on synaptic strength and demonstrated the emergence of stable bimodal weight distributions. Those sets of synapses with correlated inputs were more strongly potentiated than those without. However, this model did not include any biophysical mechanism for STDP. Whereas most versions of STDP model the time difference between preand postsynaptic spikes explicitly, as in the above study, Shouval et al (2002) used a model of NMDA-R-dependent calcium signaling to effect long-term potentiation and depression in a similar spike-timing-dependent manner to traditional STDP. We implemented a biophysical model inspired by this that used weight dependence along with modified calcium dynamics. We found that there is systematic potentiation of inputs with strong correlation, and there is depression of inputs with weak correlation.