Selectivity and Metaplasticity in a Unified Calcium-Dependent Model

Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)

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Luk Chong Yeung, Brian Blais, Leon Cooper, Harel Shouval


A uni(cid:12)ed, biophysically motivated Calcium-Dependent Learning model has been shown to account for various rate-based and spike time-dependent paradigms for inducing synaptic plasticity. Here, we investigate the properties of this model for a multi-synapse neuron that receives inputs with di(cid:11)erent spike-train statistics. In addition, we present a physiological form of metaplasticity, an activity-driven regulation mechanism, that is essential for the ro- bustness of the model. A neuron thus implemented develops stable and selective receptive (cid:12)elds, given various input statistics