Probabilistic versus Incremental Presynaptic Learning in Biologically Plausible Synapses

In this paper, the presynaptic rule, a classical rule for hebbian learning, is revisited. It is shown that the presynaptic rule exhibits relevant synaptic properties like synaptic directionality, and LTP metaplasticity (long-term potentiation threshold metaplasticity). With slight modifications, the presynaptic model also exhibits metaplasticity of the long-term depression threshold, being also consistent with Artola, Brocher and Singer's (ABS) influential model. Two asymptotically equivalent versions of the presynaptic rule were adopted for this analysis: the first one uses an incremental equation while the second, conditional probabilities. Despite their simplicity, both types of presynaptic rules exhibit sophisticated biological properties, specially the probabilistic version.