The trade-off between temporal precision and effect amplitude of inhibitory plasticity regulation determines separability of learned representations
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Synaptic plasticity, the process by which synapses change in an activity-dependent manner, is assumed to be the basis of learning. Experimental evidence demonstrates that activity originating from other synapses in close proximity to an observed one can influence the outcome of plasticity including activity from inhibitory synapses. Under the assumption that the regulatory effect of inhibition is mediated by hyperpolarisation, we identify a trade-off between temporal precision and effect amplitude due to the treatment of postsynaptic activity in three different voltage-dependent plasticity models. Generally, inhibitory regulation of plasticity enhances the competition between lateral neurons driving the development of functionally relevant connectivity structures in recurrent excitatory-inhibitory networks. Thus, all models show signs of the ability to perform Independent Component Analysis (ICA) and lead to receptive field development. Models which are highly sensitive to local synaptic information tend to result in a higher degree of separation between learned features. This work stresses the importance of considering inhibition in plasticity research as well as indicates that learned representations are influenced by the available information at a synaptic site.