Weight dependence in BCM leads to adjustable synaptic competition

Abstract

Models of synaptic plasticity have been used to better understand neural development as well as learning and memory. One prominent classic model is the Bienenstock-Cooper-Munro (BCM) model that has been particularly successful in explaining plasticity of the visual cortex. Here, in an effort to include more biophysical detail in the BCM model, we incorporate 1) feedforward inhibition, and 2) the experimental observation that large synapses are relatively harder to potentiate than weak ones, while synaptic depression is proportional to the synaptic strength. These modifications change the outcome of unsupervised plasticity under the BCM model. The amount of feed-forward inhibition adds a parameter to BCM that turns out to determine the strength of competition. In the limit of strong inhibition the learning outcome is identical to standard BCM and the neuron becomes selective to one stimulus only (winner-take-all). For smaller values of inhibition, competition is weaker and the receptive fields are less selective. However, both BCM variants can yield realistic receptive fields.

Metaplasticity: a key to visual recovery from amblyopia in adulthood?

“…Whether through endogenous or exogenous approaches, neuromodulatory signaling acts to disinhibit cortical principal neurons [40 ▪▪ ]. Interpreted through the lens of the BCM theory, decreasing inhibitory tone in primary visual cortex makes it more likely that amblyopic eye inputs fall above the threshold for potentiation and undergo LTP [49,59 ▪ ]. Furthermore, the BCM theory would predict that a decrease in neuronal stimulus selectivity caused by reduced inhibition would allow for increased likelihood of correlation of pre and postsynaptic responses to amblyopic eye inputs [22].…”

Tuning Bienenstock–Cooper–Munro learning rules in a two-terminal memristor for neuromorphic computing

“…7,8 The prominent feature of the BCM learning rule is the sliding frequency threshold, which can be controlled in the normal range to avoid the modulation imbalance of artificial synapses. 7,[9][10][11][12][13] In this study, the BCM learning rule can be successfully simulated by applying presynaptic pulses. 8,14 However, in biology, the BCM learning rule is non-monotonous with an enhanced depression effect (EDE) in the low-frequency section.…”