… The critical difference between our learning rules and classical ones is that the learning
rates themselves undergo plasticity. We derived three rules, based on three different …

… -based, Hebbian synaptic plasticity has been the primary focus of neuroscientists for decades,
it is inherently limited. Three-factor plasticity rules … to generate inferences and predictions …

… Therefore, one of the primary lessons from our model is that when local synaptic plasticity
rules are sensitive to high-frequency bursts, then pyramidal neurons possess the necessary …

… , agents infer hidden states under plausible policies with a generative model based on a …
This means the agent predicts how it will behave and then verifies those predictions based on …

… in a deep layer cannot infer how a change in its … synaptic plasticity rule, DECOLLE, derived
from a surrogate gradient approach with linear scaling in the number of neurons. The rule …

… -dependent plasticity of adult human synapses, and then highlight rules and mechanisms
of … temporal sequences of unit activity are used to infer synaptic connectivity [72,73]. Recent …

… generative model is sometimes called self-evidencing [63]. In brief, active inference casts the
brain … For generative models based upon continuous states of the world, these schemes are …

… plausible plasticity rules operating across multiple timescales are implemented in the models
and … of subsequent avalanche sizes allows inference about the underlying model and self-…

… This real value is said to be “hidden,” as during inference, we only use its sign to get the
binary … 4 and 5, we provide a learning rule where synapses also feature a network of elements …

… So-called, spike-timing-dependent plasticity (STDP) is a Hebbian learning rule in which the
… induced by TMS, they were able to infer the effective connectivity and activity, respectively. …