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Found 5 papers, 3 papers with code
Learning fixed points of recurrent neural networks by reparameterizing the network model
A natural approach is to use gradient descent on the Euclidean space of synaptic weights.
Meta-Learning Biologically Plausible Plasticity Rules with Random Feedback Pathways
In this study, we develop a meta-learning approach to discover interpretable, biologically plausible plasticity rules that improve online learning performance with fixed random feedback connections.
Evaluating the extent to which homeostatic plasticity learns to compute prediction errors in unstructured neuronal networks
Homeostatic inhibitory synaptic plasticity is a promising mechanism for training neuronal networks to perform predictive coding.
On the relationship between predictive coding and backpropagation
Artificial neural networks are often interpreted as abstract models of biological neuronal networks, but they are typically trained using the biologically unrealistic backpropagation algorithm and its variants.
A model of reward-modulated motor learning with parallelcortical and basal ganglia pathways
Many recent studies of the motor system are divided into two distinct approaches: Those that investigate how motor responses are encoded in cortical neurons' firing rate dynamics and those that study the learning rules by which mammals and songbirds develop reliable motor responses.
