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Found 5 papers, 3 papers with code
Desiderata for normative models of synaptic plasticity
In this review, we organize work on normative plasticity models in terms of a set of desiderata which, when satisfied, are designed to guarantee that a model has a clear link between plasticity and adaptive behavior, consistency with known biological evidence about neural plasticity, and specific testable predictions.
Adaptive coding efficiency in recurrent cortical circuits via gain control
Using published V1 population adaptation data, we show that propagation of single neuron gain changes in a recurrent network is sufficient to capture the entire set of observed adaptation effects.
Flexible Phase Dynamics for Bio-Plausible Contrastive Learning
Many learning algorithms used as normative models in neuroscience or as candidate approaches for learning on neuromorphic chips learn by contrasting one set of network states with another.
Impression learning: Online representation learning with synaptic plasticity
Understanding how the brain constructs statistical models of the sensory world remains a longstanding challenge for computational neuroscience.
Learning efficient task-dependent representations with synaptic plasticity
Neural populations encode the sensory world imperfectly: their capacity is limited by the number of neurons, availability of metabolic and other biophysical resources, and intrinsic noise.
