Topic: Hebbian-like plasticity

A high-dimensional nonlinear recurrent neural network is driven by periodic, incoherent inputs while its synapses undergo Hebbian-like plasticity. Depending on drive and plasticity strengths, the network can retain autonomous limit-cycle activity after both the inputs and learning are removed. Dynamical mean-field theory and finite-size simulations characterize the resulting rhythmic memories and transitions between chaotic, fixed-point and oscillatory regimes. Memory robustness depends on the interval between removing the input and stopping plasticity. These results suggest a mechanism by whi

How can neural networks learn to efficiently represent complex and high-dimensional inputs via local plasticity mechanisms? Classical models of representation learning assume that feedforward weights are learned via pairwise Hebbian-like plasticity. Here, we show that pairwise Hebbian-like plasticity only works under unrealistic requirements on neural dynamics and input statistics. To overcome these limitations, we derive from first principles a learning scheme based on voltage-dependent synaptic plasticity rules. Here, recurrent connections learn to locally balance feedforward input in indivi