Feedback Control and Gain Modulation in Neural Networks

We applied feedback control on neuronal excitability (i.e., response gains) and modulated synaptic weights by Hebbian learning to build more biologically realistic neural networks. We established feedforward neural networks and recurrent neural networks in Python to simulate non-linear functions. We investigated the properties of transfer of learning and adaptation under perturbation in these neural networks.