Martino Sorbaro

[1/n] Here's a body of relevant literature, including old foundational work: The recent excitement about neural networks, Crick Learning representations by recirculation, Hinton and McClelland

Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm, O'Reilly A learning rule for asynchronous perceptrons with feedback in a combinatorial environment, Almeida

Generalization of back-propagation to recurrent neural networks, Pineda Contrastive Hebbian learning in the continuous Hopfield model, Movellan A learning algorithm for Boltzmann machines, Ackley et al

Difference target propagation, Lee et al Weight perturbation: An optimal architecture and learning technique for analog VLSI feedforward and recurrent multilayer networks, Jabri et al Equivalence of backpropagation and contrastive Hebbian learning in a layered network, Xie et a

Dendritic error backpropagation in deep cortical microcircuits, Sacramento et al Kickback Cuts Backprop's Red-Tape: Biologically Plausible Credit Assignment in Neural Networks., Balduzzi et al A more biologically plausible learning rule for neural networks, Mazoni et al

An objective function for STDP, Bengio Stdp-compatible approximation of backpropagation in an energy-based model, Bengio et al Towards biologically plausible deep learning, Bengio et al How to do backpropagation in a brain, Hinton

An approximation of the error backpropagation algorithm in a predictive coding network with local hebbian synaptic plasticity, Whittington et al Towards a biologically plausible backprop, Scellier et al

Equilibrium propagation: Bridging the gap between energy-based models and backpropagation, Scellier et al Learning in spiking neural networks by reinforcement of stochastic synaptic transmission, Seung Decoupled neural interfaces using synthetic gradients, Jaderberg et al

The papers of Bengio and the the recent paper of @somnirons at NIPS (?). They are not introductions, but I believe they are very good :)

Thanks for the reference! @tyrell_turing and Lillicrap also have a recent review on the topic in CONB.