# Random feedback weights support learning in deep neural networks

@article{Lillicrap2014RandomFW, title={Random feedback weights support learning in deep neural networks}, author={T. Lillicrap and D. Cownden and D. Tweed and C. Akerman}, journal={ArXiv}, year={2014}, volume={abs/1411.0247} }

The brain processes information through many layers of neurons. This deep architecture is representationally powerful, but it complicates learning by making it hard to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame to a neuron by computing exactly how it contributed to an error. To do this, it multiplies error signals by matrices consisting of all the synaptic weights on the neuron's axon and farther downstream. This… Expand

#### Paper Mentions

#### 120 Citations

Random synaptic feedback weights support error backpropagation for deep learning

- Computer Science, Medicine
- Nature communications
- 2016

A surprisingly simple mechanism that assigns blame by multiplying errors by even random synaptic weights is presented, which can transmit teaching signals across multiple layers of neurons and performs as effectively as backpropagation on a variety of tasks. Expand

Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights

- Computer Science, Medicine
- Neural Computation
- 2017

These problems can be solved by two simple devices: learning rules can approximate dynamic input-output relations with piecewise-smooth functions, and a variation on the feedback alignment algorithm can train deep networks without having to coordinate forward and feedback synapses. Expand

Unsupervised learning by competing hidden units

- Computer Science, Biology
- Proceedings of the National Academy of Sciences
- 2019

A learning algorithm is designed that utilizes global inhibition in the hidden layer and is capable of learning early feature detectors in a completely unsupervised way, and which is motivated by Hebb’s idea that change of the synapse strength should be local. Expand

Backpropagation and the brain

- Computer Science, Medicine
- Nature Reviews Neuroscience
- 2020

It is argued that the key principles underlying backprop may indeed have a role in brain function and induce neural activities whose differences can be used to locally approximate these signals and hence drive effective learning in deep networks in the brain. Expand

Continual Learning with Deep Artificial Neurons

- Computer Science
- ArXiv
- 2020

Deep Artificial Neurons (DANs) are introduced, and it is shown that a suitable neuronal phenotype can endow a single network with an innate ability to update its synapses with minimal forgetting, using standard backpropagation, without experience replay, nor separate wake/sleep phases. Expand

Training the Hopfield Neural Network for Classification Using a STDP-Like Rule

- Computer Science
- ICONIP
- 2017

It is shown that the well-known Hopfield neural network (HNN) can be trained in a biologically plausible way and several HNNs with one or two hidden layers are trained on the MNIST dataset and all of them converge to low training errors. Expand

Learning to solve the credit assignment problem

- Computer Science, Biology
- ICLR
- 2020

A hybrid learning approach that learns to approximate the gradient, and can match or the performance of exact gradient-based learning in both feedforward and convolutional networks. Expand

Biologically feasible deep learning with segregated dendrites

- Computer Science
- 2016

A spiking, continuous-time neural network model that learns to categorize images from the MNIST data-set with local synaptic weight updates and demonstrates that deep learning can be achieved within a biologically feasible framework using segregated dendritic compartments. Expand

Training a Network of Spiking Neurons with Equilibrium Propagation

- Computer Science
- 2018

It is shown that with appropriate step-size annealing, the Equilibrium Propagation model can converge to the same fixed-point as a real-valued neural network, and that with predictive coding, it can make this convergence much faster. Expand

Training a Spiking Neural Network with Equilibrium Propagation

- Computer Science
- AISTATS
- 2019

It is shown that with appropriate step-size annealing, the Equilibrium Propagation model can converge to the same fixed-point as a real-valued neural network, and that with predictive coding, it can make this convergence much faster. Expand

#### References

SHOWING 1-10 OF 34 REFERENCES

A Fast Learning Algorithm for Deep Belief Nets

- Computer Science, Medicine
- Neural Computation
- 2006

A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. Expand

Learning in Spiking Neural Networks by Reinforcement of Stochastic Synaptic Transmission

- Psychology, Medicine
- Neuron
- 2003

The hypothesis that the randomness of synaptic transmission is harnessed by the brain for learning, in analogy to the way that genetic mutation is utilized by Darwinian evolution is considered. Expand

Supervised and Unsupervised Learning with Two Sites of Synaptic Integration

- Computer Science, Medicine
- Journal of Computational Neuroscience
- 2004

Compared to standard, one-integration-site neurons, it is possible to incorporate interesting properties in neural networks that are inspired by physiology with a modest increase of complexity, thanks to recent research on the properties of cortical pyramidal neurons. Expand

Equivalence of Backpropagation and Contrastive Hebbian Learning in a Layered Network

- Mathematics, Medicine
- Neural Computation
- 2003

A special case in which they are identical: a multilayer perceptron with linear output units, to which weak feedback connections have been added suggests that the functionality of backpropagation can be realized alternatively by a Hebbian-type learning algorithm, which is suitable for implementation in biological networks. Expand

Backpropagation without weight transport

- Computer Science
- Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)
- 1994

The feasibility of an architecture equivalent to backpropagation, but without the assumption of weight transport is formally and empirically demonstrated. Expand

A more biologically plausible learning rule for neural networks.

- Medicine
- Proceedings of the National Academy of Sciences of the United States of America
- 1991

A more biologically plausible learning rule is described, using reinforcement learning, which is applied to the problem of how area 7a in the posterior parietal cortex of monkeys might represent visual space in head-centered coordinates and shows that a neural network does not require back propagation to acquire biologically interesting properties. Expand

Learning representations by back-propagating errors

- Computer Science
- Nature
- 1986

Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain. Expand

Learning Representations by Recirculation

- Mathematics, Computer Science
- NIPS
- 1987

Simulations in simple networks show that the learning procedure usually converges rapidly on a good set of codes, and analysis shows that in certain restricted cases it performs gradient descent in the squared reconstruction error. Expand

Is backpropagation biologically plausible?

- Computer Science
- International 1989 Joint Conference on Neural Networks
- 1989

The authors finds that in several posited implementations these design considerations imply that a finely structured neural connectivity is needed as well as a number of neurons and synapses beyond those inferred from the algorithmic network presentations of backpropagation. Expand

Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning

- Machine Learning
- 1992

This article presents a general class of associative reinforcement learning algorithms for connectionist networks containing stochastic units. These algorithms, called REINFORCE algorithms, are shown… Expand