The state-of-the art machine learning approach to training deep neural networks,
backpropagation, is implausible for real neural networks: neurons need to know their …

Backpropagation (BP) uses detailed, unit-specific feedback to train deep neural networks
(DNNs) with remarkable success. That biological neural circuits appear to perform credit …

We study the loss surface of DNNs with $ L_ {2} $ regularization. Weshow that the loss in
terms of the parameters can be reformulatedinto a loss in terms of the layerwise activations …

An established normative approach for understanding the algorithmic basis of neural
computation is to derive online algorithms from principled computational objectives and …

During development, biological neural networks produce more synapses and neurons than
needed. Many of these synapses and neurons are later removed in a process known as …

The brain effortlessly solves blind source separation (BSS) problems, but the algorithm it
uses remains elusive. In signal processing, linear BSS problems are often solved by …

Feature-learning deep nets progressively collapse data to a regular low-dimensional
geometry. How this phenomenon emerges from collective action of nonlinearity, noise …

This paper investigates how to solve image classification with Hopfield neural networks
(HNNs) and oscillatory neural networks (ONNs). This is a first attempt to apply ONNs for …

In this survey, we examine algorithms for conducting credit assignment in artificial neural
networks that are inspired or motivated by neurobiology, unifying these various processes …

Recent work on sample efficient training of Deep Neural Networks (DNNs) proposed a semi-
supervised methodology based on biologically inspired Hebbian learning, combined with …