The biggest problem restricting the development of SNN is the training algorithm. Backpropagation (BP)-based training has extended SNNs to more complex network structures and datasets. However, the traditional design of BP ignores the dynamic characteristics of SNNs and is not biologically plausible. ...
But backpropagation algorithm is neither biologically plausible nor neuromorphic implementation friendly because it requires: 1) separate backward and forward passes, 2) differentiable neurons, 3) high-precision propagated errors, 4) coherent copy of weight matrices at feedforward weights and the backward...
Although the case for backpropagation as potentially biologically plausible has recently been strengthened [132–134], its extension through time is difficult to reconcile with biology [135] or implement efficiently in a finite engineered system for online learning — precisely because it requires ...
The desired output of a network, given some input. Deviation from the target is quantified with an error function. Unsupervised learning Learning in which the error function does not involve a separate output target. Instead, errors are computed using other information readily available to the netwo...
For both humans and machines, the essence of learning is to pinpoint which components in its information processing pipeline are responsible for an error in its output, a challenge that is known as ‘credit assignment’. It has long been assumed that cre
signals for the hidden layers, and we show that the combination of these losses help with optimization in the context of local learning. Using local errors could be a step towards more biologically plausible deep learning because the global error does not have to be transported back to hidden ...
The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, t