12 citations · 15 across the 4 of their papers we have counts for
4 papers
A Review of Neuroscience-Inspired Machine Learning
Alexander Ororbia, Ankur Mali, Adam Kohan +2
One major criticism of deep learning centers around the biological implausibility of the credit assignment schema used for learning -- backpropagation of errors. This implausibilit…
Temporal Weights
Adam Kohan, Ed Rietman, Hava Siegelmann
In artificial neural networks, weights are a static representation of synapses. However, synapses are not static, they have their own interacting dynamics over time. To instill wei…
Signal Propagation: A Framework for Learning and Inference In a Forward Pass
Adam Kohan, Edward A. Rietman, Hava T. Siegelmann
We propose a new learning framework, signal propagation (sigprop), for propagating a learning signal and updating neural network parameters via a forward pass, as an alternative to…
Error Forward-Propagation: Reusing Feedforward Connections to Propagate Errors in Deep Learning
Adam A. Kohan, Edward A. Rietman, Hava T. Siegelmann
We introduce Error Forward-Propagation, a biologically plausible mechanism to propagate error feedback forward through the network. Architectural constraints on connectivity are vi…