4 ms·
This is strictly not a neural network, so there is no backpropagation. Credit assignment is done on each edge using a local rule (Eg. using only its current sta
by RaisinLoaf69 5y ago
This is strictly not a neural network, so there is no backpropagation. Credit assignment is done on each edge using a local rule (Eg. using only its current state and the state of touching edges). To scale the network you just have to add more edges (no limit on the amount). We have a design for a tiny version of this network using transistors that could have order 10^6 edges on the size of a microchip.
- igorkraw 5y agoI've looked over the paper now, unless I'm misunderstanding this seems very similar to the general trend of hebbian learning/STDP/local predictive coding/teacher forcing (for those unfamiliar, these are all distinct but the basic idea is always to have a signal adjust based on the difference with some local target. Hebbian learning is the basic "fire together wire together" principle, STDP is a specific instantiation that works with specific types of memristors, teacher forcing comes from RNN training and imposes the ground truth input on intermediate step, local predictive coding I can't recall the precise thing but but basically diffuses a local output error through a network similar to what is done on a single layer here [which can actually approximate backpropagation! It's very cool]). How would you differentiate yourself against this/what would you say is the core benefit of this approach?