Cognitive Memory Network
Alex Pappachen James, Sima Dimitrijev

TL;DR
This paper introduces a resistive memory network with a hierarchical modular architecture that demonstrates cognitive functions like character recognition, overcoming hardware limitations of traditional neural networks through evolutionary training.
Contribution
It presents a novel resistive memory network architecture that is hardware-efficient and capable of cognitive tasks, trained via evolutionary processes.
Findings
Successfully recognized noisy, rotated, scaled, and shifted characters
Demonstrated hardware advantages over conventional neural networks
Validated cognitive functionality through character recognition
Abstract
A resistive memory network that has no crossover wiring is proposed to overcome the hardware limitations to size and functional complexity that is associated with conventional analogue neural networks. The proposed memory network is based on simple network cells that are arranged in a hierarchical modular architecture. Cognitive functionality of this network is demonstrated by an example of character recognition. The network is trained by an evolutionary process to completely recognise characters deformed by random noise, rotation, scaling and shifting
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
