Symphony from Synapses: Neocortex as a Universal Dynamical Systems Modeller using Hierarchical Temporal Memory — mentioned by dmos62

Mon, 19 Nov 2018 18:57:32 GMT

Sounds interesting, but this 2016 paper has only one citation. I'm reading it now and this paragraph from the introduction compares it to traditional Deep Learning networks: > Our artificial Feynman Machines have several interesting properties which distinguish them from existing Deep Learning and similar systems. In particular, due to the much higher density and locality of processing, a Feynman Machine-based system can perform at least comparably while dramatically reducing the footprint in computational power, training data and fine-tuning. Feynman Machines can be arbitrarily split into…

Paper and discussion history. A mention is not an endorsement.

Symphony from Synapses: Neocortex as a Universal Dynamical Systems Modeller using Hierarchical Temporal Memory — HN submission

Thu, 17 Dec 2015 23:14:28 GMT

Symphony from Synapses: Neocortex as a Hierarchical Temporal Memory

3 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Symphony from Synapses: Neocortex as a Universal Dynamical Systems Modeller using Hierarchical Temporal Memory · HackerPapers discussions

Symphony from Synapses: Neocortex as a Universal Dynamical Systems Modeller using Hierarchical Temporal Memory — mentioned by dmos62

Symphony from Synapses: Neocortex as a Universal Dynamical Systems Modeller using Hierarchical Temporal Memory — HN submission