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"OH WATERS, TEEM WITH MEDICINE TO KEEP MY BODY SAFE FROM HARM, SO THAT I MAY LONG SEE THE SUN." - Rig Veda
Deixo-vos com a introdução mas, garanto-vos, o artigo é muito menos seco e muito mais interessante do que esta faz parecer.
This paper is structured in five parts: Parts 1 and 2 introduce a class of dynamic systems called SONkANNs - self-organizing networks (k times iterated) of attractor neural networks. The usefulness of these systems is derived in two different ways:
Part 1 analyzes the brain as a dynamic system and searches for the simplest way to capture the essence of the workings of the brain in a toy realm.
Part 2 searches for the simplest system that can exhibit complicity or superemergence in a computer.
We consider it significant that these two arguments, based on neuroscience and dynamic systems theory, independently lead to the same system architecture. The class of systems we single out for further investigation are networks of Hopfield nets with the following properties:
The basic nets are configured for 2D-symbol recognition.
The network of Hopfield nets is governed by a regulatory network that can implement learning and shift attentional focus.
The network of networks is connected to an outside world through sensory-input/action-output channels.
We call such networks SOHNs (Self-Organizing Hopfield Networks).
Part 3 consists of a technical analysis of SOHNs, and part 4 concentrates on two interesting properties they have:
Semantics: Based on the existential connection to a valid outside world, and propagated through similarities between the symbol sets of neighboring Hopfield nets, the SOHNs can develop semantic content.
Brainwaves: The workings of the regulatory network can give rise to brainwaves which in suitable semantic networks can perform emergent information processing on a higher organizational level.
In Part 5 we suggest a method to investigate the space of SOHNs for outstanding cases of brainwave processing in semantic networks that can be considered cases of supermergence or consciousness. Our solution is to use the SOHNs as brains for actors in a virtual world that ``users'' can interact with as a computer game. If users are given an easy way to design/modify/train SOHNs, which they can then use to compete with their peers, we expect that a lot of ingenuity will be applied to find the SOHNs that act in the most intelligent manner. If the space of SOHNs allows for consciousness to emerge, we expect that this approach will eventually find systems that actually do so.