The exploration of a new hypothesis on or about the neurocognitive connectome.

It may all be bullshit, but thoughts over the past two days have led me down what I observe is an interesting garden path concerning the components of a Clique Space(TM) neural network and how a participating Neuron might conceive of itself and its neighbourhood.

Traditionally, a neuron shares physical connections with a subset of its neighbours. Each of these physical connections is assigned a weight and any given connection is assigned a value for its weight based on a process of "learning". That has always appeared as a reasonable if not abridged overview of the neural way things are generally known in this world.

I feel that I can give a slightly different slant on the above view. Clique Space is no different from this view in that Neurons indeed share similar physical connections (synapses) with a subset of their brethren; each connection is indeed realised through an algorithmic structure known as a synapse. However, what is slightly different is the following: weights are not assigned to synapses.

A Clique Space Client Device (a Neuron is a type of Client Device) is identified individually by a Connection. Connections are Elements which contain one or more features that are expressed by Participants (also Elements) which are members of Cliques. Cliques denote some cooperative endeavour is being undertaken. Synapses are a type of bipartite Clique which models the engagement of two Client Devices through the "this Element" feature of their individual Connections.

While a Client Device may not share a synapse with every one of its cohorts, it can still be aware of every Connection that makes up the entire population. Weights are assigned to the Connections known to a Neuron to represent other Neurons - even those Neurons with which the host Neuron doesn't share a synapse directly. Now, I think at this moment, there is an opportunity to do something rather useful with this knowledge; something that uses another structure developed over the past 11 years I have been working on my proof-of-concept. This something appears to me to advance the traditional connectionist model of neurons. Instead of assigning a weight to a synapse, weights are assigned to Connections.

A deliberator is a thinker (a type of Java thread) which is created to deliberate the arrival of a deliberation. Deliberators are associated with the Connection assigned as the first Neuron (actually Client Device but we will not split hairs here) to have entertained the topic being deliberated. Now, getting hypothetical, the wonderful thing about creating deliberators in this way is that a group of deliberators may be created to represent receipt of deliberations sent from neighbouring Neurons all of which are received at around the same time. Apart from the difference in originator, each deliberator is cogitating the same topic and hence each deliberator is constructed to have a identical parameters such as activation and bias because all deliberators are instructed by the same feature.

Accumulation of Connection weights for deliberators on this same topic may cross a threshold which when reached will cause the deliberator on which the threshold was crossed to send deliberations through synapses to its neighbours. Some neighbours may share synapses and may receive the deliberation immediately and others may only receive the deliberation through a neighbour who has faithfully passed the deliberation on.

This is all done by assigning weights to the Connections instead of to the synapses. I think that is a big deal, and an advancement in neuroscience that was published in this blog entry before anyone else in this world knew that assigning weights to representations of nodes in a Neuron's knowledge of its neural neighbourhood would be better than assigning them merely to the physical connections it would share with its neighbours.

This mechanism, and more as would answer most other questions about my concept is all there in my code. Really, it is... almost. I'm sitting on the greatest advancement in neuroscience since the perceptron!