Graph Continuous Thought Machines

We propose a method by which a neural graph continuous thought machines dispositional nodes connections may be designed faithful to a human brain. A graph continuous thought machine replaces the synapse and neuron level models with a graph cnn .In some sense, the nodes of the graph at any one time represent the instantiation of the nodes of the dispositional neural model it is part of. Instantiating only those nodes that are currently firing. The GCNN then outputs the next graph as the system searches graph space for solutions as guided by learnt property vectors.The outputs from its neural synchronization matrix then modulate the attention given to inputs as well as to the nodes of the dispositional network. This way it designs The dispositional neural models connections (disposition for particular graphs to be next after others). We then employ neural training modules which are spiking neural networks which have their nodes mapped with keys from a musical keyboard. In particular when exposed to the state of teacher systems the nodes are trained to musically harmonize, while when exposed to the state of the untrained agent they are dissonant. The agent then tries to maximise consonance in the spiking network by using it as a reward signal. By this method the agent is trained to perform like the teacher system. We introduce text conditioned neural training modules, that condition the input on text. We show a method to modulate not just the behavior of the system , but the connectivity of the dispositional network of a GCTM. https://www.researchgate.net/publication/392733228_Text_Conditioned_Self_Architecture_Search_for_Building_Brain_Like_Connectivity_by_Describing_It