have a paper on graph continuous thought machines that replace the synapse model and the neuron models with a graph convolutional network. The gcnn outputs the

have a paper on graph continuous thought machines that replace the synapse model and the neuron models with a graph convolutional network. The gcnn outputs the next graph in the thought process as guided by learnt property vectors. What's interesting is that the synchronization matrix regulates the attention given to the nodes as well as the input. So these nodes may be seen as neurons in their own right. And consecutive graphs have connections between them that sent virtual signals and caused them to spike. The nodes and potential nodes exist in a dispositional neural network, and only the nodes that are currently activated are instantiated in the gcnn. So as the outputs from the synchronization matrix modulate attention, a subset of the attended dispositional neurons will represent memory. While other parts of the dispositional network and parts of the input represent keys that index the next presentation of memory. In fact only the pre frontal cortex dispositional nodes will contribute to the synchronization matrix. So the pfc performs read and write operations to memory this way.