We propose a model for binding of variables such as the thematic role of a word in a sentence
or episode (e.g., agent or patient), to concrete fillers (e.g., a word or concept). Our model is based
on recent experimental data about corresponding processes in the human brain. One source of
information are electrode recordings from the human brain, which suggest that concepts are
represented in the medial temporal lobe (MTL) through sparse sets of neurons (assemblies).
Another source of information are fMRI recordings from the human brain, which suggest that
subregions of the temporal cortex are dedicated to the representation of specific roles (e.g.,
subject or object) of concepts in a sentence or visually presented episode. We propose that
quickly recruited assemblies of neurons in these subregions act as pointers to previously created
assemblies that represent concepts. We provide a proof of principle that the resulting model
for binding through assembly pointers can be implemented in networks of spiking neurons, and
supports basic operations of brain computations, such as structured information retrieval and
copying of information. We also show that salient features of fMRI data on neural activity
during structured information retrieval can be reproduced by the proposed model.
This is pretty much the same as the spa_parse example (a single input, that you can put information into one State in some conditions, and in another State in other conditions), but they don't model the control system at all. But it would still be interesting to do a direct comparison
This paper is a very different approach to variable binding,
https://arxiv.org/pdf/1611.03698.pdf
This is pretty much the same as the spa_parse example (a single input, that you can put information into one State in some conditions, and in another State in other conditions), but they don't model the control system at all. But it would still be interesting to do a direct comparison