  Background: Deep neural networks have proven to be powerful computational
tools for modeling, prediction, and generation. However, the workings of these
models have generally been opaque. Recent work has shown that the performance
of some models are modulated by overlapping functional networks of connections
within the models. Here the techniques of functional neuroimaging are applied
to an exemplary large language model to probe its functional structure.
Methods: A series of block-designed task-based prompt sequences were generated
to probe the Facebook Galactica-125M model. Tasks included prompts relating to
political science, medical imaging, paleontology, archeology, pathology, and
random strings presented in an off/on/off pattern with prompts about other
random topics. For the generation of each output token, all layer output values
were saved to create an effective time series. General linear models were fit
to the data to identify layer output values which were active with the tasks.
Results: Distinct, overlapping networks were identified with each task. Most
overlap was observed between medical imaging and pathology networks. These
networks were repeatable across repeated performance of related tasks, and
correspondence of identified functional networks and activation in tasks not
used to define the functional networks was shown to accurately identify the
presented task. Conclusion: The techniques of functional neuroimaging can be
applied to deep neural networks as a means to probe their workings. Identified
functional networks hold the potential for use in model alignment, modulation
of model output, and identifying weights to target in fine-tuning.
