Cognitive and Molecular Challenges to Statistical Inference Across Healthy Aging

Mammalian brains represent information using distributed population codes which provide a number of advantages from robustness to high representational capacity. However, for downstream readout neurons such codes pose formidable high-dimensional learning problems as a very large number of synaptic connections must be adjusted during learning in search of a suitable readout. Our recent theoretical work hypothesized that these high-dimensional learning problems can be simplified by inductive biases implemented through stimulus-independent noise correlations which express the degree to which a pair of neurons covary in their trial-to-trial fluctuations. While noise correlations have traditionally been viewed as providing constraints on representational capacity our recent work demonstrates that they simultaneously constrain readout learning. In some biologically relevant cases, they could theoretically speed learning by shaping the geometry of the underlying neural space to focus the gradient of learning onto task-relevant dimensions. However, this hypothesized role of noise correlations in shaping learning has not yet been empirically tested. Here the investigators elaborate an experimental framework to test the predicted role of noise correlations, as measured through covariation in fMRI multi-voxel BOLD activity patterns for a given stimulus, on learning in both familiar and novel contexts. In familiar contexts, useful noise correlations may be induced by top-down inputs from the prefrontal cortex that signal relevant task dimensions. Thus, the strength of noise correlations in task-relevant dimensions would predict faster learning about task-relevant features. On the other hand, in novel contexts when the relevant task dimensions are unknown, noise correlations may force gradients onto task-irrelevant dimensions and thus impair learning. Therefore, suppressing noise correlations, which might be achieved through neuromodulatory signaling, may speed learning by reducing bias early during learning or after a change in the task-relevant stimulus. Across our Aims, the investigators develop a plan to test the most basic predictions of our computational model using fMRI to characterize the geometry of noise correlations and pupillometry as a proxy for neuromodulatory signaling in human subjects. The planned research will provide the first empirical test of the role of noise correlations in learning.