@article{Context-Dependent:2720,
      recid = {2720},
      author = {Johnston, William Jeffrey},
      title = {Reliable and Context-Dependent Computation in the Brain},
      publisher = {University of Chicago},
      school = {Ph.D.},
      address = {2020-12},
      pages = {217},
      abstract = {The brain is a distributed computational system. While the  brain has been understood to exhibit at least weak  modularity for over a century, numerous important questions  about the degree and consequences of that modularity  remain. My thesis work consisted of three projects, which  are each related to distinct questions about the nature and  function of modularity in the brain.

First, I investigated  how the neural code within distinct regions of the brain  could be made reliable in the face of the unreliability of  individual neurons. In this work, I found that in- creasing  the dimensionality of neural representations through  conjunctive mixing of multiple stimulus features improves  the reliability of those representations by orders of  magnitude relative to representations without mixing. This  work provides an explanation for a commonly observed  phenomenon in experiments: The apparent random conjunctive  mixing of stimulus features in single neurons. However, it  also intersects with questions about modularity. In  particular, the benefits that can be derived from this  conjunctive mixing depend strongly on the size of the  neural population available to participate in the  representation – that is, on the size of a particular brain  region. Further work will explore how this pressure for  larger regions is tempered by other constraints in the  brain.

Second, I investigated how the neural code across  distinct regions of the brain could be made reliable in the  presence of multiple heterogeneous objects that are  represented only partially within each region. As an  example, two cats in the world have both visual and  auditory representations in the brain. To guide behavior,  the brain must integrate these different facets of the same  animals. We describe the necessary conditions for this  integration process to be reliable. Further, we outline a  tradeoff, in which the fidelity of each individual  representation can be increased at the cost of a greater  risk of catastrophic integration errors, in which the  auditory features of one cat are integrated with the visual  features of the other cat. More generally, this work  provides another constraint on the modularity exhibited by  the brain. We show that redundancy in the information  represented by distinct brain regions is absolutely  necessary for reliable integration. Thus, this work  illustrates a pressure for the brain to use fewer distinct  modules, so that it can satisfy the overall goal of  redundancy reduction for producing efficient neural  codes.

Third, I performed electrophysiological experiments  to investigate the role of a particular brain region, the  lateral intraparietal area (LIP), in two distinct tasks.  These experiments revealed that the putative function of  LIP in the representation of both visually guided actions  and the behavioral relevance of different parts of the  visual field is highly task- dependent. In particular, our  results indicate that LIP may serve this role primarily in  the context of directed tasks, while it is less engaged in  undirected, free-viewing behavior. These results illustrate  the extreme context-dependence of many of our inferences  about the functional role of different brain  regions.

Together, my thesis work refines our  understanding of the role and reason for modularity in the  brain – and points to numerous directions for future work.  In particular, my work provides many of the necessary tools  for beginning to build a more comprehensive normative  theory of neural modularity that will be necessary to a  comprehensive understanding of the brain.},
      url = {http://knowledge.uchicago.edu/record/2720},
      doi = {https://doi.org/10.6082/uchicago.2720},
}