Maxwell A. Bertolero

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Network-based analyses of brain imaging data consistently reveal distinct modules and connector nodes with diverse global connectivity across the modules. How discrete the functions of modules are, how dependent the computational load of each module is to the other modules' processing, and what the precise role of connector nodes is for between-module(More)
A complex system can be represented and analyzed as a network, where nodes represent the units of the network and edges represent connections between those units. For example, a brain network represents neurons as nodes and axons between neurons as edges. In many networks, some nodes have a disproportionately high number of edges. These nodes also have many(More)
We investigate whether holism presents a problem for in-ductive inference by examining the relationship between the size of a Bayesian network that represents human conceptual knowledge and the computational complexity of probabilistic inference in that network. We find that, despite prior claims, holism may not be a problem for inductive inference, as(More)
The thalamus is globally connected with distributed cortical regions, yet the functional significance of this extensive thalamocortical connectivity remains largely unknown. By performing graph-theoretic analyses on thalamocortical functional connectivity data collected from human participants, we found that most thalamic subdivisions display network(More)
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