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In this video article, accompanying the paper “An approach to learning the hierarchical organization of the frontal lobe”, we discuss a data driven approach to learning brain connectivity. Hierarchical models of brain connectivity are useful to understand how the brain can process sensory information, make decisions, and perform other high-level tasks. Despite extensive research, understanding the structure of the prefrontal cortex (PFC) remains a crucial challenge. In this work, we propose an approach to studying brain signals and uncovering characteristics of the underlying neural circuity, based on the mathematics of Gaussian processes and causal strengths. For discovering causations, we propose a metric referred to as double-averaged differential causal effect, which is a variant of the recently proposed differential causal effect, and it can be used as a principled measure of the causal strength between time series. We applied this methodology to study local field potential data from the frontal lobe, where the interest was in finding the causal relationship between the medial and lateral PFC areas of the brain. Our results suggest that the medial PFC causally influences the lateral PFC.
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Tracers in neuroscience: Causation, constraints, and connectivity
This paper examines tracer techniques in neuroscience, which are used to identify neural connections in the brain and nervous system. These connections capture a type of “structural connectivity” that is expected to inform our understanding of the functional nature of these tissues (Sporns in Scholarpedia, 2007). This is due to the fact that neural connectivity constrains the flow of signal propagation, which is a type of causal process in neurons. This work explores how tracers are used to identify causal information, what standards they are expected to meet, the forms of causal information they provide, and how an analysis of these techniques contributes to the philosophical literature, in particular, the literature on mark transmission and mechanistic accounts of causation.
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- Award ID(s):
- 1945647
- PAR ID:
- 10231003
- Date Published:
- Journal Name:
- Synthese
- ISSN:
- 0039-7857
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s11229-020-02970-z
