Representations of Sensory Signals and Abstract Categories in Brain Networks
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0000283.pdf
Description
Published version
Size
408.21 KB
Format
Unknown
Checksum (MD5)
d388ae772f6bc6317a472d15ef5ad575
Author(s)
Pinotsis, Dimitrios
Siegel, Markus
Miller, Earl K
Date Issued
2019
Journal
2019 Conference on Cognitive Computational Neuroscience
Publisher
Cognitive Computational Neuroscience
Citation
Pinotsis, Dimitris, Siegel, Markus and Miller, Earl. 2019. "Representations of Sensory Signals and Abstract Categories in Brain Networks." 2019 Conference on Cognitive Computational Neuroscience.
Version
Final published version
Abstract
Many recent advances in artificial intelligence (AI) are rooted in visual neuroscience. However, ideas from more complicated paradigms like decision-making are less used. Although automated decision-making systems are ubiquitous (driverless cars, pilot support systems, medical diagnosis algorithms etc.), achieving human-level performance in decision making tasks is still a challenge. At the same time, these tasks that are hard for AI are easy for humans. Thus, understanding human brain dynamics during these decision-making tasks and modeling them using deep neural networks could improve AI performance. Here we modelled some of the complex neural interactions during a sensorimotor decision making task. We investigated how brain dynamics flexibly represented and distinguished between sensory processing and categorization in two sensory domains: motion direction and color. We found that neural representations changed depending on context. We also trained deep recurrent neural networks to perform the same tasks as the animals. By comparing brain dynamics with network predictions, we found that computations in different brain areas also changed flexibly depending on context. Color computations appeared to rely more on sensory processing, while motion computations more on abstract categories. Overall, our results shed light to the biological basis of categorization and differences in selectivity and computations in different brain areas.
MIT Department
Picower Institute for Learning and Memory
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution 3.0 unported license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.32470/ccn.2019.1290-0
