Journal
NATURE NEUROSCIENCE
Volume 16, Issue 9, Pages 1170-1178Publisher
NATURE PORTFOLIO
DOI: 10.1038/nn.3495
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Funding
- Gatsby Charitable Foundation
- National Eye Institute grant [R01EY020958-01]
- National Science Foundation [IIS-1132009, BCS0446730]
- Army Research Office grant [W911NF-12-1-0262]
- Multi-University Research Initiative grant [N00014-07-1-0937]
- National Institute on Drug Abuse [BCS0346785]
- Swiss National Fund [31003A 143707]
- James S. McDonnell Foundation
- Swiss National Science Foundation (SNF) [31003A_143707] Funding Source: Swiss National Science Foundation (SNF)
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There is strong behavioral and physiological evidence that the brain both represents probability distributions and performs probabilistic inference. Computational neuroscientists have started to shed light on how these probabilistic representations and computations might be implemented in neural circuits. One particularly appealing aspect of these theories is their generality: they can be used to model a wide range of tasks, from sensory processing to high-level cognition. To date, however, these theories have only been applied to very simple tasks. Here we discuss the challenges that will emerge as researchers start focusing their efforts on real-life computations, with a focus on probabilistic learning, structural learning and approximate inference.
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