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035 (MiAaPQ)AAI28496964
040 MiAaPQ|beng|cMiAaPQ|dNTU
100 1 Morais, Michael J
245 10 Approximate Bayesian Methods for Optimal Neural Coding and
Decision-Making
264 0 |c2021
300 1 online resource (139 pages)
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
500 Source: Dissertations Abstracts International, Volume: 82-
12, Section: B
500 Advisor: Pillow, Jonathan W
502 Thesis (Ph.D.)--Princeton University, 2021
504 Includes bibliographical references
520 One fundamental goal of theoretical neuroscience is to
understand the normative principles governing the
functional organization of neural circuits, and, in turn,
to what extent they can be considered optimal. Calling
neural representations of information in the brain
̀̀optimal'' implies a multifarious equilibrium that
balances robustness against flexibility, completeness
against relevance, and so on, but it need only imply a
solution to some optimization program. The exact forms of
these programs varies with the modeling goals, neural
circuits, tasks, or even animals under investigation. With
this dissertation, we explore how we can define neural
codes as optimal when they generate optimal behavior -- an
easy principle to state, but a hard one to implement. Such
a principle would bridge a gap between classical
hypotheses of optimal neural coding, efficient coding and
the Bayesian brain, with a common unified theory.In the
first study, we analyzed neural population activity in V1
while monkeys performed a visual detection task, and found
that a majority of the total choice-related variability is
already present in V1 population activity. Such a
prominent contribution of non-stimulus activity in
classically sensory regions cannot be incorporated into
existing models of neural coding, and demands models that
can jointly optimize coding and decision-making within a
single neural population.In the second study, we derived
power-law efficient codes, a natural generalization of
classical efficient codes, and show they are sufficient to
replicate and explain a diverse set of psychophysical
results. This broader family can maximize mutual
information or minimize error of perceptual decisions,
suggesting that psychophysical phenomena used to validate
normative models could be more general features of
perceptual systems than previously appreciated.In the
third study, we translated the problem of joint model
learning and decision-making into Bayesian machine
learning, and extended a family of methods for decision-
aware approximate inference to include a novel algorithm
that we called loss-calibrated expectation propagation.
How this problem can be solved by a non-biophysical system
could be a constructive reference point for future studies
into joint coding and decision-making, and the normative
principles that drive decision-related variability in
optimal sensory neural codes
533 Electronic reproduction.|bAnn Arbor, Mich. :|cProQuest,
|d2021
538 Mode of access: World Wide Web
650 4 Neurosciences
650 4 Statistics
650 4 Logic
653 Approximate inference
653 Bayesian statistics
653 Decision-making
653 Efficient coding
653 Neural coding
653 Perception
655 7 Electronic books.|2local
690 0317
690 0463
690 0395
710 2 ProQuest Information and Learning Co
710 2 Princeton University.|bNeuroscience
773 0 |tDissertations Abstracts International|g82-12B
856 40 |uhttps://pqdd.sinica.edu.tw/twdaoapp/servlet/
advanced?query=28496964|zclick for full text (PQDT)
912 圖書館PQDT110|b1110406
