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Optimal Adaptation Principles in Neural Systems.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Optimal Adaptation Principles in Neural Systems./
Author:	Krishnamurthy, Kamesh.
Description:	1 online resource (163 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
Contained By:	Dissertation Abstracts International79-07B(E).
Subject:	Neurosciences. -
Online resource:	click for full text (PQDT)
ISBN:	9780355619713

Optimal Adaptation Principles in Neural Systems.
Krishnamurthy, Kamesh.

Optimal Adaptation Principles in Neural Systems. - 1 online resource (163 pages)

Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.

Thesis (Ph.D.)--University of Pennsylvania, 2017.

Includes bibliographical references

Animal brains are remarkably efficient in handling complex computational tasks, which are intractable even for state-of-the-art computers. For instance, our ability to detect visual objects in the presence of substantial variability and clutter surpasses any algorithm. This ability seems even more surprising given the noisiness and biophysical constraints of neural circuits. This thesis focuses on understanding the theoretical principles governing how neural systems, at various scales, are adapted to the structure of their environment in order to interact with it and perform informa- tion processing tasks efficiently. Here, we study this question in three very different and challenging scenarios: i) how a sensory neural circuit the olfactory pathway is organised to efficiently process odour stimuli in a very high-dimensional space with complex structure; ii) how individual neurons in the sensory periphery exploit the structure in a fast-changing environment to utilise their dynamic range efficiently; iii) how the auditory system of whole organisms is able to efficiently exploit temporal structure in a noisy, fast-changing environment to optimise perception of ambiguous sounds. We also study the theoretical issues in developing principled measures of model complexity and extending classical complexity notions to explicitly account for the scale/resolution at which we observe a system.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355619713Subjects--Topical Terms:

593561
Neurosciences.
Index Terms--Genre/Form:

554714
Electronic books.

Optimal Adaptation Principles in Neural Systems.
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245 1 0 $a Optimal Adaptation Principles in Neural Systems.
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300 $a 1 online resource (163 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
500 $a Advisers: Vijay Balasubramanian; Joshua I. Gold.
502 $a Thesis (Ph.D.)--University of Pennsylvania, 2017.
504 $a Includes bibliographical references
520 $a Animal brains are remarkably efficient in handling complex computational tasks, which are intractable even for state-of-the-art computers. For instance, our ability to detect visual objects in the presence of substantial variability and clutter surpasses any algorithm. This ability seems even more surprising given the noisiness and biophysical constraints of neural circuits. This thesis focuses on understanding the theoretical principles governing how neural systems, at various scales, are adapted to the structure of their environment in order to interact with it and perform informa- tion processing tasks efficiently. Here, we study this question in three very different and challenging scenarios: i) how a sensory neural circuit the olfactory pathway is organised to efficiently process odour stimuli in a very high-dimensional space with complex structure; ii) how individual neurons in the sensory periphery exploit the structure in a fast-changing environment to utilise their dynamic range efficiently; iii) how the auditory system of whole organisms is able to efficiently exploit temporal structure in a noisy, fast-changing environment to optimise perception of ambiguous sounds. We also study the theoretical issues in developing principled measures of model complexity and extending classical complexity notions to explicitly account for the scale/resolution at which we observe a system.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Neurosciences. $3 593561
650 4 $a Biophysics. $3 581576
650 4 $a Artificial intelligence. $3 559380
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773 0 $t Dissertation Abstracts International $g 79-07B(E).
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