Neuronal Circuit Computation of Choice

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

All of the models developed in preceding chapters present analyses at the level of action potential firing rates in major output neurons. This is, however, only one kind of neurbiological modeling. A large and dynamic community of theorists also develop more biophysically detailed models that often make detailed and testable predictions about the dynamics of both neuronal firing rates and behavior. This chapter presents an example of that approach in the study of decision making. The chapter begins by developing biophysically plausible accumulator models of the type described in Chapter 19. It then goes on to show how such a circuit can be endowed with realistic reward-dependent learning to guide value-based decision making. A detailed explanation of how models of this kind account for dopamine-dependent reward learning is provided. The chapter concludes with a discussion of the behavior of models of this class in strategic games, during probabilistic inference and during "irrational" decision making.

Original languageEnglish (US)
Title of host publicationNeuroeconomics: Decision Making and the Brain: Second Edition
PublisherElsevier Inc.
Pages435-453
Number of pages19
ISBN (Print)9780124160088
DOIs
StatePublished - Sep 2013

Fingerprint

Decision Making
Reward
Learning
Action Potentials
Dopamine
Neurons

Keywords

  • Biophysical Modeling
  • Drift Diffusion
  • Network Modeling
  • Neural Dynamics
  • Value Learning

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Wang, X-J. (2013). Neuronal Circuit Computation of Choice. In Neuroeconomics: Decision Making and the Brain: Second Edition (pp. 435-453). Elsevier Inc.. https://doi.org/10.1016/B978-0-12-416008-8.00023-1

Neuronal Circuit Computation of Choice. / Wang, Xiao-Jing.

Neuroeconomics: Decision Making and the Brain: Second Edition. Elsevier Inc., 2013. p. 435-453.

Research output: Chapter in Book/Report/Conference proceedingChapter

Wang, X-J 2013, Neuronal Circuit Computation of Choice. in Neuroeconomics: Decision Making and the Brain: Second Edition. Elsevier Inc., pp. 435-453. https://doi.org/10.1016/B978-0-12-416008-8.00023-1
Wang X-J. Neuronal Circuit Computation of Choice. In Neuroeconomics: Decision Making and the Brain: Second Edition. Elsevier Inc. 2013. p. 435-453 https://doi.org/10.1016/B978-0-12-416008-8.00023-1
Wang, Xiao-Jing. / Neuronal Circuit Computation of Choice. Neuroeconomics: Decision Making and the Brain: Second Edition. Elsevier Inc., 2013. pp. 435-453
@inbook{7ea3beb5191248a690b355097e271eeb,
title = "Neuronal Circuit Computation of Choice",
abstract = "All of the models developed in preceding chapters present analyses at the level of action potential firing rates in major output neurons. This is, however, only one kind of neurbiological modeling. A large and dynamic community of theorists also develop more biophysically detailed models that often make detailed and testable predictions about the dynamics of both neuronal firing rates and behavior. This chapter presents an example of that approach in the study of decision making. The chapter begins by developing biophysically plausible accumulator models of the type described in Chapter 19. It then goes on to show how such a circuit can be endowed with realistic reward-dependent learning to guide value-based decision making. A detailed explanation of how models of this kind account for dopamine-dependent reward learning is provided. The chapter concludes with a discussion of the behavior of models of this class in strategic games, during probabilistic inference and during {"}irrational{"} decision making.",
keywords = "Biophysical Modeling, Drift Diffusion, Network Modeling, Neural Dynamics, Value Learning",
author = "Xiao-Jing Wang",
year = "2013",
month = "9",
doi = "10.1016/B978-0-12-416008-8.00023-1",
language = "English (US)",
isbn = "9780124160088",
pages = "435--453",
booktitle = "Neuroeconomics: Decision Making and the Brain: Second Edition",
publisher = "Elsevier Inc.",

}

TY - CHAP

T1 - Neuronal Circuit Computation of Choice

AU - Wang, Xiao-Jing

PY - 2013/9

Y1 - 2013/9

N2 - All of the models developed in preceding chapters present analyses at the level of action potential firing rates in major output neurons. This is, however, only one kind of neurbiological modeling. A large and dynamic community of theorists also develop more biophysically detailed models that often make detailed and testable predictions about the dynamics of both neuronal firing rates and behavior. This chapter presents an example of that approach in the study of decision making. The chapter begins by developing biophysically plausible accumulator models of the type described in Chapter 19. It then goes on to show how such a circuit can be endowed with realistic reward-dependent learning to guide value-based decision making. A detailed explanation of how models of this kind account for dopamine-dependent reward learning is provided. The chapter concludes with a discussion of the behavior of models of this class in strategic games, during probabilistic inference and during "irrational" decision making.

AB - All of the models developed in preceding chapters present analyses at the level of action potential firing rates in major output neurons. This is, however, only one kind of neurbiological modeling. A large and dynamic community of theorists also develop more biophysically detailed models that often make detailed and testable predictions about the dynamics of both neuronal firing rates and behavior. This chapter presents an example of that approach in the study of decision making. The chapter begins by developing biophysically plausible accumulator models of the type described in Chapter 19. It then goes on to show how such a circuit can be endowed with realistic reward-dependent learning to guide value-based decision making. A detailed explanation of how models of this kind account for dopamine-dependent reward learning is provided. The chapter concludes with a discussion of the behavior of models of this class in strategic games, during probabilistic inference and during "irrational" decision making.

KW - Biophysical Modeling

KW - Drift Diffusion

KW - Network Modeling

KW - Neural Dynamics

KW - Value Learning

UR - http://www.scopus.com/inward/record.url?scp=84902427665&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84902427665&partnerID=8YFLogxK

U2 - 10.1016/B978-0-12-416008-8.00023-1

DO - 10.1016/B978-0-12-416008-8.00023-1

M3 - Chapter

SN - 9780124160088

SP - 435

EP - 453

BT - Neuroeconomics: Decision Making and the Brain: Second Edition

PB - Elsevier Inc.

ER -