NDLI: A plausible neural circuit for decision making and its formation based on reinforcement learning

Please wait, while we are loading the content...

A plausible neural circuit for decision making and its formation based on reinforcement learning

Content Provider	Springer Nature Link
Author	Wei, Hui Dai, Dawei Bu, Yijie
Copyright Year	2017
Abstract	A human’s, or lower insects’, behavior is dominated by its nervous system. Each stable behavior has its own inner steps and control rules, and is regulated by a neural circuit. Understanding how the brain influences perception, thought, and behavior is a central mandate of neuroscience. The phototactic flight of insects is a widely observed deterministic behavior. Since its movement is not stochastic, the behavior should be dominated by a neural circuit. Based on the basic firing characteristics of biological neurons and the neural circuit’s constitution, we designed a plausible neural circuit for this phototactic behavior from logic perspective. The circuit’s output layer, which generates a stable spike firing rate to encode flight commands, controls the insect’s angular velocity when flying. The firing pattern and connection type of excitatory and inhibitory neurons are considered in this computational model. We simulated the circuit’s information processing using a distributed PC array, and used the real-time average firing rate of output neuron clusters to drive a flying behavior simulation. In this paper, we also explored how a correct neural decision circuit is generated from network flow view through a bee’s behavior experiment based on the reward and punishment feedback mechanism. The significance of this study: firstly, we designed a neural circuit to achieve the behavioral logic rules by strictly following the electrophysiological characteristics of biological neurons and anatomical facts. Secondly, our circuit’s generality permits the design and implementation of behavioral logic rules based on the most general information processing and activity mode of biological neurons. Thirdly, through computer simulation, we achieved new understanding about the cooperative condition upon which multi-neurons achieve some behavioral control. Fourthly, this study aims in understanding the information encoding mechanism and how neural circuits achieve behavior control. Finally, this study also helps establish a transitional bridge between the microscopic activity of the nervous system and macroscopic animal behavior.
Starting Page	259
Ending Page	281
Page Count	23
File Format	PDF
ISSN	18714080
Journal	Cognitive Neurodynamics
Volume Number	11
Issue Number	3
e-ISSN	18714099
Language	English
Publisher	Springer Netherlands
Publisher Date	2017-02-18
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Neural circuit Behaviors Decision-making Network flow Reinforcement learning Biomedicine Neurosciences Computer Science Artificial Intelligence (incl. Robotics) Biochemistry Cognitive Psychology
Content Type	Text
Resource Type	Article
Subject	Cognitive Neuroscience

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A plausible neural circuit for decision making and its formation based on reinforcement learning.

Route searching based on neural networks and heuristic reinforcement learning

Transitory memory retrieval in a biologically plausible neural network model

Computational models of reinforcement learning: the role of dopamine as a reward signal

Parallel reinforcement learning for weighted multi-criteria model with adaptive margin

Energy coding in neural network with inhibitory neurons

Energy coding in biological neural networks

Neural network modelling of the influence of channelopathies on reflex visual attention

Erratum to: Functional model of biological neural networks

A plausible neural circuit for decision making and its formation based on reinforcement learning

Similar Documents

A plausible neural circuit for decision making and its formation based on reinforcement learning.

Route searching based on neural networks and heuristic reinforcement learning

Transitory memory retrieval in a biologically plausible neural network model

Computational models of reinforcement learning: the role of dopamine as a reward signal

Parallel reinforcement learning for weighted multi-criteria model with adaptive margin

Energy coding in neural network with inhibitory neurons

Energy coding in biological neural networks

Neural network modelling of the influence of channelopathies on reflex visual attention

Erratum to: Functional model of biological neural networks

A plausible neural circuit for decision making and its formation based on reinforcement learning