Event Title

Segmental Overlay of the Algorithms of the Brain: A Computational Model of the Visual System

Location

CSU Ballroom

Start Date

21-4-2014 2:00 PM

End Date

21-4-2014 3:30 PM

Student's Major

Psychology

Student's College

Social and Behavioral Sciences

Mentor's Name

Dawn Albertson

Mentor's Email Address

dawn.albertson@mnsu.edu

Mentor's Department

Psychology

Mentor's College

Social and Behavioral Sciences

Second Mentor's Name

Rebecca Bates

Second Mentor's Email Address

rebecca.bates@mnsu.edu

Second Mentor's Department

Integrated Engineering

Second Mentor's College

Science, Engineering and Technology

Description

A computational model of the brain would give researchers a better understanding of the processing power of the brain as well as insight into information processing algorithms. This project takes a software engineering approach to modeling processes in the brain. Rather than attempt to describe all the actions of a neuron or collection of neurons at once, it must be shown that each component of the brain's processing can be described and then combined with the others without loss or confliction. Starting from the most basic structures to the most complex, each component can theoretically be defined as a collection of attributes and actions, such as those used in objects and classes in an object-oriented programming language. Before developing the actual code for implementation, models are first described using a unified modeling language (UML) class diagram. To narrow the scope of the computational model presented here, only the visual system is described through UML class diagrams. Only neurons that have function beyond simple information relay in the visual system are defined and their layers of algorithms described. In this model of the visual system, a photon interacts with a photoreceptor and the signal that propagates from this source is followed to the occipital lobe and returns through motor pathways that move the eye. Class diagrams and code segments representing the model will be presented here. This process can be extended to allow for additional research to help explain information gaps within the brain system as a whole.

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Apr 21st, 2:00 PM Apr 21st, 3:30 PM

Segmental Overlay of the Algorithms of the Brain: A Computational Model of the Visual System

CSU Ballroom

A computational model of the brain would give researchers a better understanding of the processing power of the brain as well as insight into information processing algorithms. This project takes a software engineering approach to modeling processes in the brain. Rather than attempt to describe all the actions of a neuron or collection of neurons at once, it must be shown that each component of the brain's processing can be described and then combined with the others without loss or confliction. Starting from the most basic structures to the most complex, each component can theoretically be defined as a collection of attributes and actions, such as those used in objects and classes in an object-oriented programming language. Before developing the actual code for implementation, models are first described using a unified modeling language (UML) class diagram. To narrow the scope of the computational model presented here, only the visual system is described through UML class diagrams. Only neurons that have function beyond simple information relay in the visual system are defined and their layers of algorithms described. In this model of the visual system, a photon interacts with a photoreceptor and the signal that propagates from this source is followed to the occipital lobe and returns through motor pathways that move the eye. Class diagrams and code segments representing the model will be presented here. This process can be extended to allow for additional research to help explain information gaps within the brain system as a whole.

Recommended Citation

Boyd, Scott. "Segmental Overlay of the Algorithms of the Brain: A Computational Model of the Visual System." Undergraduate Research Symposium, Mankato, MN, April 21, 2014.
http://cornerstone.lib.mnsu.edu/urs/2014/poster_session_B/33