Neural Network Model of Working Memory in Fragile X Syndrome

With a $67,000 grant from FRAXA Research Foundation in 2000, Dr. Mina Johnson-Glenberg at the University of Wisconsin created a neural network model to understand long-term and working memory in individuals with fragile X syndrome.

Mina Johnson-Glenberg, PhD, at University of Wisconsin, FRAXA research grant
$67,000 Grant
Mina Johnson-Glenberg, PhD
Principal Investigator
University of Wisconsin
2000 FRAXA Research Grant

by Mina Johnson-Glenberg, 10/1/2000

This study will attempt to bridge one of the gaps between neurology and behavior. Based on the research regarding hippocampal size and differences in the dendrites and spines of those with fragile X syndrome, I will be researching how long-term and working memory interact in two different populations.

A computational neural network will be created to simulate memory processing leading to a finer-grained model of the information processing style and cognitive capabilities of those with fragile X. To this end, I have designed a working memory sequential memory task that feels like a card game. Long-term and sequential working memory will also be assessed in a typically developing mental age match group. A neural network model that simulates the interplay of hippocampal and prefrontal memory in typically developing children will first be created. Parameters in the typically developing model will be adjusted until the pattern of results mimics the results of those with fragile X.

Neural networks are powerful tools that can provide insights into the particular strengths and weaknesses found in the fragile X cognitive profile. Analyzing such models can help to guide the development of remediation programs and provide neurologically theory-driven explanations or various cognitive deficits (e.g., why individuals with fragile X have such trouble learning to read in the usual sequential phonemic manner).