This site uses cookies

By using this site, you consent to our use of cookies. You can view our terms and conditions for more information.

A flexible model of working memory

Flora Bouchacourt
Princeton University ~ Princeton Neuroscience Institute
Tim Buschman

Working memory is fundamental to cognition, allowing one to hold information “in mind.” A defining characteristic of working memory is its flexibility: we can hold anything in mind. However, typical models of working memory rely on finely tuned, content-specific attractors to persistently maintain neural activity and therefore do not allow for the flexibility observed in behavior. Here, we present a flexible model of working memory that maintains representations through random recurrent connections between two layers of neurons: a structured “sensory” layer and a randomly connected, unstructured layer. As the interactions are untuned with respect to the content being stored, the network maintains any arbitrary input. However, in our model, this flexibility comes at a cost: the random connections overlap, leading to interference between representations and limiting the memory capacity of the network. Additionally, our model captures several other key behavioral and neurophysiological characteristics of working memory.



working memory
neural network model
Follow-up to my question Last updated 2 years ago

Hi Flora. Now that I've read your Neuron paper I think that I can formulate my question more clearly. OK, so we were discussing how in your model there are two mechanisms for interference: divisive inhibition and representational overlap. But this is partly because in the model random connectivity between sensory and random networks is "hand-cod...

Fabian Soto 1 comment
Cite this as:

Bouchacourt, F. M., & Buschman, T. (2021, July). A flexible model of working memory. Paper presented at Virtual MathPsych/ICCM 2021. Via