| Paper: | PS-2B.7 |
| Session: | Poster Session 2B |
| Location: | Symphony/Overture |
| Session Time: | Friday, September 7, 19:30 - 21:30 |
| Presentation Time: | Friday, September 7, 19:30 - 21:30 |
| Presentation: |
Poster
|
| Publication: |
2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania |
| Paper Title: |
A flexible model of working memory |
| Manuscript: |
Click here to view manuscript |
| DOI: |
https://doi.org/10.32470/CCN.2018.1231-0 |
| Authors: |
Flora Bouchacourt, Tim Buschman, Princeton Neuroscience Institute, United States |
| Abstract: |
Working memory provides the workspace for holding and manipulating thoughts. It is flexible: we can hold anything in mind. However, typical models of persistent activity rely on tightly tuned attractors and do not allow for the flexibility observed in behavior. Here we present a novel model of working memory that maintains representations through random reciprocal connections between two layers of neurons: a selectively tuned layer and a randomly connected, untuned layer. As the recurrent interactions are unstructured, the network is flexible: it is able to maintain any input. However, adding multiple memories lead to interferences in the untuned layer, which result in a capacity limitation on the number of items that can be maintained. This is due to divisive-normalization-like reduction in neural responses coming from E/I balance in the network. Furthermore, it has been shown that time and load have a degrading effect on memory precision. Interferences in the network provide a possible mechanism for this psychophysical finding, as well as key neurophysiological results. Thus, we present a simple network model that allows for flexible representations while still capturing behavioral and neural hallmarks of working memory. |
