Technical Program

Paper Detail

Paper: PS-2A.35
Session: Poster Session 2A
Location: H Lichthof
Session Time: Sunday, September 15, 17:15 - 20:15
Presentation Time:Sunday, September 15, 17:15 - 20:15
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Feature-binding in working memory through neuronal synchronization
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Joao Barbosa, IDIBAPS, Spain; Kartik Sreenivasan, NYU Abu Dhabi, United Arab Emirates; Albert Compte, IDIBAPS, Spain
Abstract: Swap-errors occur in working memory (WM) tasks when a wrong response is in fact accurate relative to a non-target stimulus. These errors reflect the failure to bind in memory the conjunction of features that define one object, and the mechanisms implicated remain unknown. Here, we tested the mechanism of synchrony across feature-specific neural assemblies. We built a biophysical neural network model for WM composed of two 1D attractor networks for WM, one representing colors and the other one locations. Within each network, gamma-oscillations were induced during bump-attractor activity through the interplay of fast recurrent excitation and slower feedback inhibition. These two networks are then connected via weak excitation, accomplishing color-location binding through the selective synchronization of pairs of bumps across the networks. Association-encoding was accomplished by stimulating simultaneously the corresponding bumps in each network, and feature-decoding by stimulating the cued location with a .5s pulse, which impacted strongly the corresponding phase-locked bump. In some simulations, “color bumps” abruptly changed their phase relationship with “location bumps” from which we derived a neural prediction: swap-errors are associated with a lower phase consistency of oscillatory activity in the delay period. Finally, we tested this prediction in MEG recorded from n=30 humans.