Modeling Neural and Performance Correlates of Attention
The application of covert visual attention has clear behavioral effects: it can increase accuracy and reduce reaction time on challenging visual tasks. Neural recordings performed while animals execute these tasks have captured several neural correlates of attention. Questions remain, however, regarding how the observed neural correlates relate to the observed behavioral changes. Utilizing the ability of deep convolutional neural networks to perform visual tasks, we explore the impact that biologically-inspired attentional modulations have on performance in challenging visual tasks that require feature and spatial attention. Interestingly, the modulations that most closely match the biology perform best in this artificial network. We also build a more biologically-realistic version of a CNN that incorporates recurrently-connected E and I cells at each convolutional layer. This model also replicates the behavioral effects of applying attention, and allows for a more direct comparison between neural data and the model.
Date: 15 February 2017, 13:00 (Wednesday, 5th week, Hilary 2017)
Venue: Tinsley Building, Mansfield Road OX1 3TA
Speaker: Miss Grace Lindsay (Columbia University)
Organisers: Dr Rafal Bogacz (University of Oxford), Dr Tim P Vogels (University of Oxford), Dr Rui Ponte Costa (University of Oxford)
Part of: Oxford Neurotheory Forum
Booking required?: Not required
Audience: Public
Editor: Rui Costa