| ... | @@ -35,8 +35,7 @@ Donders Institute for Brain, Cognition and Behaviour, Nijmegen |
... | @@ -35,8 +35,7 @@ Donders Institute for Brain, Cognition and Behaviour, Nijmegen |
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This talk will describe our recent methodological advances in understanding information processing in the human brain and artificial vision systems. A central theme of our work is the combination of neuroimaging and deep learning, a powerful computational framework for obtaining models of cortical information processing and task-performing vision systems. Operating in this interdisciplinary research area, I will cover our recent work in which we demonstrated that neural network architectures with recurrent connectivity provide better models of human visual processing (estimated via representational dynamics and behavioural measurements). This insight was made possible by a novel mechanism to directly infuse brain data into large-scale recurrent
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This talk will describe our recent methodological advances in understanding information processing in the human brain and artificial vision systems. A central theme of our work is the combination of neuroimaging and deep learning, a powerful computational framework for obtaining models of cortical information processing and task-performing vision systems. Operating in this interdisciplinary research area, I will cover our recent work in which we demonstrated that neural network architectures with recurrent connectivity provide better models of human visual processing (estimated via representational dynamics and behavioural measurements). This insight was made possible by a novel mechanism to directly infuse brain data into large-scale recurrent
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neural networks. In addition, we have shown that recurrent connectivity in artificial vision systems leads to computational benefits. Recurrence enables systems to flexibly trade-off speed for accuracy while exhibiting overall higher object recognition performance. Together, these findings suggest that recurrence is required to capture the representational dynamics of the human visual system.
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neural networks. In addition, we have shown that recurrent connectivity in artificial vision systems leads to computational benefits. Recurrence enables systems to flexibly trade-off speed for accuracy while exhibiting overall higher object recognition performance. Together, these findings suggest that recurrence is required to capture the representational dynamics of the human visual system.
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### Talk by Julia Sidorova:
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### Talk by Julia Sidorova: "Towards AI capable of solving long-standing open problems in research"
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"Towards AI capable of solving long-standing open problems in research"
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Computer Science and Engineering, Blekinge Institute of Technology (BTH), Sweden
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Computer Science and Engineering, Blekinge Institute of Technology (BTH), Sweden
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* When: Monday, 25 November 2019, 1:00pm
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* When: Monday, 25 November 2019, 1:00pm
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