Task representations in neural networks trained to perform many cognitive tasks
| Autor: | H. Francis Song, Guangyu Robert Yang, Madhura R. Joglekar, Xiao Jing Wang, William T. Newsome |
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| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Elementary cognitive task Computer science Decision Making Models Neurological Task (project management) 03 medical and health sciences Cognition 0302 clinical medicine Feature (machine learning) Humans Learning Computer Simulation Neurons Artificial neural network Working memory business.industry General Neuroscience Cognitive flexibility Brain Memory Short-Term 030104 developmental biology Categorization Neural Networks Computer Artificial intelligence business Neuroscience 030217 neurology & neurosurgery |
| Zdroj: | Nature Neuroscience. 22:297-306 |
| ISSN: | 1546-1726 1097-6256 |
| Popis: | The brain has the ability to flexibly perform many tasks, but the underlying mechanism cannot be elucidated in traditional experimental and modeling studies designed for one task at a time. Here, we trained single network models to perform 20 cognitive tasks that depend on working memory, decision making, categorization, and inhibitory control. We found that after training, recurrent units can develop into clusters that are functionally specialized for different cognitive processes, and we introduce a simple yet effective measure to quantify relationships between single-unit neural representations of tasks. Learning often gives rise to compositionality of task representations, a critical feature for cognitive flexibility, whereby one task can be performed by recombining instructions for other tasks. Finally, networks developed mixed task selectivity similar to recorded prefrontal neurons after learning multiple tasks sequentially with a continual-learning technique. This work provides a computational platform to investigate neural representations of many cognitive tasks. |
| Databáze: | OpenAIRE |
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