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Task representations in neural networks trained to perform many cognitive tasks.

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  • 1Center for Neural Science, New York University, New York, NY, USA.

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Neural networks trained on multiple cognitive tasks develop specialized units and compositional representations, mimicking brain flexibility. This computational approach aids understanding of neural mechanisms for diverse cognitive functions.

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Area of Science:

  • Computational neuroscience
  • Cognitive science
  • Artificial intelligence

Background:

  • Traditional studies face limitations in elucidating brain mechanisms for multiple tasks.
  • Understanding cognitive flexibility requires models capable of handling diverse tasks.

Purpose of the Study:

  • To investigate the neural mechanisms underlying cognitive flexibility using computational models.
  • To explore how single network models perform multiple cognitive tasks.

Main Methods:

  • Trained single recurrent neural network models on 20 diverse cognitive tasks.
  • Developed a measure to quantify relationships between neural representations of tasks.
  • Employed a continual-learning technique to train networks sequentially.

Main Results:

  • Recurrent units formed functionally specialized clusters after training.
  • Task representations exhibited compositionality, enabling task recombination.
  • Networks displayed mixed task selectivity, mirroring prefrontal neuron activity.

Conclusions:

  • Computational models can reveal mechanisms of cognitive flexibility.
  • Specialized units and compositional representations are key to performing multiple tasks.
  • This work offers a platform for studying neural representations across various cognitive tasks.