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Temporal encoding in deep reinforcement learning agents.

Dongyan Lin1,2, Ann Zixiang Huang3,4, Blake Aaron Richards5,3,4,6,7

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Time cells and ramping cells, crucial for temporal and memory tasks, naturally emerge in artificial neural networks. These computational models reveal how these brain cells support behavior through dynamic representations, offering new insights into neural computation.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Neuroscientists have identified time cells and ramping cells in the brain, believed to support temporal computations and memory.
  • Understanding the precise behavioral contributions of these cells is challenging due to limitations in animal experiments.

Purpose of the Study:

  • To investigate the emergence and function of time and ramping cells in artificial neural networks.
  • To explore how these cells contribute to temporal and working memory tasks in simulated environments.

Main Methods:

  • Utilized deep reinforcement learning models with recurrent neural networks.
  • Simulated interval timing and working-memory tasks.
  • Analyzed the emergence and information content of time and ramping cells within the models.

Main Results:

  • Time and ramping cells naturally emerged in the neural networks.
  • These cells carried information about time and working memory content.
  • Their primary behavioral contribution was providing dynamic representations for policy computation.
  • The information encoded by these cells was dependent on task demands and input variables.

Conclusions:

  • Time and ramping cells can emerge in artificial systems and contribute to temporal and mnemonic tasks.
  • Their role in behavior may be complex, involving dynamic representations rather than direct encoding.
  • Computational models offer a valuable tool for studying the function of neural circuits in behavior.