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Silvan C Quax1, Michele D'Asaro2, Marcel A J van Gerven2

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Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Systems Neuroscience

Background:

  • Visual cortex exhibits a hierarchy of time scales for information processing.
  • Recurrent neural networks (RNNs) are used to model temporal dynamics in neuroscience and machine learning.
  • Standard RNN derivations often neglect neuronal time constants, limiting their ability to capture biological temporal processing.

Purpose of the Study:

  • To investigate the impact of adaptive time constants in RNNs for modeling neural temporal dynamics.
  • To enhance the expressive capacity of RNNs by incorporating biologically relevant time scales.
  • To determine if learning time constants can reveal the underlying temporal structure in neural data.

Main Methods:

  • Compared standard RNN approximations with a lenient approximation that accounts for neuronal time constants.
  • Utilized simulated neural data to evaluate model performance.
  • Assessed the model's ability to recover underlying time scales from data.

Main Results:

  • The model with adaptive time constants demonstrated superior performance in predicting simulated neural data.
  • The adaptive model successfully recovered the time scales of the underlying processes.
  • A hierarchy of time scales emerged when the model adapted to data with multiple temporal scales.

Conclusions:

  • Incorporating adaptive neuronal time constants significantly enhances RNNs' ability to model temporal dynamics in the brain.
  • Learning time constants provides insights into the brain's temporal processing mechanisms.
  • The emergence of a time scale hierarchy highlights its importance for processing complex temporal information.