Recurrent Neural Circuits Overcome Partial Inactivation by Compensation and Re-learning

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Figure 2.
Figure 2.

Inactivation of the integrating circuit reduces sensitivity and increases decision times, with larger effects when a larger portion of neurons are silenced. a, Inactivation schematic. Colored ovals indicate the affected proportion of the P2 network. b,c, Psychometric functions of example networks after training (gray) and after a weak (5–10%, b) or strong (30%, c) inactivation. d, Chronometric function of the same network in panel b following weak inactivation. e, Changes of sensitivity (slope of psychometric function) across 10 trained networks for various inactivation sizes. f, Effects of inactivation on bias (shift of the mid-point of the psychometric function from 0% coherence) across the trained networks. g, Changes of mean decision times across the trained networks. Error bars show s.e.m. Error bars in panels e–f are computed over 10 trained networks. Maximal trial duration set to 500 steps.

  1. The Journal of Neuroscience vol. 44 no. 16
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