  It is well known that artificial neural networks are vulnerable to
adversarial examples, in which great efforts have been made to improve the
robustness. However, such examples are usually imperceptible to humans, and
thus their effect on biological neural circuits is largely unknown. This paper
will investigate the adversarial robustness in a simulated cerebellum, a
well-studied supervised learning system in computational neuroscience.
Specifically, we propose to study three unique characteristics revealed in the
cerebellum: (i) network width; (ii) long-term depression on the parallel
fiber-Purkinje cell synapses; (iii) sparse connectivity in the granule layer,
and hypothesize that they will be beneficial for improving robustness. To the
best of our knowledge, this is the first attempt to examine the adversarial
robustness in simulated cerebellum models.
  The results are negative in the experimental phase -- no significant
improvements in robustness are discovered from the proposed three mechanisms.
Consequently, the cerebellum is expected to be vulnerable to adversarial
examples as the deep neural networks under batch training. Neuroscientists are
encouraged to fool the biological system in experiments with adversarial
attacks.
