  In this paper, we examine how deep learning can be utilized to investigate
neural health and the difficulties in interpreting neurological analyses within
algorithmic models. The key contribution of this paper is the investigation of
the impact of a dead neuron on the performance of artificial neural networks
(ANNs). Therefore, we conduct several tests using different training algorithms
and activation functions to identify the precise influence of the training
process on neighboring neurons and the overall performance of the ANN in such
cases. The aim is to assess the potential application of the findings in the
biological domain, the expected results may have significant implications for
the development of effective treatment strategies for neurological disorders.
Successive training phases that incorporate visual and acoustic data derived
from past social and familial experiences could be suggested to achieve this
goal. Finally, we explore the conceptual analogy between the Adam optimizer and
the learning process of the brain by delving into the specifics of both systems
while acknowledging their fundamental differences.
