  Understanding brain function, constructing computational models and
engineering neural prosthetics require assessing two problems, namely encoding
and decoding, but their relation remains controversial. For decades, the
encoding problem has been shown to provide insight into the decoding problem,
for example, by upper bounding the decoded information. However, here we show
that this need not be the case when studying response aspects beyond noise
correlations, and trace back the actual causes of this major departure from
traditional views. To that end, we reformulate the encoding and decoding
problems from the observer or organism perspective. In addition, we study the
role of spike-time precision and response discrimination, among other response
aspects, using stochastic transformations of the neural responses, here called
stochastic codes. Our results show that stochastic codes may cause different
information losses when used to describe neural responses and when employed to
train optimal decoders. Therefore, we conclude that response aspects beyond
noise correlations may play different roles in encoding and decoding. In
practice, our results show for the first time that decoders constructed
low-quality descriptions of response aspects may operate optimally on
high-quality descriptions and vice versa, thereby potentially yielding
experimental and computational savings, as well as new opportunities for
simplifying the design of computational brain models and neural prosthetics.
