"Decision-making in the presence of uncertainty is a pervasive computation.
Latent variable decoding—inferring hidden causes underlying visible
effects—is commonly observed in nature, and it is an unsolved challenge
in modern machine learning.
On many occasions, animals need to base their choices on uncertain
evidence; for instance, when deciding whether to approach or avoid an
obfuscated visual stimulus that could be either a prey or a predator. Yet,
their strategies are, in general, poorly understood.
In simple cases, these problems admit an optimal, explicit solution.
However, in more complex real-life scenarios, it is difficult to determine the
best possible behavior. The most common approach in modern machine
learning relies on artificial neural networks—black boxes that map each
input to an output. This input-output mapping depends on a large number
of parameters, the weights of the synaptic connections, which are optimized
during learning.(...)
