I can’t argue that working from “the bottom up” is a bad approach; my own works starts with the biology and builds from that point. This is one of the reasons I am invested with the basic HTM model - much of what it does is in close alignment with how I think the biology works.
I will offer that blind adherence to a strict top down or bottom up stance limits your navigation in the problem search-space. As you move from the known to the unknown each step adds a degree of uncertainty. At some point, the amount of uncertainty builds to the point where you really don’t know anything. Having some “goal” helps constrain the search space for faster convergence on a solution.
Each method should inform the other to aid in faster understanding.
BTW: the " Deep Predictive Learning: A Comprehensive Model of Three Visual Streams" paper postulates that this is exactly what is going on in the cortex/thalamus streams. The “forward” stream from the senses going up the hierarchy interacts with the guidance from the “reverse” pathways including the hypothalamus/thalamus/forebrain/cortex as a feedback or training signal. This is NOT the classic ANN back propagation but is instead more plausible local error processing. I highly recommend this paper to anyone thinking about system-level on-line learning. Not an easy read but well worth the effort.