  As part of understanding how the brain learns, ongoing work seeks to combine
biological knowledge and current artificial intelligence (AI) modeling in an
attempt to find an efficient biologically plausible learning scheme. Current
models of biologically plausible learning often use a cortical-like combination
of bottom-up (BU) and top-down (TD) processing, where the TD part carries
feedback signals used for learning. However, in the visual cortex, the TD
pathway plays a second major role of visual attention, by guiding the visual
process to locations and tasks of interest. A biological model should therefore
combine the two tasks, and learn to guide the visual process. We introduce a
model that uses a cortical-like combination of BU and TD processing that
naturally integrates the two major functions of the TD stream. The integrated
model is obtained by an appropriate connectivity pattern between the BU and TD
streams, a novel processing cycle that uses the TD part twice, and the use of
'Counter-Hebb' learning that operates across the streams. We show that the
'Counter-Hebb' mechanism can provide an exact backpropagation synaptic
modification. We further demonstrate the model's ability to guide the visual
stream to perform a task of interest, achieving competitive performance
compared with AI models on standard multi-task learning benchmarks. The
successful combination of learning and visual guidance could provide a new view
on combining BU and TD processing in human vision, and suggests possible
directions for both biologically plausible models and artificial instructed
models, such as vision-language models (VLMs).
