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It is likely that human-level online learning for vision will require a brain-like developmental model. We present a general purpose model, called the Self-Aware and Self-Effecting (SASE) model, characterized by internal sensation and action. Rooted in the biological genomic equivalence principle, this model is a general-purpose cell-centered in-place learning scheme to handle different levels of development and operation, from the cell level all the way to the brain level. It is unknown how the brain self-organizes its internal wiring without a holistically-aware central controller. How does the brain develop internal object representations? How do such representations enable tightly intertwined attention and recognition in the presence of complex backgrounds? Internally in SASE, local neural learning uses only the co-firing between the pre-synaptic and post-synaptic activities. Such a two-way representation automatically boosts action-relevant components in the sensory inputs (e.g., foreground vs. background) by increasing the chance of only action-related feature detectors to win in competition. It enables develop in a “skull-closed” fashion. We discuss SASE networks called Where-What networks (WWN) for the open problem of general purpose online attention and recognition with complex backgrounds. In WWN, desired invariance and specificity emerge at each of the what and where motor ends without an internal master map. WWN allows both type-based top-down attention and location-based top-down attention, to attend and recognize individual objects from complex backgrounds (which may include other objects). It is proposed that WWN deals with any real-world foreground objects and any complex backgrounds.