  How does the neocortex learn and develop the foundations of all our
high-level cognitive abilities? We present a comprehensive framework spanning
biological, computational, and cognitive levels, with a clear theoretical
continuity between levels, providing a coherent answer directly supported by
extensive data at each level. Learning is based on making predictions about
what the senses will report at 100 msec (alpha frequency) intervals, and
adapting synaptic weights to improve prediction accuracy. The pulvinar nucleus
of the thalamus serves as a projection screen upon which predictions are
generated, through deep-layer 6 corticothalamic inputs from multiple brain
areas and levels of abstraction. The sparse driving inputs from layer 5
intrinsic bursting neurons provide the target signal, and the temporal
difference between it and the prediction reverberates throughout the cortex,
driving synaptic changes that approximate error backpropagation, using only
local activation signals in equations derived directly from a detailed
biophysical model. In vision, predictive learning requires a
carefully-organized developmental progression and anatomical organization of
three pathways (What, Where, and What * Where), according to two central
principles: top-down input from compact, high-level, abstract representations
is essential for accurate prediction of low-level sensory inputs; and the
collective, low-level prediction error must be progressively and
opportunistically partitioned to enable extraction of separable factors that
drive the learning of further high-level abstractions. Our model self-organized
systematic invariant object representations of 100 different objects from
simple movies, accounts for a wide range of data, and makes many testable
predictions.
