  A feature of the brains of intelligent animals is the ability to learn to
respond to an ensemble of active neuronal inputs with a behaviorally
appropriate ensemble of active neuronal outputs. Previously, a hypothesis was
proposed on how this mechanism is implemented at the cellular level within the
neocortical pyramidal neuron: the apical tuft or perisomatic inputs initiate
"guess" neuron firings, while the basal dendrites identify input patterns based
on excited synaptic clusters, with the cluster excitation strength adjusted
based on reward feedback. This simple mechanism allows neurons to learn to
classify their inputs in a surprisingly intelligent manner. Here, we revise and
extend this hypothesis. We modify synaptic plasticity rules to align with
behavioral time scale synaptic plasticity (BTSP) observed in hippocampal area
CA1, making the framework more biophysically and behaviorally plausible. The
neurons for the guess firings are selected in a voluntary manner via feedback
connections to apical tufts in the neocortical layer 1, leading to dendritic
Ca2+ spikes with burst firing, which are postulated to be neural correlates of
attentional, aware processing. Once learned, the neuronal input classification
is executed without voluntary or conscious control, enabling hierarchical
incremental learning of classifications that is effective in our inherently
classifiable world. In addition to voluntary, we propose that pyramidal neuron
burst firing can be involuntary, also initiated via apical tuft inputs, drawing
attention towards important cues such as novelty and noxious stimuli. We
classify the excitations of neocortical pyramidal neurons into four categories
based on their excitation pathway: attentional versus automatic and
voluntary/acquired versus involuntary. Additionally, we hypothesize that
dendrites within pyramidal neuron minicolumn bundles are coupled via
depolarization...
