  Neocortical neurons have thousands of excitatory synapses. It is a mystery
how neurons integrate the input from so many synapses and what kind of
large-scale network behavior this enables. It has been previously proposed that
non-linear properties of dendrites enable neurons to recognize multiple
patterns. In this paper we extend this idea by showing that a neuron with
several thousand synapses arranged along active dendrites can learn to
accurately and robustly recognize hundreds of unique patterns of cellular
activity, even in the presence of large amounts of noise and pattern variation.
We then propose a neuron model where some of the patterns recognized by a
neuron lead to action potentials and define the classic receptive field of the
neuron, whereas the majority of the patterns recognized by a neuron act as
predictions by slightly depolarizing the neuron without immediately generating
an action potential. We then present a network model based on neurons with
these properties and show that the network learns a robust model of time-based
sequences. Given the similarity of excitatory neurons throughout the neocortex
and the importance of sequence memory in inference and behavior, we propose
that this form of sequence memory is a universal property of neocortical
tissue. We further propose that cellular layers in the neocortex implement
variations of the same sequence memory algorithm to achieve different aspects
of inference and behavior. The neuron and network models we introduce are
robust over a wide range of parameters as long as the network uses a sparse
distributed code of cellular activations. The sequence capacity of the network
scales linearly with the number of synapses on each neuron. Thus neurons need
thousands of synapses to learn the many temporal patterns in sensory stimuli
and motor sequences.
