  Understanding the basic operational logics of the nervous system is essential
to advancing neuroscientific research. However, theoretical efforts to tackle
this fundamental problem are lacking, despite the abundant empirical data about
the brain that has been collected in the past few decades. To address this
shortcoming, this document introduces a hypothetical framework for the
functional nature of primitive neural networks. It analyzes the idea that the
activity of neurons and synapses can symbolically reenact the dynamic changes
in the world and thus enable an adaptive system of behavior. More
significantly, the network achieves this without participating in an
algorithmic structure. When a neuron's activation represents some symbolic
element in the environment, each of its synapses can indicate a potential
change to the element and its future state. The efficacy of a synaptic
connection further specifies the element's particular probability for, or
contribution to, such a change. As it fires, a neuron's activation is
transformed to its postsynaptic targets, resulting in a chronological shift of
the represented elements. As the inherent function of summation in a neuron
integrates the various presynaptic contributions, the neural network mimics the
collective causal relationship of events in the observed environment.
