Condition Integration Memory Network: An Interpretation of the Meaning of the Neuronal Design

05/21/2021 ∙ by Cheng Qian, et al. ∙ 0

This document introduces a hypothesized framework on the functional nature of primitive neural network. It discusses such an idea that the activity of neurons and synapses can symbolically reenact the dynamic changes in the world and enable an adaptive system of behavior. More specifically, the network achieves these 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. A neuron's activation is transformed to its postsynaptic targets as it fires, 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.

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