Condition Integration Memory Network: An Interpretation of the Meaning of the Neuronal Design
This addresses a fundamental gap in neuroscientific theory for researchers, but it is incremental as it builds on existing ideas without empirical validation.
The paper tackles the problem of understanding the operational logic of the nervous system by proposing a hypothetical framework where neurons and synapses symbolically reenact environmental dynamics, enabling adaptive behavior without algorithmic structures.
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.