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

TL;DR

This paper proposes a hypothetical neural network framework that interprets neuronal activity as symbolic reenactments of environmental changes, emphasizing non-algorithmic, dynamic, and causal relationships in neural processing.

Contribution

It introduces a novel interpretative model of neural function that explains how neurons and synapses symbolically represent and reenact environmental dynamics without relying on traditional algorithms.

Findings

Neurons symbolize environmental elements and their changes.

Synaptic efficacy indicates the probability of environmental change.

The network mimics causal relationships through neural summation.

Abstract

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.