An Introduction to Cognidynamics

TL;DR

This paper introduces Cognidynamics, a theoretical framework for understanding cognitive system dynamics driven by optimal objectives, using Hamiltonian equations and neural networks, with implications for biological plausibility and consciousness.

Contribution

It develops a novel dynamic programming-based theory of cognition, linking energy exchange, neural propagation, and attention mechanisms within a unified mathematical framework.

Findings

Neural propagation schemes exhibit locality in space and time.

Energy dissipation is crucial for attention and consciousness.

The framework offers insights into biologically plausible learning algorithms.

Abstract

This paper gives an introduction to \textit{Cognidynamics}, that is to the dynamics of cognitive systems driven by optimal objectives imposed over time when they interact either with a defined virtual or with a real-world environment. The proposed theory is developed in the general framework of dynamic programming which leads to think of computational laws dictated by classic Hamiltonian equations. Those equations lead to the formulation of a neural propagation scheme in cognitive agents modeled by dynamic neural networks which exhibits locality in both space and time, thus contributing the longstanding debate on biological plausibility of learning algorithms like Backpropagation. We interpret the learning process in terms of energy exchange with the environment and show the crucial role of energy dissipation and its links with focus of attention mechanisms and conscious behavior.