An Introduction to Cognidynamics
It proposes a foundational framework for understanding cognitive dynamics, potentially impacting neuroscience and AI by linking energy dissipation to attention and consciousness.
The paper introduces Cognidynamics, a theory modeling cognitive systems through dynamic programming and Hamiltonian equations to create biologically plausible neural propagation schemes with spatiotemporal locality, addressing the debate on learning algorithm plausibility.
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.