An issue centered on stochastic coupling, attention, tract-level information theory, and computational creativity.
Coupling methods provide powerful tools for studying long-time behavior of stochastic dynamical systems. Hairer (2001) introduced an influential framework of asymptotic coupling to obtain exponential mixing results for stochastic partial differential equations. In his presentation, the...
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We compare two mathematical frameworks for understanding selective attention in neural systems: the synaptic-based Free Energy Principle of Friston, which posits that brains minimize prediction error through hierarchical Bayesian inference, and the ephaptic coupling framework of Chawla and Morgera,...
This paper develops an explicit capacity analysis of the axon tract modeled as a vector Gaussian communication channel with perfect feedback. Building on the generalized ephaptic equation and its modifications, we show how the tract can be reduced to an effective linear Gaussian channel with...
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This paper presents and analyzes a series of Python-based generative systems designed to explore computational creativity through explicit generative spaces, rule-based perceptual evaluation, and iterative refinement. The systems, developed sequentially as godtron, davincitron, and vincination,...
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