Chapter 10. Connectionist Neuroarchitectures in Cognition and Consciousness Theory Based on Integrative (Synchronization) Mechanisms
Frontiers in Artificial Intelligence and Applications July 21, 2023 DOI: 10.3233/faia230142 (opens in new tab)
Summary
AI-generated from the abstractCognitive neuroarchitectures, developed since the mid-1980s within connectionism, are theoretical models that explain perceptual and linguistic performances of self-organizing neuronal networks in the human brain, addressing the binding problem. Neurocognition is organized by integrative phase synchronization mechanisms that orchestrate information flow in networks with positive and negative feedback loops across subcortical and cortical areas. This dynamic perspective bridges the gap between discrete, abstract symbolic descriptions of propositions and their continuous, numerical modeling. By conceptualizing these architectures as nonlinear dynamical systems, they enable more accurate modeling of cognitive processes using neurodynamics in abstract n-dimensional phase spaces.
Study at a glance
| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Key finding | Proposes that cognitive neuroarchitectures, modeled as nonlinear dynamical systems using phase synchronization mechanisms, can bridge the gap between abstract symbolic descriptions and continuous numerical modeling of neurocognitive processes. |
Abstract
The topic of this chapter are cognitive neuroarchitectures, which have been developed since the mid-eighties in connectionism as theoretical models to explain, based on empirical-experimental data, as neurophysiologically plausible as possible perceptual and linguistic performances of self-organizing neuronal networks in the human brain that are related to the binding problem. Thus, neurocognition can be viewed as organized by integrative (phase) synchronization mechanisms that orchestrate the flow of neurocognitive information in self-organizing networks with positive and/or negative feedback loops in subcortical and cortical areas of the brain. This dynamic perspective on cognition contributes significantly to bridging the gap between the discrete, abstract symbolic description of propositions in the mind and their continuous, numerical, and dynamic modeling in terms of cognitive neuroarchitectures in connectionism. This dynamic binding mechanism in connectionist cognitive neuroarchitectures thus has the advantage of enabling more accurate modeling of cognitive processes by conceptualizing these neuroarchitectures as nonlinear dynamical systems. This means that neurocognition is modeled using a neurodynamics in abstract n-dimensional phase spaces in the form of nonlinear vector fields or vector flows.