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Neurodynamical Computing at the Information Boundaries of Intelligent Systems
Joseph D Monaco1, Grace M Hwang2
1Dept of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD USA.
Cognitive Computation
|August 12, 2024
Summary
Biological intelligence remains elusive for artificial intelligence. This perspective proposes a shift from brain-as-computer models to a dynamical systems approach, integrating embodied cognition and perceptual control theory for a new understanding of neural computation.
Area of Science:
- Neuroscience
- Cognitive Science
- Artificial Intelligence
Background:
- Current artificial intelligence (AI) models lack key features of biological intelligence.
- Cognitivist brain-as-computer theories present methodological and epistemic biases.
- Existing frameworks fail to fully capture the complexity of neural mechanisms.
Purpose of the Study:
- To challenge prevailing AI and cognitive science paradigms.
- To propose an alternative framework for understanding biological intelligence.
- To bridge theoretical gaps in computational neuroscience.
Main Methods:
- Synthesizing historical approaches to intelligent systems.
- Integrating dynamical systems theory and perceptual control theory.
- Reconceptualizing cell assemblies as reentrant dynamical flows.
Main Results:
- Identified limitations in cognitivist and brain-as-computer theories.
- Proposed cell assemblies as a minimal supraneuronal organizational level.
- Highlighted the role of embodiment and situational embedding in neural computation.
Conclusions:
- A shift towards embodied cognition and dynamical systems is necessary for AI and neuroscience.
- Reentrant dynamical flows (cell assemblies) offer a neurodynamical base layer for computation.
- This integrated approach can overcome neurosymbolic stalemates in AI research.
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