Target-specific inhibition in E-I recurrent networks creates three dynamical classes: quiescent or asynchronous chaos in balanced cases, and persistent activity with either synchronous chaos or coherent oscillations in excitation-dominated cases, where oscillations suppress chaos.
author Plenz, D
7 Pith papers cite this work. Polarity classification is still indexing.
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Information defined as maximum-caliber deviation derives IIT 3.0 cause-effect repertoires from constrained entropy maximization and equates to prediction error under CLT and LDT.
The no-barber principle prohibits selection rules in the inaccessible game that appeal to external adjudicators, favoring the symmetric monoidal category NCFinProb over the cartesian FinProb as its internal language due to the absence of canonical copying maps.
Recurrent networks driven by low-dimensional sensory dynamics generically embed those dynamics as smooth internal manifolds, with prediction accuracy forcing state separation up to a resolution limit set by prediction error.
Introduces intrinsic difference from maximal specification to assess cause-effect repertoire availability, establishing a necessary differentiation-specification tradeoff for intrinsic existence in IIT.
Tuning a human connectome model via standardized metrics yields emergent alpha-band oscillations, infra-slow rhythms, and higher perturbational complexity in both spontaneous and evoked regimes.
Self-organising memristive networks exhibit collective nonlinear dynamics that can support physical learning with parallels to biological plasticity and potential for energy-efficient edge intelligence.
citing papers explorer
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From Chaos to Synchrony in Recurrent Excitatory-Inhibitory Networks with Target-Specific Inhibition
Target-specific inhibition in E-I recurrent networks creates three dynamical classes: quiescent or asynchronous chaos in balanced cases, and persistent activity with either synchronous chaos or coherent oscillations in excitation-dominated cases, where oscillations suppress chaos.
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Information as Maximum-Caliber Deviation: A bridge between Integrated Information Theory and the Free Energy Principle
Information defined as maximum-caliber deviation derives IIT 3.0 cause-effect repertoires from constrained entropy maximization and equates to prediction error under CLT and LDT.
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The No Barber Principle: Towards Formalised Selection in the Inaccessible Game
The no-barber principle prohibits selection rules in the inaccessible game that appeal to external adjudicators, favoring the symmetric monoidal category NCFinProb over the cartesian FinProb as its internal language due to the absence of canonical copying maps.
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Embedding of Low-Dimensional Sensory Dynamics in Recurrent Networks: Implications for the Geometry of Neural Representation
Recurrent networks driven by low-dimensional sensory dynamics generically embed those dynamics as smooth internal manifolds, with prediction accuracy forcing state separation up to a resolution limit set by prediction error.
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Intrinsic cause-effect power: the tradeoff between differentiation and specification
Introduces intrinsic difference from maximal specification to assess cause-effect repertoire availability, establishing a necessary differentiation-specification tradeoff for intrinsic existence in IIT.
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Emergent complexity and rhythms in evoked and spontaneous dynamics of human whole-brain models after tuning through analysis tools
Tuning a human connectome model via standardized metrics yields emergent alpha-band oscillations, infra-slow rhythms, and higher perturbational complexity in both spontaneous and evoked regimes.
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Self-Organising Memristive Networks as Physical Learning Systems
Self-organising memristive networks exhibit collective nonlinear dynamics that can support physical learning with parallels to biological plasticity and potential for energy-efficient edge intelligence.