A self-consistent framework with generalized local order parameters is derived for the Kuramoto model with dyadic and triadic interactions on hypergraphs, showing bistability onset depends on eigenvector correlations between dyadic and triadic structures.
Cutts and Stephen J
11 Pith papers cite this work. Polarity classification is still indexing.
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A taxonomy of SNN training algorithms is presented with the release of NeuroTrain, an open benchmarking framework for reproducible comparisons across datasets and architectures.
Count-FM is a new flow-matching method for count data based on birth-death processes that achieves better sample quality with fewer parameters than baselines on simulations and real scRNA-seq and spike-train data.
Derives information-maximizing rules for baseline weights and release probabilities in Tsodyks-Markram synapses, producing onset-sensitive presynaptic terms and anti-causal connectivity in recurrent networks.
Next-token prediction on multi-modal tokenized sleep signals yields embeddings that match supervised performance with far less labels and generalize to daytime heart data.
Feature visualization on TRIBE v2 brain encoders recovers the known ventral visual hierarchy from V1 to V4 and produces distinctive patterns for MT, FFA, and PPA, with optimized stimuli driving ~4x higher activation than natural images.
A controlled benchmark for context-sensitive memory shows adaptive plasticity (especially homeostatic) enables recall under weak support, with quantum-like models preserving order sensitivity better than Markov controls but without universal advantage.
In spiking ResNets, 1FC ensembles defined by pairwise correlations show ReLU-like cofiring-to-response mapping whose gain scales with ensemble size, with reliable class encoding restricted to infrequent high-cofiring events.
A brain-inspired hierarchical model with inverse structural extraction and HPC-MEC dissociation achieves structural abstraction and generalization in visual world models via velocity-driven path integration.
Analysis of 1,223 AI-HCI papers shows declining focus on human epistemic sovereignty and rising optimization of autonomous agents, leading to a proposal for scaffolded cognitive friction via multi-agent systems to preserve human cognitive agency.
citing papers explorer
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Self-consistent analysis of the Kuramoto model with higher-order interactions
A self-consistent framework with generalized local order parameters is derived for the Kuramoto model with dyadic and triadic interactions on hypergraphs, showing bistability onset depends on eigenvector correlations between dyadic and triadic structures.
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NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Framework
A taxonomy of SNN training algorithms is presented with the release of NeuroTrain, an open benchmarking framework for reproducible comparisons across datasets and architectures.
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Flow Matching for Count Data
Count-FM is a new flow-matching method for count data based on birth-death processes that achieves better sample quality with fewer parameters than baselines on simulations and real scRNA-seq and spike-train data.
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Reshaping Neural Representation via Associative, Presynaptic Short-Term Plasticity
Derives information-maximizing rules for baseline weights and release probabilities in Tsodyks-Markram synapses, producing onset-sensitive presynaptic terms and anti-causal connectivity in recurrent networks.
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Next-Token Prediction Learns Generalisable Representations of Sleep Physiology
Next-token prediction on multi-modal tokenized sleep signals yields embeddings that match supervised performance with far less labels and generalize to daytime heart data.
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Feature Visualization Recovers Known Cortical Selectivity from TRIBE v2
Feature visualization on TRIBE v2 brain encoders recovers the known ventral visual hierarchy from V1 to V4 and produces distinctive patterns for MT, FFA, and PPA, with optimized stimuli driving ~4x higher activation than natural images.
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A quantum-like benchmark for context-sensitive associative memory with adaptive plasticity
A controlled benchmark for context-sensitive memory shows adaptive plasticity (especially homeostatic) enables recall under weak support, with quantum-like models preserving order sensitivity better than Markov controls but without universal advantage.
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Rare Events, Real Signals: Functional Ensembles as Units of Computation in Deep Spiking Networks
In spiking ResNets, 1FC ensembles defined by pairwise correlations show ReLU-like cofiring-to-response mapping whose gain scales with ensemble size, with reliable class encoding restricted to infrequent high-cofiring events.
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Structure Abstraction and Generalization in a Hippocampal-Entorhinal Inspired World Model
A brain-inspired hierarchical model with inverse structural extraction and HPC-MEC dissociation achieves structural abstraction and generalization in visual world models via velocity-driven path integration.
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Cognitive Agency Surrender: Defending Epistemic Sovereignty via Scaffolded AI Friction
Analysis of 1,223 AI-HCI papers shows declining focus on human epistemic sovereignty and rising optimization of autonomous agents, leading to a proposal for scaffolded cognitive friction via multi-agent systems to preserve human cognitive agency.