A taxonomy of SNN training algorithms is presented with the release of NeuroTrain, an open benchmarking framework for reproducible comparisons across datasets and architectures.
09-07-02382.1989
6 Pith papers cite this work. Polarity classification is still indexing.
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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.
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 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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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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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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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.