A taxonomy of SNN training algorithms is presented with the release of NeuroTrain, an open benchmarking framework for reproducible comparisons across datasets and architectures.
Behavioral Timescale Synaptic Plasticity: A Burst in the Field of Learning and Memory
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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.
Coarse wiring statistics set the dynamical regime while precise connections set activity geometry in a parameter-free model of the complete larval Drosophila connectome.
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.
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.
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