Stronger inhibitory-to-excitatory synapses in working-memory RNNs reproduce inhibitory dominance, excitatory hypofunction, and task impairment, while resilience training preserves performance at the cost of generalization to longer delays.
Maynard, Leonardo Collado-Torres, Lukas M
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Local 2- and 3-cycles enhance RNN computational capacity for Boolean functions, predicted by structural statistics, while adding interneurons boosts large networks.
SRast is a generalist framework using self-supervised decoupling of gene and spatial representations plus flow matching for physically consistent super-resolution of spatial transcriptomics data with strong zero-shot generalization.
TabPFN on radiomic features matched or outperformed image foundation models for IDH mutational status prediction in glioma MRI, with BiomedCLIP strongest among visual encoders and performance sensitive to cohort shifts and calibration.
citing papers explorer
-
Modelling chronic stress as an excitatory-inhibitory perturbation in recurrent working-memory networks
Stronger inhibitory-to-excitatory synapses in working-memory RNNs reproduce inhibitory dominance, excitatory hypofunction, and task impairment, while resilience training preserves performance at the cost of generalization to longer delays.
-
Identifying structural design principles shaping the computational abilities of recurrent neural networks
Local 2- and 3-cycles enhance RNN computational capacity for Boolean functions, predicted by structural statistics, while adding interneurons boosts large networks.
-
Towards Universal Spatial Transcriptomics Super-Resolution: A Generalist Physically Consistent Flow Matching Framework
SRast is a generalist framework using self-supervised decoupling of gene and spatial representations plus flow matching for physically consistent super-resolution of spatial transcriptomics data with strong zero-shot generalization.
-
A Benchmark of (MRI-) Foundation Models to Predict IDH Mutational Status in Glioma
TabPFN on radiomic features matched or outperformed image foundation models for IDH mutational status prediction in glioma MRI, with BiomedCLIP strongest among visual encoders and performance sensitive to cohort shifts and calibration.