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.
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NERD uses RL-trained diffusion models on fMRI data to model higher-order uncertainty representations, outperforming controls and linking individual differences to neurofeedback success.
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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.
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Characterizing higher-order representations through generative diffusion models explains human decoded neurofeedback performance
NERD uses RL-trained diffusion models on fMRI data to model higher-order uncertainty representations, outperforming controls and linking individual differences to neurofeedback success.