Michael M. Halassa

This paper introduces a multiplicative coupling mechanism between RNN and FNN architectures to enhance learning speed and information gating in neural networks.

The paper shows that individual differences in cognitive flexibility are linked to specific neural oscillations and feedback sensitivity during rule learning.

The paper identifies a conserved excitatory neuron gradient in human mediodorsal thalamus using single-nucleus and spatial transcriptomics, linking it to psychiatric disorder risk.

This study identifies the mediodorsal thalamus as critical for belief dynamics in a schizophrenia mouse model and shows that enhancing its activity rescues cognitive deficits.

This study uses fMRI and dynamic causal modeling to show that ventromedial prefrontal cortex (vmPFC) modulates connections from dorsolateral PFC to orbitofrontal cortex based on prior belief during decision strategy re-evaluation.

This study uses computational models to show that the mediodorsal thalamus improves decision-making by reducing sensory noise, enhancing working memory, and enabling context switching.

This paper trains a spiking recurrent neural network to perform a rule-dependent cognitive task, showing neural dynamics similar to experimental data in rodents and monkeys.

The paper develops a circuit model of thalamic neurons to show how top-down inputs to reticular neurons enhance attentional control by improving signal detection and discrimination.

This study dissociates perceptual and executive deficits in ADHD using a task that varies low-level stimulus features and measures task-irrelevant motor output, finding higher perceptual variability and TIMO in ADHD participants.