Cue–reward learning rate scales proportionally with the time between rewards. Consequently, learning over a fixed duration is independent of the number of trials. This challenges trial-based dopamine ...

Astrocytes in the basolateral amygdala dynamically track fear state and support fear memory retrieval and extinction.

Dereddi, Djannatian, and colleagues show that oligodendrocytes use mechanical cues to measure axon size. The stretch-activated channel TMEM63A converts membrane tension into calcium signals, which cal...

Neuropsychopharmacology - Corticosterone accelerates behavioral inflexibility via plasticity-related gene expression in the dorsal striatum

Neuropsychopharmacology - Orbitofrontal BDNF puts the brakes on alcohol intake and relapse

The authors mapped spontaneous and choice activity across mouse prefrontal cortex. The activity maps aligned with intrinsic connectivity rather than anatomical subregions, suggesting that connectivity...

Animals must exploit environmental regularities to make adaptive decisions, yet the learning algorithms that enabels this flexibility remain unclear. A central question across neuroscience, cognitive science, and machine learning, is whether learning relies on generative or discriminative strategies. Generative learners build internal models the sensory world itself, capturing its statistical structure; discriminative learners map stimuli directly onto choices, ignoring input statistics. These strategies rely on fundamentally different internal representations and entail distinct computational trade-offs: generative learning supports flexible generalisation and transfer, whereas discriminative learning is efficient but task-specific. We compared humans, rats, and mice performing the same auditory categorisation task, where category boundaries and rewards were fixed but sensory statistics varied. All species adapted their behaviour near-optimally, consistent with a normative observer constrained by sensory and decision noise. Yet their underlying algorithms diverged: humans predominantly relied on generative representations, mice on discriminative boundary-tracking, and rats spanned both regimes. Crucially, end-point performance concealed these differences, only learning trajectories and trial-to-trial updates revealed the divergence. These results show that similar near-optimal behaviour can mask fundamentally different internal representations, establishing a comparative framework for uncovering the hidden strategies that support statistical learning. ### Competing Interest Statement The authors have declared no competing interest. Wellcome Trust, https://ror.org/029chgv08, 219880/Z/19/Z, 225438/Z/22/Z, 219627/Z/19/Z Gatsby Charitable Foundation, GAT3755 UK Research and Innovation, https://ror.org/001aqnf71, EP/Z000599/1

Lieber Institute for Brain Development Appoints Keri Martinowich, Ph.D., as Chief Scientific Officer Baltimore, MD (January 6, 2025) — The Lieber Institute for Brain Development (LIBD) announced the a...

Neuropsychopharmacology - Striatal and hippocampal contributions to value-based learning in adolescence

Memories are stored in a sparse population of neurons active at the time of an event, an engram ensemble, and reactivation of the engram ensemble drives memory recall. Although the amygdala is essent...

Molecular Psychiatry - The serotonin 1B receptor is required for some of the behavioral effects of psilocybin in mice

Axons of dopamine (DA) neurons express nicotinic receptors (nAChRs) that have been shown to both facilitate and suppress striatal DA release, but the mechanisms underlying these opposing actions are u...

The neurobiological origins of social behaviors are poorly understood. Previous studies have linked the function of a single Krüppel-associated box...

Ogg et al. use in vivo imaging techniques to record activity in the locus coeruleus (LC) and the orbitofrontal cortex (OFC) during a reversal learning task in freely moving rodents. They show that man...