Learning to fear novel stimuli by observing others in the social affordance framework.

Fear responses to novel stimuli can be learned directly, through personal experiences (Fear Conditioning, FC), or indirectly, by observing conspecific reactions to a stimulus (Social Fear Learning, SFL). Although substantial knowledge exists about FC and SFL in humans and other species, they are typically conceived as mechanisms that engage separate neural networks and operate at different levels of complexity. Here, we propose a broader framework that links these two fear learning modes by supporting the view that social signals may act as unconditioned stimuli during SFL.

Impaired glymphatic system in genetic frontotemporal dementia: a GENFI study.

The glymphatic system is an emerging target in neurodegenerative disorders. Here, we investigated the activity of the glymphatic system in genetic frontotemporal dementia with a diffusion-based technique called diffusion tensor image analysis along the perivascular space. We investigated 291 subjects with symptomatic or presymptomatic frontotemporal dementia (112 with [] expansion, 119 with [] mutations and 60 with [] mutations) and 83 non-carriers (including 50 young and 33 old non-carriers).

Dissecting abstract, modality-specific and experience-dependent coding of affect in the human brain.

Emotion and perception are tightly intertwined, as affective experiences often arise from the appraisal of sensory information. Nonetheless, whether the brain encodes emotional instances using a sensory-specific code or in a more abstract manner is unclear. Here, we answer this question by measuring the association between emotion ratings collected during a unisensory or multisensory presentation of a full-length movie and brain activity recorded in typically developed, congenitally blind and congenitally deaf participants.

Convolutional neural networks for vision neuroscience: significance, developments, and outstanding issues.

Convolutional Neural Networks (CNN) are a class of machine learning models predominately used in computer vision tasks and can achieve human-like performance through learning from experience. Their striking similarities to the structural and functional principles of the primate visual system allow for comparisons between these artificial networks and their biological counterparts, enabling exploration of how visual functions and neural representations may emerge in the real brain from a limited set of computational principles. After considering the basic features of CNNs, we discuss the opportunities and challenges of endorsing CNNs as models of the primate visual system.