Neural Circuit Mechanisms of Associative Learning and Adaptive Behavior

Through evolution, animals acquired the ability to respond with sub-second precision to environmental stimuli and to associate those stimuli with positive or negative outcomes. This capacity for associative learning is essential for shaping motivated behaviors, enabling organisms to seek rewards and avoid threats in dynamic environments. Although the fundamental principles of associative learning have long been described, the cortical and limbic circuit mechanisms that encode, integrate, and update these associations remain incompletely understood.
Our research focuses on the neural circuits underlying associative learning and motivated behaviors, with particular emphasis on interactions between cortical and limbic networks. We investigate how sensory information is encoded, how emotional valence is assigned, and how learned associations are translated into adaptive behavioral responses. Importantly, we study these processes in both physiological conditions and in pathophysiological contexts such as depression and post-traumatic stress disorder (PTSD), where maladaptive associative processes contribute to persistent negative affect and dysfunctional behavior.
To uncover the cellular and circuit substrates of positive and negative associative learning, we use an integrative, multidimensional approach combining omics strategies, multi-site large-scale electrophysiology, neurotransmitter and calcium dynamics through 1- or 2-photon microscopy, and circuit-specific optogenetic manipulations coupled with behavior.
By identifying the genetic and functional neuronal engrams embedded within cortical–limbic circuits, our work aims to reveal how sensory experiences are transformed into motivated actions in health and how their dysregulation leads to psychiatric disease.

Funding Agency

Fundação para a Ciência e Tecnologia

Funding Agency

Fundação para a Ciência e Tecnologia

Project Reference

Project Members

Main Project Outcomes

S. Queirós, “Right ventricular segmentation in multi-view cardiac MRI using a unified U-net model”, in E. Puyol Antón et al. (eds) Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge. STACOM 2021. Lecture Notes in Computer Science, vol 13131, pp. 287-295, Springer, Cham, 2022.

“Best Paper Award in the M&Ms-2 Challenge”, by M&Ms2 Challenge organizers and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society.

Main Project Outcomes

Papers: 1- Domingues AV, Carvalho TTA, Martins GJ, Correia R, Coimbra B, Bastos-Gonçalves R, Wezik M, Gaspar R, Pinto L, Sousa N, Costa RM, Soares-Cunha C, Rodrigues AJ. Dynamic representation of appetitive and aversive stimuli in nucleus accumbens shell D1- and D2-medium spiny neurons. Nat Commun. 2025 Jan 2;16(1):59. doi: 10.1038/s41467-024-55269-9.; 2 – Deseyve C, Domingues AV, Carvalho TTA, Armada G, Correia R, Vieitas-Gaspar N, Wezik M, Pinto L, Sousa N, Coimbra B, Rodrigues AJ, Soares-Cunha C. Nucleus accumbens neurons dynamically respond to appetitive and aversive associative learning. J Neurochem. 2024 Mar;168(3):312-327. doi: 10.1111/jnc.16063. Advanced courses: 1 – Techniques and Methodologies for Brain-Circuit level Analysis (2026 – https://www.med.uminho.pt/en/post-graduation/courses/brain-circuit); 2 – EMBO Practical COurse on Computational approaches for neuronal and behavioral data analysis (2024 – https://meetings.embo.org/event/25-data-analysis). Media: 1 – A table talk about the team’s work on the national network RTP2 – https://www.youtube.com/watch?v=yGNy-UCcE0M; Podcast “Comunicar Ciência – Antema Minho” (https://creators.spotify.com/pod/profile/antenaminhociencia/episodes/T4-E15–Como-o-crebro-codifica-o-que–bom-ou-mau-e31j7pb)