RT Journal Article
T1 Minimal Neural Network Conditions for Encoding Future Interactions
A1 Díez Hermano, Sergio
A1 Aparicio Rodriguez, Gonzalo
A1 Manubens, Paloma
A1 Sánchez Jiménez, Abel
A1 Calvo Tapia, Carlos
A1 Levcik, David
A1 Villacorta Atienza, José Antonio
AB Space and time are fundamental attributes of the external world. Deciphering the brain mechanisms involved in processing the surrounding environment is one of the main challenges in neuroscience. This is particularly defiant when situations change rapidly over time because of the intertwining of spatial and temporal information. However, understanding the cognitive processes that allow coping with dynamic environments is critical, as the nervous system evolved in them due to the pressure for survival. Recent experiments have revealed a new cognitive mechanism called time compaction. According to it, a dynamic situation is represented internally by a static map of the future interactions between the perceived elements (including the subject itself). The salience of predicted interactions (e.g. collisions) over other spatiotemporal and dynamic attributes during the processing of time-changing situations has been shown in humans, rats, and bats. Motivated by this ubiquity, we study an artificial neural network to explore its minimal conditions necessary to represent a dynamic stimulus through the future interactions present in it. We show that, under general and simple conditions, the neural activity linked to the predicted interactions emerges to encode the perceived dynamic stimulus. Our results show that this encoding improves learning, memorization and decision making when dealing with stimuli with impending interactions compared to no-interaction stimuli. These findings are in agreement with theoretical and experimental results that have supported time compaction as a novel and ubiquitous cognitive process.
PB World Scientific Publishing Company
SN 0129-0657
SN 1793-6462
YR 2025
FD 2025
LK https://hdl.handle.net/20.500.14352/119312
UL https://hdl.handle.net/20.500.14352/119312
LA eng
NO Diez-Hermano, S., Aparicio-Rodriguez, G., Manubens, P., Sanchez-Jimenez, A., Calvo-Tapia, C., Levcik, D., & Villacorta-Atienza, J. A. (2025). Minimal neural network conditions for encoding future interactions. International Journal of Neural Systems, 35(04), 2550016. https://doi.org/10.1142/S0129065725500169
NO This research was supported by the Ministry of Science and Innovation (Spain), Grant PID2022-138659NB-I00, awarded to J.A.V-A, and by the project National Institute for Neurology Research (Programme EXCELES, ID Project No. LX22NPO5107) — Funded by the European Union — Next Generation EU.
NO Ministerio de Ciencia e Innovación (España)
NO European Commission
DS Docta Complutense
RD 2 sept 2025