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Biology subjects

Domine, C.

Publications and source records attributed to Domine, C..

2 recordsLinked to original sources

Replay of procedural experience is independent of the hippocampus

Sleep is critical for consolidating all forms of memory1-3, from episodic experience to the development of motor skills4-6. A core feature of the consolidation process is offline replay of neuronal firing patterns that occur during experience7,8. This replay is thought to originate in the hippocampus and trigger the reactivation of ensembles of cortical and subcortical neurons1,3,9-18. However, non-declarative memories do not require the hippocampus for learning or for sleep-dependent consolidation19-26 meaning what drives their consolidation is unknown. Here we show, using an unsupervised method, that replay occurs in the dorsal striatum of mice during offline consolidation of a non-declarative, procedural, memory and that this replay is generated independently of the hippocampus. Replay occurred at both real-world and time-compressed speeds and was also prioritised both at the level of the individual neurons and the type of neural sequence. Complete bilateral lesions of the hippocampus had no effect on any feature of this replay. Our results demonstrate that procedural replay during consolidation of a non-declarative memory is independent of the hippocampus. These results support the view that replay drives active consolidation of all types of memory during sleep but challenges the idea that the hippocampus is the source of this replay.

neuroscience↗

Geometric Epitope and Paratope Prediction

Antibody-antigen interactions play a crucial role in identifying and neutralizing harmful foreign molecules. In this paper, we investigate the optimal representation for predicting the binding sites in the two molecules and emphasize the importance of geometric information. Specifically, we compare different geometric deep learning methods applied to proteins inner (I-GEP) and outer (O-GEP) structures. We incorporate 3D coordinates and spectral geometric descriptors as input features to fully leverage the geometric information. Our research suggests that surface-based models are more efficient than other methods, and our O-GEP experiments have achieved state-of-the-art results with significant performance improvements.

immunology↗