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Found 4 papers, 1 papers with code
Capturing cross-session neural population variability through self-supervised identification of consistent neuron ensembles
Classification of consistent versus unfamiliar neurons across sessions and accounting for deviations in the order of consistent recording neurons in recording datasets over sessions of recordings may then maintain decoding performance.
Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation
This means that a decoder trained to predict behaviour on one day performs worse when tested on a different day.
Targeted Neural Dynamical Modeling
These approaches, however, are limited in their ability to capture the underlying neural dynamics (e. g. linear) and in their ability to relate the learned dynamics back to the observed behaviour (e. g. no time lag).
Hippocampal representations emerge when training recurrent neural networks on a memory dependent maze navigation task
We observe that once a recurrent network is trained to learn the structure of its environment solely based on sensory prediction, an attractor based landscape forms in the network's representation, which parallels hippocampal place cells in structure and function.
