no code implementations • 19 May 2022 • Justin Jude, Matthew G. Perich, Lee E. Miller, Matthias H. Hennig
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.
2 code implementations • NeurIPS 2021 • Cole Hurwitz, Akash Srivastava, Kai Xu, Justin Jude, Matthew G. Perich, Lee E. Miller, Matthias H. Hennig
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).
no code implementations • 3 Feb 2021 • Cole Hurwitz, Nina Kudryashova, Arno Onken, Matthias H. Hennig
Modern recording technologies now enable simultaneous recording from large numbers of neurons.
no code implementations • 2 Dec 2020 • Justin Jude, Matthias H. Hennig
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.
1 code implementation • NeurIPS 2019 • Cole L. Hurwitz, Kai Xu, Akash Srivastava, Alessio P. Buccino, Matthias H. Hennig
Determining the positions of neurons in an extracellular recording is useful for investigating functional properties of the underlying neural circuitry.