Search Results for author:
Found 6 papers, 3 papers with code
Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos
To address this gap, we established the Sensorium 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice.
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.
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).
Building population models for large-scale neural recordings: opportunities and pitfalls
Modern recording technologies now enable simultaneous recording from large numbers of neurons.
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.
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
Determining the positions of neurons in an extracellular recording is useful for investigating functional properties of the underlying neural circuitry.
