35 citations · 84 across the 11 of their papers we have counts for
11 papers
Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos
Polina Turishcheva, Paul G. Fahey, Michaela Vystrčilová +22
Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural…
Capturing cross-session neural population variability through self-supervised identification of consistent neuron ensembles
Justin Jude, Matthew G. Perich, Lee E. Miller +1
Decoding stimuli or behaviour from recorded neural activity is a common approach to interrogate brain function in research, and an essential part of brain-computer and brain-machin…
Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation
Justin Jude, Matthew G Perich, Lee E Miller +1
Neural population activity relating to behaviour is assumed to be inherently low-dimensional despite the observed high dimensionality of data recorded using multi-electrode arrays.…
Targeted Neural Dynamical Modeling
Cole Hurwitz, Akash Srivastava, Kai Xu +4
Latent dynamics models have emerged as powerful tools for modeling and interpreting neural population activity. Recently, there has been a focus on incorporating simultaneously mea…
Building population models for large-scale neural recordings: opportunities and pitfalls
Cole Hurwitz, Nina Kudryashova, Arno Onken +1
Modern recording technologies now enable simultaneous recording from large numbers of neurons. This has driven the development of new statistical models for analyzing and interpret…
Hippocampal representations emerge when training recurrent neural networks on a memory dependent maze navigation task
Justin Jude, Matthias H. Hennig
Can neural networks learn goal-directed behaviour using similar strategies to the brain, by combining the relationships between the current state of the organism and the consequenc…
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
Cole L. Hurwitz, Kai Xu, Akash Srivastava +2
Determining the positions of neurons in an extracellular recording is useful for investigating functional properties of the underlying neural circuitry. In this work, we present a…
Statistical models of neural activity, criticality, and Zipf's law
Martino Sorbaro, J. Michael Herrmann, Matthias H. Hennig
In this overview, we discuss the connections between the observations of critical dynamics in neuronal networks and the maximum entropy models that are often used as statistical mo…
Scaling Spike Detection and Sorting for Next Generation Electrophysiology
Matthias H. Hennig, Cole Hurwitz, Martino Sorbaro
Reliable spike detection and sorting, the process of assigning each detected spike to its originating neuron, is an essential step in the analysis of extracellular electrical recor…
Local learning rules to attenuate forgetting in neural networks
Michael Deistler, Martino Sorbaro, Michael E. Rule +1
Hebbian synaptic plasticity inevitably leads to interference and forgetting when different, overlapping memory patterns are sequentially stored in the same network. Recent work on…
Optimal encoding in stochastic latent-variable Models
M. E. Rule, M. Sorbaro, M. H. Hennig
In this work we explore encoding strategies learned by statistical models of sensory coding in noisy spiking networks. Early stages of sensory communication in neural systems can b…