A novel method and software provides researchers with the capability to rapidly, flexibly, and robustly perform Bayesian parameter estimation of theoretically meaningful models in cognitive neuroscience that were heretofore intractable.
Building on simple unsupervised matrix factorization techniques, the seqNMF algorithm successfully recovers neural sequences in a wide range of simulated and real datasets.
Emergent property inference, a novel machine learning methodology, learns distributions of neural circuit model parameters that produce computational properties and provides novel scientific insight through the quantification of the rich parametric structure it captures.
The Real-Time Experimental Control with Graphical User Interface (REC-GUI) framework can facilitate cutting-edge neuroscience research by providing precise experimental control using high-level programming environments familiar to many experimentalists.
A standard for complex mouse behavior that can be successfully reproduced across laboratories, along with open-access data and tools to implement provide a resource for reproducibility through collaborative open-science approaches.
Neural ensemble activity in the human motor cortex contains dynamical structure that is independent of movement parameters and is not well-explained by current models.
Biologically plausible changes in the excitabilities of single neurons may suffice to selectively modulate sequential network dynamics, without modifying of recurrent connectivity.
Human Neocortical Neurosolver is a new user-friendly software tool for researchers to develop and test hypotheses on cellular/circuit origins of human MEG/EEG signals using a biophysical model.
