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.

Effectively connected neurons self-organize forming networks with a small-world architecture composed of modules, with most likely few and nearby neurons, integrated by more topologically important and high firing rate neurons.

Building on simple unsupervised matrix factorization techniques, the seqNMF algorithm successfully recovers neural sequences in a wide range of simulated and real datasets.

A novel statistical algorithm for mining high-dimensional spike train (count) data for significant spatio-temporal patterns reveals new insights into task and brain area dependent functional organization of neural activity.

A computational framework and analysis of rodent CA1 data reveals that theta oscillations sequentially sweep through the past, present, and future at a spatial scale that varies with the characteristic speed of the animal.

A brain–computer interface for real-time identification of transient neural activity patterns enables causal inference of the role of these patterns in cognition through closed-loop manipulation.

Pynapple advances data analysis in systems neuroscience with a versatile, open-source framework that uses common objects to handle diverse data streams, ensuring reliable and reproducible analysis while maintaining long-term backward compatibility.

A novel methodological framework for cross-frequency coupling and spike-field coherence in multichannel (LFP, EEG, MEG) electrophysiology data reveals new manifestations of coupling dynamics.

Biologically plausible changes in the excitabilities of single neurons may suffice to selectively modulate sequential network dynamics, without modifying of recurrent connectivity.

The neural population of the Aplysia's pedal ganglion are a low-dimensional spiral attractor, and the parameters of the attractor directly define the properties of the Aplysia's escape locomotion behaviour.