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Talk by Dr. Alex Williams

Stanford University

03 Mar 2021 16:00
03 Mar 2021

We hereby announce the next talk in the
'Computation and Systems Neuroscience Virtual Seminar' in short: 'CSN Virtual Seminar'

Point process models for sequence detection in high-dimensional neural spike trains


Dr. Alex Williams
Stanford University

on Wed, 3rd of Mar 2021, 4pm

Watch the recorded talk at


Sparse sequences of neural spikes are posited to underlie aspects of working memory, motor production, and learning. Discovering these sequences in an unsupervised manner is a longstanding problem in statistical neuroscience. Promising recent work utilized a convolutive nonnegative matrix factorization model to tackle this challenge. However, this model requires spike times to be discretized, utilizes a sub-optimal least-squares criterion, and does not provide uncertainty estimates for model predictions or estimated parameters. We address each of these shortcomings by developing a point process model that characterizes fine-scale sequences at the level of individual spikes and represents sequence occurrences as a small number of marked events in continuous time. This ultra-sparse representation of sequence events opens new possibilities for spike train modeling. For example, we introduce learnable time warping parameters to model sequences of varying duration, which have been experimentally observed in neural circuits. We demonstrate these advantages on experimental recordings from songbird higher vocal center and rodent hippocampus.

Link to paper:
Link to code:

Guests are welcome.