Universal models for binary spike patterns using centered Dirichlet processes

Il Memming Park, Evan Archer, Kenneth Latimer, Jonathan William Pillow

Research output: Contribution to journalConference articlepeer-review

8 Scopus citations


Probabilistic models for binary spike patterns provide a powerful tool for understanding the statistical dependencies in large-scale neural recordings. Maximum entropy (or "maxent") models, which seek to explain dependencies in terms of low-order interactions between neurons, have enjoyed remarkable success in modeling such patterns, particularly for small groups of neurons. However, these models are computationally intractable for large populations, and low-order maxent models have been shown to be inadequate for some datasets. To overcome these limitations, we propose a family of "universal" models for binary spike patterns, where universality refers to the ability to model arbitrary distributions over all 2m binary patterns. We construct universal models using a Dirichlet process centered on a well-behaved parametric base measure, which naturally combines the flexibility of a histogram and the parsimony of a parametric model. We derive computationally efficient inference methods using Bernoulli and cascaded logistic base measures, which scale tractably to large populations. We also establish a condition for equivalence between the cascaded logistic and the 2nd-order maxent or "Ising" model, making cascaded logistic a reasonable choice for base measure in a universal model. We illustrate the performance of these models using neural data.

Original languageEnglish (US)
JournalAdvances in Neural Information Processing Systems
StatePublished - 2013
Event27th Annual Conference on Neural Information Processing Systems, NIPS 2013 - Lake Tahoe, NV, United States
Duration: Dec 5 2013Dec 10 2013

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


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