Cosyne 2013

We’ve recently returned from Utah, where several of us attended the 10th annual Computational and Systems Neuroscience (CoSyNe) Annual Meeting.  It’s hard to believe Cosyne is ten!  I got to have a little fun with the opening-night remarks, noting that Facebook and Cosyne were founded only a month apart in Feb/March 2004, with impressive aggregate growth in the years since:


The meeting kicked off with a talk from Bill Bialek  (one of the invited speakers for the very first Cosyne—where he gave a chalk talk!), who provoked the audience with a talk entitled “Are we asking the right questions.” His answer (“no”) focused in part on the issue of what the brain is optimized for: in his view, for extracting information that is useful for predicting the future.

In honor of the meeting’s 10th anniversary, three additional reflective/provocative talks on the state of the field were contributed by Eve Marder, Terry Sejnowski, and Tony Movshon.  Eve spoke about how homeostatic mechanisms lead to “degenerate” (non-identifiable) biophysical models and confer robustness in neural systems. Terry talked about the brain’s sensitivity to “suspicious coincidences” of spike patterns and the recent BAM proposal (which he played a central part in advancing). Tony gave the meeting’s final talk, a lusty defense of primate neurophysiology against the advancing hordes of rodent and invertebrate neuroscience, arguing that we will only understand the human brain by studying animals with sufficiently similar brains.

See Memming’s blog post for a summary of some of the week’s other highlights.  We had a good showing this year, with 7 lab-related posters in total:

  • I-4. Semi-parametric Bayesian entropy estimation for binary spike trains. Evan Archer, Il M Park, & Jonathan W Pillow.  [oops—we realized after submitting that the estimator is not *actually* semi-parametric; live and learn.]
  • I-14. Precise characterization of multiple LIP neurons in relation to stimulus and behavior.  Jacob Yates, Il M Park, Lawrence Cormack, Jonathan W Pillow, & Alexander Huk.
  • I-28. Beyond Barlow: a Bayesian theory of efficient neural coding.  Jonathan W Pillow & Il M Park.
  • II-6. Adaptive estimation of firing rate maps under super-Poisson variability.  Mijung Park, J. Patrick Weller, Gregory Horwitz, & Jonathan W Pillow.
  • II-14. Perceptual decisions are limited primarily by variability in early sensory cortex.  Charles Michelson, Jonathan W Pillow, & Eyal Seidemann
  • II-94. Got a moment or two? Neural models and linear dimensionality reduction. Il M Park, Evan Archer, Nicholas Priebe, & Jonathan W Pillow
  • II-95. Spike train entropy-rate estimation using hierarchical Dirichlet process priors.  Karin Knudson & Jonathan W Pillow.